Theory and Methods RRI Input-Output Archive

9-1977

The Washington Projection and Simulation Model The Washington Projection and Simulation Model

Philip J. Bourque

Richard S. Conway

Charles T. Howard

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THE WASHINGTON

PROJECTION AND SIMULATION

MODEL

Philip J. Bourque Richard S. Conway, Jr.

Charles T. Howard

Graduate School of Business Admin istration • University of Washington

Input-Output Series • September 1977

THE WASHINGTON PROJECTION AND SIMULATION MODEL

THE WASHINGTON PROJECTION AND SIMULATION MODEL:

AN INPUT-OUTPUT ECONOMETRIC MODEL OF WASHINGTON STATE

by

Philip J. Bourque Professor, Business Economics

Richard S. Conway, Jr. Research Associate

Charles T. Howard Research Assistant

Graduate School of Bus iness Administration University of Washington

Seattle, Washington September 1977

This study was conducted under contract with the Washington State Department of Commerce and Economic Development, with primary financial support provided by the Pacific Northwest Regional Com­mission. This material is the result of tax-supported research and as such is not copyrightable; it may be freely reproduced with customary crediting of the source.

PREFACE

This study is a report of our efforts to develop a systematic and comprehensive model for the economy of Washington State. The research strategy represents a synthesis of the three dominant approaches to regional economic analysis: export base theory, income-expenditure models, and input-output analysis. Rather than viewing input-output and econo­metrics as competitive or alternative methodologies, we have attempted to integrate them into a single regional forecasting framework.

The result is the Washington Projection and Simulation Model (WPSM), a large scale simultaneous-equation system linked to the INFORUM projec­tion model for the U.S. economy. Because WPSM is explicitly structured, dynamic, comprehensive, versatile, and consistent, it has the ability not only to yield baseline forecasts but to simulate regional economic change through time under a variety of alternative assumptions. We hope that, as a policy-analytic forecasting system, the model will serve the many needs of professional planners.

While WPSM is the most intensive and complete modeling effort of the Washington economy undertaken up to this time, we are acutely aware that it can easily become a museum piece if it is not continuously maintained. Even the best forecasting system needs to be updated on a regular basis. Furthermore, the model design could be improved as additidnal observations are accumulated and as we learn more about the system in practical appli­cations. Hopefully, the awareness of the need that motivated the initial funding of WPSM will be succeeded by a recognition of the need to sustain it in the years ahead. One of the tragedies of regional economic fore­casting is the short-sighted view that the models are expendable but that the projections are durable! Forecasting should not be a periodic occasion, but a process of continuously monitoring possible future outcomes to support the on-going requirements of planners and policy-rnakers--a means of reducing uncertainty about possible outcomes and an instrument for appraising the implications of alternative circumstances. Models allowed to languish can­not satisfactorily perform these functions.

Computerized models may give the misleading illusion that forecasting has been reduced from an art to machine mechanics. In fact, WPSM, as a system that organizes complex relationships, channels the user's attention from those variables whose outcomes are more predictable to those where judgment and discretion are required most. Since WPSM is complex, it requires professional skill and judgment to run it properly. The quality of the forecasts still depends upon the competence of the person who pushes the buttons, as well as upon the inherent integrity of the model design.

WPSM has been developed under contract with the Washington State Department of Commerce and Economic Development, and we wish to express our appreciation to that agency and to the Pacific Northwest Regional Conanission for their financial support. We are also grateful to Professor

Clopper Almon of the University of Maryland, who generously provided special tabulations of data from the INFORUM model, and to Mr. Ronald Kutscher of the U.S. Bureau of Labor Statistics, who supplied informa­tion from that agency's national economic projections.

Our part-time staff was lean but competent. Eleanor Hungate was a superb analytical assistant who left no stones unturned in the search for data and who aided the authors in all phases of the endeavor. Research assistants Cynthia Bourque and Jacqueline Smoczyk performed well the many routine duties associated with the project. We appreciate their cheerful assistance.

TABLE OF CONTENTS

PART I APPROACHES TO REXHONAL ECONCMIC MODELING

CHAPTER 1 RmIONAL ECONOMIC MODELS

CHAPTER 2 AN OVERVIEW OF WPSM

PART II SPECIFICATION OF THE WASHINGTON PROJECTION AND

SIMULATION MODEL

CHAPTER 3 EXPORTS

CHAPTER 4 STATE AND LOCAL GOVERNMENT EXPENDITURES

CHAPTER 5 PRIVATE INVESTMENT

CHAPTER 6 PERSONAL CONSUMPTION

CHAPTER 7 INDUSTRY OOTPUT

CHAPTER 8 VALUE ADDED

CHAPTER 9 PERSONAL INCCME

CHAPTER 10 EMPLOYMENT AND POPULATION

CHAPTER 11 IMPORTS

PART III PREDICTIONS AND COOCLUSION

CHAPTER 12 PREDICTION TESTS OF WPSM

CHAPTER 13 FUTURE PROJECTIONS

CHAPTER 14 CONCLUSION

REFERENCES

APPENDIX A DEFINITION OF VARIABLES IN WPSM

APPENDIX B SPECIFICATION OF WPSM

APPENDIX C PROJECTIONS FROM 1972 TO 1985

PAGE

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39

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69

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105

107

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135

139

143

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183

PART I APPROACHES TO REGIONAL ECONOMIC MODELING

CHAPTER 1 REGIONAL ECONOMIC MODELS

What are the long-term energy requirements of the region? What is t he economic impact on the state of expanding the petroleum refining industry? Where is future regional growth--or decline, for that matter-­l i kely to occur? What has been the role of the aerospace industry in the state's economy? These are examples of questions that commonly confront r egional analysts. More specifically, the questions above are ones currently being asked by persons in government, business, and aca­demic i nstitutions in Washington State, as this regional society "plans" f or its future.

A common characteristic of these questions is the fact that under­l ying each answer is a prediction about the regional economy, or at least some aspect of it. The future energy needs of the state depend upon its populati on, the production levels of industry, and the size and nature of state and local government programs, among other things. The impact on r egional jobs and income of expanding petroleum refining is essentially related to the anticipated size of the expansion and the degree to which t he pe t roleum industry is tied to the economy through its interindustry linkages. The location of future regional growth depends in part upon which of the so-called basic sectors of the economy will expand and which of them will not. The importance of aerospace not only depends upon its volume of production and its links to the local economy but also upon its susceptibility to national business fluctuations.

A prerequisite for answering these questions--and for regional anal­ysis and decisionmaking regarding the future in general--is a satisfactory forecasting model. The Washington Projection and Simulation Model (WPSM), an input-output econometric model of Washington State, is one such model designed t o meet this need. Although the model is not, and cannot be, the crystal ball desired by decisionmakers, it does provide a suitable framework wi thin which intelligent regional forecasting and analysis can be carried out.

The primary purpose of this paper is to present the specification of WPSM. The discus sion is divided into three major parts. The introductory section describes the general properties of regional econometric models as well as the overall features of WPSM. In a more technical discussion, Part II gives the formulation of the Washington model, detailing the structure of its various components. The third section presents annual predic tions of WPSM, which cover the period of observation as well as the time span to 1985. Also included are values of multipliers and results of sensitivity tests as a means of evaluating the properties of the system. Fi nally, the conclusion describes a number of uses of WPSM and summarizes its s t rengths and weaknesses.

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Characteristics of Regional Economic Models

There are a number of approaches to regional economic forecasting. Ordered by their degree of complexity, we might list four: time trend extrapolations, time series analysis, gross-income-originating econo­metric models, and input-output econometric models. No single approach has a clear-cut advantage over any other. Which method to employ depends upon the purpose at hand, and there is always a trade-off involved in this choice. Complex models are more difficult to construct and operate, but they also contain more information and have greater analytical capa­bilities.

Time Trend Extrapolations

The simplest regional model is an equation which relates the pro­jected economic variable to time. For example, if we were interested in forecasting personal income, Yt, at some point in time, t, we might choose the following equation:

This particular formulation states that income increases by some fixed amount each year.

(1-1)

The advantages of a time trend, or extrapolation, model are three­fold: conceptually, the model is not difficult to grasp; the date require­ments for estimating the parameters (a and bin the above example) are minimal; and the parameter-estimating procedure is straightforward, often only entailing the use of a hand calculator . Furthermore, for many pur­poses, predictive errors with time trend models are of tolerable size.

Its major drawback is that the model has no causal structure; that is, there is no economic theory underlying the determination of the predicted variable, and therefore no explanatory variables in the equation. Thus, continuing our example, we would have no insight into why personal income was at one level and not another; nor could we ascertain how much income would change if, say, the exports of the region were increased by $100 million.

Times Series Analysis

A time series, or autoregressive, model simply relates the predicted variable to past values of itself. Thus, the general form of a forecasting equation for income would be

(1-2)

This model has advantages over a time trend formulation when the movement of the variable in question displays not only a time trend but also

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periodic fluctuations about that trend. On the other hand, the mathe­matical form of time series equations can become quite complex, creating additional difficulties in estimating parameters. One further drawback to time series models is that they also do not involve any causative factors.

Gross-Income-Originating Econometric Models

Econometric model is a generic term referring to models that use statistical inference techniques to estimate from empirical data the direction and magnitude of relationships of different economic variables. Thus, in contrast to time trend and time series models, an econometric approach attempts to explain the behavior of economic variables. There are two families of econometric models available to regional economists: econometric models that stress macroeconomic relationships, and input­output models that emphasize the interindustry structures of regions.

Regional macroeconomic models resemble their national counterparts, usually following the specification outlined by Klein (1969). However, because little data exist on final demand expenditures for most regions, especially on exports and imports, a gross-income-originating, or value added, rather than an income-expenditures formulation is most often adopted. 1

The logic of gross-income-originating models is not complex. In one sense, they represent the culmination in the development of the traditional economic base models popularized in the 1940's and 1950's. Referring to the elementary framework depicted in Figure 1-1, the gross incomes of export-oriented industries (particularly in manufacturing) are related to the incomes of the corresponding national industries, fore­casts of which are taken from national econometric models. Gross income in the residentiary, or locally-oriented, sector is tied primarily to the income earned in the export industries, Given value added in each regional industry, it is then possible to predict wages, personal income, employment, and population as well as any other related variable.

Gross-income-originating models are also relatively easy to construct. This is largely due to the fact that data for these variables are fairly abundant, with quarterly and annual historical series on a regional level spanning many years. This permits the development of models capable of yielding quarterly and annual forecasts with near and intermediate term horizons (often up to two years for a quarterly model and ten years for an annual model).

Experience so far shows promise for gross-income-originating models, although as Glickman (1974, p. 1) points out, "the level of work can be

1Among models of this type, see Adams et al.(1975) and Glickman (1974). Note that these models are prototypes of the one constructed by Boeing Computer Services for Washington State.

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Figure 1-1

The Structure of a Regional Gross-Income-Originating Econometric Model

Regional value added - of

residentiary Employment industries

Population - by ... -- industry H

Wages Personal - by +-i ncome -

industry Regional u.s. va lue value added

~ added - of -export

by

industries industry

\ -7-

compared to that on national economies which took place twenty years ago." Indeed, very little is known about how well the models actually operate. The usual theoretical and statistical problems, such as misspecification, multicollinearity, and serial correlation, are evident; and few tests of ex ante predictions and the dynamic properties of such models have been made. A further weakness of the models, despite the existency of causal relationships, is their unsuitability for many policy-related simulations. In this regard, Glickman (1974, p. 19) notes that "previous experience with regional econometric models (of this type) indicates that relatively few are capable of usage in a policy-analytic manner."

WPSM as an Input-Output Econometric Model

In contrast to macroeconomic models, the strength of input-output forecasting models lies in their depiction of the detail of regional interindustry structures and their ability to address analytic questions. The chief distinction between this approach and gross-income-originating models is that imbedded in the input-output model ia a series of equa­tions describing flows of outputs from each regional industry to all other local industries and to sectors of final demand. Thus, this for­mulation contains a product side as well as an income side. As a conse­quence of this structure, whatever problem that can be currently analyzed with a static input-output model--for example, the measurement of the economic impact of the location of a new industry, or an assessment of allocation schemes for scarce resources (such as natural gas)--can be 2 handled, but now more effectively, with an input-output econometric model,

As with macroeconomic models, virtually nothing is known about the predictive powers ·of regional input-output models, although economists have tinkered with them for about fifteen years.3 A partial explanation of this may be that past input-output projection systems have been con­structed with a single purpose in mind, such as providing information for a particular decision. To our knowledge, no regional forecasting model has been maintained on an ongoing basis. As n consequence, in contrast to the experience at the national level (Almon et al., 1974, and the U.S. Bureau of Labor Statistics, 1975), these models have been neither updated nor properly tested.

However, the most serious drawb11ck to the interindustry 11ppronch 11t present is the amount of regional datn necessary for successful implemen­tation; ,md in this regard, the principle of "gnrbnge in-gnrbnge out"

2Miernyk has been a pioneer in the usage of input-output models in this manner; for example, see Miernyk (1970n) nnd Miernyk nnd Sears (1974).

3Hoch (1959) constructed one of the first regional input-output fore­caeting modelff, for the Chicago nre11. Others hnve been hui lt by Ml~rnyk (1968, 1970b), Tiebout (1969), ,md Eaen1on (197L).

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applies. First, a region must have one, and preferably two or three, input-output table. Second, it is necessary to have historical data on all final demand variables as well as on output, income, and employment by industry. Third, since interindustry models are primarily used in the formulation of development strategies that are by nature long-term, the historical series of data should extend over a considerable period of time.

WPSl-1 is an input-output econometric model with all of the inherent advantages and disadvantages. However, with regard to the problem of data, Washington State is fortunate in that it has three input-output tables for the years 1963, 1967, and 1972 (Bourque and Weeks, 1969, Beyers et al., 1970, and Bourque and Conway, 1976). These provide bench­mark measures of economic activity in the state and the factual basis upon which to construct a projection-simulation model. Although the tables only represent three observations, they provide a wealth of infor­mation on the changing interindustry structure of the state during a period characterized ZY both significant long-term growth and wide swings in economic activity. Furthermore, utilizing the input-output tables and supporting information, it is possible to develop the necessary historical data on final demand components for non-input-output years. Of course, this does not mean that the hurdle created by data requirements has been altogether removed, only that it has been significantly lowered. However, before proceeding further with more technical matters, it would be useful to present an overview of WPS:M.

40ver the ten-yea r period, Gross State Product (in current dollars) grew 110 percent, which c ompared with a 95 percent growth in Gross Nationa l Product, itself representing a h i storically rapid growth rate. At the same time, due largely to great fluctuations in aerospace exports, the economy experienced an upturn and downturn of dramatic proportions. As an indicator of this, the state's unemployment rate, which was 6.8 percent in 1963, hit a low of 4.5 percent in 1966 and a high of 10.1 per­cent in 1971, before dropping slightly to 9.5 percent in 1972. For com­ments about the behavior of the economy during this period, presented in an input-output context, see Bourque (1971), Beyers (1972), Conway (1975), and Bourque and Conway (1975).

CHAPTER 2 AN OVERVIEW OF WPSM

The Washington Projection and Simulation Model has been designed with five characteristics in mind:

• structural explicitness • comprehensiveness • detail • consistency • flexibility

The first characteristic of the model, that of structural explicit­ness, is the most important one in that the other four more or less follow from it. By an explicit structure we mean that there is a clearly traceable line of logic underlying the relationships between economic variables in the model. In other words, the model is specified such that the immediate as well as the more removed causes of, say, a rise in the consumer demand for beverages are known. This means that we take into account both economic theory and the strength of statistical relation­ships when selecting the behavioral equations that comprise the model. As much as possible, this is an attempt to remove the mysticism pervading many of the models that have been built strictly on the basis of statis­tical correlation.

Since we strive for structural explicitness, and because the Wash­ington economy is a complex organism, the model is necessarily comprehen­sive. Projections from WPSM literally involve thousands of predictions, ranging from individual intersectoral sales to total intermediate and final demands; from employment by industry to total residential population; arid from the distribution of industrial inputs to total personal income. As such, we try not to distort the view of economic behavior by handling economic variables in isolation. When one variable is disturbed, the model permits us to follow the repercussions as the economic effects "ripple" throughout the system.

At the same time that it is comprehensive, WPSM is also detailed, especially with regard to the interindustry structure. The advantage of identifying 55 industries, as WPSM does, is that economic investigations involving particular industries can be more easily performed. In this detail, the model begins to bridge the gap between macro- and microeco­nomic behavior and analysis.

A fourth characteristic of the model's predictions is their internal consistency. For example, the forecast of total consumption is consistent with the projection of regional disposable income, which in turn is in line with predictions of wage rates and employment by industry. Private investment by industry is consistent with industrial output. Expenditures by state and local government are consistent with the income and population

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of the society that it serves. And overall the view of the regional economy is consistent with the national economic environment within which it finds itself.

Lastly, WPSM is operationally flexible. Due to the particular con­struction of the model, its behavior can be altered in a number of ways. For example, if one disagreed with the view of the national economy underlying the regional forecasts, one could readily make modifications (by changing the values of the relevant exogenous variables) to reflect this new view, generating an alternative set of regional forecasts. If one disliked the formulation of a particular relation, such as the resi­dential housing function, another equation could be proposed and, if found more reasonable from a theoretical and statistical standpoint, easily inserted into the model. Or, if one found fault with the assump­tions regarding input-output coefficient change, the necessary changes could also be introduced without complication.

Features of WPSM

To be more specific about its features, WPSM is a model that makes annual forecasts covering an intermediate and long-range period, that is, a period of from five to twenty years into the future (see Table 2-1).1 As yet, the model is not structured to be sensitive to regional cycles and is therefore not suitable for short-term forecasting. Consequently, one should not expect WPSM to explain all of the historical variation in regional growth over time, an impossible task in any event. For example, the model cannot be expected to tell us whether 1985 will be a year of recession or recovery for the economy; for that will in part depend upon such short-term factors as the phase of the national housing cycle, which conditions the demand for wood products, and world weather, which influ­ences the demand for wheat. However, in order to be useful for longer­run analytical and planning purposes, WPSM should tell us the long-run position of the economy in 1985 apart from such short-term deviations.

The model itself is a system of linear and nonlinear equations which is designed to explain, or predict, the behavior of 456 endogenous vari­ables. The simultaneous solution of this system of equations in a given year constitutes the set of predictions for that year. As there are 456 "unknowns," the model must contain a like number of equations: 421 are behavioral equations, such as the consumption function, which predicts the volume of consumer expenditures from the level of disposable income;

1 Of course, the model can make predictions as far into the future as there are values for the exogenous variables. However, as we extend the projection horizon, we increase the uncertainty associated with the predictions. For this reason, forecasters with models such as WPSM have limited their horizons to about 15 years.

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Table 2-1

Features of WPSM

Projection Horizon

5-20 years (intermediate and long-range)

Model Size

456 endogenous variables 53 lagged endogenous variables 75 exogenous variables (including 55 U.S. output variables)

421 behavioral equations 35 identities

Industry Detail

55 industries, each having projections of output value added regional intermediate inputs imports wages, salaries, and proprietors' income employment regional intermediate sales sales to final demand sectors

Other Selected Endogenous Variables

Gross State Product personal income disposable income personal consumption residential housing investment state and local government education expenditures labor force unemployment rate population

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and 35 are accounting identities, such as the definition of disposable income, which is defined as personal income less personal tax and nontax payments. To round out the model, there are 53 lagged endogenous vari­ables, whose values are determined by a prior year's solution (e.g., popu­lation of the previous year), and 75 exogenous variables, whose values are determined completely outside the system (e.g., the output of the national plywood industry).

With regard to detail, we identify 55 regional industries. This disaggregation follows the sectoring plan for the 1972 Washington input­output table, with the exception of the construction industry, where the model is further disaggregated into five construction sub-sectors. For each industry, there are projections of output, value added, regional intermediate inputs, imports, labor income (i.e., wages, salaries, and proprietors' income), employment, regional intermediate sales, and sales to final demand sectors. Given these forecasts, we also have the capa­bility of projecting input-output tables for each prediction year.

Other economic variables generated by WPSM include Gross State Prod­uct, total persons employed, labor force, unemployment rate, population, personal income, disposable income, disposable income per capita, personal consumption, residential housing investment, equipment investment, state and local expenditures, and highway construction. We should clarify the role in the model of such variables as population. At present, there is no comprehensive demographic model underlying the prediction of residential population. One could argue that forecasts of population from WPSM are therefore subject to a greater degree of uncertainty than one would find i n demographic formulations. The question then arises, why not have popu­la t ion determined outside the model? For baseline forecasting this prob­ably would be the proper approach, and the model could be run under these circumstances. However, WPSM is also designed for conditional forecasting and impact analysis. In this case, when one is trying to assess how the economy would respond to forces like a decline in food product exports, it is necessary to have population as a variable endogenous to the system. This is because population itself would be subject to change under these conditions and because population is a key explanatory variable of other dependent variables in the system. Similar remarks would of course apply to regional disposable income.

2An alternative specification of the population component has been made but has not been integrated into the system as yet. Among other things, the sub-model includes a sex-age distribution of population and demographic factors such as births, deaths, and labor force participation r ates. The parameters of this sub-model have been estimated by Charles Sawyer of the Washington State Department of Commerce and Economic Development.

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How the Model Works

Although the structure of the model, with 456 equations, might appear complicated, the view of the Washington economy underlying it is quite simple. WPSM essentially follows from export base concepts, which identify two sets of economic demands placed upon the region, export (or external) demands and residentiary (or internal) demands.

The export demands are considered a primary driving force behind regional economic growth, as shown in Figure 2-1. In the context of WPSM, exports include foreign exports and exports to the rest of the u.s. as well as federal government expenditures. For example, we count as exports of aircraft the sales to foreign and domestic airlines as well as deliveries to the federal government. Exports of regional industries are largely determined by the national economic environment. Specifically, exports are projected on the basis of the predicted demands placed upon national industries, as represented by the output of the corresponding U.S. industries, and the extent to which Washington State can satisfy these demands. In general, this is not a fixed-shares formulation, since an allowance is made for an increasing or decreasing share of the national market served by the region. Some exceptions to this general specifica­tion occur, as in the case of logging, where exports are related directly to the foreign exports of u.s. logs, since virtually all logs shipped from Washington go overseas to Japan. Projections of national industrial requirements and other U.S. economic variables in turn come from the INFORUM model of Clopper Almon, an input-output econometric model of the u.s.3

The local output required to support these external sales triggers the first set of internal demands, the interindustry demands. For example, the demand for regionally-produced aircraft establishes a chain of demands which affect output in the local electrical machinery and services indus­tries, among others. The induced output in these sectors in turn sets up further intermediate demands, all of which are depicted by the equations in the output sub-model. These input-output relations constitute the core of WPSM.

However, the linkages in the model are far from completely specified at this point. Industrial output and independently determined productivity estimates combine to predict the number of jobs in the economy. This determines the number of persons employed, which when coupled with pre­dictions of unemployment and labor force participation lead to projections of the labor force and residential population.

3see Almon et al. (1974). Other national models of this type include the Wharton Annual and Industry Forecasting Model (Preston, 1972) and a model developed by the U.S. Bureau of Labor Statistics (1975).

Imports

Coefficient Change

'

Output (I/0 Relations)

Income

it

Employment and

Population

I I

Consumption -

Exports

State and Local

Goverrunent

----~ Investment

_ Output /

\

_Highways/

\

- I \

National Econometric

Model

\ Productivity Rates ·-------......::.....----------' Wage Rates, Tax Rates, Per Capita Property Income and Transfer Payments

Figure 2-1

Washington Projection and Simulation Model

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In the course of production, income is earned, primarily in the form of wages, salaries, and proprietors' income. Separate equations determine property income, transfer payments, employer contributions to social security, and personal tax and nontax payments, which result in a determination of disposable income.

Disposable income and population together are key factors explaining the second tier of internal demands, which include the final demands of the consumption, investment, and state and local government sectors. Consumer spending is related directly to the disposable income of house­holds. Residential construction is linked to household income as well as to the current stock of housing, interest rates, and the cost of con­struction. Other private fixed investment is tied to industrial output levels, while inventory change is explained by changes in these levels. Disposable income also influences state and local government expenditures, but factors such as school-age population and federal highway expenditures also enter into the determination.

At the same time that these feedback loops are operating, so-called leakages are present in the system. These take the form of savings, taxes, and imports, and represent income or revenue that is not redirected into the flow of locally-oriented demands. Although WPSM predicts personal taxes, it stops short of a complete specification of taxes and savings. On the other hand, the model does generate forecasts of imports. Despite the lack of attention paid to these variables, they are an important component of the system. Without the drain caused by taxes, savings, and imports, the flow of demand running through the feedback loop joining, say, output, income, and consumption would run interminably, causing the model to "explode" as prediction values grow to infinity. Speaking in mathematical terms, the leakages permit the system of equations consti­tuting the economic model to converge to a solution .

The Economic Future of Washington State

What is in store for the Washington economy? WPSM provides a picture of the economic future, but it is one still clouded with uncertainty. The projections are made on the basis of three presumptions: that the model is properly specified; that the parameters of the equations are correctly estimated; and that the values of the exogenous variables are known. If WPSM op,rated under these conditions, the projections would be accurate, apart from random disturbances. However, it is clear that each of these conditions does not in fact hold, at least to a degree.

In order to gain some appreciation of the art involved with economic forecasting, one should at least be aware of the major exogenous assump­tions underlying our forecasts. These largely deal with economic condi­tions at the national level, which essentially reflect the INFORUM point of view as of the swmner of 1976, the date of their latest forecasting effort:

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• a peacetime economy • no devastating economic event, such as an oil embargo • a 1985 population of 234,000,000 persons • a slow decline in the unemployment rate to 5.6 percent by 1985 • per capita disposable income (in 1972 dollars) of $5005 in 1985

As a gauge of the uncertainty inherent in the prediction of national variables, consider a comparison of the above projections with those from the Bureau of Labor Statistics (BLS) model. The set of projections made by BLS in 1974 is generated under the same population assumption. How­ever, the national unemployment rate is assumed to stablize at 4.0 per­cent by 1980, with per capita disposable income climbing to $5745 by 1985. Apart from the unemployment picture, the disparity in the income projec­tions results from differing points of view with regard to productivity gains, BLS being much more optimistic. As a consequence, the two models and their operators present quite different scenarios for the future of the U.S. economy.

Table 2-2

Projections of Selected Washington Economic Variables to 1985

Annual Rate Actual Projected of Change

1972 1985 (%)

Population (thous.) 3,418 4,073 1.4 Employment (thous.) 1,335 1,716 2.0 Unemployment rate(%) 9.5 7.5 -1.8 Disposable income (mil. $72) 13,473 21,072 3.5 Per capita disposable income ($72) 3,940 5,170 2.1 Gross State Product (mil. $72) 19,171 30,124 3.5 Exports (mil. $72) 11,631 17,260 3.1 Consumption (mil. $72) 12,000 18,716 3.5 Investment (mil. $72) 2,336 3,994 4.2 State and local (mil. $72) 2,979 4,289 2.8

Keeping such shortcomings of forecasting in mind, a look at the WPSM projections of selected aggregates reveals a number of interesting trends (see Table 2-2 and Figure 2-2). Population is expected to increase to 4,073,000 persons by 1985, representing a growth rate somewhat more rapid than the national rate. This is triggered by an expansion in the number of persons employed, which is projected to increase at an average annual rate of 2.0 percent between 1972 and 1985. The unemployment rate is expected to decline but still remain above the national figure, in part as a response to relatively higher seasonal unemployment in Washington

Cl) 'M I,; 0 ci, ~

a,~

§8 -.<I ~N ~ r--~ O"I X: ~

Cl) Cl) C:

"'0 0 C: Cl) ~ M Cl) ~ :, ~ 0

~~ 0 in N ~

Thousands of Persons

4250

4000

3750

3500

3250

3000

1965 1970

Disposable Income

I I

I I

I I

I , I ,

/ , I ,

/ , ,, I /

I I I/ , , , ,, ,

I

I I

I I

I I

\ I

,/

Millions of , 1972 Dollars

I /

/ /

/ 20,000 , , , , I Population

I I

,I ,

, ,

, ., ., , / ., ,, ,

/ , , , , ,'Exports ,

Actual

Predicted

18,000

16,000

14,000

12,000

10,000

1975

Figure 2-2

1980 1985

Washington Population, Disposable Income, and Exports, 1963-1985

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State. Despite the burden of the unemployed, per capita disposable income is anticipated to remain about three percent above the national average. Underlying all of this, Gross State Product is projected to grow at a 3.6 percent annual rate over this 13-year period.

Table 2-3

Projections of Washington Employment in Selected Industries to 1985 (thousands of jobs)

Annual Rate Actual Projected of Change

1972 1985 (%)

Agriculture 60.0 42.3 -2.7 Food products 27.0 30.6 1.0 Wood and paper products 74.2 84.0 1.0 Aerospace 41.4 34.6 -1.4 Transportation and trade 360.2 495.3 2.5 Services 229.7 363.7 3.6 Government 268.2 317.1 1.3 Other 310.1 405 .2 2.1

Total 1,370.8 1,772.8 2.0

Turning to the industrial sector, in terms of the number of jobs, growth is expected to occur in most sectors, with the noncommodities leading the way (see Table 2-3). Services in particular are projected at a rate well above the overall annual average of 2.0 percent for this period. Slower growth in manufacturing is generally foreseen, with an actual decline in the number of jobs for aerospace anticipated. Agri­cultural jobs also will continue to decrease, mirroring the ongoing gains in productivity in that sector. Finally, a slowing of the growth rate is predicted in the government sector, in part due to the leveling off of school-age population and a curtailing of federal government activity "

PART II SPECIFICATION OF THE WASHINGTON

PROJECTION AND SIMULATION MODEL

CHAPTER 3 EXPORTS

WPSM is composed of nine distinct but interrelated sub-models (or blocks): exports, state and local government expenditures, private investment, personal consumption, industry output, value added, income, employment and population, and imports. Part II discusses in detail the specification of each of these blocks.

The present chapter deals with modeling the first of the final demand components, Washington State exports. The following discussion covers export theory, export data, and the results of model estimation. Through­out this chapter, the reader should keep in mind the critical role played by exports in determining the region's economic future.

Export Theory

Regarding his national projection model, Almon (1966, p.24} has remarked that "wither the consumer's dollar leads, the American economy follows; and where our consumption projections go, the rest of the model trails along." If we substitute the word "exports" for "consumption", a similar thing can be said for a regional economy: the economic growth of a region is largely dependent upon the demand for its exports; and fore­casts of total regional activity follow directly from projections of the growth in the volume of these external sales. This is the hypothesis of the export base theory of regional economic growth and a general premise upon which WPSM is built.

However, the export base hypothesis in itself is not useful for making projections of exports. It simply states that a region will grow if its external sales are increased. In this sense, the hypothesis is a conditional statement about regional growth. In order to understand the determinants of exports, we must turn to other regional economic theories, and in particular to the theory of location.

The volume of a region's exports depends upon both demand and supply factors. For Washington State, the potential demand for its goods and services beyond its borders is measured by the total demand for goods and services by the nation, foreign nations, and the federal government. What portion of this demand is actually supplied by the state is a function of its comparative advantage. This relation might be stated mathematically as

(3-1)

where WEXi tis the value of good i exported from Washington in time t, USXi,t is the value of U.S. output for good i, and WCOSTi,t/USCOSTi,t is

-22-

the measure of the cost of producing and delivering to the purchaser a unit of good i from Washington relative to the unit cost from the rest of the U.S. Note that USXi tis a measure that incorporates national, foreign, and federal demand~ for good i. For example, the demands for U.S. aircraft by U.S. commercial airlines, by foreign governments and foreign cormnercial airlines, and by the federal government will in a given year sum to a total that equals the output of the U.S. aircraft industry. 1

Relative unit costs are themselves a function of a number of vari­ables. Distance is a key factor in location theory, which attempts to answer the question of where economic activity will occur. In general, a region that is closer to major markets (i.e., population and industrial centers) and important material sources (e.g., forests and mineral depos­its as well as population and industrial centers) will tend to incur relatively lower transportation costs associated with the production and delivery of goods. This in turn will mean lower unit costs and greater exports from the region. But distance is not the only determinant of unit costs. Wage rates, the productivity of labor and capital, the cost of energy, the availability of supporting services, and taxes or subsi­dies, among other factors, influence a region's unit costs.

For equation (3-1), we expect that

(3-2)

and

c:) '\•lEXi t / a (WCOST i t /USCOST i t) .( 0. ' ' '

(3-3)

That is, an increase in the demand at the national level, ceteris paribus, will increase state exports; and an increase in the relative unit costs in the region, ceteris paribus, will lead to a decline in exports. More­over, a tracing of the movements of USXi t and WCOSTi t/USCOSTi t (or the variables underlying relative cost) over'time should explain historical and future changes in HEXi t•

' The Export Hodel

In practice, the state of regional data precludes estimating an export equatior. as specified by equation (3-1). In our case, it is neces­sary to simplify it into the following form:

1we should point out that in defining USXi t we have not deleted Washington outputs from national outputs. We £611ow the usual presumption in regional analysis that whereas national activity significantly influ­ences regional activity, the reverse is not true. This seems to be reasonable in this instance, since our largest- exports volumes represent less than 15 percent of the output of the corresponding nati onal industry.

-23-

(3-4)

This states that Washington exports are related to U.S. output and time. The latter explanatory variable is intended to be a proxy for the region's changing comparative advantage in the production of good i. The sign of the partial derivative of exports with respect to time can be positive, zero, or negative depending upon whether relative unit costs in the state are declining, remaining constant, or increasing.

Our assumption permitting the use of time as a variable explaining export behavior is that comparative advantage changes slowly and per­sistently over time. Is this reasonable? As changes in comparative advantage represent changes in a region's relative wage rates and labor productivity, its proximity to markets and supply sources (especially natural resources), and other factors which one would expect to vary slowly over time, the assumption of a slowly changing comparative advan­tage, particularly for a region's larger export sectors, seems reasonable. This of course is a statement that has not been empirically tested.

It should also be pointed out that the assertion about a slowly varying regional comparative advantage is made in the context of the long-run development of the region. In any given year, random factors can cause an export share to deviate from its long-run path. For example, unusually good local weather conditions can result in an excellent har­vest, in turn causing agricultural and processed food exports to be above their long-run growth paths. Or, a strike in the local plywood mills can reduce the region's supply below its expected share of national output. In other words, one cannot expect our export sub-model to explain all of the historical variation in the region's exports over time. Similarly, projections from the equation will not tell us whether 1985 will neces­sarily be a better year than 1984 for the plywood industry. The export sub-model, like WPSM in general, is designed not to pick up short-term fluctuations but rather to project the long-run patterns of regional growth.

The Export Data

Input-Output Data

The three input-output tables constitute the core of information on Washington State exports. Export data drawn from these tables are pre­sented in Table 3-1. It shows the 15 largest exporting industries, ordered according to their 1972 volumes. Exports are valued in 1972 dollars and include exports to the rest of the U.S., foreign exports, and federal procurements. The table also indicates the share of national output represented by each industry's exports.

overall, relatively few of the 55 industries defined in the 1972 input-output table account for the bulk of external trade. Aerospace ·

Table 3-1

Washington Exports, 1963, 1967, and 1972 (millions of 1972 dollars)

l963 1967 1972 Percent Fraction Percent Fraction Percent Fraction

Industry of of U.S. of of U.S. of of U.S. No. Name Exports Total Output Exports Total Output Exports Total Output

39 Aerospace 1,537.8 20. 7 0. 0962 2,931.8 29.2 0.1221 2,053.1 19 .o 0.1281 53 Trade 584.0 7.9 0.0033 829.0 8.3 0.0038 1,134.0 10.5 0.0042 32 Aluminum 406.2 5.5 0.0868 557 .4 5.6 0.0869 719. 7 6.7 0.0907 43 Trans. services 467.0 6.3 0.0080 673.0 6.7 0.0092 682.5 6.3 0.0080 17 Sawmills 554.6 7.5 0.0936 484.6 4.8 0.0783 637.5 5.9 0.0946 8 Canning 285.0 3.8 0.0327 326.7 3.3 0.0303 371.4 3.4 0.0306

54 Fin., ins., real. 230.6 .3 .1 0.0020 251.1 2.5 0.0018 368.2 3.4 0.0022 23 Paperbd. mills 217 .5 2.9 0.0117 241.9 2.4 0.0109 339.5 3.1 0.0129 41 Shipbuilding 274.3 3.7 0.0938 365.8 3.6 0.0993 321.3 3.0 0.0746 18 Plywood 242.1 3.3 0.1397 244.1 2.4 0.1086 278.7 2.6 0.0948 40 Motor vehicles 108.8 1.5 0.0022 129.7 1.3 0.0023 272.9 2.5 0.0036 22 Paper mills 269.1 3.6 0.0369 228.1 2.3 0.0266 267.2 2.5 0.0265 27 Petroleum 70 .2 0.9 0.0026 60.8 0.6 0.0019 265.5 2.5 0.0068

1 Field crops 185.9 2.5 0.0097 232.9 2.3 0.0111 259.2 2.4 0.0107 16 Logging 60.8 __Q_& 0.0148 120.8 1.2 0.0252 220.2 ..b.Q 0.0434

Subtotal 5,493.9 73.9 7,677.7 76.5 8,190.9 75 . 7 Other 11942.9 -1§.._J_ 21364.3 23.5 2.625.8 24.3

Total* 7,436.8 100.0 10,042.0 100.0 10,816.7 100.0

*Excludes federal government value added.

-25-

exports are by far the greatest in volume, almost double the size of trade exports in 1972, which is second in rank. Five sectors make up about one­half of the volume, and the 15 sectors shown in Table 3-1 constitute about 75 percent of the external sales in 1972.

This concentration of regional export activity suggests that our analysis should focus on only a few industries. Export equations for all 55 sectors have been estimated, but only the 21 largest have been given close attention. These 21 sectors make up 87 percent of the exports and include the two major agricultural sectors (field crops and vegetables and fruits), two key food processors (canning and beverages), the entire for­est products sector, the three transportation equipment industries, and four services (trade; transportation services; finance, insurance, and real estate; and construction).

Export Estimates in Intervening Years

One immediate problem with the study of exports is the lack of his­torical data. Ostensibly, we have three observations, one in each of the three input-output years. We should note that this situation is much better than that faced by most input-output forecasters.2 Nonetheless, more observations, if they can be made with a fair degree of accuracy, are preferable. 2

We have developed a methodology that permits us to make export esti­mates for the seven years between 1963 and 1972 not covered by input­output studies. In general, we estimate the exports of an industry from estimates of its output and the local demands for that output.

The expression for the estimate of exports from industry i in time tis given by

WEX. t = 1,

wx i,t

(3-5)

where WEXi,t is the exports of industry i, wxi,t is the output of i, rij,t is the purchases coefficient representing the proportion of the total inputs of j required from i, WCTOTt is total household consumption, cit is the fraction of consumption spent on i, WITOTt is total regional ' investment, and WSLTOTt is total state and local government spending.

2Initially, we proposed to analyze export shares only in the three input-output years. However, the variability of the shares in those years, as evident in Table 3-1, prompted us to develop export data for the intervening years.

-26-

As an example, consider the exports of aerospace in 1965. An esti­mate of aerospace shipments is made from the Annual Survey of Manufactures, 1965. From this we deduct local demands in 1965, which are dependent upon the activity levels of each of the local sectors (including the final demand sectors) that require inputs from aerospace and the corresponding regional input coefficients. Since aerospace serves only two local mar­kets, aerospace (industry 39) and transportation services (43), we can express the estimate of aerospace exports as

(3-6)

The estimate of transportation services output in 1965 is based upon wage and employment data in that year and output, wage, and employment observa­tions in the 1963 and 1967 input-output years. The two regional coeffi­cients are estimated as averages of these same coefficients as measured by the 1963 and 1967 interindustry studies. This follows from an assumption that each input-output coefficient changes at a constant rate between the values in base-year tables.

This procedure should produce fairly reliable estimates of exports, especially f or the largest exporting industries. Errors in exports can result from errors either in the estimates of output, final demands, or regional coefficients. Sufficient data exist for good output and final demand estimates (nearly as good as those in the input-output years), par­ticularly for the large manufacturing sectors. There is some question about the assumption of constantly changing coefficients. However, since local demands tend to represent a small portion of the total demand on the outputs of the major exporters (about three percent in the case of aerosp ace ) , l arge relative errors in local demands are tolerable in these cases, as thes e errors translate into relatively small errors in the export estimates. 3

U.S. Output Variables

The major explanatory variable in the export equations is the U.S. output of good i in time t, USXi t • The most important sources of infor­mation regarding the past and fJture behavior of national activity are the nationa l interindustry forecasting models, all of which are tied to the Bureau of Economic Analysis' input-output tables for the U.S. As noted previously, there are three operational U.S. interindustry fore­casting models available: the INFORUM model developed by Clopper Almon and his associates at the University of Maryland; the Wharton interindus­try model of Ross Preston; and the BLS model of the U.S. Department of Labor. Generally, they provide annual forecasts of the national economy to 1985.

~easurement errors in the dependent variables also do not lead to biased or inconsistent parameter estimates in our export regression equations, as long as the measurement errors are random in nature.

-27-

One important consideration in the choice of the model with which to link is the problem of model alignment. This is more than simply matching up the industries of the regional and national models acco~ding to nominal output content. There are broader conceptual and definitional problems. For instance, among the models there are differing definitions of industry output: each model transfers and redefines economic activity in its own way, and some models use a variety of dummy industries. These conventions tend to complicate the task of linkage.

We have linked WPSM with the INFORUM model. As Table 3-2 on sector definitions indicates, there qre relatively few problems involved with matchifg the Washington model sector definitions with those of the INFORUM model. Indeed, a drawback of the Wharton and BLS models is their highly aggregated sectoring plans; and calculations have suggested that, as long as we stick with our 55-industry model, an aggregated national model may not provide sufficient information about the specific export markets of our Washington economy.

Price Deflators

It is necessary in our export sub-model to relate real export values to real U.S. output values, since our forecasts, as well as the national projections, are stated in 1972 dollars. For this purpose, regional out­put price indices should be developed. In some cases, this is possible. For example, there is a softwood plywood price index that permits us to estimate a regional price index for the output of the plywood industry. However, for most industrial outputs we must rely upon national price indices, which have been made available by Almon.5

The Results

Export Forecasting Equations

Regression analysis is used to estimate the export equation for each industry as given by (3-4); and depending upon the statistical results,

4we should re-emphasize that the matching of industries refers only to the correspondence of industries in terms of the SIC classification. It does not mean that the scope of activities covered by the Washington and U.S. sectors, as represented by their respective control totals, are strictly comparable. For example, the U.S. input-output tables, upon which the national projection models are based, handle secondary products, government enterprises, and imports in a quite different manner than the Washington tables. For our regional forecasting purposes, the U.S. indus­try outputs are simply an index of the behavior of the national markets served by Washington exports.

5An unpublished paper by Willet (1974) gives some information on wholesale prices in Washington during the period.

No. Indus try Name

1 Field and seed crops 2 Vegetables and fruits 3 Livestock and products 4 Other Agriculture 5 Fishing 6 Meat products 7 Dairy products 8 Canning and preserving 9 Grain mills

10 Beverages 11 Other food products 12 Textiles 13 Apparel 14 Mining 15 Forestry 16 Logging 17 Sawmills 18 Plywood mills 19 Other wood products

20 Furniture and fixtures 21 Pulp mills 22 Paper mills 23 Paperboard and other paper products 24 Printing and publishing 25 Industrial chemicals 26 Other chemicals 27 Petroleum refining 28 Glass and products 29 Cement, stone, and clay products 30 Iron and steel

Table 3-2

Sector Definitions of the WPSM, U.S. Input-Output and INFORUM Models

1967 SIC

Oll(exc . pt. 0119), pt. 014 012, pt. 0119, pt. 014

013, pt. 014, 072 019, 071, 073

09 201 202 203 204 208

Other 20 22 23

10-14 08

241 242 2432

Other 24, pt. 3791

25 261 262

Other 26 27

281, 287, 289 Other 28

29 321-323 Other 32

331, 332, 3399

U.S. I/0 Sector Nos.

2.01-03, 2.06 2.04-05

1.01-03, pt. 4.00 2.07, pt. 4.00

pt. 3.00, pt. 4.00 14.01

14.02-06 14.07-13 14.14-17 14.21-23

14.18-20, 14.24-32 16.01-04, 17.01-10, 18.01-03

18.04, 19.01-03 5.00, 6.01-02, 7.00-10.00

pt. 3.00, pt. 4.00 20.01

20.02-04 20.06

20.05, 20.07-09, 21.00, pt. 61.06

22.01-04, 23.01-07 24.01 24.02

24.03-07, 25.00 26.01-08 27.01-04

28.01-04, 29.01-03, 30.00 31.01-03 35.01-02 36.01-22

37.01-02, 37.04

INFORUM Sector Nos.

4-6, pt. 7 pt. 7

1-3, pt. 10 pt. 7, pt. 10 pt. 8, pt. 10

24 25 26 27

31, 32 28-30, 33,

36-40 41, 42

11-15, 17, pt. 8, pt.

46,

43 44 45

47, pt.

48, 49 50

pt. 51

34

18 10

153

pt. 51, 52-54 55-60 64-67 68-74 76-78

86 87-90 pt. 91

No.

31

32 33 34

35

36 37

38

39 40

41 42

43 44 45 46 47 48-52 53 54

55

Industry Name

Other nonferrous metals

Aluminum Fabricated structural metals Other fabricated metals

Nonelectrical motive equipment

Machine tools and shops Nonelectrical industrial equipment

Electrical machinery

Aerospace Motor vehicles

Shipbuilding Other manufacturing

Transportation services Electric companies Gas companies Other utilities Communications Construction Trade Finance, insurance, and real estate

Services

Table 3-2 (continued)

Sector Definitions of the WPSM, U.S. Input-Output and INFORUM Models

1967 SIC

Other 33

3334, 3352, 3361 344

Other 34 3391, 3392

351-353

354, 359 Other 35

36

372 371, 374, 375, pt. 379

373 30, 31, 38, 39

40-47, pt. 9190 491, pt. 493 492, pt. 493

pt. 493, 494-497 48

15-17, 656 50, 52-59

60-64, 65(exc. 656), 66, 67

70, 72, 73, 75, 76, 78-82, 84, 86, 89, 074

'U.S. I/0 Sector Nos.

38.01-03, 38.05-07, 38.09-10, 38.12-13

38.04, 38.08, 38.11 40.04-09

37.03, 38.14, 39.01-02, 40.01-03, 41.01-02, 42.01-11

43.01-02, 44.00, 45.01-03, 46.01-04

47.01-04, 50.00 48.01-06, 49.01-07, 51.01-04, 52.01-05

53.01-08, 54.01-07, 55.01-03, 56.01-04, 57.01-03, 58.01-05

60.01-04 59.01-03, 61.03-05, pt. 61.06, 61.07

61.01-02 32.01-04, 33.00, 34.01-03,

62.01-07, 63.01-03, 64.01-12 65.01-07, 78.01, pt. 79.01

68.01, 78.02, 79.02 68.02 68.03

66.00, 67.00 11.01-05, 12.01-02

69.01-02 70.01-05, 71.02

pt. 3.00, 72.01-03, 73.01-03, 75.00,

76.01-02, 77.01-05

INFORUM Sector Nos.

92-94, 96-98, pt. 99

95 103, 104

pt . 91, pt • 99 100-102, 105-110

111-114

115-117, 126 118-125

129-142

147-149 144, 145, 151, 152, pt. 153

150 80-85, 156-160,

162-166 167-173, 194

176 178 179

174, 175 19, 20

180, 181 182, 183, 185

pt. 8, 186-193

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one of the following four linear forms is adopted:

WEX. = a + bUSXi,t + ct + ut (3-7a) 1,t

WEXi, t = a + bUSXi t + ut (3-7b) '

WEX. = bUSX. + ut (3-7c) 1,t 1,t

WEX. = a+ ut. (3-7d) 1,t

The term ut in the equation set represents the fact that the model is stochastic, ut being an error term whose expected value and variance are assumed to be zero and constant, respectively. In all cases, U.S. output is kept as an explanatory variable only if parameter bis positive, b being the marginal propensity to export. Equation (3-7a) is chosen when the parameter c is statistically significant, usually but not always at the 95 percent level of confidence. Intercept a is dropped from equation (3-7b) when it is not significantly different from zero, which yields the constant shares relationship, equation (3 - 7c). When both time and U.S. output fail to explain variation in Washington exports, equation (3-7d) is adopted, which states that the volume of exports of good i is constant over time.

Estimated using the Ordinary Least Squares (OLS) method on annual observations from 1963 to 1972, export equations for the 15 largest export­ing industries are reported in Table 3-3. The first three columns of numbers give the regression coefficients of the export equations, below which in parentheses are their respective "t" statistics. The table also includes for each regression equation the coefficient of determination, il2, which has been adjusted for degrees of freedom; the Durbin-Watson sta­tistic, DW, which tests for evidence of autocorrelation; and the standard error of the estimate, SEE. In parentheses, the standard error of the estimate is also expressed as a percentage of the mean value of the observed exports, which approximates the average percentage error of the predictions over the ten observations.

The last column of Table 3-3 shows the name of the U.S. output vari­able when it is included in the forecasting equation. In parentheses are the INFORUM sector numbers to which each variable corresponds. In most cases, this variable is the output of the corresponding national industry. Exceptions to this occur in the aerospace, finance, insurance, and real estate, and logging export equations. Since aerospace in the state is dominated by The Boeing Company, primarily a producer of aircraft, the output of U.S. aircraft appears to be a better predictor. U.S. insurance output is the explanatory variable for finance, insurance, and real estate, since over 90 percent of the exports in this sector originate from insur­ance establishments. Foreign exports from the U.S. logging industry are a superior predictor of regional logging exports, as virtually all of Wash­ington external sales are to foreign markets. Initially, we felt that total Washington commodity exports would be a good predictor of trade and

Table 3-3

E!J!ort Forecasting Eguations

U.S. Output Industry Regression Coefficients (Percent Variable

No. Name Constant {a) u.s. Outeut {b) Time {c2 a:2 DW .SEE of mean} {INFORUM sector}

39 Aerospace 0.2902 0.89 1.58 232.6 (9.1) Aircraft (35. 7) (147)

53 Trade -498.4 0.0058 0.93 1.69 45.9 (5.7) Trade (-4.1) (10.9) (180-1)

32 Aluminum 0.0932 0.88 2.21 32.7 (5. 7) Aluminum (56.0) (95)

43 Trans. services -276.6 0.0201 -33.26 0.93 2.10 25.1 (4.0) Trans. services ( -2. 7) (4.6) (-2.5) (167-73, 194)

17 Sawmills 0.0901 0.24 1.15 39.6 (7 .2) Sawmills (53. 7) (44)

8 Canning 0.0311 0.54 1.52 25.1 (7.6) Canning (41.6) (26)

54 Fin., ins., re. 0.0091 0.83 0.79 22.4 (7.8) Insurance (40 . 9) (183)

23 Paperbd. mills 0.0120 0.64 1.89 21.7 (8.0) P aperbd. mi 11 s (39.9) (51-4)

41 Shipbuilding 0.1060 0.42 1.05 40.0 (9.5) Shipbuilding (30.1) (150)

18 Plywood 209.5 0.1521 -16.98 0.51 1.32 18.9 (7.4) Plywood (0.1) (3.2) (-3.3) (45)

40 Motor vehicles -140.8 0.0015 12.59 0.79 1.15 24.2 (14.0) Motor vehicles (-0.1) (1.0) (3.2) (144-5, 151-3)

22 Paper mills 251.1 12.9 (5.1)

27 Petroleum -383.3 0.0154 0.62 1.07 46.9 (37.9) Petroleum (-3.0) (4.0) (76-8)

1 Field crops 0.0096 0.62 1.55 18.1 (9.0) Field crops (35.4) (4-6)

16 Logging 0.5396 0.91 2.68 20.3 (13.0) Logging foreign (26.2) exports

(pt. 43)

-32-

transportation exports, since for the most part trade and transportation exports are margins associated with the delivery of locally produced goods. However, U.S. trade and transportation output variables have per­formed much better, apparently because of the independent growth of these services in the region with the emergence of the Puget Sound area as an interregional and international center of cormnerce.6

We have accepted statistical export equations for nearly all of the 55 industries. For the construction sectors and the meat products indus­try, however, exports are exogenous to the model. Exports of construc­tion represent federal government projects within Washington State and therefore are subject to decisions of policy. The regression equation for meat products measures such a sharp decline in exports that negative values are forecast. These predictions consequently have been altered. Judgment also has led to the alteration of forecasts in three other indus­tries--printing, fabricated structural metals, and other fabricated met­als--although these adjustments are minor.

It is clear from Table 3-3 that our simple model does not explain all of the variation in the export series; but by and large the statisti­cal results appear very satisfactory, especially for the larger exporting sectors. The standard error of the estimate as a percentage of the mean for the first eight exporters, whose volumes comprise 60 percent of total external sales, is 9 percent or less. Larger relative standard errors are more common for the smaller exporters. For three regression equa ­tions--sawmills, dairy products, and ferrous metals (the latter two not shown in the table)--there are conspicuously low coefficients of determi­nation. In each case, the ten observations for both exports and U.S. outputs stray very little from their mean values. Although the evidence is not strong to support a constant shares relationship, this model has been chosen in each instance.

Special note should be made of the Durbin-Watson statistic. In a number of equations, the statistic has low values, indicating evidence of autocorrelation. If autocorrelation does exist, the estimated regression equation may lead to poor forecasts. However, study of the residuals reveals an interesting pattern. There is a general tendency for the equa­tions to overpredict exports in the period 1966-1969 (see Table 3-4 and Figure 3-1). This evidence of a contraction of exports seems reasonable considering the state of the economy at that time. Triggered by growth in aerospace, the region was midstream in an economic boom. Characteristic of this rapid growth was a shortage of locally-produced inputs, evident in the increased propensity to import as measured by the 1967 interindustry study. We would expect this situation to have had two interrelated effects

6Note that the growth of trade exports is not nearly as great as that implied by the three input-output tables. This is because trade exports were underestimated in both the 1963 and 1967 studies.

12,000

11,000

10,000

9,000

8,000

7,000

6,000

I

I I

I

I-....._

Predicted Tota l Exports

1963

Actual Total Exports

✓

/ ✓ Predicted Exports Excluding

1965

Aerospace

/ /

., ,, ;" .,,

1967

,,, ,,, __ ,---- .....

Exports Excluding Aerospace

1969 1971

Figure 3-1

Predicted and Actual Washington Exports, 1963- 1972 (millions of 1972 dollars)

-34-

on regional exports. First, shortages in the local market would lead to higher prices and a diversion of some goods and services away from the national market. Second, shortages of inputs would tend to hinder the normal expansion of exports that one would expect given the growth in demands in the national market. In any event, low Durbin-Watson statistics suggest a close monitoring of export behavior. Of course, this word of caution pertains to all sub-models of WPSM.

Some Tests of the Model

The statistics in Table 3-3 depict the performance of the model for individual export series over the observation period; but an important question is how the model predicts export behavior overall. Table 3-4 compares predicted and actual total exports (including federal government value added) during the 1963-1972 period, while Figure 3-1 displays these results graphically. The table shows prediction errors, expressing them in percentages of the actual exports. One summary statistic on the model's performance, the average percentage error, is also shown.

Table 3-4

Predicted and Actual Washington Exports, 1963-1972 (millions of 1972 dollars)

Total Exports* Exports Excluding Aerospace*

Predicted Actual Percent Predicted Actual Percent Year Exports Exports Error Exports Exports Error

1963 8,319.0 8,409.6 -1.1 6,672.0 6,871.8 -2 .9 1964 8,825.1 9,468.9 -6.8 7,083.1 7,310.8 -3.1 1965 9,487.0 9,464.2 0.2 7,545.2 7,494.9 0.7 1966 10,608.0 10,342.0 2.6 8,160.1 8,105.4 0.7 1967 11,746.0 11,189.0 5.0 8,535.5 8,257.2 3.4 1968 12,540 . 0 12,261.0 2.3 8,877.7 8,693.4 2.1 1969 12,358.0 12,370.0 -0.1 8,980.1 8,847.8 1.5 1970 11,594.0 11,883.0 -2.4 8,859.9 8,808.9 0.6 1971 11,360.0 11,476 .o -1.0 8,929.3 8,988.6 -0.7 1972 11,821.0 11,671.0 1.3 9,594.0 9,617.9 -0.2 Average percentage error 2.3 1.6

*Includes federal government value added.

As a predictor of total exports, t he model does very well. The average percentage error without regard to sign in the ten years is only 2.3 percent; and in only two years, 1964 and 1967, does the error of the

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Table 3-5

Predicted and Actual Washington Exports for Selected Sectors, 1963-1972

(millions of 1972 dollars)

Transportation Equipment Lumber and Wood Products Predicted Actual Percent Predicted Actual Percent

Year Exports Exports Error Exports Exports Error

1963 2,010.7 1,920.9 4.7 922.6 951.5 -3.0 1964 2,148.3 2,595.8 -17.2 982.5 1,036.8 -5.2 1965 2,410.3 2,407.3 0.1 1,015.3 1,032.9 -1.7 1966 2,960.0 2,765.2 7.0 1,013.0 978.3 3.5 1967 3,757.5 3,427.3 9.6 1,092.1 964.6 13.2 1968 4,228.7 4,108.4 2.9 1,147.3 1,098.9 4.4 1969 3,967.1 4,146.1 -4.3 1,090.2 1,061.5 2.7 1970 3,304.2 3,650.1 -9 .5 1,150.2 1,077.9 6.7 1971 3,033.0 3,042.5 -0.3 1,115.1 1,186.9 -6.0 1972 2,879.8 2,647.3 8.8 1,340.8 1,356.4 -1.2 Average percentage e+ror 6.4 4.8

Food Products Paper Products Predicted Actual Percent Predicted Actual Percent

Year Exports Exports Error Exports Exports Error

1963 656.8 658.5 -0.3 628.0 629.8 -0.3 1964 720.8 676.5 6.5 644.7 648.7 -0.6 1965 706.8 729 .3 -3.1 648.2 677.4 -4.3 1966 738.3 791.5 -6.7 671.3 721.2 -6.9 1967 770.3 784.5 -1.8 677 .9 642.0 5.6 1968 777 .8 747.0 4.1 711.9 681.0 4.5 1969 830.8 799.1 4.0 735.0 714.9 2.8 1970 849.9 853.9 -0.5 710.8 716.2 -0.8 1971 855.4 894.2 -4.3 689.3 649.8 6.1 1972 887.9 848.3 4.7 720.2 767 .4 -6.2 Average percentage error 3.6 3.8

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estimate exceed 3.5 percent. There are two major turning points in the series, a downturn in 1969 and an upturn in 1972. The model catches the upturn but misses the downturn by one year, showing it instead in 1968. Once again one can note the tendency of the model to overestimate total exports during the aerospace boom.

Table 3-5 shows how the model predicts exports of four selected sec­tors. These sectors represent aggregations of related industries. For instance, transportation equipment includes aerospace, motor vehicles, and shipbuilding. It is apparent, as it is with the predictions of total exports, that the randomness of the disturbance terms in individual equa­tions leads to a reduction in the relative prediction error with aggrega­tion, which is a trait of forecasting models. This can be seen if one compares the average percentage error of the aggregated sectors with the comparable standard error percentages for the associated industries in Table 3-3. For example, the standard errors as percentages of their means are higher for each of the transportation equipment industries than for the aggregated sector.

It is also clear in Table 3-5 that by comparison there is some dif­ficulty with forecasting the exports of transportation equipment, as evident by an average percentage error of 6.4 percent. This is particu­larly troublesome given the volume of external sales in this case. Referring back to Table 3-4, if we exclude just aerospace exports from the total export predictions, we note a significant reduction of the average percentage error, from 2.3 to 1.6 percent. Furthermore, none of the errors in the ten years of observation now exceeds 3.5 percent.7

Conclusion

In general, the export model seems to perform well. Total exports over the observation period are predicted with a high degree of accuracy. Prediction errors of individual export series are much higher, but this result is not unexpected, given that the model is disaggregated into 55 industr ies. An axiom of forecasting is that it is more difficult to predic t i ndividual behavior than group behavior. Of the individual indus­tries, aerospace stands out as the greatest contributor to prediction error, primarily because of the size of its export volume.

7comparison of the two export series in Table 3-4 can lead to some interesting conjectures about the structure of the Washington economy. For instance, the annual growth rate of total real exports over the nine­year period averaged 3.7 percent, a very healthy rate by any standard. However, the growth rate of exports which exclude aerospace was even higher, at 3.8 percent. Furthermore, total exports excluding aerospace did not display any dramatic turnarounds; in fact, there was only one negligible downturn in the nine years (see Figure 3-1).

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A basic weakness of the model at this point remains the brevity of the observation period. However, it may be possible soon to increase the number of observations. Shortly, information will become available for measuring the region's exports in 1973 and 1974. Moreover, at least for the larger exporters, we may be able to make measurements on exports back to 1958. Thus, it is conceivable that the observation period can be extended from ten to 17 years, a much more satisfactory foundation upon which to conduct our statistical analysis.

With regard to the forecasts (which will be presented in Chapter 13), we should re-emphasize that the statistical relationships estimated above are based upon historical patterns of behavior. Forecasting with these equations is made with the presumption that there will be a continuation of this behavior into the future. One should not make predictions from a statistical relationship of exports unless a thorough examination of rel­evant forces, such as a newly emerging market or a future supply constraint, indicate that the basic forces affecting the region's comparative advantage are in fact unchanging. If there are foreseeable events that will shift the movement of an industry's export share off its observed path, then it is necessary to alter judgmentally the statistical forecasts.

CHAPTER 4 STATE AND LOCAL GOVERNMENT EXPENDITURES

Whereas the export block can be described as externally oriented, exports being predicted from variables that are exogenous to the Washing­ton Projection and Simulation Model, state and local government expendi­tures are for the most part related to economic variables that are determined inside of WPSM, such as income and population. This chapter describes the specification of the state and local government sub-model, beginning first with its theoretical foundation.

The Determinants of State and Local Government Expenditures

The Components of State and Local Spending

The objective of the state and local sub-model is to predict govern­ment expenditures by the sectors from which the purchases are made. The most direct approach would entail two steps: first, to make predictions of total state and local expenditures; and second, to apply the 1972 base year coefficients to distribute the purchases among the supplying sectors (i.e., Washington industries, households, and the import sector). Our procedure is somewhat more complicated, since we disaggregate total expen­ditures into a number of components and do not restrict ourselves to a constant (1972) coefficient assumption.

State and local government expenditures, which exclude transfer payments (such as welfare payments and unemployment compensation), are classified into educational .and non-educational expenditures, with a further distinction being made between construction and non-construction outlays. Note that in general input-output conventions do not distinguish between capital and current account expenditures for state and local gov­ernment. However, since it is felt that the behavior of capital expendi­tures is different from that of operational expenditures, and since the major local demand from capital spending stems from construction projects, isolation of construction activities would appear to be beneficial to our modeling efforts. Construction in turn is disaggregated into educational buildings, other buildings, highways, and other non-building structures. Also counted are purchases from the construction sector for maintenance and repair, but these are on current account and treated like all other non-construction purchases.

Modeling State and Local Government

One can adopt three points of view with regard to the behavior of state and local spending. The first is that expenditures are subject to the policies of government officials, in which case the variables might be considered as manipulatable and thus exogenous to the model. This in fact has been a c011111on approach taken by model builders. The second

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hypothesizes that expenditures are dependent upon the level of govern­ment revenues, a viewpoint which would require a model of the tax and non-tax revenue-generating process. The third considers expenditures as a response to the demand for the services provided by the state and local sector. It would seem that to a degree there is some truth in each of these viewpoints, and a proper specification of the state and local block would therefore necessitate consideration of each.

Nevertheless, WPSM essentially keeps to the demand formulation. Accordingly, non-construction expenditures are postulated to be functions of regional income and population. In particular, the educational compo­nent is related to school-aged population, per capita disposable income, and population, while non-educational expenditures are dependent upon disposable income. Intuitively, this specification argues that as regional population and income grow, the demands increase for publicly provided educational, health, and safety services. For example, if students in the state were to increase in number, we would expect greater educational spending for textbooks and the services of teachers, among other things. Although this represents a demand model similar in character to consumer demand specifications, the formulation does not necessarily contradict a tax revenue specification, especially given the prominence of income as an explanatory variable. One could argue that tax revenues determine expen­ditures, but that taxes are in turn predictable from total income, What­ever the case, as the estimated model will show, one apparently can antic­ipate state and local operational spending from information about the overall performance of the regional economy.

Construction purchases, which account for approximately one-quarter of total state and local expenditures, i nitially are postulated to depend upon demand factors different from those for non-construc tion expenditures, Although this is probably the case , construction equations estimated with such often used variables as interest ra t es, capital stocks, change in income, and change in population yield either incorrect signs or statis­tically insignificant values for the regress i on coefficients. The explan­atory variables settled upon include disposable income, change in school­aged population, and U.S. highway construction, the latter being an exoge­nous variable. In general, the cons t ruction equations are no t as s t r ong as the non-construction equations.

As noted above, once these tota l expenditures are predicted, it is necessary to translate them into demands for the products and services provided by Washington firms and households. With the construction sector disaggregated into four new-construction industries--residential housing, non-residential buildings, highways, and other non-building structures-­state and local construction expenditures represent direct demands upon these industries. For non-construction expenditures, it is necessary to distribute the totals by sector. As described in the following discussion of data, coefficients for both the educational and non-educational compo­nents have been estimated for 1972. With the exception of the value added coefficient, WPSM is presently being run under a constant coefficient

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assumption with regard to the state and local sector. That is, the pro­portion of educational expenditures going to printing and publishing is assumed to be the same in 1980 as it was in 1972. For value added, there are excellent time series data upon which to observe and make forecasts of changes in the two value added coefficients. For the other coefficients, one could analyze the changes predicted by the INFORUM model as an indica­tion of possible developments in the Washington coefficients. Of course, national models deal only with so-called technical coefficient change, arising out of technological change, variations in product mix, price changes and substitutions, and economies of scales. Our problem of pre­dicting future regional coefficients is compounded by our need to consider changes in trade patterns as well, since possibilities for variations in the propensity to import are much greater in small regional economies.

State and Local Government Data

Historical Expenditures Series

Unlike other final demand sectors, there are excellent historical data for state and local government expenditures. The primary sources of information are Governmental Finances and the Census of Governments, but other useful publications include State Government Finances, Public Employment, and the Survey of Current Business, all of which are published by the U.S. Department of Commerce.

Data have been collected for 16 years, from 1958 to 1973. Informa­tion before 1958 is not usable and data after 1973 are not yet available. Overall, Government Finances has proven to be a relatively clean and com­plete data source, requiring very few adjustments to convert the series into the form needed. The data have been disaggregated into educational and non-educational expendi tures as well as into construction and non­construction outlays. Also, historical series on value added have been developed.

Table 4-1 indicates the procedure for estimating total state and local expenditures for 1972. The control total estimate begins with total general expenditures for state and local governments for F.Y. {fiscal year ) 1971-1972 and F.Y. 1972-1973. Total general expenditures already exclude total outlays for utilities (electric, gas, water supply, and local tran­sit) and liquor stores, the activities of which are incorporated into the industrial sector of WPSM. Transfer payments, here listed as cash assis ­tance payments, are not considered direct expenditures by government and are deducted from the total. National income accounting conventions also lead to the exclusion of interest on general debt and capital outlays fo land and existing structures. We further deduct the expenditures of othe= utilities (sewerage and other sanitation), since they also have been counted in the utilities industry control totals. However, we add back all capital outlays for utilities, which are expenditures by state and local government but not for current operations. Next, we subtract the

Table 4-1

Procedure for Estimating Total State and Local Government Expenditures for 1972

(millions of current dollars)

Item

1 Total general expenditures 2 Less: Cash assistance payments 3 Interest on general debt 4 Land and existing structures expenditures 5 Total sewerage expenditures 6 Total other sanitation expenditures 7 State ferries expenditures 8 Department of Natural Resources forestry expenditures 9 Plus: Capital expenditures for sewerage

10 Capital expenditures for other sanitation 11 Capital expenditures for utilities 12 Capital expenditures for liquor stores

Equals: Total state and local government expenditures

Total state and local government expenditures, 1972

F.Y. 1971-2 FoY. 1972-3

3,068.8 163.7 116.2

80.2 58.6 20.7 14.7 12.7 40.1 1.2

104.7 0.2

2,748.4

3,612.0 186.7 130.5

71.1 87.3 22.6 16.1 14.1 67 .o 1.4

99.2 0.2

3,251.4

2,999.9

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operating expenses for the state ferry system (which has been included in transportation services) and for forest management by the Department of Natural Resources (which has been transferred to the forestry sector). Finally, the calendar year estimate is made by averaging the two fiscal year figures.

As a check of the above procedure, we have applied the same steps to estimate state and local expenditures for the U.S. in 1963, 1966, and 1967, years for which U.S. input-output control totals are available. In each case, the procedure yields estimates that differ from the control totals by less than 1.0 percent, a quite tolerable descrepancy.

The second step in estimating state and local outlays is to disag­gregate the control total into educational and non-educational components. This separation is straightforward, as Governmental Finances provides estimates of educational spending.

The third step, that of breaking out construction outlays, requires more data manipulation (see Table 4-2). Construction for four types of structures are estimated: educational buildings, other buildings, high­ways, and other non-building structures. The Census of Governments provides data for F.Y.'s 1962, 1967, and 1972, but not for other years; for the non-census years only capital outlays are reported. However, with the use of construction-capital outlay ratios from the census years and construction-capital ratios at the national level for all years, good estimates of total construction can be obtained. Sufficient infor­mation, primarily in the form of these ratios, is also on hand to make fairly reliable estimates of educational buildings and highway construc­tion. Less confidence can be placed i n the estimates of other buildings and other non-building structures. One last adjustment to the construc­tion series is to bring it into line with input-output conventions, that is, to make the estimates correspond with the new-construction-put-in­place concept. National relationships have been utilized in this task. Once more a replication of this overall procedure with national data indicates the basic soundness of our construction estimates.

Finally, independent series of value added are estimated on the basis of two sources, the Survey of Current Business and Public Employment. The latter reports detailed state and local employment and payroll data. Value added for government is defined as total employee compensation, which includes wage as well as non-wage compensation (such as payments for health insurance and employer contributions to soc i al security).

Mention should be made of the deflation method used to convert these current dollar series into constant 1972 dollars. The control total is deflated by the GNP implicit price deflator for total state and local expenditures. New construction deflators for the U.S. reported in recent issues of the Survey of Current Business are applied to the construction series. The national state and local value added deflator is employed with value added. Lastly, given these newly calculated real expenditures,

TyPe

Educational Highways

Table 4- 2

Procedure for Estima ting St a te arid Local Government New Construction (nillions of current dollars )

F.Y . 1971-2 F.Y. 1972-3 1972 Cnpi t a l Capital Estimated Estimated Outl ay Construction Outlay Construction Construction

buildings 139,5 104,3 126.4 94.5 99.4 267.7 214.7 305.2 244 .8 229.8

Other structures * 348.4 284 .5 447.9 367.1 325.9 ---Total 755.6 603.5 879.5 706.4 655.1

*Includes non -educational buil dings and non-building structures.

1972 Estimated New Construe tion

100,3 222.4 269.0 591.7

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all other purchases (i.e., the non-value added operational expenditures) fall out in 1972 dollars as a residual.

Base Year Purchases Coefficients

As total state and local government expenditures are broken down into two functions--educational andnon-educationa~-it is necessary to make estimates of the purchases coefficients vector for each function in order to translate the control totals into demands placed upon the Washington economy. Using the corresponding 1967 U.S. input-output vec­tors of technical (total) purchases, the 1972 Washington regional vector for total state and local government spending, which now excludes new construction, is disaggregated according to the two functions.

In general, the estimating procedure involves a reconciliation of U.S. input-output technical purchases estimates with the 1972 Washington regional purchases estimates. Involving five steps, the procedure is straightforward, although some judgment is required. The first step aggregates the 1967 U.S. input-output technical purchases (including value added but excluding new construction) following the WPSM sectoring plan (i.e., by the two state and local functions and the 51 non-new­construction industries defined by WPSM). Next, the two purchases vec­tors are scaled to sum up to the 1972 educational and non-educational control totals for Washington State. At this point, we have an initial estimate of the technical purchases (in dollar flows) for the two Wash­ington state and local functions. The third step brings the initial technical purchases vectors in line with the regional purchases esti­mates for 1972. One objective is to make sure that the total require­ments from a given industry exceed the corresponding regional purchases, as given in the 1972 input-output table. The adjustments of the third step are for the most part minor. This is comforting in that this exer­cise has given credibility to our 1972 input-output state and local gov­ernment estimates. The fourth step estimates the regional purchases by function, employing the assumption that the ratios of regional to tech­nical purchases from a given industry are equal for both the educational and non-educational vectors. Lastly, the regional flows are converted into vectors of coefficients, the results of which ar! shown in Appendix B, which presents the detailed specification of WPSM.

The Estimated Model

The principal equations for the state and local sub-model are shown in Table 4-3. Included at the bottom are definitions of the dependent and independent variables. The usual statistics for the regression

1For further discussion on how these coefficients are used in WPSM, see Chapter 7, which describes the industry output equations.

Table 4-3

Principal Equations of the State and Local Govenunent Expenditures Sub-Model

i2 DW SEE

OSLEDUPC5-20 = -0 .4055 + 0 .40231/YDPC (-11.2) (35.0)

0.0001253HCYCLE (-7.4)

0. 00008541:.JCYCLELl (-4.8)

0.99 1.13 0.0174

t·JSLEDU = WSLEDUPC5-20(WPOP5-20)

IJSLOTH = -300.0 + 0.1178\ ;YD - 0.08606l/CYCLE - 0.2294HCYCLEL1 - 0.1967WCYCLEL2 (-5.9) (22 .5) (- 2 .4) (-5.5) (-4.6)

HSLEDUBLD = -18.8 + 0.0121WYD + l.47llffiPOP5-2011 (-0.4) (3.4) (3.5)

WSLOTHBLD = -23.6 + 0,0114HYD (-2.1) (10.3)

0,0271HCYCLE (-3.3)

0.0359ilCYCLELl (-4.1)

WSLHHlAY = -116.0 + 0.0229USHH!AY + 0.00481JYD + 0.03641,iGYCLE + 0.0436WCYCLEL1 (-203 ) (4.5) (2.4) (2.5) (2.4)

HSLNONBLD = exogenous

0.97 2.08

0.49 1.13

0.88 1.46

0.89 1.62

WSLEDUPC5-20 WSLEDU \JSLOTH 1JSLEDUBLD WSLOTHBLD \:JSLHillAY !JSLNONBLD HYD

~ashington state and local education expenditures per capita aged 5 to 20 ~ashington state and local education expenditures

WYDPC f-JCYCLE 1-JCYCLELl WCYCLEL2 '.aIPOP5-20 l-!DPOP5-20Ll USHIHAY

Washington other s tate and local expenditures Washing ton state and local education buildings Washington state and local other buildings Washington state and local highways Washington state and local other non-buildings Washington disposable income Washington disposable income per capita Washington change in disposable income ~ashington change in disposable income lagged one year Washington change in disposable income lagged two years Washington population aged 5 to 20 Hashington change in population aged 5 to 20 lagged one year U.S. highway construction

38.9

26.9

8.7

15.4

(Percent of :Mec1n)

(1.9)

(5.0)

(18.7)

(11.5)

(7.2)

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equations, which have been estimated using the Ordinary Least Squares method on annual data from 1958 to 1973, are also shown in the table.

The two operating expenditures equations are stronger than the construction equations, as we have pointed out previously. In the first equation, there is evidence of autocorrelation, but the high coefficient of determination indicates that this should be of minor concern. For construction, the regression statistics are less comforting, indicating that projections have to be made with more than the usual amount of caution. For other non-building construction, the problem of forecasting is serious, as historical behavior is apparently no guide to the future, and since this component is relatively large in size.

Special note should be taken of the cyclic variables (here defined as changes in disposable income) that have been introduced into these equations. Although the objective of WPSM is long-term forecasting, it is sometimes necessary to include terms that account for short-run dis­turbances about the trends so that the influence of the basic growth factors can be discerned. Such seems to be the case with the state and local sub-model. For example, without the cyclic indicators in the other state and local expenditures equation, the coefficient of determination and the Durbin-Watson statistic stand at 0.80 and 0.42, respectively, both well below the values reported in Table 4-3. This is evidence of bias in the regression coefficient of disposable income in the formula­tion without the cyclic variables.

As for a theory about the nature of this short-term phe?omenon, one can only speculate. With the exception of highway construction, the cyclic variables enter with negative signs. This means that state and local spending has been countercyclical over the period of observation. One explanation for this might be found in the planning process of state and local governments. Since budgets are prepared in advance, and since total expenditures must be matched by tax receipts in Washington State, there might be a tendency for governments to "underspend" during years of "unexpectedly" high growth rates and to overspend during periods of unexpectedly low growth rates. Alternatively, countercyclical buffers might be in operation, including such mechanisms as capital borrowing and financial aid from the federal government.

Apart from the regression analysis reported here, there is no point in testing the sub-model at this time. One reason for this is the fact that, with the exception of USHIWAY, all explanatory variables are them­selves forecasted internally by WPSM. Thus, an assessment of prediction errors in the state and local block should take into account forecasting errors in the independent variables. It is more appropriate to leave this task until WPSM can be tested in its entirety. That is the subject of Chapter 12.

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Conclusion

It is evident from this investigation that economists can reasonably model the pattern of state and local government spending. This conclusion has implications not only for baseline forecasting but also for multiplier analysis. Typically, input-output practitioners, working mostly with static models, do not include state and local government as an endogenous sector in estimating the values of multipliers. However, the apparently strong relationship between state and local expenditures and income and popul ation suggests that the exclusion of this linkage is not only unnec­essary but can conceivably lead to a sizeable bias in the estimates of multipliers.

Nevertheless, there remain problems with this block. The theory is not \,ell developed, in particular with regard to the dichotomy of forces set up by tax revenue constraints on the one hand and regional demands for public services on the other. The construction equations have the greatest room for improvement, from both a theoretical and statistical standpoint. This latter finding should not be surprising, as capital spending, being rather "lumpy" in nature, is always difficult to predict. This point becomes all too clear in the following chapter on private investment.

CHAPTER 5 PRIVATE INVES'IMENT

Our approach to forecasting is to wed economic theory with regional data to produce a reasonable projection model. In the case of private investment, this marriage is shakey at best and the offspring is of ques­tionable character, as Almon might say. Although economists have devoted much attention to the theory of capital spending, providing insight into its determinants, the complexities of investment decision-making have not been completely unravelled, as evident by the loose-fitting investment functions. The problem of modeling regional investment is compounded by a poor data base. Apart from new plant and equipment expenditures for manufacturing, which represent only a small portion of total capital pur­chases, no direct measures are available.

At this point, one might well ask, why proceed ahead? The honest response is that we must, that is, if we are to build an input-output forecasting model at all. However, as the following study reveals, many of the problems posed here are either surmountable or of minor signifi­cance; and as long as we realistically lower our expectations about the investment block, we can conclude that our modeling task has met with some success.

Modeling Fixed Investment

An important characteristic of investment demands in Washington State is that the construction industry is the only local sector receiving a significant direct impact. As Table 5-1 on fixed investment indicates, over one-third of our capital requirements are imported; and of the remain­ing portion purchased locally, about 90 percent entails output from con­struction. Thus, our model should place emphasis on the investment in structures. To meet this end, fixed investment is divided into three cate­gories--residential buildings, non-residential structures, and equipment.

Residential Housing

Residential construction is the most important component of private investment. In 1972, housing expenditures constituted 29 percent of total fixed investment. More significantly, it represented 46 percent of invest­ment demand placed on local industries. In other words, with the specifi­cation of a housing forecasting equation, our task of modeling investment spending would be about half complete.

ntere are a number of approaches that economists have taken in model­ing residential housing. However, the endpoints are roughly the same iy each case, with equations incorporating both demand and supply factors.

1the interested reader might compare our model with that of Almon (Almon et al., 1974, pp. 89-105),who adopts a stock adjustment formulation.

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Table 5-1

Washington Fixed Investment, 1972 {millions of dollars)

Locally purchased Construction Trade and transportation Heavy metals Motor vehicles Nonelec. indus. eqp.

Imports Total

Investment

1,543.1 1,365.0

91.8 22.7 13.5 12.5

931.9 2,475.0

Percent

62 .3 55.2

3.7 0.9 0.5 1.5

37.7 100.0

We postulate a market model, hypothesizing that the price of housing adjusts until the market is cleared, that is, until demand equals supply. The equilibrium condition is expressed as

D =I+ S, (5-1)

where Dis the demand for housing during the time period in question, I is the investment in residential structures, and Sis the initial stock of housing. This relation implies that the demand for housing units (or more correctly, the demand for the services from housing) in conjunction with the existing stock of houses determine the level of residential investment.

Turning to the determinants of demand, we assume that individual consumers maximize utility subject to their budget constraints. Accord­ingly,

d = d(Ph, y, P, r), (5-2)

where dis the per capita demand for housing, Ph is the price of housing, y is per capita income, Pis the price of all other items purchased by the consumer, and r is the appropriate interest rate. Theoretically, higher income and lower consumer prices lead to greater demands, while higher housing prices and higher interest rates result in lower demands. With population symbolized by pop, total demand is given by

D = d(pop).

On the supply side, profit-maximizing behavior on the part of the construction industry suggests that

(5-3)

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(5-4)

where Ch is the cost of a house to the builder. As profits rise (i.e., as either Ph increases or~ decreases), we would expect investment to increase.

From equations (5-1) to (5-4), we can solve for either D, I, or Ph in terms of the other variables. Solving for investment, I, yields the reduced form equation

* I= I (pop, y, P, r, Ch, S)o (5-5)

It can be shown that the signs of the partial derivatives of this function are unambiguous; specifically, ~ 1/ ~ pop > O, c) I/~ y > O, ~ 1/) P > O, ) I/ c) r < 0, ~I/~ Ch < 0, and ~ I/~ S ~ O.

As one might expect, the residential housing equation actually esti­mated is slightly different. Total disposable income is substituted for population and per capita income. In the place of the interest rate, an interest rate differential is used. In this instance, it is defined as the difference between Moody's AAA bond rate and the interest rate for prime 4-6 month conmercial paper, and is a measure of the availability of credit in the mortgage market. 2 The sign of the regression coefficient for the interest rate differential is expected to be positive. For the cost of housing, an index is developed by dividing the Boeckh construction cost index by the GNP deflator for personal consumption.

Special attention should be given to the stock variable, since there is no information available on housing stock in Washington State. Fol­lowing the suggestion of Almon (Almon et al., 1974, pp. 89-97), we con­struct a stock variable. Let s0 ~e the actual stock in year zero, It the construction during year t, and d the retirement rate (assumed to be two percent). Then the actual stock at the end of rear t, St, is the sum of the surviving part of the initial stock, S

0(1-d )t • S

0(0.98)t, plus the

surviving part of construction since year zero. Hence,

(5-6)

If we substitute (5-6) into our investment function (5-5), we see that S0 is implicitly estimated during the course of estimating the coefficients of the stock terms. This formulation therefore avoids having to know the initial stock; but this is only made possible by assuming a value for the demolition rate.

2Almon (Almon et al., 1974) and Evans (1969), among others, have found this to be superior to other interest rate formulations.

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Non-Residential Structures

For modeling non-residential structures we follow a line of reasoning similar to that for residential housing. However, the equation ultimately accepted is of a more elementary form.

Although other variables have been tested, the only variable aside from the stock of non-residential structures entering into the equation is industrial output, representing the demand for capital. Specifically, the output variable is a weighted average of manufacturing and non-manu­facturing output~ the weights being determined by the historical amount of investment in structures per dollar of output for the two broad sectors. The weighted output, WA'WTl, is given as

WXWT 1 = 0 • 2 4WXMFG + 0 • 7 6WXNONMFG , (S-7)

where WXMFG is total manufacturing output and WXNONMFG is total non-manu­facturing output. Non-manufacturing output has a weight roughly three times as great as that for manufacturing, because investment levels per dollar of output have been about three times greater in non-manufacturing over the period of observation from 1958 to 1972. 'nle purpose of the application of weights is to explain changes in investment levels because of the increasing importance of non-manufacturing output over time.

The stock variable is constructed in the manner described in the section on residential housing. Once more a demolition rate of two per­cent is assumed.

Since we make a distinction between building and non-building struc­tures, one final step is to disaggregate total non-residential structures into these two components. On the basis of observations at the national level, the split as assumed to be into equal parts.

Equipment

The least important component of private fixed investment is the expenditures for equipment, since this spending has a relatively small impact on the Washington economy. For example, in 1972 about 85 percent of the equipment purchases by the private sector were made out-of-state. Thus, of the total expenditures of $963.9 million in that year, only $178.1 million represented demands placed on local producers. For this reason, it is less important to have precise equipment investment forecasts, which is a fortunate circumstance, as there are some apparent problems with the forecasting equation.

In national models, there have been generally two approaches to fore­casting equipment expenditures. The first is the use of a capital coeffi­cients matrix, which essentially measures the investment needed to increase productive capacity by one dollar. This model has been criticized because of the very simplified specification of the accelerator principle. The

-53-

approach is also difficult to implement empirically, especially at the regional level, as Miernyk and ShellhaDDller (1968) have discovered with the West Virginia model. The second approach is to model investment spending by purchasing industry. These models are very disaggregated (e.g., there are 89 equations in the Almon system) and involve the user cost of capital and rather complicated lag structures in output. At the regional level, implementation of this detailed model is not possible because of data inadequacies. But, as we have pointed out above, a com­plicated specification does not seem to be warranted in any event.

We adopt a very simple model~ relating equipment investment solely to output. The output variable is again weighted; in this case,

WXWT2 = 0.29WXMFG + 0.71WXNONMFG. (5-8)

Given the projected aggregate, we then allocate total equipment expenditures according to the industries from which the purchases are made. For this purpose, non-construction coefficients from the invest­ment sector of the 1972 input-output table are calculated. At this time, WPSM is running under a constant coefficient assumption with regard to these parameters.

New Construction and Equipment Expenditures

The following paragraphs outline the calculations of private fixed investment and construction (including public construction) for Washington State from 1958 to 1972 (see Table 5-2). In summary, there is no way of knowing whether these estimates are correct--at least as a matter of degree, they certainly are not correct. The figures, as well as the pro­jections that arise from them, can only be checked for their reasonable­ness.

General Estimating Methodology

Independently published data are available for residential building construction, non-residential building construction, non-building con­struction, state and local government construction, federal government construction, and manufacturing investment in plant and equipment for each year between 1958 to 1972. It is fortunate that the best information covers construction, since the construction industry is the only local sector that receives a substantial direct impact from regional investment demands, as we have pointed out. Nevertheless, even for construction the data are incomplete. Therefore, the goals of the estimating procedure are two-fold: to arrive at final estimates which (1) are reasonably con­sistent with published figures and (2) look reasonable in light of regional activity levels. With regards to the latter, consistency checks are made by comparing the relationship of output and investment in Washington with that relationship in the national economy. As the following passages reveal, this approach requires considerable data massaging with a liberal application of judgment.

Table 5-2

Priva te Investment and Public Construction in Washing t on, 1958-1972 {millions o f current dollars ~

1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972

Pr iva t e i nves tment 986. 3 1160 .6 11 56 . 6 1101 .4 1298 .3 1285.6 1291.2 1453.4 1800.3 2081.0 2359 .2 2388 .2 2165 . 2 2323 . 8 2469.8 Residential bu ildings 356 . 9 515. 5 449 . 6 407 .9 490.2 513. 7 427 . 3 410.5 448. 2 648 . 5 889.8 866.7 645 .3 667 . 4 71 2 . 5 Nonresiden t ial struc tures 274.3 240.9 256 . 3 271. 2 291.4 281. 3 293.6 372 . 2 523 .9 506. 2 516.9 517. 8 584 .6 679 . 3 637.0

Manufact uring structures 78. 5 31.4 30 . 4 38 . 5 42 . 9 39 . 6 36.5 61. 0 169 . 7 157 . 0 115 . 5 64.9 120. 8 175 . 7 87. 8 Buildings 78.5 31.4 30.4 38 . 5 42.9 39. 6 36 .5 61.0 169 . 7 157 .0 11 5. 5 64. 9 120. 8 175 .7 87. 8

Nonmanufac t uring structures 195 .8 209.5 225 . 9 232 . 7 248.5 241.7 25 7 . l 31 l.2 354 . 2 349.2 401.4 452 .9 463 .8 503 . 6 549 . 2 Buildings 97 . 9 104.8 11 2 .9 11 6. 4 124 .2 120 . 9 128. 5 155.6 177 . 1 174.6 200. 7 226.6 231.9 25 1 . 8 274. 6 Other nonbuilding struc tures 97 . 9 104 . 7 l l3 . 0 11 6 . 3 124. 3 120 . 8 l 28 . 6 l 55 .6 177 . 1 174 . 6 200.7 226 . 5 231.9 251. 8 274.6

Equi pmen t 355. 1 404 .2 450.7 422 . 3 516 . 7 490 . 6 570. 3 670 . 7 828 .2 926 .3 952. 5 1003 . 7 935 . 3 977 .1 112 0 . 3 Manufac tur i ng equi pment 82 . 0 80.5 111.4 102 . 6 143 . 5 103. 8 140.9 182 . 3 248 . 5 307 . 7 259 . 2 241. 2 190 . 2 188.8 260 . 2 Nonmanufacturing equipment 2 7 3 : 1 323. 7 339 .3 319 . 7 373 .2 386 . 8 429 . 4 488.4 579 . 7 618 . 6 693.3 762.5 745 . l 788.3 860.1

Fed e ral construc ti on 141.4 l39 .5 156 .6 150 . 8 11 9 .8 l42 .9 200 .2 239. 5 263 .2 256 .0 239. 6 204.4 209.0 277 . 3 272. 7 Buildings 14 . 4 15 .6 14.3 17 . 3 18 .3 19.7 l 9 .3 2 1. 6 21.3 15 . 0 13 . 0 13. 2 12. 6 18.5 22. 4 Other nonbui l ding s tructures 12 7 .o 123 .9 142.3 l3 3 .5 lOl . 5 123 . 2 180 .9 217 .9 241 .9 241 . 0 226 . 6 191. 2 196 . 4 258 . 8 250.3

St a t e and l oca l cons truction 316. I 293 . 1 283 .0 322 .5 343~6 359.3 463. 6 562 .4 522 .2 544. 7 564 . 5 607. 5 649. 3 623 . 5 59 1. 7 Educa tiona l build ings 65 .0 65.5 69 .2 80.7 85 .6 87.8 78 .2 8!. 6 99 .3 118. 4 147.3 173.5 188 . 0 149 . 7 100 . 3 Other buildings 45.0 34 . 7 28 .5 31.2 32 . 6 33 . 7 40.9 43 .4 41. J 39 . 0 47 . G 75. 9 103.J 110. 6 109 . 9 Hi ghways 102 . J 9J . 2 79. 4 93.6 11 5 .6 129 .0 136 . J 155. l 19 0 . 5 2 16. 4 204 .9 194. 7 197. 3 201. 7 222 .4 Other nonbuilding s truc t u r es 103.8 99 . 7 105.9 ll 7 .0 109 . 8 108 . 8 208.2 282 . ) 191 . l 170 .9 164 . 7 163 . 4 160 . 7 16 1. 5 159.l

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Public Construction

(i) State and local government construction. The procedure for making state and local government estimates has been presented in Chapter 4. Overall, we have great confidence in their reliability, as the under­lying data appear excellent. The estimates are based upon detailed annual capital expenditures information for a variety of government functions as well as upon construction data for the census years. The estimates are made consistent with the C-30 new construction-put-in-place public expen­ditures figures published in Construction Reports, which brings them in line with our input-output construction concepts.

The estimates are shown in Table 5-2. The total is disaggregated into four components: educational buildings, other state and local govern­ment buildings, highways, and other non-building structures. It should be pointed out that a small portion of buildings, about 10 percent, includes residential buildings, but this component is not isolated.

(ii) Federal government construction. The estimates of federal gov­ernment new construction are not as good as the state and local government estimates, although considerable effort has been expended in making them.

With the exception of the publication entitled Federal Outlays in Washington by the National Technical Information Service, which is avail­able only for recent years, there is no comprehensive source of informa­tion on federal expenditures in the state. It is therefore necessary to piece construction estimates together from budgetary data of the various government agencies. The best information is found in the Appendix to the Budget of the President.

Actual construction outlays reported in the budget closely correspond to the C-30 new construction-put-in-place estimates of federal government. Five agencies--the Corps of Engineers, the Bureau of Reclamation, Bonne­ville Power Administration (BPA), the Atomic Energy Commission (AEC), and the military--are identified as responsible for the bulk of construction, not only in the U.S. but particularly in the state. There is excellent information on state construction projects by the Corps and the Bureau of Reclamation. The Budget annually reports the obligations incurred and the location of each project, so the estimating procedure simply involves going from obligations incurred to actual outlays, the latter totals being reported for the two agencies. For BPA, the AEC, and the military, the estimating procedure is much cruder, but the flows involved are much smaller. A very small residual construction component in the state is estimated as one and one-half percent of national construction after the construction for the five agencies is deducted. This percentage is chosen because it is roughly Washington's share of national income.

From this total construction figure for the state, estimates are made for the building and non-building components (see Table 5-2). All con-

\ -56-

struction for the five agencies is asstm1ed to be of non-building struc­tures. From C-30 information it is possible to estimate the proportions of the residual construction that are building and non-building. On the average, 85 percent of this construction is building. This percentage is applied to the Washington residual construction component to obtain the estimate of building construction. Again, a small portion of building construction {about five percent) is for residential buildings.

As Table 5-3 shows, an unusually high proportion of federal non­building construction occurs in the state. On the other hand, the build­ing estimates are in line with our share of income, which seems very reasonable. As previously noted, these estimates are not precise. It is especially important not to take too much stock in the figures for any single year. But overall the general level and pattern of activity are credible.

Table 5-3

Federal Government Construction in Washington and the U.S., 1960, 1965, and 1970

(millions of current dollars)

Total Buildings Non-Buildings

1960 1965 1970

Wash.

157 240 209

Percent U.S. of u.s. Wash.

3,622 4,014 3,290

4.3 6.0 6.4

14 22 13

Private Residential Construction

Percent U.S. of U.S. Wash.

1,024 1,460

902

1.4 1.5 1.4

143 218 196

Percent u.s. of U.S.

2,598 2,554 2,388

5.5 8.5 8.2

There are two series of data from which it is possible to make resi­dential construction estimates--from state contract construction values reported by F. w. Dodge and from state pennit data from Construction Reports, series C-40.

Two steps are entailed in the estimating procedure. First, the historical Dodge contract values are "smoothed" by taking a two-year moving average, calculated as the average of the reported values in the given year and the year prior to that. This removes some of what appears to be erratic behavior in the Dodge figures. The averaging process follows f~om an assumption that there is a six-month lag between contracts and actual construction; that is, we assume that one-half of the contracts in any given year represents construction that actually takes place in the following

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year. It may well be incorrect to smooth this series, but removing the extremeties of the peaks and troughs should not bias our long-run fore­casts to any appreciable degree.

In part because the Dodge series represents only residential con­struction under contract, it understates total construction-put-in-place. Therefore, the second step is to "blow up" the Dodge figures to put them on the same footing as the C-30 series. For this, we use the C-30 and Dodge residential construction series for the U.S.:

(5-9)

where WIRES is estimated Washington residential construction, WRd sand USRd s are smoothed residential construction Dodge contract value~ for Wash{ngton and the U.S., respectively, and USRc 30 is the value of new residential construction-put-in-place in the U.S. A last minor adjust­ment to this estimate is to remove public residential housing, which is already counted in public building construction.

The results are once more found in Table 5-2. Table 5-4 repeats the series as well as exhibits the original Dodge data, the smoothed Dodge series, and the historical permit-issuing data. Year-to-year per­centage changes are calculated as a means of comparing the four series. The similarity in the percentage changes in the Dodge and permit-issuing series indicates that the two are largely measuring the same thing. It would seem that the permit-issuing series would also tend to anticipate actual construction somewhat, as we assume the Dodge series does. Com­paring the smoothed Dodge data with the original series reveals that not only are the fluctuations tempered by averaging but the turning points are delayed in most instances. The net effect on the present Washington estimates is that the direction of change in these estimates are contrary to the direction of change in the original Dodge series in three years, 1961, 1963, and 1965.

In summary, our feeling is that the general level of housing con­struction as indicated by the estimates is reasonable, but that the timing of fluctuations may well be off in some instances. At any rate, we will stand by these estimates, at least for now.

Manufacturing Plant and Equipment Expenditures

No estimating procedure is involved with manufacturing plant and equipment expenditures. All figures are taken directly from either the Census of Manufactures or the Annual Survey of Manufactures. Nevertheless, we should maintain a healthy skepticism about these data, especially in the survey years. There appear to be some peculiar movements, such as plant investment in 1971 (see Table 5-2). The Bureau of Economic Analysis report entitled Interindustry Transactions in New Structures and Equipment, 1963 and 1967 shows that virtually all manufacturing structures should be classified as non-residential buildings.

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Table 5-4

Washington Residential Construction, 1958-1972 (millions of current dollars)

Smoothed Permit-Est. Percent Dodge Percent Dodge Percent Issuing Percent

Constr. Change Contr. Change Contr. Change Value Change

1958 357 287 251 1959 516 +44.5 368 +28.2 328 +30.7 1960 450 -12.8 263 -28 .5 316 -3.7 1961 408 -9.3 291 +10.6 277 -12.3 1962 490 +20.1 376 +29.2 334 +20.6 ... 1963 514 +4.9 347 -7.3 362 +8.4 304 1964 427 -17.0 292 -15.9 320 -11.6 257 -15.5 1965 411 -3.7 337 +15.4 315 -1.6 293 +14.0 1966 448 +9.0 392 +16.3 365 +15.9 360 +22.9 1967 649 +44.9 609 +55.4 501 +37.3 597 +65.8 1968 890 +37.1 704 +15.6 675 +34. 7 707 +18.4 1969 867 -2.6 574 -18.5 657 -2.7 537 -24.0 1970 645 -25.6 449 -27 .8 512 -22.1 416 -22.5 1971 667 +3.4 483 +7.6 466 -9.9 485 +16.6 1972 713 +6.9 589 +21.9 536 +15.0 548 +13.0

Non-Manufacturing Plant and Equipment Expenditures

The weakest link in this chain of estimates is for non-manufacturing plant and equipment expenditures. In fact, we have no regional data on these at all. As a consequence, we must resort to making estimates strictly on the basis of national relationships.

There are many possible estimating procedures, each providing different, though similar, results. The method we choose is to base investment spending on investment-employment ratios in the U.S. and employment levels in the state:

IHNONMFG = WNNONMFG(USIN0NMFG/USNNON:MFG), (5-10)

where WINONMFG and WNNONMFG are private non-manufacturing investment and employment in Washington, respectively. Separate estimates in this manner are made for structures and equipment. Structures are further disaggregated into building and non-building structures. From Interindustry Transactions in New Structures and Equipment, 1963 and 1967, the average percentage of non-manufacturing structures for buildings in the U.S. in those two years is found to be 50 percent. Using this percentage for Washington, one-half of non-manufacturing structures are estimated to be buildings and one-half are taken to be non-buildings.

-59-

The assumption that national investment-employment ratios apply to Washington does not seem unreasonable, although most certainly there are differences because of, for example, differing mixes of industries within this broad grouping in the two economies. In general, we would expect capital stock to be related to employment levels. But since a major portion of investment is for replacement of worn-out durable goods, we would also expect investment to be somewhat related to employment. However, using the national ratios probably has a smoothing effect on local investment estimates, as some of the volatility of net investment, which is more related to changes in activity levels, is most likely lost in the estimating process.

Investment and Construction in the Aggregate

At this point, we have built up the investment and construction estimates from estimates of their respective components. The question now is whether the estimates in the aggregate appear reasonable.

Four checks of consistency have been made: a comparison of total investment and GSP in Washington with total investment and GNP in the U.S.; a comparison of the relative movement of regional investment with that of national investment (both relative to total activity in their respective economies); a comparison of the state investment figures here with those made by others, namely those in the Washington input-output tables; and a check on the implied estimates of construction, in total and by the three major types--residential buildings, non-residential buildings, and non-building structures. With the exception of the input­output check, there emerges no readily apparent inconsistency in the investment and construction series. In the case of the input-output estimates, the present investment estimate in 1972 is virtually equal to the 1972 input-output figure, which is very comforting. On the other hand, the estimates differ appreciably in the other two input-output years, especially in 1967. For 1967 we feel that investment in the input­output table has been greatly overstated. As a percentage of GSP, it exceeds 23 percent, which now seems very unlikely. A more careful esti­mate in 1963 has been made, and the difference with ours is not large. Investigation indicates that this disparity can be traced to the estimate of private structures, in particular to residential housing. In this instance, we feel that the input-output figure is too low. In any event, the picture of investment over time presented here is quite different than that shown by the three interindustry tables.

Price Deflators

National price deflators are used to transform the current dollar series reported in Table 5-2 into constant 1972 dollar~. For equipment the implicit GNP deflator for that category is used. Four individual national deflators, which have been newly revised by the Bureau of Eco­nomic Analysis, are applied to the construction series. The first three cover residential buildings, non-residential buildings, and highways. For other non-building structures, the public construction deflator is borrowed.

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Fixed Investment Forecasting Equations

The three fixed investment regression equations are shown in Table 5-5, accompanied by definitions of the variables. The residential housing equation has been estimated using the Ordinary Least Squares method on annual data from 1958 to 1974, while the period of observation for the other two dependent variables covers 1958 to 1972.

Many variations of the residential housing equation have been tested, but the superior fit includes, as independent variables, disposable income lagged one year, the differential between long-term and short-term inter­est rates lagged one year, relative construction cost, and the current stock of housing, the latter being an expression composed of two terms, WSRES and TIME98. All of the signs of the equation are theoretically correct; and the coefficients are significant at the 95 percent level of confidence. With regard to historical predictions, the equation seems to fare well. Its corrected coefficient of determination, at 0.87, is quite high, in spite of the wide swings in housing construction. Reference to the plot of the equation in Figure 5-1 further shows that it picks up all turning points, with the exception of 1965, where it is predicted one year earlier. As one last check, when comparing our projections with Almon's under the same growth conditions, we find roughly the same fore­casts. This does not necessarily mean that our model is correctly speci­fied, only that we are in good company.

For the non-residential structures forecasting equation, similar results are obtained. All regression coefficients have the correct sign and are statistically significant at the 95 percent level of confidence, or nearly so. Prediction errors over the observation period are not large, and the model also gives reasonable projections. Under the growth condi­tions of the INFORUM model, Washington investment relative to U.S. invest­ment remains fairly constant at about 1.5 percent, more or less its his­torical value.

The value of the corrected coefficient of determination for equipment investment is unexpectedly high. Ostensibly, this is due to mild fluctua­tions in the historical equipment investment series coupled with a strong time trend. The apparent behavior of investment over time is in turn a possible consequence of the estimating procedure used to develop the series, a procedure that probably has a smoothing effect on the fluctuations. We note that the Durbin-Watson statistic indicates problems of autocorrelation, which suggests that the regression equation could be off the long-run growth path. But once more a comparison with Almon's projections indicates that the model yields reasonable forecasts.

Inventory Change

Inventory change in general is not a very important demand component, especially over the long run. Nevertheless, neglecting it would bias our

Table 5-5

Fixed Investment Forecasting Equations

DW (Percent

SEE of mean)

WIRES= 7459.8 + 0.4752WYDL1 + 12645.6USDRL1 0.87 1.72 56.8 (3.1) (8.9) (6.1)

-2780.7USCH/CP - 0.4614HSRES - 8360.1TIME98 (-4.4) (-5.4) (-3.1)

WIOTHSTR = 2966.2 + 0.1026WX-WT1 - 0.1744WSOTHSTR o. 77 1.80 58.6 (1.8) (2.9) (-2.6)

-3351.5TIME98 (-2.0)

WIEQP = -332.5 + 0.1080WXIIT2 (-3.5) (13.2)

0.93 0.83 68.4

WIRES WIOTHSTR WIEQP WSRES WSOTHSTR HYDLl WX}/Tl WXWT2 TIME98 USDRLl USCH/CP

Washington residential investment Washington investment in other structures Washington investment in equipment Washington "stock" of residential housing Washington "stock" of other structures Washington disposable income lagged one year Washington weighted output for structures Washington weighted output for equipment (0.98)t U.S. interest rate differential lagged one year U.S. ratio of housing cost to consumption cost

(7.9)

(10.4)

(8.3)

Residential Investment

1000

900

800

700

600

1958

\ '\

1960

\

\ I

'

1962

\ \ \ /

\/

1964

Actual

Predicted

1966 1968 1970

Figure 5-1

Actual and Predicted Residential Investment, 1958-1974 (millions of 1972 dollars)

1972 1974

projections on the low side.

Definitions and Conventions

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Our definitions and conventions of inventory change should be as consistent as possible with those of the U.S. input-output study, which in turn are consistent with the national income and product accounting procedures. The following passage from the Survey of Current Business (July 1969) gives the national input-output definitions and conventions for inventories:

The inventory change shown for each industry represents the change in inventories of the industry's products regard­less of which industry actually owns or holds the inventories. (This is different from the customary industry inventory figures which represent inventories held by each industry.) Inventories are so classified in the input-output table in order to provide the balance between the output of each indus­try and the total consumption of its products. Current pro­duction includes products which end up in inventories and are therefore not reflected in consumption. On the other hand, consumption may come from inventories of the producer, of the consumer, or of trade companies as well as from current output. To the extent it comes from inventories, it is not included in current production. Therefore, adding inventory increases of products of the industry to, and subtracting depletions from, the consumption of that industry's products achieves the bal­ance with gross output of that industry.

Following these conventions would lead us to inventory change esti­mates for all industries producing storable commodities. The service sectors, with the exception of trade and transportation, would show no inventory change. For trade and transportation, the inventory estimates would be only of margins associated with inventory items, reflecting the input-output convention of using producers' prices.

It should be pointed out that the national conventions are not the conventions of the 1972 Washington input-output study. In our regional estimates, we account for only a portion of total inventory change, that being the change in work-in-process and finished goods held by manufac­tures, We do not take into account such inventories as the raw materials of processors or the goods on hand of trade establishments. If we were to estimate total inventory change in the state in such a manner as to give us a total figure comparable in scope to that in the national accounts, we would have to make estimates of these neglected components. Furthermore, we would have to estimate how much of these inventories emanated from local producers and how much was imported.

However, it is clear from the standpoint of estimation that we cannot ever account for inventories in such a manner. Among other things, there

-64-

is no direct information on, or no way of estimating, the amount of inventory build-up consisting of imported conmodities.

Nevertheless, for forecasting purposes, we should attempt to project the inventory build-up in Washington that represents demands placed on local producers. Once again, neglect of this demand would bias our pro­jections downward. How this projection of inventory change is accom­plished is the subject of the next two sections.

The Inventory Change Model

The most connnonly used model, and indeed the one used by Almon, for inventory change is the stock-adjustment model. One reason for the choice of this form is that it leads to cyclic behavior, which is characteristic of inventory investment. It is hypothesized that there is a desirable inventory-sales ratio, vi, for good i. Given sales of good i in time t, Si(t), a desired stock of inventory is specified as viS 1(t). If the rate of adjustment between ~he desired stock in a given period and the inventory in the previous period, Vi(t-1), is b, inventory change is specified as:

(5-11)

In implementing his model, Almon makes two assumptions. First, he sets Vi equal to Vi(l971)/Si(l971), assuming that the 1971 inventory-sale~ ratio is a desirable one. He further sets b equal to 0,60. (He states in his book that he is now estimating individual inventory change equations, presumably each having unique values for Vi and b.) Thus, his inventory investment model for good i is given by

V.(t) - V. (t-1) = 0.60(v.(197l)S.(t)-V.(t-l)). l. l. l l. l

(5-12)

For our purposes, we can make two more simplifications. First, it can be shown that once an economy is on its long-run growth path, with a constant or slm,;ly changing rate of growth, the difference in inventory projections assuming b=0.60 and assuming b=l.00 is negligible. In other ~wrds, for our long-run model, we can assume an instantaneous adjustment without significantly affecting our long-run projections. This leads us to

(5-13)

where

V-(t-1) = vi(l97l)Si(t-l), l.

(5-14)

which yields

Vi(t)-Vi(t-1) = vi(l97l)(Si(t)-Si(t-l)) (5-15)

or

AV.(t) = vi(l97l)ASi(t). l.

(5-16)

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The second simplification is to substitute A Xi (t), the change in gross output for industry i, for ASi(t). The sales variable, as Almon uses it, is defined as the domestic use of good i by final demand and other industries plus exports. The relationship between X and Sis given by

(5-17)

where Mi represents the imports (at the national level) of good i and aii is the units of product i required to produce one unit of itself. Once more it can be shown that for an economy growing at a roughly con­stant rate

Substitution yields

A v = .{\. i (5-18)

(5-19)

However, .AVi(t), as defined by the above equation, strictly speaking is not the desired projection of inventory change in Washington. This equation estimates for a given change in gross output of industry i the expected change in inventories of i held by all holders, including those outside the state. In principle, goods produced in Washington but held as inventories elsewhere have been previously counted in our state's exports. As we are only concerned with local inventory demands, vi(l971) must be reduced to some value, v~(l971), to reflect only local holdings. Thus, we might restate the above1 equation as

* AVi(t) = vi(l971)AXi(t), (5-20)

Estimating the Parameters

* The following procedure attempts to estimate vi(1971). We first must estimate vi(l971) as used in the Almon equations, since he does not report these values directly. Following the line of argument above and noting that Almon's projections are more or less on their long-run growth paths by 1985, we estimate vi(l971) as

vi(1971) = AV. (1985)/ AX. (1985). 1 1

(5-21)

It would have been preferable to use ,Asi(1985) in place of Axi(l985); but Si(l984) is not reported, while Xi(l984) is. In any event, the loss of precision is not great.

The second step is to estimate the proportion of inventory change associated with sales of good i held by Washington sectors. For most goods, a major portion of inventory is held as work-in-process and finished goods by the processors themselves. Thus, if we could estimate what portion of

-66-

total inventory constitutes work-in-process and finished goods, we could allocate this directly to local inventory holdings. Using our estimates of vi(l971), Almon's reported sales for 1971 (Si(l971)), and inventory estimates from the Survey of Manufactures, estimation of these portions is possible,

Deducting estimated work-in-process and finished goods from total expected inventory holdings leaves us with residual inventories held by both local and out-of-state establishments, A reasonable estimate of the local portion of this residual is the ratio of internal sales (sales to local industries, consumption, investment, and state and local government) to total sales (which include sales to the federal government and exports as well as internal sales), as measured in 1972.

From the above estimates, we can make estimates of * vi (1971). The values of these are sho,;m in Table 5-6.

Table 5-6

Parameter Estimates for the Inventory Change Equations

HPSM WPSM \\!PSM * * v~(l971) Sector v.(1971) Sector v . (1971) Sector 1 l.

1 0.28 15 0,00 29 0,04 2 o.oo 16 0.18 30 a.as 3 0.32 17 0,10 31 0.17 4 o.oo 18 0,06 32 0.12 5 o.oo 19 0.04 33 o.os 6 0.03 20 0.15 34 0.11 7 0.01 21 0 . 04 35 0.11 8 0,13 22 0.02 36 0.09 9 0.01 23 0.04 37 0.12

10 0.07 24 0.07 38 0.08 11 0.10 25 0,08 39 0.20 12 0.08 26 o. 11 40 0,04 13 0.10 27 0,03 41 0.11 14 0.04 28 0.14 42 0.12

To complete our specification of this sub-model, we must take into account trade and transportation margins, since our inventory change equations are stated in terms of producers' prices. To do this, we simply add up inventory change in all sectors and apply to this subtotal the appropriate trade and transportation margins. Taken from the 1967 U.S. input-output study, these margins are estimated to be 0.0445 and 0.0200, respectively.

-67-

Conclusion

If nothing else, this discussion has demonstrated that with respect to regional investment forecasting we have a long path yet to follow before we reach any place of real comfort. Although investment theory is not as well developed as one might like, the biggest obstacle in our sojourn is the paucity of investment data for Washington State. It can­not be overemphasized that the historical data for the region are only estimates, and in no way is their quality comparable to that for national investment and construction figures. These estimates have been made consistent with published regional data and historical movements in eco­nomic activity in the state as well as have been checked for reasonable­ness with national figures. But only the god in charge of income and product accounts for Washington State knows for sure their true accuracy.

Given the meager data upon which to estimate regional investment equations, one might well ask, why not borrow national investment func­tions (properly scaled, of course)? From our experience here, there would appear to be much merit in this approach. This certainly would be the case for equipment expenditures, which have minimal feedback on the local economy and for which there is little information. And, as we show in the next chapter, this strategy is indeed employed for household consumption spending.

CHAPTER 6 PERSONAL CONSUMPTION

Many analysts have expended considerable effort in modeling personal consumption, since household spending is a major determinant of economic activity. Some economists, such as Emerson (1971), have even attempted to carefully specify regional consumption behavior, under the presumption that expenditures patterns vary from region to region.

We question the value of such an extensive effort in our case, since we have very little usable information on consumer behavior in the state. Instead, we feel that we can borrow from work done at the national level. Adopting a national consumption model would require two assumptions: that aggregate consumption relative to disposable income is the same in Wash­ington as in the U.S., and that changes in spending patterns at the national level are reflected in changes at the regional level. Note that this stops short of assuming that the mix of goods and services purchased by Washington households is the same as that purchased by U.S. consumers. By using the 1972 input-output estimates for regionally produced goods and services purchased by local households, the WPSM model would capture the peculiarities of Washington consumption patterns.

Specification of the regional consumption model requires three things: (1) a forecast of total consumption; (2) an estimate of base-year coefficients that transform total spending into demands placed upon local industries; and (3) estimates of changes in these coefficients over time.

Forecasting Total Consumption

Fi tting U.S. aggregate consumption (USCTOT) to disposable income (USYD), both in constant dollars, yields the following equation:

USCTOO' = 16830.3 + 0.8840USYD. (4.8) (147.5)

R =1 . 00, DW=l.80, SEE=4617.1 (0.9), OLS 1950-1974

(6-1)

This appears to be an excellent fit, even better than a formulation con­sistent with the permanent income hypothesis, which relates consumption to current disposable income and last year's consumption. The aggregate model also seems to give reasonable projections. Table 6-1 compares the ratio of total consumption to total disposable income from our model with the ratios from the more complex specifications of INFORUM and the Bureau of Labor Statistics (BLS). Assuming the same national disposable income level as INFORUM for 1985, our ratio of consumption to income is about 1.5 percent higher. At the level of income predicted by BLS for that year, our ratio is lower by about 0.5 percent. In other words, equation (6-1) is projecting consumption relative to disposable income somewhere

-70-

in between the INFORUM and BLS forecasts. It is interesting that these two national models diverge in their projections to the extent that they do, not only in their levels of consumption but also in their average propensities to consume.

Table 6-1

Projections of U.S. Consumption in 1985 (billions of 1972 dollars)

Consumption Disposable income Ratio

Comparison with INFORUM Projection

INFORUM

1,037.1 1,175.2

0.8825

Ours

1,055.7 1,175.2 0.8983

Comparison with BLS Projection

BLS

1,260.9 1,397.2

0.9025

Ours

1,251.9 1,397.2 0.8960

Of course, equation (6-1) is inappropriate for forecasting Washington consumption. In particular, it is necessary to scale the intercept to the proper size. Over the last 25 years, Washington's disposable income as a percentage of U.S. income has averaged 1.72 percent. Scaling the intercept by this percentage gives

WCTOT = 291.2 + 0o8840WYD, (6-2)

where the prefix W refers to Washington State.

One further test of the model is to ask how equation (6-2) would have predicted Washington consumption for the three input-output years. Table 6-2 shows the results. The first collll!ln gives the predictions from equa­tion (6-2). The prediction errors for 1963 and 1972 are small, about 2 percent in both years. However, the error in 1967 is quite large, around 10 percent, We think that the error for 1967 is less of a reflection of the operation of the model than of the input-output estimate for that year, although one could believe that, given the booming economy at that time, the conslll!lption level relative to disposable income was somewhat lower than normal in that year.

The second column in Table 6-2 shows predictions on the basis of the following equation:

WCTOT = 89.9 + 0.8840WYD.

Equation (6-3) differs from (6-2) only in that the intercept has been reduced in size, so that the model now predicts the 1972 input-output

(6-3)

-71-

consumption estimate exactly. Note that this correction is only a minor adjustment, and that it can be "justified" given the relatively low t­value of the intercept in equation (6-1). Note also that this adjustment improves the predictions in the two other years as well, with 1963 now having only negligible error.

Table 6-2

Predictions of Washington Consumption, 1963, 1967, and 1972 (millions of 1972 dollars)

1963 1967 1972

Predicted Model 1 Model 2

8,393.9 10,732.1 12,201.3

8,192.8 10,530.9 12,000.0

Actual I/0 Estimates

8,166.0 9,714.8

12,000.0

On the basis of this analysis, we choose to use equation (6-3) as our aggregate consumption function: it is a very simple function; its national counterpart gives reasonable projections; and it passes through two of the base-year input-output estimates.

Regional Consumption Coefficients

In order to project regional consumption coefficients into the future, we require good base-year estimates of regional coefficients as a starting point. The 1972 Washington input-output table is the source of these estimates.

Given the base-year consumption coefficients, the problem of coeffi­cient change is handled in a manner like that of interindustry coefficient change, a discussion of which follows in Chapter 7. We first make a distinction between technical and trade changes by viewing each regional coefficient in the consumption column, ric• as the product of a trade coefficient, tic' and a technical coefficient, aic:

(6-4)

According to equation (6-4), percentage changes in technical require­ments translate directly into the same percentage changes for regional requirements. Estimates of changes in technical coefficients can be obtained from either the INFORUM or the BLS models. Table 6-3 reports the implied projected technical coefficients from Almon's model for 1972, 1975, 1980, and 1985. One notable feature of the table is that for the most part coefficient changes are small.

No.

1 2 3 4 .5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24-25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48-52 53 54 55

Table 6-3

Projected National Consumption Technical Coefficients from INFORUM Model, 1972 1 1975 1 1980 1 and 1985

Industry Name

Field and seed crops Vegetables and fruits Livestock and products Other agriculture Fishing Meat products Dairy products Canning and preserving Grain mills Beverages Other food products Textiles Apparel Mining Forestry Logging Sawmills Plywood mills Other wood products Furniture and fixtures Pulp mil ls Paper mills Paperboard and other paper products Printing and publishing Industrial chemicals Other chemicals Petroleum refining Glass and products Cement, stone, and clay products Iron and steel Other nonferrous metals Aluminum Fabricated structural metals Other fabricated metals Nonelectrical motive equipment Machine tools and shops Nonelectrical industrial equipment Electrical machinery Aerospace Motor vehicles Shipbuilding Other manuf acturing Transportation services Electric companies Gas companies Other utilities Communications Construction Trade Finance, insurance, and real estate Services Owner dwellings Other

Total

1972

.00695

.00260 ,00031 .00142 .03483 .01583 .01447 .00521 .02053 .02558 .00940 ,02994 .00020 ,00142 .00005

.00048

.00878

.00322 • 00791 .00091 .01462 .01853 ,00077 .00063

,002 27 .0009 2 ,00014 .00121 .01738

.04762

.00102

.02443

.01472 ,01577 .00858 .00415 .01742

.22348

.09736 ,15780 .10100 ,04014

l.00000

1975

.00540

.00214

.00031

.00099

.03552

.01589

.01569

.00543 ,02036 .02433 ,00888 .02907 ,00022 .00099 ,00005

.00045

.00821

.00329

.00815

.00097

.01493

.01931

.00068

.00051

,00156 .00091 .00014 .00119 .01669

• 0!+089 .00094 ,02545 ,01606 .01628 .00862 .00427 .019 24

.22059

.10162

.15738

.1047 5

.04166 1.00000

1980

,00457 .00210 ,00030 .00074 ,03376 .01474 .01563 .00551 ,01969 , 02339 .00916 .02811 ,00030 .00074 ,00004

.00046

.00828

.00332

.00814

.00095 ,01577 .01886 .00070 .00048

.00176

.00091

.00015

.00129

.01647

.04519 ,00095 . 02511 .01877 .01558 .00791 ,00385 .01854

.21912

.10438

.15780

.10486

.04172 1.00000

1985

.00374

.00196

.00030

.00049

.03302

.01348

.01603

.00564

.01835 ,02214 .00897 .02684 ,00036 ,00049 ,00003

,00045 ,00814

.00344

.00807

.00096

.01597

.01885 ,00071 .00042

.00169

.00086

.00015

.00131

.01650

.04948

.00098

.02457

.01933

.01602

.00775

.00383

.01854

.21589

.10453

.16049

.10764

.04151 1,00000

-73-

Adjustment for variations in trade coefficients, which are defined as the portion of total household requirements from a given industry that is supplied locally, are more difficult to make, as we do not have much historical information. It has been possible to make some rough estimates of trade coefficients for 1963 and 1967, which has enabled us to identify a few strong shifts in trade patterns. However, present forecasts of most trade coefficients are based largely on judgment.

Conclusion

Admittedly, a consumption model consisting of one linear equation and a series of coefficient changes does not appear impressive. One obvious improvement in our scheme would be to disaggregate total consumption into a few of its major components. One useful division might be into nondur­able goods, durable goods, and services, since this would isolate the purchases of durables, which is the most volatile component of household spending. Nevertheless, our present approach seems to be a good one for now, especially given the limited regional data at our disposal and the availability of national studies.

CHAPTER 7 INDUSTRY OUTPUT

Although every sub-model is directly or indirectly tied to every other sub-model in our system of simultaneous equations, it is not an overstatement to say that the output block is the core of the Washington Projection and Simulation Model. Through the 55 input-output equations, all final demands, as well as the intermediate demands that arise from them, are transformed into industry outputs. At the same time, industry outputs determine employment and income, which in turn become driving forces behind local consumption, investment, and government spending. The objective in this chapter is to show the derivation of our output forecasting equations. A convenient starting point is a brief account of the nature of input-output models.

Input-Output Models

An input-output model represents the demand and supply relationships of an economy in equilibrium. Some economists prefer to make the dis­tinction between an input-output system of accounts and an input-output model . The input-output accounting system is simply a statement of the interindustry transactions of an economy for a given period of time. It is specified by three sets of accounting relationships. The first set of identities shows in value terms the sales distribution of the outputs of each regional industry:

for i = 1, n, (7-1)

where Xi is the total output of "selling" industry i; Xij is the output sol d to regional industry j; Ci stands for the sales to local households; INi represents the sales to the regional private investment sector (in­cluding inventory change); GSLi is the output going to state and local government (for both educational and non-educational functions); and Ei is the sector's exports (including sales to the federal government as well as fo r eign and domestic exports). Equation (7-1) states that the total value of output of each regional industry must equal the sum of the values of sales to each of its markets, including the sectors of final demand.

The second equation depicts the purchase pattern of each industry's inputs :

for j = 1, n, (7-2)

where Xj is the value of total input of "purchasing" industry j; Xij is the purchased input from regional industry i; VAj, representing value added, is the payment to the factors of production (principally to labor

-76-

· capital, land, and entrepreneurs); and Mj is the total input of imported goods and services. Equation (7-2) states that the value of total input of industry j must equal the sum of the values of each purchased input.

The accounting framework of the input-output system is completed with the identity that total output (sales) of each industry must equal total input (purchases):

for i = j. (7-3)

These three sets of relations underlie all input-output transactions tables. A transaction table consistent with the formulation of our input-output system is shown on a following page. In this instance, it is the 1967 27-industry input-output table for Washington State. The 27 regional industries are listed down the side of the table as sellers of output and across the top of the table as purchasers of input. Row i shows the flow of output from regional industry i to each of its markets; and colwnn j indicates the value of each input purchased by industry j.

Although an input-output table, as a benchmark measure of regional economic activity, has a variety of uses in and of itself, with t.he addi­tion of behavioral relationships to the accounting identities, an input­output model can readily be formulated for making forecasts.

The behavioral asslD!lption employed in projection exercises is that the ratio of the value of the supply of input by industry i to industry j to the total input required by industry j is either constant or predic­table over the relevant forecasting horizon. This ratio is usually called the direct purchase, or regional input, coefficient, rij' and is defined as

(7-4)

e define a value added coefficient, vaj, and an import coefficient, mj, f or industry j by resorting to similar assumptions; that is,

(7-5)

and

(7-6)

rom these definitions, it follows that

(7-7)

-77-

Together these coefficients constitute the parameters of the interindus­try model.

The behavioral equations are incorporated into the accounting frame­work by substituting equation (7-4) into equation (7-1), yielding

for i = 1, n. (7-8)

Equation (7-8) is the general form of the output equations in WPSM.

The Estimated Output Equations

Regional Coefficients

Unlike the equations in the four final demand blocks, which are estimated with time-series data, the parameter estimates of the output equations are based upon cross-sectional data. The regional coefficients have been taken directly from the 1972 51-industry input-output model for Washington State (Bourque and Conway, 1977). Since WPSM has five (rather than one) construction sectors, it has been necessary to disaggregate the construction coefficients shown in the 1972 study according to these five components. The procedure for this step has already been discussed in Chapter S.

Output Equations

Since there are 55 output equations in total, only three are pre­sented here for illustrative purposes. The equations, which are shown in Table 7-1, cover fishing, aerospace, and residential construction.

The first equation shows that the output from fishing in Washington goes to other establishments in fishing, to the canning industry, to Washington consumers, and to exports. The coefficients of the first two terms are regional input-output coefficients, calculated in the manner described above. Shown also is a coefficient associated with total con­sumption. The source of this coefficient has been explained in Chapter 6, but it is essentially equivalent to a regional coefficient, in this case representing the fraction of total consumer purchases bought directly from the fishing industry. As pointed out in previous chapters, coeffi­cients of this kind also arise in conjunction with state and local educa­tional and non-educational operating expenditures (WSLEDU and WSLOTH) and equipment investment (WIEQP), but examples of these variables are not found in Table 7-1. For fishing exports, the coefficient is unity, since fishing exports are predicted in the export block.

The aerospace and residential construction output equations are simi­lar in nature. As well as sales to other regional industries and to the

-78-

export sector, the aerospace equation shows output going to inventory change. The residential construction industry has only one market defined by WPSM, that being residential investment. Since residential investment is a variable predicted in the investment block, the coefficient of this term is also unity.

Table 7-1

Output Equations for Fishing, Aerospace, and Residential Construction. 1972

WX05 = 0.00252WX05 + 0.07255WX08 + 0.00016HCTOT + WEXOS

WX39 = 0.02197WX38 + 0.00859WX39 + 0,00116WX43 + WIINV39 + WEX39

WX48 = WIRES

wxos Washington output from industry 5 (fishing) WX:08 Washington output from industry 8 (canning and preserving) WX38 Washington output from industry 38 (electrical machinery) WX:39 Washington output from :i,ndustry 39 (aerospace) WX:43 Washington output from industry 43 (transportation services) WX:48 Washington output from industry 48 (residential construction) WEXOS Washington exports from industry 5 WEX39 Washington exports from industry 39 WIRES Washington residential investment WIINVOS Washington investment in inventory by industry 5 WIINV39 Washingt on investment in inventory by industry 39 WCTOT Washington total consumption

Coefficient Change

Coefficient change is a critical issue in input-output forecasting. Indeed, most of the criticism of interindustry models focuses upon this problem. There are many theoretical reasons why one would expect regional coefficients to vary over time--technological change, variations in prod­uct mix, price changes and input substitutions, and shifts in trade pat­terns--and these quite naturally have made many economists skeptical of the utility of input-output models. However, the question is not whether theoretical limitations exist, but rather to what degree they impinge upon the quality of the forecasts. In other words, the question regarding coefficient change and the validity of input-output models is essentially an empirical one.

"' ... ~ ::,

• 0 • ;!!;

TABLE 1 THE

GROSS FLOWS IN

WASHINGTON'S ECONOMY

1967 (All FIGUHS IN MIWONS Of DOU.US

At '1IODUCHS' Pitas)

IESOUICE

2 3 4 5 6 7 8

~ READ SALES ACROSS -PURCHASING INDUSTRIES FINAL DEMAND

MANUFACTURING NON-COMMODITY GOVHNMENl

9 10 11 12 13 14 15 16 17 18 9 20 21 22 23 24 25 26 27

111 flllD ANO SHO CIOP5 1 t ., 34., 0 .1 10.0 S4.0 26 .3 130.0 156.3 210.3 1

~ LIVESTOCK ANO ,tOOUCTS

~r--:-:-::---~~------;~2-+---t-'-1.'---t---t---t-"-' -' t--ii---+--+-+-+--t-----i--+---t--+-+------if---t-+-+-+--t-----i--+- +-+---11--'~~-~·11--~~~-~·f-- +--+--+-__::"~•t-__::+ __::'=' ·~·1-="~··•+ 2=-i 5r.===::=:'~.•~;,~="=:=n~•"""=~=-=:=.:=:..=•-•a_,.--t~3+-+ -t--f-'-·'+--·-'t-"-·'+ -"-·'+-- +--f-f---t-+-+-'-'-+--- f--+--+- +-+---l--+---l--'-'-:.+--":+--"-t--+-+--l-----4_:_"='='ll-=~=-='-+--+--~1.•+---l-_:_"='='+--'='=·'1-="='~•1-="'=·•+3=-l

tjf" aa M .a.wn , ,. 4 10.9 .I 145.3 .2 ll.2 6.0 .6 11.2 4 .l 56. 1 265.S 4 MIAT ANO OAIIY PIOOUC"n

i tj( 201 ANO 202

CANNING, l'ltHHVING ANO IIVIIAGH SIC 203 AND 201

G•AIN MIW AND OTHH fOOO - oouas SIC 204 205-7 AND 20t TEXTllH ANO Afl'PAIIL ~It' 11.1'1

Pl.YWOOO MKU O SIC 2432 Z ,Af'll AHO AU/ED ,tOOUCU • s,c 26

C :t;~NG AND ,WUSMtNG

:f CHIMICAL<D ~ f'lTIOUUM ~ l'aOOUCTS ~C 21-29

~ !:~i ClAT AHO GUSS N00UCTS

~ IION ANO STEEL SIC 331 -332 3391 AND 3399

NONIHIOUS MflAlS SIC 333 334 335 336 ANO 3392

FAIIICATEO MITAL NOOUCTS II'" 3'

MACHINHY u: 3S-:16

AHOSf'ACI SIC 372 OTHH TIANS,Oll'ATIOH@ lftl.1/H•fMf SIC 37 I XC. 372

OTHH MANUfACTUIING (II'" 10 30 31 38 AN> 30

CONSTIUCTION

~ ~-~~:;~T_:~':av,as 0 SIC 0 -47

~ COMMUNICATION ANO UTILITlls@ ~ $1 1'" ...... ,

0 TIAOfS U 1.11': ,n..1.9

Z flNANCI, INSUIANa ANO HAL O u ·ou,,: ~C 60-67 fEXC. 6561

Z ~_:v1cu

5 1.0 J6.6 2.0 3 .S 4.0 4.0 359.1 S0.3 6 .7 444.0 502.7 5

6 7 32.2

10

11 .2

12

13 11 .•

14

15

16 •

17 .,

18 .,

19

20

21

.7

,.,

,., .3 67.1

...

2J 1.2 S.6 1.6 S.2 49.1 6 .7 19.0 .4 11.S 10.2 1.8

24 •.J 3 .9 3 .0 8.6 1..S 20.0 , .. , .o 4 .0 2 .8

..•

25 3 .0 6.• 1.2 7 .8 19.0 , .s .3 11.9 • -2 1$.2 .• 3 .4 1.9

26 2.S 1.6 1.2 2.'2 6 ,6 u 4.6 1.6 1.0 .9

27 6.3 9 .S S.6 4.2 2.1 1.0 S.3 3.S 1.6 3.2

40.3 134.S 37.1 25.6 241.• m .6 " ·' 226.6 11.s 51.3 1$.6 51.7

IJS.O

S8 .s

,., ,.,

,.,

,.,

1.5.9 442.0

9S.O 131.7

633.2 6

311 .0 239,4 306.4 7

'"·' n ., 8 53.4 442.1 IOD.0 9

3S.O

••.o

.!ISl .2

22U 10

to6.9 11

1611.S 12

.SJ.3 13

1611.2 14

,0,0 15

614 .1 16

161.9 17

212.S 332.3 18

494 .S 393.1 2473.0 1536.0 19

3 .0 286.S 103.7 S.I 411.1 437.2 20

111.2 21

9.9 36.S 1259 .0 66S.7 147.7 2IOl,9 21$1 ,4 22

30 .0 l .9 256 .4 96.5 6.0 271.S 143.8 Sll.3 837 .7 23

10.0 71 .2 79.1 421.3 319.0 JO.I •03.0 12'.3 24

7.9 242.4 1923.3 123.0 It.I 205.5 2273.9 2516.3 25

7 .4 49.1 297 252 .0 S3'2.2 6 .3 113.9 657.S 909.S 26

116.1 92.8 449.0 1137.0 , .o 36.7 1200.1 1649.1 27

127.3 304.0 3"1$2.2 SIS4.6 143J.'2 782 .8 1362 .0 $057.0 95'.0 14,742 .6 18,69,.1

I 11ST Of UNITED $TATIS 23.9 44.0 21 .$ 150 .4 1'20.7 122 .4 59.6 137 .0 19,7 232 .2 99.0 1267 .0 15'.8 2"1 .2 736 $ 17$.6 148 .0 l9'l .6 4329.9 1423.8 15'M. I 175.2 3193.1 7513.0

! f--,.,..- ,-•• -------- - - t-="-t-"",..:.., t-'-"'-1--""--f--'-',_--,+--,-_,+--'-+---, . .:.., +--'-,,-_,+=+,--, . .:.., t--'-"-t--'-"-+--'-+-""_,+,,=,:.:_,+-'-,.:..,,+=+=;...:.::=-,_:.:c::.+=.:+=+=+c:=i--:c:.+=c:..Jf-= .. ::.,.--,11----,.,=.=,, f-'= + -"-::::.+--=-+---=-+-=--½-'--,oo:::.~, ½-'"' .. =,_=,, H

VAlUI CHATED@ 140.9 ,0, 1 2,1 .0 213.3 100.0 2$1.8 Ill.I 39.2 329.8 76.1 424 .0 11$ .8 261.1 95.S 195.0 127 .4 18'2 .3 1019.0 240.0 729 ,0 506 .1 570 .6 1903.6 7$2.3 IOS2.S 9955.4 1219.7 1s,.o ao4 .o 21n.1 12,n1.1

TOT AL P'URCHASES JI0.3 2ff.6 l09.6 26S.S 502.7 633.2 306.• 92 .I 800 .0 221.8 106.9 169.S 6S3.3 168.'2 90.0 614.1 261.9 332.3 2536.0 437.2 111 .2 1$8.4 137 .7 8'24 .l 2$16.3 909.S jl6'9. I 18,694.8 7898.1 3026.3 1712 .0 2166.0 $0$7.0 9$4.0 20,IU.•I 39,SOl .2

fOOINOTlS, © INCLUOlS HANFOIO ATOMIC lfrlHGY WOIKS <D tNClUOIS IIIMHTON NAVAL SHl,YAIO (i) INCWOIS GOVHNMfNT UTILITIES ,,.u.o: s. 8'AI © 1NaUOfS COMl'INSAllON o, EMPLOYUS, HNTAl ,ATMfNlS, NU INTHEST ,AYMfNTS, CAl'tTAl CONSUM,nON AllOWANaS. I USINl:5S TAX ,ATMINU, ANO IUSIM.SS INCOME.

Graduate School of Business Administration University of Washington

-80-

In light of the existing empirical evidence on coefficient change, one finds it difficult to take a strong stand on the issue. Indicative of the lack of concensus, Carter (1967, p. 223), in her study of the 1947 and 1958 U.S. input-output tables, concludes that "the two tableaux (retain) a striking family resemblance;" whereas Tilanus (1966, p. 134), in a time series analysis of each coefficient in the Dutch tables, reports that "the dispersion of the observations around the average (or around the trend for that matter) is substantial."

Nevertheless, we are compelled to make projections of coefficient change, even when such projections are based on little more than hunches. One consolation in this forecasting process is that if one is not happy with the present assumptions regarding future coefficients, one can readily alter them and run out an alternative set of forecasts.

In order to facilitate forecasting, we view each regional coeffi­cient as being influenced by two elements, a technical coefficient, aij' and a trade coefficient, tij• The relationship can be expressed as

(7-9)

where the technical coefficient represents the total purchases of an input from a given industry without regard to the location of that indus­try; and the trade coefficient measures the proportion of the total input that originates from local establishments. Decomposing regional coeffi­cients in this manner permits us to concentrate on the two primary, but more or less independent, forces behind regional coefficient change.

In the projection of technical coefficients, we place heavy reliance on forecasts made at the national level. This reflects an expectation that future changes in production techniques in the nation will be shared by Washington producers. This falls short of saying that everywhere tech­niques will be the same, since only the rates of change in Washington technical coefficients are assumed to be similar to those projected at the national level. Although we have undertaken a study of coefficient projections in the BLS model, we have largely adopted the INFORUM view of the future, but not because we feel that it is necessarily more accurate. For our purposes, the changes predicted by INFORUM have been modified to take into account dist inct temporal patterns in the technical coefficients observed in the 1963, 1967, and 1972 Washington total purchases tables.

In the case of forecasting trade coefficients, it has been necessary to depend solely upon evidence found in the three Washington interindustry studies. Calculations of trade coefficients in these three years indicate that very few industries are experiencing strong and persistent shifts in trade patterns. One of the most apparent is other manufacturing (which includes fabricated plastic products and scientific instruments), for which there seems to be a strong import substitution tendency with respect to these goods.

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Although WPSM has the capability of changing individual coefficients, the model is presently being run under the assumption that all coefficients across a given row change at the same rate. Selected projected annual rates of change for regional coefficients are shown in Table 7-2. For example, input coefficients representing purchases !.!:2m, vegetables and fruit are assumed to remain constant over the forecasting period. On the other h~nd, the relative purchases from logging are expected to decline at an annual rate of 0.5 percent a year. As the table shows, import substi­tution is expected to continue for other manufacturing goods, although at a less rapid rate in the 1980-1985 period. The study thus far suggests that coefficient change will occur at relatively slow rates; but the pro­jections of technical change and import substitution certainly need closer scrutiny.

Table 7-2

Selected Projected Annual Rates of Regional Coefficient Change, 1975-1980 and 1980-1985

(percentages)

Selling industries

Vegetables and fruit Logging Aluminum Other manufacturing Finance, insurance, and real estate

1975-1980

o.o -0.5 2.0 2.0

-1.0

Historical Output Series

1980-1985

o.o -0.5 o.o 1.5

-1.0

Mention should be made of the derivation of the historical output series for the 55 industries. Although these outputs are not used in estimating input-output coefficients, they have an important function in

· the development of WPSN. In the first place, they are necessary for eval­uating productivity change over time and therefore fonn the foundation for the projections of employment. Moreover, historical output figures are employed in tracking tests, whereby we evaluate the predictive ability of WPSM over our period of observation.

The objective is to derive output estimates from 1958 to 1972 in constant 1972 dollars that are definitionally comparable to the estimates in the 1972 input-output table. Refer to Table 3-2 for the sector defini­tions of the 55 industries. Details regarding the output estimates of the 1972 study are given in the publication entitled The 1972 Washington Input­Output Study (Bourque and Conway, 1977), but a few additional notes about

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the estimating procedure for the hi s t orical series are in order.

For t he r esource indus t ries --which include the four agricultural industries, f i shing , mini ng , and forestry--there are excellent data on an annua l bas i s. The ma j or publications drawn upon include issues of the Annual Crop Report, Fi sheries Statistics of the U.S., Minerals Year­~, and Fores t Produc t s Stati stics, Washington. A possible source of measurement error is the deflati on of the current dollar outputs, since we must r esort t o national deflators at this juncture. Of course, this i s a probl em i nher ent in all of our constant dollar series.

Excel lent info rmation from t he Census of Manufactures and the Annual Sur vey of Manuf ac tur es is also on hand f or the manufacturing industries, especially for t he larger ones. The survey figures are of course subject t o sampl i ng errors. Furthermore , they do not pr ovide value of shipment figures for a number of small er sec t ors , in which case it has been nec­essary to break out shipment esti mates from repor t ed aggregate outputs. Thi s disaggregati on involves t he us e of census year sales, wage, and employment estimates with wage and employment data (provided by the Wash­ington State Employment Securi t y Department ) for the intervening years.

Very little pub l i shed data are avai lable for the service sector series, except for the four new construc t ion industries , whose output estimates have been discussed in Chapt er 5. The initi al point of depar­ture for the service output s are t he three i nput -output estimates for 1963, 1967, and 1972. For the non- input-output years, es t imates are made l argely on the basis of Washington empl oyment and personal income data, the latter provided by the U.S . Bureau of Economic Analys i s. The goal is to derive series that (1) make sense in terms of national output -employee and output-income ratios and (2) run t hrough the i nput-output base-year figures. In general , these est imat es are l es s r eliable than those for the resource and manufacturing s ectors.

Conc lus ion

In one sense, the output equati ons pr esent li t tle difficulty. Based on cross-sectional data, parameter estimat es are easy to obta i n once an input-out put table is ava i lable . On the other hand, sinc e WPSM revolves about the output block, it is cri tical t hat these regional coefficients are accurately stated over the proj ecti on period. Unfortunately, one thing about which we can be sure is that the coefficients themselves are not constant over time . Wi t h three input -output tables for Washington Sta te and investigations of t echnica l coefficient change at the national level , we can make a running s tart at projecting regional coefficients. Nevertheless , it is clear that much l eg work remains on this issue.

CHAPTER 8 VALUE ADDED

Gross State Product (GSP) is an important measure of regional eco­nomic activity, being a measure of the value of output produced by the resources employed within Washington State. In WPSM, we actually view GSP from two angles. First, it is the sum of the sales to final demand, that is, the output purchased by the consumption, investment, government, and net exports (i.e., exports less imports) sectors. Second, GSP, or more precisely, Gross State Income, measures the income earned by the regional factors of production, principally labor, capital, land, and entrepreneurs. Value added, as it is also termed, covers the compensa­tion of employees, net interest payments, capital consumption allowances, rental payments, business taxes, and profits. According to income and product accounting procedures, Gross State Income equals Gross State Product, as shown by the figures in Table 8-1, which are drawn from the 1972 input-output table.

Table 8-1

Gross State Income and Gross State Product in Washington State, 1972

(millions of dollars)

Industrial value added Personal consumption value added State and local government value added Federal government value added

Gross State Income

Personal consumption Private investment Change in inventories State and local government expenditures Federal government expenditures U.S. exports Foreign exports

Less: U.S. imports Less: Foreign imports

Gross State Product

14,949.1 1,608.4 1,691.6

909.5 19,158.6

12,000.0 2,475.0

-139.5 2,979.1 2,439.5 7,294.2 1,952.7 8,630.7 1.211.1

19,158.6

Apart from functioning as an index of economic activity, value added plays no explicit role in WPSM; that is, value added does not enter into any of our behavioral equations as an explanatory variable, However, this does not mean that it should not have a part. One can argue that

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value added, not gross output, should be the pivotal variable in the system. Since value added represents the !!il contribution of regional resources to the value of output by each industry, conceptually it is a superior variable upon which to make projections of, for example, income, employment, and investment. One reason for the choice to by-pass value added as a causal variable stems from the difficulty of disaggregating value added into its income components. In particular, we have had only limited success in measuring capital consumption allowances and indirect business taxes. Furthermcre, it is impossible to estimate how much of the property income earned by capital operating in Washington State ends up as personal income of in - state residents. For this and other reasons, we cannot derive regional personal income directly from value added accounts.

The Model

Value added in each industry, WVAi, is predicted from the corre­sponding output , WXi, in the following manner:

(8-1)

The value added coefficient, vai, is estimated using the 1972 transactions table.

In general, we would expect errors in value added predictions to arise from two sources apart from prediction errors in output, namely, measurement error in base-year coefficients and temporal change. A study of value added coefficients over time should give us some insight into these factors.

We have conducted an investigation of value added coefficients and have found them to be relatively stable, or at least without any percep­tible trends. There is little evidence to suggest that we should not adopt the 1972 estimates and proceed under a constant coefficient assump­tion. However, we should qualify our findings by noting that our analysis is not without shortcoming. First, since we must rely upon national value added deflators, and since they are only published at the two-digit SIC level, the study has been undertaken with very aggregated value added and output series. Second, there is no historical information on value added in Washington for some sectors, including three non-cormnodity industries-­fishing, mining, and forestry. In these cases, we have looked at national value added coefficients, which are calculated from the Bureau of Economic Analysis' gross income originating series and the INFORUM gross output figures. Nevertheless, on the basis of a number of checks of reasonable­ness, it appears that our projection of total value added (i.e., Gross State Income) is not out of line.

In any event, considering the structure of WPSM, wherein value added is not linked to other variables in the system, the problem of forecasting value added coefficients is at this time of minor concern.

CHAPTER 9 PERSONAL INCOME

Personal income and personal disposable income are interesting vari­ables to project in themselves, being measures of received and spendable income, respectively. But more important is the role of disposable income as a key factor in the prediction of other endogenous variables in the system, such as household consumption, state and local government expen­ditures, and residential construction. A careful specification of the income model is therefore imperative. A careful specification is also feasible, given the excellent income data available for the state.

Data

Personal Income

The series maintained by the U.S. Bureau of Economic Analysis (BEA) is our central store of information on personal income in Washington State. A sample of the data is shown in Table 9-1, giving four major components of personal income--labor and proprietors' income, property income, transfer payments, and personal contributions for social insur­ance. A residence adjustment converts the series from a place-of-work basis to a place-of-residence basis. Deducting personal tax and non-tax payments from personal income leads to personal disposable income.

Less: Equals:

Plus: Equals:

Less: Equals:

Table 9-1

Personal Income in Washington State, 1972 (millions of dollars)

Labor and proprietors' income Property income Transfer payments Personal contributions to social insurance Personal income, place-of-work Residence adjustment Personal income, place-of-residence Personal tax and non-tax payments Personal disposable income

11,952 2,148 1,906

561 15,445

135 15,579 2,106

13,473

Labor and proprietors' income includes wages and salaries, non-wage compensation excluding employers' contributions to social insurance (e.g., contributions to medical insurance plans and private pensions),

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and proprietors' income. Property income includes personal dividend, interest, and rental payments. Note that whereas labor and proprietors' income originates from economic activity located in Washington State, property income is earned by local residents in part from property located outside the state. In practice, we cannot distinguish between in-state and out-of-state property earnings, which is an important consideration in our modelin8 of property income. We are also unable to determine the geo­graphical source of transfer payments, which include retirement and dis­ability payments, veterans' benefits,unemployment compensation, and other public assistance.

For modeling purposes, it is necessary to disaggregate further labor and proprietors' income by industry. The BEA series aids considerably in this effort by reporting labor and proprietors' income at the two-digit SIC level. For a number of these two•digit industries, such as lumber and wood products, we must estimate income in greater industry detail. In this break-out procedure, we utilize Employment Security Department and Census wage data. We should point out that there is not a direct corre­spondence between our industry labor and proprietors' income series and that of BEA because of differences in the industrial classification of activities.

A Com:nent About Deflation

WPSM is designed to predict both disposable income and Gross State Product in real terms. It is therefore important that we give careful consideration to !:.!.!1_ labor and proprietors' income and real value added. Although l abor earnings are part of value added, it does not follow that both series are deflated by the same deflator. Indeed, while one may more easily conceptualize the deflation of value added, especially when consid­ering value added as the flip-side of Gross State Product, it is difficult to think in terms of real income. This problem of deflating income and value added is highlighted in the following example.

Consider for the moment our present method of projecting income in the state and local government education sector. Total operating expenses in real terms are predicted from disposable personal income and school-age population. Value added is then predicted by multiplying this total expen­diture by the value added coefficient. Since value added in the government sector entails only employee compensation, one might think that a forecast of value added is also a forecast of personal income earned in this sector. If our model were stated in current value terms, this would be true. How­ever, as the model is in real terms, this is not the case. The problem stems from the fact that value added, or gross product originating, is deflated by one deflator, while labor income is deflated by another.

In the measurement of the real contributions to Gross National Product by the government sector, the national accounting conventions essentially assume that productivity increases for public employees are zero. (Actually, the newly revised GPP accounts do assume that a change in productivity

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occurs when there is a change in the quality of employees, as measured by a variation in the distribution of workers by rank.) Thus, current­dollar compensation (viewed as value added) is deflated such that real growth in GNP in the government sector is essentially commensurate with growth in the number of full-time equivalent employees. On the other hand, in order to measure real personal income, the national accounts by convention effectively deflate any contribution to personal income by the GNP deflator for personal consumption expenditures (PCE). This is done to show the real purchasing power of personal income. Thus, in this case, current-dollar compensation (now viewed as personal income) is deflated by the PCE deflator.

In general, these accounting procedures lead to diverging sets of real value added and personal income coefficients, as Table 9-2 indi­cates. The figures show that while the real value added coefficient has remained relatively constant, the real personal income (compensation) coefficient has grown considerably. This is because compensation per employee in government has increased with time, while there has been no change in productivity, at least according to accounting conventions.

Table 9-2

U,S. State and Local and Federal Government Expenditures and Value Added and Personal Income Coefficients

(billions of current and 1972 dollars)

1963 1967 1972

Compensation (current dollars) 58.1 85.1 137.4 Value added ($72) 104.8 127.2 137.4 Compensation ($72) 77.8 104.7 137.4 Total expenditures ($72) 197.6 248.3 253.1 Value added coefficient ($72) 0,530 0.512 0.543 Personal income coefficient ($72) 0,394 0,422 0.543

In short, by real income we mean the purchasing power of income. Thus, by deflating, say, personal taxes by the PCE deflator and deducting the constant-dollar taxes from real personal income, we are simply mea­suring, in this instance, the loss of real purchasing power.

Personal Income Model

The Structure

The structure of the income block is depicted in Figure 9-1. Starting at the top, the demand for exports induces industrial production, which in

WN

USYSSRT

•

WYSSRT

USYPROPPC - WYPROPPC

- WPOP

USYTPPC - WYTPPC

WUNEMRT

Figure 9-1

Personal Income Sub-Model

WEX

-- wx

WYL -

WYSS -

-~ WYPROP -

--__.,. WYTP --

-

- WCTOT

WITOT -

--- WSLTOT -

- WYP -_.., WPD

Wl'TA:X -

WYTA:XRT

'

USYTA:XRT

]

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turn leads to earnings in the form of labor and proprietors' income. Given the Washington contribution rate, which is predicted from the national rate, personal contributions to social insurance then follow from labor and proprietors' income.

Although in the course of production, rents, interests, and profits are earned that to some extent wind up in the pockets of Washington resi­dents, we predict property income in a more indirect manner, From output we forecast employment and population. Population is then combined with Washington per capita property income projections, which are once more based on U,S, estimates, to give total property income. The presumption in this formulation is that market forces dictate that local and national per capita property incomes move more or less together. He rely upon the same reasoning to predict transfer payments, except that the local unem­ployment rate enters into the per capita transfer payments equation as an additional explanatory variable.

Together, labor and proprietors' income, personal contributions to social insurance, property income, and transfer payments determine per­sonal income. Disposable income follows from a deduction of personal tax and non-tax payments from personal income. Taxes are estimated by apply­ing the projected tax rate to personal income.

The description of the role of income in WPSM is completed with the addition of a linkage that runs from disposable income to the locally­oriented final demand sectors, that is, to consumption, investment, and state and local government.

The Estimated Equations

The personal income forecasting equations exhibited in Table 9-3 follow the structure depicted in Figure 9-1. From a statistical stand­point, the regression equations look good, the equations being estimated on annual observations from 1950 to 1974. As there is a problem of auto­correlation with the per capita property income and transfer payments equations with the OLS fits, they have been estimated using the Cochran­Orcutt method. The regression coefficients do not change much with dif­ferencing, which provides strong support for our specification of the two equations. Moreover, the values of the regression coefficients on the corresponding U,S, variables are theoretically reasonable, being about unity. Note that in the tax rate equation, the regression coefficient on the . U,S. tax rate is only 0.7948. This reflects the fact that Washington State has no personal income tax. If in the future an income tax is imposed, the present tax rate function may become inoperative.

As a forecaster of disposable income, the block represented by the · equations in Table 9-3 appears to do well. Over the observation period-­with total labor and proprietors' income, the U.S. exogenous variables, and the Tfoshington unemployment rate all known--the average predic tion error is only 0.5 percent.

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Table 9-3

Personal Income Forecasting Equations

DW SEE

WYPROPPC = 0.0622 + 0.9412USYPROPPC 0.94 1.64 0.0103 (2.7) (19.6)

WYPROP = WYPROPPC(WPOP)

WY!'PPC = 0.0016 + 1.0453USYIPPC + 0.4100WUNEMRT 0.97 1.71 0.0088 (0.1) (24.4) (2.5)

WYTP = WYTPPC(WPOP)

WYSSRT = 0.0015 + 0.9223USYSSRT 0.99 2.43 0.0013 (2.1) (42.7)

WYSS= WYSSRT(WYLTOT)

WYP = WYLTOT + WYPROP + WYTP - WYSS

WTAXRT = 0.0194 + 0.7948USTAXRT 0.90 1.40 0.0036 (2.9) (15.0)

WTAX = WTAXRT(WYP)

WYD = WYP - WTAX

WYPROPPC WYPROP WYTPPC WYTP WYSSRT WYSS WYLTOT WYP WTAXRT WTAX WYD WPOP WUNEMRT USYPROPPC USYTPPC USYSSRT USTAXRT

Washington property income per capita Washington property income Washington transfer payments per capita Washington transfer payments Washington contribution rate to social insurance Washington contributions to social insurance Washington total labor and proprietors' income Washington personal income Washington personal tax and non-tax payment rate Washington personal tax and non-tax payments Washington disposable income Washington population Washington unemployment rate U.S. property income per capita U.S. transfer payments per capita u.s. contribution rate to social insurance U.S. personal tax and non-tax payment rate

(Percent of mean)

(5.5)

(9.2)

(4.3)

(3.0)

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The Problem of Coefficient Change

The income sub-model described in Table 9-3 takes total labor and proprietors' income as given. As depicted in Figure 9-1, this component of personal income is predicted from industry outputs. The forecasting equation for each industry is simply

where once more the income coefficient, yli' is estimated using 1972 base-year data,

For projection purposes, we could invoke a constant coefficient assumption with regard to the income coefficient. Although our analysis reveals that this turns out not to be a bad assumption, we can do a lit­tle better,

In order to understand the logic to projecting income coefficients, we have to jump ahead of ourselves and consider also our means of fore­casting employment. The employment equation takes the same form as equation (9-1), namely

(9-2)

where n is the inverse of a commonly used productivity measure, gross output per worker. J. Since WYLi and i.JXi represent total labor and propri­etors' income and total employment (actually total jobs) for industry i, respectively, the ratio WYLi/WNi is a measure of earnings per worker. Dividing equation (9-1) by (9-2), we see that these earnings equal the ratio of the income coefficient to the employment coefficient, that is,

(9-3)

or, rearranging,

(9-4)

From equation (9-4), it is apparent that in order to predict the income coefficient, we need to take into consideration both future earn­ings per worker and future productivity change. Indeed, we have such information for :i.ndustries nationally. At the present time, we employ the INFORUM projections of productivity and earnings, which have been modified to take into account historical differences occurring at the regional and national levels, to make projections of labor and propri­etors' income coefficients. As Table 9-4 indicates, future income coef­ficients by and large are expected to vary slowly. In general, we predict increases in coefficients to occur in industries with little

1we should emphasize that in the context of WPSM the term "produc­tivity" means gross output per worker and not value added per worker.

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projected productivity gains, such as construction and state and local government (see Table 10-3). Similarly, we anticipate decreases to occur where industry productivity gains are rapid, such as in the vegetables and fruit and aerospace sectors.

Table 9-4

Selected Projected Annual Rates of Income Coefficient Change, 1975-1980 and 1980-1985

(percentages)

Industry

Vegetables and fruit Meat products Sawmills Cement, stone, and clay products Machine tools and shops Aerospace Comnunications Residential construction State and local government education

An Alternative Specification

1975-1980

-1.3 0.6

-0.2 0.1 0.1

-2.7 -0.3 1.2 1.6

1980-1985

-1.6 0.6 0.5 0.1 0.3

-2.5 -0.4 1.9 1.6

Although the.re is a logic to altering income and employment coef­ficients, this approach does not take full advantage of all available information. Furthermore, as our coefficients are changed ''by hand", it would be difficult t o deal with projections under alternative views of national--and thus regional--productivity and income changes. 2 Under these circumstances, it would be quite easy to have regional income and employment coefficients that were either inconsistent with each other or inconsistent with national forecasts.

One way of circumventing these problems would be to develop explicit income and employment equations. A number of formulations should be tested, but one possible specification for the income equation is the following:

(9-5)

2Analysis of a variety of national productivity and income scenarios

would be an important step in baseline forecasting, since productivity and income changes represent a key to disparities in the INF0RUM, BLS, and Wharton forecasts of the U.S. economy.

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An equation where Washington annual earnings per worker is dependent upon national earnings per worker is an equilibrium formulation, which presumes that changes occurring at the national level will lead to changes at the regional level. The inclusion of the Washington unemployment rate is intended to pick up variations in local market conditions, the hypothesis being that earnings are relatively low when the regional unemployment rate is high.3

Conclusion

Despite the likelihood of improvement in the income block, especially with the estimation of explicit income equations, the present specifica­tion seems to be substantially superior to that of previous input-output models. For example, the static Washington model now in use for impact analyses identifies as income only value added, which in turn becomes the sole determinant of consumption. In the static formulation, each dollar of value added is assumed to exert an equal influence upon consumption, despite the fact that the personal income content of value added varies from industry to industry. The WPSM specification has overcome this prob­lem by more precisely identifying the sources--both industry-originating and non-industry-originating--of regional personal income. Still, as we have noted, better specifications for the personal income sub-model are in the wind. But these must wait for the development of WPSM II.

3A preliminary test of equation (9-5) using the pulp industry as an example yields the following results:

\.JYL21/WN21 = 0.3941 + 1.0370USYL21/USN21 - 9 .5866HtJNEMRTI,l (0.9) (23.4) (-1.8)

R2 = 0.98, DH= 1.33, SEE= 0.30 (3.7), OLS 1958-1972

Although the unemployment rate term is not statistically significant at the 95 percent level of confidence in this case, the results look prom­ising.

CHAPTER 10 Fl1PLOYMENT AND POPULATION

Like personal disposable income, employment and population are not only important economic variables in their own right, but are basic deter­minants of other variables in WPSM. Employment underlies our predictions of population, which in turn has a direct linkage to state and local gov­ernment spending. As Figure 9-1 in the previous chapter shows, population even enters into the determination of personal income. Although some untangling is necessitated, we are blessed with an abundance of published information on regional employment and population. Once again this clears the way for a careful formulation.

Employment and Population Data

There are many sources of information on employment and population in Washington State. However, it seems that each series tends to be based either on different concepts or on different means of measurement. As a consequence, when one inquires about population in Washington State, one must specify whether he is speaking of total population including armed forces abroad, total resident population, or civilian resident population. Furthermore, one must state whether he wants Bureau of Census estimates or the ones from the Population Studies Division of the Washington State Office of Program Planning and Fiscal Management, the agency responsible for the "official" state population figures. If anything, the situation is even worse for employment measures. One must distinguish between total jobs, total persons employed, total civilian employment, and total wage and salary workers, as well as between Bureau of Census, Bureau of Economic Analysis, and Washington State Employment Security Department estimates.

Employment and population estimates for 1972 used in WPSM are given in Table 10-1. Also included are the sources for these figures. The first five entries in Table 10-1 cover the number of jobs in the Hashington econ­omy. Jobs is a difficult concept to define, and differs from the concept of persons employed in a number of respects. Among the important sources of descrepancy is the fact that a given person may hold more than one job, except in the case of proprietors, where it is assumed that each proprietor has only one job. Furthermore, jobs include those held by persons under 16 years of age, whereas the Census count of persons employed includes only those 16 years and older.

It has been necessary for us to estimate jobs by industry, but in each case the jobs estimates are firmly grounded on other published employment figures. In the case of agriculture, we adopt the ESD estimate of employ­ment, with only minor adjustments being made for some agricultural services employment. For the manufacturing industries, we start with the Census esti­mates of employment from the Census of Manufactures. Slight modifications are made to account for auxiliary employment, which is estimated only in total by the Census, and for the self-employed in manufacturing. Jobs in

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service-related industries are estimated from ESD wage and salary employ­ment data and national job-employment ratios in services, as reported by the BLS. The count of civilian government jobs is based on ESD figures but is altered to reflect the transfer of employment in government enter­prises to the industrial sector as well as of "capitalized" labor to the construction industry.

Table 10-1

Washington State Employment and Population, 1972 (thousands)

Agricultural jobs 60.0 WPSM (ESD) Manufacturing jobs 243.2 WPSM (Census) Nonmanufacturing jobs 799.5 WPSM (ESD) Civilian government jobs 222.2 WPSM (ESD) Total civilian jobs 1,324.9 WPSM Civilian persons employed 1,289.0 Census Civilian persons unemployed 136.0 Census Civilian labor force 1,425.0 Census Resident military employment 46.0 WPSM Total labor force 1,471.0 WPSM Persons not in labor force 1,947.0 WPSM Resident population 3,418.0 Census

As indicated in Table 10-1, the series on civilian persons employed, civilian persons unemployed, the civilian labor force, and resident pop­ulation are taken directly from the Census. We make an estimate of resi­dent military employment, which we add to the civilian labor force to obtain an estimate of the total labor force.

In order to derive our forecasting equations in this block, it has been necessary to estimate the non-job series historically. Unfortunately, the Census series for civilian employment and labor force begin only in 1967. In particular, this limits the data upon which to model the total labor force participation rate. There are longer historical series for estimating the unemployment rate and military employment equations, the two other regression equations in the block. In order to project produc­tivity, it has been necessary to estimate jobs by industry in all three input-output years, for 1963 and 1967 as well as for 1972.

Specification of Employment and Population

The Structure

The specification of employment and population is shown in Figure 10-1. Predictions of regional output and Washington productivity, the

Figure 10-1

Employment and Population Sub-Model

HX Washington~-~--~ U.S.

Productivity ~roductivity

I

WJOBCIV . WDJOBCIV WUNEMRTLl USUNEMRT

I I ,i,

USMIL WNCIV WUNEM - WUNEMRT

I " •

WNMIL HLFCIV

I

HLF WNRT USNRT

WPOP

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latter based on projections of u.s. productivity, combine to yield fore­casts of civilian jobs by industry. Taking account of multiple job holders and jobs held by persons under 16 years of age, total civilian jobs are converted into total civilian persons employed.

Civilian persons employed and civilian persons unemployed make up the civilian labor force. The number of unemployed persons is determined by applying the unemployment rate to the civilian labor force. The local unemployment rate is in turn dependent upon the change in the number of local jobs, the unemployment rate in the previous year, and the U.S. unemployment rate.

The civilian labor force plus the projections of military employment, which are based on forecasts of U.S. military personnel, give the total labor force. Finally, given the total labor force participation rate, which is predicted from the U.S. rate, Washington population follows from projections of the labor force.

The Forecasting Equations

As the model is now specified, there are only three regression equa­tions in the employment and population block--for the unemployment rate, for militar y employment, and for the labor force participation rate. The statistical r esults for these equations are given in Table 10-2, along with the identities needed to take one from predictions of civilian employ­ment to resident population.

With t he exception of the unemployment rate equation, the results are satisfactor y. The unemployment formulation presents a curious case. As given in Table 10-2, not all t-values are significant, and there is evidence of autocorrelation, which makes us even more uneasy with the equa­tion. As i t turns out, adding one more term, disposable income, consider­ably improves the results. All regression coefficients become significant at the 95 percent level of confidence, and the corrected coefficient of determination and the Durbin-Watson statistic increase to 0.92 and 2.05, respectively. However, as a forecaster, this latter model appears unrea­sonable, giving unemployment rates of 13 percent by 1985, at a time when the U.S. unemployment rate is predicted to be only 6 percent. Disposable income app arently picks up a strong upward drift in the unemployment rate over the observa tion period from 1951 to 1975 not explained by the U.S. unemployment rate . As a consequence, we opt for the equation in Table 10-2, but being quite aware of probable problems of misspecification.

A further note should be added about the participation rate equation. Being estimated on annual data only from 1967 to 197~ there is an unsat­isfactorily short period of observation upon which to make forecasts. How­ever, over a test period from 1958 to 1967, this equation seems to perform well in the sense that, given total employment and the unemployment rate, the prediction of population is fairly accurate.

--

Table 10-2

Employment and Population Forecasting Equations

DW SEE

WUNEMRT = 0.0140 (1.3)

0.00022WDNCIV + 0.4907WUNEMRTL1 0.69 0.73 0.0092 (-2.5) (3.2)

+ 0.5038USUNEMRT (2.0)

WUNEM = WUNEMRT(WLFCIV)

WLFCIV = WNCIV + WUN.EM

WNFEDMIL = -12.0 + 0.0276USNFEDMIL (-1.0) (6.5)

o. 76 1.04 6.1

WLF = WLFCIV + HNFEDMIL

WNRT = 0 .1080 + 0. 7 688USNRT 0.78 1.42 0.0048 (1.8) (5 .4)

WPOP = HLF /WNRT

WUNEMRT WUNEM WLFCIV WNFEDMIL WLF WNRT WP0P WDNCIV WUNEMRTLl WNCIV USUNEMRT USNFEDMIL USNRT

Washington unemployment rate Washington persons unemployed Washington civilian labor force Washington military employment Washington total labor force Washington labor force participation rate Washington population Hashington change in civilian jobs Wash j_ngton unemployment rate lagged one year Washington civilian persons employed U.S. unemployment rate U.S. military employment u.s. labor force participation rate

(Percent of mean)

(14.3)

(9.1)

(1.1)

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Productivity Change

As noted in Chapter 9, the employment equation for industry i is expressed as

(10-1)

In order to project jobs, it is necessary to forecast not only output but the coefficient, ni, which is the inverse of the gross output-per-job pro­ductivity measure. Forecasts of productivity are taken from the INFORUM projections but modified to take into account historically observed regional and national disparities.

Annual rates of productivity change for selected industries are given in Table 10-3. One characteristic of these projections is the tapering off of growth rates in the 1980-1985 period. The higher rates in the earlier period reflect an anticipated recovery from the recession of 1974-1975, a time of little, or no, advancement in productivity in most industries.

Table 10-3

Selected Projected Annual Rates of Productivity Change in Terms of Output per Job, 1975-1980 and 1980-1985

(percentages)

Industry 1975-1980

Vegetables and fruit 5 .O Meat products 2.7 Sawmills 3.5 Cement, stone, and clay products 2.7 Machine tools and shops 1.5 Aerospace 5.9 Communications 3.8 Residential construction 2.0 State and local government education 0.7

Alternative Specifications

1980-1985

5.0 2.4 1.0 2.4 1.0 5.4 3.6 1.0 o. 7

As it stands, the employment and population block operates reasonably well in terms of its tracking of past series as well as its projections into the future. Nevertheless, improvements are possible. The first would be an explicit formulation of a productivity function. As with an income equation, this would allow us to make better use of the employment and output information available to us, and to escape from having to manually change the employment coefficients. One conceivable specification is the following:

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(10-2)

where WDXi is the change in output of industry i. This last variable is intended to capture short-run variations in productivity because of a postulated reluctance by £inns to adjust immediately the number of workers to new, and possible transitory, levels of production. 1

The second improvement would be a more detailed specification of population. For example, by disaggregating population into age and sex groupings, we would probably realize superior projections of the overall labor force participation rate. Furthermore, such groupings might help us to better formulate other components of WPSM. One that comes to mind is transfer payments, of which a large part is retirement benefits for the elderly. Relating this to, say, persons aged 65 and over could possibly reduce the prediction errors associated with transfer payments. Additional improvements in the population model might also be made by distinguishing in a given year between native and migrant populations. To do this, one would have to consider birth and death rates as well as the special age­sex characteristics of migrants. In closing, we should point out that we have calibrated a population model of the form described here. However, there has not been sufficient time for testing or for integrating this component into :,!PSM.

Conclusion

Even with the incorporation of these forementioned i mprovements, the question remains whether WPSM can project population more accurately than demographic models now existing for the state. If one has greater faith in othe r predictions, this information can be fed directly into WPSM, since the model has the c apability of overriding any endogenous variable. Of course, it is possible that under these conditions intolerable inconsis­tencies might arise in the employment and population forecasts, requiring a reconc iliation of predictions in some manner.

However, the purpose of modeling population is not solely to make base­line forecasts. WPSM is also designed to be used as a tool for analyzing

1 A test run of this model on the pulp industry gives very satisfactory results :

WX2l/;-IN21 = 25.9 + Oo3705USX21/USN21 + 0.1038WDX2 1 (9.7) (9.0) (4.7)

-2 R = 0.90, DH = 1. 73, SEE = 1.9 (3 .8), OLS 1958-1972

2 Charles Sawyer of the Washington State Department of Commerce and Economic Development has been responsible for estimating this population model.

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economic impacts on the region and for assessing alternative growth scenarios. For these purposes, population must not be given to the model but rather explained by it, since population itself is a dynamic predictor of other internally determined economic variables.

CHAPTER 11 IMPORTS

The last component of WPSM is the import block, Imports are calcu­lated as residuals and play no part in the determination of other variables in the system, However, the import block does have a function, being an indicator of the reasonableness of WPSM's projections. Before discussing this role, it is useful to describe how imports are modeled.

The Import Model

In WPSM we make no distinction between foreign imports and imports from the rest of the U.S. For each purchasing sector, we estimate only a single total.

Imports by sector are forecasted as a residual by deducting projected regional intermediate inputs and value added from the projected total input (output) of that sector. For industry j, the expression for imports is given by

n WM- = HX · - Lr• .wx. - va.\ JX .

J J i=l iJ J J J' (11-1)

where HMj is the total imports of industry j, HX j is the total input, rij is the regional coefficient representing purchases by industry j from industry i, and Vaj is the value added coefficient for j. The last two te rms in equation (11-1) signify total regiona l intermediate inputs and value added, respectively. Note that regional purchases are sununed over the selling industries in the local economy; that is, in reference to an input-output table of transactions, they are summed down the column.

The Function of the Import Block

We have shown the regional and value added coefficients in (11-1) to point out that imports depend in part upon the changes in these coeffi­cients. Since we do project regional and value added coefficients, we are implicitly projecting for each sector that sector's propensity to import. In other words, we are making an implicit forecast of the import coeffi­cient, m .• This point might become clearer by recalling that for each sector jJinput coefficients must sum to unity; that is,

n Lr. . + va . + mj = 1, i=l iJ J .

(11-2)

Accordingly, any changes in the r .. 'sand va. imply changes in m .• iJ J J

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Equations (11-1) and (11-2) provide one test of the reasonableness of WPSM's forecasts. Regional and value added coefficients must change such that no sector's imports, or iq,ort coefficient, are less than zero. Indeed, any dramatic variation in the import coefficients might be a signal for unwarrantable changes in the other input coefficients.

Aggregate imports provide two other checks. We can monitor projec­tions of total imports as a percentage of total inputs. In the present baseline forecasts these percentages are declining slightly over time, which seems reasonable in light of common notions about regional growth and import substitution. Secondly, we can test our projected trade balance, that is, the relationship between future exports and imports. our baseline forecasts indicate that net exports are expected to show the same relative surplus that they have displayed in the past.

PART III PREDICTIONS AND CONCLUSION

CHAPTER 12 PREDICTION TESTS OF WPSM

When a model is assembled, there is a tendency for it to become merely a toy in the hands of the forecaster, Sometimes distracted by the fun of playing with it, and often impressed by its gadgets and mech­anisms, the user is likely to forget that the model is not a finished product, Indeed, in a sense it can never be a finished product. If forecasting is to be a serious effort, modeling must be an ongoing pro­cess, Builders must continually monitor, test, update, and re-estimate their models as better theory and new data become available, This is particularly true in our case, since WPSM represents an initial step into the relatively unknown territory of regional input-output economet­ric models.

The following prediction tests are designed to assess the fore­casting precision and properties of WPSM as it is now specified, Three basic tests are performed. The first assesses the model's ability to track certain economic variables over a period of observation, In the second, we make measurements of some of WPSH's multipliers, comparing them with multipliers from a static input-output model of Washington State, Lastly, we test the constant regional coefficient hypothesis by comparing our present baseline forecast, which tries to take account of changing regional coefficients, with a forecast without such coefficient changes. This third test is of particular interest because of the c~n­cern expressed by input-output practitioners and others about the constant coefficient restriction.

Historical Predictions from 1963 to 1972

Limitations of a Tracking Test

The strongest test of a forecasting model such as WPSM ,iould be an assessment of ex ante predictions, that is, an evaluation of the model's predictive capabilities after it had been forecasting for a number of years. However, at this point in time, ·wi th our system only recently constructed, such a test would not be possible. Instead, we have to rely upon a weaker test of WPSM's forecasting powers, namely a tracking test over the period of observation used to calibrate the model.

Although some variables in WPSM can be tr acked over longer spans of time, most can only be follmved between 1963 and 1972. Admittedly, this is a very short period of time for a tracking test. In fact, this ten­year period is shorter than our forecasting hori zon, which currently runs from 1972 to 1985. From the standpoint of validating HPSM, this is an unfortunate circumstance. However, for purposes of testing , the year s bet,,1een 1963 and 1972 may be better than their number would suggest. During this time span, the region experienced not only substantial long­term growth but also wide swings in industrial activity as the region

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reacted to a boom and bust in the aerospace sector. Thus, this period displays a considerable range of economic behavior over which to esti­mate and test our model.

Apart from the brevity of the observational period, the effective­ness of a tracking test is limited because of the amount of information known, or given, to the model during the test. Future predictions will be in error in part because of errors in the projected values of the exogenous variables. For our tracking test we abstract from this prob­lem by taking these values as given. Thus, we are gauging only the precision of the specification of WPSM; and our assessments of predic­tion errors over the 1963-1972 period will tend to overstate our overall forecasting capabilities.

Given to WPSM for the tracking test are values of the following U.S. variables: outputs in 50 industries, highway construction, the interest rate differential, the relative cost of housing construction, property income per capita, transfer payments per capita, the contribu­tion rate to social insurance, the personal income tax rate, the labor force participation rate, the unemployment rate, the proportion of pop­ulation aged 5 to 20 years, federal military employment, and federal civilian employment. Also known are some variables endogenous to WPSM: Washington exports of meat products, state and local government other nonbuilding construction, federal building construction, federal non­building construction, and inventory change by industry. Finally, imposed upon the model are average coefficient change rates such that the model is "pinned" to 1963 and 1972 with respect to output, income, and employment. In other words, for each output equation regional coef­ficients are given in 1963 and 1972 such that if WPSM were to predict every one of the corresponding explanatory variables (i.e., Washington outputs and final demands) exactly in those years, the output of the industry in question would be predicted without error as well. Like­wise, income and employment coefficients are given such that exact pre­dictions of output would lead to exact predictions of income and employ­ment in these two years. Over the intervening years, each of these coefficients moves at a constant rate between the two terminal values.

The Predictions

Depicted in Figure 12-1 are predictions of disposable income and resident population. The average prediction errors without regard to sign are 2.1 and 2.6 percent, respectively, as shown in Table 12-1. In both cases, there is a tendency for WPSM to overestimate these two vari­ables during the mid-period from 1965 to 1968, which corresponds to the years of the Boeing Boom. In the later years, when the bottom falls out of the aerospace market and the economy falters, WPSM under-predicts income and population. In particular, the model is oversensitive to population, indicating significant declines in 1968 and 1969 when in fact the population decreases only slightly in a later year, 1972. This is due to the elementary formulation of the employment and population

--- --- -- - -- - - ~- ,

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Table 12-1

Prediction Errors for Selected Variables of Tracking Test from 1963 to 1972

(millions of 1972 dollars)

Variable

Disposab le income Labor and proprietors' income Res i den t population (thousands of persons) Per sons emp l oyed (thousands of persons) Unemployment rate (percentage) Total expor ts Consumpti on Stat e and loc al government expenditures Fixed i nves t ment Fiel d and seed crop output Canning and preserving output Logging output Plywood mill output Pulp mill output Paperboard output Petroleum output Cement, stone, and clay output Aluminum output Aerospace output Shipbui lding ou tput Transportation s ervices output Residential cons t ruction output Trade output Services output

Base Mean

11,696.6 10,757.7

3,198.9 1,329.3

7.0 10,853.5 10,429.7

2,518.1 2,352.2

288.9 426.0 465.7 331.2 204.5 388.1 426.5 184.1 649.0

2,425.4 368.1

1,111.5 765.6

3,549.5 2,217.1

Average Absolute

Difference

251.2 165 .8 82.5 33.6 1.0

242.6 215.7

39.9 140.3

16 .1 21.2 23.1 21.3 8.9

16.9 41.8 9.5

25.2 217.0

27.9 16.3 94.1 61.6 61.2

Difference as Percent of Mean

2.1 1.5 2.6 2.5

14.3 2.2 2.1 1.6 6.0 5.6 5.0 5.0 6.4 4.3 4.4 9.8 5 . 1 3.9 8.9 7.6 1.5

12.3 1.7 2.8

Millions of 1972 Dollars

13,000

11,000

9,000

Thousands of Persons

3,400

3,200

3,000

1963

,, ,,

1963

I

/

Disposable Income

1967 .1972

Resident Population

I I

I

I I

I

I I

I I

1967

, .... ', Predicted

'~ ... ---

1972

Figure 12-1

Predictions of Disposable Income and Resident Population

-111-

block, which does not pick up short-term fluctuations in productivity and the labor force participation rate, among other things. We should point out that beyond 1972 population prediction errors decline, indicating that our baseline population forecasts are getting back on track.

The boom and bust years are clearly revealed in the graph of exports in Figure 12w2. Despite the extreme fluctuation, our model predicts total exports quite well. Both turning points are picked up, although the first is missed by one year. The average prediction error, as given in Table 12-1, is only 2.2 percent. Once more we see a familiar pattern to predic­tion errors, with WPSM overestimating exports in the middle years and underestimating them in the later years. In terms of cause and effect, it would be correct to say that the direction of errors in exports underlay the error pattern in income and population.

Three other major final demand components are shown in Figure 12-2. Given our modeling of household spending, prediction of consumption follows predictions of disposable income, the average error also being 2.1 percent. WPSM does a good job of tracking state and local government expenditures, displaying an error averaging only 1.5 percent. Larger errors are associ­ated with forecasts of fixed investment, but this is not surprising given the volatility of this series. WPSM actually seems to perform quite well with this variable, catching all three turning points and yielding an average error of 6.0 percent.

Looking at other predictions in Table 12-1, we see that the error in labor and proprietors' income is only 1.5 percent. This indicates that a significant portion of the error in disposable income stems from inaccu­r at e predictions of property income and transfer payments. The errors in thes e two instances are 4.8 and 5.7 percent, respectively. The average error for persons employed is 2.6 percent. However, because of changes in the accounting of Washington employment, we can only track this series from 1967 t o 1972. The prediction error in the unemployment rate is 14.3 per­cent, an imprecision that seem.a great until we consider the wide swings that characterize movement of this variable.

As for industry outputs, prediction errors tend to cluster about 5 percent on the average. Larger errors are found where we might expect them, i n the capital goods sectors, 1uch as aerospace, shipbuilding, and residen­tial construction. Smaller errors occur in the non-cOlllllOdity sectors, such as transportation services, trade, and services. This greater precision is fortunate, since these industries account for a large share of income and employment in Washingt•n State.

Some Export Multipliers

Aerospace Multipliers

A second test of WPSM is to measure how it responds to a shock in its system, such as an increase in an industry's exports. Is the reaction a reasonable one? In other words, are the multipliers of WPSM of credible

12,000

10,000

8,000

3,000

2,600

2 , 200

1,800

Exports Consumption

,, 12,000 Predicted

10,000

s,oop

1963 1967 1972 1963 1967

State and Local Government Fixed Investment

Predicted

1963 1967

3,000

tual 2,600

2,200

1,800

1972

Predicted

\ \

1963

Figure 12-2

Predictions of Exports, Consumption,

1967

State and Local Government, and Fixed Investment· (millions of 1972 dollars)

Predicted

1972

I

\ I .......... ,

1972

-113-

proportions? This i s an important issue, since improbable multipliers signal problems of model misspecification, which of course would preclude use of WPSM for impact studies.

The impact on the economy of an increase in aerospace exports is shown in Table 12-2. For this simulation, it is assumed that aerospace exports increase above the baseline forecast by $100 million in 1976 and remain at that relative level until 1985. As a response to this incre­ment in exports, total value added, or Gross State Product, jumps to $115 million i n 1976 and continues rising until 1979, where it peaks at $125 million. At this point, a decline begins, with value added reaching, but apparently leveling off, around $100 million by 1985.

The smooth reaction path of Gross State Product to this impact stands in contr ast to the response patterns taking place in the components of final demand, as shown in Table 12-2 and Figure 12-3. Exports of course increase to $100 million in 1976 and remain at that level throughout the test period. Because of its link to income, consumer demand reacts in a manner s i milar to that of value added, rising and declining slightly after the ini t i al jump in 1976 . On the other hand, state and local government spendi ng remains fla t un t il three years after the increment in exports, at which time it increases to about $16 million. The delay is due to the government sector's t endency to slowly adjust to new levels of income and population in the region . Opposing the movement in public spending, and demons t ra ting the pr i nciple of acceleration, fixed investment peaks early and then declines sharp l y . The slide in capital spending in the later years i s mainly responsible for the tapering of total value added.

As fo r other variables, disposable income follows the path of value added but at a l ower l evel. Persons employed behaves similarly, although the decl ine in the later years i s much steeper. This is due to productiv­ity ga i ns taking pl ace i n t he economy, which imply fewer workers in the f u tur e required t o support the $100 mi ll ion aerospace impact. Population i n turn is tied t o employment but with a slight lag, which is most apparent i n the first years of t he impact. Population does not respond as quickly a s employment to i nc r eas ed ac tivity, in part because of the adjustment in the number of persons unemployed. As Table 12-2 indicates, the unemploy­ment r at e drops 0. 1 percent in the first two years of the impact before r etur ning to its long - r un "equilibrium" level.

A second perspective of the aerospace impact is given in Table 12-3, which disaggregates t ota l value added by sector. About 40 percent of the impac t occurs in aerospace itself. The jagged movement of this series in the f irs t years stems from inventory adjustments taking place in aerospace. Trade and services are next in importance, constituting about 25 percent of the total value added. The construction industry is also significantly affected, but only in the early years during the capital-spending phase of the impac t. For the most part, other manufacturing, to the degree that it is affected, follows the construction cycle; in the long run the impact is sma ll. Simi larly, the r esource industries feel very little of the impact. As f or government va l ue added, it displays its unique pattern, jumping up in 1979 to represent about 10 percent of the total incremental value added generated in the economy.

Table 12-2

Impact of $100 Million Increase in Aerospace Exports from 1976 to 1985

(millions of 1972 dollars)

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

Aerospace exports 0 100 100 100 100 100 100 100 100 100 100 Value added 0 115 121 121 125 122 116 111 107 103 101 Consumption 0 56 63 65 69 69 65 62 59 56 53 State and local expenditures 0 2 -1 3 15 16 16 11 17 16 15 Fixed investment 0 18 52 38 27 22 16 11 8 7 6 Disposable income 0 63 72 74 79 18 73 70 66 63 61 Persons employed (thousands) 0 5.9 6.4 6.2 6.6 6.4 5.9 5.5 5.2 4.8 4.5 Population (thousands) 0 9.3 12.5 13.9 15.1 15.0 14.5 13.7 12 .8 12 .o 11.3 Unemployment rate (percent) 0 -0.1 -0.1 0 0 0 0 0 0 0 0

120

100

80

60

40

20

0

1976 1978

-115-

Exports

Fixed Investment

and Expenditures

1980

Figure 12-3

1982 1984

Impact of $100 Million Increase in Aerospace Exports From 1976 to 1985 on Value Added, Consumption,

Fixed Investment, and State and Local Expenditures (millions of 1972 dollars)

Table 12- 3

Impact of $100 Mi l l ion Incr eas e in Aerospace Exports from 1976 to 1985 on Value Added of Selected Sectors

(millions of 1972 dollars)

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

Aerospace 0 59 44 48 47 47 47 47 47 47 47 Resource 0 2 2 2 2 2 l l l l l Other manufacturing 0 6 10 8 7 7 6 5 4 4 4 Cons true tion 0 5 20 13 9 7 4 2 l l 0 Transportation 0 2 2 2 2 2 2 l l 1 l Trade 0 14 17 17 17 16 15 14 13 13 12 Services 0 13 14 14 15 14 14 13 12 12 12 Other sectors 0 15 17 17 18 18 17 17 17 15 14 Government 0 -1 -5 1 8 9 10 11 10 10 9 Total 0 115 121 121 125 122 116 111 107 103 101

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Static and WPSM Multipliers

So-called Type II income multipliers are the multipliers most com­monly drawn upon by users of the Washington State input-output tables (Bourque and Conway, 1977). A Type II multiplier is defined as the value added generated directly, indirectly, and through induced house­hold consumption in all industries of the economy per dollar of final demand delivered from a given industry. Despite the fact that static multipliers of this sort have become standard tools of regional analysis, they are subject to a number of restrictions. The first is the usual input-output assumption of constant coefficients over time. The second restriction is the simplified specification of the income-consumption linkage. For Washington input-output multipliers, each dollar of value added is assumed to induce an equal amount of constDllption spending, regardless of the personal income content of that value added dollar. The last restriction deals with the concept of closure. Type II multi­pliers capture the effects of induced consumption but not induced state and local government or private investment spending. The latter link­ages are almost always disregarded in impact assessments, usually accom­panied by an argument to the effect that they are either too difficult to specify, operate only in the long run, or are negligible in size.

Since WPSM is designed with the intent of overcoming the restric­tions of static input-output models--coefficients are not held constant, the income and consumption blocks are more completely specified, and gov­ernment and capital spending is allowed to respond to changes in output, income, and population--the question arises, bow do the static multipliers and WPSM's dynamic multipliers com.pare? Table 12-4 gives an answer. In all six sets shown, the values of the WPSM multiplier• are greater than the corresponding static multipliers, as one would expect given the greater degree of closure in WPSM. What is of interest is the size of the differ­ence. For example, in 1980 the WPSM multipliers on average are about 40 percent higher than their static counterparts, although individual dispar­ities vary from 33 percent greater in the case of petroleum to 63 percent higher for trade. By 1985, with the end of the investment cycle, differ­ences are smaller, ranging from 15 to 48 percent.

What this comparison suggests is that disregarding the economic link­ages to state and local government and investment spending can lead to sizeable understatements in the estimates of long-run economic impacts. This is not to say that static Type II multipliers have all of a sudden become obsolete for impact assessments, only that the investigators should be aware of their limitations.

The relationship between WPSM and static multipliers is revealed in one last experiment. We rerun the aerospace impact twice, first short ­circuiting investment spending and then closing off the state and local government sector as well. In other words, as with the static Type II multiplier, we impose the restriction that these two sectors are unre­sponsive, directly or indirectly, to the increase in aerospace exports.

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Table 12-4

A Comparison of WPSM and Static Input-Output Value Added Multipliers for Selected

Industries Assuming Increase in Exports from 1976 to 1985 (dollars of valye added per dollar of exports)

Industry TYJ>e 1975 1976 1978 1980 1985

Aerospace WPSM 0 1.15 1.21 1.22 1.01 Static 0 0.86 0.86 0.86 0.86

Logging WPSM 0 1.83 1.97 2.04 1.85 Static 0 1.49 1.49 1.49 1.49

Pulp mills WPSM 0 1.33 1.67 1.75 1.59 Static 0 1.18 1.18 1.18 1.18

Petrole\Dll WPSM 0 0.43 0.52 0.53 0.46 Static 0 0.40 0.40 0.40 0.40

Aluminum WPSM 0 0.92 1.05 1.13 1.11 Static 0 0.75 0.75 0.75 0.75

Trade WPSM 0 1.66 2 .37 2.49 2.19 Static 0 1.53 1.53 1.53 1.53

Comparing the first and second rows of Table 12-5, we see that when the investment linkage is severed, multipliers drop in value, especially in the first years, which again represent the investment phase of the impact. Bypassing the investment sector leaves a distinctive step-like configuration to the path of the multiplier. In the first three years, the multiplier hovers about 0.82. By 1979, when government spending adjusts, the multiplier jumps to 0.95, where it tends to remain. There is a slight decline in the last couple of years, but it is not nearly as great as that seen in the unrestricted multiplier. In fact, it is quite apparent that the fall in the unrestricted multiplier is due al•et entirely to the termination of the investment cycle. This further sug­gests that had we run the unrestricted impact beyond 1985, we would have observed the aerospace multiplier continuing to flatten out.

When state and local government spending is also bypassed, the step to the multiplier in the second row is removed, with the multiplier now remaining relatively constant around a value of 0.80. This is only slightly below the value of the previously reported estimates of the Type II static multiplier, the difference being due to the varying manner in which the two models specify the output-income-consumption relationship and handle coefficient change. However, by and large, the two multipliers are now conceptually as well as numerically equivalent.

A Test of the Constant Regional Coefficient H:Ypothesis

Most forecasters adopt the constant coefficient assumption to render input-output models operational. Since coefficients are subject to forces

- - - - - --

Table 12-5

Aerospace Value Added Multipliers Under Various Assumptions Regarding Closure Given an Increase in Exports from 1976 to 1985

(dollars of value added per dollar of exports)

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

Aerospace multiplier unrestricted (WPSM) 0 1.15 1.21 1.21 1.25 1.22 1.16 1.11 1.07 1.03 1.01 Aerospace multiplier with investment

exogenous (WPSM) 0 0.81 0.79 0.84 0.95 0.95 0.98 0.98 1.00 0.99 0.97 Aerospace multiplier with investment

and state and local exogenous (WPSM) 0 0.80 0.80 0.81 0.80 0.81 0.80 0.79 0.79 0.78 0.78

Aerospace static multiplier 0 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86

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that could lead to change, critics, as well as input-output practitioners themselves, have questioned th~ validity of this assumption. Their con­cern centers on how the quality of the forecast is affected when this assumption is invoked.

Instead of answering this particular question, the following test addresses a related, but narrower, issue, namely the sensitivity of the WPSM forecasts to our projections of regional coefficients (i.e., the ri 1 's in the output equations). The experiment is to generate forecasts £of the 1972-1985 period under the constant coefficient restriction, com­paring them with our present baseline projections which incorporate coef­ficient change.

As reported in Table 12-6, selected results indicate that projections tend to be lower under the constant coefficient restriction. By 1985 the forecasts of disposable income and the number of persons employed under this assumption are about 10 percent below the baseline projections. Industry outputs reflect this decline, although there are exceptions, such as the output of logging. In a number of important cases, there appear to be significant reductions in output, as in the case of trade and ser­vices, both of which entail great levels of income and employment, as we have noted previously. Indeed, the major portion of the total reduction in income and employment can be traced to the declines in these two sectors.

These results suggest that although coefficient change can notably alter the projections of certain sectors, our forecasts overall are not sensitive to coefficient change, at least to the degree that we have pro­j ected it. In fact , as Table 12-6 indicates, most of the differences with the constant coeffici ent pr ojections appear by 1975. Beyond that time, the restricted f orecasts more or less follow our baseline forecasts, albeit at a l ower level .

We should stress that this is not a true test of the constant coeffi­cient hypothes i s . I t may well turn out to be that variations in coeffi­cients are a ma j or source of baseline prediction errors, and that our apparently moderate view of coefficient change is incorrect. In this mat­ter, however, we must be patient. Only with the passing of time and the application of !!.!!!!!, pr edi c tion tests can we legitimately evaluate the accuracy of any of our projections.

Conclusion

The purpose of t esting a model is to assess its validity. Is WPSM a well specified model? Unfortunately, as we have pointed out, only time will tell. However, the tracking test, the evaluation of export multi­pliers, and the constant coefficient exercise indicate that WPSM is a reasonable model ; that is, the system simulates regional macroeconomic behavior in a cr edi ble fashion.

Table 12-6

A Comparison of Projections With and Without Coefficient Changes, 1975 and 1985 (millions of 1972 dollars)

Variable

Disposable income Persons employed (thousands of people) Vegetables and fruit output Logging output Plywood output Petroleum output Cement, stone, and clay output Electric companies output Trade output Services output

Without

13,967.0 1,171.8

371.6 507.6 222.4 511.7 121.0 550.2

3,976.0 2,624 7

1975

With

15,706.0 1,333.6

403.2 529.2 239.4 587.1 179.4 631.7

4,844.2 3,293.9

Percent Difference

-11.1 -12.1 -7.8 -4.1 -7 .1

-12 .8 -32.6 -12.9 -17.9 -20.3

Without

18,812.0 12,801.0

507.7 765.1 448.1 906.7 229.5 791.4

6,209.8 3,861.9

1985

With

21,039.0 14,265.0

551.2 764.9 474.8

1,119.8 279.5 805.0

7,447.3 4,829.3

Percent Difference

-10.6 -12.2 -7.9 0.2

-5.6 -19.0 -17.9 -1.7

-16.6 -20.0

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Still another test of WPSM would be to ask how reasonable the base­line projections themselves appear. We can address this question by comparing our forecasts with those from a national model, such as the INFORUM model. However, we must leave this task for the next chapter, after we present our baseline projections in greater detail.

CHAPTER 13 FUTURE PROJECTIONS

Now that the model has been built and tested, it is time to unveil its forecasts for the future. Some readers may judge WPSM solely by the credibility of these forecasts. To this we must cry foul, at least for the time being. Despite the fact that the following projections have been subjected to certain tests of reasonableness, as we show later in the chapter, there has been insufficient time to undertake a careful scrutiny. In particular, we have not yet evaluated the INFORUM national forecast, which is the source of the values of our exogenous variables, and consequently a major determinant of our predictions. Nor have our projections been subject to critical review by economists out in the private and public sectors, a step that is absolutely critical if we are to learn about, and make allowances for, new developments occurring in the region. In short, we present these projections only for purposes of demonstration, and caution the reader about their tentative nature.

Forecast to 1985

Economic Growth Indicators

Table 13-1 shows selected indicators of future economic growth. Figure 13-1 graphically displays disposable income and resident population, two of the more important economic barometers. For the complete set of forecasts, refer to Appendix C. As shown in Figure 13-1, the first year of the projection is 1973, even though that year is now recorded history. A check with some recently published income, employment, and population figures indicates that, with the exception of 1975, h1PSM traaks the 1972-1976 period fairly well. For 1975 a downturn has been incorrectly pre­dicted. However, by 1976 it appears that WPSM's projections are more or less in line with the actual path of the economy, although forecasts for certain variables even now are obviously in error, such as the unemploy­ment rate.

Beyond 1975 WPSM foresees slower long-run growth for the economy, Gross State Product and disposable are both projected to increase at about 3 percent a year over the 1976-1985 period, which compares with 4 percent growth rates between 1963 and 1976. Resident population is also expected to grow less rapidly, reaching nearly 4.1 million persons by 1985. Dis­posable income per capita is expected to grow at a 2.1 percent annual rate, down from a 2.5 percent figure over the 1963-1976 period. Projections of unemployment call for a constant 7.5 percent unemployment rate beginning about 1980. Regarding this forecast, we would expect greater unemployment in Washington State than in the U.S., because of the higher proportion of persons seasonably employed in the northwest, among other factors. On the other hand, a 7.5 percent rate may be too high, given that the INFORUM projection is about 6.0 percent during this time.

Gross State Proquct Disposable income

Table 13-1

Indicators of Economic Growth, 1972-1985 (millions of 1972 dollars)

19721 1976 1980 1985

19,171.1 22,881.0 26,503.0 30,124.0 13,473.0 15,734.0 18,471.0 21,072.0

Resident population (thousands) 3,418.0 3,658.9 3,879.6 4,072.6 Disposable income per capita (dollars) 3,940.0 4,300.0 4,760.0 5,170.0 Persons employed (thousands) 1,335.0 1,509.7 1,623.9 1,716.0 Unemployment rate (percent) 9.5 7.3 7.5 7.5 Exports 11,631.3 12,785.0 14,986.0 17,260.0 Consumption 12,000.0 13,997.0 16,416.0 18,716.0 State and local expenditures 2,979.1 3,326.8 3,694.4 4,288.7 Private investment 2,335.5 3,319.7 3,721.7 3,993.8

1A£tual. 2Estimates of historical growth.

Annual Percentage Growth Rate 2 1976-1985 1963-1976

3.1 4.1 3.3 4.2 1.2 1.7 2.1 2.5 1.4 2.2

3.4 3.3 3.3 4.2 2.9 4.4 2.1 4.8

Disposable Income (1972 $ mi l.)

20,000

18,000

16,000

14,000

12,000

10,000

1963

Resident Population ( thou.)

/ ;' ,,,, ,, ,,,,

, / ,,,,,,, 4,000 .,,,

/ ,,,, ,,,

Actual Resident

Population

1967

Predicted

✓ ;' / ,, ,,,

/ I / I

/ , ,, I

,,,, ~

/ ' ✓ Predicted Resident / ,,,, Disposable

Population / "" Income I / I /

,I

I I I I ,, ,,

I ,,,. - ;;, ,,,,, ----1"- ----,, /

Disposable Income

1972 1976 Figure 13-1

1980

Disposable Income and Resident Population, 1963-1985

3,800

3,600

3,400

3,200

3,000

1985

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Underlying these forecasts of income and population is a 3.4 percent annual growth rate projected for regional exports. Somewhat surprisingly, this does not represent a less rapid pace than that over the past fifteen years. However, we anticipate slower future growth from other components of final demand. The projection of consumption follows the path of income. The growth rate in the public sector is anticipated to drop sharply, in part due to slackening educational demands with the continuing decline in school enrollment. Expected increases in investment are also much lower in the future, although the volatility of this series makes it difficult to compare growth rates over different periods.

Export and Employment Projections

WPSM makes literally thousands of forecasts, ranging from Gross State Product to individual interindustry transactions; and it would be too time-consuming to comment on even a portion of them here. But two forecasts that are of interest to regional analysts are those of exports and employment. A brief overview of the export and employment picture by industry provides good insight into the economy's changing structure, especially with regard to its strong and weak points. Furthennore, the employment forecasts permit one to compare WPSM's outlook with other rro­jections, such as that of the Washington State Department of Revenue. Keep in mind that our employment variable is stated in terms of the num­ber of jobs, which differs from other employment concepts, such as wage and salary workers. For purposes of comparison, one should therefore focus on the relative growths predicted, not on the absolute levels.

As noted previously, total exports from Washington State are antici­pated to continue growing at about the same rate. However, as Table 13-2 shows, this generalization does not hold for all sectors. A quickening growth rate is expected in agriculture, as is the case for wood and paper products. We should point out that for the latter sectors the growth rates shown may not be strictly representative of long-tenn growth, since 1976 was a year on the heels of a national recession, which substantially reduced demand for wood and paper products. All else being equal, this would tend to lead to understatements of historical growth and overstate­ments of projections in these two cases. Food product exports are expected to grow at the same pace; but reduced rates are foreseen for primary metals and transportation equipment. As transportation equipment is a key sector, we comment further on this forecast in the following section. As for trade and transportation services, the rapid expansion, both historical and pro­jected, reflects two distinct phenomena: the growth of margin revenues associated with the exports of commodities from Washington State, and the development of the Puget Sound area as a national and international trade and transportation center. Whether Puget Sound continues to grow as a commercial center has great implications for the future welfare of the

1see the publication entitled Economic Forecast of Washington State, Calendar Years 1976-1980.

Table 13-2

Export Growth for Selected Industries, 1972-1985 (millions of 1972 dollars)

1976

Agriculture 512.9 501.5 Food pro due ts 848.3 961.8 Hood products 1,356.4 1,318.0 Paper products 767 .4 742.2 Primary metals 794.2 811. 7 Transportation e quipment 2,647.3 3,311.7 Trade and transportation 1,051.0 2 ,021 .7

1Actual. 2Estimates of historical growth.

1980 1985

574.1 660.4 1,112.2 1,275.0 1,699.3 1,903.3

812.4 893.0 983.5 1,107.7

3,585.8 4,149.1 2 ,618.0 3,199.9

Annual Percentage Growth Rate

1976-1985 1963-19762

3.1 2.0 3.2 3.0 4.2 2.5 2.1 1.3 3.5 4.0 2.5 4.3 5.2 5.2

Table 13-3

Employment for Selected Sectors, 1972-1985 (thousands of jobs)

19721

1976 1980 1985

Agriculture 60.0 57.3 50.4 42.3 Food products 27.0 28.8 29.6 30.6 Wood products 56.4 58.2 63.7 65.9 Paper products 17.8 17.2 17.5 18.1 Primary metals 13.3 15.3 16.4 17.1 Transportation equipment 62.5 75.8 66.6 61.8 Transportation services 63.5 65.7 72.0 75.3 Construction 72 .2 92.7 96.9 96.2 Trade 296. 7 358.0 397 .4 420.0 Finance, insurance, and real estate 69.3 80.6 89.9 101.6 Services 229.7 294.9 319.6 363.7 State and local government 190.6 201.0 216.6 238.5 Federal government 77 .6 79.9 79.4 78.6

1Actual. 2Estimates of historical growth.

Annual Percentage Growth Rate 2 1976-1985 1963-1976

-3.3 -1.9 0.7 0.3 1.4 1.3 0.6 -0.4 1.2 3.5

-2.2 -0.7 1.5 1.3 0.4 3.3 1.8 3.4 2.6 3.9 2.4 5.8 1.9 3.9

-0.2 -1.1

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state. However, we must admit that, on the whole, very little is known about the nature of this development. As a consequence, the reader must view these forecasts with more than the usual caution.

Employment forecasts for selected sectors are found in Table 13-3. Three characteristics of these projections are noteworthy: the steep decline in agricultural jobs; the relatively unchanging employment pic­ture in manufacturing, that is, apart from the transportation equipment sector; and the continued advances in the non-commodity industries. Al­though the prospects for agricultural output are bright, the employment outlook is not, due to expected rapid gains in productivity. This decline in agricultural jobs is of course a continuation of a historical trend. In contrast, slower productivity advancements are projected for manufacturing, which tends to give a boost to employment levels. How­ever, this characterization does not seem to apply to transportation equipment, especially to aerospace. If one accepts the INFORUM view of the future, one should expect a fairly steep decline in the number of aerospace jobs, even in the face of expanding production. As for the service-oriented sectors (including government), it is not an overstate­ment to say that the employment future of the state lies in their hands. For example, the increase in the total number of jobs in the entire economy between 1976 and 1985 is expected to be 195,200. For the non­commodity sectors, the increase amounts to 200,500. In other words, all of the projected growth in jobs is found outside the resource and manufacturing sectors. However, growth rates in services are expected to be below those of the past fifteen years, reflecting the economy's overall slower pace.

An Alternative View of Aerospace

The present baseline forecast suggests that aerospace employment will decline to about 35,000 workers by 1985. This prediction is based on essentially three projected developments between 1975 and 1985: (1) that U.S. aerospace output will grow at about a 3.1 percent average annual growth rate; (2) that Washington's export share of national output will remain constant; and (3) that output per job in the local industry will grow at an average annual rate of 5.5 percent. Given the constant marke t share, the fact that U.S. output is projected to grow at a rate slower than that for productivity accounts for the anticipated reduction in aerospace jobs.

In light of Almon's national projections, the baseline aerospace forecast seems reasonable. On the other hand, it falls well below the concensus view in Washington State of the aerospace future. At least two other regional forecasts foresee 1980 and 1985 employment levels around 55,000 workers. Are these projections also reasonable? The answer seems to be in the affirmative. For example, if we adopt not the INFORUM but the BLS view of the aerospace industry, we come up with quite different figures. The BLS projections translate into 6.0 percent and 2.7 percent annual growth rates for U.S. output and local productivity, respectively, over the 1975-1985 period. Again assuming a constant market share, the resulting regional aerospace employment forecast calls for 60,000 employees by 1985.

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Of course, changes in aerospace activity would in turn cause the economy to alter its growth path. The impact of higher aerospace employ­ment on selected economic variables is shown in Table 13-4. Detailed forecasts for this scenario can also be found in Appendix C. The addi­tional 25,000 jobs in aerospace would lead to an overall expansion of 98,000 civilian persons employed. Following in line, total population would increase by about 223,000 persons. Disposable income would rise slightly faster than population, resulting in a slightly higher dispos­able income per capita. The more rapid rate of job creation would also tend to depress the unemployment rate slightly.

Table 13-4

Changes in the Baseline Forecast with Greater Anticipated Aerospace Growth. 1975-1985

Aerospace output ($72 mil.) Aerospace employment (thousands) Civilian employment (thousands) Population (thousands) Disposable income ($72 mil.) Disposable income per capita ($72) Unemployment rate(%)

1Predicted, no t actual.

2,246.3 42.8

1,332.8 3,436.2

14,259.0 4,150.0

10.0

Original Baseline

3,243.2 34.6

1,668.7 4,072.6

21,072.0 5,170.0

7.5

1985 Higher

Aerospace

4,268.1 60.0

1,766.3 4,296.2

22,389.0 5,210.0

7.3

What this exercise demonstrates is not the superiority of one pro­jection over another; rather, it provides one example of the uncertainty inherent in economic forecasting. If the reader feels that possibly we have presented an extreme case--that surely one forecast is clearly more reasonable--place yourself back to 1968 and consider the problem of pre­dicting aerospace employment at t hat time. Some projections called for the number of jobs to expand t o about 150,000 by 1985, which seemed quite pl ausible given the 100,000 then currently employed. Of course, we know the punch line to this .story: by 1971 aerospace employment had plumneted t o about 40,000, causing a drastic reassessment of the future of aero­space and the Washington State economy.

Some Tests of Reasonableness

If there is a truth in forecasting it is that no one knows forcer­t ain what the future holds. One therefore cannot assess the accuracy of a set of forecasts until after the fact, a predicament that makes it

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difficult to speak in terms of the validity of WPSM. However, we can ask whether our projections seem reasonable, although we must concede that what may appear reasonable to us may not be so to someone else. Among the possible questions regarding this issue, we ask just one: are the HPSM forecasts of economic variables consistent with each other and with national projections? Of course, consistency has been an objective of our modeling effort all along; but the question still remains whether the forecasts overall seem to hold together. Is the growth of income consistent with projections of population? Is the ratio of private investment to Gross State Product reasonable? How does the employment picture by industry in Washington State match up to that in the U.S.?

Major economic indicators for Washington State and the U.S. are shown in Table 13-5. As usual, the U.S. forecasts are taken from the INFORUM model. For purposes of comparison, ratios of the corresponding series have been calculated. Ratios for Gross State Product, population, and disposable income reveal that Hashington is expected to grow slightly faster than the rest of the nation, moving from about 1.6 percent of total national economic activity to about 1.8 percent. Not only does this moderately faster growth seem reasonable in light of commonly held notions about the Washington State economic outlook, but the ratios at each point in time indicate that WPSM's projections of these three vari­ables are more or less in line with each other. Per capita disposable income in Washington State is anticipated to remain slightly above the national level, reflecting the continuation of a past trend. However, since much of this difference is due to the fact that Washington has no personal income tax, a situation that may soon change, a narrowing of this difference is certainly conceivable. The projections of the unem­ployment rate seem high, as we have previously noted; but this is one variable where our confidence with any prediction would amount to a ",~et finger in the wind. 11

As for components of final demand, Table 13-5 shows that the HPSN forecast of the average propensity to consume remains about one or two percentage points below that of the U .s. The fraction of Gross State Product for investment is also lower than its national counterpart, due in part to differences in accounting procedures and to the fact that much of the state's capital is publicly owned. 2 Evidence of the degree of government investment is found in the public construction series. The three percent share of national public construction anticipated for the state is well above the figure one would expect on the basis of Wash­ington's share of income and population. State and local government operating expenditures as a fraction of dispos able income are expected to remain higher in Hashington than in the U.S. Although both state and national forecasts suggest relative declines in the future, the projected paths are somewhat different. However, here as well as with the other

2As an example of differences in the accounting of investment, the Washington accounts disregard the inventory change in stocks of imported goods.

Table 13-5

A Comparison of Economic Indicators for Washington State and the U.S., 1972 1 1980 1 and 1985

Washington Gross State Product ($72 mil) U.S. Gross National Product ($72 mil) Ratio

Washington population (thousands of persons) U.S. population (thousands of persons) Ratio

Washington disposable income ($72 mil) U.S. disposable income ($72 mil) Ratio

Washington disposable income per capita ($72) U.S. disposable income per capita ($72) Ratio

Washington unemployment rate(%) U.S. unemployment rate(%) Ratio

Washington average propensity to consume (fraction) U.S. average propensity to consume (fraction) Ratio

Washington investment to GSP (fraction) U.S. investment to GNP (fraction) Ratio

Washington public construction ($72 mil) U.S. public construction ($72 mil) Ratio

Washington state and local to income (fraction) U.S. state and local to income (fraction) Ratio

1972

19,171 1,171,120

0.016

3,418 209,300

0.016

13,473 803,084

0.017

3,940 3,837 1.03

9.5 5.6

1.70

0.891 0.913

0.98

0.122 0.161

0.76

864 30,180 0.029

0,179 0.151

1.19

1980 1985

26,503 30,124 1,459,090 1,645,950

0.018 0.018

3,880 4,073 222,770 234,070

0.017 0.017

18,471 21,072 1,032,539 1,171,520

0.018 0.018

4,760 5,170 4,635 5,005 1.03 1.03

7.5 7.5 6,1 5.6

1.23 1.34

0.889 0.888 0.894 0.905 0.99 0.98

0.140 0.133 0.169 0.166

0.83 0.80

1,053 1,150 35,410 37,960 0.030 0.030

0.158 0.162 0.143 0.138 1.10 1.17

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final demands, there is no signal that our projections have gone too far astray.

For a final check, we look at state employment projections as frac­tions of the U.S. forecasts. Table 13-6 shows that state shares tend to increase slightly, following our anticipations of the relative growth in regional income and population, Notable exceptions are the slippages in paper products and transportation equipment; but neither development is surprising. In short, we once again see no apparent eyesores in the WPS}1 forecasts.

Table 13-6

1:lashington Shares of U.S. Employment for Selected Sectors, 1972, 1980 1 and 1985

1972

Agriculture 0.016 Food products 0.015 Wood products 0 .092 Paper products 0.026 Primary metals 0.0ll Transportation equipment 0,036 Transportation services 0.022 Construction 0.016 Trade 0.017 Finance, insurance, real estate, and services 0.016 State and loc al government 0.018 Federal government 0,018

1980 1985

0.016 0.018 0,017 0.017 0.091 0.093 0.022 0.022 0.0ll 0.0ll 0,035 0.031 0.024 0.024 0,019 0,018 0.019 0.019 0,018 0.013 0.018 0.018 0.019 0.019

- ---- -- - - --

CHAPTER 14 CONCLUSION

The purpose of this study has been to develop a regional model of the Washington State economy suitable for forecasting and policy analysis. The result is the Washington Projection and Simulation Model, a 456-equation econometric model built around an input-output framework. Formally tied to a national input-output model, the INFORUM model of Clopper Almon, WPSM is capable of projecting the growth of the regional economy over the inter­mediate and long range (i.e., a five to 20-year time· horizon) under a variety of externally and internally imposed conditions.

Strengths and Weaknesses

The obvious strength of WPSM lies in its explicit structure, partic­ularly with regard to its input-output linkages. As a consequence of this construction, the model is a comprehensive and detailed formulation of the behavior of the Washington State economy. WPSM not only predicts variables typically found in input-output systems--output and income by industry, interindustry transactions, consumer expenditures, private investment, state and local government spending, exports (including sales to the fed­eral government), and imports--but also such important variables as Gross State Product, disposable income, regional employment, and population. The model's structure also ensures a set of consistent predictions. Each industry's input requirements (including its labor input} are consistent with its sales; household consumption, private investment, and government spending are consistent with the income and population of the region; and the s.tate's exports are consistent with the region's external economic environment. Finally, because of its structure, WPSM is operationally flexible. The behavior of the model can be readily altered to incorporate changing economic conditions (e.g., higher aerospace exports or lower sales of energy) that might condition the course of regional growth. This char­acteristic, coupled with the others mentioned here, makes WPSM especially useful for simulating the effects on the state of certain government poli­cies and other changes in Washington's economic climate.

The obvious weakness of the model is its data base, a pool of cross­sectional and historical time series data. Most glaring is the paucity of information on final demand, a condition that has hindered regional input­output forecasting efforts in the past. However, Washington State is fortunate in having three input-output tables upon which to develop a reasonable annual series on exports by industry. Excellent data on state and local government spending are also found, and an aggregate consumption function based on national data appears to be a reasonable substitute. We have even been fairly successful with investment expenditures, primarily because of the fact that the only component that is critical for long-run regional forecasting purposes is investment in structures, for which ade­quate time series information is available.

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Nevertheless, the span of time covered by the data--it ranges from 25 observations (in the case of certain income, employment, and population variables) to three (in the case of the input-output cross-sections)--is generally short. This limits our ability to model more precisely the complex relationships between the variables in the system and to describe certain types of behavior, especially that of a short-term or cyclical nature. Most forecasting equations are restricted to only one or two explanatory variables because of the "degrees of freedom" problem. Hope­fully, the condition of the data base will improve with time, and with that improvement will come superior forecasting models.

Perhaps another serious shortcoming of WPSM is that relative price changes and consequent substitution effects are not explicitly incorporated into the model as determinants of the production and consumption patterns of the various sectors of the economy. This problem is not totally ignored, but we have been able to modify production and consumption coefficients on this account only to the extent that the INFORUM model has captured the effects of relative · price changes at the national level. For the present, the influence of prices must be handled principally through such ad hoc adjustments, rather than more formally by market-clearing supply and demand equations.

Whether the model specification we have adopted is basically strong or weak depends upon whether one views the cup as half-full or half-empty. The formulations that we have chosen to describe the behavior of consumers, investors, producers, and government, while reasonable, are obviously incom­plete, not only with regard to the role of prices but also with respect to the influence of stocks. Again, dat a have been the limiting factor. With the exception of population and labor force, there are virtually no satis­factory economic measures of the stocks of regional resources and their rates of utilization over time, despite the fact that the size and value of the physical infrastructure has long been recognized as a significant factor affecting regional development. Stocks of water, land, forests, minerals, and man-made capital are important determinants of regional output and income, but, as Czamanski (1973) has pointed out, wealth accounting is the least developed form of social accounting.

Of course, we are not only aware of the severe data deficiencies that restrict the scope of our modeling efforts, but we also recognize that the sharpness of existing economic data is often not keen enough to force a clear-cut selection among plausible hypotheses of regional economic behavior. Even when historical data provide strong evidence that behavioral relation­ships have been correctly identified, there is always the question as to what degree past experience is relevant to the future. It is an article of faith that the responses measured in the past to specified influences will be repeated in the future in exactly the same manner.

Uses of WPSM

Since WPSM is a general-purpose model, it is difficult to know in

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advance all the possible uses to which it may be put. The immediate objec­tive of the development of WPSM has been to provide the State of Washington with a policy-,-analytic forecasting model for use in development planning. The projection model is now operational and on the State computer system. It is anticipated that Washington State will employ WPSM to prepare inter­mediate and long-term projections and as a simulator to explore the impli­cations of various economic situations that policymakers might encounter.

While WPSM is a general-purpose model, it is not an all-purpose model. This means that in its present form it is not likely to serve many of the particularized needs of potential users. However, the general equilibrium nature of WPSM provides a framework to which satellite models can be attached. The blocks of the model may also be reformulated or expanded to meet special purposes. For example, sub-models which relate water requirements to indus­trial output, occupational labor demands to employment, or energy facilities planning (or conservation policies) to energy demand may be added to the basic structure. Since the variables encompassed by WPSM are comprehensive, many activities that are associated with these variables can be related to the model for either forecasting or analytical purposes.

We should re-emphasize that the model has been designed primarily to permit us to probe the implications of intermediate and long-tern national economic growth upon the development of Washington State. As such, it is not particularly suited for cyclical analysis. While certain features of short-run change are reflected in some of the equations of the model, the forecasting equations are fitted to annual data and therefore do not closely follow the course of business cycles. Moreover, certain flows are measured in units which are only mildly sensitive to fluctuations (e.g., labor inputs are measured in terms of jobs rather than manhours), and some of the specifi­cations are reasonable for long-term but not short-term predictions (e.g., the inventory model). The distinction between cyclical and trend models is not a clear one; it seems to be more dependent upon the complexity of the model and its ability to depict the behavior of variables within phases of business cycles rather than as a result of a dichotomy imposed by theoretical considerations alone. One further implication of our focus upon long-term projections is that ex~ predictions made from WPSM should not be appraised by reference to short-term experience.

A Final Word

Like other large-scale econometric models, WPSM is a forecasting frame­work which needs to be continuously updated as new observations become available. The model should also be refonnulated as better theory is devel­oped. As Tiebout (1969) has pointed out, it is useful for public and private planning to have a regional model which can be easily modified and updated, and therefore to think of forecasting as an ongoing process. It is in this spirit that WPSM has been built.

The model, like a new automobile, has been factory tested. Undoubtedly, defects will be found during its break-in period; and it will need to be

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tuned frequently if it is to be kept in good running condition. Accessories can also be added. For example, it should not be difficult to attach sub­models to forecast the values of energy demand, water requirements, or even tax yields, variables which are related to the transmission mechanism of the system, that is, to production, income, employment, consumption, and investment. Of course, how well WPSM predicts depends significantly upon the exogenous inputs into the model, which is another way of saying that even good cars can be wrecked by bad drivers.

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Almon, C., Jr. et al. (1974) 1985: Interindustry Forecasts of the American Economy. Lexington, Mass.: Lexington Books.

Beyers, W. B. (1972) "On the Stability of Regional Ipterindustry Models: The Washington Data for 1963 and 1967," Journal of Regional Science, Vol. 12, 363-374.

Beyers, w. B. et al. (1970) Input-Output Tables for the Washington Economy. Seattle: Graduate School of Business Administration, University of Washington.

Bourque, P. J. (1971) "An Input-Output Analysis of Economic Change in Washington State," University of Washington Business Review, Vol. 30, 5-22.

Bourque, P. J. and R. s. Conway, Jr. (1976) The Input-Output Structure of the Washington Economy. Seattle: Graduate School of Business Administration, University of Washington.

Bourque, P. J. and R. s. Conway, Jr. (1977) The 1972 Washington Input­Output Study. Seattle: Graduate School of Business Administration, University of Washington.

Bourque, P. J. and E. Weeks (1969) Detailed Input-Output Tables for Washington State. 1963. Pullman: Washington State University.

Carter, A. P. (1967) "Changes in the Structure of the American Economy, 1947 to 1958 and 1962," Review of Economics and Statistics, Vol. 49, 209-224.

Conway, R. s., Jr. (1975) "A Note on the Stability of Regional Interindustry Models," Journal of Regional Science, Vol. 15, 67-72.

Conway, R. s., Jr. (1977) "The Stability of Regional Input-Output Multipliers," Environment and Planning, Vol. 9, 197-214.

Czamanaki, S. (1973) Regional and Interregional Social Accounting. Lexington, Mass.: Lexington Books.

Emerson, M. J. (1971) Interindustry Projections of the Kansas Economy. Topeka: State of Kansas.

Evans, M. K. (1969) Macroeconomic Activity. New York: Harper and Row.

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Glickman, N. J. (1974) "Econometric Analysis of a Regional System I: A Forecasting Model," Discussion Paper No. 290, Department of Economics, University of Pennsylvania.

Hoch, I. (1959) ''Forecasting Economic Activity for the Chicago Region: Final Report," Chicago Area Transportation Study,

Isard, W. (1960) Methods of Regional Analysis, Cambridge, Mass.: The M.I.T. Press.

Klein, L. R. (1969) "The Specification of Regional Econometric Models," Papers, Regional Science Association, Vol. 23, 105-115.

Klein, L. R. (1971) An Essay on the Theory of Economic Prediction. Chicago : Markham Publishing Company.

McCracken, M. C, (1973) An Overview of Candide Model 1.0. Ottawa: Economic Council of Canada.

Miernyk, W. H. (1968) ''Long-Range Forecasting with a Regional Input­Output Model," Western Economic Journal, Vol. 6, 165-176.

Miernyk , W. H. (1970a) Simulating Regional Economic Development. Lexington, Mass.: Heath Lexington,

Miernyk, w. H. (1970b) ''The West Virginia Dynamic Model and its Impli­cations," Growth and Change, Vol. 1, 27-32.

Miernyk, w. H. and J. T. Sears (1974) Air Pollution Abatement and Regional Economic Development . Lexington, Mass.: Lexington Books.

Miernyk, w. H. and K. Shellhammer (1968) Simulating Regional Economic Development with an Input-Output Model. Morgantown: Regional Research Institute, West Virginia.

Preston, R. s. (1972) The Wharton Annual and Industry Forecasting Model. Philadelphia: Department of Economics, University of Pennsylvania,

Richardson, H. w. (1972) Input-Output and Regional Economics, New York: John Wiley and Sons.

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-141-

u.s. Bureau of Labor Statistics (1975) The Structure of the U.S. Economy in 1980 and 1985. Washington D. C.: U.S. Department of Labor.

Willet, B. R. (1974) ''The Development of Wholesale Price Indexes for the State of Washington," M.B.A. paper, University of Washington.

- -------- ----

APPENDIX A DEFINITIONS OF VARIABLES IN WPSM

Following is the list of variables found in the Washington Projection and Simulation Model. The first column shows the variable number. The first digit(s) of that number indicates the block to which the variable belongs. There are nine blocks covering the endogenous variables: exports, state and local government expenditures, private investment, personal con­sumption, output, value added, personal income, employment and population, and imports. The tenth block contains the lagged endogenous variables. Finally, the eleventh block covers the exogenous variables. In total, there are 456 endogenous variables, 53 lagged endogenous variables, and 75 exogenous variables.

The second column gives the variable name. In order to make the model easier to read, the following nomenclature is adopted:

variable name• prefix+ root+ root modification+ suffix

(l) Prefixes:

D change us u.s. W Washington

(2) Roots:

C CYCLE EX I M N POP SL VA X y YL

consumption cyclic indicator (change in disposable income) exports (including federal government expenditures) investment imports employment population state and local government expenditures value added output income wages and salaries and proprietors' income

(3) Suffixes:

01 industry 1 (field crops)

55 industry 55 (services) Ll lagged one year L2 lagged two years

-144-

PC per capita RT rate TOT total

The last column in the list gives the variable definition. For fur­ther discussion of these definitions, refer to the corresponding chapters on model specification in Part II.

LISTING Of RECORDS 0 THROUGH 1500 , FROM THE OEFINTN FILE , LAST UPDATED ON 12/15

101 WEXOl WASt-4I NGTON EXPORTS FROM INDUSTRY l 102 WEX02 WASHINGTON EXPORTS FROM INDUSTRY 2 }OJ WEXOJ WASHINGTON EXPORTS FROM INDUSTRY 3 1114 WEX04 WASHINGTON EXPORTS FROM INDUSTRY 4 105 WEX05 IIIASHINGTON EXPORTS FROM INDUSTRY s 106 WEX06 WASHINGTON EXPORTS FROM INDUSTRY 6 107 WEX07 WASHINGTON EXPORTS FROM INDUSTRY 1 108 WEX08 IIIASHINGTON EXPORTS FROM INDUST~Y 8 109 WEX09 •ASHINGTON EXPORTS FROM INDUSTRY 9 110 .WEXlO WASHINGTON EXPORTS FROM INDUSTRY 10 111 WEXll WASHINGTON EXPORTS FROM INDUSTRY 11 112 WEX12 WASHINGTON EXPORTS FROM INDUSTRY 12 113 IIIIEXlJ WASHINGTON EXPORTS FROM INDUSTRY 13 li4 WEX14 IIIASHINGTON EXPORTS FROM INDUSTRY 14 115 WEXlS l1USHINGTON EXPORTS FROM INDUSTRY 1s 116 WEX16 IIIASHINGTON EXPORTS FROM INDUSTRY 16 I

117 IIIIEXl 7 IIIIASHINGTON EXPORTS nwM INDUSTRY 17 ..... .i:-

1!8 WEX18 WASHINGTON EXPORTS FROM INDUSTRY 18 VI I

119 IIEX19 WASHINGTON EXPORTS FROM INDUSTRY 19 120 WEX20 WASHINGTON EXPORTS FROM INDUSTRY 20 121 WEX21 WASHINGTON EXPORTS FROM INDUSTRY 21 122 IIEX22 •ASHINGTON EXPORTS FROM INDUSTRY 22 liJ ll£X23 WASHINGTON EXPORTS FROM INDUSTRY 23 124 WEX24 IIIIASHINGTON EXPORTS FROM INDUSTRY 24 125 IIIEX25 WASHINGTON EXPORTS FROM INDUSTRY 2s 1~6 WEX26 WASHINGTON EXPORTS FROM INDUSTRY 20 127 WEX27 WASHINGTON EXPORTS FROM INDUSTRY 21 128 WEX28 WASHINGTON EXPORTS FROM INDUSTRY 28 129 IIIIEX29 WASHINGTON EXPORTS FROM INDUSTRY 29 130 IIIEXJO WASHINGTON EXPORTS FROM INDUSTRY 30 lJl IIIEXJl WASHINGTON EXPORTS FROM INDUSTRY Jl 132 WEX32 WASHINGTON EXPORTS FROM INDUSTRY 32 133 WEX33 IIIASHINGTON EXPORTS FROM INDUSTRY 33 134 WEX34 WASHINGTON EXPORTS FROM INDUSTRY 34 135 WEXJS WASHINGTON EXPORTS FROM INDUSTRY 35 136 WEX36 WASHINGTON EXPORTS FROM INDUSTRY 36 lJ7 WEX37 WASHINGTON EXPORTS FROM INDUSTRY 37 1J8 WEX38 WASHINGTON EXPOPTS FROM INDUSTRY 38 l.J9 WEX39 WASHINGTON EXPORTS FROM INDUSTRY 39

140 WEX40 WASHINGTON EXPORTS FROM INDUSTRY 40 141 WEX4l WASHINGTON EXPORTS FROM INDUSTRY 41 142 WEX42 WASHINGTON EXPORTS FROM INDUSTRY 42 143 WEX43 WASHINGTON EXPORTS FROM INDUSTRY 43 144 WEX44 WASHINGTON EXPORTS FROM INDUSTRY 44 1~5 WEX45 WASHINGTON EXPORTS FROM INDUSTRY 45 l't6 111EX46 WASHINGTON EXPORTS FROM INDUSTRY 46 147 WEX47 WASHINGTON EXPORTS FROM INDUSTRY 47 148 111EX48 WASHINGTON EXPORTS FROM INDUSTRY 48 149 WEX49 WASHINGTON EXPORTS FROM INDUSTRY 49

ltiO WEXSO WASHINGTON EXPORTS FROM INDUSTRY so 151 WEXSl WASHINGTON EXPORTS FROM INDUSTRY Sl 1~2 WEX52 WAStHNGTON EXPORTS FROM INDUSTRY 52 153 WEX53 WASHINGTON EXPORTS FROM INDUSTRY SJ 1~4 WEX54 WASHINGTON EXPORTS FROM INDUSTRY 54 1~5 WEXSS WASHINGTON EXPORTS FROM INDUSTRY 55 1~6 WEXTOT WASHINGTOfl; TOTAL EXPORTS 201 '-SLEOUPCS-20 WASH STATE AND LOCAL EDUC EXPEN PER CAPITA AGED S-20 202 WSLEOU WASHING TON STATE AND LOCAL EDUCATION EXPENDITURES 203 WSLOTH WASHINGTON OTHER STATE ANO LOCAL EXPENOITURES 204 WSLOP WASHINGTON STATE ANO LOCAL OPERATING EXPENDITURES 205 WSLEOUBLD WASHINGTON STATE ANO LOCAL EDUCATIONAL BUILDINGS I

I-

206 WSLOTHBLD WASHINGTON STATE ANO LOCAL OTHER BUILDINGS ~ a-

207 IISLHIWAY WASHINGTON STATE ANO LOCAL HIGHWAYS I

208 WSLNONBLO WASHINGTON STATE ANO LOCAL OTHER NONBUlLDING 209 WSLCON WASHINGTON STATE ANO LOCAL CONSTRUCTION 210 WSLTOT WASHINGTON TOTAL STATE AND LOCAL EXPENDITURES 211 WSLEOUTOT WASHINGTON STATE ANO LOCAL TOTAL OTHER EXPENDITURES 212 liSLOTHTOT WASHINGTON STATE ANO LOCAL TOTAL EDUCATION EXPENDITURES 301 WSRES WASHINGTON ~STOCK~ Of RESIDENTIAL HOUSING 3Q2 IIIIRES WASHINGTON RESIDENTIAL INVESTMENT 303 WSOTHSTR wAStHNGTON ~STOCK~ OF OTHER STRUCTURES 304 WXMFG WASHINGTON TOTAL MANUFACTURING OUTPUT 305 WXNONMFG WASHINGTON TOTAL NONMANUFACTURING OUTPUT 306 WXWTl IIIASHINGTON WEIGHTED OUTPUT FOR STRUCTU~ES 307 wIOTHSTR WASHINGTON INVESTMENT IN OTHER STRUCTURES 308 WIOTHBLD WASHINGTON INVESTMENT IN OTHER BUILDlN~S 309 liINONBLD WASHINGTON INVESTMENT IN NONBUILOINGS 310 WXliT2 WA~HINGTON WEIGHTED OUTPUT FOR EQUIPMENT 311 WIEQP WASHINGTON INVESTMENT IN EQUIPMENT 312 WIFIXTOT WASHINGTON TOTAL FIXED INVESTMENT 313 Ii IlNVO l WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY l 314 WI INV02 WASHINGTON INVESTMENT IN INVENTORIES BY INOUSTRy 2 315 WINNVOJ WASHINGTON INVESTMENT IN INVENTORIES BY INOUSTRY 3

316 W IINV04 IOSHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 4 317 •IINVOS WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 5

318 W IINV06 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 6

319 •IINV07 WASHINGTON INVESTMENT IN INVENTORIES B'< INUUSTRY 1 3i0 WIINV08 WASHINGTON INVESTMENT IN INVENTORIES BY INUUsTRY 8

Jil WIINV09 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 9 34:'.2 ~IIIINVlO WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 10 323 WIINVll WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 1 l 324 IIIIIINV12 IIIIASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 12 325 ~IIIINV13 WASHINGTON INVESTMENT IN INVENTORit::S BY INOUSTRY }3 32t., •IINV14 WASHINGTON INVESTMENT IN INVENTORIES er, INDUSTRY 14 3~1 WIINVlS WASHINGTON INVESTMENT IN INVENTORIES B'< INDUSTRY 15 3~8 WIINV16 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 16 3~9 • IINV l 7 WASHINGTON INVESTMENT IN INVENTORIES BY INlJUSTRY 17 330 IIIIIIN\118 111ASHINGTON INVESTMENT IN INVENTORIES BY INUUSTRy 18 331 IIIIIN\119 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 19 332 WIIN\120 WASHINGTON JN\IESTME.NT IN INVENTORIES BY INUUSTRY 20 333 IIIIIINV2l WASHINGTON INVESTMENT IN INVENTOR JES BY INOUSTRY 21 3.J4 WJINV22 WASHINGTON INVESTMENT IN INVENTOR JES BY INOUSTRY 22

33!:> WIINV23 WASHINGTON INVESTMENT IN INVENTORIES BY INOUSTRY 23 J3t., •IINV24 IIASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 24 337 WIIN\125 WASHINGTON INVESTMENT IN INVENTORil:S BY INDUSTRY 25

I .....

338 WIINV26 WASHINGTON INVESTMENT IN INVENTORit.S BY INUUSTRY 26 ~ ....,

339 W IINV27 WASHINGTON INVESTMENT IN INVENTORIES BY INUUSTRY 21 I

340 WIINV28 WASHINGTON INVESTMENT IN INVENTORIES BY INUUSTRy 28

341 .wIINV29 WASHINGTON INVESTMENT IN INVENTORIES BY INUUSTRY 29

3'+2 •IINV30 WASHINGTON INVESTMENT IN INVENTORIES BY lNUUSTRY JO 343 IIIIIN\131 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 31 344 IIIJIN\132 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 32

3'+5 • IINV 33 WASHINGTON INVESTMENT IN INVENTORIES By INDUSTRY 33 346 •IINV34 WASHINGTON INVESTMENT IN JNVENTORit:S BY INUUSTRY 34 347 111IINV35 WASHINGTON INVESTMENT IN INVENTORIES BY INiJUSTRY 35

348 WIINV36 IIIASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 36 349 WJINVJ7 IIIASHINGTON INVESTMENT IN INVENTOR JES BY INUUSTRY 37 3!:>0 WIINV38 WASHINGTON INVESTMENT IN INVENTORit.S BY INOUSTRY 38

3~1 1111 I INVJ9 WASHINGTON INVESTMENT IN JNVENTORJE.S B'f INDUSTRY 3~ 3!:>2 WIINV40 WASHINGTON INVESTMENT IN JN\IENTORJt.S B ·t INDUSTRY 40

J!:>J -iiIINV41 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 41

3~4 'fllJINV42 WASHINGTON INVESTMENT IN INVENTORIES BY INDUSTRY 42

3~5 •IINVSUB WASHINGTON INVENTORY INVESTMENT SUBTOTAL 3!:>6 •IINV43 WASHINGTON INVESTMENT IN INVENTORIES BY INUUSTRy 4)

3!:> 7 WJINV53 WASHINGTON INVESTMENT IN INVENTORIES B'f INUUSTRY 53 3!:>8 WJINVTOT WASHINGTON TOTAL INVESTMENT IN INVENTORIES 3~9 •ITOT WASHINGTON TOTAL INVESTMENT

401 WCTOT WASHINGTON TOTAL CONSUMPTION 501 WXOl WASHINGTON OUTPUT FROM INDUSTRY 1 502 WX02 WASHINGTON OUTPUT FROM INDUSTRY 2 503 WXOJ WASHINGTON OUTPUT FROM INDUSTRY 3 504 WX04 WASHINGTON OUTPUT FROM INDUSTRY 4 505 WX05 WASHINGTON OUTPUT FROM INDUSTRY 5 506 WX06 WASHINGTON OUTPUT FROM INDUSTRY 6 507 WX07 WASHINGTON OUTPUT FROM INDUSTRY 1 508 wxoe WASHINGTON OUTPUT FROM INDUSTRY 8 509 WX09 WASHINGTON OUTPUT FROM INDUSTRY 9 510 WXlO WASHINGTON OUTPUT FROM INDUSTRY 10 511 WXll WASHINGTON OUTPUT FROM INDUSTRY 11 512 wx12 WASHINGTON OUTPUT FROM INDUSTRY 12 513 WXlJ WASHINGTON OUTPUT FROM INDUSTRY 13 514 WX14 WASHINGTON OUTPUT FROM INDUSTRY 14 515 WXlS WASHINGTON OUTPUT FROM INDUSTRY 15 516 WX16 WASHINGTON OUTPUT FROM INDUSTRY 16 517 WX1 7 WASHINGTON OUTPUT FROM INDUSTRY 17 518 WX18 WASHINGTON OUTPUT FROM INDUSTRY 18 519 wXl9 WASHINGTON OUTPUT FROM INDUSTRY 19 520 WX20 WASHINGTON OUTPUT FROM INDUSTRY 20 521 WX21 WASHINGTON OUTPUT FROM INDUSTRY 21

I .... 522 WX22 WASHINGTON OUTPUT FROM INDUSTRY 22 .i:-

00

523 WX23 WASHINGTON OUTPUT FROM INDUSTRY 23 I

524 WX24 WASHINGTON OUTPUT FROM INDUSTRY 24 525 wX25 WASHINGTON OUTPUT FROM INDUSTRY 25 526 WX26 WASHINGTON OUTPUT FROM INDUSTRY 26 527 WX27 WASHINGTON OUTPUT FROM INDUSTRY 21

528 WX28 WASHINGTON OUTPUT FROM INDUSTRY 28 529 WX29 WASHINGTON OUTPUT FROM INDUSTRY 29 530 WXJO WASHINGTON OUTPUT FROM INDUSTRY JO 531 WX3l WASHINGTON OUTPUT FROM INDUSTRY 31 532 WX32 WASHINGTON OUTPUT FROM INDUSTRY 32 533 WX33 WASHINGTON OUTPUT FROM INDUSTRY 33 534 WX34 WASHINGTON OUTPUT FROM INDUSTRY 34 5J5 WX35 WASHINGTON OUTPUT FROM INDUSTRY JS 536 WX36 WASHINGTON OUTPUT FROM INDUSTRY 36 537 WX37 WASHINGTON OUTPUT FROM INDUSTRY 37 5J8 WX38 WASHINGTON OUTPUT FROM INDUSTRY 38 5J9 WX39 WASHINGTON OUTPUT FROM INDUSTRY 39 540 WX40 WASHINGTON OUTPUT FROM INDUSTRY 40 541 ■X4l WASHINGTON OUTPUT FROM INDUSTRY 41 5'+2 WX42 WASHINGTON OUTPUT FROM INDUSTRY 42 543 WX43 WASHINGTON OUTPUT FROM INDUSTRY 43

UA u I . T 1-,J ) Lil Ill M 1

544 WX44 WASHINGTON OUTPUT FROM INOU~TIH' 44 545 WX45 WASHINGTON OUTPUT FROM INDUSTRY 45 546 .ix46 WASHINGTON OUTPUT FROM INDUSTRY 46 547 WX47 WASHINGTON OUTPUT FROM INDUSTRY 47 548 WX48 WASHINGTON OUTPUT FROM INDUSTRY 48 549 •X49 WASHINGTON OUTPUT FROM INDUSTRY 49 5!:>0 IIIIX50 WASHINGTON OUTPUT FROM INDUSTRY 50 5!:>l WXSl '1ASHINGTON OUTPUT FROM INDUSTRY 51 5~2 liXS2 WASHINGTON OUTPUT FROM INDUSTRY 52 5!:>3 WX53 WASHINGTON OUTPUT FROM INDUSTRY 53 5!:>4 -'X54 WASHINGTON OUTPUT FROM INDUSTRY 54 5!:>5 wxss WASHINGTON OUTPUT FROM INDUSTRY 55 601 WVA0l WASHINGTON VALUE ADDED FOR INDUSTRY l 602 WVA02 WASHINGTON VALUE ADDED FOR INDUSTRY 2 603 wVA03 WASHINGTOt-; VALUE ADDED FOR INDUSTRY 3 604 •VA04 WASHINGTON VALUE ADDEO FOR INDUSTRY 4 605 •VA05 WASHINGTON VALUE ADDEO FOR INDUSTRY 5 606 11VA06 WASHINGTON VALUE ADDED FOR INDUSTRY 6 607 . WVA07 WASHINGTON VALUE ADDED FOR INDUSTRY 7

608 WVA08 WASHINGTON VALUE ADDEO FOR INDUSTRY 8 609 •VA09 WASHINGTON VALUE ADDEO FOR INDUSTRY 9

610 lliVAl 0 WASHINGTON VALUE ADDEO FOR INDUSTRY 10 I .... 611 WVAll WASHINGTON VALUE ADDEO FOR INDUSTRY 11 ~

\0

612 WVA12 WASHINGTON VALUE ADDED FOR INDUSTRY 12 I

613 WVA13 WASHINGTON VALUE ADDEO FOR INDUSTRY 13 614 . WVA14 WASHINGTON VALUE ADDED FOR INDUSTRY 14 615 WVA15 WASHINGTON VALUE ADDEO FOR INDUSTRY 15 616 WVA16 WASHINGTON VALUE ADDEO FOR INDUSTRY 16 617 IIWVAl 7 il!IASHINGTON VALUE ADDEO FOR INDUSTRY 17 618 WVA18 WASHINGTON VALUE ADDED FOR INDUSTRY 18 619 WVA19 WASHINGTON VALUE ADDEO FOR INDUSTRY 19 6~0 WVA20 WASHINGTON VALUE ADDEO FOR INDUSTRY 20 6~1 WVA21 WASHINGTON VALUE ADDEO FOR INDUSTRY 21 6i2 WVA22 WASHINGTON VALUE ADDED FOR INDUSTRY 22 623 •VA23 WASHINGTON VALUE ADDEO FOR INDUSTRY 23 624 wVA24 WASHINGTON VALUE ADDEO FOR INDUSTRY 24 6£5 WVA25 WASHINGTON VALUE ADDED FOR INDUSTRY 25 6£6 IIIVA26 WASHINGTON VALUE ADDED FOR INDUSTRY 26 627 WVA27 WASHINGTON VALUE ADDEO FOR INDUSTRY 27 6~8 WVA28 WASHINGTON VALUE ADDEO FOR INDUSTRY 28 6~9 WVA29 WASHINGTON VALUE ADDEO FOR INDUSTRY 29 630 IIIVA30 WASHINGTON VALUE ADDEO FOR INDUSTRY 30 6.J l WVA31 IIIASHINGTON VALUE ADDED FOR INDUSTRY 31 6J2 WVA32 WASHINGTON VALUE ADDED FOR INDUSTRY 32

6J3 WVA33 WASHINGTON VALUE ADDEO FOR INDUSTRY 33 634 WVA34 WASHINGTON VALUE ADDEO FOR INDUSTRY 34 635 WVA35 WASHINGTON VALUE ADDEO FOR INDUSTRY 35 6.J6 WVA36 WASHINGTON VALUE ADDEO FOR INDUSTRY 36 637 WVA37 WASHINGTON VALUE ADDED FOR INDUSTRY 37 638 liVA38 WASHINGTON VALUE ADDEO FOR INDUSTRY 38 639 WVA39 WASHINGTON VALUE ADDEO FOR INDUSTRY 39 640 llliVA40 WASHINGTON VALUE ADDEO FOR INDUSTRY 40 641 WVA4l WASHINGTON VALUE ADDEO FOR INDUSTRY 41 6'+2 WVA42 WASHINGTON VALUE ADDEO FOR INDUSTRY 42 643 WVA43 WASHINGTON VALUE ADDEO FOR INDUSTRY 43 644 •VA44 WASHINGTON VALUE ADDEO FOR INDUSTRY 44 645 •VA45 WASHINGTON VALUE ADDEO FOR INDUSTRY 45 646 liVA46 WASHINGTON VALUE ADDED FOR INDUSTRY 46 647 WVA47 WASHINGTON VALUE ADDEO FOR INDUSTRY 47 648 llliVA48 WASHINGTON VALUE ADDEO FOR INDUSTRY 48 649 WVA49 WASHINGTON VALUE ADDEO FOR INDUSTRY 49 650 liVA50 WASHINGTON VALUE ADDEO FOR INQUSTRY 50 6bl WVA5l WASHINGTON VALUE ADDEO FOR INDUSTRY 51 652 WVA52 WASHINGTON VALUE ADDEO FOR INDUSTRY 52 6~3 WVA53 WASHINGTON VALUE ADDEO FOR INDUSTRY 53 654 WVA54 WASHINGTON VALUE ADDED FOR INDUSTRY 54 I .... 655 WVA55 WASHINGTON VALUE ADDED FOR INDUSTRY 55 V,

0

656 WVAC WASHINGTON VALUE ADDEO FOR CONSUMPTION I

657 WVAIFIX WASHINGTON VALUE ADDEO FOR FIXED INVESTMENT 658 WVAIINV WASHINGTON VALUE ADDEO FOR INVENTORY CHANGE 659 WVASLEDU WASHINGTON VALUE ADDEO FOR STATE ANO LOCAL EDUCATION 660 WVASLOTH WASHINGTON VALUE ADDED FOR OTHER STATE AND LOCAL 6&1 WVAFED WASHINGTON VALUE ADDEO FOR FEDERAL GOVERNMENT 662 WVATOT WASHINGTON TOTAL VALUE ADDED 701 WYL0l WASH WAGES• SALA!~ IES • AND PROP INCOME FROM INDUSTRY l 702 WYL02 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 2 703 WYL0J WASH WAGES, SALM<IES, AND PROP INCOME fROM INDUSTRY 3

704 WYL04 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 4

705 WYL05 WASH WAGES• SALAIHESt AND PROP INCOME FROM INDUSTRY 5 706 WYL06 WASH WAGES• SALARIES, AND PROP INCOME FROM INDUSTRY 6 707 •YL07 WASH WAGES• SALARIES, AND PROP INCOME FROM INDUSTRY 1

708 WYL08 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 8

709 WYL09 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 9

710 IIIYLlO WASH WAGES• SALARIEs, ANO PROP INCOME FROM INDUSTRY 10 711 WYLll WASH WAGES, SALAl~IES, ANO PROP INCOME FROM INDUSTRY 11 712 WYL12 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 12 713 WYLlJ WASH WAGES• SALARIES, AND PROP INCOME fROM INDUSTRY 13 714 WYL14 WASH WAGES• SALARIES• AND PROP INCOME FROM INDUSTRY 14

715 WYL15 WASH WAGES• SALAklES, AND PROP INCUMt. 1-'~0M INDUSH(l' 1~ 7lt, WYL16 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 16 717 WYL17 WASH WAGES, SALAfHEs, AND PROP INCOME fROM INDUSTRY 17 718 WYL18 WASH liAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 18 7.l9 IIYL19 WASH WAGES, SALARIES• ANO PROP INCOME FROM INDUSTRY 19 72.0 WYL20 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 20 7cl WYL21 WASH liAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 21 7t!.2 WYL22 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 22 7t!.3 WYL23 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSH~Y 23 7t!.4 •Yl24 WASH WAGES• SALARIES, AND PROP INCOME FROM INDUSTRY 24 7c5 i11YL25 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 25 7t!.6 WYL26 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 26 7i.7 WYL27 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 27 7t!.8 wYL28 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 28 7t.9 WYL29 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 29 7JO •YL30 WASH liAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 30 7Jl ri1YL31 WASH IIIAGES• SALARIES, AND PROP INC0t-4E FROM INDUSTRY 31 7J2 WYL32 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 32 733 WYL33 WASH WAGES• SALARIES• AND PROP INCOME FROM INDUSTRY 33 7J4 IIIIYL34 II/ASH WAGES• SALARIES, AND PROP INCOME FROM INDUSTRY 34 735 WYL35 WASH IIAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 35 I

736 WYL36 WASH WAGES• SALARIES, ANO PROP INCOME fROM INDUSTRY 36 I-' V,

7J7 WYL37 lliASH WAGES• SALARIES, ANO PROP INCOME fROM INDUSTRY 37 I-' I

7J8 IIYL38 WASH WAGES• SALARIES, ANO PROP INCOME fROM INDUSTRY 38 7J9 wYL39 WASH WAGES, SALARIES, ANO PROP INCOME FROM INDUSTRY 39 740 WYL40 WASH WAGES• SALARIES• ANO PROP INCOME fROM INDUSTRY 40 7'+1 IIYL4l WASH WAGES• SALARIES• AND PROP INCOME FROM INDUSTRY 41 7'+2 WYL42 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 42 743 ilWYL43 WASH .iAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 43 7'+4 WYL44 II/ASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 44 745 ilWYL45 WASH WAGES• SALARIES, ANO PROP INCOME fROM INDUSTRY 45 7'+6 lliYL46 WASH WAGES, SALARIES, ANO PROP INCOME FROM INDUS'TRY 46 7'+7 WYL47 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 47 7'+8 •YL48 WASH WAGES• SALARIES, ANO PROP INCOME FROM INDUSTRY 48 749 WYL49 WASH WAGES• SALM<IES• ANO PROP INCOME FROM INDUSTRY 49 750 wYL50 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 50 751 WYL51 IIASH WAGES• SALARIES, AND PROP INCOME FROM INDUSTRY 51 752 WYL52 IIASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 52 7~3 WYL53 WASH WAGES, SALARIES, AND PROP INCOME FROM INDUSTRY 53 7':,4 WYL54 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 54 755 WYL55 WASH WAGES• SALARIES• ANO PROP INCOME FROM INDUSTRY 55 756 WYLC IIASH WAGEStSALARIES,ANO PROP INCOME FROM HOUSEHOLDS 757 WYLSLEDU WASH WAGEStSALARIES, AND PROP INCOME FROM EDUCATION 758 llf.YLSLQTH WASH WAGEStSALARIES, AND PROP INCoME FROM OTHER STATE+LOCAL

7~9 7b0 7bl 762 7b3 7b4 7b5 7b6 7t,7 7b8 769 710 771 712 713 774 775 776 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826

lliYLFEDMILPN WYLFEDMIL WYLFEDCIVPN WYLFEDCIV WYLFED WYLTOT WYPROPPC WYPROP WYTPPC WYTP •YSSRT WYSS WYP WTAXRT WTAX w)'O •YDPC •CYCLE WNOl WN02 WNO) WN04 WNOS WN06 WN07 WN08 lliN09 WNlO WNll WN12 WN13 WN14 WNlS WNl6 IIIN17 WN18 WNl9 ■ N20 WN21 WN22 WN23 WN24 WN25 WN26

WASHINGTON FEDERAL MILITARY LABOR INCOME PER EMPLOYEE WASHINGTON FEDERAL MILITARY LABOR INCOME WASHINGTON FEDERAL CIVILIAN LABOR INCOME PlR EMPLOYEE WASHINGTON FEDERAL CIVILIAN LABOR INCOME WASH WAGES•SALARIES,ANU PROP INCOME FROM FEDERAL WASHINGTON TOTAL WAGES• SALARIES, ANO PROP INCOME lliASHINGTON PROPERTY INCOME PER CAPITA WASHINGTON PROPERTY INCOME WASHINGTON TRANSFER PAYMENTS PER CAPITA WASHINGTON TRANSFER PAYMENTS WASHINGTON RATE or CONTRIBUTIONS TO SOCIAL SECURITY WASHINGTON SOCIAL SECURITY CONTRIBUTIONS WASHINGTON PERSONAL INCOME WASHINGTON PERSONAL TAXES ANO NONTAX PAYMENT RATE WASHINGTON PERSONAL TAXES ANO NONTAX PAYMENTS WASHINGTON DISPOSABLE INCOME WASHINGTON DISPOSABLE INCOME PER CAPITA WASHINGTON CYCLIC INDICATOR WASHINGTON EMPLOYMENT IN INDUSTRY l WASHINGTON EMPLOYMENT IN INDUSTRY 2 WASHINGTON EMPLOYMENT IN INDUSTRY 3 WASHINGTON EMPLOYMENT IN INDUSTRY 4 WASHINGTON EMPLOYMENT IN INDUSTRY 5 WASHINGTON EMPLOYMENT IN INDUSTRY 6 WASHINGTON EMPLOYMENT IN INDusTRY 7 WASHINGTON EMPLOYMENT IN INDUSTRY 8 WASHINGTON EMPLOYMENT IN INDUSTRY 9 WASHINGTON EMPLOYMENT IN INDUSTRY 10 WASHINGTON EMPLOYMENT IN INDUSTRY 11 WASHINGTON EMPLOYMENT IN INDUSTRY 12 WASHINGTON EMPLOYMENT IN INDUSTRY 13 WASHINGTON EMPLOYMENT IN INDUSTRY 14 WASHINGTON EMPLOYMENT IN INDUSTRY 15 WASHINGTON EMPLOYMENT IN INDUSTRY 16 WASHINGTON EMPLOYMENT IN INDUSTRY 17 WASHINGTON EMPLOYMENT IN INDUSTRY 18 WASHINGTON EMPLOYMENT IN INDUSTRY 19 WASHINGTON EMPLOYMENT IN INDUSTRY 20 WASHINGTON EMPLOYMENT IN INDUSTRY 21 WASHINGTON EMPLOYMENT IN INDUSTRY 22 WASHINGTON EMPLOYMENT IN INDUSTRY 23 WASHINGTON EMPLOYMENT IN INDUSTRY 24 WASHINGTON EMPLOYMENT IN INDUSTRY 25 WASHINGTON EMPLOYMENT IN INDUSTRY 26

I I-' V, N I

8i7 WN27 WASHINGTON EMPLOYMENT IN INDUSTRY 27 8i8 WN28 WASHINGTON EMPLOYMENT IN INDUSTRY 28 829 WN29 WASHINGTON EMPLOYMENT IN INDUSTRY 29 830 WN30 WASHINGTON EMPLOYMENT IN INDUSTRY 30 8Jl WNJl WASHINGTON EMPLOYMENT IN INDUSTRY 31

8.J2 •N32 WASHINGTON EMPLOYMENT IN INDUSTRY 32 833 WNJJ WASHINGTON EMPLOYMENT IN INDUSTRY 33 834 WN34 WASHINGTON EMPLOYMENT IN INDUSTRY 34 835 WNJS WASHINGTON EMPLOYMENT IN INDUSTRY JS 836 WN36 WASHINGTON EMPLOYMENT IN INDUSTRY 36 837 WN37 WASHINGTON EMPLOYMENT IN INDUSTRY 37 8J8 WN38 WASHINGTON EMPLOYMENT IN INDUSTRY 38 839 -iN39 WASHINGTON EMPLOYMENT IN INDUSTRY 39 840 WN40 WASHINGTON EMPLOYMENT IN INDUSTRY 40 841 WN4l WASHINC,TON EMPLOYMENT IN INDUSTRY 41 842 WN42 WASHINGTON EMPLOYMENT IN INDUSTRY 42 843 WN43 WASHINGTON EMPLOYMENT IN INDUSTRY 43 844 WN44 WASHINGTON EMPLOYMENT IN INDUSTRY 44 845 wN45 WASHINGTON EMPLOYMENT IN INDUSTRY 45 846 wN46 WASHINGTON EMPLOYMENT IN INDUSTRY 46 847 WN47 WASHINGTON EMPLOYMENT IN INDUSTRY 47

I

848 WASHINGTON EMPLOYMENT IN INDUSTRY 48 WN48 I-' V,

849 wN49 IIIASHINGTON EMPLOYMENT IN INDUSTRY 49 w I

850 wNSO WASHINGTON EMPLOYMENT IN INDUSTRY so 851 WNSl WASHINGTON EMPLOYMENT IN INDUSTRY 51 8S2 WNS2 -'ASHINGTON EMPLOYMENT IN INDUSTRY 52 8!:>3 WN53 WASHINGTON EMPLOYMENT IN INDusTRY 53 854 i111N54 wASHINGTON EMPLOYMENT IN INDUSTRY 54 855 WN55 WASHINGTON EMPLOYMENT IN INDUSTRY 55 856 WNC WASHINGTON EMPLOYMENT OF DOMESTIC SERVANTS 857 WNSLEDU WASHINGTON EMPLOYMENT IN STATE ANO LOCAL EOUCATJON 858 WNSLOTH WASHINGTON EMPLOYMENT IN OTHER STATE AND LOCAL 859 WNFEOMIL WASHINGTON EMPLOYMENT IN MILITARY 860 WNFEOCIV WASHINGTON EMPLOYMENT IN OTHER fEOt::RAL 861 WNFEO WASHINGTON EMPLOYMENT IN FEDERAL GOVERNMENT 862 WJOBCIV WASHINGTON CIVILIAN JOBS 863 WNCIV -'ASHINGTON CIVILIAN EMPLOYMENT (PERSONS) 864 WONCIV WASHINGTON CHANGE IN CIVILIAN EMPLOYMENT (PERSONS> 8b5 WNTOT WASHINGTON TOTAL EMPLOYMENT (PERSONS) 866 WUNEMRT WASHINGTON UNEMPLOYMENT RATE 8t>7 WEMRT WASHINGTON EMPLOYMENT ~ATE 868 WLfCIV WASHINGTON CIVILIAN LArlOR fORC£ 869 WLf WASHINGTON LABOR FORCE 870 WNRT WASHINGTON LABOR FORCE PARTICIPATION RATE

871 WPOP WASHINGTON POPULATION 812 IIIPOPS-20 WASHINGTON POPULATION AGED 5-20 873 WOPOPS-20 WASHINGTON CHANGE IN POPULATION AGED s-20 901 WMOl WASHINGTON IMPORTS BY INDUSTRY l 902 WM02 WASHINGTON IMPORTS BY INOUSTRY 2 903 WM03 WASHINGTON IMPORTS BY INDUSTRY 3 904 WM04 WASHINGTON IMPORTS BY INDUSTRY 4 905 WMOS WASHINGTON IMPORTS BY INDUSTRY 5 906 WM06 WASHINGTON IMPORTS BY INDUSTRY 6 907 WM07 WASHINGTON IMPORTS BY INDUSTRY 7 908 WM08 WASHINGTON IMPORTS BY INDUSTRY 8 909 WM09 WASHINGTON IMPORTS BY INDUSTRY 9 910 WMlO WASHINGTON IMPORTS BY INDUSTRY 10 911 WMll WASHINGTON IMPORTS BY INDUSTRY 11 912 WM12 WASHINGTON IMPORTS BY INDUSTRY 12 913 WMl3 WASHINGTON IMPORTS BY INDUSTRY 13 914 WM14 WASHINGTON IMPORTS BY INDUSTRY 14 915 WMlS WASHINGTON IMPORTS BY INDUSTRY 15 916 WM16 WASHINGTON IMPORTS BY INDUSTRY 16 917 WM17 WASHINGTON IMPORTS BY INDUSTRY 17 918 WM18 WASHINGTON IMPORTS BY INDUSTRY 18 919 WM19 WASHINGTON IMPORTS BY INDUSTRY 19

I .... 9~0 WM20 WASHINGTON IMPORTS BY INDUSTRY 20

VI .p.

921 WM21 WASHINGTON IMPORTS BY INDUSTRY 21 I

922 WM22 WASHINGTON IMPORTS BY INDUSTRY 22 923 WM23 WASHINGTON IMPORTS BY INDUSTRY 23 924 WM24 WASHINGTON IMPORTS BY INDUSTRY 24 925 WM25 WASHINGTON IMPORTS BY INDUSTRY 25 926 WM26 WASHINGTON IMPORTS BY INDUSTRY 26 927 iiM27 WASHINGTON IMPORTS BY INDUSTRY 27 928 WM28 WASHINGTON IMPORTS BY INDUSTRY 28 929 WM29 WASHINGTON IMPORTS BY INDUSTRY 29 930 WM30 WASHINGTON IMPORTS BY INDUSTRY 30 931 WM31 WASHINGTON IMPORTS BY INDUSTRY 31 932 WM32 WASHINGTON IMPORTS BY INDUSTRY 32 933 •MJ3 WASHINGTON IMPORTS BY INDUSTRY 33 934 WM34 WASHINGTON IMPORTS BY INDUSTRY 34 935 IIIIM35 WASHINGTON IMPORTS BY INDUSTRY 35 936 WM36 WASHINGTON IMPORTS BY INDUSTRY 36 937 WM37 WASHINGTON IMPORTS BY INDUSTRY 37 938 llliM38 WASHINGTON IMPORTS BY INDUSTRY 38 939 iiM39 WASHINGTON IMPORTS BY INDUSTRY 39 940 •M40 WASHINGTON IMPORTS BY INDUSTRY 40 941 WM41 WASHINGTON IMPORTS BY INDUSTRY 41

942 943 944 945 946 947 948 949 9~0 9!:>l 9!:>2 953 9~4 955 956 957 958 9!:>9 9b0 9bl 9b2

1001 1ou2 1003 1004 1005 l 00t, 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 l 0 £'. 0 l O. l

0

WM42 WM43 -.M44 WM45 WM46 IIIIM47 WM48 WM49 -.MSO llliMSl WM52 WM53 WH54 WMSS WHC WMIFIX WMIIN\I wMSLEDU WMSLOTH WMEX WMTOT WDPOPS-20Ll •POP5-20Ll WCYCLEL2 INCYCLEll WYOLl IIIINC I VL l WUNEMRTLl WSRESLl WIRESLl wsoTHSTRLl WIOTHSTRLl WXOlLl WX02Ll WX03Ll IIIX04L l WXOSLl WX06Ll •X07Ll IIIIX0SL l wX09Ll WX l OLl wool l ti, }( 1 ·. 1

WASHINGTON IMPORTS BY INDUSTRY 42 WASHINGTON IMPOR TS BY INDUSTRY 43 WASHINGTON IMPORTS BY INDUSTRY 44 WASHINGTON IMPORTS BY INDUSTRY 45 WASHINGTON IMPORTS BY INDUSTRY 46 WASHINGTON IMPORTS BY INDUSTRY 47 WASHINGTON IMPOR i S BY INDUSTRY 48 WASHINGTON IMPORTS BY INDUSTRY 49 WASHINGTON IMPORTS BY INDUSTRY 50 WASHINGTON IMPORTS BY INDUSTRY 51 WASHINGTON IMPORTS BY INOU5TRY 52 WASHINGTON IMPORTS BY INDUSTRY 53 WASHINGTON IMPORTS BY INDUSTRY 54 WASHINGTON IMPORTS BY INDUSTRY 55 WASHINGTON IMPORTS BY CONSUMERS WASHINGTON IMPORTS BY FIXED IN~ESTMENT WASHINGTON IMPORTS BY INVENTORY CHANGE WASHINGTON IMPORTS BY STATE AND LOCAL ~OUCATION WASHINGTON IMPORTS BY OTHER STATE AND LOCAL WASHINGTON IMPORTS BY EXPORTS WASHINGTON TOTAL IMPORTS WASHINGTON CHANGE IN POPULATION AGED 5-20 LAGGED ONE YEAR WASHINGTON POPULATION AGED 5-20 LAGGED ONE YEAR WASHINGTON CYCLIC INDICATOR LAGGED TWO YEARS WASHINGTON CYCLIC INDICATOR LAGGED ONE YEAR WASHINGTON DISPOSABLE INCOME LAGGED ONE YEAR WASHINGTON CIVILIAN EMPLOYMENT LAGGED UNE YEAR WASHINGTON UNEMPLOYMENT RATE LAGGED ONE YEAR WASHINGTON -STOCK- OF ~ESIDENTIAL HOUSING LAGGED ONE YEAR WASHINGTON RESIDENTIAL INVESTMENT LAGGED ONE YEAR WASHINGTON jSTOCK- Of OTHER STRUCTUPES LAGGED ONE YEAR WASHINGTON INVESTMENT IN OTHER STRUCTU~ES LAGGED ONE YEAR WASHINGTON OUTPUT OF JNOUSTRy l LAGGED ONE YEAR WASHINGTON OUTPUT OF INDUSTRY 2 LAGGED ONE YEAR WASHINGTON OUTPUT OF INDUSTRY 3 LAGGED ONE YEA~ WASHINGTON OUTPUT OF INDUSTRY~ LAGGED ONE YEAR WASHINGTON OUTPUT OF INDUSTRY 5 LAGGED ONE YEAR WASHINGTON OUTPUT OF INDUSTRY 6 LAGGED ONE YEAR WASHINGTON OUTPUT Of INDUSTRY 7 LAGGED ONE YEAR WASHINGTON OUTPUT or INDUSTRY 8 LAGGED ONE YEAR i.ASHINGTON OUTPUT Of INDUSTRY 9 LAGGED ONE YEAR WASHINGTON OUTPUT OF INDUSTRY 10 LAGGED ONE YEAR WA SHINGTON OUTPUT OF INDUSTRY ll LAGGEU ON£ YEAR WA HlNGTON OUTPUT OF INDUSTRY 12 LAGGED ONE YEAR

I .... V, V, I

1024 111Xl3Ll IOSHINGTON OUTPUT OF INDUSTRY 13 LAGGED ONE. YEAR 10~5 •Xl4Ll WASHINGTON OUTPUT OF INOUST~Y 14 LAGGEU ONI:. YEAR 1026 WXlSLl WASHINGTON OUTPUT Of INDUSTRY 15 LAGGEU ONt:.. YEAR 1027 WX16Ll WASHINGTON OUTPUT Of INDUSTRY 16 LAGGED 0NI:. YEAR 1028 WX17Ll WASHINGTON OUTPUT Of INDUSTRY 17 LAGGEU ONE YEAR 10i9 WX18Ll WASHINGTON OUTPUT OF INDUSTRY 18 LAGGED ONl YEAR 1030 WX19Ll WASHINGTON OUTPUT OF INDUSTRY 19 LAGGED ONE. YEAR lOJl WX20Ll WASHINGTON OUTPUT OF INDUSTRY 20 LAGGED ONE YEAR 1032 IIIX21Ll WASHINGTON OUTPUT OF INDUSTRY 21 LAGGED ON[ YEAR 1033 WX22Ll WASHINGTON OUTPUT OF INDUSTRY 22 LAGGED ONt. YEAR 1034 WX23Ll WASHINGTON OUTPUT Of INDUSTRY 23 LAGGEU ONE YEAR 1035 · WX24Ll WASHINGTON OUTPUT OF INDUSTRY 24 LAGGED ONE YEAR 1036 WX25Ll WASHINGTON OUTPUT Of INDUSTRY 25 LAGGED ONI:. YEAR 1037 \ljX26Ll WASHINGTON OUTPUT OF INDUSTRY 26 LAGGED ONI:. YEAR 1038 WX27Ll WASHINGTON OUTPUT Of INDUSTRY 27 LAGGED ON£ YEAR 1039 WX28Ll WASHINGTON OUTPUT Of INDUSTRY 28 LAGGED ONE:. YEAR 1040 •X29Ll WASHINGTON OUTPUT OF INDUSTRY 29 LAGGED ONE YEAR 1041 llwX30Ll WASHINGTON OUTPUT OF INDUSTRY 30 LAGGED ONI::: YEAR 1042 llliX31Ll IIASHINGTON OUTPUT Of INDUSTRY Jl LAGGED ONE YEAR 10'+3 WX32Ll IIASHINGTON OUTPUT Of INDUSTRY 32 LAGGED ONE YEAR 1044 llliX33Ll WASHINGTON OUTPUT OF INDUSTRY 33 LAGGED ONl YEAR I

1045 WX34Ll WASHINGTON OUTPUT Of INDUSTRY 34 LAGGED ONE:. YEAR ..... V,

1046 wx35Ll WASHINGTON OUTPUT Of INDUSTRY JS LAGGED ONE YEAR 0\ I

1047 WX36Ll WASHINGTON OUTPUT Of INDUSTRY 36 LAGGED ONE YEAR 1048 WX37Ll IIASHINGTON OUTPUT Of INDUSTRY 37 LAGGED ONI:. YEAR 1049 WX38Ll WASHINGTON OUTPUT Of INDUSTRY 38 LAGGED ONt:. YEAR 1050 WX39Ll IIASHINGTON OUTPUT OF INDUSTRY 39 LAGGED ONI:. YEAR 1051 WX40Ll WASHINGTON OUTPUT Of INDUSTRY 40 LAGGED ONI:. YEAR 1052 IIIX41Ll WASHINGTON OUTPUT Of INDUSTRY 41 LAGGED ONI:. YEAR 1053 WX42Ll WASHINGTON OUTPUT Of INDUSTRY 42 LAGGED ONI:. YEAR 1101 TIME TIME 1102 TIME98 (0.98) TIME 1103 DUMMY DUMMY 1104 USXOl u.s. OUTPUT OF INDUSTRY 1 1105 usxo2 u.s. OUTPUT Of INDUSTRY 2

1106 USXOJ u.s. OUTPUT OF INDUSTRY 3 1107 USX04 u.s. OUTPUT OF INDUSTRY 4 1108 usxos u.s. OUTPUT OF INDUSTRY 5

1109 USX06 u.s. OUTPUT Of INDUSTRY 6 1110 USX07 u.s. OUTPUT OF INDUSTRY 7

1111 usxos u.s. OUTPUT Of INDUSTRY 8

1112 USX09 u.s. OUTPUT OF INDUSTRY 9

1113 usx10 u.s. OUTPUT Of INDUSTRY 10 1114 USXll u.s. OUTPUT Of INDUSTRY 11

1115 usx12 u.s. OUTPUT Of . INDUSTRY 12 1116 US.Xl3 u.s. OUTPUT OF INDUSTRY 13 11 J. 7 USX14 u.s. OUTPUT OF INDUSTRYl4 1118 USXlS u.s. OUTPUT Of INDUSTRY 15 1119 USX16 u.s. OUTPUT OF INDUSTRY 16 1120 lJSX17 u.s. OUTPUT OF INDUSTRY 17 1121 US.Xl8 u.s. OUTPUT Of INDUSTRY 18 1122 US.Xl9 u.s. OUTPUT OF INDUSTRY 19 11~3 usx20 u.s. OUTPUT OF INDUSTRY 20 1124 USX21 u.s. OUTPUT OF INDUSTRY 21 1125 USX22 u.s. OUTPUT Of INDUSTRY 22 1126 USX23 u.s. OUTPUT OF INDUSTRY 23 1127 USX24 u.s. OUTPUT OF INDUSTRY 24 11~8 USX25 u.s. OUTPUT Of INDUSTRY 25 1129 USX26 u.s. OUTPUT Of INDUSTRY 26 1130 USX27 u.s. OUTPUT Of INDUSTRY 27 1131 USX28 u.s. OUTPUT Of INDUSTRY 28 1132 USX29 u.s. OUTPUT OF INDUSTRY 29 1133 USX30 u.s. OUTPUT OF INDUSTRY 30 1134 USX31 u.s. OUTPUT OF INDUSTRY 31 1135 lJSX32 u.s. OUTPUT OF INDUSTRY 32 l l 36 USX33 u.s. OUTPUT Of INDUSTRY 33

I I-"

1137 USX34 u.s. OUTPUT OF INDUSTRY 34 \J1 .....

ll.J8 USXJS u.s. OUTPUT Of INDUSTRY 35 I

1139 USX36 u.s. OUTPUT Of INDUSTRY 36 1140 USX37 u.s. OUTPUT OF INDUSTRY 37 1141 USX38 u.s. OUTPUT Of INDUSTRY 38 11 '+2 USX39 u.s. OUTPUT OF INDUSTRY 39 11 '+3 USX40 u.s. OUTPUT OF INDUSTRY 40 l 1 '+4 USX41 u.s. OUTPUT OF INDUSTRY 41 l 145 USX42 u.s. OUTPUT Of INDUSTIH 42 11'+6 USX43 u.s. OUTPUT OF INDUSTRY 43 1147 USX44 u.s. OUTPUT OF INDUSTRY 44 1148 USX45 u.s. OUTPUT Of INDUSTRY 45 11 '+9 USX46 u.s. OUTPUT OF INDUSTRY 46 11so USX47 u.s. OUTPUT OF INDUSTRY 47 11s1 USX48 u.s. OUTPUT Of INDUSTRY 48 11s2 USX49 u.s. OUTPUT Of INDUSTRY 49 11=>3 USX50 u.s. OUTPUT OF INDUSTRY so llS4 USXSl u.s. OUTPUT OF INDUSHH 51 11=>5 USX52 u.s. OUTPUT Of INDUSTRY 52 1156 USX53 u.s. OUTPUT OF INDUSTRY 53 llS7 USX54 u.s. OUTPUT OF INDUSTRY 54 llS8 USX55 u.s. OUTPUT Of INDUSTRY 55

11~9 llt>O llt>l llb2 llt>J llt>'+ 1165 1106 llb7 llt>8 l}t>9 1110 1171 1172 1173 1174 1175

USX4lfE0 USHIWAY wSLNONBLO USROLl USYPROPPC USYTPPC USYSSRT USTAXRT USNRT USUNEMRT USPOP5•20RT USNfEOMIL USNfEOCI\J IIEX06E WEX49E WEXSlE USCH/CP

U.S. OUTPUT Of G0VtRNM£NT SHIPBUILDING U.S. HIGHWAY CONSTRUCTION WASHINGTON STATE ANO LOCAL OTHER NONBUlLDJNG CONSTRUCTION u.s. INTEREST RATE OIFfERENTIAL LAGGED ONE YEAR U.S. PROPERTY INCOME PER CAPITA U.S. TRANSfER PAYMENTS PER CAPITA U.S. CONTRIBUTION RATE TO SOCIAL SECURITY U.S. PERSONAL TAX RATE U.S .LABOR FORCE PARTICIPATION RATE u.s. UNEMPLOYMENT RATE u.s. PROPORTION Of POPULATION AGED 5•20 U.S. FEDERAL MILITARY DOMESTIC SERVANTS EMPLOYMENT U.S. FEDERAL CIVILIAN EMPLOYMENT WASHINGTON EXPORTS FROM INDUSTRY 6(EXOuENOUS) WASHINGTON EXPORTS fROM INOUSTRY 49tEX0GEN0US) WASHINGTON EXPORTS FROM INDUSTRY SltEXOGENOUS) US RATIO Of THE COST Of HOUSING INDEX TO THE CONSUMPTN INDEX

I I"-' VI 01) I

APPENDIX B SPECIFICATION OF WPSM

Contained in this appendix is the detailed specification of the Washington Projection and Simulation Model. Grouped by block, there is a total of 456 equations, of which 421 are behavioral equations and 35 are identities.

On the left-hand-side of each equation are the number and name of the dependent variable. On the right-hand-side are the names of the independent, or explanatory, variables and their corresponding estimated parameters.

Note that not all parameters remain constant throughout the fore­casting period. In particular, the coefficients of some of the output, employment, and income equations vary over time. The regional purchases coefficients in the output equations change to reflect anticipated vari­ations in technical requirements and trade patterns. The parameters in the industry job equations are altered to take into account expected productivity advances. The coefficients of the industry labor income equations vary to be consistent with the projected growth in earnings per worker. The value shown in the equations for each of these variable parameters is the 1972 baseline estimate. For further information on coefficient change, refer to the appropriate chapters in Part II.

liPSMl EQUATIONS BLOCK l EXPORTS

101 liEX0 l = .009600 USXOl 102 _,EX02 = -242.500000 ♦ .056800 usxo2 13.450000 TIME 103 WEX03 = -89.100000 ♦ .002100 usxo3 l Ot+ liEX04 = 29.500000 ♦ 1.000000 DUMMY 105 _,EX05 = 2.400000 ♦ 1.000000 DlJMMY 106 lliEX06 = 1.000000 WEX06E 107 W(X07 = .002000 USX07 108 WEX08 = .031100 usxo8 109 _,EX09 :: .009400 USX09 110 WEXl0 = -133.600000 ♦ .020100 usx10 111 WEXl l = 89.200000 ♦ 1.000000 DUMMY 112 WEX12 = .000400 USX12 113 WEX13 = -104.400000 ♦ .004200 USX13 ♦ 4.010000 TIME 114 _,EX14 = 14.400000 ♦ 1.000000 DUMMY 115 _,EX15 = 10.100000 ♦ 1.000000 DUMMY 116 _,EX16 = .539600 USX16 117 WEX17 = .090100 USX17 118 _,EX18 = 209.500000 ♦ .152100 USX18 16.980000 TIME

119 WEX19 = -136.200000 ♦ .050500 USX19 120 _,EX20 = 33.100000 ♦ 1.000000 DlJMMY l~l WEX21 :: 32.000000 ♦ .236700 usx21 6.950000 TIME li2 WEX22 = 251.100000 ♦ 1.000000 DUMMY 123 _,EX23 :: .012000 USX23 124 _,EX24 = 35.000000 ♦ 1.000000 DUMMY

I ... (J\

125 WEX25 = .006900 usx~5 ... 126 WEX26 = 3.400000 ♦ .000138 USX26

I

127 _,EX27 = -383.300000 ♦ .015400 usx21 14!8 lliEX28 = -2.600000 ♦ .001000 usx28 129 WEX29 = 8.800000 ♦ 1.000000 DUMMY lJO WEX30 = .001000 USX30 lJl liEX31 = 42~400000 ♦ .005600 USX31 3.800000 TIME 132 liEX32 = .093200 USX32 1J3 liEX33 = .003000 USX33 134 liEX34 = 45.000000 ♦ 1.000000 DUMMY 13S WEX35 = .003600 usx35 136 liEX36 = -48.500000 ♦ .001000 USX36 137 WEX37 = -61.800000 ♦ .005500 USX37 138 liEX38 = .001100 usx38 139 liEX39 = .290200 USX39 140 liEX40 = -140.800000 ♦ .001500 USX40 ♦ 12.590000 TIME

l '+ l liEX41 = .106000 USX41 1'+2 liEX42 = -1<;4.800000 ♦ .oossoo USX42 11t3 WEX43 = -276.600000 ♦ .020100 USX43 33.260000 TIME 1'+4 liEX44 = 42.100000 ♦ 1.000000 DUMMY 14S WEX45 = 1.200000 ♦ 1.000000 DUMMY l1t6 liEX46 = 2.000000 ♦ 1.000000 DUMMY 14 7 liEX47 = .000877 USX47 148 WEX48 = 1.000000 DUMMY l1t9 liEX49 = 1.000000 -.Ex49E 150 liEX50 = 1.000000 DUMMY 151 liEXSl = 1.000000 liEX5lE 1~2 WEX52 = 1.000000 DUMMY 153 liEXSJ = -498.400000 ♦ .005800 usx53

l~'- liEX51t = .009100 USX54 1~5 liEXS5 = 57.800000 ♦ .000300 usxs5

IMPSMl EQUATIONS BLOCK EXPORTS

156 WEXTOT = -43.000000 ♦ 1.000000 Wt:.X0l ♦

♦ 1.000000 WEX04 ♦ 1.000000 Wt.X0S ♦

♦ 1.000000 wExoa ♦ 1.000000 WEX09 ♦

♦ 1.000000 WEXl2 1.000000 Wf.Xl3 ♦

♦ 1.000000 WEX16 1.000000 Wf.Xl7 ♦

♦ 1.000000 WEX20 ♦ 1.000000 WEX21 ♦

♦ 1.000000 WEX24 ♦ 1.000000 WtX25 ♦

♦ 1.000000 WEX28 ♦ 1.000000 Wt.X29 ♦ 1.000000 WEX32 ♦ 1.000000 WE.X33 ♦

♦ 1.000000 WEX36 ♦ 1.000000 WE.X37 ♦

• 1.000000 WEX40 ♦ 1.000000 IIIEX4l . ♦ 1.000000 WEX44 ♦ 1.000000 Wf.X45 • . 1.000000 WEX48 ♦ 1.000000 WE.X49 ♦

♦ 1.000000 WEX52 ♦ 1.000000 Wt.XS) ♦

♦ .261700 USNfEOMIL ♦ .173200 USNfEOCIV

1.000000 wEX02 ♦

1.000000 WEX06 ♦

1.000000 Wf.XI0 ♦

1.000000 IIIEXl4 . 1.000000 WE.XIS ♦

1.000000 WEX22 ♦

1.000000 IIIEX26 ♦

1.000000 WEXJ0 ♦

1.000000 WEX34 ♦

1.000000 WEX38 • 1.000000 WEX42 ♦

1.000000 WEX46 . 1.000000 WEXS0 • 1.000000 WEX54 ♦

1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

WEX03 WEX07 WEXll IIIEX 15 WEX19 WEX23 WEX27 WEX3l WEX35 WEX39 WEx43 WEX47 WEXSl WEXSS

I ..... a,

"' I

wPSMl EQUATIONS BLOCK 2 STATE

201 WSLEOUPCS-20= -.405500 • 0 402300 WYOPC

202 •SLEOU = 1.000000 (WPOP5-20 l IWSLEOUPC5-201

203 WSLOTH = -Joo.000000 • 0 117800 WYO • 196700 WCYCLEL2

204 WSLOP = 1.000000 WSLEDU • 1.000000 WSLOTH

205 IIISLEOUBLD = -18.810000 • .012100 WYO

206 WSLOTHBLD = -23.620000 • .011400 WYO

207 IIISLHIWAY = -116.000000 • 0 0229llO USHJWAY

• • 043600 WCYCLELl 208 IIISLNONBLD = 1.000000 IIISLNONBLO 209 WSLCON = 1.000000 WSLEDUBLD • 1.000000 WSLOTHBLO

210 WSLTOT = 1.000000 WSLOP . 1.000000 WSLCON

211 IIISLEDUTOT = 1.000000 WSLEOU • 1.000000 WSLEOUBLO

212 WSLOTHTOT = 1.000000 WSLOTH • 1.000000 WSLOTHBLO

• LOCAL GOVERNMENT

.000125 WCYCLE

.086100 WCYCLE

• 1.471000 WDPOP5-20Ll .021100 WCYCLE

• .004800 WYO

• 1.000000 WSLHIWAY

• 1.000000 WSLHIWAY

.000085 WCYCLELl

.229400 WCYCLELl

.035900 WCYCLELl • .036400 WCYCLE

• 1.000000 WSLNONBLD

♦ 1.000000 WSLNONBLD

I ,... "' ..., I

WPSMl EQUATIONS BLOCK 3 INVESTMENT

301 WSRES = .980000 WSRESLl . 1.000000 WlRESLl 302 WIRES = 7459.800000 • .475200 WYDLl 2780.700000 USCH/CP • 12646.000000 USROLl

.461400 WSRES 8360.000000 T1ME98 303 WSOTHSTR = .980000 WSOTHSTRLl . 1.000000 WIOTHSTRLl 304 WXMFG = 1.000000 IIIX06 • 1.000000 1111\07 • 1.000000 WX08 • 1.000000 WX09 . 1.000000 wx10 • 1.000000 wx l l • 1.000000 wx12 ♦ 1.000000 WX13

• 1.000000 WX16 • 1.000000 WX17 • 1.000000 WXl8 • 1.000000 WX19 ♦ 1.000000 WX20 . 1.000000 WA21 ♦ 1.000000 wx22 • 1.000000 WX23 • 1.000000 WX24 • 1.000000 Wl\25 ♦ 1.000000 WX26 • 1.000000 WX27 ♦ 1.000000 WX28 • 1.000000 WA29 ♦ 1.000000 WX30 • 1.000000 WX31 • 1.000000 WX32 • 1.000000 WX33 . 1.000000 WXJ4 • 1.000000 WX35 • 1.000000 WX36 • 1.000000 W/1.37 • 1.000000 WX38 • 1.000000 WX39 • 1.000000 WX40 ♦ 1.000000 W/1.41 • 1.000000 WX42

305 WXNONMFG = 1.000000 WXOl ♦ 1.000000 WA02 • 1.000000 WX03 • 1.000000 WX04 • 1.000000 WX05 ♦ 1.oooouo WX14 ♦ 1.000000 WX15 • 1.000000 WX43 ♦ 1.000000 WX44 ♦ 1.000000 WX45 • 1.000000 WX46 • 1.000000 WX47 ♦ 1.000000 WX48 ♦ 1.000000 WA49 • 1.000000 WX50 ♦ 1.000000 WX51 • 1.000000 IIIX52 • 1.000000 WX53 . 1.000000 Wl\54 . 1.000000 WX55

306 WXWTl = .240000 WX!olfG ♦ • 760000 W/1.NQNMFG 307 WJOTHSTR = 2966.200000 ♦ .102&00 wxwn .174400 WSOTHSTR 3351.500000 TIME98 308 WIOTHBLD = .500000 WIOTHSTR 309 WJNONBLO = .500000 WIOTHSTR 310 WXWT2 = .290000 WXMFG • .110000 WXNONMFG 311 WJEQP = -332.500000 • .108000 WXWT2 I

312 WJFJXTOT = 1.000000 WIRES ♦ 1.000000 WlOTHSTR • 1.000000 WIEQP .... a-

313 WIINVOl = .280000 IIIXO 1 .280000 WAO lll "'" I

314 WJINV02 = .000000 WX02 • .oooouo wxo2u 315 WINNV03 = .320000 WX03 .320000 WX03Ll 316 WllNV04 = .000000 WX04 ♦ .000000 WX04Ll 317 WIINV05 = .000000 WX05 • .000000 WX05Ll 318 WI INV06 = .025000 WX06 .025000 WA06Ll 319 WI INV07 = .010000 WX07 .010000 WX07Ll 320 WJINV08 = • 135000 WX08 .135000 WX08Ll 321 WI INV09 = .015000 WX09 .01sooo WX09Ll 322 WJ INVlO = .075000 wx10 .075000 wx l OLl 323 WJJNVll = .105000 WXll .105000 WX l lll 324 WJ INV12 = .085000 wx12 .oa5ooo WA12Ll 325 WIINV13 = .100000 IIIX 13 .100000 WX13Ll 326 11JJNV14 = 0040000 WX14 0 0400UO WA14Ll 327 1111 INV15 = .000000 wx15 • .000000 11Xl5Ll

328 WIINV16 = .185000 WX}6 .1850UO W/l.l6Ll 329 WllNVl 7 = .100000 WX17 .100000 wx l 7Ll 330 WIINV18 = .060000 WX18 .060000 Will8Ll 331 WJ INV19 = .041)000 WX}9 .040000 Wl\l9Ll 3J2 WI JNV20 = .155000 wx20 .155000 W/l.20Ll 3J3 WJINV2l = .040000 wx21 • 0400-UO Wil21Ll 3J4 WJINV22 = .025000 wx22 .025000 WX22Ll 335 WJINV23 = .045000 WX23 .045000 WA23Ll 3J6 WJJNV24 = .015000 WX24 .075000 WX24Ll 3J7 WJINV25 = .oa5000 WX25 .oa5000 WX25Ll 338 IIJJNV26 = • 110000 WX26 .110000 WX26Ll 339 WJ INV27 = .035000 IIIX27 .035000 llll\27L l

340 WI JNV28 = .140000 WX28 .140000 WX28Ll

341 WJJNV29 = .045000 WX29 .045000 WX29Ll

3'+2 WJINV30 = .050000 WX30 .osoooo WX30Ll

11/PSMl EQUATIONS BLOCK 3 INIIESTMENT

343 WIIN\131 = .110000 WX31 .110000 WX31L l 344 WIIN\132 = .125000 WXJ2 .125000 Wl\32L 1 345 WJIN\133 = .055000 WXJ3 .0550UO Wl\33L l 3 .. 6 WIIN\134 = .110000 WX34 • 110000 WX34Ll 347 WIIN\135 = .115000 WX35 .115000 WX35Ll 348 •IIN\136 = .095000 WX36 .095000 WX3t,Ll 349 WJIN\137 = .125000 WXJ7 .125000 WX37Ll 350 WJIN\138 = .005000 WXJ8 .oa5000 WX38Ll 3!:>l lliIIN\139 = .200000 WX39 .200000 WX39Ll 352 WI IN\140 = 0045000 WX40 .045000 Wl\40L 1 J!:,3 •I IN\141 = • 115000 WX41 .115000 WX41Ll 354 WIIN\142 = .125000 WX42 .125000 WX42Ll 3!:>5 WJIN\ISUB = 1.000000 WIINIIOl ♦ 1.000000 WllN\102 ♦ 1.000000 WINNI/OJ • 1.000000 WIIN\104

• 1.000000 WIIN\105 ♦ 1.000000 WIIN\106 ♦ 1.000000 WIIN\107 • 1.000000 WIJN\108 ♦ 1.000000 IMIJN\109 ♦ 1.000000 !Ml IN\110 ♦ 1.000000 WIIN\111 ♦ 1.000000 WIIN\112

♦ 1.000000 WIIN\113 ♦ 1.000000 WllN\114 ♦ 1.000000 WIIN\115 • 1.000000 WIJN\116 ♦ 1.000000 IMIIN\117 • 1.000000 WI IN\118 • 1.000000 WIIN\119 .. 1.000000 WIIN\120 ♦ 1.000000 WIIN\121 ♦ 1.000000 Wl Hi\122 ♦ 1.000000 WIIN\123 ♦ 1.000000 WI IN\124 ♦ 1.000000 IMIIN\125 ♦ 1.000000 WIIN\126 ♦ 1.000000 Ill IN\127 • 1.000000 WIIN\128 ♦ 1.000000 WIJN\129 ♦ 1.000000 WIIN\130 ♦ 1.000000 WIIN\131 • 1.000000 WIJN\132 • 1.000000 WIIN\133 ♦ 1.000000 WIIN\134 ♦ 1.000000 WIIN\135 ♦ 1.000000 Wl JN\136 ♦ 1.000000 WI JN\137 ♦ 1.000000 IIIIIN\138 ♦ 1.000000 WIIN\139 ♦ 1.000000 WIIN\140 ♦ 1.000000 WIIN\141 ♦ 1.000000 WIIN\142

356 •IIN\143 = .020000 WIINIISUB I .... 357 WIIN\153 = .044500 WIINIISUB a,

3~8 WIINIITOT = 1.000000 WIINIISUB ♦ 1.000000 WlIN\143 ♦ l .000000 WIIN\153 V, I

359 WJTOT = 1.000000 WIFIXTOT ♦ 1.000000 WllNIITOT

401 WCTOT = 89.900000

IIIPSMl EOUATJONS BLOCK 4 cONSUM~TION

♦ .tJ84000 wYO

I ..... a, a, I

IIIPSMl EQUATIONS BLOCK 5 OUTPU T

SIil WXOl = .038650 WXOl ♦ .009970 W/\02 ♦ .201640 WX03 ♦ .015600 WX04 • .167730 WX09 • .017340 wx10 • .006940 WXll • .000290 WCTOT • • 000000 WIEQP ♦ 1.000000 WIINVOl ♦ .000260 WSLEOU • .000580 WSLOTH • 1.000000 liEXOl

502 wxo2 = .008350 WX02 • .001010 li/\03 • .000820 WX07 • .206700 WX08 • • 009550 wx10 ♦ .160380 WXll ♦ .0001so WX43 • .000290 WX55 • .002480 WCTOT • .000000 WIEQP • 1.000000 WIINV02 • .000260 WSLEOU

• 0000990 liSLOTH • 1.000000 WEX02 503 WX03 = .095870 WX03 ♦ .326920 WX06 ♦ .438450 WX07 • .000210 WX08

• .011100 WXll ♦ • 006450 wx12 • . • 000120 WX13 • .005780 WCTOT

• .000000 WIEQP ♦ 1.000000 WlNNV03 ♦ .000000 WSLEOU • .000490 WSLOTH • 1.000000 WEX03

504 WX04 = .002690 WX02 ♦ .001100 WX03 ♦ .060660 WX04 • .000430 WXll

• .003080 wx1s • • 000110 WX44 • .000980 WX48 • .000430 WX49

• • 001310 wxso • .000760 WASS ♦ .001760 WCTOT • .000000 WIEQP

• 1.000000 WIJNV04 ♦ .000000 WSLEOU • .000160 WSLOTH • 1.000000 WEX04 sos wxos = .002520 wxos ♦ .072550 wxo5 • .000160 WCTOT • .000000 WIEQP

• 1.000000 WIJNV05 ♦ .000000 WSLEOU • .000000 WSLOTH ♦ 1.000000 WEXOS

506 WX06 = .005040 wxos • .024110 WX06 • .001240 wxoe • .027180 WX09

• .003900 WXll • • 000050 WX39 • .001810 WX42 ♦ .001080 WX43 ♦ .001210 WX44 • .000220 wxss • .021680 WCTOT • .000000 WIEQP

• 1.000000 WI JNV06 • .000110 WSLEOU • .000750 WSLOTH ♦ 1.000000 WEX06

507 WX07 = .002s20 wxos • .000920 WX06 • .143560 WX07 • .003510 WX08

• .008240 WX}l • • 000310 WX43 • .000690 WX44 ♦ .000360 wxss I .. .012750 WCTOT ♦ • 000000 WlEQP • 1.000000 WIINV07 ♦ .000430 WSLEOU ... "' .....

♦ .003140 WSLOTH • 1.000000 WEX07 I

508 liX08 = .002520 wxos • .004090 WX07 • .005370 WX08 • .006940 WX09

• .004330 WX}l • • 000310 WX43 • .000110 WX44 • .000470 wxss

• .008880 WCTOT ♦ .000000 WlEQP • 1.000000 WIINV08 • .000110 WSLEDU

• · .000830 WSLOTH • 1.000000 lit.X08 509 WX09 = .098420 WX03 ♦ .000610 WX06 • .007230 WX08 • +043380 WX09

♦ .041180 WXll ♦ • 000850 WX18 • .000290 WX41 • .000150 WX43 ♦ .0002so wxss ♦ .001340 WCTOT ♦ .000000 WIEOP • 1.000000 WIINV09

• .000260 WSLEDU • • ooosso IIISLOTH • 1.000000 WEX09

510 IIIXlO = • 005040 wxos ♦ .000410 WX07 • .044940 WXlO • .004330 WXll .

• .ooooso WX39 • .000390 WX43 • .000650 WX5S • .006660 WCTOT

• .000000 WIEQP • 1.000000 WllNVlO • .000000 WSLEOU • .000080 liSLOTH

• 1.000000 WE,1(10 511 illXll = .010180 WX03 ♦ .005040 wxos • .004580 WX06 • .006130 WX07

• .013850 WX08 ♦ .011510 wxo9 • .021230 WXlO • .037710 WXll

• .002510 wx2s ♦ .ooooso WX39 • .000450 WX42 • .000690 liX43

• .001720 WX44 • .000100 WX53 • .000440 wxss • .007080 WCTOT

• .000000 WIEQP ♦ 1.000000 WlINVll ♦ .000110 WSLEDU ♦ .000830 WSLOTH

• 1.000000 WEXll 512 WX12 = .0002so WXOl • .000210 wxo2 • .012590 wxos • .000120 wx 13

• .000380 wx1s • .003370 wx20 • .000290 WX41 • .000080 WX43

• .000110 WX44 • .000140 WCTOT • .000000 WIEQP • 1.000000 WIJNV12

• .000000 WSLEOU ♦ .000000 WSLOTH • 1.000000 WEX12

513 liXl3 = .005090 WXOl • .001740 WA09 ♦ .000110 WXlO • .003590 WXlJ

• .006450 WX31 • .ooos10 WX41 • .000080 WX43 • .000140 WX48

• .000120 WXS3 • .000010 WX54 • .000650 wxss • .002080 WCTOT

• .000000 WIEOP • 1.000000 WlINVlJ • .000000 WSLEOU • .ooooao WSLOTH

• 1.000000 WEX13 ii IOI)( l t, = .000760 liXOl • .000540 WA02 • .002110 wx 11 ♦ .013090 WX}4

.000440 WX2 l • .000290 wx22 ♦ .000450 WX23 • .000840 WX25 (EQUATION CONTINUED ON NEXT PAGE>

WPSMl EQUATIONS BLOCK 5 OUTPUT

• .000110 WX27 • .056770 WX28 • .078530 WX29 • .010750 WX31

• • 000210 WX39 . .0001so WA43 • .031040 WX44 • .002380 WX48 • • 003030 WX49 • .051780 WA50 • .008370 WX51 . .011860 WX52 • .000040 WX55 • .000100 WCToT . .000000 WIEQP • 1.000000 WIINV14 • .000110 WSLEDU • .000910 w::.LoTH • 1.000000 WEX14

5i5 WX15 = .036170 w.q5 • .311910 WX16 • .068040 WX17 • .011360 WX18 • .000110 WX44 • • 000050 WCTOT • .000000 WIEQP • 1.000000 WIJNV15

• .000000 WSLEDU • .000000 W!:>LQTH • 1.000000 WEX15 516 WXl6 = .002310 wx15 • .OA56tl0 WX16 • .228190 WX17 • .182570 WX18

• .0171B0 WX19 • • 06l5b0 wx21 • .039050 wx22 • .028480 WX23

• • 002150 WX31 • .000190 wx51 . .000000 WCTQT ♦ .000880 WIEQP • 1.000000 WI I NV 16 • .000000 W!:>LEOU . .000000 WSLOTH • 1.000000 WEX16

517 WX17 = .003930 WX14 • .0003tl0 wx1s • .009750 WX16 • .041430 WX17

• .034070 WX18 . • 119210 WX19 • .038790 WX20 • .112930 wx21 ♦ .023720 wx22 • .017940 WX23 • .001870 WX30 • .001200 WX35 • .000690 WX37 ♦ .000210 WA39 ♦ .002780 WX40 • .005150 WX4l • .000900 WX42 • • 0001~0 WX43 • .001120 WX44 • .048250 WX48

• • 003750 WX49 • .001310 WASO • • 011990 wx51 • .008110 WX52

• .000290 WX53 • .0003Jo WCToT • .002630 WJEQP • 1.000000 WI JNV17

• .000110 WSLEDU ♦ • oo05tlO W!:>LOTH • 1.000000 WEX17 518 1111)(18 = .006270 WX16 . .006080 WX17 . .032940 WX18 • .032960 WX19

• +003370 wx20 • • 035430 Wll21 • .003760 WX27 • .002780 WX40

• .002s00 WX41 • .0239~0 WA48 • .008360 WX49 • .009570 wxso

• +008180 WXSl • .003120 wxs2 • .000190 WXS3 ♦ .000000 WCTOT I .... • .000000 WIEOP • 1.000000 WllNVlf\ • .000090, WSLEOU • .000580 WSLOTH "' 00

I

• 1.000000 WEX18 519 WX19 = .002690 WX02 • .000610 Wl\06 . .001030 WX08 . .000110 WXIO

• .000430 WX}l • • 000110 WX16 • .013610 WX17 • .000850 WX18

• .015430 WX19 • .013490 wx20 • .000290 IIIX22 • .000900 WX23

• .000420 wx2s • .0028b0 WX26 • .000110 WX27 • .004370 WX28

• .001010 WX29 • .000930 WX30 • .002150 WX31 • .000350 WX32

• +001440 WX34 • .001200 WX35 • .001390 WX37 . .001760 liX38

• .000160 WX39 • .000620 WX40 • .000290 1iX41 • .001360 WX42

• .0001so 1iX43 • .028810 WX48 • .007210 WX49 • .000870 liXSO

• .007420 WXSl • .oo31co wxs2 • .000100 WX53 • .000010 liXSS

• .000210 WCTOT • .0054.JO WlEQP • 1.000000 WI INV19 • .000090 WSLEDU

• .000410 WSLOTH . 1.000000 w!:..X 19

520 wx20 = • 005060 wx20 . .000110 WX39 • .002580 WX41 • .000180 WX47

• .002240 WX48 • • 000870 WX49 • .000190 wxs1 • .000100 WXS3

• .000010 WX54 • .002410 wCToT • .000000 WIEQP • 1.000000 lil1NV20

• .002100 liSLEDU • .000410 WSLOTH • 1.000000 WEX20

521 lliX21 = .000570 WX18 • .004430 wx21 • .042520 wx22 • .058970 WX23

• +004610 wx2s • • 000000 WCTOT • .000000 WIEQP • 1.000000 WI JNV21

• .000000 WSLEDU . .000000 W!:>LOTH • 1.000000 WEX21

5a WX22 = .000010 WX02 • .000850 WX03 . .001730 WXll • .012920 WX13 . .000570 WX18 • .oo03!:,0 WX19 • • 021920 WX20 • .000440 WX21

• .002600 WX22 • .078030 wx23 • .048050 WX24 • .000420 WX2S

• .ooooso WX39 • .000310 WA40 • .000290 WX41 . .001010 WX42

• .000230 WX43 . .000110 WX44 • .002940 WX47 • .003860 WXS3

• .000690 WX5'+ . .ooOltlO WASS • .000680 WCTOT • .000000 WlfQP

• 1.000000 WI INV22 . .ooos10 w::.LEDU • .000330 WSLOTH • 1.000000 WEX22

523 WX23 = .ooo5lo WXOl . .002960 WA02 . .011600 WX06 • .033130 WX07

• .021000 WX08 • .0121so WX09 • .024770 IIIXl O • .018640 liX 1 l

• .000120 WX13 • .002620 WX14 • .000110 WX16 • .001580 WX17

• .002560 WX18 • .001150 w.q9 • .005060 wx20 . .002660 WX21 (EQUATION CONTINUED ON NEXT PAGEi

WPSMl EQUATIONS BLOCK 5 OUTPUT

• .022560 wx22 ♦ .039010 Wll23 ♦ .000830 WX24 • .001120 WX25 ♦ • 005710 WX26 ♦ .000510 WJl.27 • • 056 770 WX28 ♦ .006410 WX29 • .000230 WX32 ♦ .002870 WJl.34 ♦ .003510 WX38 • .001610 WX39 ♦ • 000620 WX40 • .000290 WJl.41 • .001810 WX42 • .000150 WX43 ♦ +002410 WX44 ♦ .000450 WX45 • .000610 WX46 ♦ .000370 WX47 • .000420 WX48 ♦ .000430 WX49 • .000190 wx51 ♦ .001010 WX53 • .003790 WXS4 ♦ .000910 WX55 • .001320 WCTOT • .000000 WIEOP • 1.000000 WI INV23 ♦ .000600 WSLEDU ♦ .000990 WSLOTH ♦ 1.000000 WEX23

524 WX24 = .000920 WX06 • .001240 W-'08 ♦ .001110 WXlO ♦ .002110 WXll

• .000110 wx15 ♦ .000110 WJl.16 • .000570 WX18 ♦ .000350 WX19

• .000290 wx22 ♦ .000220 WJl.23 • .012840 WX24 ♦ .000420 WX25 ♦ .005710 WX26 ♦ .0005Jo WJl.29 ♦ .000930 WX30 ♦ .000120 WX32 ♦ .002400 W)(J5 ♦ .000690 WX37 • .oooa8o WX38 • .000160 WX39

• .000620 WX40 ♦ .000290 WX41 ♦ .000450 WX42 • .000620 WX43

• • 002010 WX44 ♦ .001300 WX45 ♦ .001830 WX46 ♦ .004770 WX47 ♦ .000140 WX49 ♦ .017620 WX53 ♦ .020040 WX54 ♦ .014550 WX55

• .004010 WCTOT ♦ .000000 WIEQP ♦ 1.000000 WlINV24 ♦ .001280 WSLEDU

• .000250 WSLOTH ♦ 1.000000 WEX24 525 WX25 = .033820 WXOl ♦ .027480 wxo2 ♦ .007070 WX03 ♦ .026000 WX04

♦ .002520 WX05 ♦ .000920 WX06 ♦ .000820 WX07 ♦ .000410 WX08 ♦ .000350 wx10 ♦ .003470 WXll ♦ .003930 WXl4 • .002690 WX15 ♦ .000350 WX16 ♦ • 000570 WX18 ♦ .044730 wx21 • .021100 wx22 ♦ 0003360 WX23 ♦ • 039360 WX25 ♦ .020000 WX26 • .002380 WX27 ♦ .002800 WX30 ♦ .0021so WX31 • .000230 WX32 ♦ .002870 WX34 I ,-

♦ 0001200 WX35 ♦ 0001230 WX36 ♦ 0000690 WX37 ♦ .000880 WX38 a,

'° ♦ 0000050 WX39 ♦ .001240 WX40 ♦ .001120 WX41 ♦ 0000450 WX42 ♦ 0000150 WX43 ♦ 0001220 Wll46 ♦ 0000140 WX49 ♦ 0000380 WX51

• 0000260 WX53 ♦ oooo8oo WX55 ♦ oOOOOOO WCTOT • oOOOOOO WIEQP ♦ loOOOOOO WlINV25 • o000170 WSLEDU ♦ .001570 WSLOTH • 1.000000 WEX25

5i:!6 WX26 = 0002260 WX03 • .001730 WJl.04 ♦ .000610 WX06 ♦ 0000410 WX07 ♦ 0000380 .WX l 5 ♦ .000350 WX16 • .oooa5o WX17 • 0008800 WX18 ♦ 0000700 WX19 ♦ .003370 wx20 ♦ .000580 wx22 • 0003590 WX23 ♦ .000830 WX24 ♦ .002930 WJl.25 • .048570 WX26 ♦ .000110 WX27 ♦ .001010 WX29 ♦ .000930 WX30 ♦ .002150 WX31 • .000470 WX32 ♦ +001880 WX33 ♦ • 002870 WX34 • .002400 WX35 ♦ .000690 WX37 ♦ +001760 WX38 ♦ .000540 WX39 • .000620 WX40 ♦ .001430 WX41 ♦ 0008600 WX42 ♦ .000340 WA44 ♦ .000180 WX47 ♦ +001190 WX48

• .000870 WX49 ♦ .001740 WX50 ♦ .000760 wx51 ♦ +006870 WX52

• .000600 WX53 ♦ .000140 WX54 • .ooosoo WX55 • .000210 WCTOT

• .000000 WIEQP ♦ 1.000000 WIINV26 ♦ .000260 WSLEDU ♦ 0000660 WSLOTH

• 1.000000 WEX26 527 '027 = .008390 WXOl ♦ .003500 wxo2 • .005090 WX03 • 0019060 WX04

• .065490 WX05 ♦ 0000920 WX06 • .002860 WX07 ♦ +000620 WX08 ♦ oOOo5ao WX09 ♦ .000350 wx10 ♦ .001300 WXll • 0000720 WX13

• 0006540 WX14 ♦ • 001150 wx15 ♦ 0003830 WX16 • .001580 WX17 ♦ .000570 WX18 ♦ .000100 WJl.19 ♦ .001690 wx20 ♦ 0037200 WX21

• 0015910 wx22 ♦ .000900 wx23 ♦ 0000410 WX24 ♦ 0002510 WX25 ♦ .002860 WX26 ♦ .006630 WX27 ♦ .004370 WX28 ♦ o0080lo WX29 ♦ 0000930 WX30 ♦ .008600 WJl.31 ♦ .003490 WX32 ♦ .001250 WX33

♦ 0000720 WX34 ♦ .001200 WX35 ♦ 0001230 WX36 ♦ .000690 WX37

• 0000970 WX39 ♦ 0000930 WA40 ♦ 0002860 WX41 ♦ .000900 WX42

♦ 0029640 WX43 ♦ .001030 WX44 ♦ .000910 WX45 • 0006100 WX46

♦ .001100 WX47 ♦ .006570 WX48 ♦ 0010380 WX49 • 0055270 wxso

♦ 0018270 WXSl ♦ .023100 WJl.52 • .002110 WX53 ♦ 0001450 WXS4

• .001850 wxss ♦ .012750 WCTOT ♦ .000000 WIEQP • loOOOOOO WIINV27 (EQUATION CONTINUED ON NEXT PAGEi

IIIPSMl EQUATIONS BLOCK c; OUTPUT

♦ +004880 WSLEDU ♦ .005120 W~LOTH ♦ 1.000000 WEX27 528 WX28 = .000920 WX06 ♦ .010330 IIIA08 ♦ +049890 WXlO ♦ .001730 WXll

♦ .000420 WX25 ♦ .000000 WCTOT ♦ .000000 WIEQP ♦ 1.000000 WI INV28 ♦ +000090 WSLEDU ♦ .000000 WSLOTH ♦ 1.000000 WEX28

529 WX29 = +000250 wxo 1 ♦ .000540 wxo2 • .000280 WX03 ♦ .000310 WX06 ♦ .001160 WX09 ♦ .019630 WAl4 ♦ .001540 WX15 ♦ .000110 WX27 ♦ +154380 WX29 ♦ .001870 WA30 ♦ .002150 WX3} ♦ .000350 WX32 ♦ .002150 WX34 ♦ .001200 WX35 ♦ .003680 WX36 ♦ .000690 WX37 ♦ .000110 WX39 ♦ .000290 WA41 ♦ .001360 WX42 ♦ .000080 WX43 ♦ .000110 WX44 ♦ .0001so WA47 ♦ .056220 IIIX48 • +068930 WX49 ♦ +073980 wxso ♦

0 0447cO wxs1 ♦ .023100 WX52 ♦ .000050 WXS3 ♦ .000110 WX55 ♦ .000430 WCTOT ♦ .000000 WIEQP ♦ 1.000000 WIJNV29 ♦ .000110 WSLEDU ♦ .001490 WSLOTH ♦ 1.000000 WEX29

5.JO WX30 = .001020 WXOl ♦ .000200 WX03 ♦ .001730 WX04 ♦ .002620 WX14 ♦ +000350 WX16 ♦ .000120 WX l 7 ♦ .000280 WX18 ♦ .000350 WX19 ♦ +000440 WX2l ♦ .000580 WA22 ♦ .000220 WX23 ♦ .000340 WX27 • +013990 WX30 ♦ .006450 WA3l ♦ .000580 WX32 ♦ .068840 WX33 • .0222so WX34 ♦ .050480 WA35 ♦ .033130 WX36 ♦ .021540 WX37 ♦ .002640 WX38 ♦ .000380 WA39 ♦ .027520 WX40 ♦ .002000 WX4l ♦ .000080 WX43 ♦ .000110 WA44 ♦ .000180 iiX47 ♦ .004200 WX48 ♦ +006200 WX49 ♦ .002180 WX50 ♦ .039390 wx51 ♦ +009360 WXS2

• .000010 WCTOT ♦ .000000 wlEQP ♦ 1.000000 WIINV30 ♦ .000000 WSLEDU ♦ .000160 IIISLOTH ♦ 1.000000 Wt.X30

5-H IIIX3l = .002520 WX05 ♦ .000890 WA21 ♦ .000870 iiX22 ♦ .000110 iiX27 I ... ♦ • 000930 WX30 ♦ • 0021so WA3} ♦ .002090 WX32 ♦ .002500 WX33 ....

0

♦ .004310 WX34 ♦ .000690 WX37 ♦ .001240 iiX40 ♦ .002000 WX4l I

♦ .000450 WX42 ♦ .000110 WA44 • .000180 WX47 ♦ .001260 WX48 ♦ .001590 WX49 ♦ .003240 wxs1 ♦ .000620 wxs2 ♦ .000000 WCTOT ♦ .000000 iilEOP ♦ 1.oooouo iii INV31 ♦ .000000 WSLEOU ♦ .000000 WSLOTH ♦ 1.000000 WEX31

SJ2 111)(32 = .000250 WXOl ♦ .001750 WXl9 ♦ .001690 wx20 ♦ .000110 WX27

• .023660 iiX3l ♦ .157410 wx32 ♦ .040050 WX33 ♦ .015080 WX34 ♦ .001230 WX36 ♦ .010420 Wll.37 ♦ .005270 WX38 ♦ .000210 WX39 ♦ .003090 WX40 ♦ .000290 WX41 ♦ .001810 WX42 ♦ .002240 WX48 ♦ .002880 WX49 ♦ .005900 WAS} ♦ .000620 111xs2 ♦ .000010 WCTOT ♦ .002100 WIEQP ♦ 1.000000 WIINV32 • .000000 iiSLEDU • .000080 WSLOTH

• 1.000000 WEX32 533 WX33 = .0011so WXI5 ♦ .000970 w,11.17 ♦ .000280 WXlB ♦ .000350 WX19

♦ .000890 wx21 ♦ .000010 WA22 ♦ .000510 WX27 ♦ .000930 WX30 ♦ .000230 WX32 ♦ .035670 WX33 ♦ .0021so iiX34 ♦ .001200 WX35 ♦ .008590 WX36 ♦ .006950 WX37 ♦ .000000 WX38 ♦ .001010 WX39 ♦ .002160 WX40 • .002860 WX41 ♦ • 000170 WX44 ♦ .000180 WX47 ♦ +016360 WX48 ♦ .043980 WX49 ♦ .027420 wxso ♦ .030830 WXSI

♦ .0}4360 WXS2 ♦ .000110 WX53 ♦ .000040 WX55 ♦ .000030 WCTOT

♦ .019880 WIEOP ♦ 1.000000 WlINV33 ♦ .000340 WSLEOU ♦ .001400 WSLOTH

♦ 1.000000 WEX33 534 111)(34 = .001020 WXOl ♦ .000810 w,11.02 ♦ .ooosso WX03 ♦ .001220 IIIX06

♦ .034730 WXOB ♦ .oo75co WA09 ♦ .135530 WAlO ♦ .002600 WXII ♦ .001310 WX14 ♦ .002260 WX16 ♦ .000490 WX17 ♦ .000200 WXlB

♦ .002100 WX19 ♦ .001690 Wll.20 ♦ .001770 wx21 ♦ .001160 wx22

♦ +005030 IIIX25 ♦ .014290 Wll.26 ♦ .000110 WX27 ♦ .001010 WX29

• .000930 WX30 ♦ .0021so Wll.3 l ♦ .000470 WX32 • .010010 WX33

♦ .010050 WX34 ♦ .001210 WA35 ♦ .001230 WX36 ♦ +003470 WXJ7

♦ • 007910 IIIX38 ♦ .001340 WA39 . .003090 WX40 • .003720 WX4l

♦ +009950 WX42 ♦ 0 000080 WA43 ♦ .002410 WX44 ♦ +000450 WX45

(EQUATION CONTINUED ON NEXT PAGEi

WPSM l [ QUATJONS BLOCK s OUTPUT

• .002240 WX48 ♦ .002450 WX49 ♦ .000440 WX50 • .001520 WXSl • • 001870 WX52 ♦ .000240 WX53 ♦ .000010 WX55 ♦ .ooooJo WCTOT • .oon,20 WIEOP ♦ 1.000000 WlIN\134 ♦ 0000090 W5LEOU ♦ .000080 WSLOTH ♦ 1.000000 WEX34

SJS WX35 = 0000350 WX16 ♦ .010820 WX35 ♦ .002290 WX41 ♦ .000110 WX44 ♦ .000140 WX49 ♦ .000020 WCTOT ♦ .005430 WIEQP • 1.000000 WIJN\135 • • 000110 WSLEDU ♦ .001240 WSLOTH ♦ 1.000000 WEX35

536 WX36 = .002520 WX05 • .001220 WA06 • .001030 wxo8 • .000350 WXlO

• 0003930 WX14 ♦ .004350 WX16 ♦ .000970 WX17 ♦ .000850 WX18 ♦ 0000350 WX19 ♦ .0016~0 wx20 ♦ .000530 WX29 ♦ 0013060 WX30 ♦ .002210 WX32 ♦ .010640 WX33 ♦ .020820 WX34 • .024040 WX35 ♦ 0051530 WX36 • .024320 WX37 • .011420 WX3A • .003220 WX39 ♦ 0003090 WX40 ♦ .001430 WX41 ♦ .004070 WX42 ♦ .000390 WX43 ♦ .000110 WX44 ♦ .000420 WA48 ♦ .000430 WX49 ♦ .000440 WX50 ♦ .000570 WX51 ♦ .000240 WX53 ♦ .002550 WX5S ♦ .000160 WCTOT ♦ .001050 WIEOP • 1.000000 WIIN\136 • .000940 WSLEOU • 0000160 WSLOTH ♦ 1.000000 WEX36

537 WX37 = .000610 WX06 ♦ .001240 wxo8 • .000120 WXl3 ♦ .001090 WX17 ♦ .000850 WX18 ♦ .002810 WX19 ♦ 0000890 wx21 ♦ .002600 wx22 ♦ .000670 WX23 • .001260 wx2s ♦ .ooos10 WX27 ♦ .000530 WX29 • .002150 WX31 ♦ .000930 WX32 • .002500 WX33 ♦ .002400 WX35 ♦ .027800 WX37 ♦ .004390 WX38 ♦ .000860 WX39 • 0000310 WX40 ♦ 0000290 WX41 ♦ .000900 WX42 • .000690 WX44 ♦ 0000180 WX47 ♦ .000280 WX48 • .000290 WX49 • .000220 WX5s ♦ .000000 WCTOT I

♦ .010950 WIEOP .. 1.000000 WIIN\137 ♦ .000510 WSLEOU ♦ .000160 WSLOTH .... -.J ....

♦ 1.000000 WEX37 I

538 WX38 = .000610 WX06 ♦ .000240 WX17 ♦ .000280 WX18 ♦ 0000700 WX19 ♦ .001260 WX25 ♦ .000580 WX32 • .004810 WX35 ♦ 0005560 WX37

• 0013180 WX38 ♦ 0001070 WX39 ♦ 0001430 WX41 ♦ 0000900 WX42 ♦ 0000520 WX44 ♦ .004770 WX47 ♦ .001960 WX48 ♦ 0003030 WX49

• 0000440 wxso • .001110 wx51 ♦ .002500 WX52 ♦ .000020 WXS3

• 0000040 WX55 ♦ .000040 WCTOT • .002190 WIEOP • 1.000000 WIJN\138

• +000090 WSLEDU • .000000 WSLOTH • 1.000000 WEX38

539 WX39 = 0021970 WX38 ♦ .008590 WX39 • .001160 WX43 • oOOOOOO WCTOT ♦ 0000000 WIEOP • 1.000000 WllN\139 ♦ .000000 WSLEOU • .000000 WSLOTH

• 1.000000 WEX39 540 WX40 = .ooo35o wx10 ♦ .004810 WX35 ♦ .006180 WX40 ♦ .000150 WX43

♦ .000140 WX48 • 0000440 WX50 ♦ .000190 wx51 • oOOl 170 WCTOT ♦ .011820 WIEOP ♦ 10000000 WllN\140 ♦ .000000 WSLEOU • oOOOOOO WSLOTH

• 10000000 WEX40 541 WX4l = 0055420 wxos • 0000120 WX17 ♦ o000050 WX39 ♦ 0010020 WX4l

• .001310 WJl.43 • o000580 WCTOT • 0000440 WIEQP ♦ loOOOOOO WIJN\141

♦ 0000000 WSLEDU • .010330 WSLOTH ♦ 1.000000 WEX41

542 WX42 = .000210 WX02 • 0002520 WX05 ♦ 0000310 WX06 • .001230 WX07

• 0000830 WX08 • 0001060 wx10 ♦ 0000870 WXll • 0002620 WX14

• 0000380 wx15 • .000350 WX16 • .000280 WXl8 • 0001750 WX19

• 0028670 wx20 • .ooouo WX23 ♦ 0001680 WX25 • 0002860 WX26

♦ 0000170 WX27 • .000930 WX30 ♦ 0002150 WX31 • 0001440 WX34

• 0002400 WX35 • 0004910 WX36 ♦ 0002780 WX37 • 0013180 WX38

• .001880 WX39 • 0001240 WX40 • 0001430 WX41 • 0022170 WX42

• .000080 WX43 ♦ .000110 WA44 • 0000450 WX45 • .000550 WX47

• +003920 WX48 • 0004760 WX49 • 0001740 WX5o • 0002090 WXSl

• .002500 WXS2 ♦ 0001020 WX53 • .001450 WX54 • 0005270 wxss

• +000640 WCTOT • .004290 WlEOP • loOOOOOO WIIN\142 • 0001200 WSLEDU

• 0001490 Ir/SLOTH • 10000000 WEX42

lliPSMl EQUATIONS BLOCK 5 OUTPUT

543 iliX43 = .010110 WXOl ♦ .008080 w.xo2 ♦ .019800 W.X03 ♦ .008670 WX04 • .032750 WX05 ♦ .020150 WllOt, • .004910 WX07 • .029760 wxo8 • .019660 WX09 ♦ • 014510 WJl.lO ♦ .007800 wx 11 • .006450 wx12 • .001440 WX l 3 ♦ • 006540 Wl\14 ♦ .011160 WX15 • .006270 WX16 • .035480 WX17 ♦ • 040320 WJl.18 • .027350 WX19 • .006750 wx20 • .030560 WX2l • • 030950 wx22 ♦ .027580 WX23 • .016570 WX24 ♦ .014660 lliX25 ♦ • 014290 WX26 • .007650 WX27 ♦ .017470 WX28 • .048080 WX29 ♦ .015860 Wll30 ♦ .034410 WX31 • .014530 WX32 • .008760 WX33 ♦ • 006460 WX34 • .004810 WX35 ♦ .004910 WX36 ♦ .002780 WX37 ♦ .002640 W>-38 • .000210 WX39 • .001860 WX40 ♦ .005440 WX4l ♦ .011310 WJl.42 ♦ .074100 WX43 • .005350 WX44 • .001360 WX45 ♦ .002440 WX46 • .005510 WX47 ♦ .015520 WX48

• .014420 WX49 ♦ .036120 WX5o • .016180 wx51 ♦ .013110 WX52 ♦ .007140 WX53 ♦ .006540 WX54 ♦ .006440 WX55 • .016670 WCTOT

• .008760 WIEQP ♦ 1.000000 WlINV43 ♦ .OOA990 WSLEDU ♦ .007850 WSLOTH ♦ 1.000000 WEX43

5'+4 WX44 = .005090 WXOl ♦ .003230 Wl\02 . .004240 WX03 ♦ .003470 WX04 ♦ .002520 lliX05 ♦ .002150 WX06 ♦ .004910 WX07 ♦ .004750 WX08 ♦ .003470 WX09 ♦ .002480 WX}O ♦ .018640 wx11 ♦ .012900 wx12 ♦ .001310 WX14 ♦ .000730 WX17 ♦ .003690 WX18 ♦ .014610 wx21

• .009550 wx22 ♦ .015250 wx23 ♦ .002010 loX24 • .017170 WX25 ♦ .005710 WX26 ♦ • 0047b0 WJl.27 ♦ .013100 WX28 ♦ .014960 WX29 ♦ .023320 WX30 ♦ .012900 WX31 ♦ .042780 WX32 ♦ .003750 WX33 ♦ +006460 WX34 ♦ .004810 WX35 ♦ .003680 WX36 ♦ .002780 WX37 I

♦ .003510 WX38 ♦ .002090 Wl\39 ♦ .004640 WX40 ♦ .002000 WX4l ,... .....

♦ .002110 WX42 ♦ .005170 WX43 • .237970 WX44 ♦ .002210 WX45 ..., I

♦ .013410 Wl\46 ♦ .003120 WX47 ♦ .000840 WX48 ♦ .001150 WX49

• .000870 WX50 ♦ .000760 Wl\51 ♦ .010600 WX53 ♦ .010260 WX54 ♦ .010180 WX55 ♦ 0 0163b0 WCTOT ♦ .000000 WIEQP ♦ 1.000000 DUMMY ♦ .004370 WSLEDU ♦ .004050 WSLoTH • 1.000000 WEX44

545 WX45 = .001530 WX06 ♦ .004090 WX07 ♦ .008270 WX08 ♦ .002830 WXlO ♦ .018640 WX l l ♦ .012900 wx12 ♦ .001310 WX14 ♦ .000730 WX17

• .003690 WX18 ♦ .037640 111x21 ♦ .018220 wx22 ♦ .010090 WX23

• .029730 WX25 ♦ .008570 Wl\26 • .016330 WX27 • .026200 WX28

• .018700 WX29 ♦ .013990 WX30 • .034410 WX3l • .004650 WX32

• .001250 WX33 ♦ 0 0064b0 WX34 . .002400 WX35 ♦ .001230 WX36

♦ .000690 WX37 . .000970 WX39 • .001240 WX40 • .000290 WX4l ♦ • 000450 WX42 • .000310 Wl\43 • .000690 WX44 • .346360 WX45 ♦ .001220 WX46 • .0001ao WX47 ♦ .000140 WX48 ♦ .001430 WX53

• .001030 WX54 ♦ .002690 WX55 • .004630 WCTOT • .000000 WIEOP

• 1.000000 DUMMY • .003080 WSLEDU • .001730 WSLQTH ♦ 1.000000 WEX45

546 WX46 = • 006360 wxol ♦ .003500 wxo2 • .004240 WX03 • .017330 WX04

• .000610 WX06 ♦ .000410 WX07 • .000830 WX08 • .000110 WXlO ♦ • 001300 WXll • .000120 WX13 • .002620 WX14 • .001090 WX17 ♦ .000280 WX18 • .0003so WX19 ♦ .010630 wx21 ♦ .001660 wx22

• .002910 WX23 ♦ .000410 WX24 ♦ .001680 WX25 ♦ .001360 WX27

♦ • 001010 loX29 ♦ .000930 Wll30 • .000470 WX32 ♦ .000630 WX33

• .001440 WXJ4 ♦ .001200 WJ\35 ♦ .000690 WX37 • .000880 WX38

♦ .000640 lliXJ9 ♦ .000620 WX40 ♦ .000570 WX41 ♦ .000450 WX42

♦ .000310 WX43 ♦ .000340 WA44 ♦ .000450 WA45 ♦ .075610 WX46

♦ .000920 WX47 ♦ .000280 WX48 ♦ .000580 WX49 ♦ .000870 wxso

♦ .001670 WX53 ♦ .002200 WA54 ♦ .001130 WX55 ♦ .009290 WCTOT

♦ .000000 WIEQP ♦ 1.000000 DUMMY ♦ .003420 WSLEOU ♦ .001240 WSLOTH

♦ 1.000000 WEX46 547 WX47 = .003560 WXOl ♦

0 005390 Wl\02 ♦ .003960 WX03 ♦ .010400 WX04

(EQUATION CONTINUED ON NEXT PAGE)

WPSMl EQUATIONS BLOCK 5 OUTPUT

♦ .002520 WX05 ♦ .002750 WX06 ♦ .002860 WX07 ♦ .002690 WX08 • .001740 WX09 ♦ .001420 wx10 ♦ .003470 WXll ♦ .012900 wx12 ♦ .004310 WX13 ♦ .002620 WX14 ♦ .002690 WX15 ♦ .001570 WX16 ♦ .001090 WX17 ♦ .001100 WX18 ♦ .003510 WX19 ♦ .005060 wx20 ♦ .001330 WX2l ♦ .002890 wx22 ♦ .003140 WX23 ♦ .012010 WX24 ♦ .003350 wx25 ♦ .008570 WX26 ♦ .001020 WX27 ♦ .005340 WX29 ♦ .003730 WX30 ♦ .002150 IIIA31 ♦ .001050 WX32 • .014390 WX33 ♦ .007900 WX34 ♦ • 009620 WX35 ♦ .011040 WX36 ♦ .011120 WX37 ♦ .007910 WX38 ♦ .004140 WX39 ♦ .008660 WX40 ♦ .002860 WX41 ♦ .005880 WX42 ♦ .008720 WX43 ♦ .005170 WX44 ♦ .002210 WX45 ♦ .006710 WX46 ♦ .003490 WX47 ♦ .004060 WX48 ♦ .005620 WX49 ♦ .005660 111x50 ♦ .004760 liA51 ♦ .002500 111x52 ♦ .011010 liX53 ♦ .020730 WX54 ♦ .033820 liA55 ♦ .019090 WCTOT ♦ .000000 WIEQP ♦ 1.000000 DUMMY ♦ .006590 W5L[OU ♦ .020900 WSLOTH ♦ 1.000000 WEX47

548 IIIX48 = 1.000000 DUMMY ♦ 1.000000 WIRES ♦ 1.000000 DUMMY ♦ 1.000000 DUMMY ♦ 1.000000 DUMMY ♦ 1.000000 WEX48

549 IIIX49 = 1.000000 DUMMY ♦ 1.000000 WlOTHBLD ♦ 1.000000 DUMMY ♦ 1.000000 WSLEOUBLD ♦ 1.000000 WSLOTHBLD ♦ 1.000000 Wt::X49

550 111x50 = 1.000000 DUMMY ♦ 1.000000 DUMMY ♦ 1.000000 DUMMY ♦ 1.000000 DUMMY ♦ 1.000000 WSLHIIIIAY ♦ 1.000000 IIIE.X50

5~1 WX51 = 1.000000 DUMMY ♦ 1.000000 llllNONBLD ♦ 1.000000 DUMMY • 1.000000 DUMMY ♦ 1.000000 IIISLNONBLD ♦ 1.000000 wt::x51

552 wx52 = .012110 WXOl ♦ .008080 1111102 ♦ .001010 WX03 ♦ .006930 WX04 ♦ .000610 WX06 ♦ .000820 Wl\07 ♦ .000410 wxo8 ♦ .000350 WXlO I

♦ .000870 WXll ♦ • 000120 WA13 ♦ .005240 WX14 ♦ .001100 WX15 .... .....

♦ .002090 WX16 ♦ .0025so WX17 ♦ .000570 WX18 ♦ .001050 WX19 ..., I

♦ .003370 WX20 ♦ .003100 Wl\21 ♦ .005790 wx22 ♦ .004040 WX23 ♦ .001660 WX24 ♦ .006700 Wl\25 ♦ .002860 WX26 ♦ .002890 liX27 ♦ .004370 WX28 ♦ .005340 WX29 ♦ .001870 WX30 ♦ .001160 WX32 ♦ .001880 WX33 ♦ .000120 WA34 • .009620 WX35 • .001390 WX37 ♦ .001760 WX38 ♦ • 000430 WX39 ♦ .000310 WX40 • .002000 WX4l

• .001360 WX42 ♦ .004630 WX43 ♦ .000860 WX44 ♦ .000450 WX45 ♦ .009760 WX46 ♦ .003860 Wl\47 ♦ .000200 WX48 ♦ .000140 WX49 ♦ .000440 WX50 ♦ .000190 Wl\51 ♦ .003710 WX53 ♦ .013640 WX54 ♦ .003380 WX55 ♦ .004180 WCTOT ♦ .000000 WIEQP ♦ 1.000000 DUMMY ♦ .002020 WSLEOU ♦

0 0155JO W=>LOTH ♦ 1.000000 WEX52 553 WX53 = .037630 WXOl ♦ .041760 wxo2 ♦ .035070 WX03 ♦ .039860 WX04

♦ .080600 WX05 • .015570 Wl\06 ♦ .024540 WX07 ♦ .036790 WX08 ♦ .023710 WX09 • .029020 WlllO ♦ .024270 WXll ♦ .012900 wx12 ♦ .012200 WX13 ♦ .013090 WX14 • .013470 WX15 • .010110 WX16 ♦ .027340 WX17 ♦ .035210 WX18 ♦ .037520 WX19 ♦ .026980 WX20 ♦ .016830 wx21 ♦ .020250 wx22 ♦ .027580 WX23 ♦ .015330 WX24

• .013400 wx25 ♦ .020000 Wl\26 ♦ .003060 WX27 ♦ .013100 WX28 ♦ .020300 WX29 ♦ .044780 WX3Q ♦ .015050 WX31 ♦ .006630 WX32 ♦ .018150 WX33 ♦ .010050 Wl\34 ♦ .015630 WX35 • .018400 WX36

• .02221+0 WX37 • .018450 WX38 • .002260 WX39 ♦ .009890 WX40

• .022330 WX4l • .016290 WX42 • .0115eo WX43 • .003280 WX44

• .002210 WX45 ♦ .004270 Wl\46 ♦ .004960 WX47 ♦ .068670 WX48 ♦ .046720 WX49 • .041340 Wl\50 ♦ .038440 WX51 ♦ .046820 WX52 ♦ .009520 wx53 • .009090 WX54 ♦ .016550 WX55 ♦ .208330 WCTOT ♦ .071600 Wl[QP ♦ 1.000000 W11NV53 .001970 WSLEOU ♦ .000510 WSLOTH ♦ 1.000000 IIIEX53

554 WX54 = .009150 WXOl ♦ .005120 wxo2 ♦ .004810 WX03 ♦ .005200 WX04

♦ .017630 WX05 ♦ .002140 1111106 ♦ .006950 WX07 ♦ .002480 WX08 ♦ .002310 IIIX09 ♦

0 004600 wx10 ♦ .006070 i.x11 ♦ .003590 liX13 (EQUATION CONTINUED ON NEXT PAGE)

WPSMl EQUATIONS BLOCK 5 OUTPUT

• .009160 WX14 ♦ .0019<::0 WX15 • .007840 WX16 • .005470 WXI7 • • 004830 wx1a ♦ .004560 111Xl9 • .005060 IIIX20 ♦ .003540 WX21 • .005210 wx22 ♦ .003590 WX23 • .004560 WX24 • .003350 WX25 • • 005710 WX26 ♦ .0078<::0 WX27 ♦ .004370 IIIX28 ♦ .oo5aao WX29 • .004660 IIIX30 ♦ .0037<'.0 WX32 • .005630 WX33 • .003590 WX34 • .004810 WX35 • • 003680 WX36 ♦ .004170 WX37 • .003510 WX38 • .001880 WX39 ♦ • 002100 WJ\40 • .002580 WX41 • .004070 WX42 • .012200 WX43 • • 004660 WJ\44 • .003640 WX45 • .007930 WX46 • .006060 WX47 • .005870 WX48 • .007930 WX49 • .007830 wxso ♦ .006470 WX5l ♦ .003750 wx52 • .011900 WX53 • .073350 WX54 • .015130 wxss ♦ • 064030 WCTOT • .000000 WIEQP ♦ 1.000000 DUMMY ♦ .006250 WSLEOU ♦ .013880 WSLOTH ♦ 1.000000 WEX54

555 WX55 = .046530 WX0l ♦ .0180~0 wxo2 • .013570 WX0J ♦ .032930 WX04 • .020150 WX05 ♦ .011290 WJ\06 ♦ .027400 WJ\07 • .022320 wxoa • .028340 WX09 ♦ .013090 WJ\10 ♦ .022970 wx11 ♦ .006450 11/X l 2 • .010110 WX13 ♦ .020940 111Xl4 ♦ .004620 WX15 ♦ .025600 WX16 • • 0170 l O WX17 • .013060 WX18 ♦ .015780 WX19 • .028670 111x20 • .020010 wx21 ♦ .010uo IIIJ\22 • .021970 WX23 • .036040 WX24 • .051090 wx25 • .042860 WX26 ♦ .004590 WX27 ♦ .013100 WX28

• .003740 IIIX29 ♦ .023320 WJ\30 ♦ .015050 WX3l ♦ .004880 WX32 ♦ .018770 WX33 ♦ .019380 WX34 • .025240 WX35 ♦ .018400 WX36 ♦ .017370 WX37 ♦ .014060 111/J\38 • • 030130 IIIIX39 ♦ .008350 WX40

• .012600 WX4l • .0171'10 WA42 ♦ .022460 WX43 • .031040 WX44

• .006820 WX45 • .018290 II/X46 ♦ .036900 WX47 ♦ .033430 WX48 I

♦ .040230 IIIIX49 • .0287<'.0 wxso ♦ .030450 WX51 • .014360 WX52 ,..... ..., • .048810 WX53 ♦ .0776~0 WJ\54 ♦ .049650 WX55 ♦ .144940 WCTOT l'-

I

♦ .000000 WIEOP ♦ 1.000000 DUMMY • .007780 WSLEOU ♦ .026270 WSLOTH ♦ 1.000000 WEX55

IIIPSMl EQUATIONS BLOCK 6 VALUE ADDED

6Ul WVA0l = .641500 WXOl 602 lliVA02 = .783670 WX02 603 illVA03 = .285350 WX03 604 illVA04 = .587520 WX04 605 WVA05 = .584380 WX05 606 illVA06 = .168500 i11X06 607 WVA07 = .185690 IIIX07 608 lliVA08 = .374740 WX08 609 WVA09 = .248700 WX09 610 lliVAl0 = .444440 wx10 611 illVAll = .361070 WXll 612 WVA12 = .619350 wx12 613 WVA13 = .450110 i11Xl3 614 lliVA14 = .623040 WX14 615 WVA15 = .889570 WXI5 616 WVA16 = .441830 WXI6 617 illVAl 7 = .451520 WX17 618 lliVA18 = .417940 WX18 619 lliVA19 = .354490 WXI9 620 WVA20 = .519390 wx20 621 illVA21 = .365370 wx21 622 'r1VA22 = .431010 WX22 623 WVA23 = .450220 IIIX23 I

624 WVA24 = .661970 WX24 .... ..... 625 WVA25 .621020 wx25 = ..,,

I

626 lliVA26 = .451430 WX26 627 '14VA27 = .197960 WX27 628 WVA28 = .685590 lliX28 629 lliVA29 = .460470 WX29 630 illVA30 = .627800 i11X30 631 illVA3l = .563440 WX31 632 WVA32 = .302840 WX32 633 illVA33 = .450560 lliX33 634 WVA34 = .437900 WX34 635 WVA35 = .472360 WX35 636 WVA36 = .553370 WX36 637 WVA37 = .503130 11iX37 638 WVA38 = .500880 WX38 639 WVA39 = .463740 WX39 640 WVA40 = .266540 WX40 641 WVA4l = .646440 WX4l 642 WVA42 = .564250 WX42 643 '14VA43 = .711870 WX43 644 WVA44 = .605100 WX44 645 WVA45 = .347270 WX45 646 WVA46 = .825610 WX46 647 WVA47 = .837530 WX47 648 wVA48 = .404200 WX48 649 WVA49 = .412400 WX49 650 illVA50 = .470410 WX50 651 WVA51 = .462230 i11X5l 6!:>2 WVA52 = .601750 WX52 653 WVA53 = .809520 WX53 654 WVA54 = .706940 WX54 6!:>5 WVA55 = .761250 WX55

wPSMl EQUATIONS BLOCK 6 VALUE AODLO

656 ill/AC = .134030 WCTOT 6~7 W\/AJflll. = .000000 WXWT2 658 W\/AJIN\/ = .000000 WI JN\/43 659 il\/ASLEDU = .791220 WSLEDU 660 111\/ASLOTH = 0S93980 WSLOTH 6bl W\/AFEO = 9.484000 WNFEOMIL ♦ 13.323000 WNFEDCJ\/ 6b2 111\/ATOT = 1.000000 WIIAOl ♦ 1.000000 WIIA02 ♦ 1.000000 WIIA03 ♦ 1.000000 IIIIIA04

• 1.000000 WIIA05 ♦ 1.000000 WIIA06 ♦ 1.000000 WIIA07 -♦ 1.000000 WIIA08 ♦ 1.000000 WIIA09 ♦ 1.000000 WIIAlO • 1.000000 WI/All ♦ 1.000000 WIIA12

• 1.000000 WIIA13 ♦ 1.000000 WliA14 • 1.000000 WIIA15 • 1.000000 WIIA16

• 1.000000 WI/A 17 . 1.000000 WIIA18 • 1.000000 WIIA19 • 1.000000 WIIA20

• 1.000000 WIIA21 ♦ 1.000000 IIIIIA22 • 1.000000 WIIA23 • 1.000000 WIIA24

• 1.000000 WIIA25 • 1.000000 IIIIJA26 • 1.000000 WIIA27 • 1.000000 WIIA28

• 1.000000 WIIA29 ♦ 1.000000 WVAJO • 1.000000 WIIA31 ♦ 1.00000.0 W\/A32

• 1.000000 WIIA33 • 1.000000 WVAJ4 ♦ 1.000000 WIIA35 ♦ 1.000000 WIIA36

• 1.000000 WIIA37 ♦ 1.000000 WIIAJ8 • 1.000000 W\/AJ9 ♦ 1.000000 WIIA40 ♦ 1.000000 WIIA41 • 1.000000 WIIA42 • 1.000000 WIIA43 ♦ 1.000000 lli\/A44 ♦ 1.000000 WIIA45 ♦ 1.000000 WIIA46 • 1.000000 WIIA47 • 1.000000 WIIA48

• 1.000000 WIIA49 • 1.000000 WIIA50 . 1.000000 WIIA5l • 1.000000 WIIAS2

• 1.000000 WIIA53 • 1.000000 WIIA54 • 1.000000 W\/A55 • 1.000000 WI/AC

• 1.000000 WIIAIFlll • 1.000000 WIIAJ IN\/ • 1.000000 WIIASLEOU • 1.000000 WIIASLOTH

• 1.000000 W\/AFEO I .... ..... "' I

WPSMl EQUATIONS BLOCK 7 INCOME

701 WYLOl = .430460 WXOl 702 liYL02 = • 719290 WX02 703 WYL03 = .222570 WX03 704 WYL04 = .431540 WX04 705 liYLOS = .435770 wxos 706 WYL06 = .087000 WX06 707 WYL07 = .132920 WX07 708 WYL08 = .162880 wxo0 709 WYL09 = .094270 WX09 710 •YLlO = .139770 wx10 711 WYLll = .229300 WXll 712 liYLl2 = .270970 wx 12 713 WYL13 = .262740 WX13 714 WYL14 = .339010 liX14 715 WYL15 = .056560 WX15 716 WYLl6 = .288230 WX16 717 WYL17 = .245080 WX17 718 WYL18 = .249010 WX18 719 liYLl9 = .224750 WX19 no WYL20 = .359190 wx20 721 illYL21 = .198410 wx21 7'!2 WYL22 = .255130 wx22 723 WYL23 = .203590 WX23 724 WYL24 = .446150 WX24 I ... 725 WYL25 = .293130 WX25

.... .... U.6 WYL26 = .257140 WX26

I

7'c.7 illYL27 = .084860 WX27 728 WYL28 = .358080 WX28 729 IIIYL29 = .287930 lliX29 730 WYL30 = .358210 lliX30 731 WYL3l = .423660 WX31 732 WYL32 = .132990 WX32 733 WYL33 = .346060 WX33 734 liYL34 = .230440 WX34 735 WYL35 = .370190 WX35 736 WYL36 = .499390 WX36 7J7 WYL37 = .388460 WX37 738 WYL38 = .331280 WX38 739 WYL39 = .323180 WX39 740 lliYL40 = .201300 WX40 7'+1 WYL4l = .612370 WX41 742 WYL42 = .349320 WX42 743 WYL43 = .573020 WX43 744 WYL44 = .14 7270 llfX44 745 •YL45 = .065450 WX45 746 WYL46 = .260970 liX46 747 WYL47 = .318340 WX47 748 ■ YL48 = .319280 WX48 749 WYL49 = .325760 WX49 7!:,0 WYL50 = .371580 WX50 751 WYL51 = .365120 WX5l 752 WYL52 = .475330 WX52 753 WYL53 = .497050 WX53 754 WYL54 = .428400 WXS4 7!:15 WYL55 = .621370 wxss

wPSMl EQUATIONS BLOCK 7 INCOME

756 WYLC = .005830 wcror 757 WYLSLEDU = 0714040 WSLEOu 758 •YLSLOTH = .536630 WSLOTH 759 WYLF"EDMILPN = 2.304900 ♦ 0258800 TIME 7b0 WYLF"EDMIL = 1.000000 IWYLFEDMILPN I IWNfEDMlL )

7bl WYLF"EDCIVPN = 4.843900 ♦ .339400 T lME. 7b2 WYLF"EDCIV = 1.000000 IWYLFEDCIVPN I lwNfEDCIV )

7b3 WYLF"EO = 1.000000 WYLFEDMIL ♦ 1.000000 WYLFEDCIV 7b4 WYLTOT = 1.000000 WYLO l ♦ 1.000000 IIIYL02 ♦ 1.000000 WYL03 ♦ 1.000000 WYL04

♦ 1.000000 WYLOS ♦ 1 0000000 IIIYL06 ♦ 1.000000 WYL07 ♦ 1.000000 WYL08 ♦ 1.000000 WYL09 ♦ l 0000000 WYLlO ♦ 1.000000 WYL 11 ♦ 1.000000 WYL12 ♦ 1.000000 WYL13 ♦ 1.000000 WYL14 ♦ 1.000000 WYL15 ♦ 1.000000 WYL 16 ♦ 1.000000 WYL17 ♦ 1 0000000 WYL18 ♦ 1.000000 111Yll9 ♦ 1.000000 WYL20 ♦ 1.000000 WYL2l ♦ 1 0000000 WYL22 • 1.000000 WYL23 . 1.000000 WYL24 • 1.000000 WYL25 ♦ 1.000000 WYL26 ♦ 1.000000 IIIYL27 ♦ 1.000000 WYL28 ♦ 1.000000 WYL29 ♦ l.000000 WYL30 ♦ 1.000000 WYL31 ♦ 1.000000 WYL32 • 1.000000 WYL33 ♦ l.000000 WYL34 • 1.000000 WYL35 ♦ 1.000000 WYL36 ♦ 1.000000 WYL37 ♦ l.000000 WYL38 ♦ 1.000000 WYL39 ♦ 1.000000 WYL40 ♦ 1.000000 WYL41 ♦ l.000000 WYL42 ♦ 1.000000 WYL43 ♦ 1.000000 WYL44 ♦ 1.000000 WYL45 ♦ 1.000000 WYL46 ♦ 1.000000 WYL47 ♦ 1.000000 WYL48 ♦ 1.000000 WYL49 ♦ 1.000000 WYLSO ♦ 1.000000 WYLSl ♦ 1.000000 WYL52 ♦ 1.000000 IIIIYL53 ♦ 1.000000 WYL54 ♦ 1.000000 WYLS5 ♦ 1.000000 WYLC ♦ 1.000000 WYLSLEDU ♦ 1.000000 WYLSLOTH ♦ 1.000000 WYLFED

7b5 WYPROPPC = .062200 ♦ 0941200 USYPROPPC I .... 7b6 IIIYPROP = 1.000000 (WYPROPPC ) (ii/POP )

..... 00

7b7 IIIYTPPC = .001600 ♦ l. 045300 USYTPPC ♦ .410000 WUNEMRT I

7b8 IIIYTP = 1.000000 IWYTPPC ) IWPOP )

7t>9 IIIYSSRT = .001soo ♦ 0922300 USYSSRT

710 ilYSS = 1.000000 IWYSSRT ) IWYLTOT )

771 WYP = 1.000000 WYLTOT ♦ 1 0000000 WYPROP ♦ 1.000000 IIIYTP 1.000000 IIIYSS

712 IIIITAXRT = .019400 ♦ 0794800 USTAXRT

773 IIIITAX = 1.000000 IWTAXRT ) IWYP )

774 ilfYD = 1.000000 WYP 1 0000000 WTAX 775 IIIYOPC = 1.000000 (WYO I I (IIIPOP )

716 IIIICYCLE = 1.000000 WYO l • 000000 WYDLl

IIIPSMl EQUATIONS BLOCK 8 EMPLOYMENT ANO POPULATION

801 WN0l = .047550 WXOl 802 WN02 = .079740 WX02 803 WN03 = .024600 IIIX03 804 WN04 = .051990 WX04 805 WN05 = .060450 WX05 80t, WN06 = .008850 WX06 807 WN07 = .011040 WX07 808 WN08 = .023150 wxo8 809 WN09 = .008680 WX09 810 WNlO = .011320 wx10 811 WNll = .023840 WXll 812 11Nl2 = .038710 wx12 813 111Nl3 = .046660 WX13 814 •Nl4 = .027490 wx 14 815 WN15 = .014620 wx15 816 1111Nl6 = .032570 WX16 817 IIINl 7 = .026120 WX17 818 •Nl8 = .024420 IIX18 819 WN19 = .026650 WX19 820 WN20 = .043840 wx20 821 WN2l = .015500 WX2l 8.:2 WN22 = .019090 wx22 823 WN23 = .017260 WX23 I

824 WN24 = .048050 WX24 ... .... 825 WN25 = .022190 wx25 "' I

82b WN26 = .025710 WX26 827 WN27 = .004250 WX27 828 WN28 = .030570 WX28 829 WN29 = .026180 WX29 830 WN30 = .029850 WX30 5;,i WN31 = .036560 WX31 832 IIIIN32 = .009770 WX32 833 WN33 = .028790 WX33 834 WN34 = .022250 WX34 835 WN35 = .031-250 WX35 8J6 IIIN36 = .045400 WX36 0.n IIIN37 = .031970 WX37 838 WN38 = .032510 WX38 839 WN39 = .022240 WX39 840 WN40 = .013910 WX40 841 IIIIN4l = .047520 WX41 842 IIIIN42 = .038010 WX42 843 WN43 = .049010 WX43 844 WN44 = .011730 WX44 845 IIIIN45 = .005450 WX45 84b lliN46 = .027440 WX46 847 WN47 = .028090 WX47 848 WN48 = .028530 WX48 849 WN49 = .029130 WX49 850 WN50 = .033070 WX50 8!:>l WN51 = .032730 WX51 852 •N52 = .042450 WXS2 853 IIIN53 = .070640 WXS3 854 WN54 = .047730 WX S4 855 IIIN55 = .083540 WX55

lolPSMl EQUATIONS BLOCK 8 EMPLOYMENT AND POPULATION

856 WNC = .002670 WC TOT 857 WNSLEDU = .108810 WVASLEDU 858 WNSLOTH = .121360 WVASLOTH 859 WNFEDMIL = -12.000000 • .027600 U:>NFEDMIL 860 IIINFEDC IV = S.300000 + .013000 U~NFEDCJV 81>1 WNFED = 1.000000 WNFEDMIL • 1.000000 WNFEDCIV 862 WJOBCIV = 1.000000 WNOl ♦ 1.oooouo WN02 ♦ 1.000000 WNOJ + 1.000000 WN04 . 1.000000 WNOS + 1.000000 WN06 • 1.000000 lolN07 • 1.000000 WN08

• 1.000000 WN09 • 1.000000 WNlo • 1.000000 WNll • 1.000000 WN12 • 1.000000 WN\3 • 1.000000 WN 14 • 1.000000 WNlc:; • 1.000000 WNl6 • 1.000000 WN17 • 1.000000 WNlB • 1.000000 WN19 • 1.000000 WN20 + 1.000000 i1N2l • 1.000000 WN22 • 1.000000 WN23 • 1.000000 WN24 • 1.000000 WN25 • 1.000000 WN26 • 1.000000 WN27 + 1.000000 WN28 • 1.000000 WN29 • 1.000000 WN30 • 1.000000 wN3l • 1.000000 WN32 + 1.000000 WN33 • 1.000000 WN34 • 1.000000 WN3S • 1.000000 WN36 • 1.000000 WN37 • 1.000000 WN38 • 1.000000 WN39 • 1.000000 WN40 • 1.000000 WN4l • 1.000000 WN42 • 1.000000 WN43 • 1.000000 WN44 ♦ 1.000000 WN45 • 1.000000 WN46 • 1.000000 wN47 • 1.000000 WN48 • 1.000000 WN49 • 1.000000 WNSO • 1.000000 WNSl • 1.000000 WN52 • 1.000000 WN53 • 1.000000 WNS4 • 1.000000 WNSS • 1.000000 WNC • 1.000000 WNSLEDU ♦ 1.000000 WNSLOTH ♦ 1.000000 WNFEDCIV

863 IINCJV = .973000 WJOBCIV 864 •ONCIV = 1.000000 WNCIV 1.000000 WNCIVll 865 IIINTOT = 1.000000 IINCIV . 1.oooouo 11f'<FEDMIL I ..... 866 IIIUNEMRT = .014000 • .490700 WUNEMRTLl • .503800 USUNEMRT .000220 WDNCIV a,

0

867 WEMRT = 1.000000 1.000000 WUNEMRT I

868 liLFCIV = 1.000000 (lilNCIV I I ( 111EMRT )

869 IIILF = 1.000000 WNFEOMJL • 1.000000 WLFCIV 810 ilNRT = .108000 • • 76tl800 USNRT ~71 WPOP = 1.000000 IIIILF l/(WNRT )

872 lliPOPS-20 = 1.000000 (wpOP l <usPOPS-20fH )

873 WOPOPS-20 = 1.-000000 WPoPs-20 1.000000 W~OPS-20Ll

liPSMl EQUATIONS BLOCK 9 IMPORTS

901 liMOl = liXOl WIIAOl I# INSO l 902 WM02 = WX02 WIIA02 i#lNS02 903 WM03 = WX03 W\IA03 WINS03 904 liM04 = WX04 W\IA04 1111NS04 905 IIIM05 = wxos W\IAOS WINSOS 906 WM06 = IIIX06 W\IA06 111INS06 907 WM07 = WX07 - 111\IA07 111INS07 908 liM08 = wxoa W\IA08 WINsOa 909 IIIM09 = iiX09 W\IA09 IIIINS09 910 IIIMlO = liXlO W\/AlO WINSlO 911 liMll = liX 11 W\/All WINSll 912 WM12 = liXl2 W\/Al2 WINS12 913 liM13 = WX13 - W\/Al3 WINS13 914 iiMl4 = WX14 - W\/Al4 WINS14 915 WMlS = WXlS - W\/AlS lilNSlS 916 liMl6 = liXl6 - ii\/Al6 lilNSl6 917 liM17 = WX17 - W\/Al7 ill INSl 7 918 i11Ml8 = WX18 - W\/Al8 lilNS18 919 111Ml9 = WX19 - W\/Al9 lilNS19 920 liM20 = WX20 ii\lA20 IIIINS20 9~1 WM21 = WX2l W\IA21 WINS21 922 IIIM22 = WX22 - WIIA22 IIIINS22 923 WM23 = WX23 - w\lA23 IIIIN523

I

924 WM24 = WX24 - 1i\lA24 IIIINS24 .... 925 WM25 = liX25 - W\IA25 WINS25

00 .... I

926 IIIM26 = WX26 W\IA26 WINS26 927 WMZ7 = iiX27 - W\IA27 WINS27 928 liM28 = WX28 - WIIA28 WINS28 929 liM29 = WX29 - W\IA29 IIIINS29 9JO WM30 = liX30 - W\IA30 i#INS30 9Jl liM31 = WX31 - W\IA31 WIN531 932 IIIM32 = WX32 - WIIA32 WINS32 9.;3 WM33 = WX33 - WVA33 - WIN533 934 WM34 = WX34 - W\IAJ4 i#INS34 935 WMJS = iiX35 - W'IA35 - lilNS35 936 IIIM36 = WX36 - WVA36 IIIINS36 937 WM37 = WX37 - lll'IA37 lilNS37 938 WM38 = liX38 - W'IA38 - WINS38 939 WM39 = WX39 - WI/AJ9 WINS39 940 WM40 = WX40 - WI/A40 - WINS40 941 WM4l = WX4l - WIIA4l W INS4 l 942 IIIM42 = WX42 · - WI/A42 WINS42 943 IIIM43 = WX43 - W'IA43 - WINS43 944 IIIIM44 = IIIX44 - WIIA44 - WINS44 945 liM45 = IIIX45 - W'IA45 - IIIINS45 946 WM46 = IIIX46 lll'IA46 IIIIINS46 947 IIIIM47 = liX47 - WVA47 - WINS47 948 IIIIM48 = WX48 - WIIA48 - WIN548 949 IIIM49 = WX49 - W\IA49 i#INS49 950 IIIIM50 = wxso - WIIASO - WINSSO 951 IIIM5l = WXSl - WIIA51 - i#INS51 952 IIIM52 = WX52 - W'IA52 - i#INS52 9!:>3 WM53 = WX53 - W'IA53 - WINS53 9!:>4 WM54 = WX54 - WIIA54 - WINS54 9!:>5 WM55 = wxss - WI/ASS - ·w INSSS

wPSMl EQUATIONS BLOCK 9 IMPORTS

95t, WMC = WCTOT - WVAC WIN401 957 WMIFIX = WIFIXTOT - WVAIFIX - WIN3ll - WIN30C - WIN308 WIN309 958 WMIINV = WIINVTOT - wVAIINV WIN3l3 - WIN314 - WIN315 WIN316 WIN3l7

- WIN318 - wIN319 - WIN)cO - WIN321 - W!N322 - WIN323 - W!N324

- W!N325 - WIN326 - 11IN327 - WIN328 - W!N329 - il!N330 - W!N331 - ill!N332 - io11N333 - IIIIN334 - WIN335 - WIN336 - WIN337 - WIN338 - WIN339 - wIN340 - WIN341 - WIN342 - WIN343 - WIN344 - WIN345 - WIN346 - WIN347 - WIN348 - WIN349 - W!N3SO - WIN351 - WIN352 - WIN353 - WIN354 - IIIN356 - W!N3S7

959 WMSLEOU = W5lf0UTOT - WVASLEDU li!N202 - WIN205 91:>0 WMSLOTH = WSLOTHTOT - WVASLOTH - WIN203 - \odN200 - WIN207 WIN208 91:>l WMEX = WEXTOT - IIVAFED - IIIN!Ol - WIN102 - WINl03 - WIN104 W!NlOS

- WIN106 - WIN107 - W!N}08 - WINI09 - WINllO - WINlll - W!Nll2

- WIN113 - WlNll4 - WINJlS - 11lN116 - WIN117 - W!Nll8 - WIN119

- WIN120 - WIN121 - ll!Nl22 - WIN123 - WIN124 - WIN125 - WIN126

- WIN127 - WIN128 - •IN129 - WIN130 - WIN131 - WIN132 - WIN133 - W!Nl34 - WIN135 - WIN136 - WIN137 - W!Nl38 - WIN119 - W!Nl40

- ll!Nl4l - wIN142 - W!Nl43 - 11IN144 - WIN145 - IIIN146 - WINl47

- Wl"4148 - WIN149 - Wlf\llSO - WINISI - WIN152 - WINIS3 - IIIIN154

- IIINISS 9b2 IIMTOT = 1.000000 WMOI • 1.000000 WM02 . 1.000000 IIMOJ • 1.000000 WM04

• 1.000000 WMOS . 1.000000 wM06 . 1.000000 WM07 ♦ 1.000000 WM08

• 1.000000 WM09 ♦ 1.000000 WMlO ♦ 1.000000 WMl l ♦ 1.000000 WM12

• 1.000000 WM13 ♦ 1.000000 IIIM 14 • 1.000000 WMIS • 1.000000 WM16 ' ,... ♦ 1.000000 WMJ7 ♦ 1.000000 WM18 ♦ 1.000000 11Ml9 ♦ 1.000000 WM20 00

N

• 1.000000 WM21 • 1.000000 WMc2 . 1.000000 WM23 . 1.000000 IIIM24 ' • 1.000000 WM25 • 1.000000 WM26 . 1.000000 IIM27 • 1.000000 IIM28 . 1.000000 WM29 ♦ 1.000000 WM30 • 1.000000 WM31 ♦ 1.000000 WM32 ♦ 1.000000 WM33 ♦ 1.000000 WM34 • 1.000000 111M3S • 1.000000 WM36

• 1.000000 WM37 • 1.000000 WM38 ♦ 1.000000 WM39 ♦ 1.000000 WM40 ♦ 1.000000 iliM41 ♦ 1.000000 WM42 ♦ 1.000000 WM43 ♦ 1.000000 WM44 ♦ 1.000000 WM45 ♦ 1.000000 111M46 • 1.000000 WM47 . 1.000000 WM48 ♦ 1.000000 WM49 ♦ 1.000000 WMSO ♦ 1.000000 WMS! ♦ 1.000000 WM52 ♦ 1.000000 WMS3 ♦ 1.000000 WM54 ♦ 1.000000 WM55 ♦ 1.000000 WMC

• 1.000000 WMJF!X . 1.000000 WM I I NV ♦ 1.000000 WMSLEDU . 1.000000 WMSLOTH

• 1.000000 WMEX

APPENDIX C PROJECTIONS FRCM 1972 TO 1985

Appendix C shows preliminary projections from 1972 to 1985 for each of the 456 endogenous variables in WPSM. All value forecasts are stated in constant 1972 dollars. Also given are input-output tables for 1972, 1980, and 1985.

It should be re-emphasized that these forecasts are for demonstration purposes, and that different assumptions about the economic future would result in alternative predictions. With regard to this, we have not assessed the INFORUM projections of our national exogenous variables; nor have the values of these variables or the coefficient changes incorporated in our predictions been subjected to critical appraisal by outside review­ers. Nevertheless, these projections are useful in evaluating the reason­ableness of the model design. As a tentative statement of the future of the regional economy, the forecasts may even be of more general interest, provided that their limitations are recognized.

There are in fact two projections shown on the following pages. The first, listed as BASE, is the projection of the economy assuming a decline in aerospace employment, an expectation compatible with the INFORUM view of the national aerospace industry. The second, listed as co,n,, shows the Washington economy assuming a higher level of growth in the aerospace sec­tor. The differences between these two forecasts are discussed at length in Chapter 13. The primary reason for showing two baseline forecasts is the uncertainty about the aerospace outlook. By presenting two sets of forecasts, as well as the absolute (DIFF) and percentage (PCNT) differences between them, we also demonstrate the analytical capabilities of WPSM in conditional forecasting exercises.

VARIABLE NAME l Y72 !973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

wEX0l BASE. 234.84 259.cO 254.44 238.97 26b.OS 272.63 278 • l 5 282085 288.21 293.73 COMP 234084 259020 254.44 238.97 266005 272o63 278.15 282085 288.21 293.73 Dlff o00 oOO .oo .oo .oo oOO .oo .oo .oo oOO PCNT .o .o .o .o .o .o .o .o .o .o

wEX0l BASE 299081 306.02 312.36 318.82 COMP 299.81 306.02 312.36 318.d2 DJFf .oo oOO .oo .oo PCNT o0 .o .o o0

BASE Ml::AN= 279.0l PCNT DifF MEANS= oO AV ABS 01rr= oOO otrF PCNT or BASE MEANz .o

WEX02 BASE 115.52 186.55 180.00 190.00 169.66 182.51 189.79 197.9A 207.70 215.27 COMP 175.52 1A6.55 1so.oo 190.00 169.66 182.51 189.79 197098 207.70 215.27 Dlff .oo .oo .oo .oo .oo .oo .oo oOO oOO oOO PCNT .o oO oO .o .o .o oO .o .o .o

wEX02 BASE 223.18 231059 240.58 250013 COMP 223.18 231.59 240.58 250.13 Dlff o00 oOO .oo oOO PCNT o0 oO .o .o

BASE MEAN: 202.89 PCNT OJfF MEANS= .o AV ABS DIFr= oOO OIFF PCNT Of BASE MEAN= .o

WEX03 BASE 24.02 24030 2s.02 28.40 36.23 38.29 43.66 46006 48.69 5lo29 COMP 24002 24.30 2s.02 28.40 36.23 38.29 43.66 46006 48.69 51.29 OJFf .oo oOO .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX03 BASE 53.90 56.56 59027 62.03 COMP 53.90 56.56 5<Jo27 62.03 Dlff oOO .oo .oo .oo PCNT oO .o o0 .o I

BASE MEAN= 42.75 PCNT DIFf Mt.ANS= .o AV ABS DlfF= .oo Dlff" . PCNT or BASE MEANc .o ,... OD \JI I

WEX04 BASE 29.50 29050 29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50

COMP 29050 29.50 29.50 29.50 29.50 29.50 29.SO 29050 29.50 29050

Dlff o00 oOO .oo oOO .oo .oo .oo oOO oOO .oo

PCNT .o .o .o oO .o oO .o .o .o .o

wEX04 BASE 29.50 29.50 29.50 29050 COMP 29.50 29050 29.50 29.50 Olff oOO oOO .oo .oo PCNT .o .o .o .o

BASE MEAN= 29.50 PCNT DifF" MEANS= .o AV ABS DIFF= .oo OIFr PCNT or BASE MEAN= ~o

WEX0S BASE 2.40 2.40 2.40 2.'+0 2.1+0 2.40 2.40 2.40 2o40 2o40

COMP 2.40 2o40 2.40 2.40 2.40 2.40 2.40 2.40 2.40 2.40

Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo oOO

PCNT o0 .o .o .o .o .o .o oO .o .o

WEX05 BASE 2o40 2o40 2.40 2.40 COMP 2.40 2.40 2.40 2.40 Dlff .oo .oo .oo .oo PCNT .o oO .o .o

BASE MEAN= 2o40 PCNT DIFF MEANS= .o AV ABS l)Jffs o00 Dlff PCNT Of BASE MEANa .o

WEX06 BASE 42.00 41.00 4loOO 40.00 39.00 38.00 31.00 36.00 35000 34.00

COMP 42.00 41000 41.00 40000 3<.J.00 38.00 31.00 36.00 35.00 34.00

Olff .oo oOO .oo oOO .oo .oo • 00 .oo oOO .oo

PCNT .o .o .o .o .o .o oO .o .o .o

WEX06 BASE 33.00 32000 31.00 30.00 COMP 33.00 32000 31.00 30.00

Olff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 36.36 PCNT OJff Mt.ANS= .o AV ABS l)JfF= .oo DIFF PCNT Of BASE MEAN= .o

YAR[ASI.E '°' A"E 19 72 197 3 197:. l i h J 9 1 b !-, 77 1 978 \9 7Q 1980 1981 H '3 2 1-.eJ 1 .,,, .. l 9 8S

111E ll ll7 BA SE J l , l l 28 , 00 29,o .. 29 , '1 6 29 , 57 J O, e, l 3 1 , (1 6 3 l,4Q 32 , 0J n.s5 COM? Jl. 11 2!:l, 00 29,1)4 29 , '16 2-., 5 7 30 , 61 31, 0 6 J l,4Q 32 , 03 32 ,55 DI H .oo ,llO . oo , oo , 00 , 0 0 • (I Q • 00 • 0.0 .oo PCkT .o ,0 .o .c .o .o .o . o . o .o

IIEX07 BASE JJ, 05 ) J,55 3 .. , 06 3 .. , :>8

COMP 33,05 33,SS 3 .. ,06 J4 ,S8 OI FF .oo , 06 , 00 , JO ?CNT . o ,0 .o ,O

BA.SE ,,.EAN= J ·l , .. 8 P G!l;T D!F• /oftA/li..S= • 0 ·~ ABS OIF•• ,00 -0 lFF" "-0-li OF BASE MEAN= •. o

•EXOa BASE J'lO ,too '+0'+, JO i+ I J , 0 :3 .. l <:I, J-, .. to2.18 .. 6 I, St> .. 78,SO to95 , -6 l 513,37 530,81

c~ )90,46 40 .. • JO .. I O. 08 .. li!,.39 4 .. 2.1e •bl ,Sb .. 18,50 495,61 513,37 530,81

DIFF . oo , \J O , v 0 .vo ,00 .-o 0 , OU ,.0(\ • .oo .o.o PCl'if .o .o .o .o • .o .o .o ... 0 ~o .o

111EX08 BASE 54ci, 14' 566, ll 2 58 .. , '+ 9 oO J,59 COMP 540,-l'- 56t>, il 2 58 .. . .. 9 60 J,:i9 DIFr .oo , oo , 00 , oo PCl'ii .o ,O ,0 • 0

BASE i'OEA,._.= '+8 9, l l PC~T O!i' F "' t::A NS= . " Av ABS OiFF= ,oo Dir• PC'<T Or BASE MEAN= .o

•EXQ9 BASE I I 2, '-8 1 1 1,<:16 l 09 , 7i. i 12 , ,,.3 I I 9 , i. 5 l23,d2 127,IO 130,40 133,96 137,28 CQf4P 112 .•8 111 ,,;6 10 -. .1 .. i lc,93 11 9 , .. 5 123,82 121.1 0 130,t+O 133.96 137,28

DIFF .oo , vO ,00 .vo ,1)0 ,00 • 0 .0 ,00 .oo .oo PCNT .o ,0 • 0 ,O .o .a .o .o .o .o

wEll09 BASE. 1'+0 ,63 l i.t+. \J 7 lio 7, 59 i 5 1 ,20 COM'P 140,63 1 ..... 0 7 14 7, 59 ,51.20 O[H .oo • \j 0 ,00 ,uo PCIIIT .o . a . o • 0

BASE •EA'f: 12 e ,7S ? CIII T i) [i', Mt::Ai'iS= • 0 A¥ ABS Otr,= • 0 0 D[.,=- PCIIIT Or BASE MEAN= .o I ... 0D a-

•ElllO BASE 222,bJ 2l> S ,30 21t0 ,'+3 2 l 5, "2 21o2.2s 2b7,66 28 l, '46 291o,29 308,56 J21.26 I

COl"P 2U.0J 21oo,Ju 21oo,<.3 .2. l :i. ,.2 242,25 267,6 6 281,'4!: 29 .. ,29 308,56 321,26

OIH .oo • ..)Q , 00 • ll 0 ,00 ,oo • I) 0 ,00 ,oo .oo PCNT .o ,0 . o • 0 .o .o • 0 • 0 .o .o

aEXlO BASE 332, l• )..3,29 35 .. , 7 I J66,39 COMP 332,lio JioJ,29 35 .. , 7 I J6o,J9 o IF• .oo • .l 0 , 00 • V 0 PCl'tT ,0 • 0 ·" • a

BASE "41::Al'j: 26~ • '1·S P c , r 0 1,, "!t.Ai-.S= • 0 ... Ae S u !FF= ,o o Cl l•• PC ~T o, BASE. MEAN= .o

111011 ilASE e9,2o a9,2 0 ~" • 20 .3 9,2 0 8 9 ,2 0 89 ,20 8 9 ,20 ~9,2 0 89,20 1'9,20

CQ!f? a:., 20 ,;9 ,20 8 :, , 2 0 0 9 ,20 89,20 09,20 89,20 89,2 0 89,20 89,20

0 !Fi' , 00 • 0 0 • Q l) , 0 0 , 0 0 , 00 • I) 0 ,O Q .oo .oo PCl'IT • 0 • 0 . ,) • 0 • 0 • 0 • 0 . o .o .o

•E•ll 3ASE a ... 2 o ; 9 .2 J 9 :,, 2 ·) ~~.2 0 CO,._P 8 9 ,20 i! <; • .:: .; a~.2 0 "" • .:: 0 u lFF , 0 0 . ~ ~ • :j I) . ~c ;,,_ .. , ,,) • 0 , :; • 0

o ASE .. EA .. = 3;,c: c c ,::-. T )!•• '4-t. • ~,s; " ... A3 S Ci !F•= . co ') !•• PC•,T OF 8ASE MEAN= • 0 . .., w0. 12 i34SE 12 . ~o l 3 ... Ci l J, 2 8 12, 7 0 I,. • I) 15,2 3 l6, ry 2 \ 6,72 l 7 ,1+6 )7,97

c~.- 12,90 I 3 . : ~ ) ), 2~ l 2, IC l ._, l 3 15,2) 16, 0 2 )6,7 2 l 7, <.6 17,97

C: !l'F • \j 0 • , 0 • () 0 .• ~ 0 • ij 0 , 0 0 • 0 I) , 0 0 .oo .oo ;;c., T ,Ii , J • 0 • 0 .o • Cl • 0 • 0 • 0 .o

•E.J.i2 3ASE I 8. "7 I 8 , • 8 l • , 5 0 2 (11 \; 3 co .. .- le ," 7 l '! , , a l;. :;o 2 0 • .; J

~ lF i' • OQ . ,;.. ~ • \J ,; . ' . "" ,,;

;;C"- T , \j • . j • 0 • 0

=, s~ w:_ ,,,: I ~, 1 7 ;;. C ,, T J ! r ► Ml ~ A•, S;: . ,. & ' Ao S :, l FF: , 0 0 DI•• P C•, T OF BASE MEAN• .o

VARIABLE NAME 1972 1973 19 74 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WEX13 BASE 105065 l l2o l5 114059 118 o 54 129056 l38o l8 145098 153021 l6lol4 167059 COMP 105065 ll2ol5 114059 118054 129 056 l38ol8 145098 15 3021 l6lol4 167059 Olff oOO .oo .oo .oo .oo .oo oOO .oo .oo oOO PCNT .o .o .o .o oO .o .o .o .o o0

WEXlJ BASE 174.34 181014 1sa .oo 194091 COMP 174. 34 l8lol4 188000 l94o9l Olff oO0 oUO oOO oOO PCNT o0 .o .o oO

BASE MEANz 148093 PCNT Diff MEANS: oO AV ABS Dlffz oOO Olff PCNT OF BASE MEAN: .o

WEX14 BASE 14040 14040 14040 l4o40 14.40 14.40 14040 14040 l4o40 14.40 COMP 14040 l4o40 14.40 14.40 14.40 14040 l'+.40 14040 14040 14040 Dlff oOO oOO oOO oOO oOO oOO oOO oOO oOO oOO PCNT oO oO .o oO oO oO oO oO oO oO

WEX14 BASE 14040 l4o'+O 14040 l4o40 COMP l4o40 14040 14040 14040 Dlff oOO oOO oOO oOO PCNT oO oO .o oO

BASE MEAN= l4o40 PCNT OifF MEANS: oO AV ABS DlfF: oOO Olff PCNT Of BASE MEAN• o0

wEXl5 BASE 10070 lOo70 l0o70 lOolO 10070 10o70 10070 10070 lOo70 10070 COMP 10070 lOo70 10070 10.10 10070 lOo70 10070 10070 lOo70 10070 Dlff oOO .oo oOO .oo .oo oOO oOO .oo oOO .oo PCNT o0 .o .o .o .o .o oO .o .o .o

WEXlS BASE 10.10 10070 10.10 10.10 COMP 10070 10070 10.10 10070 OIFF oOO oOO .oo .oo PCNT o0 .o oO .o

BASE MEAN= 10.10 PCNT OJff MEANS= oO AV ABS DIFF= oOO Olff PCNT Of BASE MEAN= .o I .... 00 ....

WEX16 BASE 219008 242082 286053 243oJ6 238050 237042 246006 253061 261 o 17 269.80 I

COMP 219.08 242082 286053 243o36 238.SO 237042 246006 253061 261017 269080

OIFf .oo oOO oOO oOO oOO .oo oOO oOO oOO .oo

PCNT .o oO .o .o .o .o .o .o oO oO

wEXl6 BASE 278.43 287007 295070 304087 COMP 278043 287007 295070 304087 DIH .oo oUO oOO oOO PCNT .o .o .o .o ✓,

BASE MEAN= 261.74 PCNT Ol f f Mt.ANS= oO AV ABS DIFf= oOO OIH PCNT OF BASE MEAN= • . o "

WEX17 BASE 620016 621.69 646.92 584012 642086 722006 755040 77 lo 08 785049 796093

COMP 620016 621069 646.92 584. 12 642.86 722.06 755.40 771.0fl 785.49 796.93

DIFf oOO oOO oOO .oo oOO .oo oOO .oo .oo oOO

PCNT oO .o .o oO oO .o oO .o oO .o

WEX17 BASE 808065 820.'+5 832o52 844069 COMP 808.65 820045 832o52 844069 DIH ~00 oOO .oo oOO PCNT .o .o .o .o

BASE MEAN= 732.36 PCNT OJff MlANS= .o AV ABS DIFF= · .oo Olff PCNT OF BASE MEAN:r .o

WEX18 BASE 285009 260005 233079 178. 18 236018 296.62 313056 320061 327.66 331022

COMP 285.09 260.05 233.79 178.18 236.18 29b o62 313056 320061 327.66 331022

OIH o00 oOO oOO oOO .oo .oo .oo oOO oOO oOO

PCNT o0 oO oO oO oO oO oO oO oO oO

WEX18 BASE 33'to32 338002 342033 347041 COMP 334o32 338.02 342033 347.41 OIFF .oo .oo oOO .oo PCNT oO .o .o oO

BASE MEAN= 296007 PCNT OifF Mt.ANS= •O AV ABS OIFF= oOO OIFF PCNT Of BASE MEAN= .o

VARIABLE NAME 1912 197.J l'il74 197':> 1976 1977 1978 }979 198-0 1981 1982 198.J 1984 198~

wEX19 BASE 216.39 222.J5 182.00 148.57 200.43 266.03 289.67 307.39 324.86 34-0 • 92 COMP 216.39 222.35 182.00 148.!:>7 200.43 266.03 289.67 307.39 324.86 340.92 DIFF .oo .uo .oo .oo .oo .oo .oo .oo .oo • 0-0 PCNT .o .o .o .o .o .o .o .o .o .o

wEX19 BASE 341. 33 372.59 389.15 40o.J2 COMP 341.33 372.59 389.15 -406.32 Dlff .oo .uo .oo .uo PCNT .o .o .o .o

BASE ME.AN= 286.29 PCNT Dlff ME.ANS= .o AV ABS OifF= .oo DIFF PCNT Of BASE MEAN= .o

wEX20 BASE. 33.10 33.10 33.10 33.10 33.10 33.10 JJ.10 33.10 33.10 33.10 COMP 33.10 33.10 3J.10 33.10 3.J.10 33.10 33.10 33.10 33.10 33.10 OIFF .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX20 BASE. 33.10 33.10 33.10 33.10 COMP 33.10 33.10 3J.10 33.10 Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 33.10 PCNT DIFF MEANS= .o AV ABS DIFF= .oo DIFF PCNT Of BASE MEAN= .o

IIIEX2I BASE 153.91 156.19 166.28 144.1:!9 148.60 151. 11 153.63 156.39 160.09 164.03

COMP 1S3.91 156.19 166.28 l44 0 b9 }41:l.60 151. 11 153.63 156.39 160.09 164.03

DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o . -0 .o .o .o .o .o .o .o IIIEX21 BASE 161:l.20 172.61 111.50 l82ob6

COMP 168.20 172.61 111.50 182.86 OIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 161.16 PCNT Dlff MEANS= .o AV ABS Olff= .oo Dlff PCNT Of BASE MEAN= .o I .... 00 00

IIIEX22 BASE 25 l. l O 251.10 251.10 251.10 251.10 251.10 251.10 251.10 251.10 251.10 I

COMP 251.10 251.10 251.10 2s1.10 251.10 251.10 251.10 251.10 251.10 251.10 DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

wEX22 BASE 251.10 251.10 251.10 2s1.10 COMP 251.10 251. l 0 251.10 251.10 DIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN: 251.10 PCNT DIFF MEANS= .o AV ABS DIFF= .oo Olff PCNT Of BASE MEAN= .o

IIIEX23 BASE 315.16 330.00 327.19 316 • .::4 342.47 363.65 376.}6 388.06 401.24 412.09

COMP 315.16 330.00 327 .19 Jl6.24 342.47 363.65 376. l 6 388.06 401.24 412.09

DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

wEX23 BASE 423.34 434.89 446.77 458.96 COMP 423.34 434.89 446.77 458.96 DIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 381.16 PCNT Oiff Mt.ANS= .o AV ABS OIFf= .oo Olff PCNT OF BASE MEAN= .o

IIIEX24 BASE 3':>. 00 35.00 J~.oo 35.UO 35.00 35.00 35.oo JS.on 35.00 35.00

COMP JS.DO 35.UO 35.00 35.00 35.00 35.00 JS.oo 35.oo 35.00 35.00

DIFF .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

wEX24 BASE 35.00 35.00 35.00 35.00 COMP JS.00 35.00 35.oo 35.00 DlFf .oo .oo .oo .uo PCNT .o .o .o .o

BASE MEAN= 35.00 PCNT Diff Mt.ANS= •O AV ABS !JIFF= .oo DIFF PCNT Of -SASE MEAN= .o

IIA~IABLE NAME 1972 1973 l 97 4 1975 }976 1977 1978 1979 1980 1981 1982 1983 1984 198':>

WEX25 BASE 187 . 38 201. 00 197 . 55 188.94 208.76 230.85 241 • 25 251.00 262.30 272.77 COMP 187.38 201.00 197.55 188.94 208.76 230.85 241 • 25 251.00 262.30 212.11 OJFf .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX25 BASE 283.06 293.74 304.82 310.J2 COMP 283.06 293.74 304.82 316.32 OIFf .oo .oo .oo .110 PCNT .o .o .o .o

BASE MEAN= 246.12 PCNT OJff MEANS= .o AV ABS OJFF= .oo OIFF PCNT OF BASE MEAN= .o

WEX26 BASE 7.73 8.23 8.22 7.B6 8.31 8.79 9.06 9.34 9.63 9.88 COMP 7.73 8.23 8,22 7.B6 8.37 8.79 9.06 9.34 9.63 9088 OIH .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX26 BASE 10.12 10.37 10.02 l0otl9 COMP 10.12 10.37 10.62 10.89 Olff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN=- 9.22 PCNT Diff MEANS= •O All ABS OIFF= .oo OIFF PCNT OF BASE MEAN= .o

WEX27 BASE 234.53 248.10 216.50 225.99 264.52 305.48 332.18 353.64 376.37 399.14 COMP 234.53 248.10 210.50 225.99 264.52 305.48 332.18 353.64 376.37 399.14 Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WEX27 BASE 417.73 436.75 456.23 476.17 COMP 417. 73 436.75 456.23 476.17 OIH .oo .oo .oo .oo PCNT .o .o .o .o I

BASE MEAN= 338odl PCNT OJff MEANS= •O AV ABS DIFF= •00 OlfF PCNT OF BASE MEANs .o .... 00

"' I

WEX28 BASE 2.99 3.40 3.35 3.11 3.73 4. 16 4.46 4o75 5.05 5.28

COMP 2.99 3.40 3.35 3.11 3.73 4. 16 4.46 4.75 5.05 s.2a

DIH .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WEX28 BASE s.s3 s.19 6.06 6.34 COMP 5.53 s.19 6.06 6034 DIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 4.57 PCNT OJff MEANS= .o AV ABS DIFF= .oo OIFF PCNT OF BASE MEAN= ~o

WEX29 BASE a.so a.so a.so 8od0 a.so a.so a.so B.80 a.so a.so

COMP s.ao a.so a.so s.1::10 a.so a.so a.so a.so a.so a.so

DIFf .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WEX29 BASE a.so a.so a.so a.so COMP 8.80 a.so a.so a.so OIFf .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN• 8080 PCNT Diff MEANS= .o AV ABS OIFF= .oo DIFF PCNT OF BASE MEAN= .o

WEX30 BASE 38.01 39.10 39.94 37.26 42.69 45.19 46.o3 46.76 47.61 47.82

COMP 38.01 39.70 39.94 37.26 42.69 45.19 46.03 46.76 47.61 47.82

DIFf .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WEX30 BASE 48.29 48.17 49.25 49.73 COMP 48.29 48.77 49.25 49.73 DIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 44.79 PCNT OJFF MEANS= .o AV ABS DIFF= .oo DIFF PCNT Of BASE MEAN= .o

'IARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 )979 1980 )981 1982 1983 1984 1985

lllf[X.J l BASE 45.19 39.00 29 .75 19. '+ 8 29.13 33.26 33. 34 12 . 21 31-.09 29.05 COMP 45. l 9 39 oll0 29 .75 19.'+8 2<.J.13 33.26 33014 32.21 31.09 29.05 DIFF .oo .uo .oo .uo .oo .oo .oo • . oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

lllfEX31 BASt: 27 .15 25.30 23.48 2l.b9 COMP 27.15 25.30 23 .48 21.69 Dlff .oo .uo .oo .uo PCNT .o .o .o .o

BASE MEAN:: 29.94 PCNT DJFF MEANS= .o A'I ABS DIFF= .oo DIFF" PCNT OF" BASE MEAN= .o

lllfEX32 BASE 709.62 689.b8 71'+.l q 653.80 73'i.91 803.57 842.06 872.7? 904.79 9?7.53 COMP 709.62 68 ... 68 714.lq 653.80 739.91 803.57 842.06 872.72 904.7'i 927.53 Dlff .oo .oo .o o .uo .oo .oo .oo .oo .oo .oo PCNT .o .. o .o .o .o .o .o .o .o .o

lllfEX32 BASE 953.62 980. 37 1ooe.oo 1036.JO COMP 953.62 qa o.37 1008.00 1036.JO Dlff .oo .oo .oo • llO PCNT .o .o .o .o

BASE MEAN= 845044 PCl'-il DIFF MEANS= .o A'I ABS l) IF'F= .oo D IFF PCNT OF" BASE MEAN 2 .o

■EX33 BASE 40.39 42.00 39.50 37.33 41.24 45.65 48oil 49.42 50.83 51.83 COMP 40.39 42.00 39.50 37.33 41.24 45.65 48oll 49.42 so.aJ 51.83 DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o WEX3J BASE 52.83 53.84 54.87 55.'12

COMP 52.83 53.84 54.87 ss.92 DIFf .oo .uo .oo .uo PCNT .o .o .o .o

l:IA5E MEAN= 47.41 PCNT OifF Mt.ANS= .o A'I ABS OIF"f= .oo DIFF" PCNT OF" BASE MEANs .o I .... "' 0

WEX34 l:IASE 45.00 45.00 4~.oo 45.00 45.00 45.00 45.oo 45.oo 45.00 45.00 I

COMP .r.s.oo 45.00 45.oo 45.UO 4S.oo 45.00 45.oo 45.00 45.00 45.00 DI ff .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .u .o

WEX.34 BASE 45.00 45.00 45.oo <+5.0o COMP 45.00 45.00 .. s.oo 45.UO Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN:: 45.00 PCIIIT DifF MEANS= .o AV ABS Dlff:: .oo Olff PCNT Of BASE MEANz .o

WEX.35 BASE 75.11 86.'+0 88 .59 78.81 82.92 86.95 89oA6 92.85 96.38 99.51 COMP 75.11 86.<+0 8do59 78.tH s2.92 86.95 89066 92.85 96.38 99.51 Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX35 BASE 102.25 105.06 107.95 110.'12 COMP 102.25 loS.06 I 07 o'i5 110. <,2 Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN:: 93.10 PCNT DJff Mt:.•NS = . o AV ABS DIFF= .oo Dlff PC 1'1 T OF BASE MEAN= .o

111EX36 BAS E 35.30 46.00 4t!o 08 40.'+8 56.93 64.71 68. 27 71.87 76.70 79.94

COMP 35.30 46.00 <+d.08 40.'+8 50.93 t,4 • 71 68. 27 71.87 76.70 79.94

Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

wEX36 BASE BJ.SI 8 7 • 18 90. 95 94 . t! J COMP 83.51 87.18 90 . 'i5 94.83 Dlff .oo .oo .oo .oo PCN T .o .o .o .o

BASE MEAN= t, 7 ... 8 PCNT D!f f Ml:ANS= . n AV ABS l) Jff:: .oo n lfF PC NT Of AASE MEAN= .o

VARIABLE NAME 1972 1973 19 74 197':> 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

iiE.X 3 7 BASE 9 9 .74 127.95 125.29 l 08 • '0 137.43 153.98 164.44 175.84 188.28 197.81 COMP 9 9 .74 127 • 95 12 5 .29 10 8 . 9 7 137.43 15 3 .98 164 . 44 175.84 l8A.28 19 7.81 0lfF . oo .uo .oo .oo .oo .oo .no .oo .oo .oo PCNT .o .o .o .o • 0 .o .o .o .o .o

iiE.X37 BASE 20 8 .27 219.15 230.4 8 l42. c 5 COMP 208.27 c19.l5 230.48 l 42.c5 0IH .oo .oo .oo . o o PCNT .o .o .o .o

BASE MEAN:: 169.99 PCNT 0!Ff Mt. ANS= . o AV ABS DIFF= .oo 0lff PC NT Of BASE MEAN= .o

iiEX38 !:!ASE 90.95 96.90 96,46 91.35 101.09 109,66 ll5.<;8 120.47 125.38 129.26 COMP 90.95 96.90 96.46 9 1. JS 101.09 109.66 ll5.58 120.47 125.38 129.26 DIH .oo .oo .oo .oo .oo .oo ,00 .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

iiEX38 BASE 133.33 137.54 141.87 146.34 COMP 133.33 137.54 141.87 146.34 0IH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 116.87 PCNT DI FF Mt.ANS= •O AV ABS I.JIFF= •00 0IFF PCNT OF BASE MEAN= .o

iiEX39 BASE 2221,00 2466.fO 2788,50 2347.(0 2570.00 2'=>67.70 26b6.90 2686.40 2762.70 2844.00 COMP 2221.00 2466.70 278!!.50 2347.70 2426.90 2590.60 2912.70 3249.10 348b.70 3718.20 DIH .oo .oo .oo .oo -143,10 22.90 245.80 562.70 724.00 874.20

PCNT ,0 .o .o .o -5.6 .9 9.2 20.9 26.2 30.7

iiEX39 BASE 2927.00 3012.JO 3100,20 3190.~0 COMP 3834.10 3952.20 4074.90 4199.'=>0 DlfF 907.10 939.90 974.70 100~.oo PCNT 3 l .-0 31.2 31,4 31•6 I

!:!ASE MEAN= 2725.54 PCNT 0JFF Mt:ANS= 16.0 AV ABS DlfF= 457,39 0lFf PCNT OF BASE MEAN= 16.8 .... '° .... I

liEX40 BASE 245.50 261.27 264.28 ~68.91 301.21 332.20 34b.Fll 365.82 383.70 396.35

COMP 245.50 261.27 264.28 c:'.68. 'ill 301.21 332.20 346.Fll 365.8;> 383.70 396.35

0IH .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

iiEX40 BASE 412.83 429.41 446.09 462.1:18 COMP 412.83 429.41 446.09 '+62.tla 0IFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 351.6b PCNl OJFF ME.ANS= •O AV ABS DlfF= •00 0lfF POJT OF BASE MEAN= .o

11EX'+l BASE 407.25 414 .46 422.30 4U:1,38 434.49 '+03.22 423.68 4]0.04 439.37 4 56,97

COMP 407.25 4)4.46 422.30 418.38 434.49 '+03.22 423.~8 430.04 439.37 456.97

DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

iiEX41 BASE 466.61 '+76.26 485.90 495.66 COMP 466.61 476.26 485.90 495.66 DIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 44l.U4 PCNT DIFF i'!l:.ANS= .o Av ABS lilFF= .oo 0lFF PCNT OF BASE MEAN= .o

iiEX42 BASE 130.69 158.70 156.06 14 7 • '=>5 171 .04 200.46 218.74 2 39.6fi 257.13 273.39

COMP 130.69 158 .70 156.06 147.55 171.04 200.46 218.74 2 39.M, 257.13 273.39

0IH .oo .oo ,00 .oo ,00 .oo ,00 .oo .oo .oo

PCNT .o .o .o .o • 0 .o .o .o .o .o

iiEX'+2 BASE 288.57 304.28 320.56 J37.'+1 COMP 288.57 304.28 )20.56 J37.'+1 DIFF . oo .oo .oo .vo PCNT . o .a • 0 ,O

BASE MEAN = 228 , d 7 PC NT 0!FF Mt. AN S= . o AV Al:IS DIFF= .oo 0lFF" PCNT OF" BASE MEAN:z .o

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198!:>

wEX43 BASI:. 703.46 781.<+9 760.29 fl<+.09 784.64 881.-01 936.q9 990.91'. 1·049.20 1087.10 COMP 703.46 78 l .49 H,0.29 714.09 784.64 l:HH.0l 936 .. 9<;1 990.W, 1049.20 lOR-7.10 Olff .oo .oo .oo .oo .oo .oo .-0-0 .oo .oo .oo PCNf .o .o .o .o .o .o .o .o .o .o

IIIIEX43 BASE 1131 .60 1178.90 1220.10 1281 .20 COMP 1131.60 1178.90 1220.10 1291.20 OIFF .oo .oo .oo .oo 1-'CNT .o .o .o .o

BASE MEAN= 964.97 PCNT OIFF MEANS= .o Al/ ABS DIFF= .oo oIFr: PCNT OF BASE MEAN= .o

IIIIEX44 BASE 42.10 42.10 42.10 42.10 42.10 42.10 42.10 42.10 42.10 42.10 COMP 42.10 42.10 42.10 42.10 42.10 42 .10 42.10 42.10 42. l O 42.10 DIH .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

1111EX44 BASE 42.10 42.10 42 .10 42.10 COMP 42.10 42.10 42 .10 42.10 OIFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 42.10 PCNT OIFr: MEANS= •O Al/ ABS DIFF= .oo OlFF PCNT OF BASE MEAN• .o

IIIIEX45 BASE 1.20 1.20 1.20 1.20 1.20 1.20 1 • 20 1.20 l .20 1.20 COMP 1.20 1.20 1.20 1.20 1.20 1.20 l • 20 1.20 1.20 1.20

OIH .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o wEX45 BASE 1.20 1.20 1.20 1.20

COMP 1.20 1.20 1.20 1.20 OIH .oo .oo .oo .oo PCNT .o .o .o .o I

BASE MEAN= 1.20 PCNT OIFF MEANS= .o Av ABS OIFF= .oo OlFF PCNT OF BASE MEAN= .o ... '° "' I

IIIIEX46 BASE 2.00 2.00 2.00 2.uo 2.00 2.00 2.00 2.00 2.00 2.00

COMP 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 OIFF .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

wEX46 BASE 2.00 2.00 2.00 2.uo COMI-' 2.00 2.00 2.00 2.00 OIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 2.00 PCNT OIFF Mt.ANS= .o Al/ ABS OIFF= .oo OIFF PCNT OF BASE MEAN:1 .o

IIIIEJl.47 BASE 30.62 31.13 32.57 32.23 34.61 36.86 38. 71 40.77 42.90 44.76

COMP 30.62 31.13 32.57 32.23 34.61 36.86 38.71 40.77 42.90 44.76

DIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

wEX47 BASE 46.59 48.49 50.47 52.!:>3 COMP 46.59 48.<+9 50.47 52.53 OIH .oo .oo .oo .uo PCNT .o .o .o .o

bASE MEAN= 40.23 PCNT O[FF Mt.ANS= .o Al/ AHS DIFF= .oo DIFF PCNT OF AASE MEAN= .o

wl:.X'+8 HASE. .oo .uo .oo .uo .oo .oo .oo .oo .oo .oo

COMP .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

DIH .oo . oo .oo .1.10 .oo .oo .no .oo .ou .oo

PCNT .o .o .o .o .o .o .o .n .o .o

wEX48 BASE. .oo .oo .oo .oo COMP ,00 .oo .oo .uo DIH .oo .uo ,00 . oo PCNT .o .o .o .o

BASE ME.AN= .oo PCNT OIFF Mt.AN5= .o Al/ ABS UIFF= .oo DIFF PCNT OF ~ASE. MEAN= .o

IIAHIABLE NAME 1972 1973 1~74 1975 1976 1977 1978 J979 1980 1981 1982 }983 1984 1985

wEX49 BASE 22.40 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 ?0.00 COMP 22.40 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 OIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

wEX49 BASE 20.00 20.00 20.00 20.00 COMP 20.00 20.00 20.00 20.00 DIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 20.11 PCNT OJFF MEANS= .o AV ABS OIFF= .oo OIFF PCNT OF BASE MEAN= .o

wEX50 BASE .oo .uo .oo .uo .oo .oo .oo .oo .oo .oo COMP .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo DIH .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEX50 BASE .oo .oo .oo .uo COMP .oo .oo .oo .uo OIFF .oo .uo .oo .oo PCNT .o .o .o .o

BASE MEAN= .oo PCNT OJFF MEANS= .o Al/ ABS DIFF= .oo Olff PCNT OF BASE MEAN= .o

wEX51 BASE. 250.30 260.00 260.00 260.UO 260.00 260.00 260.oo 260.00 260.00 260.00 COMP 250.30 260.00 260.00 260.00 260.00 200.00 260.00 260.00 260.00 260.00 Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WEXSl BASE 260.00 260.00 260.00 260.UO COMP 260.00 260.00 260.00 l60 0 UO OIFF .oo .oo .oo .oo PCNT •• o .o .o .o I

BASE MEAN• 259.31 PCNT OJFF Mt.ANS= .o All ABS DIFF= .oo OIFF PCNT Of BASE MEAN" .o .... "' ..,

I

wEX52 BASE .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

COMP .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

Olff .oo .oo .oo .oo .oo .oo .no .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

wEX52 BASE .oo .oo .oo .oo COMP .oo .oo .oo .oo Olff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= .oo PCNT OJff Mt.ANS= .o All ABS Olff= .oo DIFf PCNT Of BASE MEAN= .o

wEX!:>3 BASE 1068.90 1142.40 1121.so 1120 • .:io 1231.10 1335.60 1412.20 14~8.60 1568.80 1634.50

COMP 1068.90 1142.40 1121.50 1120 • .:io 1237.10 1335.60 }412.;>0 14A8.60 1568.80 1634.50

Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

wEX53 BASE 1102.20 1112.10 1844.JO 1918.70 COMP 1102.20 1112.10 1844.30 1~18.70 Olff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 1455.23 PCNT Diff Mt.ANS= .o AV ABS Dlff= .oo Olff PCNT Of BASE MEAN= .o

liEX54 BASE 379.28 386.75 374.55 362 0 b5 378.46 395.50 408.45 422.09 437.47 450.63

COMP 379.28 386.75 374.55 J62.85 378.46 395.50 408.45 422.09 437.47 450.63

Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

1-'CNT .o .o .o .o .o .o .o .o .o .o

wEX54 BASE 463.28 476.28 489.64 503.38 COMP 463.28 476.28 489.64 !:>03.38 Olff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 423.47 PCNT OJFF Mt.ANS= .o AV ABS Dlff= .oo !'1 lfF PCNT Of BASE MEAN= .o

V.ARIABLE NAME 1972 )973 1~74 197':> J'H6 1977 1'178 1~19 1980 J-981 1982 1983 1984 198':>

w0.55 BASE 127.54 132.20 132.00 131.tlJ 135.33 138.92 142.22 _}45.-45 148.69 -151 • 48 COMP 127.54 :132.20 132.00 I 31 • tl3 135.JJ 138.92 142.22 145045 148.69 151 .48 DI-Ff .oo .oo .oo .-00 -.oo - •. oo .J>O .oo • .oo .o.o PGNT .o .o .o .. o -.o .• o .11 .o .-o .o

w.EX515 'BASE l 54 .18 156. 96 159.-82 162.76 COMP l 54 .18 156.96 159.82 l-62.16 Dlrf .oo .oo .oo .oo PCNT .o .o .o .o

BASE HEAN: 144024 PCNT DifF ME.ANS= .o AV ABS DIFf= .oo Oiff PCNT Of BASE MEAN= .o

liEXTOT BASE. 11821.00 12421.00 12670.00 11778.00 12785.00 13507.00 14069.00 l44AB.OO 14986.00 154?0.00 COMP 11021.00 12421.00 12670.00 1111&.oo 12042.oo 13530.00 14315.oo 15051.00 15710.00 16294.00

Dlff .oo .oo .oo .oo -143000 23.00 24bo0-0 5f>J.oo 724.00 874.00 PCNT .o .o .o .o -1.1 .2 1.7 3.9 408 5.7

liEXTOT BASE 15843.00 10J10.oo 16777 .oo l U60.00 COMP 16750.00 l 7250.00 l 7752.00 18269.00 DIH 907.00 940.00 91s.oo 1009.00 PCNT 5.1 508 5.8 5•8

BASE HEAN= 14295.36 PCNT OJFF Mt::ANS= Joi AV ABS Olff: 457.43 Olff PCNT OF BASE HEAN= 3.2

-.SLEOUPC5-20 BASE 1.16 · 1.15 1.19 1.26 1.16 1.1s 1.28 lo3? I. 35 lo4l

COMP l .16 l • l 5 I• I 9 l • 26 1.16 1.15 1.25 1.29 1.32 1.39

Olff .oo .oo .oo .oo .oo -.01 -.oJ -.03 -.03 -.02

PCNT .o .o .o .o .4 -.1 -2.1 -2.6 -2.J -1.6 liSLt::OUPC5-20 BASE 1.47 1.so I .52 1.56

COMP l .46 1.49 1.52 1.so Olff -.01 -.oo -.oo .oo PCNT -.e -.J -.1 • I I

BASE MEAN= l • :!2 PCNT OIH MEANS= - • 1 AV AHS DIH= .01 OifF PCNT OF BASE MEAN= .8 .... '° .,. I

liSLEOU BASE 1190.10 llA4.50 1208.90 1273.':>0 1229.10 1228.50 tJ42.oo 1368000 1385.20 1420.90 COMP 1190.10 11A4o50 1208.90 1273.50 1231.60 1222.80 1328.JO 1361.70 1396.30 1454.80

Olff .oo .oo .oo .oo 2.50 -5.10 -13.90 -o.JO 1 l. l 0 33.90 PCNT .o .o .o .o .2 -.5 - I .o -.5 .8 2.4

liSLt::OU BASE 1465.60 1476.50 1492.Jo 1526.40 COMP 1520.40 1546.40 1570.00 1011.uo DIFF 54.80 f,9.90 11.10 84.60 PCNT 3.1 4.7 5.2 5.5

BASE MEAN= 1342.25 PCNT OIFF Mt::ANS= 1•6 AV ABS DlrF= 25074 DlfF PCNT OF RASE MEAN= 1.9

liSLOTH BASE 1191.30 1236.30 1167.60 1163.10 1404.60 1210.so 1205.00 1458.20 1536.60 1572.00

COMP l 191 0 30 1230.30 1167.60 1163.10 1402.70 1281 .so 1201.10 14?6.00 1505.10 1566.20

OIFF .oo .oo .oo .oo -1.90 11.00 -4 • l O -32.20 -JI.SO -s.eo PCNT .o .o .o .o -.1 .9 -.3 -2.? -2.0 -.4

liSLOTH BASE 1676.70 1806.50 1852.80 lt:192.20 COMP 1101.00 1&12.10 1951 .20 2001.20 Olff 31.10 66.20 98.40 115.00 PCNT lo9 3.7 5.3 601

BASE MEAN= 1459.bl PCNT OIH Mt.ANS= 1•2 AV ABS DIH= 28•37 OlFF PCNT OF BASE MEAN= J.9

liSLOP BASE 2381.40 2420.!:IO 2376.50 2'+36.60 2633.70 2'+99.30 2547.80 2826020 2921.80 2993.00

COMP 2381.40 2420.tlO 2376.50 2<+36.bO 2634.30 2504.70 2529.AO 27A7.70 2901.50 3020.90

DIH .oo .oo .oo .oo .60 5 ... o -18.00 -1&.50 •20.30 27.90

PCNT .o .o .o .o .o .2 -.7 -1 .4 -.7 .9

liSLUP BASE 3142.30 3283.10 3345.10 34lts.b0 COMP 3228.20 3419.10 3521.20 JblB.20 Olff 85.90 136. 00 176.JO l 99.o bO PCNT 2.1 4. l 5.3 s.0

BASE MEAN= 280l•d7 PCNT O!FF MEANS= 1•4 AV ABS DIFF= 50•59 DlFF PCNT OF BASE MEAN= 1.8

VARIABLE NAME 1972 1973 l '174 1"75 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WSLEOUBLO BASE 130.46 94.08 162.21 i39.16 162.59 256.83 188.64 169.91 186.17 194.84 COMP 130.46 94.08 162.21 139.16 161.87 253.89 199.c;l 189.67 212.55 220.69 DIFf .oo .oo .oo .oo -.12 -2.94 10.01 19.u, 26.38 25.85 PCNT .o .o .o .o -.4 -1.1 5.8 11.ti 14.2 13.3

WSLlOUBLO BASE 190.58 204.17 214086 229.85 COMP 216.25 226.t:>2 234095 249.J2 Dlff 25.67 22.45 20.09 19.47 PCNT 13.5 11.0 9.4 8•5

BASE HEAN: 180.Jl PCNT Diff MEANS= 6.6 AV ABS Olf"f: 12.44 DIFF PCNT Of BASE MEAN: 6.9

IIISLOTHBLO BASE 119.ll l ll .84 103.30 J.32.34 122.01 92.20 130.24 13B.70 140.52 150.43 COMP 119.11 111.84 103.30 J.32.34 123000 91.49 122.g3 130.15 133.t>O 148.51 Dlff .oo .oo oOO .oo .93 -072 -7.31 -8.55 -6092 -loQ2 PCNT oO .o .o .o .0 -.8 -5.6 -6.2 -4.9 -1.3

WSLOTHBLO BASE 168 0 01 172.79 174037 182.14 COMP 170.24 179.73 183.44 192.78 Olff 2.23 6.94 9o07 10.f:>4 PCNT 1.3 4.0 5.2 s.8

BASE MEAN= 138.43 PCNT Diff MEANS= •2 AV ABS Dlff= 3o94 DifF PCNT Of BASE MEAN= 2.8

WSLHIWAY BASE 199.03 200.79 197.79 200.J4 245.98 292.06 256014 259.13 272025 268.36 COMP 199.0J 200.19 197.79 200.34 243053 294089 271005 280.99 295.90 289.60 OJH .oo .oo • oo. .oo -2.45 2.83 14.91 21086 23.65 21.24

PCNT .o .o. .o .o -1 .o 1.0 5.B 8.4 8.7 7.9

WSLHIWAY BASE 254.96 260.50 269020 269020 COMP 272096 274.38 281083 281005 OIH 18000 13.88 12.63 11 .t1s PCNT 7•. l 5.3 4o7 4.4 '

BASE MEAN= 246.12 PCNT OJ.ff MEANS= 4o0 AV ABS DIFF= 10.24 DifF PCNT OF BASE MEAN• 4.2 .... "' VI

' IIISLNONBLD BASE 159.10 154.40 151.00 159070 162040 165 .10 167.90 110.80 173. 70 176.70

COMP 159010 154.40 157.oo 159.70 162.40 165.10 167.90 170.AO 173. 70 176.70

OIH .oo oOO .oo .oo oOO .oo .oo .oo .oo oOO

PCNT .o .o oO .o oO .o oO .o oO oO

WSLNONBLO BASE 179.70 102.10 185.80 189.00 COMP 179070 1112.10 185.80 l.89.00 DIH .oo oOO .oo .oo PCNT .o .o .o oO

BASE MEAN= 170.29 PCNT OJFF MEANS= •O AV ABS DIFF= .oo OifF PCNT OF BASE MEAN=- .o

WSLCON BASE 607 o 71 561. 11 620031 f:>31.54 693003 806019 142.q2 738.53 772.64 790.33

COMP 6070 71 561.ll 620.31 f:>31.54 6 90.79 805037 761040 17106! 815.75 835.50

OIH oOO .oo .oo oOO -2.24 -.82 18048 33008 43oll 45ol7

PCNT .o .o oO oO -.3 -ol 20s 4 0 c; 506 5o7

WSLCON BASE 793o25 820.J.7 844.23 870019 COMP 839.16 863.43' 886001 912. 15 OJFF 45.91 43.26 41 o 78 41 o 'il6 PCNT 508 5o3 4o9 408

BASE HEAN= 735 • l 5 PCNT Oiff MEANS= 3oO AV ABS Dlff= 22•56 OIFF PCNT OF BASE MEAN= 3.1

WSLTOT BASE 2989010 2981.90 2996.80 3068.20 1326.80 3305050 3290.70 3564080 3694.40 3783.30

COMP 2989.10 2981.90 2996.80 3068.20 3325.10 JJl0.10 3291.;>0 3559030 3717.20 3856.40

OIH .oo oOO .oo oOO -l.70 4.60 .so -5.50 22.00 73.10

PCNT .o .o oO .o -.1 • l .o -o2 06 lo9

WSLTOT BASE 3935.60 4103.20 4189.40 4288.70 COMP 4067040 4202.50 4407.20 4530oJO DIH 131080 179.30 217080 241.bO PCNT 3.3 4o4 5.2 506

BASE MEAN= 3537.03 PCNT DtfF MEANS= 1 o 7 AV ABS OIFF= 62076 nifF PCNT OF BASE MEAN= 1.8

VARIABLE NAME 1972 -!973 1974 1975 i976 -1977 1978 1979 1980 1981 1982 1983 1984 .198~

WSLEOUTOT BASE 1320.50 1278.60 1371.10 1412.70 1391.70 1485.30 1530.60 1537.90 1571.40 1615.80

COMP 1320.50 1278.60 1371.10 1412.70 1393.50 1476.70 1527.60 1551.40 1608.90 1675.50

Olff .oo .oo .oo .uo 1.80 -8.60 -3.oo 13050 37.'50 59.70 PCNT .o .o .o .o .1 -.6 -.2 .9 2o4 3.7

WSLEOUTOT BASE 1656.20 1680.70 1107.20 1756.20 COMP 173-t>.60 1113.00 1804.90 1860.30 Olff .80.40 92.30 97.70 104.10 PCNT 4o9 5.5 5.7 5.9

BASE MEAN= 1522.56 PCNT Oiff Mt.ANS= 2.2 AV ABS Dlff= 35.61 Dlff PCNT Of BASE MEAN= 2.3

WSLOHHOT BASE 1668.60 1703.30 1625.70 lb55.50 1935.00 1820.20 1760.10 2026.80 2123.10 2167.50

COMP 1668.60 1703.30 1625.70 1055.~o 1931.60 1833.30 1763.SO 2007.90 2108.30 2181.00 Dlff .oo .oo .oo .oo -3.40 13.10 3.40 -18.90 -14.80 13.50 PCNT .o .o .o .o -.2 .1 .2 -.9 -.1 .6

liiSLOTHTOT BASE 2279.40 2422.50 2482.20 2s32.so COMP 2330.80 2509.50 2602.30 2010.00 Dlff 51.40 87.00 120.10 137.50 PCNT 2.3 3.6 4.8 5.4

BASE MEAN= 2014.46 PCNT DifF MEANS= 1.4 AV ABS OlfF= 33.oa Dlff PCNT Of BASE MEAN= 1.6

WSRES BASE 9329.50 9904.60 10308.00 1os50.uo 10000.00 11371.00 12150.00 12900.00 135116.00 143;>2.00

COMP 9329.50 9904.60 10308.00 1os50.oo 10808.00 11371.00 12121.00 12923.00 13733.00 14666.00

Dlff .oo .oo .oo .oo .oo .oo -29.oo ?3.00 147.00 344.00

PCNT .o .o .o .o .o .o -.2 .2 l • 1 2.4

WSRES BASE 15129.00 15836.00 16483.00 17167.00 COMP 15678.00 16585.00 17389.oo 18198.00 Dlff 549.00 749.00 906.00 1031.00 PCNT 3.6 4.7 5.5 6 • 0 I

BASE MEAN= 12846.01 PCNT DIFF ME.ANS= 2-1 AV ABS Olff= ;>69.86 Dlff PCNT OF BASE MEAN= 2.1 .... .,, "' I

WIRES BASE 761.66 601.59 448.35 468.26 779.48 1006.20 993. 71 943.40 1001.10 1093.90

COMP 761. 66 601.59 448.35 '+68.26 779.48 <;77 .as 1044.00 1068.90 1207.80 1304.60

Olff .oo .oo .oo .oo .oo -28.32 50.29 125.50 200.10 210.10

PCNT .o .o .o • 0 .o -2.s s.1 13.3 19.9 19.3

WIRES BASE 1009.50 963.23 1014.20 1034.UO COMP 1220.30 1136.00 ll5b.60 1154.SO DlfF 210.80 172.77 142.40 120.so PCNT 20.9 17.9 14.0 11 • 7

BASE MEAN= 866.08 PCNT DIFF ME.ANS= 9.9 AV ABS DIFf= 90.10 DIFF PCNT Of BASE MEAN= 10.4

WSOTHSTR BASE 7370.30 7868.70 8420.40 8950.00 9399.60 10015.00 10661.oo 11219.oo 11873.00 124111.00

COMP 7370.30 7868.70 8420.40 8950.00 9399.60 10005.00 106sa.oo )1310•00 11970.00 12655.00

Olff .oo .oo .oo .oo .oo -10.00 -3.oo 31.00 97.00 174.00

PCNT .o .o .o .o .o -.1 -.o • 3 .8 l .4

liiSOTHSTR BASE 13068.00 136;>1.00 14162.oo 14702.uo COMf> 13321.00 13943.00 14540.00 15121.00 DIFF 253.00 322.00 310.00 425.00 PCNT 1.9 2.4 2.7 2.9

BASE MEAN= 10990.79 PCNT DifF ME.ANS= l • l AV ABS DIFF= 120.93 OIFF PCNT Of BASE MEAN= l • 1

WXMfG BASE. 10973.00 11828.00 12122.00 10957.00 12642.00 13265.00 13021.00 14lflleOO 14746.00 15160.00

COMP 10973.00 11828.00 12122.00 10957.UO 12446•00 13347.00 14185.1)0 14964000 15700.00 16307.00

Olff .oo .oo .oo .uo -196.00 82.00 364.00 783.00 954.00 1147.00

PCNT .o .o .o .o -1.6 .6 2.6 5.5 6.5 7.6

WXMfG bASE 15soc.oo 15944.00 16400.00 16tl48.00 COMP 16660.00 17-141.00 17629.00 18113.00 DIFF 1158.00 1197.00 1229.oo 1~65.UO PCNT 7.5 7.5 7.5 7.5

BASE MEAN= 13884 • 93 PCNT DifF Mt.ANS= 4 • l AV ABS OIFf= <;98.21 DJFF PCNT Of -SASE MEAN= 4.3

VARIABLE NAME. 1972 l 973 1974 1975 1976 1977 1978 1979 1980 1981

1982 1983 1984 198~

WXNONMfG BASE 15024000 160)6000 16434 00 0 16493000 1860 4000 19735000 20250000 ?.0820000 21732000 22334000

COMP 15024000 16036000 16434000 16493oUO 18536000 19773000 20576000 214 1H oOO 22657000 23421000

DIH oOO oOO .oo oOO -68000 38000 326000 67lo00 925000 1087000

PCNT oO .o .o .o -o4 .2 lo6 3.2 4o3 4o9

WXNONMfG BASE 22700000 23264000 23948000 24558000

COMP 23845000 24410.00 25095000 25715000

DIH 1145000 1146000 ll47ooo 1157000

PCNT 5o0 4o9 4 .8 4o7 BASE ME.AN= 20138000 PCNT DIFF M£ANS= 2.1 AV ABS DIFF= s50o7l OlfF PCNT Of BASE MEAN= 2o7

IIIXWTl BASE 14052000 15026.00 15399000 15165000 11113000 18182000 18707ooo 192?7000 20056000 206J3.00

COMP 14052000 15026000 15399000 15165000 17074000 18230000 19042000 19924000 20987.00 21714000

OIH oOO oOO oOO oOO -99000 48000 335000 697000 931.00 1101.00

PCNT oO .o .o .o -o6 o3 1.e lo6 406 5o3

IIIXWTl BASE. 20973.00 21501000 22136000 22708oUO

COMP 22120.00 22666000 23304.00 23890oll0

Dlff 1147000 1159000 1168.00 1182000

PCNT 5o5 5.4 5o3 5.2

BASE ME.AN= 18637043 PCNT DtFF ME.ANS= 2o9 AV ABS DIFf= 56lo93 OIFF PCNT OF BASE MEANa: 3o0

WIOTHSTR BASE 645.82 709005 698.08 621:1.56 ao3.12 1:146033 831005 820024 845oll 836051

COMP 645.82 709005 698.08 628o56 793000 853004 866004 8A6o2l 923074 9!9.18

Dlff oOO oOO oOO oOO -l0ol2 6071 34099 1'>5o97 78063 82067

PCNT oO oO .o oO -1.3 oB 4o2 Boo 9o3 9.9

WIOTHSTR BASE 814065 813023 823067 824093

COMP 888.29 875099 877052 872027

DIH 73.64 62076 53.85 47.34

PCNT 9.0 1.1 6.5 5.7 I

BASE MEAN= 781045 PCNT DJFF MEANS= 4o5 AV ABS Dlff= 36•91 Dlff PCNT Of BASE MEAN= 4.7 ...... "' .....

I

WJOTHBLD BASE 322091 354053 349.04 Jl4 • .:8 401.56 423. lb 415052 410.12 422.55 4!8.25

COMP 322091 354.53 349004 .314028 396.SO 426.52 433.02 443.11 461.87 459.59

Dlff .oo .oo oOO .oo -5.06 3o36 11 oc;o 32099 39.32 41.34

PCNT oO oO .o oO -lo3 08 4.2 800 9.3 9o9

IIIIOTHBLO BASE 407.33 406061 411.83 412046

COMP 444.15 437099 438. 1f:, 436.13

Dlff 36082 31 • .38 2t>o93 23067

PCNT 9o0 7o7 bo5 5.7

BASE MEAN= 390072 PCNT DJFF MEANS= 4o5 AV ABS DIFF= 18•46 DIFF PCNT Of BASE ME.AN= 4.7

WINONBLO BASE 322.91 354.53 349.04 314.28 401.56 423.16 415.52 410.12 422055 418.25

COMP 322.91 354.53 349.04 Jl4.i8 396.50 426052 433.02 443oll 461 .87 459059

Dlff .oo .oo .oo .oo -5.06 3.36 11.50 32.99 39.32 41.34

PCNT .o oO .o oO -1.3 .0 4.2 8.0 9.3 9.9

WINONBLD BASE 407.33 406.61 4llo83 4l2•4t,

COMP 444.15 437.99 438.76 436.13

Dlff 36.82 31.38 26.93 23.67

PCNT 9.0 1.1 6.5 5.7

BASE MEAN= 390.12 PCNT OJff Mt.ANS= 4.5 AV ABS DIFF= 18•46 Olff PCNT Of BASE MEAN= 4.7

WXWT2 BASE 13850.00 14815000 15184.00 14888000 16875.00 17859.00 18385.00 18895.00 19706.00 20254.00

COMP 13850.00 14815.00 15184.00 14888.00 16770.00 17909.00 18723.00 !9598.00 20639.00 21358.00

Olff .oo .oo .oo .oo -105.oo 50.00 338.00 703-00 933.00 1104000

PCNT .o .o .o .o -.6 .3 1.0 3.7 4.7 s.5

WXWT2 BASE 20613.00 21141.00 21759000 22322.00

COMP 21761.00 22302.00 22930.00 23510.00

Dlff 1148.00 1161.00 1111.00 1188.00

PCNT 5.6 5.5 5.4 5.3

BASE MEAN= 18324.71 PCNT DJ f f MEANS= 3.0 AV AHS UIFF= 564036 Olff PCNT Of BASE MEAN= 3. l

VARIABLE NAME 1972 1973 1974 1975 1976 1971 1978 1979 1980 1981 1982 1983 1984 1985

WlEQP BASE 1163.30 1267.60 1307.30 lc75 0 40 1490.00 1596.20 1653.10 1708.20 1795.80 1854.90 COMP 1163.30 1267.60 1307.30 1275.40 1478.60 1601.70 1689.60 1784.10 1896050 1974.20 OIFf oOO .oo oOO .oo -ll.40 5.50 36.SO 75.90 100070 119.30 PCNT .o .o .o oO -o8 03 2o2 4.4 5.6 6.4

WIEQP BASE 1893.70 1950.70 2017040 2078030 COMP 2011.10 2076.10 2144000 2c06o60 OIFf 124.00 125.40 126.60 128.JO PCNT 605 604 6.3 6•2

BASE MEAN= 1646.56 PCNT OtfF Mt.ANS= 306 AV ABS OlfF= 60.97 OIFF PCNT OF BASE MEANz: 3o7

WlflXTOT BASE 2570.70 2578.20 2453070 2372.20 3072.60 3448.80 3477.90 3471.80 3648.50 3785.30 COMP 2570.70 2578.20 2453.70 2372020 3051010 3432060 3599.60 3739010 4028.10 4198.00 OlFf .oo oOO .oo .oo -21.so -16020 121.10 267.30 379.60 412070 PCNT oO .o .o .o -.1 -.5 3o5 1.1 l0o4 l0o9

IIIFIXTOT BASE 3717 .so 3727020 3855.30 3937.30 COMP 4126.30 4088010 4178.10 4233040 OIFF 408050 360.90 322.00 296010 PCNT lloO 9o7 8.4 7o5

BASE MEAN= 3294009 PCNT DifF ME.ANS= 5.5 AV ABS DIFF= )86024 OIFF PCNT OF BASE MEAN= 507

lillNVOl BASE 3.00 12.12 -4.99 -2.06 18.21 -S.36 7.49 -.14 4.68 .84 COMP 3.oo 12.12 -4.99 -2006 18.01 -4061 7o46 054 4.68 1038

DIFf .oo .oo oOO .oo -.20 .74 -.03 069 oOO 054

PCNT .o .o .o .o -1.1 -13.9 -o4 -47502 oO 64o7 lillNVOl BASE 3o53 2o41 2086 2o61

COMP J.34 2o72 2.02 2.13 OIFf -.18 .30 -.04 o 13 PCNT -502 12.5 -1 .s 4e8

BASE MEAN= 3o27 PCNT DIFF Mt.ANS= 4o2 AV ABS OIFF= 020 OIFF PCNT OF BASE MEAN= 6.2 I .... .,, <»

111IJNV02 BASE .oo oOO .oo .oo .oo oOO oOO .oo .oo .oo I

COMP oOO .oo oOO oOO .oo .oo oOO .oo .oo oOO

OIFf .oo oOO .oo .oo .oo .oo .oo oOO .oo .oo

PCNT oO .o oO .o oO .o .o oO .o .o

WIINV02 BASE .oo .oo oOO oOO COMP .oo .oo .oo .oo OIFf .oo oOO oOO oOO PCNT oO .o .o oO

BASE MEAN= .oo PCNT OifF Mt.ANS= oO AV ABS DIFF= .oo OIFF PCNT OF BASE MEAN= oO

IIINNV03 BASE .so 7o43 2.40 1.,n 10.09 -4002 1.92 leOR 5.64 -.04 COMP .00 7o43 2.40 1.21 is.so -2ol9 0.79 3ol0 6039 lo37 DIFf .oo .oo .oo .oo -060 1.83 .86 2.03 .75 1.40 PCNT .o .o .o .o -307 -45.6 10.9 )87o9 13.3 -360206

WINNV03 BASE 2.10 2.42 2.20 1.1:14 COMP 2.79 3o0S 2.45 2.13 OIFF .09 063 .11 oi:'9 PCNT 3.2 25•9 7.6 15.7

BASE MEAN= 3.'+2 PCNT DtfF Mt.ANS= 1506 AV ABS OIFf= •62 DIFF PCNT Of BASE MEANs 18. l

1illNV04 BASE .oo oOO .oo .uo .oo .oo .oo .oo .oo .oo

COMP oOO .oo .oo .uo .oo .oo .oo .oo oOO .oo

DIFF .oo .oo .oo .oo oOO .oo .oo .oo .oo .oo

PCNT .o .o .o .o oO oO oO .o oO .o

IIIIJNV04 BASE oOO .oo .oo .oo COMP .oo .oo .oo .oo Olff .oo oOO .oo .oo PCNT oO .o .o .o

BASE MEAN= oOO PCNT Diff MEANS= oO AV ABS UIFF= oOO OIFF PCNT OF BASE MEAN= oO

VARIABLE NAME 1972 1973 1974 1975 1976 1977 197A 1979 1980 1981

1982 1983 1984 1985 WIINV05 BASE .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

COMP .oo .oo .oo .uo .oo .oo .oo .on .oo .oo

Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WlINV05 BASE .oo .oo .oo .oo COMP .oo .oo .oo .oo Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= .oo PCNT DIFF MEANS= .o AV ABS DIFF= .oo DIFF PCNT OF BASE MEAN:s .o

WI INV06 BASE 6.90 .05 -.oo -.i:!6 .66 .24 .20 .26 .31 .16

COMP 0.90 .05 -.oo - • .:6 .64 .31 .30 .39 .40 .2s

Olff .oo .oo .oo .oo -.OJ .01 .10 .13 .10 .09

PCNT .o .o .o .o -4.4 28.6 47.0 48.7 Jl.O 57.7

WIINV06 BASE .13 .to .19 .16 COMP .18 .£4 .22 .19 OIFF .os .04 .OJ .OJ PCNT 35.8 19.1 16.4 15•4

BASE MEAN= .b6 PCNT DIFF Mt::ANS= 6.4 AV ABS OIFF= .os OIFF PCNT OF BASE MEAN• 1.0

WIINV07 BASE .JO .01 .os -.01 .1s • 04 .02 .os .os .01

COMP .JO .01 .os -.01 .14 • Ob .os .oa .oa .OJ

Olff .oo .oo .oo .oo -.01 .02 .02 .03 .02 .02

PCNT .o .o .o .o -5.3 46.3 108.7 69.6 46.2 230.0

WllNV07 BASE .01 .02 .02 .01 COMP .02 .OJ .02 .01 OIFF .01 .01 .01 .oo PCNT 220.0 38• l 40.0 so.o I

BASE MEAN= .06 PCNT Oiff MEANS= l8o3 AV ABS DIFF= .01 Dlff PCNT Of BASE MEAN:: 20.4 .... "' "' I

WllNV08 BASE -3.10 3.09 .12 .99 S.49 3.02 2.91 3. 14 3.32 2.84

COMP -3.10 3.b9 .12 .99 S.41 3.21 3.13 3.45 3.54 3.07

Olff .oo .oo .oo .oo -.08 .19 .23 .31 .22 .22

PCNT .o .o .o .o -1 .4 6.4 1.a 10.0 6.7 7.9

WlINV08 BASE 2.84 3.11 3.14 3.16 COMP 2.95 3.21 3.22 J.23 Diff .11 .10 .08 .01 PCNT 3.9 3.2 2.6 2d

BASE MEAN= 2.52 PCNT DIFF Mt.ANS= 4•1 AV ABS DIFF= • 12 oIFr PCNT Of BASE MEAN= 4.6

WIINV09 BASE 2.30 -.oo -.03 .04 .22 • Ob .10 oOA .10 .06

COMP 2.30 -.oo -.03 • 04 .22 .01 oil .09 .Il .01

DI ff .oo .oo .oo .uo -.oo • 0 l .01 .02 .01 .01

PCNT .o .o .o .o -1.a l9o4 a.8 21.3 1.a 19.4

WllNV09 BASE .oe .08 .08 .oa COMP .oa .oe .08 .08 Olff .oo .oo .oo .oo PCNT 3.9 603 2.s 2•6

BASE MEAN: .23 PCNT DIFF MEANS= 2.0 AV ABS DIFF= .01 OIFF PCNT OF BASE MEAN= 2.2

WlJNV 10 BASE .Jo 2.59 -.21 -2.67 3.22 2.22 1 • 26 1.32 1.49 1.19

COMP .30 2.59 -.21 -2.01 3.19 2.30 1 • 36 1.46 1 • 59 1.2a

DI ff .oo .oo .oo .oo -.03 .08 .10 .14 .10 .10

PCNT .o .o .o .o -1 .o 3.5 7.9 10.3 &.6 8 • 1

WIINVlO BASE l.OJ 1.14 l • 14 1.14 COMP 1 • 08 1.1a l. 18 1.11 OIFF .05 .04 .03 .03 PCNT 4.8 3.7 3.0 2.0

BASE MEAN= 1.oa PCNT DIFF Mt.ANS= 4o2 AV ABS DIFF= .05 OIFF PCNT or BASE MEAN= 4.6

VARIABLE NAME 1972 1973 1~74 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198!:>

WllNVl l BASE 3.70 .24 .01 - • .J7 1.44 .38 .44 .53 • 6 l .31 COMP 3.70 . 24 .01 - • .J7 1.38 .s2 .59 074 .75 .46 Olff .oo .oo .oo .oo -.os .13 .1s .21 • l 5 • 15 PCNT .o .o .o .o -3.7 34.5 34.7 40.3 24.2 48.I

Ill l JNV 11 BASE .31 .43 .39 .JS COMP .38 .49 .44 .J9 OIFF .07 .06 .os • 04 PCNT 22.1 1s.o 12.7 11 • 7

BASE MEAN= .63 PCNT DIFF MEANS= 10.q AV ABS DJFF= oOA OlfF PCNT OF BASE MEAN= 12.1

WllNV12 BASE .10 .02 .03 -.06 .16 .09 .01 .01 .01 .os COMP .10 .02 .03 -.06 .16 .10 .00 .01 .01 .os Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o 2.2 4.2 4.5 2.8 4.3

1111 INV12 BASE .os .os .os .us COMP .os .us .os .05 OIFf .oo .oo .oo .uo PCNT 2.2 2.0 .o 2.0

BASE MEAN= .06 PCNT OIFF MEANS= 1.q AV ABS DlfF= .oo OlfF PCNT OF BASE MEAN= 1.9

WI JNV13 BASE 5.20 .29 .23 • .JO 1.58 .93 .q1 .86 .98 .73 COMP 5.20 .29 .23 .Jo 1.56 .97 .95 .92 1.02 .11

Olff .oo .oo .oo .oo -.01 .03 .04 .06 .04 .04

PCNT .o .o .o .o -.8 3.5 4.6 6.7 4.2 s.s WIINV13 BASE .78 .s1 .82 .t;i

COMP .so .113 .83 .b2 Dlff .02 .02 .01 .u1 PCNT 2.6 2.2 1.7 1.5 I

BASE MEAN= 1.09 PCNT DifF ME.ANS= 1.7 AV ABS Ulff= .02 Olff PCNT OF BASE MEAN= 1.9 "' 0 0 I

WI INV14 BASE .oo .08 .02 .01 .47 .27 -.10 .04 .13 .os

COMP .oo .00 .02 .01 .45 .29 -.01 .12 • 18 .06

Dlff .oo .oo .oo .oo -.02 .03 .o9 .os .05 .01

PCNT .o .o .o .o -3.6 9.4 -89.9 183.3 38.3 28.6

WllNV14 BASE -.04 .os .09 .os COMP -.os .03 .08 .04 Dlff -.oo -.02 - • 01 . -.01 PCNT 11.9 -46.9 -13.8 -11.0

BASE MEAN= .08 PCNT OIFF MEANS= 17.J AV ABS DlfF= .02 OIFF PCNT Of BASE MEAN=- 29.0

WI I NV 15 BASE .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo COMP .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo

Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WllNVlS BASE .oo .oo .oo .oo COMP .oo .oo .oo .oo Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= .oo PCNT D I FF Mt.ANS= .o AV ABS DIFF= .oo Olff PCNT OF BASE MEAN= .o

wIINVl6 BASE 5.90 3.67 10 .50 -21.65 }5.92 5.95 3.}6 2.36 3.37 2.10

COMP s.9o 3.67 10.so -21.t>S 15.88 5.88 3.71 2.10 3.72 2.68

Dlff .oo .oo .oo .oo -.04 -.07 .ss .J4 .36 -.02

PCNT .o .o .o .o -.2 -1 .2 11.s 14.2 10.5 -.7

WIINV16 BASE I • 71 2 .88 2.85 2.lj4 COMP l • 71 2ob7 2 • 76 i!. • 76 DIFF .oo -.21 -.oq -.us PCNT .3 -7.3 -J.2 -2.8

BASE MEAN= 3.01 PCNT O!FF MtANS= 1.7 AV At;S DIFF= -11 [)!Ff PCNT Of BASE MEAN= 4.2

VARIABLE NAME 1972 1973 1974 197:> 197t, 1977 1978 1979 1980 1981

1982 1983 1984 198:>

WIJNV17 BASE -3.60 -.52 l. 5 I -8.77 11.79 10 • 9:, J.21 1 .84 2.11 2.01

COMP -3.60 -.52 1.51 -a.11 11.76 10.sJ 3.86 2.35 3.20 2.13

OIFF .oo .oo .oo .oo -.03 - .12 .s9 .51 .49 .06

PCNT .o .o .o .o -.2 -1.1 18.0 27. S 1s.o 2.8

lllllNVl 7 BASE .81 l. 77 2.09 1 .es

COMP .82 l • 52 1 .93 l. 76

DIFF .oo -.24 -.It, -.12

PCNT .s -1308 -7.8 -6.J

BASE MEAN= 1.99 PCNT DIFF MEANS= 3.5 AV ABS DlfF= .17 DlfF PCNT OF BASE MEAN= 8.4

WIINV18 BAS£ .20 -1.e2 -1.78 -3.69 5.11 4.51 .95 .so .so .SJ

COMP .20 -1.82 -1.78 -J.69 s.16 4.47 1.13 066 .96 .s6

DIFF .oo .oo .oo .oo -.01 -.03 • 19 .11 .16 .03

PCNT .o .o .o .o -.2 •• a 19.7 33.9 1906 4.9

1111 JNV18 BASE .13 .40 .58 • :,t,

COMP .13 .32 .52 .s2

DIFF .oo -.OB -.os -.04

· PCNT 1.6 -19.o -9.3 -1.0

BASE MEAN= .so PCNT OJFF MEANS= 406 AV ABS DIFF= .os DIFF PCNT OF BASE MEAN= 10.1

IIIIINV19 BASE .90 .28 -1.68 -1.21 J.22 J.45 1.13 .94 1.2J 1.00

COMP .90 .28 -1.68 -1.21 J.21 J.42 1.31 1.11 1.42 1.os

DIFF .oo .oo .oo .oo -.01 -.OJ .18 • 19 .19 .os

PCNT .o .o .o .o -.3 •• 9 15.9 20.2 15•4 4.5

WJJNV19 BASE -.03 1.45 .98 • 'J1

COMP -.01 1.J8 .93 .93

DIFF .01 -.01 -.os -.04

PCNT -so.o -4.9 -s.s -3.9 I

BASE MEAN= .90 PCNT OJFF MEANS= 3.2 AV ABS DIFF"' .Ot, OJFF" PCNT OF BASE MEANa 6.5 N 0 .... I

IIIJJNV20 BASE .10 .20 -.02 -.12 .12 .14 • l 6 • 16 .24 • 12

COMP .10 .20 -.02 -. 12 .69 .19 .21 .29 .34 • 19

DIFF .oo .oo .oo .oo -.03 .os .11 .13 .10 .01

PCNT .o .o .o .o -3.9 39. 1 65.8 78.S 38.9 61.0

WJINV20 BASE .o5 • 12 .13 .10

COMP .os .14 .1s .11

DIFF .OJ .01 .01 .01

PCNT 61.5 11•4 8.9 9 .t,

BASE MEAN= .1s PCNT DIFF MEANS= 2J.4 AV ABS DIFF= .04 OIFF PCNT OF BASE MEAN:: 26.0

WJJNV2l BASE 21.80 -.73 046 -.95 .JI .17 .J4 • l S .20 • l 9

COMP 21.80 -.73 .46 -.95 .31 .11 .1s .H, .20 .20

DIFF .oo .oo .oo .oo -.oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o -.6 2.4 2.8 3.2 1.5 2.1

1111 JNV21 BASE .21 .22 .24 .26

COMP .21 .22 .24 .26

DIFF .oo .oo .oo .oo

PCNT .s .5 .o .o

BASE MEAN= 1.62 PCNT OIFF MEANS= • l AV ABS DIFF= .oo OJFF PCNT OF BASE MEAN= • l

WIJNV22 BASE -J.00 • l 9 .oo -.04 .19 .09 .06 .07 .os .o5

COMP -J.00 .19 .oo -. 04 .19 .10 .08 .09 .10 .06

DIFF .oo .uo .oo .oo -.01 .01 .02 .02 .02 .01

PCNT .o .o .o .o •J.l 13•5 26.2 32.4 1s.1 29.2

llllJN\122 BASE .04 .06 .os .us

COMP .os .06 .06 .us

Olff .01 .oo .oo .oo

PCNT 13.J 809 7.3 s.s

BASE MEAN= -.15 PCNT DIFF Mt.ANS= -4.J AV ABS OJFF= .01 OIFF PCNT OF BASE MEAN:a -4.9

VAIHABLE NAME 1972 1973 l 'H4 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WIJNV23 BASE -2.00 .98 -.24 -. 75 1.71 1.09 .65 .65 .74 .56 COMP -2.00 .98 -.24 -.75 I .68 1.14 .10 .12 • 7li .60 OIH .oo .oo .oo .oo -.02 .04 .05 .01 .04 .04 PCNT .o .o .o .o -1 .4 3.9 1.1 10.4 5.4 1.0

WIJNV23 BASE .57 .bl .62 .63 COMP .58 .62 .63 .b3 OIFf .01 .01 .01 .01 PCNT 2.1 1.a 1.3 1.0

BASE MEAN= .42 PCNT DifF Mt:ANS= 4.3 All ABS DIFf= .0 2 OIFF PCNT OF BASE ME.AN= 5.2

WIJNV24 BASE 1.20 I .30 .59 .14 2.05 .87 .11 .89 1.05 .68 COMP 1.20 1.30 .59 .14 l • 97 1.04 1.00 1.21 1.21 .89 OIFf .oo .oo .oo .oo -.08 .11 .23 .31 .22 .21

PCNT .o .o .o .o -3.8 19.7 29.9 34.9 20.a 30.9

WI JNV24 BASE .63 .81 .01 .76 COMP .73 .89 .88 .112 DIFf • IO .oa .01 .06 PCNT 15.7 10.4 8.7 8-1

BASE MEAN= • '10 PCNT DIFF MEANS= I I• 0 All ABS UIFF= • 11 OIFF PCNT OF BASE MEAN= 12.2

WI JNll25 BASE. 2.60 1 • 98 -1.02 -.78 2.60 2.05 1.12 1 • 1 0 1.33 1.12 COMP 2.60 1.98 -1.02 -.78 2.58 2.08 1.14 1.14 I• 35 1.16

DIFF .oo .oo .oo .oo -.01 .03 .02 .04 .02 .03

PCNT .o .o .o .o -.5 1.6 1.6 3.4 1.4 2.8 WIJNV25 BASE 1.10 1.23 1.26 I.JO

COMP 1.19 1.24 1.27 I •. H OIFF .01 .02 .01 .01 PCNT .a 1.s .a .a I

BASE MEAN= 1.22 PCNT DIFF MEANS= I • o AV ABS DIFF= • 0 1 OIFF PCNT OF BASE MEAN= 1.2 "' 0

"' ' WI JNV26 BASE. -.90 .J9 - . 01 -.18 .69 .29 • I 6 .20 .2s .17

COMP -.90 .39 -.01 - • 18 .67 .33 .22 •2A .30 .21

OIF f .oo .oo .oo .oo -.02 .04 .06 .08 .os .04

PCNT .o .o .o .o -3.6 13.7 38.S 37.8 18 • 4 21.1

WIINV26 BASE .14 .19 .20 .19 COMP .15 .20 .21 .20 DIH .01 .oo .01 .01 PCNT 7.3 2.6 3.0 3.1

BASE MEAN= .13 PCNT DifF Mt.ANS= 14•7 All ABS DIFF= .02 OIFF PCNT OF BASE MEANz 11.s

IN I JNV27 BASE is.so .97 -.53 .63 3.33 2.64 I. 75 I .84 2.11 l .43

COMP 1 s.so .97 -.53 .63 3.27 2.76 1.97 2.11 2.31 1.59

OIH .oo .oo .oo .oo -.06 .12 . 22 .21 .20 .16

PCNT .o .o .o .o -1.7 4.5 12.3 14.5 9.7 11.2

WIIN1127 BASE 1.21 1.43 1.49 l .45 COMP 1.28 1 .48 1 . SJ 1 .49 OIFF .08 .os .os .us PCNT 6.4 3.3 3.2 3-1

BASE MEAN= 2. s 2 PCNT DIFF MEANS= 3.;> AV ABS DIFF= .09 orn· POIT OF BASE MEAN= 3 .5

wIINV28 BASE - • 10 .<+l -.09 -.Jo .59 .21 .i 9 .21 .23 .18

COMP -.10 • 41 -.09 -.Jo .sa .28 .21 .23 .2s .19

DIH .oo .oo .oo .oo -.oo • 0 I .o 1 .02 • O 1 .01

PCNT .o .o .o .o -.9 4.5 6 • 2 e.s s.2 7.3

111IINV28 BASE • l 7 • 19 .1 9 .19 COMP .18 .20 .20 .<'.O DIH • 0 I .01 .oo .oo PCNT 3.4 2•6 2. I 2.1

tlASE MEAN= .17 PCNT DifF MEANS= 3.c; AV ABS DIFF= .01 OIFF PCNT Of BASE MEAN= 3.9

VARIABLE NAME 1972 197J 19 74 197~ 1970 1977 1978 1979 1980 1981 1982 1983 1984 1985

WlINV29 BASE 1 .20 -.t:'.9 -.09 .Ob 2.10 1. 3 8 -.37 -.osi .58 .41 COMP lo20 -029 -.09 .Ub 2oos 1 .37 .io 034 .92 .47 Olff .oo .oo . oo oOO -.os -002 046 .42 .34 .01 PCNT .o oO . o .o -2.3 -1 o l -12605 -501.2 58.6 15.9

W 1 INV29 BASE -.29 .os .43 0 't:.1 COMP -.31 -.lo .30 • l 8 Olff -.02 -.17 - .13 - .08 PCNT 6.9 -226.3 -29.3 -31 06

BASE MEAN= .38 PCNT Oiff MEANS= 1s.3 AV ABS DIFf= .1 3 DIFF PCNT OF BA S M[ AN = 32 . 6

WllNV30 BASE 1.20 .23 -.oo -.32 .92 .33 .02 .06 • l 7 . os COMP 1.20 .23 -.oo -.J2 .90 .36 .10 .14 . l .07 OJFf .oo .oo oOO .uo -.02 .03 .oil .O A .o s .0 2 PCNT .o .o .o .o -2.6 9.4 3'-7.8 147.4 26.6 J4t .7

lilllNVJ0 BASE .oo .06 .09 .01 COMP -.01 .03 oOB .06 Olff -.01 -.02 -.02 -.01 PCNT -325.0 -3907 -20.2 -11.s

BASE MEAN= .21 PCNT OifF MEANS= 5.7 AV ABS DIFF= .07 OlfF PCNT OF BASE M[AN = 12 . l

WI INV31 BASE -.so -1.14 -1 obS -1.83 2.68 .45 -.os - .• 20 -.10 -.3 ..

COMP -.so -1.14 -1.65 -1 odl 2.67 .46 -.oo -.16 -.01 -.33

OIFF .oo .oo .oo .oo -.01 .01 004 .04 .03 oOl

PCNT .o .o .o .o -.3 1. 1 -93.5 -18.S -26.S -1 .s

WllNV31 BASE -.31 -.28 -.25 -.26 COMP -.32 -.Jo -.26 -.27 Olff -.oo -.01 -.01 -.01 PCNT 1.J 4.9 4.0 2.7

BASE MEAN= -.21 PCNT OIFF MEANS= -2.0 AV ABS DIFF= oOl DlfF' PCNT Of BASE MEAN= -4.2 ' N 0 w

WllNV32 BASE -18.80 .so 4o99 -10.91 18.88 9.26 6. 15 s.39 6006 J.23 ' COMP -18.80 .so 4.99 -10.91 is.as 9.31 0.28 SosJ 6. 15 3.26

Olff .oo .oo .oo .uo -.OJ .04 ol3 .14 .09 .OJ

PCNT .o oO .o .o -.2 .4 2 • l 2.5 1.s loO

WllNV32 BASE 4.20 4.25 4.47 4.~J COMI-' 4.19 4.21 4o44 4.52 Dlff -.01 -.OJ -.02 - • 01 · PCNT -.J -.a -.s -.3

BASE MEAN= 3.04 PCNT DIFf Mb\NS= .7 AV ABS lllFf= .04 Dfff PCNT OF BASE MEAN= 1.3

WllNVJJ BASE -. 10 .38 .oe -. 15 1.sa .95 .01 • 11 .46 021

COMP -. 10 .38 .OB -.15 l .52 lo02 .26 .36 .63 .34

Dlff oOO .oo .oo oOO -.06 .06 .2s .20 .17 .07

PCNT .o .o .o .o -3.7 6.3 42lb.7 24002 37o0 24.S

1t1INVJJ BASE .01 .20 oll 026 COMP -.oo .14 .21 .23 DJFF .oo -.06 -.os -o03 PCNT .o -31•0 -14.6 -11.2

BASE MEAN= .34 PCNT DIFF Mc.ANS= 14.0 AV ABS DlfF= oOe DIFF PCNT OF BASE MEAN= 22o9

11IINV34 BASE -.20 lo 04 -.09 -olH lo7l .66 040 041 .s1 039

COMP -o20 l • 04 -.09 -.Ill 1.65 .76 053 .59 .66 .47

DIFf oOO .oo .oo .oo -.06 .10 • l 3 .11 .09 oOB

PCNT .o .o .o .o -305 l4o7 34o0 41.9 16.l 19.J

WllN\134 BASE .29 .39 041 .J9 COMP .JO 039 042 .J9 Olff .01 .oo oOO .01 PCNT J.l loO l o O 1.5

BASE MEAN= .40 PCNT DIFF MEANS= 906 AV ABS DIFF= .os DIH PCNT OF BASE MEAN= 11.8

VARIABLE NAME 1972 1973 1974 1975 t976 1977 1978 1979 1980 1981 1982 1983 1984 1985

W llN\135 BASE -.1.0 l.60 .12 -1.32 .94 .47 .35 .47 .so .42 COMP -.10 1 .60 • 12 -1.32 .93 .48 .37 .s.o .s2 .44 Dlff .oo .oo .oo • 0-0 -.01 .02 .02 .02 .02 .02 PCNT .o .o .o .o -1 .o 3.6 5.7 5. 1 3.4 4.3

WIIN\135 BASE .37 .41 .4 l .4 l COMP .38 .4 l • 4 l .42 DIFf .01 •. o l .01 .oo PCNT 2.4 1.1 1.7 1•2

BASE MEAN= .36 PCNT DIFF MEANS= 2.3 AV ABS DIFf= .01 DIFF' PCNT OF' BASE MEAN= 2.6

WIIN\136 BASE 1.30 1.76 .42 -l.16 2.82 .84 .56 .54 .78 .48 COMP 1.30 1.76 .42 -1.lb 2.73 .97 .68 .73 .86 .57

Dlff .oo .oo .oo .oo -.09 .14 .12 .19 .01 .09

PCNT .o .o .o .o -3.l 16.2 22 • 1 34.7 9.2 18.6

WIJN\136 BASE .SJ .57 .60 .oo COMP .sJ .s9 .61 .01 Dlff .oo .02 .02 .02 PCNT .8 3.5 2.s 2.1

BASE MEAN= .76 PCNT OJFF MEANS= 5.4 AV ABS OIFF= .os DIFF' PCNT Of BASE MEAN= 1 • l

WJJN\137 BASE .10 4.87 -.52 -2.0s 5.56 2.21 1.12 1.92 2.21 1 .35

COMP .10 4.tl7 -.52 -2.05 5.50 2.37 l •84 2. l O 2.30 1.43

Olff .oo .oo .oo .oo -.06 .10 • 12 .11 .09 .08

PCNT .o .o .o .o -1.1 4.2 7.1 9.1 4.2 5.8

WIIN1137 BASE 1.53 l. t>S 1.13 1.11 COMP l .54 1.66 1.13 1.78 Dlff .01 .01 .01 .01 PCNT .s •6 .s ·6 I

BASE MEAN= l • 72 PCNT DIFF MEANS= 2.2 All ABS OIF'F= .os DlfF' PCNT Of BASE MEAN= 2.1 "' 0 ~ I

WIINV38 BASE 4.60 .Jo .02 -.54 1 .32 .86 .57 .48 .s1 .42

COMP 4.60 .Jo .02 -.54 1.29 .89 .63 .57 .61 .45

Dlff .oo .oo .oo .oo -.03 .04 .Q6 .oa .04 .03

PCNT .o .o .o .o -2.1 4.4 11.2 16.7 1.8 7.9

WIINV38 BASE .40 .45 .48 .48 COMP .40 .44 .48 .48 Olff .o<i -.oo -.oo .oo PCNT .1 -.2 -.2 .4

BASE MEAN= .74 PCNT DIFF" MEANS= 2.3 All ABS Olff::: .02 DIFF' PCNT Of BASE MEAN= 2.8

WllN1139 BASE -211.30 114.03 52.51 -124.72 97.82 -22.69 30.87 -2.84 20.07 1s.s1

COMP -211.30 114.03 52.s1 -124.72 51.66 28.41 74.28 66.30 43.36 47.59

Dlff .oo .oo .oo .oo -36.17 51.10 43.41 F.9.14 23.29 32.09

PCNT .o .o .o .o -41.2 -22s.2 140.6 -2437.o 116.l 206.9

WllN1139 BASE 17. 10 17.t:?9 17.90 18.34 COMP 17.30 2s.s2 24.61 25.32 Dlff .21 8.23 6.71 6.99 PCNT 1.2 47.6 37.S 38• l

BASE MEAN= 2 .14 PCNT DIFF MEANS=6as.7 All ABS DIFF"= 19.81 OlfF" PCNT OF' BASE MEAN=927.7

WIIN\140 BASE 20.00 -. 10 .10 .18 2.01 1.19 .10 .93 .89 .62

COMP 20.00 -• l 0 .10 • l 8 2.00 1.20 .12 .97 .92 .64

Dlff .oo .oo .oo .oo -.01 .02 .o3 .04 .02 .02

PCNT .o .o .o .o -.s 1.4 3.9 3.7 2.1 3.2

WIIN\140 BASE .80 .81 .82 .02 COMP .80 .01 .02 .e2 Dlff • 0 l .oo .oo .oo PCNT 1.0 .5 .s .s

BASE MEAN::: 2.13 PCNT DIFF Mt.ANS= .4 AV ABS OIF'f::: • 01 Olf"F PCNT Of" BASE MEAN= .s

IIARJABLE NAME 19 72 1973 1974 l'H5 l'H6 1977 1978 1979 1980 1981 1982 1983 1984 1985

lilJNV4ol BASE .40 1.06 083 -.b5 2.69 -4.57 3.26 o8l 1.30 2.2-COMP .40 1.06 083 -.b5 2.68 -4.53 3.25 .so l .32 2.29 OIFf .oo .oo .oo .oo -.01 .03 -.01 -.01 .02 .05 PCNT .o .o .o .o -.4 -.7 -.2 -1 .4 1.7 2.2

lilINV'tl BASE 1.16 1.35 1.21 1.23 COMP l • 21 1.40 l 026 1.25 Olff .05 .05 .04 .02 PCNT 4.5 3.5 3.5 lo8

BASE MEAN= .88 PCNT OJFf ME.ANS= 2.0 AV ABS Dlff= .02 Dlff PCNT Of BASE MEAN= 2.4

llilJNV42 BASE. 3.40 4.co -.76 -1 .34 5.16 4.35 2.60 3.31 2.92 2 .58 COMP 3.40 4.20 -.76 -l .J4 5.01 4.50 2.52 3.60 3.09 2.13 Olff .oo .oo .oo .oo -.09 .15 .22 .29 .17 .15 PCNT .o .o .o .o -1 .8 3.3 8.3 8.f, 5.9 5.9

llilJNV42 BASE 2.33 2.59 2.71 2. 75 COMP 2.37 2.62 2.74 2.19 OIFf • 04- .03 .03 .03 PCNT 1.9 1.3 1.2 1•2

BASE MEAN= 2.63 PCNT OJff MEANS= 2•A AV ABS Olff= 009 Dlff PCNT Of BASE MEAN= 3.3

llilJNVSUB BASE -1-0.10 163.25 62.04 -186.02 232 .19 26 • 12 81 .53 29053 68.73 44.63 COMP -140.70 163.25 62.04 -1B6.02 194.}3 81.28 130.41 106.38 96.51 800 71 Dlff .oo .oo .oo .oo -38.06 55 .16 48.88 76.85 27.78 36.07

PCNT .o .o .o .o -16.4 211 .2 59.9 260.2 40o4 80.8

llilINVSUB BASE 46.34 51.89 53088 53.10 COMP 47.12 60.12 60.61 60.49 Olff .78 8.82 6.73 7.40 PCNT 1.1 11.0 12.5 13.9 (

BASE MEAN= 41 otl9 PCNT Olff Mt.ANS= 39.3 AV ABS Dlff: 2lo89 Olff PCNT Of BASE MEAN= 52.3 "' 0

"' I

lliJJN\/43 BASE -2.81 3.27 1.24 -3.72 4.64 .52 lo63 .59 l .38 .89

COMP -2.81 3.27 1.24 -3.72 3.88 1.63 2o6l 2.13 1.93 lo6l

Dlff .oo .oo .oo .oo -.76 l • l 0 .98 l .54 .55 .12

PCNT .o .o .o .o -16.4 211.5 59.9 259.9 40 ... ao.1 lilIN\/43 BASE .93 1.04 1.08 l • 06

COMP .94 1.21 1.21 1.21 Olff .02 .18 • 13 • 15 PCNT 1.6 11.0 12.4 13.9

BASE MEAN= .84 PCNT Diff MEANS= 39o2 AV ABS Dlff= 044 Dlff PCNT Of BASE MEAN= 52.2

lliJJNV53 BASE -6.26 7.26 2.76 -a.20 10.33 1.16 3.63 1.31 J.06 l .99

COMf' -6.26 7.26 2.76 -8.28 8.64 J.62 s.ao 4o73 4.29 3.59

Olff .oo .oo .oo .oo -1.69 2.45 2.11 3.42 1.24 l.61

PCNT .o .o .o .o -16.4 211.J 60.0 260.3 40.5 80.9

lliJJNV53 BASE 2.06 2.31 2.40 2.36 COMP 2.10 2.10 2.10 2.69 Dlff .OJ .39 .Jo .33 PCNT 1.1 11.0 12.5 13•9

BASE MEAN= l .t:16 PCNT DI ff MEANS= J9o3 AV ABS Dlff= 097 Dlff PCNT Of BASE MEAN= 52.J

liJINVTOT BASE -149.78 173.78 6bo04 -l98.U2 247.16 27.80 86.79 31.44 73.16 47.51

COMP -149.78 173.78 66004 -198.02 206.65 86.52 138.82 l 1 3 • 24 102.73 85.91

OJ ff .oo .oo .oo .oo -40.Sl 58.72 52.oJ 81.80 29.57 38.40

PCNT .o .o .o .o -16.4 211.2 59.9 260.2 40o4 so.a wJJNVTOT BASE 49.33 55.24 57.36 56.52

COMP 50.15 64.63 64.52 64. 3 9 Dlff .83 9.39 1.11 7.tH PCNT 1.1 11.0 12.5 13•9

BASE "4EAN= 44ot>O PCNT Diff MEANS= 39.3 AV ABS Olff= 23•31 Dlff PCNT Of BASE MEAN= 52.J

VARIABLE NAME. 1972 }973 1n4 1975 1976 1977 1978 J979 1980 1981

1982 1983 1984 1985 WITOT BASE. 2421.00 2752.00 2519.80 2174.<!0 3319.70 3476.60 3564.70 3503.20 3721.70 3832.80

COMP 2421.00 2152.00 2519.80 2174.20 3257.80 3519.10 3738.40 3852.40 4130.80 421\3.90

OIFF .oo .oo .oo .oo -61.90 42.50 173.70 349.20 409.10 451.10

PCNT .o .o .o .o -1.9 l .2 4.9 10.0 11.0 ll.8

WlTOT BASE 3767.10 3782.40 3912.60 3993.80 COMP 4176.50 4152.70 4242.70 4297.80 OIH 409.40 370.30 330.10 304.00 PCNT 10.9 9.8 8.4 706

BASE MEAN:: 3338.69 PCNT OJff MEANS= -5.9 AV ABS DIFf;::: 201.24 OIFF PCNT OF BASE. MEAN= 6.2

wCTOT BASE 11855.00 12570.00 12041.00 12697.UO 13991.00 14595.00 15099.oO 15708.00 16416.00 16854.00

COMP 11855.oo 12570.00 12847.oo 12097.oo 13944000 14664.00 15351.00 }6206•00 17104.00 11117.00

DIFF .oo .oo .oo .oo -53.oo 69.00 252.00 498.00 688.00 863.00

PCNT .o .o .o .o -.4 .5 1.7 3.2 4.2 5. 1

WCTOT BASE 17240.00 l 7754.00 18259.00 18716.00 COMP 18203.00 18797.00 l936B.oo 19880.00 Olff 963.00 1043.00 1109.00 1164.00 PCNT 5.6 5.9 6.1 602

BASE MEAN:: 15329.07 PCNT OJH ME.ANS= 3. l AV ABS DlfF:: 478.71 OIH PCNT OF BASE MEAN:: 3.1

WXOl BASE 366.48 412. 19 394.59 387.03 452.31 433.53 460.52 460.23 477.26 4110.57

COMP 36b.48 412.19 394.59 387.03 451.58 435.35 462. 33 464.61 481.76 486.95

OIFF .oo .uo .oo .oo -.73 1.82 l .IH 4.4(1 4.50 6.38

PCNT .o .o .o .o -.2 .4 .4 1.0 .9 1.3

WXOl BASE 493.42 502.36 512.93 522.54 COMP 499.22 So9.28 519.72 S29ol:ll DIFf s.00 6.92 6.79 1.21 PCNT 1.2 1.4 1.3 1•4 I

BASE MEAN:: 454.00 PCNT Olff ME.ANS;:: .7 AV ABS DIFF= 3o32 OIFF PCNT OF BASE MEAN1: .1 N 0

"' I

WX02 BASE 353.75 380.91 384.26 402.95 399.38 420.ll 435. l 2 452.05 471.24 485.88

COMP 353.75 380.91 384.26 '+02.95 398.98 420.70 436.91 455.52 475.94 491.84

OIFf .oo .oo .oo .oo -.40 .59 1.79 3.47 4.70 s.96

PCNT .o .o .o .o -.1 • l .4 .8 1.0 1.2

WX02 BASE. 500.84 517.39 53'+.37 ':IS l • 75 COMP 507.46 524.59 542.04 559 • l:11 DIFf 6.62 1.20 7.67 8.06 PCNT 1.3 1.4 1.4 1.5

BASE MEAN= 449.29 PCNT OJH ME.ANS;:: .7 AV ABS lJIFf:: 3o32 DIFF PCNT OF BASE MEAN:: .1

WX03 BASE 333.47 357.00 364.79 368.'+2 418.98 406.83 431.86 435.4g 453.48 453.75

COMP 333.47 357.00 36'+.79 368.'+2 417.09 410.53 438.37 448.48 468.90 473.48 OIFF .oo .oo .oo .uo -1.89 3.70 6.51 12.99 15.42 19.73

PCNT .o .o .o .o -.s .9 1.s 3.0 3.4 4.3

WX03 BASE 462.48 470.'+2 477.96 484.08 COMP 482.56 492.50 so0.61 507.66 Dlff 20.oe 22.00 22.65 23.'=>8 PCNT 4.3 4.7 4 • 7 4.9

BASE MEAN:: 422.79 PCNT OJff Mc.ANS= 2•4 AV Af:jS UIFF= 10.62 DifF PCNT OF BASE MEAN= 2.s

WX04 !:!ASE 60.04 61.60 62.21 61. <; l 65.18 66.68 67.sa 68.72 10.22 7l. 10

COMP 60.04 61.60 62.21 61.91 65.05 66.81 68.2} 69.97 71.95 73.20

Dlff .oo .oo .oo .oo -.12 .13 .1,2 1.24 l. 72 2. l O

PCNT .o .o .o .o -.2 .2 .9 1.8 2.s 3.0

WX04 BASE. 71.t,8 12.01 73.60 74o'+5 COMP 73.98 75.02 70.10 11.02 Olff 2.29 2.40 2.50 2.s0 PCNT 3.2 3.J 3.4 3.5

BASE MEAN= t,7.69 PCNT OJFF ME.ANS= 1•6 AV ABS UIFF= 1•12 OIFF PCNT Of BASE MEAN= 1.1

VARIABLE NAME 1972 197:J l'H4 1975 1976 1977 l'H8 1979 1980 1981 1982 )983 )984 1985

WX05 BASE 40.79 44.ll 45.83 47.69 49.68 50.03 50.25 c:;0.54 so.es 51.38 COMP 40.79 44.ll 45.83 47.b9 49.63 50.ll 50.47 so.96 51.42 52.06 OIFf .oo .oo .oo .oo -.os .oe .22 .42 .55 .67 PCNT .o .o .o .o -.1 .2 .4 .0 l • l 1.3

wxos BASE 51.84 52.40 52.93 53.43 COMP 52.57 53.17 53.74 54.26 OIFf .73 .11 .81 .b3 PCNT 1.4 1.5 l.S 1.5

BASE MEAN:: 49.41 PCNT Oiff ME.ANS= .7 AV ABS DifF= .37 OlfF PCNT Of BASE MEAN= .7

WX06 BASE 324.74 327.02 327.oo 316.11 343.37 353.11 361.26 371 • 74 384.06 390.39 COMP 324.74 327.02 321.00 316. 71 342.22 354.66 366.64 3112.23 398.38 408.29 OIH .oo .oo .oo .oo -l.15 1.55 5.1e 10.49 14.32 )7.90 PCNT .o .o .o .o -.3 .4 1.s 2.8 3.7 4.6

WX06 BASE 395.80 403.85 4 l l .49 418.03 COMP 415.63 425.21 434.09 441.65 DlH 19.83 21.36 22.60 23.62 PCNT 5.0 5.3 5.5 5.7

BASE MEAN= 366.33 PCNT Oiff MEANS= 2-6 AV ABS DIFF= 9o87 Olff PC"IT Of BASE MEAN• 2.1

WX07 BASE. 224.80 232.02 237.24 236.03 251 .27 255.48 257.82 262.46 267.69 268.78 COMP 224.80 232.02 237.24 236.03 250.48 256.55 261.39 269.28 276.91 280.20 OIH .oo .oo .oo .oo -.79 1.01 3.57 6.82 9.22 Jl.42 PCNT ~o .o .o .o -.J .4 1.4 2.6 3.4 4.2

WX07 BASE 269.34 211.so 273.10 273.95 COMP 281 .89 284.87 287.07 288.32 OlFf 12.s5 13.37 13.97 14.37 PCNT 4.7 4.9 5. 1 5.2 I

BASE MEAN= 255.82 PCNT Oiff Mi::ANS= 2.4 AV ABS DlfF= 6•22 OIFF PCNT Of BASE MEAN= 2.4 .., 0 .... I

WX08 BASE SO l. 55 528.93 534.29 54 l .60 582.30 604.70 626.25 649.SO 674.15 695.27

COMP 501 055 528.93 534.29 541. 60 581.73 605.54 628.80 654.38 680.66 703.43

DIFf .oo .oo .oo .oo -.57 .84 2.ss 4088 6.51 8.16

PCNT .o .o .o .o -.1 • 1 .4 .a 1.0 1.2

WXOB BASE 716.34 739.44 762.74 786.21 COMP 725.34 749.19 11J.oci 797.07 DIH 9.00 9.75 10.35 10 .b6 PCNT 1.3 1.3 1.4 1•4

BASE MEAN= 638.80 PCNT OIFf Ml:ANS= .7 AV ABS DlfF= 4.53 OlFF PCNT OF BASE MEAN" .1

WX09 BASE }86.48 lA6 0 46 }84.66 187.Jl 202.14 206.32 213. 17 210.11 225.01 229. 21

COMP }86.48 1A6.46 18'+.66 187.31 201.85 206.84 21'+.29 220.38 227.79 232.76

DIH .oo .oo .oo .oo -.29 .52 l -12 2.21 2.78 3.55

PCNT .o .o .o .o -.1 .3 .5 1.0 1.2 1.5

IIX09 BASE 234.30 239.63 24'+.92 250.08 COMP 238.07 243.75 249.22 254.57 OIFf 3.11 4.12 4.30 4.49 PCNT 1.6 1.1 1.8 l•B

BASE MEAN= 214.85 PCNT Oiff MEANS= .9 AV AHS DIFf= 1•94 OIFF PCNT Of BASE MEAN= .9

WXlO BASE 319.98 354.54 351.73 :Jl6.15 359.17 388.80 405.66 423.29 1+43.26 459.10

COMP 319.98 354.54 351.73 316.15 358.74 389.39 407.59 427.05 448.34 465.45

OIH .oo .oo .oo .oo -.43 .59 1.93 3.76 5.oe 6.35

PCNT .o .o .o .o -.1 .2 .s .9 1.1 1.4

WXlO BASE 472.83 488.04 503.33 518.51 COMP 479.84 495.60 511.35 526.92 OIFF 1.01 7.56 a.02 8.41 PCNT 1.5 1.5 1.6 1•6

BASE MEAN= 4)4.60 PCNT OIFF MEANS= .0 AV ABS DIFF= 3.s1 OifF PCNT Of BASE MEAN= .8

VARIABLE NAME 1972 l 97:.l 1974 1975 1976 1977 1978 \979 1980 \981

1982 l98J l '184 1985 wXll BASE 212.75 215.os 215.75 <!12 .t2 225.93 c29.62 233.85 238.89 244.72 247.72

COMP 212.75 21s.os 21s.1s 212.22 225.43 230.36 236.06 243.}3 250.36 254.77

OIFf .oo .oo .oo .oo -.so .74 2.21 4to24 5.64 1.os PCNT .o .o .o .o -.2 .3 .9 1.8 2.3 2.0

WXl l BASE 250.74 254.85 258.64 l62.U2 COMP 258.46 263.19 26 7 .45 l71.23 OIH 1.12 8.J4 8.81 9.ll PCNT 3.1 3.3 3.4 3.5

BASE MEAN: 235.91 PCNT OIFF MEANS= 1•6 All ABS DIFF= 3o89 OIFF PCNT OF BASE MEAN= 1.6

wXl2 BASE 16.12 16.33 16.70 16.Ul 17.92 19.02 19.87 20.64 21.49 22.02 COMP 16.12 16.J3 16.70 16.0l }7.91 19.03 19.91 20.73 21 .• 61 22.11 OIH .oo .oo .oo .uo -.01 .01 .os .09 .12 .1s

PCNT .o .o .o .o -.1 • l .2 .4 .6 .1

WX12 BASE 22.56 23.14 23.72 24.Jl COMP 22.72 23.31 23.90 24.=>0 OIH .16 .17 • l 8 .18 PCNT .1 .1 .a .5

BASE MEAN= 19.99 PCNT OIFf Ml:.ANS= •4 All ABS OlfF= 008 DlfF PCNT Of BASE MEAN= .4

WX13 BASE 141.71 144.60 146.87 149.90 165.69 175.04 184.19 192.83 202.67 209.98 COMP 141.71 144.60 146.87 149.'10 165.56 175.24 184.80 194.03 204.28 211.99

OIH .oo .oo .oo .oo -.}3 .20 o6l 1.20 1.61 2.01

PCNT .o .o .o .o -.1 • l .3 .6 .0 1.0 WX13 BASE 211.02 225.'H 234 .14 242.25

COMP 220.03 228.35 23bo67 244.';l OIH 2.21 2.Ja 2.53 2ob6 PCNT 1.0 l • l l • l l • l I

BASE MEAN= 188.12 PCNT DIFF ME.ANS= 06 All ABS Dlff= l • l l DlfF PO.IT Of BASE MEAN= .& N 0 a, I

WX14 BASE 76.56 78.49 79.14 79.24 90.99 97.71 95.28 96.37 99.76 101.os COMP 76.56 78.49 79.14 79.24 90.55 97.87 97.69 100.72 105.40 101.02

Dlff .oo .oo .oo .oo -.44 .16 2.40 4.35 S.64 5.97 PCNT .o .o .o .o -.s .2 2.5 4.5 s.1 5.9

WX14 HASE 100.os 101.Js 103.60 104.85 COMP 105.91 106.63 1os.5a 109.bJ DIH 5.86 5.28 4.98 4.78 PCNT 5.9 5.2 4.8 406

BASE MEAN= 93.18 PCNT DJFF MEANS= 3.o AV ABS DIFf= 2.05 Olff PCNT Of BASE MEANs 3. 1

WAIS BASE 257.21 262.95 282.07 237.30 274.52 293.60 30 l • 6 7 307.25 315.26 321.59 COMP 257.21 262.95 282.07 237.30 274.44 293.30 302.79 3n9.37 318.39 324.75

DIH .oo .oo .oo .uo -.oa -.30 1.12 2 • 12 3.13 3.16 PCNT .o .o .o .o -.o - .1 .4 .1 l • 0 l • 0

loll l 5 BASE 325.21 331.63 330.2s 344.71 COMP 32b.39 334.25 340.59 346.!!2 OIH 3.18 2.02 2.34 2.11 PCNT 1.0 .0 .1 •0

BASE MEAN= 299.52 PCNT DifF MEANS= .5 All ABS OIFF= 1•44 D IfF PCNT OF BASE MEAN= .5

WXlb BASE 56!!.84 588.f4 645.54 =>28.45 614.53 646.76 6b3o88 676.68 694.91 709.55

COMP 568.84 588.74 645.54 52b 045 614.33 646 .15 b6bo27 680.90 101.01 7}5.60

DIH .oo .oo .oo .oo -.20 -.61 2.39 4o22 6. It, 6.05

PCNT .o .o .o .o -.o -.1 .4 .6 .9 .9

W.lll6 BASE 718.81 734.45 749.91 765.JJ COMP 724.89 739.J9 754.36 lt,9.J5 DIH 6.08 4.94 4.45 4.02 PCNT .8 .1 .6 .5

BASE MEAN= 664.74 PCNT D!FF Ml:.ANS= .4 AV ABS Ulff= 2.19 OlfF PCNT OF BASE MEAN= .4

VAl<IABLE NAME l'-n2 1973 19 74 197'> 1976 1977 1978 )979 1980 1981 . 1982 1983 1984 198'>

wJ17 BASt:: 808002 802088 818.04 fJO.Jl 848.20 957.77 990.49 1009000 1036.10 1056080 COMP 808.02 802.88 818.04 730o3l 847091 956.23 994085 1018040 1050.50 1071070 DIFF .oo oOO .oo .uo -029 -}.54 4o36 9o40 14040 14090 PCNT oO .o .o .o -.o -o2 .4 .9 l. 4 1.4

w.,1111 BASE 1065.oo 1082070 1103.70 1122050 COMP 1079090 1095020 1114.60 1132.20 DIH 14.90 12050 10.90 9.10 PCNT lo4 1.2 loO .9

BASE MEAN= 959039 PCNT DI ff MEANS= o7 AV ABS DIFF= 6063 Olff PCNT Of BASE MEAN= o1

WXlil BASE 36lo02 330074 JOloOO 239o'>l 3250 71 400o85 416062 424090 438.27 447.16 COMP 36lo02 330074 JOlooo 2390'>1 325055 400.09 4lil.96 430.05 446.05 455037 DIH o00 .oo .oo oOO -ol6 -.76 2.34 s.15 1o1B 8.21 PCNT o0 oO .o .o -.o -02 .6 lo2 108 lo8

WX18 BASE 449o30 455o98 465.66 474.99 COMP 457o56 462.97 471.74 <t80.43 DIH 8026 6099 6.08 5.44 PCNT 1.8 lo5 1.3 1 o 1

BASE MEAN= 395012 PCNT OJff MEANS= o9 AV ABS DIFf= 3o65 Olff PCNT Of BASE MEAN= .9

i11Xl9 BASE 28 l. 71 288078 246087 216067 297020 383044 4llo78 435021 465.92 491005 COMP 281. 71 288078 246087 216067 296092 382o36 415020 443o31'i 478.81 505005 DIH oOO oOO oOO oOO -o2a -1.08 3o42 8015 12089 14000

PCNT oO oO oO oO -ol -o3 08 lo9 208 2o9

i11Xl9 BASE 490.42 526083 55lo45 S75o67 COMP 504074 539034 562060 585089 Dlff l4o32 12051 11 o 15 l0o22 PCNT 2o9 2o4 2o0 }.8 I

BASE MEAN= 404050 PCNT Dlff MEANS= loS AV ABS DIFF= 6029 Olff PCNT Of BASE MEAN= lo6 N 0

'° I

wx20 BASE 70046 71078 71067 70olH 75.50 76040 77o45 78051 80.09 80086

COMP 70.46 71.78 7lo67 70.il7 75.32 76057 78030 80ol9 82039 83062

Dlff .oo .oo oOO oOO -018 016 .as lo68 2.30 2o76

PCNT oO .o .o .o -o2 02 1. 1 2ol 2.9 3.4

WX20 BASE 8lo20 82000 82089 83.57 COMP 84ol8 85007 86003 86. 78 Dlff 2.98 3o07 3ol5 3o2l PCNT 3.7 3.7 308 308

BASE MEAN= 11 •. n PCNT Diff ME.ANS= lo8 AV ABS Olff= 1045 OlfF PCNT Of BASE MEAN= 1.9

i11X21 BASE. 216.80 l98o'>9 209096 186033 194ol6 198o35 201091 205076 210076 215062

COMP 2160 80 198.59 209096 186o33 194012 198o39 202005 206004 211012 216006

OIH .oo oOO oOO oOO -004 004 ol4 028 036 044

PCNT o0 .o oO .o -.o .o 0 1 • 1 02 02

WX21 BASE 220.15 226020 232015 238057 COMP 221.21 226.68 232.65 239.09 Dlff .46 .48 .so .s2 PCNT .2 .2 o2 o2

BASE MEAN= 2llol4 PCNT Diff MEANS= • 1 AV ABS Dlff= 023 Olff PCNT Of BASE MEAN= • 1

WX22 BASE 327.17 334.64 33'-.73 332.96 340.72 344o29 346.75 349.SO 352.73 354067

COMP 327ol7 334.64 334073 332o96 340050 344o54 347.63 351.25 355.07 357.57

Dlff .oo oOO .oo oOO -022 .25 088 lo75 2.34 2.90

PCNT .o oO .o oO -.1 0 1 03 .s .1 .a

WX22 BASE 356048 358073 360096 363.0S COMP 359063 31,2.09 364047 366.69

Dlff 3o l5 3o36 3.51 3.64 PCNT .9 o9 1.0 }oO

BASE MEAN= 346.96 PCNT Diff MEANS= o4 AV ABS Dlff= lo57 Olff PCNT Of BASE MEAN= 05

VAIH ABLE NAME 1972 197 3 l'H4 197 S l 'H 6 1977 1978 !979 1980 198 1

1982 198 3 1'18 4 1985 Wll.23 BASE 420086 442066 437045 420 0 fl 4 58069 '+8 3002 4 9 7039 5 ll o89 528042 540089

COMP 420086 442 066 4 37 045 4 2 007 1 458 o l 5 4 83o44 4 98093 5} 4 o96 532.38 545.7 1

OIFF o00 .oo . oo . oo -.54 .42 10 5 4 3 . 07 3o96 4o82 PCNT .o .o . o . o - . 1 • 1 .3 06 .1 .9

Wll.23 BASE 55 3056 5 6 7 018 581.0 5 595 . 02 COMP 558065 572052 586.57 b00ob9 OIFF 5.09 5o34 5.52 5.67 PCNT .9 .9 lo0 1.0

BASE MEAN= 502. 77 PCN T DI FF MEANS= . 5 AV ABS DIFF= 2.57 DIFF PCNT OF BASE MEAN= .5

WX24 BASE 239 057 256.'16 264 .97 266 .69 294.08 305.80 316.08 328009 342.23 351042

COMP 239 . 57 25bo'16 264 . 9 7 266.69 ? 93003 307.02 320.38 336057 353066 365065 OIFF .oo oOO oOO .oo -1005 1.22 4.30 8048 llo43 l4o23

PCNT oO .o .o .o -.4 .4 1.4 206 3.3 4o0

WX24 BASE 359.90 370.80 381.68 391.89 COMP 375047 387.50 39~o32 4100 34 OIFF 15 . 57 16070 17.64 18 045 PCNT 4.3 4o5 4.6 4.7

BASE MEAN= 3 19030 PCNT DIFF MEANS= 2o4 AV ABS DIFF= 7.79 DIFF PCNT OF BASE MEAN= 2.4

Wll.25 BASE 258.30 281.6 7 269. 69 260044 29 1 001 315.15 328.35 341034 357.05 370.30

COMP 258.30 281067 269.69 260.44 290.88 315.37 328.79 342.22 358. l 5 371. 78

DIFF .oo . oo .oo .oo -ol3 .22 .44 088 l • 10 lo48

PCNT .o .o .o .o -.o • l • l .3 o3 .4 Wll.25 BASE 384. 17 398ob2 413.51 428 01:15

COMP 3850 77 400042 415043 '+30o90 DIFF lo60 1080 1092 2005 PCNT o4 .5 o5 . 5 I

BASE MEAN= 335060 PCNT DJFF MEANS= o2 AV ABS DIFF= 083 OIFF PCNT OF BASE MEAN= .2 "' ... 0 I

Wll.26 BASE 35o52 39010 39. 0 l 37 .38 4 3 068 46o35 1+7083 49066 5lo95 53.55

COMP 35o52 39010 39oOl 3 7 038 4 3 046 46.48 480 5 2 5lo06 53 076 550 71

DIFF oOO oOO oOO oOO - .22 ol3 070 l 039 lo82 2o 16

PCNT oO oO oO oO -o5 .3 lo5 208 3o5 4.0

WX26 BASE 54081 56 o 56 58042 6 0ol7 COMP 57006 5801:16 60077 62058 DIFF 2.25 20JO 2.35 2.<+o PCNT '+o l 4o l 4.0 4.0

BASE MEAN= 48014 PCNT Dif F MEANS= 2.3 AV ABS DIFF= lol2 r,JFF PCNT OF BASE MEAN= 2.3

WX27 BASE 555091 583.75 568.67 586 053 681.68 757.33 807.54 860.15 920.51 961.60

COMP 555.91 583.75 568.67 586.53 6 8 0.0l 758.98 815.37 875.66 941091 987.53

DIFF oOO .oo .oo .oo -1 067 1.65 1. 1n 15.51 2lo40 25.93

PCNT o0 oO .o .o -.2 .2 1.0 1.0 2o3 2.1

Wll.27 BASE 996 .18 10 37.30 1000.00 1121.50 COMP 1024030 1066.80 1110.90 1153.70 DIFF 28o l2 29050 30.90 32. 2 0 PCNT 2.8 2.e 2.9 2.9

BASE MEAN= 822076 PCNT DIFF MEANS= lo7 AV ABS OIFF= 13•91 OIFF PC f'-J T OF RASE MEAN= 1.1

Wll.28 BASE 24.91 27 ol:!6 27023 25o05 29025 31.14 32.53 ]4o05 35.7 1 36.99

COMP 24091 27086 27023 25.05 29021 3}.20 32067 34032 36007 37.44

OIFF oOO oOO .oo .oo -o03 .05 ol4 o2A .36 .45

PCNT .o oO .o .o -ol .2 o'+ oB loO 1.2

lllll.28 BASE 38.24 39.62 41.00 42.39 COMP 38.73 40.15 41056 42.98 DIFF .49 053 .56 .59 PCNT lo3 1.3 1.4 1•4

BASE MEAN= 33028 PCNT DIFF Mc.ANS= .7 AV ABS OIFF= .25 DlfF PCNT OF BASE MEAN• 08

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981

1982 1983 1984 1985 11iX29 BASE 186.41 179.96 110.02 179.26 226.05 256.91 248.88 247.13 260.21 269.45

COMP 186.41 179.96 170.02 179.26 224.94 255.42 257.73 U,5.41 286.11 296.75

Olff .oo .oo .oo .oo • l • 1 l -1 .49 a.as )8.28 25.90 27.30

PCNT .o .o .o .o -.5 -.6 3.6 7.4 10.0 10.1

wx29 BASE 263.15 265.01 274.67 280.1:12 COMP 290.03 288.08 294.98 299.25 Dlff 26.88 23.07 20.31 18.43 PCNT 10.2 8.7 7.4 606

BASE MEAN= 236.85 PCNT DJFF MEANS"' 5.o AV ABS DIFF= 12-26 D lfF PCNT OF BASE MEAN= 5.2

lliX30 BASE 115.21 119.85 119.82 113.34 131..79 138.41 138.91 140.0'3 143.49 144.51

COMP 115.21 119.85 119 • 82 113.34 131.31 138.55 140.65 143.51 147.85 149.21

OIFF .oo .oo .oo .oo -.48 .14 1 • 74 3.43 4.36 4.70

PCNT .o .o .o .o - • . 4 • l 1.3 2.4 3.0 3.3

11X30 BASE 144.62 145.83 147.75 149.25 COMP 149.08 149082 l':>l.38 l':>2.63 Dlff 4.46 3.99 3.63 3.38 PCNT 3.1 2.1 2.5 2.3

BASE MEAN= 135.20· PCNT DIFF MEANS= le6 AV ABS DIFF= 2.11 DIFF PCNT OF BASE MEAN= 1.6

IIX3l BASE 54.35 47.65 37.97 21.20 42.98 45.65 45.38 44021 43.64 41.68

COMP 54.35 47.65 37.97 21.20 42.94 45.63 45.62 44066 44.2'=> 42.31

DIFF .oo .oo .oo .oo -.04 -.01 .23 .45 .61 .63

PCNT .o .o .o .o - .1 -.o .5 1.0 1.4 1.5

lliX31 BASE 39.84 38.18 36.12 35.20 COMP 40.45 38.71 37.19 35.63 Dlff .61 .52 .47 .43 PCNT 1.5 1•4 1.3 1.2 I

BASE MEAN= 41.47 PCNT D Jf.F MEANS= AV ABS DIFF= OlfF PCNT OF BASE MEAN= .1 N

.7 .29 .... .... I

WX32 BASE 849.09 855.52 895.46 sos.11 959.27 1033.40 1082.70 1125.80 1174.30 1200.20

COMP 849.09 855.s2 895.46 808.17 959.00 1033.50 1083.80 1120.00 1177.30 1203.40

Olff .oo .oo .oo .oo -.27 .10 1 • I 0 2.20 3.00 3.20

PCNT .o .o .o .o -.o .o • 1 .2 .3 .3

WX32 BASE 1233.80 1267.80 1303.60 1339.90 COMP 1236.90 1270.70 1306.30 1342.50 DIFF 3.10 2.90 2.10 2.00 PCNT .3 .2 .2 .2

BASE MEAN= 1080eb4 PCNT DIFF Mt::ANS= • 1 AV ABS UIFF= 1•51 DIFF PCNT OF RASE MEAN= • 1

WX33 BASE !49.6t, 156.73 158.31 155.46 l 84 .19 201.64 201.82 203.82 212.26 217.24

COMP 149.66 156.73 158.31 15!:>.46 183.12 201.64 206.42 2}3.14 224.67 230.83

OIFF .oo .oo .oo .oo -1.07 .oo 4.60 9.32 12.41 13.59

PCNT .o .o .o .o -.6 .o 2.3 4.6 5.8 6.3

11iX33 BASE 217.49 221.16 226.98 231.79 COMP 230.91 233 .... 8 238.47 242.76 Olff 13.42 12.32 11.49 10.97 PCNT 6.2 5.6 5.1 4.7

BASE MEAN= l95ebl PCNT DifF MEANS= 3•2 AV ABS DIFF= 6•37 DlfF PCNT OF BASE MEAN= 3.3

WX34 BASE 144.03 153.50 152.10 145.30 }60.88 166.':H 110.53 174.30 179.53 183.12

COMP 144.03 153.50 152.70 145.30 }60.33 167.24 112.10 177.45 183.52 187.80

Dlff .oo .oo .oo .oo -.55 .33 1.57 3. IS 3.99 4.68

PCNT .o .o .o .o -.3 .2 .9 I.A 2.2 2.6

WX34 BASE: }85.75 189.32 193.11 l96.b7 COMP 190.52 194.13 }97.95 201.~6

Olff 4.77 4.tll 4.84 4.tl9

PCNT 2.6 2.5 2.5 2.5 BASE MEAN= 171 .12 PCNT DifF Mi:.ANS= 1.4 AV ABS UIFF= 2•40 DlfF PCNT OF BASE MEAN= 1.4

VARIABLE NAME }972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 198 3 1984 1985

WXJ5 BASE 85.ss 99.47 100.so 89.03 97.21 101.21 104.32 108.45 112.81 116047 COMP 85.55 99047 1000so 89003 97013 101.33 104.SS 108090 113.41 1 l 7.23 Olff .oo .oo .oo .oo -.08 .06 023 045 .60 076 PCNT oO .o .o .o -.1 • 1 .2 .4 .s .1

WX35 BASE }19.71 123.26 126.81 130.41 COMP 120.ss 124.16 121. 77 131.41 Olff .84 .90 096 1.00 PCNT .1 .1 .8 .a

BASE MEAN= 108023 PCNT Olff MEANS= •4 AV ABS Dlff= •42 DlfF PCNT Of BASE MEAN= .4

WX36 BASE 83.63 102016 106054 94oJ3 124004 132.88 138079 144052 152079 157.91 COMP 83063 l02ol6 106054 94.33 123.12 133.38 140060 148.33 157036 163.42 Olff .oo .oo .oo .oo -092 .so 1.81 3.81 4.57 5.51 PCNT oO .o oO .o -.7 .4 1.3 2.6 3.0 3o5

WX36 BASE 163.46 169052 115.81 182013 COMP 169.01 175028 181 • 73 188.22 Olff 5.55 5o76 5o92 6.09 PCNT 3o4 3.4 3.4 3.3

BASE MEAN= 137.75 PCNT Dif F ME.ANS= 2.0 AV ABS OIFF= 2•89 DlfF PCNT OF BASE MEAN= 2.1

WX37 BASE 127.47 166045 162.33 145.92 190041 4"08.59 222.32 237 o 73 255.39 266022 COMP 127047 166045 162033 145.92 189092 208.86 223.57 240038 258.80 270.25

DI ff .oo .oo .oo .uo -049 .27 lo 25 2.65 3o41 4o03

PCNT .o oO .o oO -o3 o l 06 l. l lo3 lo5 WX37 BASE 278050 291.74 305.59 319.75

COMP 282.59 295.91 309.82 324006 Olff 4.09 4.17 4o23 4.31 PCNT loS lo4 1.4 l o3 I

BASE MEAN= 227003 PCNT DIFF MEANS= o9 AV ABS Olff= 2•06 DlfF PCNT OF BASE MEAN= .9 "' .... "' I

WX38 BASE 113085 117.35 l 17 062 111.31 126083 136.91 143.56 149.27 155.93 160084

COMP 113085 117035 117.62 111.31 126050 137.02 144042 151.07 158.25 163.56

Olff oOO .oo .oo .oo -.:n o 11 086 1.80 2.32 2.12

PCNT .o .o .o oO -.3 • l 06 1.2 10s 1.1

WXJ8 BASE 165057 170.82 176.47 182.15 COMP 168031 173.55 179.20 184090 Dlff 2o14 2.73 2o73 2. 75 PCNT 1.1 1•6 1.5 105

BASE MEAN: 144089 PCNT OifF MEANS= o9 AV ABS DIFF= 1•36 DlfF PCNT OF BASE MEAN= 09

WXJ9 BASE 2037020 2607040 2869090 2<!46.30 2685040 2572o00 2726040 2112.20 2812.50 2890.00

COMP 2037020 2607040 2869.90 2246.JO 2504.60 2646070 3018010 3349060 3566040 3804030

Olff oOO oOO oOO .oo -tao.so 74.70 291070 637040 753090 914.30

PCNT .o oO oO oO -6.7 2o9 l0o7 23.S 2608 3106

WX39 BASE. 2975.50 3062.00 3151.50 3243020 COMP 3890090 4018050 41410so 44"68o 10 Dlff 915040 956.50 990000 1024.90 PCNT 3008 3lo2 3lo4 3106

BASE MEAN= 2756054 PCNT OifF ME.ANS= l6o5 AV ABS DIFF= 48 l 040 OlfF PCNT Of BASE MEAN= 17.5

WX4O l::lASE 296000 293.IH 29 7 0'H 301.92 346054 :n2094 388043 409.20 429.08 442o79

COMP 296000 293081 297.91 301092 346033 373010 389o2l 410076 43 lo l7 445.30

Olff .oo oliO oOO oOO -.21 o 16 078 lo SI\ 2o09 2os1

PCNT oO oO oO .o - o I oO 02 .4 0 'j .6

WX4O BASE 460048 478049 496. 72 :, 14 o 9 l COMP 463.15 481.26 499.58 :::,!7.85

Olff 2o67 2o17 2086 2o94 PCNT .6 06 06 .6

l::lASE MEAN= 394094 PCNT DitF Mt.ANS= o3 AV Al::lS Dlff= l • 32 Olf"F PCMT OF BASE MEAN= 03

VARIABLE NAME 1972 1973 1974 197~ 1976 1977 1978 1979 1980 )981 1982 1983 1984 1985

IIIX41 BASE 435.89 445.15 45i:'.40 44b.77 470.14 430.43 458.75 465.8) 477.16 496.64 COMP 435.89 445. l 5 452.40 44b.77 470.06 430.bJ 458.91 465.90 477.40 497.32

Dlff .oo .oo .oo .uo -.08 .20 •16 .01 .24 .68

PCNT .o .o .o .o -.o .o .o .o • l • l

WX4l BASE 506.74 518.48 529.02 539.73 COMI-' 507.87 520.03 530.95 541 .84 DIFf 1.13 l .55 l.93 2.11 PCNT .2 .3 .4 .4

BASE MEAN= 476.bS PCNT DIFF Mt.ANS= • 1 AV ABS Olff= .50 Olff PCNT Of BASE MEAN= • l

WX42 BASE 196.71 230.34 224.25 213.52 254.84 i:'89 • 71 310.54 331.01 360.4!:> 381.09

COMP 19b. 71 230.34 224.25 213.52 254.11 <!90.13 312.69 341.5 l 366.28 388 .13

Dlff .oo .oo .oo .oo -.73 .42 2.15 4.44 5.83 1.04

PCNT .o .o .o .o -.3 • l .1 1.3 1.6 1.0

IIIX42 BASE 399.75 420.51 442.20 464.25 COMP 407.15 428.18 450.13 472.4b DIFf 7.40 7.b1 7.93 0.21 PCNT 1.9 1.0 1.8 1•8

BASE MEAN= 323.23 PCNT Dlff MEANS= 1 • l AV ABS Dlff= 3.10 OlfF PCNT OF BASE MEAN= l • 1

WX43 BASE 1312.40 1432.50 1420.30 1357.70 l!:>08.80 1638.30 1707.90 1776.50 1858.20 1914.80

COMP 1312.40 1432.so 1420.JO l.J57.70 1505.90 lb41.00 1718.90 1797.80 1885.70 1947.70

DIFf .oo .oo .oo .oo -2.90 2.10 11.00 21.30 21.50 32.90

PCNT .o .o .o .o -.2 .2 .6 1.2 1.s 1.1

IIIX43 BASE 1972.60 2040.60 2112.50 2185.20 COMP 2001.20 2076.lO 2148.70 2222.00 OIFf 34.60 JS.SO 36.20 36.dO PCNT 1.0 1.1 1.7 1.1 I

BASE MEAN= 1731.31 PCNT Diff Mt.ANS= 1.0 AV ABS OIFF= 11.24 nlfF PCNT Of BASE MEAN= 1.0 N .... .., I

IIIX44 BASE 571.65 610. n 632.88 b30.86 687.79 708.28 122 • 15 7)8.87 759.24 768.33

COMP 571.65 610.11 632.88 b30.86 685.33 710. 76 131.53 757.44 784.25 799.20

DIFf .oo .oo .oo .oo -2.46 2.48 9.38 10.57 25.01 J0.87

PCNT .o .o .o .o -.4 .4 1.3 2.5 J.3 4.0

wX44 BASt. 775. 71 787.06 798.02 d07o27 COMP 809.27 822.61 835.09 845.50

DIFf 33.56 35.55 37.07 38.i:'3 PCNT 4.3 4.5 4.6 4.7

BASE MEAN= 714.21 PCNT Diff MEANS= 2.3 AV ABS DIFf= 16•66 DIFF PCNT OF BASE MEAN= 2.3

WX45 BASE 216.49 226.50 229.42 224.'+6 241.65 245.83 246.45 248.03 250.90 253.88

COMP 216.49 226.50 229.42 224.'+6 240.75 246.61 249.60 254.22 259.12 263.95

Dlff .oo .oo .oo .uo -.90 .78 3.15 6. 19 8.22 10.01

PCNT .o .o .o .o -.4 .3 1.3 2.5 J.3 4.0

IIIX45 BASE 255.98 259.40 262.82 i:'65.73

COMP 266.87 210.0s 274.69 i:'77. 9 l

DIFf 10.89 11 • 45 11.87 12.18

PCNT 4.3 4.4 4.5 406 BASE MEAN= 244.82 PCNT Diff MEANS"' 2.2 AV ABS DIFf= 5.41 OlfF PC"lT Of BASE MEAN= 2.2

liX46 ElASE 162.10 173.60 179.27 179.13 197.54 205.66 213.21 2?1.73 231.68 238.11

COMP 162.10 l 73.60 179.27 179.13 19b.75 206.55 216.48 228.31 240.74 249.55

DIFf .oo .oo .oo .uo -.79 .89 3.21 b.58 9.06 11.44

PCNT .o .o .o .o -.4 .4 1.5 3.0 3.9 4.8

iiX46 BASE 244.05 25 l • 53 258.93 265.82

COMP 256.80 265.34 273.61 281.24

DIFf 12.75 l 3 • 81 14.68 l 5.'+2

PCNT 5.2 5.5 5.7 5.0 BASE MEAN= 215 • 88 PCNT D!Ff ME.ANS= 2.9 AV ABS DlfF= 6033 Olff PCNT Of BASE MEAN= 2.9

1/ARJABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WX47 BASE 532o53 580.32 607087 bl5o28 695090 736084 773042 82lo42 873031 9llo05 COMP 532o53 580.32 607087 615o28 692074 739o91 785027 845036 906059 953070 OIFf oOO oOO oOO oOO -3o l6 3o07 ll.85 23.94 33.28 42065 PCNT .o oO oO oO -.5 • It lo5 2o9 3.8

"· 1 WX47 BASE 947.95 992oS5 1036080 1080.10

COMP 996007 1045.30 1093080 1140.t>O OIFf 48 o l2 52075 51.00 60.50 PCNT 5. l 5.3 5.5 5-6

t;ASE HEAN= 800038 PCNT DifF MEANS= 2.9 AV ABS Diff: 24.02 Olff PCNT Of BASE HEAN: 3.0

WX48 BASE 76 lo 66 601.59 448035 468o26 779048 1006020 993071 943.40 1007070 1093090 COMP 761066 601.59 448o35 468 o 26 779.48 977.88 1044000 1068090 1207.80 1304060 OIFf oOO .oo oOO oOO .oo -28o32 50029 125.50 200010 210070 PCNT oO .o oO oO oO -2.a 50 l 13o3 l9o9 19.3

WX48 BASE 1009.50 963023 1014020 1034000 COMP 1220030 1136.00 1156060 1154050 OlFf 210080 l72o 77 142.40 12ooso PCNT 20.9 l7o9 l4oO 11 o 7

BASE MEAN= 866.08 PCNT DI ff Mt.ANS= 9.9 AV ABS Dlff= 90ol0 Olff PCNT Of BASE MF.AN= 10.4

lill.49 BASE 594088 580.44 6J4o55 t>OSo78 700021 792ol9 754041 738073 769024 783o52 COMP 594088 580044 634o55 605o78 701037 79 l. 90 775o47 782093 828002 848079 OIFf oOO oOO oOO oOO -4084 -029 2lo06 44020 58078 65027

PCNT oO oO oO oO -o1 -oO 208 600 706 803 liX49 BASE 785o92 803058 821007 tl44o40

COMP 850064 864034 871015 b98o24 OIFf 64o72 60076 56008 53078 PCNT 802 706 6.8 604 I

t;ASE MEAN: 729064 PCNT Diff ME.ANS= 4o l AV ABS DIFf= 30070 DIFf PCNT Of BASE MEAN= 4o2 N ,... ~ I

WJC.50 BASE. 199.03 200079 197079 200.34 245098 292006 256014 259013 212025 268036

COMP 199003 200019 197o79 200034 243053 294089 27lo05 280099 2.95090 2A9o60

DIFf oOO oOO oOO oUO -2045 2o83 l4o9l 21086 23065 2lo24

PCNT o0 oO oO oO -1 oO loO 5.8 804 807 7o9

liJC.50 BASE 254o96 260.50 269.20 209.20 COMP 272096 274.38 281083 201.05 OlFf 10.00 13.88 12063 11 otl5 PCNT 1.1 5.3 4 • 7 4o4

t;ASE ME.AN= 246.12 PCNT Diff ME.ANS= 4o0 AV At,S Dlff:i: 10.24 Dfff PCNT Of BASE MEAN= 4.2

wx5·1 BASE 732 o 3 l 7',8.93 760.04 733098 823.96 848o26 843042 1:140.92 856025 854.95

COMP 732.31 7f,8.93 760.04 733.98 818090 851.02 860.92 873091 895057 890029

OIFf oOO oOO .oo oOO -5000 3o36 l7oso 32.99 39032 4l o34

PCNT o0 oO oO oO -o6 o4 201 3o9 406 4 o 8

WXSl BASE 847o03 849 o 3 l 857063 86lo46 COMP 883085 880.69 884056 885ol3 DlFf 3bo82 3lo38 26o93 23.t>7 PCNT 4.3 3.7 3 o 1 2o7

t;ASE HEAN= 820032 PCNT DJFF MEANS= 2.2 AV ABS DIFF= l8o45 OIFF PCNT Of BASE. MEAN= 2.2

WX52 BASE 158028 l68ot>O 170.98 l 69 e 3 l 188.99 194o38 l99o77 210009 219031 225.03

COMP }58.28 168.60 17Uo98 169.31 188.J8 19i.16 202. 10 214044 225.45 233.20

Diff .oo .oo oOO .uo -o6l 078 2.33 4.3<; 6o l4 8. 17

PCNT .o .o oO oO -o3 o4 lo2 2 o 1 208 306

WJC.52 BASE. 231029 239013 245079 25 lo "'8 COMP 240085 249085 257051 264o'+O Dlff 9o5t> 10 o l"c. 11.12 12042 PCNT 4o l 4o5 40·8 4o9

BASE MEAN= 205021 PCNT OJFF Mt.ANS= 2o3 AV ABS Dlff= 4o77 DIFI'" PCNT Of BASE MEAN= 2o3

\/AMIABLE NAME 1972 l97J 1974 197!:> 1976 1977 1978 1979 1980 1981 1982 1983 19 84 1985

WX5J BASE 4103020 4520020 47320 70 4840050 5400080 5b95 040 5914020 6154040 6445080 6643.50

COMP 4103.20 4520.20 47320 70 4tl4Uo~O 5383020 5714.60 5992 o 60 6310.60 6658050 6905030

OIFf oOO .oo .oo .uo -17.60 19.20 78.40 156.20 212.10 2t>lo80

PCNT .o .o .o .o -o3 .3 1.3 2.5 3.3 3.9

IIIXS.3 BASE 6813.60 7026.50 7251.60 7461.00 COMP 7099.40 7331.30 756tl.30 7789.90 OIFf 285.80 302.80 316.70 328.90 PCNT 4.2 4.3 4.4 4o4

!:!ASE MEAN= 5928.96 PCNT OI FF Mt.ANS= 2.3 AIJ ABS Dlff= 141.44 Dlf"F PCNT Of BASE MEAN= 2o4

WX54 BASE. 1452.40 1550.00 1567.70 1587.70 1729.80 1801040 1856.20 1923.70 2001.20 2051050

COMP 1452.40 1550.00 1587.70 1587.70 1724.60 1607.40 1878.90 19#,8.60 2063.00 2128.80

OIFf .oo .oo .oo .oo -5.20 6.00 22.10 44.90 61.80 77.30

PCNT .o .o .o .o -.3 .3 1.2 2. 3 3.1 308

WX54 BASE 2096.10 2152.90 2200.00 2260.80 COMP 2182.00 2245.20 2300.50 2362.90 OIH 85.90 92.30 91.10 102.10 PCNT 4.1 4o3 4.4 4.5

BASE MEAN= 1875.73 PCNT DifF MEANS= 2.2 AV ABS Olff= 42056 OIFF PCNT OF BASE MEAN• 2.3

WX55 BASE 2736.50 3027.50 3216.60 3290.80 )648.80 3806.40 39290 8 0 4076.10 4253040 436 7010

COMP 2736050 3027.50 3216060 3290.80 3b30o40 3823.00 3997.40 4212030 4437 060 4595.30

OIH oOO .oo .oo .oo -18.40 16060 b 7 • 60 136.20 184020 2;>8.20

PCNT .o .o .o oO -.5 .4 1.1 3.3 4.3 5.2

WX55 BASE 4463.50 4592.20 4721.80 4840.20 COMP 4712.90 4858.00 5001.30 5131.'>0 Olff 249.40 265.80 279.50 291.20 PCNT 5.6 508 5o9 600 I

BASE MEAN: 3926.48 PCNT DIFF MEANS= 3-1 AV ABS DIFF= l24•0A OIFF PCNT Of BASE MEAN= 3o2 N .... VI I

WVAUl BASE 235.10 264.42 253.13 <:48o28 290 .16 278.11 295.42 295.24 306 o l 6 308028

COMP 235.10 2l,4 • 42 253.13 248 0Z8 289.69 279.28 296.59 298.06 309005 312.38

Olff .oo .oo .oo .oo -.47 1.11 l .17 2.02 2.89 4. l 0

PCNT .o .o .o o0 -.2 .4 o4 loO .9 1.3

WVA0l BASE 316.53 322.26 329.04 :n5.21

COMP 320025 3;>6.70 333.40 J39.IH

Olff 3o72 4.44 4.36 4ob6

PCNT 1.2 1-4 1.3 1•4 BASE MEAN= 291.24 PCNT DIFF Ml:.ANS= .7 AIJ ABS DIFf= 2.13 OIFF PCNT OF BASE MEAN= o1

WVA02 BASE 211.22 298.51 301.13 315 0 78 312.99 329.23 340.99 354026 369.29 380.77

COMP 211.22 298.51 301.13 315.78 312.67 329.69 342.39 356.98 372.98 3A5.44

OIH .oo .oo .oo oOO -.32 .46 1040 2.12 3o69 4.67

PCNT .o oO oO oO -. l • l .4 .0 loO 1.2

IIII\IA02 BASE 392.49 405.46 418077 43io39

COMP 397.68 411.10 424.78 438070

DIH 5.19 5.64 b o O l 6031

PCNT 1.3 1.4 lo4 lo5 BASE MEAN= 352.09 PCNT DIFf Mt.ANS= o7 AIJ ABS DlfF= 2-60 OlfF PCNT OF BASE MEAN= .1

IIIIVA03 BASE 95016 101.87 104009 105.13 119.56 116.09 123.23 124.27 129040 129.48

COMP 95ol6 lol.87 104.09 105.13 119 • 02 117.15 125.09 127097 133.80 135.11

OIFf .oo .oo .oo .oo -.54 1.00 1.86 3.70 4.40 5.63

PCNT .o .o .o .o -o5 .9 1.s 3.n 3.4 4.3

IIIIIA03 BASE 131.97 134.24 136039 138.13

COMP 137.70 140.54 142.85 144.1:16

OIH 5.73 6.30 6046 b.13

PCNT 4o3 4.7 4 • 7 4o9

BASE MEAN= 120ob4 Pc NT O!FF MEANS = 2.4 AV ABS. OlfF= 3.03 DIFF PCNT OF BASE. MEAN= 2.s

IIARJABLE NAME 1972 197.J l'H4 1975 1976 1977 1978 1979 1980 1981 19 82 1983 19 !!4 1985

W\IA04 BASE 35.28 36.19 36.55 Jb.37 38.29 39.18 39.71 40.38 41.26 41. 77 COMP 35.28 36.19 3b o55 36.37 3 8 .22 39.25 40.07 4loll 42.27 43.01 Dlff .oo .oo .oo .oo -.01 .08 .31 .7 3 l. O l 1.23 PCNT .o .o .o .o -.2 .2 .'ii 1 .s 2.s 3.0

WVA04 BASE 42.12 42.b6 4J.24 43.74 COMP 43.46 44.07 44.71 45.~5 DIH l. 35 1 • <t l 1.47 1. s 1 PCNT 3.2 3.3 3.4 3.5

BASE MEAN= 39.77 PCNT Diff Mt.ANS= 1•6 AV ABS Olff= 066 Olff PCNT Of BASE MEAN= 1.1

WVA05 BASE 23.84 25.78 2bo78 27.87 29.03 29.2<t 29.36 29053 29.73 30.03 COMP 23.84 25.18 26.78 27.!!7 29.00 2 9.28 29.49 29.78 JO.OS 30.42 DIH .oo .oo .oo .uo -.03 .04 .13 .2s .32 .39 PCNT .o .o .o .o - • l .2 .4 .B 1. 1 1.3

WVA05 BASE 30.29 30.b2 J 0 .93 31.23 COMP 30.72 31.07 3lo40 31.71 DIH .42 .45 .47 .48 PCNT 1.4 1.s 1.5 lo6

BASE MEAN= 2!! • !!8 PCNT Diff MEANS= •1 AV Al:IS Dlff= .21 Dlff PCNT Of BASE MEAN= .1

WVA06 BASE 54.72 5 5.10 sso10 530 3 7 57086 59.50 60087 62.64 64.71 65.78

COMP 54.72 ",So 10 55.10 530 :H 57066 59076 61.78 64041 67013 68.80

DIH .oo oOO oOO .oo -ol9 .26 090 lo77 2o41 3o02

PCNl oO .o .o .o -.3 .4 1.5 2 ol'I 3.7 4.6 WVA06 BASE 66.69 68005 69034 70044

COMP 70003 71.bS 73ol4 74042 DIH 3.34 3.60 3o8l 3o 'il8 PCNT SoO So3 s.s 5.7 I

BASE MEAN= 6 lo73 PCNT Diff MEANS= 20 6 AV ABS Olff:: lo66 OlfF PCNT Of BASE MEAN:: 2o7 N ..... °' I

W\IA07 BASE 41.74 43.08 44.05 43083 4bo66 47.44 47.87 48074 49.71 49.91

COMP 41. 74 43.08 44005 43.!iJ 46.51 47.64 4!!.54 so.oo 51.42 52003

DIH oOO .oo .oo oOO -ol5 .20 066 1.26 lo 71 2.12

PCNT oO .o .o .o -.3 o4 1.4 2 .6 3.4 4.2

WVA07 BASE so.01 50041 so.11 50 .1:11 COMP 52.34 52.90 53.31 53.54 DIH 2.33 2.48 2o59 2.01 PCNT 4.7 4o9 5.1 5.2

BASE MEAN= 47.50 PCNT Oiff MEANS:: 2o4 AV ABS Dlff= 1.16 0 Iff PCNT Of BASE MEAN= 2.4

IIVA08 BASE 187.95 198.~l 200.22 202.96 2 18.21 <'.2 6.60 234.68 243.40 252.63 2f.0.54

COMP 187.95 198.21 200.22 2 02o'ilb 218.00 226.92 235064 245022 255.07 263.60

DIH .oo .oo .oo .uo -.21 .32 096 1.82 2.44 3.06

PCNT .o .o oO .o -.1 • 1 • 4 .1 1.0 1.2

WVA08 BASE. 268.44 277.lO 285083 2 94.62 COMP 271.81 280075 289.71 298.70 DIH 3.37 J.05 3o8A 4.08 PCNT lo3 lo3 1. 4 1•4

BASE MEAN= 239.38 PCNT DifF MEANS:: o7 AV ABS OlfF= 1•70 DifF PCNT OF BASE MEAN:: .1

WVA09 BASE 46.38 40.37 45.93 46059 50.21 5 l. 3 l 53o Ol 54026 55.96 5 7.01

COMP 4bo38 46.37 4!:l.93 4o.::.9 50.20 51 .44 53029 54.81 56.65 57.89

DIH .oo .oo .oo .uo -.01 .13 .28 .55 .69 .00

PCNT .o .o .o .o -.1 .3 .s 1.0 1.2 1.5

WVA09 BASE 58027 59.bO 60.91 62.19 COMP 59.21 60.b2 61 .98 63.31

OIH .94 1.03 lo07 1.12 PCNT 1.6 1.1 1.8 1.0

BASE MEAN= 5 3.-.3 PCNT DI f f ME.ANS= . g AV ABS Ulff= 048 OlfF PCNT OF BASE MEAN:: .9

VARIABLE NAME 1972 1973 l°'174 197:> }976 1977 1978 1979 1980 )981 1982 )983 }984 1985

IIVAl0 BASE 142.21 157.57 )56.32 140.:.l )59.63 172.80 180.29 lA8.13 197.00 204.04 COMP 142.21 157.57 156.32 140.51 159.44 173.06 191.15 189e80 199.26 206.87 Olff .oo .oo .oo .oo -.19 .26 • 86 l .67 2.26 2.83 PCNl .o .o .o .o -.1 .2 .5 .Q l • l 1.4

liVAl0 BASE 210 .14 216.90 223.70 c30.45 COMP 213.26 220.c6 227.26 c34. l 8 Dlff 3.12 3.36 J.56 3.73 PCNT 1.s 1.5 1.6 1•6

BASE MEAN= l84.c6 PCNT Diff Mt.ANS= •B AV ABS Dlff= 1-56 Olff PCNT Of BASE MEAN=< .8

WVAl l BASE. 76.82 77.oS 77.90 76.63 81.58 82.91 84.44 fl6.26 88.36 89.45 COMP 76.82 77.65 77.90 76.63 81.39 83.18 85.23 A7e79 90.40 9}.99 Olff .oo .oo .oo .oo -.18 .21 .00 1.53 2.04 2.54 PCNl .o .o .o .o -.2 .3 .9 1.8 2.3 2.8

liVAll BASE 90.53 92.02 93.39 94.61 COMP 93.32 95.03 96.57 97.93 Olff 2.79 3.01 3.18 3 •. '2 PCNT 3.1 3.3 3.4 3.5

BASE MEAN:: 85.18 PCNT Diff MEANS= 1-6 AV ABS Dlff= 1•40 Olff PCNT Of BASE MEAN= 1.6

liVAl2 BASE. 9.99 l O. 12 10.34 9.~l 11.10 11.78 12.31 12.1a 13.31 )3.64 COMP 9.99 10.12 l O • 34 9.91 11.09 11.79 12.33 12.84 13.38 13.73 Dlff .oo .oo .oo .oo -.01 .01 .o3 .06 .07 .09

PCNT .o .o .o .o - • 1 • l .2 .4 .s .7

liVAl2 BASE 13.98 14.33 14.69 15.06 COMP 14.07 14.44 14.80 15.17 Olff .10 .10 •ll .11 PCNT .1 .1 .1 .9 I

BASE MEAN= 12.)8 PCNT Olff MEANS= •4 AV ABS DIFf= .05 Olff PCNT Of BASE MEAN= .4 N .... .... I

liVAlJ BASE 63.79 65.08 66.11 67.47 74.58 78.79 82.91 86.80 91 .22 94.51

COMP 63.79 65.08 66 • 11 67.47 74.52 78.87 83. 18 87.JJ 91.95 95.42

Olff .oo .oo .oo .oo -.06 .09 .20 .54 .12 .90

PCNT .o .o .o .o - .1 • l .3 .6 .8 l .o

WVAlJ BASE 98.04 101.11 105.39 109.04 COMP 99.04 lo2.78 100.53 110.23 Olff .99 1.01 1.14 l.!9 PCNT 1.0 lol l • I l • l

BASE MEAN= 84067 PCNT DifF Mi::ANS= ·6 AV ABS Dlff= .so Olff PCIIIT OF BASE MEAN= .6

lliVA14 BASE 47.70 48.90 49.31 49.37 56.69 60.88 59.37 60.04 62.15 62.96

COMP 47.70 48.90 49.31 49.J7 56.42 60.98 60.86 62.76 65.67 66.68

Olff .oo .oo .oo .oo -.28 .10 1.50 2.11 3.51 3.72

PCNT .o .o .o .o -.5 .2 2.5 4.5 s.1 5.9

WVA14 BASE 62.34 63.14 64.54 65.32 COMP 65.98 66.44 67.65 68.30 Olff 3.65 J.29 3.11 2.98 PCNT 5.8 5.2 4.8 4•6

BASE MEAN= 50.o5 PCNT Oiff MEANS= 3.o AV ABS ·U IFF= 1.11 OifF" PCNT OF" BASE MEAN= J.l

lliVA15 BASE 228.81 233.91 250.92 cll.09 244.21 261.18 268.36 273.3? 280.45 2fl6.08

COMP 228.81 233.91 250.92 c 11. 09 244.13 260.'H 269.35 275.20 283.23 288.89

Dlff .oo .oo .oo .oo -.oa -.27 .99 l .8fl 2.18 2.01

PCNT .o .o .o .o -.o -.1 .4 .1 1.0 1.0

WVA15 BASE 289.30 295.01 300.90 306.64 COMt' 292.12 297.34 302.98 308.52 DI ff 2.82 2.33 2.08 1 • 88 PCNT 1.0 .0 .1 •6

BASE Ml:AN= 266.44 PCNT Oiff ME.ANS= •5 AV ABS DIFF= 1-28 Olff PCNT Of BASE MEAN= .5

VARIABLE NAME 1972 1973 l ':17 4 1975 1976 1977 1978 1979 1980 1981

1982 !983 1984 1985 WIIA16 BASE 251.33 260.12 205.22 233.49 271.52 285.76 293.32 298.9B 307.03 3!3.50

COMP 251.33 260.12 20s.22 233.'+9 271 .43 285.49 294.38 3o0.84 309.75 316.17

Olff .oo .oo .oo .oo -.09 -.21 l • 06 1 .86 2.12 2.67

PCNT .o .o .o .o -.o -.1 .4 .6 .9 .9

WIIA16 BASE 317.59 324.50 331.33 338. l 5 COMP 320.28 326.68 333.30 339.92 Olff 2.69 2.10 1.97 1.11 PCNT .8 .1 .6 .5

BASE MEAN= 293.70 PCNT DifF ME.ANS= •4 AV ABS DlFF= 1•23 DIFF" PCNT Of BASE MEAN= .4

, WVAl 7 BASE 364.84 362.52 369.36 329.75 382.98 432.45 447.22 455.56 467.82 477.19

COMP 36'+.84 362.52 369.36 329.75 382.B5 431.76 449.20 459.B3 474.30 483.92

Olff .oo .oo .oo .oo - • l 3 -.69 1.98 4.27 6.48 6.73

PCNT .o .o .o .o -.o -.2 .4 .9 lo4 1.4

WIIA17 BASE 480.87 488.IH 498.34 '::>06.84 COMP 487.61 494.52 503.25 s11.22 DI ff 6.74 s.os 4.91 4.38 PCNT le4 1.2 1.0 .9

BASE MEAN= 433 • 19 PCNT OlFf M£ANS= • 1 All ABS Olff= 3.00 Olff PCNT or BASE MEAN= .1

WIIA18 BASE 150.88 138.23 12s.00 100.10 136.13 167.53 174.!2 177.58 183. 17 186.89

COMP !50 088 138.23 12s.ao 100.10 136.06 167.21 175. 10 179.74 186.42 190.32

Dlff .oo .oo .oo .oo -.01 -.32 .98 2.16 3.25 3.43

PCNT .o .o .o .o - • l -.2 .6 1.2 loB 1.a

W\IAlB BASE 187.78 190.57 !94.62 198.52 COMP 191.23 193.49 197.16 200.79 Olff 3.'+5 2.92 2.54 2.21 PCNT 1.8 1.5 1.3 lol

BASE MEAN= 165.14 PCNT Diff MEANS= .9 AV ABS OIFf= 1.s3 Olff PCNT Of BASE MEAN= .9 I

"' ..... (I)

WIIA19 BASE 99.86 102.37 87.Sl 76.lj} 105.35 135.92 145.97 154.28 165 .16 174.07 I

COMP 99.86 102.37 87.51 76.81 105.26 135.54t 147.19 157.17 169.73 179.04

Dlff .oo .oo .oo .oo -.09 -.38 1.22 2.89 4.57 4.97

PCNT .o .o .o .o -.1 -.3 .B 1.9 2.8 2.9

W\1Al9 BASE 173.85 l p6 • 76 195.48 204.07 COMP 178.93 191.19 199.44 207 0 b9 Olff 5.08 4.43 3.96 3.62 PCNT 2.9 2.4 2.0 1-8

BASE MEAN= 143.39 PCNT DifF MEANS= 1.5 AV ABS Dlff= 2.23 OIFF PCNT Of BASE MEAN= 1.6

WVA20 BASE 36.60 37.28 31.22 36.l:ll 39.21 39.68 40.22 40078 41.60 42.00

COMP 36.60 37.28 31.22 36.81 39.12 39.77 40.67 41.65 42.79 43.43

DIFf .oo .oo .oo .oo -.10 .09 .44 .87 1.19 1.44

PCNT .o .o .o .o -.2 .2 I • I 2.1 2.9 3.4

WVA20 BASE 42.17 42.59 43.05 43.41 COMP 43.72 44.19 44.69 45.07 Olff l .55 l. 59 1.63 1.67 PCNT 3.7 3.7 3.8 3.0

BASE MEAN= 40.19 PCNT Oiff Ml:.ANS= 1.0 AV ABS OifF= 076 DIFF" PCNT Of BASE MEAN= 1.9

WIIA21 BASE 79.21 72.'=>6 76.71 68.08 70.94 72.47 73.77 75.18 11.01 78.78

COMP 79.21 72.56 76.71 68.08 70.92 72.49 73.B2 75-28 77.14 78.94

Dlff .oo .oo .oo .oo -.02 .02 .o5 • 1 0 .13 • 16

PCNT .o .o .o .o -.o .o • l • l .2 .2

W\IA21 BASE 80.66 82.65 84.82 87.17 COMP 80.82 82.82 85.oo 87.36

Dlff • 1 7 .lB • 18 .19

PCNT .2 .2 .2 .2

BASE MlAN= 77. l'+ PCNT DlfF Mt.ANS= • l AV ABS Olff= .09 DIFF" PCNT Of BASE MEAN= .1

I/ARI ABLE NAME. 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981

1982 1983 1984 1985 WI/A22 BASE 141.01 144.23 144.27 143.51 146.86 148.39 149.45 150.64 152.03 152.87

COMP 141.0l 144.<:3 144.27 143.51 146.76 148.50 l49.R3 151039 153.04 154.12

DIH .oo .oo .oo .oo -.10 .11 • 38 .75 1.01 l .25

PCNT .o .o .o .o - .1 • l .3 .5 .1 .a 11il/A22 BASE 153.64 154.62 155.58 156.48

COMP 155.00 156.06 151.09 150.05 DIH l • 36 l • 44 1.51 1.57 PCNT .9 • 'i 1.0 1.0

BASE MEAN= 149.':>4 PCNT DifF M[ANS= .4 AV ABS 0Iff= o6A Olff PCNT Of BASE MEAN= .5

llil/A23 BASE 189.48 199.29 19t>.95 189.41 206.51 217.47 223.94 230.46 237.91 243.52

COMP 189.48 199.29 196.95 189.41 206.27 .:17.65 224.63 231.85 239.69 245.69

DIH .oo .oo .oo .oo -.24 .18 .69 1.39 1.18 2. 17

PCNT .o .o .o .o - • 1 • l .3 .6 .1 .9

llil/A23 BASE 249.22 255.36 261.60 267.89 COMP 251.52 257.76 264.09 270 .44 Olff 2.30 2.40 2.49 2.s5 PCNT .9 .9 l • O 1.0

BASE MEAN= 226.36 PCNT Diff MEANS= .5 Al/ ABS DIFf= 1•16 Dlff PCNT Of BASE MEAN= .5

WI/A24 BASt. 158.59 110.10 175.40 176.54 194.67 202.43 209.24 211.10 226.54 232.63

COMP 158.59 110. 10 175.40 176.54 193.98 203.24 212.oe 222.00 234.ll 242.05

Olff .oo .oo .oo .oo -.69 .Bl 2oA4 5.62 7.57 9.42

PCNT .o .o .o .o -.4 .4 1.4 2.6 3.3 •• o

WI/A24 BASE 238.24 245.46 252.66 259.42 COMP 248.55 250.Sl 264.34 271.63 OIH 10.31 11.05 11.68 12.21 PCNT 4.3 4.5 4.6 4.7

I

BASE MEAN= 211.30 PCNT Dlff MEANS= 2.4 Al/ ABS Olff:: 5• 16 Olff PCNT Of BASE MEAN= 2.4 "' .... "' I

WVA25 !:!ASE 160.41 174.92 16 / .48 161.74 180. 73 195. 72 203.91 211.9A 221. 73 229.97

COMP 160.41 174.92 161.48 161.74 180.64 195.85 204.19 212.52 222.42 230.88

OJH .oo .oo .oo .oo -.09 .13 .28 .54 .69 .91

PCNT .o .o .o .o -.o • l • l .3 .3 .4

WI/A25 BASE 238.58 247.55 256.80 266.33 COMP 239.57 248.b7 257.99 267.bO DIH .99 1.12 1.19 1.21 PCNT .4 .5 .5 .5

BASE ME.AN= 208.42 PCNT Dlff ME.ANS= ·2 AV ABS Dlff= .52 Dlff PCNT Of f!ASE MEAN= .2

WI/A26 BASE 16.03 11.05 11 • 61 16.88 }9.72 20.92 21 .59 22.42 23.45 24.18

COMP 16.03 17.65 17.61 lo.88 19.62 20.98 21.90 23.0"i 24.27 25. 15

OIH .oo .oo .oo .oo - • l 0 .06 .31 .63 .02 .97

PCNT .o .o .o .o -.5 .3 1.5 2.8 3.5 4.0

WI/A26 BASE. 24.74 25.53 20.37 27.16 COMP 25.76 26.':>7 27 .43 28.25 Dlff 1.02 1.04 l. 06 1.00 PCNT 4.1 4. l 4.0 4.0

BASE ME.AN= 21.13 PCNT Diff MtANS= 2.3 Al/ ABS Dlff= .51 Dlff PCNT Of BASE MEAN= 2.3

WI/Al7 BASE 110.05 115.56 112.57 116.ll 134.95 149.92 159.86 170.27 182.23 190.36

COMP 110.05 115.56 112.57 11(:,.ll }34.62 150.25 161.41 173.35 186.46 195.49

Dlff .oo .oo .oo .oo -.33 .33 1.55 3.08 4.23 5.13

PCNT .o .o .o .o -.2 .2 1.0 1.8 2.3 2.1

WI/A27 BASE 197.20 205.35 213.79 222.01 COMP 202.78 211.19 219.91 228.38

DIH 5.58 5.84 0.12 6.37

PCNT 2.8 2.0 2.9 2.9 BASE MEAN= 162.t:17 PCNT Diff M[ANS= 1.7 Al/ Al:!S Olff= 2.15 DlfF PCNT Of BASE MEAN= 1.1

VARIABLE NAME 1972 1973 l 'H4 197!:i 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WI/A28 BASE 17.08 19.10 18.67 17.18 20.05 21.35 22.30 2J.34 24.49 25.36 COMP 17.08 19.10 1&.67 17.18 20.03 21.39 22.40 23.53 24.73 25.67 Olff .oo .oo .oo .oo -.02 .04 .10 • 19 .25 .JI PCNT .o .o .o .o -.1 .2 .4 .8 1.0 1.2

WVA28 BASE 2b.22 27.16 2s.11 29.06 COMP 2b.55 27.53 28.50 29.47 DIFf .34 .37 .39 .41 PCNT 1.J 1.3 1.4 1•4

BASE MEAN= 22.82 PCNT OJff Mt:ANS= .7 All ABS Olff= .17 Dlff PCNT Of BASE MEAN= .8

WIIA29 BASE 85.84 82.87 81 .97 82.54 104.09 118.30 114.60 1)3.80 119.82 124.07 COMP 85.84 82.87 81.97 82.=>4 103.58 117.61 1 18 • 68 122.21 131. 74 136.64 Olff .oo .oo .oo .oo -.51 -.69 ... oe 8.41 11.92 12.57 PCNT .o .o .o .o -.5 -.6 3.6 7.4 9.9 10.1

WVA29 BASE 121.11 122.03 120.48 129.31 COMP lJJ.55 132.65 135.83 137.79 DIFf 12.38 10.02 9.35 8.48 PCNT 10.2 a.1 1.4 606

BASE ME.AN= 109006 PCNT Diff MlANS= 5.0 AV ABS Dlff= 5e64 OlfF PCNT OF BASE MEAN= 5.2

WVAJ0 BASE 72.33 75.24 7:..22 71.15 82.74 86.89 87.21 R7.94 90.09 90. 72 COMP 72.33 75.24 15.22 71. 15 a2.44 86.98 88.30 90.09 92.82 93.67 DIFf .oo .oo .oo .oo -.30 .o9 1 • 09 2.15 2.74 2.95

PCNT .o .o .o .o -.4 • 1 1.3 2.4 3.0 3.2 WIIA30 BASE 90.80 91.55 92.76 93.70

COMP 93.59 94.06 95.04 95,82 OIFf 2.80 2.51 2.28 2,13 PCNT 3.1 2.1 2.5 2.3 I

BASE ME.AN= 84.88 PCNT DifF MEANS= 1•6 AV ABS DlFf= 1 • 36 DIFF PCNT OF BASE MEANz 1.6 "' "' 0 I

WVA31 BASE 30.62 26,85 21.39 15.32 24.22 25.72 25.s7 24.91 24.59 23.48

COMP 30.62 26.85 2i.39 15.32 24.19 25.71 25.70 2s.11 24.93 23.84 DIFf .oo .oo .oo .oo -.02 -.01 • 13 .2s .34 .36

PCNT .o .o .o .o -.1 -.o .s 1.0 1•4 1.s

WIIA31 BASE 22.45 21.51 20.69 19.83 COMP 22.79 21.81 20.95 20.01 Dlff .34 .JO .26 .24 PCNT 1.5 lo4 l.J 1.2

BASE MEAN= 23.37 PCNT DlfF MEANS= .7 AV ABS DIFF= •16 OlfF" PCNT OF BASE MEANz .7

lltl/A32 BASE 257.14 259.09 271.18 244.75 290.50 312.96 327.87 340.94 355.b4 363.47

COMP 25 7 .14 259.09 271•18 244.75 290.42 312.97 328.20 341.61 356.52 364.43

Olff .oo .oo .oo .oo -.08 .01 .33 .67 .as .96 PCNT .o .o .o .o -.o .o • 1 .2 .2 .J

WIIA32 BASE 373.66 383.95 394.79 405.79 COMP 374.60 384.81 395.59 406.56 DIFf .94 .86 .so .11 PCNT .3 .2 .2 .2

BASE MEAN= 321.21 PCNT OifF MEANS= • 1 AV ABS OIFF= .45 OIH PCNT OF BASE MEAN= • 1

WVAJJ BASE 67.43 70.bl 71.33 10.05 82.99 90.85 90.93 91.84 95.63 97.88

COMP 6 7 .43 10.01 71.33 70.05 82.51 90.85 93.ol 96.0) 101.23 104.00

DIFf .oo .oo .oo .oo -.48 -.oo 2.oe 4.20 5.60 6.12

PCNT .o .o .o .o -.6 -.o 2.3 4.6 5.9 6.3

WIIA3J BASE 97.99 99eb5 102.21 ! 04.44 COMP 104 • 04 105.20 107.44 109.38

Dlff 6.05 5.55 s.11 4.94

PCNT 6.2 5.6 s.1 4.7 BASE MEAN= 8 8.13 PC,,,T D I f f MEANS= 3.2 AV ABS DIFF= 2.87 OlFF PCNT OF BASE MEAN= 3.3

VARIABLE NAME 1972 1973 1974 197':> 1976 1977 1978 1979 1980 1981 1982 1983 1984 198::J

lliVAJ4 BASE 63.07 67.22 66.87 63.b3 70.45 73.09 74.f.8 76.33 78.62 A0.19 COMP 63.07 67.22 6b.87 63.63 10.21 73.23 7!:i.)6 11.10 80.36 82.24 OIFF .oo .oo .oo .oo -.24 .14 .69 l .38 l • 75 2.05 PCNT .o .o .o .o -.3 .2 .9 l.A 2.2 2.6

wVA34 BASE 81.34 82.91 84.56 86.12 COMP 83.43 05.01 86.68 88.26 OIFF 2.09 2.il 2.12 2 .14 PCNT 2.6 2.5 2.5 2.5

BASE MEAN= 74.93 PCNT O}fF Mt.ANS= 1.4 AV ABS DIFF= 1.05 OIFF PCNT OF BASE MEAN= 1.4

WIIA35 BASE 40.41 46.98 47.47 42.05 45.92 47.83 49.28 51.23 53.29 55.02 COMP 40.41 46.98 47.47 42.05 45.88 47.86 49.39 5 l .44 53.57 55.37 OIFF .oo .oo .oo .oo -.04 .03 • l l .21 .28 .36 PCNT .o .o .o .o - .1 • l .2 .4 .5 .6

wVAJ5 BASE 56.55 58.22 59.90 61.60 COMP 56.94 58.65 60.35 62.07 OIFF .39 .43 .45 .<+7 PCNT .1 .1 .8 .8

BASE MEAN= 51 .13 PCNT OIFF MEANS= •4 AV ABS DIFF= .20 OIFF PCNT OF BASE MEAN= .4

WIIA36 BAS[ 46.28 56.53 58.96 52.20 6!:l.64 73.53 76.80 79.97 84.55 87.38 COMP 46.28 56.53 58.96 52.20 68. 13 73.81 11.00 82.08 87.08 90.43

DIFF .oo .oo .oo .uo -.51 .28 1.00 2. 11 2.53 3.05

PCNT .o .o .o .o -.1 .4 1.3 2 .t. 3.0 3.5

iiVA36 BASE 90.45 93.81 97.29 100.19 COMP 93.52 96.99 100.56 l 04 • 16 DIFF 3.07 3.19 3.27 3.37 PCNT 3.4 3.4 3.4 3.3 I

BASE MEAN= 76.23 PCNT DIFF Mt.ANS= 2.0 AV ABS DIFF= 1•60 DIFF PCNT OF BASE MEAN= 2. 1 N N ,.... I

WVAl7 BASE 64.13 83.74 81.68 73.42 95.80 104.95 111.86 119.61 120.50 133.94

COMP 64.13 83.74 81.68 73.42 95.56 105.09 112.49 120.94 130.21 135.97

DIFF .oo .oo .oo .oo -.25 .14 .63 1.33 l • 71 2.03

PCNT .o .o .o .o -.3 .1 .6 1. l 1.3 1.5

WVA37 BASE 140.12 146.78 153.75 160.87 COMP 142.18 148.88 155.88 163.04 OIFF 2.06 2 • I 0 2.13 2.11 PCNT 1.5 1.4 1.4 1.3

BASE MEAN= 114.23 PCNT OifF ME.ANS= .9 AV ABS DIFF= 1•04 OIFF PCNT OF BASE MEAN• .9

WVA38 BASE 57.03 58.78 5&.92 55.75 63.53 68.57 71.91 74.76 78.10 A0.56

COMP 57.03 58.78 58.92 5!:>. 75 63.36 68.63 72.34 75.67 79.2b 81.92

DIFF .oo .oo .oo .oo -.11 .06 .43 .91 1.16 l • 36

PCNT .o .o .o .o -.3 • l .6 1.2 1.s 1.1

WVA38 BASE 82.93 85.56 88.39 91.23 COMP 84.30 86.93 89.76 92.bl OIFF l. 37 l .37 I• 37 1.38 PCNT 1.1 1-6 1.5 l .5

BASE MEAN= 72.57 PCNT OIF F ME.ANS= .9 All ABS DIFF= •68 OIFF PCNT OF BASE MEAN= .9

lli\lAJ9 BASE 944.74 1209.10 1330.90 1041. 70 1245.30 1192. 70 )264.30 1257.70 1304.30 1340.20

COMP 944.74 1209.10 1330.90 1041.10 })61.50 1227.40 }399.60 1553.30 1653.90 1764.20

DIFF .oo .oo .oo .oo -aJ.00 34.70 135.30 295.60 349.60 424.00

PCNT .o .o .o .o -6.7 2.9 10.7 23.5 26.8 31.6

WVA39 BASE 1379.90 1420.00 l46l.5o l':>04.00 COMP 1804.30 1863.50 1920.60 1979.30 OIFF 424.40 <+43 • ':>O 459.10 <+75._jO PCNT 30.8 31•2 31.4 31•6

BASE MEAN= 1278.31 PCNT DIFF MEANS= )6.5 AV ABS DIFF= 223.24 OIFF PCNT OF BASE MEAN= 11.5

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 )981 1982 1983 1984 1985

WVA40 BASE. 78.90 78.31 79.40 80.<+7 92.37 99.40 103.c:;3 lo9.07 l 14.37 110.02 COMP 78.90 78.31 79.40 80.<+7 92.31 99.45 103.74 109.41'1 114.92 118.69 DIH .oo .oo .oo .oo -.06 .04 .21 .41 .55 .67 PCNT .o .o .o .o - • l .o .2 .4 .5 .6

WVA40 BASE 122.74 127.!::>4 132.40 137.24 COMP 123.45 128.27 133.16 138.03 DIH .71 .73 .76 .79 PCNT .6 .6 .6 • 6

BASE MEAN= 105.27 PCNT OJFF ME.ANS= .3 AV ABS DIFF= .35 OifF PCNT OF BASE MEAN= .3

WVA41 BASE 281.78 287.76 292.45 288.bl 303.92 278.24 296.56 301.13 308.45 32 l. 05 COMP 281.78 287.76 292.45 288.bl 303.87 278.38 296066 301.17 308.61 321.49 DIFF .oo .oo .oo .oo -.05 .14 .10 .04 .16 .44 PCNT .o .o .o .o -.o • 1 .o .o • l • l

Ii VA4 l BASE 327 .57 335.17 341. 98 348.90 COMP 328.31 336.17 343.22 350.27 DIFF .74 1.00 1.24 1.37 PCNT .2 .3 .4 .4

BASE MEAN= 308.13 PCNT DtFF MEANS= • l AV ABS DIFF= .39 DIFF PCl',T OF BASE MEAN= • l

WVA42 BASE 110.99 129.•n 126.53 120.48 143.79 163.47 115.22 190.19 203.38 2)5.03 COMP 110.99 129. 'H 126.53 120.<+8 143.38 163.71 176.43 192.70 206.67 219.00

DifF .oo .oo .oo .oo -.41 .24 1.21 2.51 3.29 3.97

PCNT .o .o .o .o -.3 .1 .1 l.3 1.6 1.0 WVA42 BASE 225.56 237.27 249.51 261.95

COMP 229.73 241.60 25J.99 206.58 DIH 4.17 4.33 4.48 4.63 PCNT 1.0 1.8 1.8 1•8 I

BASE MEAN= lA2.38 PCNT DtFF MEANS= l • l AV ABS DIFF= 2.09 OIFF PCNT OF BASE MEAN= l • l "' "' "' I

WYA43 BASE 934.23 1019.70 1011.10 ~66.49 1074.10 1166.30 1215.80 1264.70 1322.80 1363.10 COMP 934.23 1019.70 1011.10 9t,6.'+9 1012.00 1168.20 1223.60 1279.80 1342.40 1386.50

OIH .oo .oo .oo .oo -2.10 1.90 1.00 15.10 19.60 23.40

PCNT .o .o .o .o -.2 .2 .6 1.2 1.5 1.1

WVA43 BASE 1404.20 1452.bO 1503.90 1~55.60 COMP 1428.90 1477.90 1529.60 1581.so OIFF 24.70 25.Jo 25.10 26.20 PCNT 1.8 1.1 1.1 1.1

BASE MEAN= 1232.47 PCNT DtFF ME.ANS= l • 0 AV ABS DIFF= 12.27 OlfF PCNT OF BASE MEAN= 1.0

WVA44 BASE 345.90 369.58 382.96 381.73 416.18 428.58 436.97 447.09 459.42 464.92

COMP 345.90 369.58 382.96 381.73 414.69 430.08 442.65 458.33 474.55 483.59

OIH .oo .oo .oo .oo -1 .49 1.50 S.68 11. 24 15.13 18.67

PCNT .o .o .o .o -.4 .3 1.3 2.5 3.3 4.0

WVA44 BASE 469.38 476.25 482.88 488.48 COMP 489.69 497.76 5o5.31 !::>11.01 DIH 20.31 21.51 22.43 23.13 PCNT 4.3 4.5 4 .6 4.7

BASE MEAN= 432.17 Po,;T DIFF MEANS= 2.3 AV ABS OIFF= 10.oe OIFF PCNT OF BASE MEAN= 2.3

IIVA45 BASE 75.18 78.66 79.67 77.95 83.92 85.37 85.59 A6o 14 87.13 A8.16

COMP 75.18 78.66 79.67 77.CJ5 83.61 8S.o4 86.68 88.28 89.98 91066

DIFF .oo .oo .oo .oo -.31 .21 1.09 2.15 2.85 3.50

PCNT .o .o .o .o -.4 .3 1.3 2.5 3.3 4.0

WVA45 BASE 88.89 90.08 91.21 92.28 COMP 92.68 94.06 95.39 96.~l OIH 3.78 3.98 4.12 4.23 PCNT 4.3 4.4 4.5 4.t,

BASE ME.AN= as.oz PCNT DtFF Mt.ANS= 2 • 2 AV ABS UIFF= lo8A OIFF Pcr-.r OF BASE MEAN= 2.2

VARIABLE NAME 1972 197.J 1974 197':> 1976 1977 197A 1979 1980 )981

1982 1983 1~84 198':, wVA46 BASE 133.83 143.33 148.01 147. 9 0 163.09 169.79 176.03 183.0f, 191.28 196059

COMP 133.83 143.33 148.0I 147.90 )62.44 170.53 1 78. 73 188.49 )98 0 7t> 206.03 DIFF .oo .oo . oo -.65 .74 2 . 70 5 .4 ) 7.48 <J.44

PCNT .o .o . o -.4 .4 I. 3 . o 3 . ... 4.8

WVA,.6 BASE 2 01 .49 01 . 01 . 77 COMP 2 1 .o l . 07 . 9 DIFF I O. l 1 • 1• 0 • l PCNT .

BA M AN■ l o. l t p H i ' V ll , I HI fl

WVA47 BA E 1,4 • 0 i 1, 11 6 • 0 J 0 .I I COMP 44 6 .01 1, 00,0 ov. 11 D l ff' .oo ,u o 00 PCNT . o •O .o • 0 • ~ r, ,, .

WVA,.7 BASE 79 • 9 4 8 I • 6()11 . 6 04 . (} } COMP 834. 24 8 7 5 . 0 9 16 - 07 'I - I DIFF 40.JO 44.21 47. 7 1 0. 10 PCNT 5.1 5.3 5.5 5 - 6

BASE ME.AN:: 670034 PCNT DifF M£ANS= 2. 9 AV AB S Dl f'f' = 0.1 0 1 ,. PCNT 0 0A M AN■ ~o

WVA48 BASE 307.86 243.16 101.22 189.27 315.07 406.71 401.f,6 38 1 . 32 407. 3 0 44 2 • 14

COMP 307.86 243016 181.22 189027 315.07 395o26 421099 432.03 488.20 527.33

Dlff .oo oOO .oo .oo .oo -11.45 20.)3 so.11 90.90 85.19

PCNT oO .o .o oO oO -208 501 1303 l9o9 l9o3

WVA48 BASE 408004 389034 409093 4170~6 COMP 493.26 459ol7 461051 466ob6 DIFF 85o22 69083 57.58 48070 PCNT 20.9 11.9 14oO 11 o 7 I

BASE HE.AN= 350007 PCIIIT DI ff MEANS= 9.9 AV ABS Dlff: 36042 DifF' PCNT Of BASE MEAN= l0o4 N N ... I

WVA,.9 BASE 245o33 239037 261.69 249.82 29 l o24 326.70 311.12 304065 317023 323013

COMP 245.33 239.37 261069 .c49o82 289o24 326.58 319.80 322088 341 ... 8 350.04

Dlff oOO oUO oOO oOO -2.00 -ol2 8068 18.23 24025 26091

PCNT oO .o oO oO -.7 -.o 208 6.o 706 8.3

WVA49 BASE 324oll 331040 338.61 J48o25 COMP 350.80 356046 361074 J70o43 DIH 26069 25006 23ol3 22 o 18 PCNT 802 706 608 604

BASE MEAN= 300o~O PCNT Diff M£ANS= 4. 1 AV ABS DlFF'= 12066 OifF' PCNT Of BASE MEAN= 4o2

WVA5O BASE 93063 94045 93004 94.t4 115071 137.39 120.49 12lo90 128007 126.24

COMP 93.63 94.45 93004 94024 114 o 56 138. 72 121.51 l 32o 18 139.19 136.23

DIH .oo oOO oOO oUO -lol5 lo33 1o02 10028 11.12 9o99

PCNT oO .o oO oO -1 oO loO 508 804 a.1 7o9

WVA5O BASE 119094 122054 126063 126063 COMP 128041 129.07 132057 132o<!l DIH 8047 6053 5o94 5o!:>8 PCNT 7ol 5o3 4.7 4o4

BASE MEAN:: 115078 PCNT DifF Mt:ANS= 4o0 AV ABS Dlf'f= 4o81 Olff PCNT OF' BASE MEAN= 4o2

WVA51 BASE 338o49 355042 354.09 339027 380086 392009 389086 388070 395079 395018

COMP 338.49 355042 354009 3 39027 378.52 393.64 397094 403095 413096 414029

DIFF oOO oOO oOO oOO -2-34 l .55 800B 15025 18017 l9oll

PCNT oO .o oO oO -o6 0 4 2 o l 3o9 406 408

WVA51 BASE 391. 52 392058 396042 398.19 COMP 40Bo54 '+07o08 408087 409ol4

Dlff 17002 14050 12 045 lOo95 PCNT 4o3 3o7 3ol 2o7

BASE MEAN= 379 018 PCNT DJ Ff ME ANS = 2.2 AV ABS Dlff= 805 3 Dlff PCNT Of' BASE MEAN= 2o2

IIARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198~

lill\lA52 l:lASE 95.24 101.46 102.89 101. a e l 13 . 72 116.97 120.21 126.42 131. 97 135.41 COMI' 95.24 lol.46 lOi.89 101. 88 113.35 117.44 121.61 129.04 135.67 140.33 DIFf .oo .oo .oo . oo -.37 .47 1.40 2.62 3.70 4.92 PCNT .o .o .o .o -.3 .4 1.2 2.1 2.8 3.6

lill\lA52 BASE 139.18 143.89 14 7 • 90 l5l.b3 COMP 144.93 150.35 154.96 159.IO DIFf 5.75 6.46 7.06 7.47 PCNT 4. l 4.5 4.B 4.9

BASE MEAN= 123.4B PCNT DifF MEANS= 2 .3 All ABS DIFF= 2•87 [)IfF PCNT OF BASE MEAN= 2.3

wllA53 BASE 3321.60 3659.iO 3831.20 39 10.so 4372.10 4610.60 4787.10 4982.10 521B.00 5378.10

COMP 3321.60 3659.20 3831.20 39 18.50 4 357.80 4626.10 4851.20 5108.60 5390.20 5590.00 OIFf .oo .uo .oo .oo -14.30 15.50 63.50 126.50 112.20 2 l l. 90 PCNT .o .o .o .o -.3 .3 1.3 2.5 3.3 3.9

lill\lA53 BASE 5515.70 5689.70 5B70.30 6039.80 COMP 5747.10 5934.80 6121>.70 6306.10 DIFf 231.40 245.10 251>.40 c66.30 PCNT 4.2 4.3 4.4 4.4

BASE MEAN = 4799.61 PCNT OifF MEANS= 2.3 Al/ ABS DIFF= 114•51 OIFF PCNT Of BASE MEAN= 2.4

w\lA54 BASE 1026.BO 1095.BO 1122.40 1122.<+o 1222.00 1273.50 1312.20 1359.90 1414.70 1450.30

COMP 1026.BO 1095.80 1122.40 1122.'+0 1219.20 12 77.70 1328.30 1391.70 1458.40 1504.90

DIFf .oo .oo .oo .oo - 3.60 4 . 20 u,.10 31.80 43.70 54.60

PCNT .o .o .o .o -.3 .3 1.2 2.3 3. l 3.8 w\lA!:>4 BASE 1481.80 1521.90 1561.50 1598.30

COMP 1542.50 1587.20 1630 . 60 11>70.40 DIFf 60.70 65.30 69.IO 12.10 PCNT 4.1 4.3 4.4 4.5 I

BASE MEAN= 1326.02 PCNT DI FF MEANS= 2.2 All Al:IS DIFF= 30.09 OIFF PCN T OF BASE MEAN= 2.3 N N -I'-I

lill\lA55 BASE 2083.10 23 04.70 2 448.60 2505.10 2111. 10 2897.60 2991 . 50 3 102.90 3237.90 3324.50

COMP 2063.10 2 304.70 2448.60 2s o5 . 10 2 763. 70 2910. 2 0 3 04 3.oo 3206 . 60 3378.20 3498.20

Diff . oo .oo .oo .oo -14.00 12.60 51.50 103.70 140.30 173.70

PCNT .o .o .o .o -.5 .4 1.1 3.3 4.3 5.2

W\IA55 BASE 3397.90 34 95.80 3594.5 0 31>8<+ . t>O COMP 3587.70 3698. 2 0 3607.20 3906.30 DIFf 189.80 202.<+o 212 .10 t21. 10 PCNT 5.6 5.0 5.9 6.0

BASE MEAN= 2 989.03 PCNT DifF MEANS = 3 •1 Al/ ABS DIFF= 94•46 OIFF PCNT OF BASE MEAN= 3.2

1111/AC BASE 1588.90 1684.70 1721.90 11 0 1.10 1876.00 1956. 10 2 023.70 2105.40 2200.30 2258.90

COMP 1586.90 1684.70 1721.90 1101.10 1B69.00 1965.50 2057.50 2112.10 2292.40 2374.60

DIFf .oo .oo .oo .oo -7.00 9.40 33.ao 66. 70 92.10 115.70

PCNT .o .o .o .o -.4 .5 1.1 3.2 4.2 5. l

1111/AC BASE 2310.70 2379.60 2447.30 2508.40 COMP 2439.80 2519.40 2595.90 21>64.50 DIFf 129.10 139.80 148.60 156.10 PCNT 5.6 5.9 6.1 6•2

BASE MEAN= 2054.54 PCNT DIFF MEANS= 3.} Al/ ABS OIFF= 64.16 OIFF PCNT OF BASE MEAN= 3. 1

lil/AlfIX BASE .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

COMP .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

OIFF .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

1111/AlFIX BASE .oo .oo .oo .oo COMP .oo .oo .oo .oo DIFf .oo .oo .oo .oo

PCNT .o .o .o .o BASE MEAN= .oo PCNT DIFF MEANS= .o AV Al:IS DlfF= .oo DIFF PCNT Of BASE MEAN= .o

IIARJABLE NAME 1972 1973 1n4 197:::» 1976 1977 1978 1979 1980 1981 1982 1983 1984 198':>

WVAIJNV BASE .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo COMP .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo DJFf .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WVAlINV BASE oOO .oo oOO oOO COMP oOO oOO .oo .oo Dlff oOO .oo oOO .oo PCNT oO oO oO .o

BASE MEAN= .oo PCNT DifF ME.ANS= oO AV ABS C.llff= oOO Olff PCNT Of BASE MEANc oO

WVASLEDU BASE 941 o 62 937.22 956052 1007.60 972051 972.00 1061.AO 1082040 1096.00 1124030 COMP 94l o62 937.22 956.52 lU07 0 60 974046 '-167o53 1050oAO 1071040 1104080 1151. 10 Dlff oOO oOO .oo oOO 1095 -4047 -11 .oo -5oOO a.so ?6080 PCNT o0 oO oO oO o2 -05 -1.0 -o5 08 2o4

WVASLEDU BASE 1159.60 1168.20 1100.00 1201. ·10 COMP 1202090 1223.50 1242.20 ll74 0 bO Dlff 43030 55030 61040 660'10 PCNT 3o7 4o7 5.2 5.5

BASE MEAN= 1062.02 PCNT OJff M[ANS= lo6 AV ABS Olff= 20.35 Dlff PCNT Of BASE MEAN= lo9

WVASLOTH BASE 707.62 734oJ2 693054 b90 084 834.30 754o82 716.21 866015 912.72 933.74 COMP 707.62 734.32 693054 690084 833.18 761.39 713.76 846099 894.03 930.26 Dlff .oo .oo .oo oOO -lol2 6.57 -2045 -19016 -18069 -3.48

PCNT oO oO oO oO -ol o9 -o3 -202 -2.0 -o4

WVASLOTH BASE 995094 1073.00 1100.50 1123.90 COMP 1014040 1112.Jo 1159000 1192.20 Olff 18046 39.30 58.50 68.JO PCNT lo9 3o7 5.3 6ol I

BASE MEAN= 866097 PCNT Diff MEANS= lo2 AV A!lS Dlff= 16086 Olff PCNT Of BASE MEANs lo9 N N VI I

WVAfED BASE 935067 905067 892o78 t!74o84 873095 873o07 872018 871030 870041 869052

COMP 935067 905.67 892o78 !!74.84 873095 613o01 872018 871 o 30 870041 869o52

Dlff oOO .oo oOO .oo oOO oOO oOO oOO .oo oOO

PCNT .o .o oO oO .o .o oO .o .o oO

WVAfED BASE 868064 861 o ·,s 866087 t!65o98 COMP 868064 867.75 866087 t!6!:>o98 Dlff oOO .oo oOO oOO PCNT .o oO oO oO

BASE MEAN= 879ol9 PCNl OJff MEANS= oO AV ABS Dlff= 000 Olff PCNT Of BASE MEAN= oO

WVATOT BASE 19164000 20463.00 20948000 20'+89000 22881.00 23856.00 24619.oo ;>5470000 26503000 27214000

COMP 19164000 20463000 20948000 20489.00 22738000 23938.00 25014.00 ?6294.00 27601.00 28568.00

Dlff .oo .oo .oo .oo -143.00 82.00 395.oo 824000 1098.00 1354.00

PCNT .o .o .o .o -.6 .3 1.6 3.2 4. l 5.0

WVATOT BASE 27816.00 28590.00 29378.00 J0124o00 COMP 29275.00 30127.00 30979.00 31780.00 Dlff 1459000 1537.00 1601.00 1656.00 PCNT 5.2 5.4 S.4 5.5

BASE MEAN= 24822050 PCNT Diff MEANS= 208 AV ABS Olff= 724093 OlfF PCNT Of BASE MEANs 2.9

WYLOI BASE 157.76 190.74 196o29 c06.'17 245.27 238.37 256.76 260.19 273.60 278.52

COMP 157076 190.74 196.29 206.97 244087 239.37 257.77 262.68 276.17 282022

Olff oOO .oo .oo .oo -.40 1.00 l • 0 1 2.49 2.57 3o70

PCNT .o .o .o .o -.2 .4 .4 1.0 .9 1.3

WYL01 BAS[ 289.12 2Q7o60 301.20 Jl6o40 COMP 292.52 301.10 3llo27 :i20.80 Olff 3.40 4. 1 o ... 07 4o40 PCNT 1.2 lo4 1.3 lo4

BASE MEAN= 251.06 PCNT DJFF MtANS= .1 AV A8S [JJff= l .94 Olff PCNT Of BASE MEAN= .a

VAklABLE NAME 1972 1973 1974 1975 1976 1977 197A 1979 1980 1981 1982 1983 1984 1985

WYL02 SASE 254.45 287.68 304.72 JJ5.=>2 328.23 340.78 348.36 351.21 367.53 372.89 COMP 254.45 287.68 304.72 :n5.52 327.90 341.26 349.AO 359.96 371.20 377.46 DIH .oo .oo .oo .oo -.JJ .48 l.44 2.1r::, J.67 4.57 PCNT .o .o .o .o -.1 • l .4 .A 1.0 1.2

WYL02 BASE 378.22 384.46 390.13 396.'19 COMP 383.22 389.81 396.34 402."18 DIH s.oo 5.Js 5.61 s. 79 PCNT 1.J 1.4 1.4 1.5

BASE MEAN= 346.27 PCNT DIFF Mt.ANS= .7 AV ABS DIFF= 2.50 DIFF PCNT Of BASE MEAN= .1

WYLOJ BASE 74.22 84.22 91.23 97.b6 llL'.51 ll0.67 119.oo l 21 .56 128.23 129.59 COMP 74.22 84.22 91.23 97.66 112.00 l 11 .67 120.50 125.19 132.59 135.23 DIH .oo .uo .oo .oo -.51 1.00 1. !10 3.63 4.36 5.64 PCNT .o .o .o .o -.s .9 1.5 3.0 J.4 4.4

WYLOJ BASE 133.41 137.05 140.64 143.!:!6 COMP 139.20 143.49 147.31 150.87 Dlff 5.79 6.44 6.67 1.01 PCNT 4.3 4.7 4.7 4.9

BASE MEAN= 115.99 PCNT DifF ME.ANS= 2.6 AV ABS DIFF= J.06 Olff PCNT Of BASE MEAN= 2.6

WYL04 BASE 25.91 27.94 29.65 31.02 J2.56 33.21 33.56 34o 0? 34.66 34.88 COMP 25.91 27.94 29.65 31.02 32.49 33.27 33.87 34.63 35.51 35.91

OIH .oo .oo .oo .oo -.06 .06 .31 .62 .as 1.03

PCNT .o .o .o .o -.2 .2 .9 1.8 2.s 3.0 WYL04 BASE 34.95 JS. 19 35.46 35.65

COMP 36.07 36.36 36.66 36.88 DIH 1.12 1.16 1.20 1.23 PCNT 3.2 3.3 3.4 3.5 I

BASE MEAN= 32.76 PCNT Diff ME.ANS= 1•6 AV AtlS DIFf= .5c; DlfF PCNT OF BASE. MEAN= 1.7 ..., ..., "' I

WYL05 BASE 11.11 18.49 18.48 l 8 • =>O 20.01 21.0J 21.99 23.02 24.13 25.29 COMP 17. 77 18.49 18.48 18.50 20.04 21.01 22.09 2J.22 24.39 25.63

DlH .oo .oo .oo .oo -.02 .OJ .io .19 .26 .33

PCNT .o .o .o .o -.1 .2 .4 .8 1 • 1 1.3

WYLOS BASE 26.49 27.80 29.15 30.54 COMP 26.86 28.~l 29.59 31.01 Dlff .37 .41 .44 .47 PCNT 1.4 1.s 1.5 1.5

BASE MEAN= 23.05 PCNT DIFF Mt.ANS= .a AV ABS lJIFf= .19 0 IFF PCNT Of BASE MEANz .8

WYL06 BASE 28.25 28.79 29.14 28.56 31.15 32.22 33.16 34.33 JS.68 36.49

COMP 28.25 28.79 29.14 28.=>6 31.04 32.37 33.66 JS .JO 37.0l 38. 16

DIFf .oo .uo .oo .uo -.ll .14 .49 .97 1. 33 1.67

PCNT .o .o .o .o -.3 • 4 1.5 2.8 3.1 4.6

WYL06 BASE 37.22 38.20 39.16 40.02 COMP 39.08 40.22 41.31 42.c8 DIH l.86 2.02 2.15 2.26 PCNT s.o 5.3 S.5 506

BASE MEAN= 33.74 PCNT DIFF Mt::ANS= 2.1 AV ABS DIFF= .93 OlfF PCNT OF BASE MEAN= 2.8

WYL07 BASE 29.88 30.04 29.91 28.99 31 • 17 32.01 32.62 33.54 34.SS 34.80

COMP 29.88 J0.04 29.91 28.'19 Jl.07 32 .14 33.07 34.41 35.74 36 .28

Olff .oo .oo .oo .uo - • l O .13 .45 .87 1.19 1.48

PCNT .o .o .o .o -.3 .4 1.4 2.6 J.4 4.3

WYL07 BASE 34.98 35.36 JS.68 35.89 COMP 36.61 37.11 37.so 37.78 OIFF l.63 1.74 1.82 1 088

PCNT 4.7 4.9 s.i s.2 BASE MEAN= 32.82 PCNT DIF F ME.ANS= 2 •4 AV ABS DlFF= .81 l)lfF PCNT Of BASE MEAN= 2.s

VARIABLE NAME l 972 1973 1974 197~ 1976 1977 1978 }979 1980 1981 1982 !98J }984 198:i

WYL08 BASE 81.69 85.72 86.16 86.90 9J.71 97.60 10 l .38 105.47 109.80 114.26 COMP 81.69 85.72 86.16 86.90 93.62 97.74 101.80 106.26 1 1 0. 86 1 I 5. 60 OIFF .oo .oo .oo .uo -.09 .14 .42 .7Q 1.06 l • 34 PCNT .o .o .o .o - .1 • 1 .4 .1 1.0 1 .2

WYL08 BASE 118 • 78 123.72 128.77 133.92 COMP 120.28 125.35 130.s1 135.17 OIFF 1 .so 1 .63 1.14 l.tlS PCNT 1.3 1.3 1.4 1.4

BASE MEAN= 104.85 PCNT OIF F ME.ANS= .7 AV ABS DIFf= .75 f1IFf PCNT Of BASE MEAN= .1

WYL09 BASE 17.58 17.45 17.16 17.29 18.66 19.04 19068 20 • 14 20.11 21.26 COMP 17.58 17.45 17•16 17.29 18.63 19.09 19.78 20.34 21.03 21.59 OIFF .oo .oo .oo .oo -.03 .os .}0 .20 .26 .33 PCNT .o .o .o .o -.1 .3 .s 1.0 1.2 1.5

WYL09 BASE 21.84 22.'+5 23.06 23.67 COMP 22.20 22.84 23.47 24.09 OIFF .35 .39 .40 .42 PCNT 1.6 1.7 1.8 1.8

BASE MEAN= 20.00 PCNT OJFF ME.ANS= .9 AV ABS DlFf= • 1 Fl Dfff PCMT Of BASE MEAN= .9

WYLl0 BASE. 44.72 49.0l 48.09 42.75 48.27 51.9'+ 53.87 55087 58.15 60.00 COMP 44.72 49.0l 48.09 42.15 48.21 s2.02 54.12 56.37 58.82 60.83

Olff .oo .oo .oo .uo -.06 .00 .26 .so .67 .83

PCNT .o .o .o .o -.1 .2 .5 .9 l. l 1.4

WYLl0 BASE 61.54 63.27 64.99 66.08 COMP 62.46 64.25 66.03 67.76 Olff .91 .98 1.04 1.00 PCNT 1.5 1•6 1.6 1•6 I

BASE MEAN= 54.94 PCNT OJFF ME.ANS= .a AV ABS DIFF= 046 Dfff PCNT Of BASE. MEAN= .8 N N .... I

WYLll BASE 48.79 49.31 49.47 48.66 52 .12 53.29 54059 56010 57.82 S8.93

COMP 48.79 49.31 49.47 48.66 52.00 53.46 55.11 51.10 59. 15 60.61

Olff .oo .oo .oo .oo - • 12 .17 .52 .99 l • 33 J.68

PCNT .o .o .o .o -.2 .J .9 1.8 2.3 2.8

WYLll BASE 60.07 61.'+8 62.84 64 .10 COMP 61.92 63.50 64.98 66.J6 OIFF 1.85 2.01 2.14 2.25 PCNT 3.1 3.3 3.4 3.5

!:!ASE MEAN= 55.54 PCNT Oiff MEANS= 1.7 AV ABS Dlff= .93 Dfff PCNT Of BASE MEANa 1.1

WYLl2 BASE 4.37 4.'+8 4.63 4.50 s.01 s.29 s.so 5.68 5.88 5.97

COMP 4.37 4.48 4.63 4.50 s.oo 5.29 s.s1 5.7} s.92 6.01

DIFf .oo .oo .oo .oo -.oo .oo .01 .03 .oJ .04

PCNT .o .o .o .o -.o • l .3 .4 .6 .7

WYLl2 BASE 6.04 6.13 6.22 6.Jo COMP 6.09 6.18 6.26 6.35 Olff .04 .04 .os .us PCNT .1 .1 .8 .9

BASE MEAN= s.<+3 PCNT OIFf Mt.ANS= •4 AV ABS Dlff= •0? Olff PCNT Of BAS£ MEAN= .4

WYLl3 BASE. 37.23 37.80 38.20 38.!lO 42.50 44.49 46.39 48.13 so.13 SI .42

COMP 37.23 37.tlO 38.20 38.1:lO 42.46 44.S4 46.SS 48.43 50.53 51 .92

OIFF .oo .oo .oo .oo -.03 .05 .JS .30 .40 .49

PCNT .o .o .o .o -.1 • 1 .3 .6 .8 1.0

WYLlJ BASE 52.81 54.24 55.64 56.99 COMP 53.35 54.81 56.24 57.bl

DI ff .54 .57 .60 .62 PCNT 1.0 l • l l • l l. l

BASE ME.AN= 46.77 PCNT Oiff ME.ANS= 06 AV ABS 0Iff= .21 f)Jff PCNT Of BASE MEAN= .6

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

wYLl4 BASE 25.96 27.36 28.35 29.18 33.38 35.70 34.67 34.93 36.0l 36.37 COMP 25.96 27.36 2a.3s 29.18 33.22 35.76 3s.ss 36.Sl 38.05 38.52 DIFF .oo .oo .oo .oo -. 16 • 06 .87 l.SR 2.04 2.1s PCNT .o .o .o .o -.s .2 2.s 4.S s.1 s.9

WYL14 BASE 35 .90 36.26 3t>.9S 37.28 COMP 38.00 38.15 38.73 38.98 OIFf 2 • 10 l .89 l • 78 1.10 PCNT s.a s.2 4.8 4.t,

BASE MEAN= 33.45 PCNT Dlff Mt.ANS= 3.0 AV ABS Dlff= 1.02 Dlff PCNT Of BASE MEAN= 3.1

wYLlS BASE 14.55 14.83 15.86 13.JO 15.39 16.46 16.91 l 7.22 17.67 18.03 COMP 14.55 14.83 15.86 13.30 15.38 16.44 16.97 11.34 17.85 10.20 Olff .oo • oo· .oo .oo -.oo -.02 .06 .12 .17 • 18 PCNT .o .o .o .o -.o -.1 ·" .7 1.0 1.0

wYLlS BASE 18.23 18.59 18.96 19.32 COMP 18.41 18.74 19.09 19.44 OIFF .18 .is • 13 • 12 PCNT 1.0 .a .1 06

BASE MEAN= 16.81 PCNT Olff Mt.ANS= .s AV ABS Dlff= oOR OlfF PCNT OF BASE MEAN= .s

WYL16 BASE 163.96 }69.35 }85.31 151.40 117.30 187.90 194.22 199.36 206.16 213.45

COMP 163.96 _169.JS 185.31 151.40 117.24 187.73 194092 200060 207.98 215.27

OIFF .oo .oo .oo .oo -.06 - .17 .10 1.24 1.a2 1.a2

PCNT .o .o .o .o -.-o -. 1 .4 .6 .9 .9 WYL16 BASE 219.26 221.11 235 o l 9 243.39

COMP 221.11 228.69 236.59 244.b7 OIH 1 .as 1.s2 1.40 1.2a PCNT .a .1 .6 .5 I

BASE MEAN= 198.10 PCNT Olff MEANS= o4 AV ABS DIFF= .as OIFF PCNT OF BASE MEAN= .4 "' "' 00 I

WYL17 BASE. 198.0J 199.52 200.14 186.61 210.30 243.75 251 .57 255.75 262.10 268.68

COMP 198.03 199.52 206 o l 4 186.61 216.22 243 03b 252.68 258.15 265.73 272.47

OIFF .oo .oo .oo .oo -.oa -.39 1. 11 2.40 3.63 3.79

PCNT .o .o .o .o -.o -.2 .4 .9 1.4 }.4

WYLl 7 BASE 272.11 278.02 284.82 291.13 COMP 275.92 281 • .:?3 287.63 293.64 OIFF 3.81 3.21 2.81 2.51 PCNT lo4 1.2 1.0 .9

BASE MEAN= 243.89 PCNT OJfF Mt.ANS= .7 AV ABS DIFf= 1.10 OlfF PCNT OF BASE MEAN= .7

WYL18 BASE 89.90 82.93 n,.01 60.90 84.40 105.84 112.o'l 116. 4'l 122.44 128.04

COMP 89.90 82.93 76.0l 60 • '10 84.35 105.64 112. 72 117.90 124.61 130.40

OIFF .oo .oo .oo .oo -.04 -.20 .63 1.41 2.11 2.36

PCNT .o .o .o .o -.o -.2 .6 1.;> 1.a 1.a

wYLl8 BASE 131.87 137.18 143.59 150.14 COMP 134.30 139.28 145.47 151.85 OIFf 2.43 2.10 1 .aa 1.11 PCNT 1.a 1.5 1.3 l • l

BASE MEAN= l l0ol3 PCNT DIFF ME.ANS= o9 AV ABS DIFF= 1.07 OIFF PCNT OF BASE MEAN= 1.0

WYL19 BASE 63.31 65.75 56.94 so.02 71.38 94.67 104.51 113 • 55 124.97 135.92

COMP 63.31 65.75 56.94 50.62 11.31 94.40 10,.39 1!5068 128.43 139.80

DIFf .oo .oo .oo .oo -.01 -.21 .a1 2.13 3.46 J.88

PCNT .o .o .o .o -.1 -.3 .a 1.9 2.8 2.9

IIIYL19 BASE 140.09 155.31 167.77 180.74 COMP 144.18 159.00 171.16 183 095 OIFF 4.09 J.69 3.39 3.21 PCNT 2.9 2•4 2.0 1•8

BASE MEAN= 1os.~1 PCNT Dif F ME.ANS= 1•6 AV ABS DIFF= 1•79 OIFF PCNT OF BASE MEAN= 1.6

IIAHIABLE NAME l't72 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

IIIYL20 BASE 25.Jl 25.'+2 25.03 24.40 26.23 26.78 21.39 2e.02 28.84 29.55 COMP 25.31 25.'+2 25.03 24.40 20.11 26.84 27.69 28062 29.67 30.56 OIH .oo .oo .oo .oo -.06 .06 .30 060 .BJ 1.01 PCNT .o .o .o .o -.2 .2 l. l 2.1 2.9 3.4t

IIIYL20 BASE 30 • 12 JO.dB 31.68 32.42 COMP 31 .23 32.03 32.88 33.66 OIH 1.10 1.16 1.20 1.25 PCNT 3.7 3.7 3.8 3.s

BASE MEAN: 20.00 PCNT OJff MEANS== 1.9 A\/ ABS Dlff:: .54 ()lff PCNT Of BASE MEAN:: l .9

IIIYL21 BASE 43.02 39.87 42.66 38.J2 40.49 41.94 43.29 44.7 3 46.46 4 8 . 34 COMP 43.02 39.87 42.66 38.32 40.48 41.95 43.32 44.79 46.54 4 8 .44 DIFf .oo .oo .oo .oo -.01 .01 .03 • 06 • 08 • l 0 PCNT .o .o .o .o -.o .o • l • I . 2 . 2

IIIYL21 BASE 50.33 52.45 54.74 57.22 COMP 50.44 52.56 5'+086 57 .J4 OIH .11 .11 .12 .12 PCNT .2 .2 .2 .2

BASE MEAN:: 45.99 PCNT DI ff MEANS== • l A\/ ABS Olff= .05 DlfF PCNT OF BASE MEAN= • l

IIIYL22 BASE 83.47 85.64 85.92 85.l2 88.42 90.06 91 .43 92.89 94.50 96.07 COMP 83.47 85.64 85.92 85.72 88.36 90 .12 91 .66 93.36 95.13 96.85 OIH .oo .oo .oo .oo -.06 .01 .23 .46 .63 .79

PCNT .o .o .o .o -.1 • l .3 .5 .1 .e IIIYL22 BASE 97.62 99.32 101.0] 102.14

COMP 98.48 l00.24 102.02 103.76 OIH .86 .92 .99 1.u2 PCNT .9 .9 1.0 l • 0 I

BASE MEAN= 92.49 PCNT DifF MEANS== .5 AV ABS Dlff:: .43 Olff PCNT Of BASE MEAN= .5 "' "' .., I

IIIYL23 BASE 85.68 89.58 0s.oo 84.12 91.99 97.16 100.35 103.59 107.25 11 O • 33

COMP 85.68 89.58 00.oo 84.12 91.88 97.24 100.t,6 104021 108.06 111.31

OIH .oo .oo .oo .oo -.ll .oa .31 .62 .al .98

PCNT .o .o .o .o -.1 • l .3 .6 .8 .9

IIIYL23 BASE 113.4t8 116.86 120.31 123.82 COMP 114.52 l l 7 • 96 l21.4t6 125.00 DIFF 1.04 1.10 1.15 1.10 PCNT .9 .9 1.0 1.0

BASE MEAN= 102.32 PCNT DJFF MEANS= .s AV ABS lHFf= .53 OlFf PCNT Of BASE MEAN= .5

IIIYLl4 BASE 106.88 112.01 112.84 l l O. '16 12l.72 128.00 132.70 138.15 144.54 1411.13

COMP 106.88 112.01 112.84 l 10 • 96 122.29 12a.51 134.51 141.73 149.37 154.13

DIH .oo .oo .oo .oo -.43 .Sl 1.01 3.58 4.83 6.00

PCNT .o .o .o .o -.4 .4 l 0 4 2.6 3.3 '- • l

IIIYL24 BASE 151 • 40 155.67 159.92 163.1:!7 COMP 157.95 162.68 167.31 l 71. 58 DIH 6.55 1.01 7.39 7.71 PCNT 4.3 4.5 4.6 4.7

BASE MEAN= l34otl4 PCNT DIFF MEANS== 2•4 AV ABS OIFF= 3•27 OlfF PCNT OF BASE MEAN= 2.4

IIIYL2S BASE 75.72 82.57 79.05 76.34 82.57 86.56 87.30 117.85 88.95 A9.95

COMP 75.72 82.57 79.05 76.34 82.54 86.62 87.42 88.08 89.23 90.30

Dlff .oo .oo .oo .oo -.04 .06 .12 .23 .28 .36

PCNT .o .o .o .o -.o • l • l .3 .3 .4

WYL25 BASE 90.98 92.05 93.10 94.14 COMP 9 l. 36 92.46 9J.53 94.59

Dlff .38 .42 .43 .45 PCNT .4 .5 .s .5

BASE MEAN= 86.22 PCNT OI FF Mt.ANS== •2 A\/ ABS DIFF= .20 OlfF PCNT OF BASE MEAN= .2

VARIABLE NAl"E 1972 1973 1974 197:> 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WYL26 BASE 9.13 9.04 9.23 8.48 9.84 10.37 10.62 10.95 11.37 11.79 COMP 9.13 9.b4 9.23 8.'>8 9.79 l0.39 10.11 11. 26 11.11 12.26 Olff .oo .oo .oo .oo -.os .03 .1s .31 .40 .47 PCNT .o .o .o .o -.s .3 1.s 2.A 3.5 4.0

WYL26 BASE 12.12 12.51 13.05 13.51 COMP 12.62 13.08 13.57 14.05 Olff .so .51 .52 .:>4 PCNT 4.1 4. l 4.0 4.0

BASE MEAN= 10.90 PCNT Oiff MEANS= 2.2 AV ABS Olff= .25 DlfF PCNT Of BASE MEAN= 2.3

WYL27 BASE 47.17 49.88 48.94 50.83 SB.JO 63.93 67.28 70.73 74.72 77 .19 COMP 47.17 49.88 48.94 so .1:13 58.16 64.07 67.93 12.01 76.45 79.27 Olff .oo .oo .oo .oo - • 14 .14 .65 l .28 l • 74 2.oa PCNT .o .o .o .o -.2 .2 1.0 1.11 2.3 2.1

WYL.!7 BASE 79.09 81.'>5 83.86 86.13 COMP 81.32 83.77 00.26 88.60 Olff 2.24 2.32 2.40 2.47 PCNT 2.a 2.8 2.9 2.9

BASE MEAN= 67.ll PCNT Olff Mt.ANS= 1•6 AV ABS DlfF= l•lO DIFF PCNT OF BASE MEAN= 1.6

WYLc8 BASE B.92 9.88 9.56 8 • 70 10.19 10.88 11.40 11.97 12.60 13.05 COMP B.92 9.88 9.56 8.70 10.1a l0.90 11.45 12.01 12. 72 13.20

Olff .oo .oo .oo .uo -.01 .02 .os .l 0 .13 .16

PCNT .o .o .o .o -.1 .2 .4 .A 1.0 1.2 liYL28 BASE 13.49 13.97 14.46 l<t.95

COMP 13.66 l 4 .16 14.66 15.16 Olff .17 .19 .20 .il PCNT 1.3 1.3 1.4 1•4 I

BASE MEAN= 11. 72 PCNT DJff MEANS= .1 AV AbS Dlff= .09 DlfF" PCNT OF BASE MEAN= .8 "' "' 0 I

WYL29 BASE 53.67 50 .1:13 49.33 48.7] 61.51 69.97 67.ii5 67.44 71. 08 73.68 COMP 53.67 50.83 49.33 48.73 61.21 69.57 10.21 72.43 78. 16 81.15

Olff .oo .oo .oo .oo -.30 -.41 2.41 4.99 1.08 7.47

PCNT .o .o .o .o -.5 -.6 3.6 7.4 10.0 l O. l

WYL29 BASE 72.03 72.61 75.33 77.09 COMP 79.39 78.93 80.90 82.15 OlfF 7.36 6.32 s.51 5.06 PCNT 10.2 8.1 7.4 6·6

BASE MEAN= 65.08 PCNT Oiff Mt.ANS= 5.0 AV ABS Dlff= 3.35 OlfF" PCNT OF BASE MEAN= 5.2

WYL30 BASE 41.27 43.79 44.05 43.08 49.75 51 .as 51.10 51.11 52.6b 53.30

COMP 41.27 43.79 44.65 43.08 49.57 51 • 93 52.]5 53.04 54.26 55.04

Olff .oo .oo .oo .oo -.18 .05 .65 1.27 1.60 1.73

PCNT .o .o .o .o -.4 • l 1.3 2.4 3.0 3.2

WYL30 BASE 53.61 54.33 5s.32 56.16 COMP 55.26 55.iH 56.68 57.43 Olff l .65 l • 49 l .36 1.21 PCNT 3.1 2.1 2.5 2•3

BASE "4EAN= 50.23 PCNT Diff Mt.ANS= 1.5 AV Al:lS DlfF= .00 DlfF PCNT Of BASE MEAN= 1.6

WYL31 BASE 2:1.02 21.JO 11.90 lJ.!>3 21 .44 22.a5 22.10 22.26 22.04 21.15

COMP 23.02 21.30 17.90 13. :>3 21.42 22.84 22.90 22.49 22.35 ?l .41

OlfF .oo .oo .oo .oo -.02 -.01 .12 .23 .31 .32

PCNT .o .o .o .o -.1 -.o .5 1.0 1.4 1.5

WYLJl BASE 20.32 l 9.!:>7 18.91 18.22 COMP 20.63 19.84 19.16 18.'>4 DlfF .31 .21 .24 .22 PCNT 1.5 1.4 1.3 1.2

BASE MEAN= 20.J8 PCNT DifF Mt.ANS= .1 AV ABS UIFF= •15 OlfF PCNT OF BASE MEAN= .1

1/AklABLE NAME 1972 1973 1974 197::> 1976 1971 1978 1979 1980 1981 1982 1983 1984 1985

liYL32 BASt:: 112.92 118.67 129.55 121.94 149.95 167.36 181.65 195.69 211.47 226.72 COMP 112 • 92 118.67 129.55 121.94 149.91 167.37 181083 196.07 212.00 227.32 DIFf .oo .oo .oo .oo -.04 .01 .10 .3,q .53 .60 PCNT .o .o .o .o -.o .o • l .2 .3 .3

WYL32 BASE 244.50 263.55 284.27 306.50 COMP 245. 11 264.14 284.85 307.08 OIFF .61 .59 .sa .s8 PCNT .2 .2 .2 •2

BASE MEAN= 193.91 PCNT DIFF MEANS= • l Al/ ABS DIFF= .29 DIFf PCNT Of BASE MEAN:z .2

liYL33 BASE 51. 79 58.30 63.31 66.83 79.10 86.51 86.SO 87.27 90.79 93.48 COMP 51.79 58.30 63.31 66.83 78.64 86.51 88.47 91.26 96.10 99.33 OIFf .oo .oo .oo .oo -.46 -.oo 1 .97 3.99 5.31 s.0s PCNT .o .o .o .o -.6 -.o 2.3 4.6 s.9 6 • 3

liYL33 BASE 94.15 96.32 99.44 102.16 COMP 99.96 101.68 104.47 lOt>.99 OIFf 5.81 5.36 s.03 4o83 PCNT 6.2 506 s.1 4.7

BASE MEAN= 82.57 PCNT OIFF MEANS= 3.3 Al/ ABS DIFf= 2o76 OIFf PCNT Of BASE MEAN= 3.3

Ii YL34 BASE 33.19 37.21 38.94 38.98 4J.03 44.51 45. 34 46.20 47.45 48.40 COMP 33.19 37.21 38.94 38.98 42.89 44.60 45.76 47.04 48.50 49.63 OIH .oo .oo .oo .oo -.1s .09 042 .84 1.05 l .24

PCNT .o .o .o .o -.3 .2 .9 l.A 2.2 2.6

liYL34 BASE 49.09 S0.04 Sl.04 51.98 COMP so.JS 51.31 52.32 53.27 OIH 1.26 1.21 1.28 1.29 PCNT 2.6 2.s 2.5 2.5 I

BASE MEAN= 44.67 PCNT OIFF MEANS= 1.4 Al/ ABS DIFf= •63 nfff PCNT Of BASE MEAN= 1.4 ..., w ,... I

liYL35 BASE 31.67 35.68 34.93 29.99 32.81 34.24 35.35 36.82 38.38 39.78

COMP 31.67 35.t>8 34.93 29.99 32.78 34.27 35.42 36.97 38.58 40.04

OIFf .oo .oo .oo .oo -.03 .02 .oa .15 .20 .26

PCNT .o .o .o .o -.1 • l .2 .4 .s .6

lilYL35 BASt:: 41.05 42.44 43.93 45.26 COMP 41. 34 42.75 44.16 45.60 OIH .29 .31 .33 .35 PCNT .1 .1 .a .9

BASE MEAN= 37.30 PCNT OIFF ME.ANS= •4 Al/ ABS DIF"f= .14 DIFf PCNT OF BASE. MEAN= .4

WYL36 BASE. 41.76 52.29 55.90 50.73 66.78 71 .60 74oA6 78.03 82.58 85.61

COMP 41.76 52.29 55.90 50.13 6bo28 71.88 75.84 80.09 as.os 88.59

OIH .oo .oo .oo .oo -.so .27 o9A 2.06 2.47 2.99

PCNT .o .o .o .o -.7 .4 1.3 2.6 3.0 3.5

WYL36 BASE 88.88 92.45 96.17 99.93 COMP 91.90 95.59 9'7.41 103.27 OIFF 3.02 3.14 3.24 3.34 PCNT 3.4 3.4 3 .4 3.3

BASE MEAN:: 74.ll PCNT Oiff MEANS= 2.0 Al/ ABS DlFf= 1.57 Olff PCNT OF BASE MEAN= 2. l

WYL37 BASE 49.52 63.49 60.81 53.t>8 10.02 78.43 84.51 9 l. 36 99.23 1os.20

COMP 49.52 63.49 60.81 53.t>B 70.64 78.53 84.99 92.39 100.56 106.79

OIFf .oo .oo .oo .uo -.18 • l 0 .47 l • 0 ;> 1.33 1.59

PCNT .o .o .o .o -.3 • l .6 1. I 1.3 1.s

WYL37 BASE l 11 .93 119.24 121.02 !35 .11 COMP 113.57 120.94 128.78 !36. 99 OlFf l.64 l • 70 l. 76 1.82 PCNT 1.s 1.4 l .4 1.3

BASE MEAN= 89.32 PCNT OIFF Mt::ANS= .g Al/ ABS DIFF= .93 Olff PCNT Of RASE MEAN= .9

VARIABLE NAME 1972 1973 }974 197!:i 1976 1977 1978 1979 1980 1981 1982 1983 }984 l 98!:i

WYL38 BASE 37.72 38 ... 5 38.12 35.f>7 42.31 '+7.55 51.90 56.18 6lo09 65.41 COMP 37.72 38.'+5 38.12 35.t>7 42.20 '+7 o!:>9 s2021 56086 62.00 66051 DIFf .oo .oo .oo oOO -.11 .04 031 068 .91 1. l 0 PCNT oO .o .o .o -.3 • l .6 l.? 1.s lo7

WYL38 BASE 69.89 71+o85 80.26 85099 COMP 71.05 76.05 Bl.SO 87.29 DIFf 1.16 1.20 1 .24 1.30 PCNT lo7 lo6 1.s loS

BASE MEAN= 56.10 PCNT OifF MEANS= loO AV ABS DIFf= .57 Dlff PCNT Of BASE MEAN= loO

WYL39 BASE 658039 805.57 847.69 f>34o29 737082 t>87.57 709015 686043 692061 693090 COMP 658.39 805057 847069 b34o~9 709.45 752o08 860. :n 9S7o85 1023.00 1090.60 OIFf oOO oOO oOO oOO -28037 64051 151018 271042 330039 3%070 PCNT oO oO oO oO -308 9o4 2lo3 39o5 47o7 57.2

WYL39 BASE 696o57 698090 70lo34 703071 COMP 1114070 ll50ot>O 1185010 1220050 OIFF 418.13 451070 483.76 5}6o -(9 PCNT 60o0 b4o6 69.0 73.4

BASE MEAN= 711 .oo PCNT Oiff MEANS= 30•7 AV ABS DIFf= 222035 Offf PCNT Of BASE MEAN= 3lo3

WYL4O BASE 59.59 58ot>l 58.89 59ol5 67062 72048 75o}9 78089 82040 85088 COMP S9o59 58061 58.89 59.15 67058 72051 75.34 79020 82.80 86037

DIFf .oo .oo oOO oOO -.04 .03 .1s 030 o 40 049

PCNT oO .o oO .o - o l oO .2 o4 .s 06 WYL4O BASE 90020 94ot>7 99026 103.92

COMP 90.73 95.22 99083 104.52 DIFf 052 .ss 057 ot>O PCNT 06 06 .6 06 I

BASE MEAN: 11.02 PCNT Diff Mt.ANS= .3 AV ABS OIFf= •2b Dlff PCNT Of BASE MEAN= .3 N ..., N I

WYL41 BASE 26f>o93 275005 282004 281 o 04 291.60 263o23 276063 276.97 279.73 285004

COMP 266093 275005 282.04 281004 291.55 263.36 276. 72 211.00 279.87 285042

DIFf .oo .oo oOO .oo -.05 .13 009 003 .14 .38

PCNT oO .o oO oO -oO .o .o oO • 1 • 1

WYL41 BASE 284.73 285.21 284.90 284056 COMP 285.36 286006 285.93 285.o7 OIFf 063 085 1.03 1.11 PCNT o2 o3 o4 • 4

BASE MEAN:: 279083 PCNT Diff MEANS= 0 1 AV ABS Dlff= •32 Dlff PCNT Of BASE MEAN= • 1

WYL42 BASE 68072 82.31 81.98 79.85 91.97 l00.89 104.36 10903?. 112.81 1J6o76

COMP 68072 82.31 8lo98 79085 91 o 70 101004 105008 110076 114063 118092

OIFf .oo .oo oOO oOO -026 • 15 072 1044 lo82 2ol6 PCNT oO .o .o oO -.3 o l .1 lo3 1.6 1.8

WYL42 BASE ll9o9l 123049 127013 130.66 COMP 122.13 125074 129.41 1320 ':17 DIFf 2.22 2025 2o28 2o31 PCNT 1 • 9 1.0 lo8 lo8

BASE MEAN= lOJ.58 PCNT Diff MEANS= 1.0 AV ABS Dlff= 1 o 1?. Dlff PCNT OF BASE MEAN= 1 • 1

WYL43 BASE 752.0l 802077 778.46 727071+ 813.ol 888.76 932.08 975.31 1026030 10<;9o60

COMP 752.0l 802077 778o46 121.14 Bl2o05 890.22 938.04 986097 1041.40 1077.80

OIFf .oo oOO .oo oOO -1 .56 }.46 S.96 llo66 15.10 18020

PCNT oO .o oO oO -.2 .2 .6 1.2 1.s lo7

WYL43 BASE 1093.80 1133080 1176.10 1219000 COMP 1113.00 1153050 1196.20 1~39050 OIFf 19020 19070 20ol0 20.50 PCNT loB lo7 1.7 1.1

BASE MEAN= 955of>7 PCNT DifF MEANS= loO AV ABS DIFf= 9.53 Dlff PCNT Of BASE MEAN= loO

VARIABLE NAME 1972 1973 l'J74 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WYL44 BASE 84.19 90.85 95.08 95.72 102.69 104.05 104.39 105.10 106.27 101.01 COMP 84.19 90.85 95.08 95072 102.32 104.42 105.75 lo7.75 109077 lllo30 OIFF .oo .oo .oo oOO -.37 .37 lo 36 2.65 3o50 4o29 PCNT oO .o .o .o - • 4 .4 1.3 2.5 3.3 4.0

WYL44 BASE 107.49 lOB.52 109.48 110.19 COMP 112.14 113.42 114.57 l l 5.41 DIFF 4.65 4.90 5.09 5.22 PCNT 4.3 4.5 4 .6 4.7

BASE MEAN= 102.22 PCNT DJf F MEANS= 2o2 AV ABS OIFF= 2•31 DIFF PCNT OF BASE MEAN= 2.3

i11YL45 BASE 14 .17 14.57 14.51 13.95 15.01 15.37 15.46 }5.61 15083 16007 COMP 14.17 14.57 l 4 • 5 l 13.95 1s.01 15042 15066 16000 H,o35 l6o 7l DIFF .oo oOO oOO oOO -006 o05 020 039 052 064 PCNT oO .o oO .o -o4 o3 lo3 2o5 3o3 4 o 0

WYL45 BASE l6o25 16052 16.79 11.02 COMP lt>o94 17025 17054 17 ollO OJFF 069 073 .76 o1B PCNT 4o3 4.4 4 .5 406

BASE MEAN= 15051 PCNT DIFF Mt::ANS= 2o2 AV ABS OIFF= 034 OIFF PCNT OF BASE MEAN= 2o2

WYL46 BASE 42030 44081 45.76 45022 48.97 50o07 50.97 52.06 53.41 <;4.46

COMP 42030 44ot:ll 45076 45.22 48078 50.29 51. 75 53060 55.50 57.07 OIFF .oo .oo .oo .oo -.19 .22 • 78 1.54 2.09 2.62

PCNT .o .o .o .o -.4 o4 l.5 3.0 3.9 408

WYL'-6 BASE 55.37 56061 57.81 58.87 COMP 58.26 59.72 61.09 62029 OIFF 2.89 3.11 3.28 3.41 PCNT 5.2 5.5 5.7 5.9 I

BASE MEAN= 5lol9 PCNT OJFF M[ANS= 2.e AV Al:lS DIFF= 1•44 OIFF PCNT OF BASE MEAN= 2.e "' .., .., I

WYL47 BASE 169.53 1A0o31 184.33 182.10 205.34 216o76 226.84 240.20 254.60 264.54

COMP 169.53 le0.31 184.33 !82ol0 204.41 211.01 230.32 247.20 264.31 276.92

OJFF .oo .oo .oo oOO -.93 .91 3.48 1.00 9.71 12.38

PCNT oO .o .o .o -.5 o4 1.5 2o9 3.8 4.7

WYL47 BASE 274.16 285090 297046 J08o64 COMP 2ea.01 301 o 11 313080 J25o93 OIFF 13.91 15.,:!l 10.34 17.29 PCNT 5.1 5.3 5.5 5•6

BASE MEAN= 235.05 PCNT OJFF ME.ANS= 2.9 AV ABS DIFF= 6•94 OIFF PCNT OF BASE MEAN= 3.0

illYL 48 BASE 243 .18 198.23 152.46 164.32 276.82 361 .62 361.41 347.23 375.34 4}5.19

COMP 243.18 198.23 152.46 164.32 276.82 351.44 379.71 393.41 449.89 495. 18

DIFF .oo oOO .oo .oo .oo -10.18 1a.30 46.18 74.55 79.99

PCNT .o .o .o .o .o -2.8 5. 1 13.3 19.9 19.3

i11YL48 BASE 390.45 379.63 407.30 423.17 COMP 471.98 447.72 464.52 472.48 OIFF 81.53 68.09 51.22 49.31 PCNT 20o9 11.9 l 4. O 11 • 7

BASE MEAN= 321.17 PCNT DIFF MEANS= 10.3 AV ABS CJIFF= 34.67 OIFF PCNT OF BASE MEAN= 10.a

WYL49 BASE 193.79 195.13 220.15 216089 255.89 290.49 279.95 277.4? 292.35 303.44

COMP 193.79 195013 220.15 216.89 254.13 290.38 287.77 294.02 314069 328.71

DIFF .oo oOO .oo .oo -1.76 - • 11 7.82 \6.60 22.34 25.27

PCNT .o .o .o .o -.1 -.o 2.e 6.0 7.6 8.3

WYL49 BASE 310.15 323 .14 336.45 J52.bl COMP 335.69 347.58 359.43 375.07 DIFF 25.54 24.44 22.98 22.46 PCNT 0.2 7.6 6.8 6.4

BASE MEAN= 274.ll5 PCNT DIF F ME.ANS= 4.3 AV ABS UIFF= 12.09 OIFF PCNT OF BASE MEAN= 4.4

WAR I ABLE NAME 1972 1973 l'H4 197!:> 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WYLS0 BASE 73.96 77.00 78.27 81.82 101.66 122.16 108.42 l 11 • 00 118.02 118 .55 COMP 73.96 11.00 78.27 81.82 100.65 123.34 114.73 120.37 128.27 127.93 DI ff .oo .oo .oo .oo -l.Ol 1.18 6.31 9.37 10.25 9.38 PCNT .o .o .o .o -1 .o 1.0 5.8 8.4 8.7 7.9

WYL50 BASE 114.77 119.49 125.82 128.21 COMP 122.87 125.85 131 • 72 133.86 DlfF 8. l O 6.36 5.90 5.65 PCNT 1.1 5.3 4.7 4.4

l:!ASE MEAN= l05o65 PCNT DifF MEANS= 4o2 AV ABS UIFT= 4.54 l)lff PCNT Of BASE MF.AN= 4.3

WYL5l BASE 267.38 289.73 297.88 294.55 334.63 348.63 350.80 353.95 364. 73 371.09 COMP 267.38 289.73 297.88 294.!:>5 332.57 350.01 358.07 367.84 381.48 389.04 Olff .oo .oo .oo .oo -2.06 1 • 38 1.21 13.89 16.75 17.95 PCNT .o .o .o .o -.6 .4 2.1 3.9 4.6 4.8

WYL5l BASE 374.64 382.79 393.88 403.16 COMP 390.92 396.93 406.24 414.23 Dlff 16.28 14.14 12.36 11.01 PCNT 4.3 3.7 3.1 2.1

BASE MEAN= 344.85 PCNT Oiff MEANS= 2.3 AV ABS Dlff= 8.08 Olff PCNT Of BASE MEAN: 2.3

IIIYL52 BASE 75.23 82.71 86.56 88.45 99.92 104.00 108. 17 l 15.12 121.62 127.16 COMP 75.23 82.71 86.56 88.45 99.60 104.42 109.43 117.51 125.02 131.78

Olff .oo .oo .oo .oo -.32 .42 l .26 2.39 3.40 4.62

PCNT .o .o .o .o -.3 .4 1.2 2.1 2.8 3.6 IIIYL52 BASE 133.19 l 40 • J l 146.96 l53.S3

COMP 138.69 146.61 153.97 161.09 DI ff 5.50 6.30 1.01 7.56 PCNT 4. l 4.5 4.8 4.9 I

BASE MEAN= 113.07 PCNT Oiff MEANS= 2•4 AV ABS Dlff= 2•77 Olff PCNT Of BASE MEAN= 2.5 "' .., ~ I

WYL53 BASE 2039.50 2190. 6 0 2 236.20 2'!30.00 2493.10 2634.30 2741.oo 2858.00 2999.30 3091.40

COMP 2039.50 2190.60 2236.20 2.::30.00 2484.90 2643.20 2777.40 2930.60 3098.30 3213.20

Olff .oo .oo .oo .oo -8.20 8.90 36.40 72.60 99.00 121.80

PCNT .o .o .o .o -.3 .3 1.3 2.5 3.3 3.9

WYL53 BASE 3170.50 3270.50 3374.30 3471.80 COMP 3303.50 3411.40 3521.70 3b24 0 80 Olff 133.00 140.90 14 7 • 40 153.00 PCNT 4.2 4.3 4.4 4.4

BASE MEAN= 2771.46 PCNT OIFF Ml:.ANS= 2.3 AV ABS DIFF= 65.80 nifF PCNT Of BASE MEAN= 2.4

WYL!:>4 BASE 622.23 636 .14 624.22 598.04 648.92 673.0tl 690.79 713.06 738.81 764.95

COMP 62 2 .23 636.14 624.22 !:>9tt. 04 047.oo 675.32 699.26 729.72 761.64 793.79

DI ff .oo .oo .oo .oo -l.92 2.24 8.47 l6o6f. 22.83 ?8.84

PCNT .o .o .o .o -.3 .3 1.2 2. 3 3.1 3.8

WYL54 BASE 789.41 818. 9 0 848.59 t177.24 COMP 821.75 854.03 886012 ~16.84 DI ff 32.34 35.13 37.53 39.60 PCNT 4.l 4.3 4.4 4.5

BASE MEAN= 717.46 PCNT OIFf Mt:ANS= 2.2 Al/ ABS Dlff= l 6 • l l 0 !FF PCNT OF BASE MEAN= 2.2

WYL55 BASE 1700.40 18;>4.tlO 1880.60 }!:166 • .:0 2069.30 2158.70 2228.60 23}1.60 2412.20 2501.40

COMP 1700.40 1824.80 1880.60 l!:IM,.20 ?058.90 2168.00 2267.oo 23118.90 2516.60 263?.10

OlfF .oo .oo .oo .uo -10.40 9.30 3tl.40 77.30 104.40 130.70

PCNT .o .o .o .o -.5 .4 l • 7 3.3 4.3 s.2

WYL55 BASE 2582.20 2683.20 2786.50 2d84.~0 COMP 2726.50 2838.60 2951 .so 3058.!:>0 DI ff 144.30 155.40 105.oo l73ob0 PCNT 5.6 5.9 5.9 6•0

BASE MEAN= 2211 .~o PCNT OIFF MEANS= 3ol AV ABS OIFF= 72o0f, nlfF" PCNT OF BASE MEAN= 3.2

IIARIABLE NAME 1972 1973 1974 197~ 1976 1977 1978 1979 1980 1981 1982 198J 1984 1985

WYLC BASE. 69.ll 73.28 74.90 74.02 8 l .60 85.09 88.03 91058 95.71 98.26 COMP 69.11 73.l8 74.90 74.02 al.30 85.49 89.50 94.48 99.72 103.29 0IFF .oo .oo .oo .oo -.31 .41 1.47 2.91 4.01 5.03 PCNT .o .o .o .o -.4 .5 l • 7 3.2 4.2 5.1

WYLC BASE 100.51 lo3.51 106.45 109.11 COMP 106.13 109.59 112.91 115.90 0IFF 5.62 6.08 6.46 6. 79 PCNT 5.6 5.9 6. l 6•2

BASE MEAN= 89.J7 PCNT 0IFF MEANS= 3.1 AV ABS l>IFF= 2.19 0IFF PCNT OF BASE MEAN= 3.1

WYLSLEOU BASE 849.77 859.33 891. 05 953.70 935.17 949.63 1054.00 1091.60 1123.00 1170.40 COMP 849. 77 859.33 891.05 953.70 937.04 945.27 1043.10 1086.60 1132.00 1198.30 0IFF .oo .uo .oo .oo 1.87 -4.36 -10.90 -5.oo 9.00 27.90 PCNT .o .o .o .o .2 -.5 -1.0 -.s .8 2.4

WYLSLEOU BASE 1226.50 1255.40 1289.20 1339.70 COMP 127c.30 1314.80 1356.20 l'+lJ.90 0IFF 45.80 59.40 67.oo 74 • .::0 PCNT 3.1 4.7 5.2 5.5

BASE MEAN= 1010.60 PCNT 0IFF ME.ANS= 1•8 AV ABS 0IFF= 21.82 0lfF PCNT Of BASE MEAN= 2.0

WYLSLOTH BAS£ 639.29 674.69 648.06 656.52 806.34 741.93 715.95 880.54 943.67 981.82 COMP 639.29 674.69 648.06 b56 0 52 805.25 748.38 713.49 861.06 924.34 978.16 0IFF .oo .oo .oo .oo -1.09 6.45 -2.46 -19.48 -19.33 -3.66 PCNT .o .o .o .o -.1 .9 -.3 -2.2 -2.0 -.4

WYLSL0TH BASE 1065.00 1167.00 1211.20 ll64.20 COMP 1084.80 1209.70 1281 .90 l 341o10 DIFF 19.80 42.70 64.70 76.90 PCNT 1 .9 3.7 5.3 6ol

BASE MEAN= 885.87 PCNT 0JFF MEANS= 1•3 AV ABS UIFF= l8o3J 0IFF POH OF BASE. MEAN= 2.1 I

"' "' VI

WYLFE.0MILPN BASE 8.00 0.26 8.52 0.11 9.03 9.29 9.55 9.81 10.07 10.33 I

COMP 8.00 s.20 0.s2 8.17 9.03 9.29 9.55 9.81 10.07 10.33

0IFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WYLFE0MILPN BASE 10.59 10.s5 l 1. 1 O 11 • 36 COMP 10.59 10.05 11 • 10 11. 36 0IFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 9.68 PCNT 0JfF MEANS= .o AV ABS DIFF= oOO 0IFF PCNT OF BASE MEAN= .o

lliYLFEOMIL BASE 434.50 423.95 438.41 439.t>2 450.10 460.42 470.61 480e6f> 490.56 5oo.32

COMP 434.50 423.95 438.41 439.62 450.10 460.42 470.61 480.66 490.56 soo.32

0IFF .oo .uo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WYLfEOMIL BASE 509.93 519040 528.73 537.'12 COMP 509.93 519.40 528.73 537.92 0IFF .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 477.51 PCNT DIFF MEANS= .o AV ABS U IFF= .oo DIFF POH OF BASE ME.AN= .o

WYLFEOCIVPN BASE 12.31 12.05 12.99 13.33 13.67 14.0l 14.)5 l4e69 15.03 !5.37

COMP 12.31 12.0s 12.99 13.33 13. 6 7 14.0l 14. 35 \4.6 9 15.0J 15.37

0IFF .oo .oo .oo .oo .oo .oo .oo . o o . o o .oo

PCNT .o .o .o .o .o .o . o . o .o .o

WYLF EOCIVPN BASE 15.70 16.04 16.38 16 .7 2 COMP 15.70 16.04 16. 38 16 . 72 0 IFF .oo . o o . oo . oo PCNT . o .o . o . o

BASE MEAN: 14.52 PCNT 0JF F Mt AN • o AV A Ul • n M AN • • 0

IJARIABLE NAME 1972 1973 1974 1975 1976 1977 197R 1979 1980 1981 1982 1983 1984 1985

liYLfEDCIIJ BASI:. 388.53 397.59 394.4} 399.87 411.83 423.87 436.ol 448.23 460.54 472.94 COMP 388.53 397.59 394.4} 399 .87 411.83 423.87 436.ol 448.23 460.S4 472.94 OIH .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

liYLf"EDCIIJ BASE 485.43 498.0l 510.67 523.43 COMP 485.43 498.01 510.67 523.43 OIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 446.53 PCNT Olf"F Ml:.ANS= .o AIJ ABS OIFF= .oo OIFF PCNT OF BASE MEAN= .o

WYLFEO BASE 823.03 821.54 832.82 839.49 861.92 884.30 906.62 928.89 951.10 973.26 COMP 823.03 821.54 832.82 839.49 861.92 884.30 906.62 928.89 951.10 973.26 OIH .oo .oo .oo .uo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WYLFED BASE 995.36 1017.40 1039.40 1061.JO COMP 995.36 1017 .40 1039.40 1061.30 DIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 924.03 PCNT DIFF MEANS= .o AIJ Al:IS DIFF= .oo OIFF PCNT OF BASE MEAN= .o

WYLTOT BASE 12053.00 12774.00 12958.00 12t>52.00 14147.oo 14748.00 15244.00 15834.00 16516.00 17062.00

COMP 12053.00 12774.00 12958.00 12t>52.00 14085.00 14835.00 15543.oo 16410.00 17289.00 10020.00

OIH .oo .oo .oo .oo -62.00 87.00 299.oO 576.00 773.00 958.00

PCNT .o .o .o .o -.4 .6 2.0 3.6 4.7 5.6 WYLTOT BASE 1754b.OO 18145.00 18753.00 19346.00

COMP 18604.00 19278.00 19954.00 20609.00 OIFf 1056.00 1133.00 1201.00 lc63.00 PCNT 6.0 602 6.4 6•5 I

BASE MEAN= 15555.71 PCNT DIFF MEANS= 3.3 AIJ ABS DIFF= S29 • 14 OIH PCNT OF BASE MEAN= 3.4 "' .., "' I

WYPHOPPC BASE .61 .t>5 .67 • t, 7 .10 .73 .76 .79 .02 .83

COMP .61 .65 .67 • t, 1 .10 .73 076 .79 .02 .83

OIFF .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WYPROPPC BASE .84 .86 .87 .aa COMP .84 .86 .87 .88 DIH .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= • 76 PCNT OIFF MEANS= .o AIJ ABS DlFF= .oo OifF PCNT OF BASE MEAN"' .o

WYPROP BASE 2040.00 2203.80 2210.00 2292.JO 2566.40 2121.20 2847.00 2997.70 3181.20 3248.30

COMP 2040.00 2203.80 2278.BO 2292.30 2560.00 2727.40 2878.20 30f.3.40 3281.30 3379.80

OIFF .oo .oo .oo .oo -6.40 6.20 30.40 65.70 100.10 131.50

PCNT .o .o .o .o -.2 .2 l. l 2.2 3.1 4.0

WYPROP BASE 3332.60 3420.40 3522.20 3588.JO COMP 3487.70 3592.80 3709.10 3785.30 OIH 155.10 172.40 186.90 197.00 PCNT 4.7 5.0 5.3 5.5

BASE MEAN= 2874.36 PCNT DIFF MEANS= 2.6 AIJ ABS UlFF= 75-12 OlFF PCNT OF BASE MEAN= 2.6

WYTPPC BASE .56 .59 .63 ob4 .61 .61 .63 .65 .67 .67

COMP .56 .59 .63 • 04 .61 .61 063 .64 .66 .66

OIFF .oo .oo .oo .oo .oo .oo -.oo -.oo -.oo -.oo

PCNT .o .o .o .o .2 .o -.3 -.5 -.4 -.4

WYTPPC BASE .67 .67 .68 .08 COMP .67 .67 .68 .08 Olff -.oo -.oo -.oo -.oo PCNT -.3 -.3 - • l - .1

BASE MEAN= .64 PCNT DIFF MEANS= -.2 AIJ ABS DIFF= .on DIFF PCNT OF BASE MEAN= .2

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981

1982 1983 1984 198~ WYTP BASE 1876.20 2004.1:!0 2138.00 2193.l:!O 2218.60 2278. 70 ?379.10 2469.70 2592.40 26 20.60

COMP 1876.20 2004.l:!O 2130.00 2193.80 2215.oo 2280.50 2396.70 2511.90 2662.20 2715.20

Dlff .oo .oo .oo .oo -3.60 1.80 17.60 42020 69080 94060

PCNT oO oO .o .o -o2 o l .7 lo7 2o7 3.6

WYTP BASE 2643.80 2688070 2742040 2163.40 COMP 2758.40 2818.lO 2003.50 2911.90 DIH 114.60 129040 141.10 148050 PCNT 4.3 4.8 s.1 5o4

BASE MEAN= 2400073 PCNT Diff ME.ANS= 2.2 AV ABS Olff= 54051 Olff PCNT Of BASE. MEAN= 2o3

WYSSRT BASE .04 o05 005 004 .04 .04 005 005 .o5 005

COMP .04 o05 005 004 004 .04 .o5 005 .o5 005

Dlff .oo .oo .oo oOO .oo .oo .oo .oo .oo oOO PCNT oO .o .o .o .o oO oO .o oO .o

WYSSRT BASE o05 .04 .04 .04 COMP o05 .04 .04 .04 Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 005 PCNT DIH ME.ANS: .o AV ABS Dlff= .oo Olff" PCNT Of" BASE MEAN= .o

WYSS BASE 522.76 690070 646.89 ~69.76 633018 661.42 7030 34 7J2o05 766061 790038

COMP 522.76 690070 646.89 569.76 630.40 665o32 111.12 758067 802.48 834.76

OIH .oo oOO .oo .oo -2.78 3.90 13.78 26.62 35.87 44.38

PCNT oO oO .o .o -.4 06 2.0 3.6 4o7 506

WYSS BASE 811.28 8}5o44 841003 867o62 COMP 860.08 866035 894.90 924ot:7 Dlff 48.80 50 o.9 l 53.87 56.65 PCNT 600 t,.2 6.4 6.5

BASE MEAN= 718.03 PCNT Diff ME.ANS= 3o3 AV ABS Olff= 24oll Olff PCNT Of BASE MEAN= 3 .4 I N ... .....

WYP BASE l544b.OO l62g2.00 1t.120.oo 16~69.00 18299.00 19086.00 l97t>7.oo 20570.00 21523.00 22141000 I

COMP 1544b.OO l62g2.00 lt.728.oo lt>St,9.00 18230000 19178.00 20100.00 21227000 22430000 23280.00

DIH .oo oOO .oo .oo -69.00 92.00 333.oO 657.oo 907.00 1139.00

PCNT oO .o oO .o -.4 o5 1.7 3o2 4o2 5.1

WYP BASE 22713.00 23439.00 24176.00 241:l30o00 COMP 23990.00 24822000 zst.52.oo 2t>382o00 OIH 1277 oOO 1383.00 l47boOO 1552000 PCNT 506 5.9 6ol 603

BASE MEAN= 20112.79 PCNT Dlff ME.ANS= 3.1 AV ABS Dlff= 634064 Olf"f PCNT Of BASE MEAN= 3o2

WTAXRT BASE .14 .13 .14 .14 .14 .14 ol4 ol4 .14 .14

COMP .14 o l 3 • 14 .14 .14 ol4 •14 .l4 .14 ol4

Olff .oo .oo .oo oUO .oo .oo .oo oOO oOO oOO

PCNT oO oO oO oO oO oO oO oO .o oO

WTAXRT BASE ol5 o 15 o 15 o 15 COMP .15 • 15 o l 5 o 15 DIH oOO oOO oOO oOO PCNT oO .o .o .o

BASE MEAN= .14 PcNT Diff ME.ANS= .o AV ABS Dlff= .oo r>lff PCNT Of BASE MEAN= .o

WTAX BASE. 2138.70 2172.90 2294.90 2309.90 2565070 267t>.10 2787.30 2900.40 3052.00 3174.70

COMP 2138.70 2172.90 2294.90 2J09. 90 ?55b.OO 2b88.90 2834.20 2993.10 3180.50 3338. l O

DIH .oo .oo .oo .oo -9.70 12.80 46.QO 92.70 128 .50 l h3.40

PCNT .o .o .o .o -.4 .s l • 7 3 . 2 4. Z 5 . I

WTAX BASE 3311.00 3454.00 3620.40 3 757.70 COMP 3497.10 3657.90 3841.30 3992.50 Dlff 186.10 203.90 220 .90 <'. 34.l:!0 PCNT 506 5. 9 6. I 6 . 2

BAS MEAN = 2012.ss PCNT Diff Mt. AN = AV AO IJI • ', !l, p ( f II~ ' f ' f I j

VARIABLE NAME 1972 1973 l'H4 1975 1976 1977 1978 )979 1980 1981 1982 1983 1984 1985

WYO BASE 13 308.00 14119.00 14433.oo l4c59.00 15734.00 16410.00 16980.oO l76i,9.00 18471.00 189t,t,.OO COMP 13308.00 14119.00 14433.oo l4c59.00 15674.00 16489.00 11266.00 10234.on 19249.00 19942.00 OIFF .oo .oo .oo .oo -60.00 79.00 206.00 565.oo 778.00 976.00 PCNT .o .o .o .o -.4 .5 1.7 3.2 4.2 s.1

WYO BASE 19402.00 19985.00 20556.00 21012.00 COMP 20493.00 21164.00 21810.00 22389.00 OIFF 1091.00 1179.00 125'+.00 1317.00 PCNT 5.6 5.9 6ol 603

BASE MEAN= 17240.29 PCNT OJff ME.ANS= 3ol AV Al:lS Dlff= 54lo79 OIH PCNT Of BASE MEAN= 3.1

WYOPC BASE 3.97 4.15 4.23 4. 15 4.30 4o40 4.51 4o63 4.1t, 4.84 COMP 3.97 4.15 4.23 4.15 4.29 4.41 4.54 4.67 4.81 4o89 DlH .oo .oo .oo .oo -.01 .01 .03 .os .o!:> .os PCNT .o .o .o .o -.1 .2 .6 1.0 l • l 1. l

WYDPC BASE 4.91 s.01 s.09 s.11 COMP 4.95 s.os 5.13 5.21 DlFF .os .04 .04 .04 PCNT .9 08 .1 .7

BASE MEAN= 4.58 PCNT OIH Ml:.ANS= .s AV ABS Dlff= .03 DIH PCNT Of BASE MEAN= .5

WCYCLE BASE 98.58 811.30 314.38 -174.63 1474.90 676. 71 569.89 689.07 801.91 494.77 COMP 98 058 811.30 314.38 -174.63 1415.30 814.68 777.44 967.6) 1015.70 692.85

DIFF .oo .oo .oo .oo -59.60 137.97 207.c;S 278.56 213.79 198.08

PCNT .o .o .o .o -4.0 20.4 36.4 40.4 26.7 40.0 WCYCLE BASE 436.43 582.33 571.45 516.05

COMP 550.37 671.80 645.93 578.96 DIFF 113.94 89.47 74.48 62.91 PCNT 26. l l5o4 13.0 12.2 I

BASE MEAN= 561.65 PCNT Dlff Ml:.ANS= 1608 AV ABS Olff= 102.60 Olff PCNT Of BASE MEAN= 18.3 N .... 00 I

WNOl BASE 17.43 19.21 18.02 17.32 18.91 16.92 16.79 15.67 15.18 14.28 COMP 17.43 19.21 18.02 17.32 18.88 n.oo 16.86 15.82 15.32 14.47

DlFF .oo .oo .oo .oo -.03 .01 .01 .1s .14 .19

PCNT .o .o .o .o -.2 .4 .4 1.0 .9 1.3

WNOl BASE 13.69 13.02 12.42 11.1n COMP 13.85 13.20 12.s8 11.98 DlFF .16 .18 .11 • 16 PCNT 1.2 lo4 1.3 1•4

BASE MEAN= 15.76 PCNT OJff Mt.ANS= •6 AY ABS OlFf= • 09 Olff PCNT OF BASE MEAN= .6

WN02 BASE 28.21 29.16 28.24 28.43 20.11 26.75 26.32 ;>5.98 25. 72 2s.20

COMP 20.21 29.16 28.24 28.43 26.74 26.79 26.43 ?6.17 25.98 25.51

DlFF .oo .oo .oo .oo -.03 .04 • 11 .20 .26 .31

PCNT .o .o .o .o -.1 • l .4 .a 1.0 1.2

WN02 BASE 24.67 24.22 23.76 23.Jl COMP 2s.oo 24.55 24.10 23.65 DlFF .33 .34 .34 .J4 PCNT lo3 1.4 1.4 1.5

BASE MEAN= 26.19 PCNT Oiff MEANS= ·6 AV ABS DlFf= •16 OlfF PCNT OF BASE MEAN= .6

WNOJ BASE 8.20 8.52 8.44 8.27 8. 79 7.97 7.90 7.44 7.24 6.76

COMP s.20 8.52 8.44 a.21 8.75 8.04 8.02 7.6':, 7.48 7.06

DlH .oo .oo .oo .oo -.04 .01 •12 .22 .25 .29

PCNT .o .o .o .o -.4 .9 1.s 3.o 3.4 4.3

WNOJ BASE. 6.44 6.12 5.80 S.'+9 COMP 6.72 6.40 6.08 5.76 DlFf .28 .29 .20 .21 PCNT 4.J 4.7 4.7 4.9

BASE MEAN= 7 • 38 PCNT OJFf Mt::ANS= 2.0 AY ABS DlFf= .is DIFF PCNT Of BASE MEAN= 2.0

IIARJABLE NAME l'H2 1973 l 974 197~ I '176 1977 l '178 1979 1980 1981

1982 1983 1984 1985 WN04 BASE. 3.12 3.07 2.98 2.ss 2.ao 2.68 2.53 2.41 2.30 2. 17

COMP 3.12 3.07 2.98 2.85 2.80 2.68 2.56 2.45 2.35 2.23 OIH .oo .oo .oo .oo -.oo .oo .02 •04 .06 .06 PCNT .o .o .o .o -.;? .2 .9 l.A 2.4 2.9

WN04 BASE 2.04 l • 93 l.83 1. 73 COMP 2.11 2.00 1.89 1.19 OIFF .01 .06 .06 .Ot, PCNT 3.2 3.3 3.4 3.5

BASE MEAN= 2.46 PCNT DlFF Ml:.ANS= 1.3 All ABS DIFF= .03 OIFF PCNT OF BASE MEAN= 1.3

IIINOS BASE 2.47 2.56 2.56 2.ss 2.61 2.57 2.53 2.so 2.46 2.44 COMP 2.47 2.56 2.56 2.~s 2.60 2.s8 2.54 2.52 2.49 2.47 OIFF .oo .oo .oo .oo -.oo .oo .01 .o;, .03 .03 PCNT .o .o .o .o - • 1 .2 .4 .e 1 • 1 1.4

wN0S BASE 2.41 2.J9 2.36 2.34 COMP 2.44 2.42 2.40 2.37 OIH .03 .04 .04 .04 PCNT lo4 1.s 1.s 1.s

BASE MEAN= 2.48 PCNT OJH ME.ANS= .7 Al/ ABS OIFF= .02 OIFF PCNT OF BASE MEAN= .1

WN06 BASE 2.87 2.84 2.78 2.04 2.78 2.78 2.77 2.18 2.79 2.11 COMP 2.87 2.84 2.78 2.04 2.11 2.so 2.91 2.8s 2.89 2.89 OIH .oo .oo .oo .oo -.01 .01 .04 .08 .10 .13

PCNT .o .o .o .o -.4 .s 1.5 2.8 J.7 4.6

WN0t> BASE 2.74 2.13 2.71 2.69 COMP 2.88 2.1H 2.86 2.84 OIH .14 .14 .1s .is PCNT s.o 5.3 s.s 5.7

BASE MEAN= 2.76 PCNT OJFF MEANS= 2.4 All ABS DIFF= .01 OIFF PCNT OF BASE MEAN= 2.s

wN07 BASE 2.48 2.44 2.37 2.2s 2.29 2.22 2.}5 2.09 2.04 1.95

COMP 2.48 2.44 2.37 2.2s 2.28 2.23 2.11 2. 14 2.11 2.03

OIH .oo .oo .oo .oo -.01 .01 .03 .oc; .01 .08

PCNT .o .o .o .o -.3 .4 1 • 4 2.6 3.4 4.3

wN07 BASE 1.86 1.79 l • 71 1 .64 COMP 1.95 1. 88 1.so 1.12 DIH .09 .09 .09 .09 PCNT 4.6 4.9 s. 1 5.3

BASE MEAN= 2.09 PCNT OJFF MfANS=- 2.0 All ABS DIFF= .04 OIFF PCNT OF BASE MEAN= 2.0

WN08 BASE. 11.61 12.1s 12.11 12.24 12.90 13.12 13-32 13.54 }J.77 14.05

COMP 11.61 12.1s 12.11 12.24 12.s8 13.14 13.37 13.64 13.90 14.21

OIH .oo .oo .oo • (JQ -.01 .02 .os .10 .13 .11

PCNT .o .o .o .o -.1 • l .4 .7 1.0 1.2

lfN08 BASE 14.31 14.61 14.91 15.20 COMP 14.49 14.81 1s.11 15.41 OJFF .18 • J. 9 .20 .21 PCNT 1.J 1.3 l.4 1•4

BASE MEAN= 13.42 PCNT OJFF MEANS= .7 All ABS DIFF= .09 OIFF PCNT OF BASE MEAN= .1

WN09 BASE l.62 1.59 1.55 1.ss l e63 1.63 l 064 l 064 l .65 l .65

COMP 1 .62 l .'=>9 l .55 1.ss 1.63 1.63 l .65 1.65 1.66 }.67

DIH .oo .uo .oo .oo -.oo .oo .01 .02 .02 .02

PCNT .o .o .o .o -.1 .2 .s 1.0 1.2 1.s

WN09 BASE l .66 1.66 1.67 l. 08 COMP 1 .68 1.69 l. 70 1 • 7 l OIFF .03 .03 .03 .03 PCNT 1.6 l • 7 1.7 1.s

BASE MEAN= 1.63 PCNT D!FF Mt.ANS" •8 All ABS DIFF= .o l OIFF PCNT OF BASE MEAN= .8

VARIABLE NAME 1972 1973 1974 197:, 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

WNlO BASE J.62 3.99 3.94 J.:.3 J.86 4.02 4.04 4o06 4.09 4.10 COMP J.62 3.99 3.94 3.:,3 J.85 4.03 4.06 4.09 4.14 4. 16 Olff .oo .oo .oo . oo -.oo • 0 l .02 .04 .os • 06 PCNT .o .o .o .o -.1 • 1 .5 .9 l • l 1 • 4

WNlO BASE 4.09 4.09 4.08 4.07 COMP 4. 15 4. 15 4. I 5 4.14 Olff .06 .06 006 .01 PCNT 1.5 1.5 1.6 lo6

BASE MEAN= 3o'J7 PCNT OJff MEANS= .7 AV ABS UIFF= .03 DIFF PCNT Of BASE MEAN= .a

WNl l BASE 5.01 5.13 s. 14 5.06 5.28 5.27 s.26 5.21 5.30 5.2a COMP s.01 s.13 s.14 5 . 06 s.21 5.2a s.31 s.31 5.42 5.44 Olff .oo .oo .oo .oo -.01 .02 .os 009 .12 .15 PCNT .o .o .o .o -.2 .J 1.0 1.8 2.J 2.8

WNll BASE 5.27 s.28 s.21 5.26 COMP 5.43 5.45 5.45 5.45 Olff .16 .17 olB .19 PCNT J.l 3.3 3.4 3.5

BASE MEAN= s.23 PCNT DJff MEANS= 1. s AV ABS DIFF= 008 Dlff PCNT Of BASE MEAN= l. 6

IIIN12 BASE .62 .63 064 .01 067 .69 .10 .11 .12 .12 COMP .62 • 63 .64 • 6 l 067 .69 071 .12 .73 .73 Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o • 1 .3 .6 .6 .1 IIIN12 BASE .12 .12 .11 .11

COMP .12 .12 .12 .12 Dlff .oo .oo .01 .01 PCNT .1 .1 .1 .1 I

BASE MEAN= .68 PCNT Diff MEANS= • 4 AV ABS DIFf= .oo OlfF PCNT Of BASE MEAN= .4 N ~ 0 I

WNlJ BASE 6.61 6.72 6080 6. c,,1 7.49 1.1s 7.99 a.20 8045 8.53 COMP 6.61 6. 72 6.80 6. '1 l 7.48 7.76 8.02 8.25 8.51 8.61 Olff .oo .oo .oo .oo -.01 .01 .03 .os .01 .oa PCNT .o .o .o .o - • l • l .3 .6 .8 1.0

IIIN13 BASE. 8.63 8.73 B.82 8 01!9 COMP 8.72 8.82 8.91 8.99 DIH .09 .09 .10 .10 PCNT 1.0 1 • l l. l l • l

BASE MEAN= 7oll9 PCNT DifF MEANS= .s AV ABS DIFF= 004 OlfF PCNT OF BASE MEAN= .6

WN14 BASE. 2.11 2.13 2.12 2. l O 2.36 2.47 2.36 2.33 2.35 2.34

COMP 2.11 2. 13 2.12 2.10 2.35 2.48 2.42 2.43 2.49 2.48

DIH .oo .oo .oo .oo -.01 .oo 006 • 11 .13 .14 PCNT .o .o .o .o -.s .2 2.s 4.5 S.6 5.9

WN14 BASE 2.21 2.26 2.26 2. 2s COMP 2.41 2.Ja 2.37 2.35 OIH .13 .12 • 11 • 1 0 PCNT 5.9 5.2 4 .8 406

BASE MEAN= 2.21 PCNT DifF MEANS= 208 AV AtlS DIFF= .01 DlfF PCNT OF BASE MEAN= 2.9

WN15 BASE J.76 3.77 J.96 3. 2 7 3.72 J.92 3.97 3.98 4.02 4.04

COMP J.76 3.17 3.96 J.,!.7 3.72 J.92 3.98 4•01 4.06 4.08

DIH .oo .oo .oo .oo -.oo -.oo .02 .03 • 04 • 04

PCNT .o .o .o .o -.o -• l • 4 .1 1.0 1.0

WN15 BASE 4.03 4.04 4.06 4.08 COMP 4.06 4.08 4.09 4.10 DIFF .04 .03 .OJ .03 PCNT 1.0 .e .1 .6

BASE MEAN= 3.90 PCNT OJFF Mt.ANS= .4 AV AtlS DIFf"' .02 OIFF PCNT OF BASE MEAN= .s

IIARIABLE NAME 1972 1973 1974 197~ )976 1977 1978 )979 19B0 19B1 19B2 19B3 1984 19B5

WN16 BASE 18.53 19.33 21.36 17.63 l9e8B 20.30 20.21 l9e9A 19.91 20.12 COMP lB.53 19.33 21.36 17.63 19.8B 20.28 20.2e 20.11 20.01:1 20.29 OIFF .oo .oo .oo .oo -.01 -.02 .01 • 12 .18 .11 PCNT .o .o .o .o -.o -.1 .4 .6 .9 .B

WN16 BASE 20.lB 20.41 20.63 20.tlS COMP 20.35 20.ss 20.76 20.96 OIFF .17 .14 .12 • l l PCNT oB .1 .6 oS

BASE MEAN= 19095 PCNT OIFF Mt.ANS= o4 All ABS OIFF= 008 DIFF PCNT OF BASE MEAN= o4

WN17 BASE 2 l o 11 2lol4 2lo7l l9o~4 2lo90 23.86 23o8l ?.3o4l 23020 23042 COMP 21. 11 2lol4 21. 71 19054 21 ol:!9 23082 23092 ?3o63 23052 23075 OIFF .oo oOO oOO oOO -.01 -004 o 11 022 .32 033 PCNT .o oO .o oO -oO -o2 o4 o9 lo4 lo4

WN17 BASE 23o37 23052 23074 23090 COMP 23o70 23079 23097 24oll OIFF .33 ot!.7 023 o2l PCNT le4 1.2 l • O .9

BASE MEAN= 22ob9 PCNT OIFF MEANS= ·6 All AbS DIFF= .is DIFF PCNT OF BASE MEAN= 06

WN18 BASE 80B2 8025 7066 6022 8017 9.70 9o73 9o58 9o53 9.47 COMP 8082 8025 7066 6022 Bol7 9068 9o79 9o69 9o70 9o65 OIFF oOO oOO .oo oOO -.oo -.02 .06 .12 .17 .17

PCNT oO .o .o .o -.o -02 06 lo?. 108 l 08

WN18 BASE 9.27 9.16 9. 12 9.06 COMP 9.44 ~031 9.23 9ol6 OIFF .17 .14 .12 .10 PCNT loB 1.s 1.3 l • l I

BASE MEAN= 8.84 PCNT OIFF Mt.ANS= 08 All ABS DIFF= 008 OIFF PCNT OF BASE MEAN= .9 "' ..,. ... I

1r1Nl9 BASE 7.51 7.87 6.B7 6016 8.17 10018 10056 10078 1101s 11.43

COMP 7.51 101n 6 o 8 7 6 o l 6 8. 16 10.1s 10.65 !Oo9B llo46 11.76

OIFF .oo oOO oOO .oo -.01 -.03 .09 .20 .31 033

PCNT oO .o .o .o -.1 -o3 .8 lo9 2.0 2o9

WN19 BASE 11.11 11 o 61 llo83 l2o0l COMP 11.43 l l o 89 12.01 12023 OIFF .32 o2B .24 o2l PCNT 2o9 2o4 2.0 l•B

BASE MEAN= 9o80 PCNT OIFF Mt.ANS= lo4 All ABS OIFF= .}4 DIFF PCNT OF BASE MEAN= 1.s

wi-120 BASE 3o09 3ol5 J.}4 3oll 3o23 3.19 3.}6 3ol?. 3.11 3.09

COMP 3.09 3.15 3ol4 3.11 3.22 3.20 3.}9 3ol9 3o20 3o20

DIFF .oo oOO oOO .oo -.01 .01 .03 007 .09 .11 PCNT o0 .o .o oO -.2 .2 l • l 2ol 2.9 3o4

IIIN20 BASE 3 • 06 3o04 3.03 3.01 COMP 3 .17 3.16 3.14 3ol2 OIFF o 11 o l l ol2 ol2 PCNT 3o7 3o7 3.B 3.9

BASE MEAN= 3.11 PCNT OifF MtcANS= 1•7 All ABS DIFF= .05 DIFF PCNT OF BASE MEAN= loB

IIIN2l BASE 3.36 3o0B 3o25 2od9 2.95 2.96 2.95 2.95 2.97 3.00

COMI-' 3.36 J.OB 3o25 2.89 2.95 2.96 2.96 2o9f> 2.97 3.00

OIFF .oo oOO .oo oOO .oo .oo oOO oOO .oo .01

PCNT .o .o .o .o .o .o • l • I o2 .2

lrlN21 BASE 3.03 3.06 J.10 3ol5 COMP 3.03 3.07 3.1 l 3.15 OIFF .01 .01 oOl • 0 l PCNT .2 .2 .2 .2

BASE MEAN= J.05 PCNT DIFF Mt.ANS= • I All A!:IS DIFF= .oo DIFF PCNT OF BASE MEAN= • l

VARIABLE NAME 1972 1973 l Y74 197~ 1976 1977 197A 1979 1980 1981 1982 1983 1984 198~

WNZ2 BASE 6.25 6.39 6.39 bo36 6.38 6.32 b.25 be lA 6.12 6.07 COMP 0.2s 6.39 6.39 bo36 6.38 6.33 6.26 6.21 6. 16 6.12 Dlff .oo .oo .oo .oo -.oo .01 .02 .03 .04 .os PCNT .o .o .o .o -. l • 1 .3 .s .1 .s

11Nl2 BASE b 0 02 5.98 5.94 5.YO COMP 6.08 6.04 6.oo 5.90 Dlff .os .06 .06 .06 PCNT .9 .9 1.0 1.0

BASE HEAN= 6.18 PCNT DIFF Mt.ANS= .4 All ABS OIFF= .03 OlfF PCNT OF BASE MEAN= .4

WN23 BASE 7.26 7.b3 7.52 1.22 1.1s a.04 a.16 a.21 8.41 8.52 COMP 7.26 7.63 1.s2 1.22 7.74 a.os a.ia a.32 8.47 8.60 OlfF .oo .oo .oo .oo -.01 .01 .02 .os .06 .os PCNT .o .o .o .o -.1 • 1 .3 .6 .1 .9

i.N23 BASE 8.63 s. 76 a.as 9.00 COMP 8.71 8.84 8.96 9.09 Olff .00 .oa .os .09 PCNT .9 .9 1.0 .9

BASE MEAN= 8 .15 PCNT OJFF MEANS= .5 AV ABS DIFF= .04 OlfF" PCNT OF BASE MEAN= .5

IIN24 BASE 11.s1 12.30 12.63 12.06 13.68 13.94 14.12 )4.37 14.69 14.85 COMP 11.51 12.Jo 12.63 12.06 13.63 14.00 14.32 14.74 15.18 }5.46

Dlff .oo .oo .oo .oo -.os .06 .}9 .37 .49 .60

PCNT .o .o .o .o -.4 .4 1.4 2.6 J.3 4. 1 IIN24 BASE 14.98 15.21 15.42 15.59

COMP 15.63 15.89 16.13 16.33 Olff .65 .69 .11 .13 PCNT 4.3 4.5 4 • 6 4.7 I

BASE MEAN= 14.00 . PCNT OIFF MEANS= 2.3 AV ABS DIFF= •32 OIFF PCNT OF BASE MEAN= 2.3 "' ..,. "' I

IIN25 BASE 5.73 6.19 s.0a 5eb3 5.97 6. 14 0.os 0.00 5.97 5.94 COMP 5.13 6.19 5.88 5.b3 5.97 6.15 6.09 0.02 5.99 5.96

Olff .oo .oo .oo .oo -.oo .oo .01 .02 .02 .02 PCNT .o .o .o .o -.o • l • 1 .2 .3 .4

WN25 BASE 5.92 5.i:19 5.87 5.84 COMP 5.94 5.92 5.90 5.87 OIH .03 .OJ .03 .03 PCNT .4 .5 .5 .5

BASE MEAN= 5.93 PCNT OIFF MEANS= •2 AV ABS DIFF= .01 OlfF PCNT OF BASE MEAN= .2

WN26 BASE .91 1.02 l • 04 1.02 1.17 1.21 1 • 23 1 • 25 1.20 1.31

COMP .91 1.02 1.04 1.02 1.16 1.22 1 • 25 le2A 1.33 1. 37

OIH .oo .oo .oo .oo -.01 .oo .02 .03 .05 .05

PCNT .o .o .o .o -.5 .3 1.5 2.8 3.5 4.0

WN26 BASE 1 .34 l • 37 1.41 1.45 COMP 1 • 39 1.43 1.47 1.51 OIH .05 • 06 .06 .06 PCNT 4.1 4.1 4.0 ,. • 0

BASE MEAN= 1.22 PCNT orFF Ht::ANS= 2.2 AV ABS OIFF= .03 OlfF PCNT OF BASE MEAN= 2.3

WN27 BASE 2.36 2.48 2.42 2.49 t!.80 3.02 3.11 3.21 3.33 3.38

COMP 2.36 2.48 2.42 2.49 2.80 3.02 3.}4 3.27 J.40 3.47

OIH .oo .oo .oo .oo -.01 .01 .oJ • Of, .08 .09

PCNT .o .o .o .o -.2 .2 1.0 l.8 2.J 2.1

WN27 BASE 3.41 3.45 J.50 3.~3 COMP 3.50 Je55 3.60 3eb4 OIH .10 .10 .10 .10 PCNT 2.a 2.a 2.9 2.9

BASE MEAN= 3.04 PCNT OJFF Ml:ANS= 1.5 AV ABS DIFF= .05 OIFF PCNT OF BASE MEAN= 1.6

'vARJABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 198'>

WN21l tlASE .76 .BJ .00 .12 .82 .a0 .as .90 .92 .94 COMP .76 .83 .ao .72 .82 .86 .ea .91 .93 .95 0IH .oo .oo .oo .oo -.oo .oo .oo .01 .01 .01 PCNT .o .o .o .o -.1 .2 .s .a 1.0 1.2

wN2t1 BASE .95 .96 .98 .99 COMP .96 .98 .99 1.00 0IH .01 .01 .01 .01 PCNT 1.J 1.3 1 .4 lo4

BASE MEAN= .88 PCNT 0Jff Ml:.ANS= .7 AV ABS 0lff= .01 0lff POJT Of BASE MEAN= .1

WN29 BASE 4.88 4.66 4.57 4.55 s.59 6.18 s.a2 S.63 s.10 5.aJ COMP 4.88 4.b6 4.57 4 • 55 5.56 6.14 6.03 6.04 6.34 6.42 0lff .oo .oo .oo .oo -.03 -.03 .21 .42 .57 .59 PCNT .o .o .o .o -.5 -.6 3.6 7.4 10.0 10 • l

WN29 BAS!:. s.55 5.46 5.52 5.51 COMP bo 12 5.93 5.93 s.tn Dlff .57 .47 .41 .36 PCNT 10.2 a.1 7.4 606

BASE MEAN= 5.39 PCNT 0iff Mt.ANS= 4.7 A'v ABS 0lff= •26 0lff PCNT Of BASE MEAN:: 4.8

wNJO BASE 3.44 3.64 3.70 J.56 4.10 4.26 4o23 4.22 4.28 4o29 COMP 3.44 J.64 J.70 J.56 4.08 4.26 ... 28 4.33 4.41 4.43 0lff .oo .oo .oo .uo -.01 .oo .os .10 • l 3 .14

PCNT .o .o .o .o -.3 • 1 1.3 2.4 3.1 3.3

WNJO BASE 4.28 4.29 4.32 4.35 COMP 4.41 4.41 4.43 4.44 0IH .13 .12 • 11 .10 PCNT 3.1 2.7 2.5 2.3

BASE MEAN= 4.01 PCNT 0Jff Mt.ANS= 1.5 AV ABS Dlff= 006 0lff PCNT Of BASE MEAN= 1.6

wNJl BASE 1.99 1.11 1.44 1.05 1.64 1.12 1.69 l.63 1.60 1.s2 COMP 1.99 1.11 l .44 1.05 1.63 1.12 1.10 1.65 l .62 1.54

DIH .oo .oo .oo .uo -.oo -.oo .01 .02 .02 .02 PCNT .o .o .o .o -.1 -.1 .s 1.0 1 ... 1.5

WN31 BASE. 1.44 1.38 1.32 1.26 COMP 1.47 1 .40 1.33 l.c7 DIH .02 .02 .02 .02 PCNT 1.s 1.4 1.3 l.J

BASE MEAN= 1.53 PCNT Dlff MEANS= .7 AV ABS Dlff= .01 0IFF PCNT OF BASE MEAN= .7

WN32 BASE 8.30 a.so 9.05 B.Jl 9.59 10.06 10.25 10.37 10.52 10.64

COMP 8.30 a.so 9.05 ti.Ji 9.59 10.06 10.26 10.39 10.55 10.67

0lff .oo .oo .oo .oo -.oo .oo .01 .02 .OJ .03

PCNT .o .o .o .o -.o .o .1 .2 .2 .3

WN32 BASE 10.82 10.99 11•18 ll.36 COMP 10.84 11.02 11.20 11.Ja 0lff .03 .02 .02 .02 PCNT .3 .2 .2 .2

BASE MEAN: 9.99 PCNT 0lff ME.ANS= • I AV ABS Diff= .01 0lff PCNT Of BASE MEAN= • l

WNJJ BASE 4 • 31 4.51 4.56 ..... a 5. 14 5.46 s.30 5.19 5.25 5.26

COMP ... 31 4.51 4.56 4.48 S.ll s.46 5.42 5.43 5.ss 5.59

DIH .oo .oo .oo .oo -.OJ .oo .12 .24 .JI . JJ PCNT .o .o .o .o -.6 .o 2.3 4 .f. 5 . 9 6.

loN J BASt. 5 , 16 s. 15 s. 18 s.1a COMP 5 .48 5 .43 5.44 5.43 O l f . 2 . c9 .26 .ts t> NT C, . 5 . I 4,7

!:I A M AN ■ • V l I-' NT 0 I H Mt.ANS= 3 , 0 /IV AH U! ·• • l • I If f f NI r f1A ' i A I 0

VARIABLE NA"4E 1'"72 197 3 1'"74 197~ 1976 1977 1978 1979 1980 1981 1982 1983 1984 198!:>

WN34 BASE 3o20 3o45 3o47 Jo :n 3o63 3o71 3o74 3o7fi 3o82 3o85 C0"4P 3o20 3o45 3o47 3o33 3o62 3o72 3o77 3o83 3o90 3o95 OJH oOO oOO oOO oUO -oOl .01 .03 007 o08 olO PCNT .o oO oO oO -.4 o2 09 loll 2o2 2o5

IIIN34 BASE 3o87 3o90 3o94 3.97 C0"4P 3o97 4o00 4o04 4o07 OIH olO olO olO .10 PCNT 206 206 205 205

BASE MEAN= J.69 PCNT Oiff Mt.ANS= loJ AV ABS Dlff= 005 Olff PCNT Of BASE MEAN= lo4

WNJS BASE 2o67 Joo0 3000 2.11 2o9l 2o99 J.03 J.11 3ol8 3o25 COMP 2o67 3.00 Joo0 2.11 2o9l 2o99 3.o4 lol2 3o20 3o27 OIH oOO oOO oOO oOO -oOO oOO oOl oOl 002 002 PCNT oO oO oO oO -ol • l o2 o4 o5 .6

WNJS BASE. 3o3l 3.37 3o44 J.!:>O COMP 3o33 3o40 3.46 3.52 Olff 002 002 003 003 PCNT o1 o7 oB 08

BASE MEAN= 3o l2 PCNT Oiff MEANS= o4 AV ABS Olff= oOI Olff PCNT Of BASE MEAN= o4

WN36 BASE 3080 4068 4o93 4o'+l s.12 6003 6020 6.36 6063 6078 C0"4P 3080 4.68 '+o93 4 o '+ l 5o67 6005 6028 6.53 6082 1.02

OIH oOO oOO oOO oOO -004 .02 o08 ol7 020 .24

PCNT oO oO oO oO -o7 o4 lo3 2 06 3o0 3o5 WN36 BASE 6095 7ol3 7o32 7o51

COMP 7ol8 7o38 7o57 7.76 OlfF 024 024 025 025 PCNT 3o4 3o4 3o4 3o3

BASE MEAN= 6003 PCNT OJFf MEANS= lo9 AV ABS OIFf= o 12 OIH PCNT Of BASE MEANa: 2.0 I ...., t

WNJ7 BASE 4o07 soJ2 so19 4066 So88 6022 6041 6062 6087 6.98 I

COMP '+o07 So32 Sol 9 4066 5087 6023 6044 6.69 6096 7o09

OIH oOO oOO oOO oOO -002 oOl 004 007 009 oll

PCNT .o .o oO .o -.3 • 1 06 l • l 1.J 1.5

IINJ7 BASE 7 .12 1.21 7 .4J 10s0 COMP 1.22 7o38 1.53 7.68 OIH .10 .10 olO .10 PCNT 1.s lo4 1.4 1 o 3

BASE MEAN= 6.26 PCNT Olff Mi::ANS= .0 AV ABS DIFf= .os Olff PCNT Of BASE MEANc .9

WNJ8 BASE 3.70 3od0 3.79 3.56 J.00 4.00 ... oo 3.98 J.97 3.91

C0"4P 3.70 3.tlO 3.79 J.56 3.87 4.00 4o03 4o02 4.03 3.97

OlfF .oo .oo .oo .oo -.01 .oo 002 .os .06 .01 PCNT .o .o oO .o -o3 • 1 .6 lo2 1.s 1.1

IIINJ8 BASE 3.84 3.78 J.73 3.68 COMP 3o91 3ot14 J.79 J.74 OIH .06 .06 .06 .06 PCNT 1.1 lo6 lo6 1.5

BASE MEAN= 3.83 PCNT OJff MEANS= 08 Av ABS Dlff= .03 Olff PCNT Of BASE MEAN= .0

WNJ9 BASE 45.31 55.09 S7o6l 42083 48018 43.43 43.32 40055 39.57 38.46

COMP 45.31 55.09 57.61 42.83 46.33 47050 s2.ss 56.51:! 58045 60.46

OIH oOO .oo .oo oOO -1.85 4.07 9o23 16.03 18.88 2lo99

PCNT .o o0 oO .o -308 9.4 2lo3 39o5 47o7 57.2

IIINJ9 BASE 37.46 36.47 JS.Si 34057 CO"4P 59096 60.04 60.0l 59.96 OlfF 22.so 23058 24.50 25.39 PCNT 60ol 6406 69oO 73.5

BASE MEAN= 42.74 PCNT OJFf MEANS= 27•5 AV ABS OIFf= 12000 Olff PCNT Of BASE MEAN= 28.l

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 )979 1980 )981 1982 1983 1984 1985

IIN40 BASE 4.12 4.18 4.33 4.48 s.02 s.26 s.35 5.49 5.61 5.75 COMP 4.12 4.18 4.33 4.48 5.01 5.21 5.36 5.51 5.64 s.1s Olff .oo .oo .oo .oo -.oo .oo .01 .o? .03 .03 PCNT .o .o .o .o -.1 • l .2 .4 .5 .6

WN4U BASE !:>.93 6.11 6.29 6.47 COMP 5.96 6.15 6.33 6.51 OIFF .OJ .04 .04 .04 PCNT .6 06 • 6 •6

BASE MEAN= 5o31 PCNT OJff MEANS= .3 A'i ABS IJIFf= •02 Olff PCNT OF BASE MEAN= .3

WN4-l BASE 20.11 21.37 21.93 21.1:11 22.63 20.36 21.34 21.30 21.44 21 .63 COMP 20.11 21.37 21.93 21.87 22.62 20.37 21 .34 21.30 21.45 21 .66 Olff .oo .oo .oo .oo -.oo .01 .01 .oo .01 .OJ PCNT .o .o .o .o -.o .o .o .o • l • l

WN4l BASE 21.38 21.20 20.96 20.12 COMP 21.43 21.26 21.04 20.1m Olff .os .06 .08 .08 PCNT .2 .3 .4 .4

BASE MEAN= 21.Js PCNT Olff MEANS= • 1 A'i ABS Dlff= .02 Olff PCNT Of BASE MEAN= • 1

IIN42 BASE 7.48 8.58 8.19 7.64 8.77 9.59 9.89 10.33 l0.63 10.88 COMP 7.48 8.Se 8.19 7.64 8.75 9.61 9.96 }0.46 10.ao 11.09 OIH .oo .oo .oo .oo -.03 .01 .01 .}4 .11 .20 PCNT .o .o .o .o -.3 • l .1 1.3 1.6 1.8

WN42 BASE 11.06 11.2a }l.49 11.69 COMP 11.21 lle48 11.10 11.90 Olff .20 .20 .21 .21 PCNT 1.9 1.a 1.8 1•8

BASE MEAN= 9.82 PCNT OJFF Mt.ANS= 1.0 A'i A&S Dlff= .10 Olff PCNT Of BASE MEAN= 1.0

WN43 BASE 64.32 68.17 t,5.63 60.92 65.74 69.31 70.}6 70.86 71.97 72.45 COMP 64.32 68.17 65.63 60.'il2 65.6} 69.42 70.61 71 • 71 73.03 73.70

OIH .oo .oo .oo .oo -.13 .11 .45 .es 1.06 l .24

PCNT .o .o .o .o -.2 .2 .6 1.2 1.s 1.1

WN43 BASE 72.93 13.10 74.55 75.34 COMP 74.20 74.99 75.82 76.61 OIFf 1.28 1.28 1.20 1.21 PCNT 1.e 1.7 1.7 1.1

BASE MEAN• 69.72 PCNT Olff MEANS= .9 A'i ABS Dlff= 064 Olff PCNT Of BASE MEAN= .9

WN41t BASE 6. 71 1.02 7.13 6.97 7.40 7.43 7.39 1.31 7.39 7.32

COMP 6.71 1.02 1.13 6.97 7.38 7.46 7.49 7.56 7.63 7.62

OIH .oo .oo .oo .oo -.03 .OJ .io olA .21o .29

PCNT .o .o .o .o -• '- .3 1.3 2.5 3.3 It. 0

wN44 BASE 7.25 1.21 1.16 1.10 COMP 7.56 7 053 7.49 7.41t Olff .31 .33 .33 • 31t PCNT 4.3 '-•S 4.7 4.7

BASE MEAN: 1.20 PCNT O[ff ME.ANS= 2• l A'i ABS Dlff= • 16 Olff PCNT Of BASE MEAN= 2.2

WN45 BASE 1.18 1.20 1.19 1.14 1.20 l .19 1.11 1.1 5 l • l '- l. 14

COMP 1.18 1.20 1.19 1.14 1.19 1.20 l.}9 l • HI 1.18 1.18

OIH .oo .oo .oo .oo -.oo .oo .02 .0 3 .04 .04

PCNT .o .o .o .o -.4 .3 1.3 2.c; 3.3 4.0

IIN45 BASE 1.13 1.12 1. 11 l • 11 COMP 1.17 l .17 1.16 l • l 6 Olff .05 .os .o s .o s PCNT 4.3 4.5 4. 5 4 . t,

BASE MEAN= J.1 6 PC NT DJ FF' M AN • AV A '- UI r• .o rwr p /IT I f1,\ 1 I Hr A

VARIABLE NAME 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 }983 1984 1985

WN4b BASE 4o45 4otis 4o69 4o'::>7 4o93 So03 So 10 so20 5o31 So37 COMP 4o45 4otis 4o69 4o'::>7 4o9l SoOS So 18 So3S 5o52 So62 OIFF oOO oOO oOO oOO -002 002 oOe o 15 .21 .26 PCNT .o .o .o oO -.4 .4 los 300 3o9 4.8

WN46 BASE So40 5.47 s.s2 s.s1 COMP 5.68 So76 5.84 So89 OIH 028 .30 o3l 032 PCNT So2 SoS 5.7 508

BASE MEAN= s.09 PCNT OJFF Ml:.ANS= 2o7 AV ABS DIFF= • 14 OIFF PCNT OF BASE MEAN= 2.1

WN47 BASE 14.96 lSodO 16003 15.73 11.11 17043 17060 17.98 18.39 18.49 COMP 14.96 15.80 160 03 15073 11.03 l7o50 17087 18050 19.09 19036 DIH .oo .oo .oo oOO -.oa 007 .21 052 .10 .86 PCNT .o oO .o .o -oS .4 1.s 2.9 3.8 4.7

WN47 BASE l80SS 18072 10.as 18093 COMP 19.49 19. 72 19089 19.99 DIH .94 loOO lo04 lo06 PCNT s.1 5.3 5.5 506

BASE MEAN= 17.47 PCNT DIFF Ml:.ANS= 206 AV ABS DIFF= 047 DIFF PCNT OF BASE MEAN= 2.1

WN48 BASE 2lo 73 17071 13062 14.68 23.95 30.30 29033 21.29 28.56 30.70 COMP 21.73 17. 71 13.62 l4ob8 23095 29o45 30olll 30091 34024 36061 OIH .oo .oo oOO .oo .oo -.as lo49 3o63 S.67 So91 PCNT oO .o .o .o .o -208 Sol 13.3 19.9 19.3

WN48 BASE 28.04 26049 27.61 27.87 COMP 33.90 3l o24 31.49 31.12 DIH 5086 4o7S 3088 3o25 PCNT 20.9 l7o9 l4o0 1106 I

BASE MEAN= 24085 PCNT DIFF MEANS= 9o7 AV ABS DIFF= 2o52 DIFF PCNT OF BASE MEAN= 10.1 N ~

"' I

WN49 BASE l7o33 17045 19069 19039 22ol6 24o36 22.73 2lo82 22.26 22045 COMP 17033 17.45 19.69 19.39 22.01 24o35 23.37 23o l2 23o9b 24.32 OIH oOO .oo .oo oOO - o 15 -oOl 064 lo3l 1 • 70 lo87 PCNT .o oO oO oO -.1 -oO 208 600 706 803

WN49 BASE 22029 22 • ':,7 22.83 23024 COMP 24ol3 24027 24038 24.72 OIH l 084 1.11 l 056 1.48 PCNT 802 706 608 604

BASE MEAN: 2 l o4 7 PCNT DJFF MlcANS= 4o0 AV ABS DIFF= 088 OIFF PCNT OF BASE MEAN"' 4o 1

WNSO BASE 6.58 6085 6.97 7o28 8.76 10019 8.76 8069 8.9'+ 8.73 COMP 6058 6085 6097 70~8 8068 10o29 9.21 9o42 9o72 9o't2

OIFF oOO oOO oOO oOO -009 olO oSl 073 078 .69 PCNT .o oO oO oO -loO 1.0 s.0 804 9.1 7.9

IIINSO BASE 0.21 a.31 a.so 8041 COMP 8079 a.1s 8089 0.10 DlfF os8 044 040 037 PCNT 1.1 So3 4o7 4o4

BASE MEAN= 8023 PCNT DIFF Ml'.ANS= 3o9 AV ABS DIFF= 033 OifF PCNT OF BASE MEAN= 4o l

wNSl BASE 23.97 2s o ·n 26070 26o't0 29005 29.31 28056 27.90 27 o8't ?7o52 COMP 23097 25.97 26070 26.'+0 28087 29042 290 I 5 29.00 29012 28085

OIH oOO .oo oOO .oo -ol8 ol2 059 1.09 lo28 lo33 PCNT .o oO oO oO -.6 .4 2ol 3.9 406 408

WNSl BASE 21000 26ot10 26.79 26.64 COMP 28017 27.79 27063 27.37 OIH l ol7 0 <.J9 084 .73 PCNT 4o3 3.7 3ol 2.1

BASE MEAN= 27017 PCNT DIFF Mt.AIIIS= 2ol AV ABS DIH= 059 OIFF POJT OF BASE MfAN= 2o2

1/AIHABLE NAME 1972 1973 1974 1975 1976 1977 1978 }979 1980 1981 1982 1983 1984 198!>

WN52 BASE 6.72 7.39 1.13 7.90 8.64 8. 71 8.77 9.04 9.25 9.40 COMP 6.72 7.39 7.73 7.90 8.61 a.1s 8.A8 9.21 9.51 9.74 OIH .oo .oo .oo .oo -.03 .04 .10 • l 9 .26 .34 PCNT .o .o .o .o -.3 .4 1.2 2. l 2.a 3.6

i!IN BASE 9.56 9.79 9.96 1 0 • l l COMP 9.96 10.23 10.43 10.61 OIH .40 .44 .47 .so PCNT 4. l 4.5 4.8 4.9

BASE MEAN= 0.10 PCNT Diff MEANS= 2.2 AV ABS Dlff= .20 Dlff PCNT Of BASE MEAN= 2.2

WN5 J BASE 289.85 314.51 324.36 326.17 358.04 370.77 378.08 386.35 397.36 402.18 COMP 289.85 314.51 324.36 326.77 356.87 372.02 383.IO 396.16 410.41:1 4}8.03 DIH .oo .oo .oo .oo -1.17 1.25 s.02 9.81 13.12 is.as PCNT .o .o .o .o -.3 .3 1.3 2.s 3.3 3.9

WN53 BASE 405.0S 410.30 415. 71 <+20.u1 COMP 422.04 4?:7.98 433.86 438.53 OIH 16.99 17.61:! 10.15 18.52 PCNT 4.2 4.3 4 .4 '+•4

BASE MEAN= 371 • .38 PCNT Diff Mt.ANS= 2.2 AV ABS OlFf= 8040 Dlff PCNT Of BASE MEAN= 2.3

WN54 BASE 69.32 73.ol 1s.02 74.65 ao.60 83.18 84.94 A.7.24 89.93 92.19 COMP 69.32 73.61 75.02 74.65 80.36 83.46 85.98 89.27 92. 71 95.67 OIH .oo .oo .oo .oo -.24 .28 1.04 2.04 2.10 J.48 PCNT .o .o .o .o -.3 .3 1.2 2.3 3.1 3.8

WN54 BASE. 94.20 96.75 99.26 101.60 COMP 98.06 l00.90 103.66 100.19 OIH 3.Bb 4.15 4.39 4.59 PCNT 4.1 4.3 4.4 4.5 I

BASE MEAN= 85.89 PCNl Diff MEANS= 2.2 AV ABS Olff= 1•9? Dlff PCNT Of BASE MEAN= 2.2 "' ~ ..., I

WNSS BASE 228.60 251.65 266.03 210.1:10 294.87 302.06 306.24 3}1.92 319.64 328.18

COMP 228.60 251.65 266.03 <!70.80 293.38 303.37 Jll.51 322.35 333.48 345.33

OIH .oo .oo .oo .oo -1 .49 1.31 s.21 }0.43 13.84 17.15

PCNT .o .o .o .o -.s .4 1.7 3.3 4.J s.2

WNSS BAS!:: 335.42 345. l O 354.84 363.73 COMP 354.17 365.07 375.84 .385.62 Dlff 18.75 19.97 21.00 21.89 PCNT 5.6 s.0 5.9 6e0

BASE MEAN= 305.o5 PCNT OIFF ME.ANS= 3.0 AV ABS Dlff= 9o36 Dlff PCNT Of BASE MEAN= 3.1

WNC 8ASE 31.65 33.56 34.30 33.90 35.88 35.91 35.67 35.62 35.74 35.22

COMP 31.65 33.56 34.30 33.90 35.74 36.09 36.26 36.75 37.24 37.03

OIH .oo .oo .oo .oo -.13 .17 060 1.13 1.so 1.01

PCNT .o .o .o .o -.4 .s 1.7 3.2 4.2 5.1

WNC BASE 34.59 34.20 33.76 33.22 COMP 36.52 36.20 JS.Bl 35.29 OIH l. 93 2.01 2.05 2.01 PCNT 5.6 5.9 6. l 6-2

BASE MEAN= 34.!:>2 PCNT OIFF Mt.ANS= 2.1 Al/ ABS lJlff= 096 Olff PCNT Of BASE MEAN= 2.a

WNSLEOU BASE }02.46 101.21 102.62 107.35 102.88 102.11 l10.7f, 112.11 112.12 114.82 COMP 102.46 101.21 102.62 107.35 103.09 101.64 109.61 lll.60 113.63 117.56 OIH .oo .oo .oo .oo .21 -.47 -1. l 5 -.s1 .91 2.74

PCNT .o .o .o .o .2 -.s -1.0 -.s .0 2.4

WNSLEDU BAS!:: 117.61 11 7 • 65 110.og 119. '14 COMP 122.00 123 • ..:Z 124.23 126.58 DlFf 4.39 s.s1 6. 14 6.64 PCNT 3.7 4.7 5.2 5.5

BASE MEAN= 110.11 PCNT DifF Mt.ANS= 1-6 AV ABS Dlff= 2.05 OlfF PO-JT Of BASE MEAN= 1.9

IIARJABLE NAME 1972 1973 1974 197!:> 1976 1977 197A 1979 1980 1981 1982 1983 1984 1985

WNSLOTH BASE 85.88 88.40 82.83 81.84 98.05 88.00 82.83 99.36 103.87 104.66 COMP 85.88 88.40 82.83 81.d4 97.91 88.76 82.s5 97.17 101.74 104.27

DIFf .oo .oo .oo .oo -.13 .11 -.28 -2.20 -2.13 -.39 PCNT .o .o .o .o -.l .9 -.3 -2.2 -2.1 -.4

WNSLOTH BASE 109.96 116.69 ll 7.89 lltl.58 COMP 112.00 120. •n 124.15 125.79 DIFf 2.04 4.28 6.U, 1.21 PCNT 1.9 3.7 5.3 6 • l

BASE MEAN= 98.'+9 PCNT Diff MEANS= l • 1 AV ABS Dlff= 1•84 OlFF PCNT Of BASE MEAN= l.9

IINH.DMIL BASE 54.32 51.34 Sl.48 so.10 49.82 49.55 49.27 49.00 48.72 48.44 COMP 54.32 51.34 5 l .48 so.10 49.82 49.55 49.27 49.00 48.72 48.44 Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

IIINFEDMIL BASE 48.17 47.89 47.62 41.34 COMP 48.17 47.89 47.62 47.34 DIFf .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= 49.50 PCNT DIFf ME.ANS= .o AV ABS DIFF= .oo DIFF PCNT OF BASE MEAN= .o

WNF"EOCIV BASE 31.56 31.43 30.36 30.UO 30.13 30.26 30.]9 30.52 30.65 30.7B

COMP 31.56 31.'+3 30.36 30.00 30.13 30.26 30.39 30.52 30.65 30.78

Olff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

IIINFEOCIV BASE 30.91 31.04 31.17 31. Jo COMP 30.91 31 • 04 31. l7 31.30 DIFf .oo .oo .oo .uo PCNT .o .o .o .o ,

BASE MEAN= 30.75 PCNT OJfF MEANS= .o AV ABS OlFF= .oo OIFF PCNT OF BASE MEAN= .o ..., ~ 00 I

IIINFEO BASE 85.88 82.77 81.84 ao.10 79.95 79.81 79.f,6 79.52 79.37 79.22

COMP 85.88 B2.77 81.84 80.10 79.95 79.81 79.66 79.52 79.37 79.22

Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

IIINFEO BASE 79.08 78.93 78.79 78.b4 COMP 79.08 78.93 78.79 78ob4 DIFf .oo .oo .oo .oo PCNT .o .o .o .o

l:lASE MEAN= ao.25 PCNT DIFF MEANS= .o AV ABS DIFF= .oo DIFF PCNT OF BASE MEAN= .o

WJOl:lClV BASE 1322.20 1402.80 1423.lO 1399.00 1527.80 1555.30 1569.60 1597.50 1628.70 1648.50

COMP 1322.20 1.:.02.so 1423.lO 1399.00 1521.80 1562.60 1595.50 1646.80 1694.60 1729.bO

OIFf .oo .oo .oo .oo -6.00 7.30 25.90 49.30 65.90 81.10

PCNT .o .o .o .o -.4 .5 l.7 3. l 4.0 4.9

wJOtlClV BASE 1662.40 1685.70 1101.10 1725.50 COMP 1751.30 1779.80 1aos.10 1826.40 OIFF 8b.90 94.10 98.00 100.~o PCNT 5.3 5.6 5.7 5.a

BASE MEAN= l5bl .09 PCNT DifF MEANS= 2•A AV ABS l)Iff= 44•10 OIFF PCNT OF BASE MEAN= 2.8

WNCIV l:lASE 1286.50 1355.40 1365.40 lJ32.tlO 1459.90 1490.70 1508.90 1540.40 1575.10 1594.20

COMP 1286.50 1355.40 1365.40 1332.80 1454.20 1497.70 1533.80 1587.80 1638.80 1672.70

DIFF .oo .oo .oo .oo -5.70 1.00 24.90 47.40 63.70 78.50

PCNT .o .o .o .o -.4 .5 l.7 3.1 4.0 4.9

111NClv BASE 1607.70 1630.20 1650.90 lb68.70 COMP 1693.60 1721.20 1745.70 l76b.30 OIFf 85.90 91.00 94.80 97.60 PCNT 5.3 5.6 s.1 5•8

BASE MEAN= 1504. 77 PCNT OifF ME.ANS= 2.s AV ABS Dlff= 42.61 OlfF PCNT OF BASE MEAN= 2.8

VARIABLE NAME 1972 }97J 1'174 l'H5 1976 1977 1978 1979 1980 1981 1982 1983 }984 1985

WONCJV BASE 28.55 68.l:16 10.00 -32.60 127.0B JO.BO 18.11 31 .49 34.79 19.08 COMP 28.55 68.l:16 10.00 -32.00 121.37 43.51 36009 54006 50098 33088 Dlff .oo oOO oOO ollO -5o 71 120 7l 17092 22057 16019 14081 PCNT .o .o .o .o -4o5 41 o3 98.6 71. 7 46o5 7706

WONCIV BASE l3o43 22057 20.66 l 7 o 1:10 COMP 20093 27.56 24050 20 .61 Dlff 7.50 5o00 J.84 2.1:11 PCNT s5.8 22. 1 18.6 15-8

BASE MEAN= 29033 PCNT Diff MEANS= 23-fl AV ABS Olff= 7.79 DIFF PCNT Of BASE MEAN= 26.6

WNTOT BASE 1340.90 1406070 1416.90 1382.90 1509.70 1540 • . 20 1558.10 1589040 1623.90 1642.70 COMP 1340.90 1406.70 1416.90 }J8c 0 90 1504000 }547o20 · 1503000 1636080 1687050 172lol0 Dlff .oo oOO .oo oUO -5070 7o00 24090 47040 63060 78.40 PCNT .o .o .o oO -.4 o5 1.6 3.o 3.9 408

wNTOT BASE 1655.80 1678010 1698.50 l 7l6oUO COHI-' 1741.80 1769010 1793.30 1ts1J.60 Dlff 8b.OO 91000 94080 97.oO PCNT 5.2 5.4 506 5o7

BASE MEAN:: 1554027 PCNT DIFf MEANS= 2.1 AV ABS OIFF= 42060 OIFf PCNT OF BASE MEAN= 2.1

wUNEMRT BASE .00 .06 .01 • lo 007 .os o08 008 .oa .08 COMP .OB .06 .07 olO .01 .OB .00 .01 .01 .01 DJFF .oo .oo .oo .oo .oo -.oo -.oo -.01 -.01 -.01

PCNT .o .o .o .o 1.4 -2.6 •b.3 -9.o •9o3 -9.l WUNEMRT BASE .08 .os .OB .us

COMP .01 007 .01 .01 Olff -oOO -oOO -oOO -oOO PCNT •604 •503 ·2o7 -2o7 I

BASE HEAN= o08 PCNT OIFf MEANS= -3o7 AV ABS DlfF= oOO OIFF PCNT OF BASE MEAN= 3o9 N

$ I

•EHRT BASE 091 093 .93 0 c,o 093 092 092 092 093 092

COMP .91 093 093 090 093 092 093 093 093 093

Dlff .oo oOO oOO oOO -oOO .oo .oo oOl oOl oOl

PCNT oO oO .o .o -.1 .2 .5 .A .0 08 WEMRT BASE .92 .92 .91 .93

COMP .93 093 .93 .93 Dlff .oo .oo .oo .oo PCNT .5 .4 .2 -2

BASE MEAN= .92 PCNT OIFf MEANS= .3 AV ABS DIFF= .oo Dlff PCNT OF BASE. MEAN= o3

WLfCIV BASE 1406080 1450040 1471060 1480 0 /0 1574060 1616.30 lb39o90 1670.70 1703050 1726.30

COMP 140b.80 1450.40 1471060 l'+80o 70 1570.60 1620.10 1057.90 1708040 1758070 1798.00

Dlff .oo .oo oOO .oo -4.00 3o80 18.00 37.70 ss.20 11.10

PCNT oO .o .o .o •o3 .2 1. 1 2o3 3.2 4o2

WLFCJ V BASE 1743.30 }764020 1785060 }!!04.t>O COMP 1B2b.70 1ass.so 1882.90 }906.30 Dlff 83.40 91.30 97.30 10lo70 PCNT 4.8 5.2 5.4 506

BASE MEAN= 1631032 PCNT DIF f MEANS= 2o4 AV ABS OIFF= 40029 Dlff PCNT Of BASE MEAN= 2.s

WLF BASE. l4bl.10 1501.70 1523.10 1530.!!0 1624.40 1665.90 1689.10 1719.70 1752.20 1774.70

COMP 1461•10 1501.70 1523.10 1530.80 1620.40 1669.60 1101.20 175 7.40 1807.40 1846.50

Dlff .oo .oo .oo .oo -4.00 3o70 10.10 37.70 ss.20 11000

PCNT .o .o .o .o -.2 .2 1. l 2.;, 3.2 4.0

WLF BASE. 1791.50 1812.10 1833.?0 lbSl.90 COMP 1874080 1903.40 1930.so l~SJot>O DIH 83.30 91.30 97.30 101.10 PC NT 4.6 s.o 5.3 5.5

BA SE MEAN= l6AO.Bl PCN T DI~ F MtANS= 2, 4 AV ABS Olff= 40029 OIFF PCNT Of BASE MEAN= 2.4

VAHJABLE NAME l'H2 197J 1974 1975 197o 1977 1978 \979 1980 1981 1982 1983 1984 198:c>

lliNRT BASE 044 044 045 045 044 • 45 045 045 045 045 COMP 044 044 045 045 044 045 045 045 045 045 DIFF oOO oOO oOO oOO oOO oOO oOO oOO oOO oOO PCNT oO oO .o .o oO oO .o oO oO oO

lliNRT BASE 045 045 045 046 COMP 045 045 045 0 '+6 DIFf .oo oOO oOO oOO PCNT oO oO oO oO

BASE MEAN= 045 PcNT Diff MEANS:: oO AV ABS Dlff= oOO OIFF PCNT Of BASE MEAN= oO

lliPOP BASE 3355000 340bol0 3412080 343bo20 3658090 3732090 3765oso 3820050 3879060 3922070 COMP 3355000 3406ol0 34l2o80 3436 020 3649080 3741030 3805080 3904040 4001070 40€11030 DIFF oOO oOO oOO oOO -9o l 0 8.40 40030 83090 122010 158060 PCNT oO oO oO oO -o2 o2 lo 1 2o2 3ol 4o0

lliPOP BASE 3953000 3991070 4038020 4072ob0 COMP 4136090 4192090 4252050 4290020 DIFF 183o90 201020 214030 223000 PCNT 4o7 s.o 5.3 5o5

BASE MEAN= 3746012 PCNT OJff MEANS= 2o3 AV ABS DIFF= 88096 DIFF PCNT OF BASE MEAN= 2o4

lliPOP5-20 BASE 1023060 l028oUO lOlBolO 1012.00 1064040 1065.70 1048070 1036010 1025040 1008010 COMP 1023060 1028000 1018010 1012000 1061070 1068010 1059090 1058090 1057070 1048090

DIFF oOO oOO oOO oOO -2070 2o40 llo20 22000 32030 40080 PCNT oO oO .o .o -o3 o2 lo 1 2o2 3ol 4o0

lliPOPS-20 BASE 995o36 9850 lS 980088 970001 COMP 1041070 1034080 1032090 l0JOo20 DI ff 4bo34 49005 52.02 53059 PCNT 4o7 5o0 5.3 5.5 I

BASE MEAN= l0l9ol5 PCNT DIFF ME.ANS= 2o2 AV ABS DlfF= 22o4J DIFf PCNT OF BASE MEAN= 2o2 N UI 0 I

lliOPOPS-20 BASE -39o38 4o35 -9092 -6009 52041 lo37 -17.04 -12057 -lOo75 -17.24 COMP -39.38 4.35 -9092 -6.09 49077 6.41 -8.24 -lo03 -lo2l -8.75

OIFF oOO .oo oOO .oo -2064 5.04 8080 11.54 9.54 8.48

PCNT .o oO .o oO -5.0 369.l -5106 -91.8 -8a.7 -49.2

lliOPOP5-20 BASE -12078 -10021 -4.27 -4o27 COMP -1023 -6088 -1 .87 -2.70 Dlff 5o56 3.34 2o40 lo57 PCNT -43.5 -3207 -5603 -3607

BASE MEAN= -6.17 PCNT DJFF MEANS=-62ol AV ABS DlfF= 4o21 Olff PCNT OF BASE MEAN=-68.2

lliMOl BASE 50032 54.13 49038 45095 53048 51003 53096 s3.68 55.39 55056

COMP 50.32 54.13 49038 45095 53.39 51025 54.18 54ol9 55091 56.30

DIFF oOO oOO oOO .uo -.09 021 .21 .s1 052 074

PCNT .o oO oO .o -o2 .4 04 1.0 .9 lo3

lliMOl BASE. 56.83 57062 58060 59045 COMP 57.49 58.42 59.37 60.27 Olff .67 019 .78 .83 PCNT lo2 1.4 lo3 lo4

BASE MEAN= 53.96 PcNT Diff ME.ANS:: o7 AV ABS DIFF= oJa OIFF PCNT OF BASE MEAN= · .1

lliMOc BASE 22002 22004 20048 19055 19.}7 19.94 20.41 20094 21.54 21.99

COMP 22002 22004 20.48 l9o55 19. l 5 19.97 20050 21.10 2lo76 22026

DIFF oOO oOO oOO oOO -.02 .03 o09 • lf', .22 021

PCNT .o .o .o .o -.1 • l .4 .a 1.0 1.2

WM02 BASE 22.44 22.94 23043 23.92 COMP 22.74 23.26 23o77 24.27

OIFF .Jo 032 .34 .JS PCNT 1.3 l .4 l .4 lo5

BASE MEAN= 2lo'+9 PCNT DifF MEANS= .1 AV ABS OIFF= .is OIFF PCNT OF BASE. MEAN= .1

VARIABLE NAME. 1972 1973 l'n4 l'H5 1976 1977 l '178 1979 -1980 1981 1982 }983 1984 198:>

WM03 BASE 63.45 64. 37 6 l. 91 58.34 66.57 64.73 68.74 f,9.37 72.31 72.39 COMP 63.45 64.37 6 l • 91 58.34 t,b.27 b5.29 69.eo 71.47 74. 7'J 75.51 DIFf .oo .oo .oo .oo -.Jo .56 1.06 2.10 2.48 3.13 PCNT .o .o .o ,O -.s .9 LS 3,0 3.4 4.3

WM03 BASE n. 78 75.09 76.33 77.31 COMP 11.00 78.b2 79.95 81.08 OIFf 3.22 3.53 J.62 3.17 PCNT 4.4 4.7 4.7 4.9

B.ASE MEAN= 68.91 PCNf OIFF MEANS= 2.4 All ABS DIFF= 1.10 nIFF PCNT OF BASE MEAN= 2.5

WMO<ft BASE 9 .78 9.10 9.45 9.04 9.46 9.62 9.69 9.79 9.93 10.02 COMP 9.78 9.70 9.45 9.04 9.44 9.64 9.78 9.96 10.18 10.31 OIFf .oo .oo .oo .oo -.02 .02 .09 .1e .24 .30 PCNT .o .o .o ,0 -.2 .2 ,9 l.A 2,5 3.0

IIIM04 BASE 10.06 10.15 10.25 10.32 COMP 10.38 10.49 10,59 l O 0.b8 DIFf .32 .34 .35 • J6 PCNT 3.2 3.3 3.4 3,5

BASE MEAN= 9.80 PCNT DIF F Mi:.ANS= lo6 AV ABS DIFF= • 16 [)!FF PCNT OF BASE MEAN= 1.6

WM05 BASE J.80 3,75 J.so 3.~1 J.27 3.22 J.JS J.08 3.00 3.01 COMP J ,80 J.75 3.50 J.,n J.27 J.22 J.i6 3.10 3.04 3.05 DIFf .oo .oo .oo .oo -.oo .oo .01 .03 .OJ .04

PCNl .o .o .o .o -.1 .2 ,4 .8 l • 1 1.3

WM05 BASE 3.01 3.01 J.01 J.01 COMP J.os 3.06 3.06 3.06 DIFf .04 .04 .os .os PCNT }.4 1,5 1.s 1,6 '

BASE MEAN= 3.22 PCNT D!FF MEANS= ·6 AV ABS DIFF= .02 DIFF PCNT OF BASE MEAN= .7 N V, .... '

IIIM06 BASE 128,64 127. 11 124,56 118.os l 28 .12 131.85 134.96 138.95 143.64 146.04

COMP 128,64 127. 11 124,56 118.05 127.69 132.42 136.98 142.8A 149.00 152.73

DIH .oo .oo .oo .oo -.43 .57 2.02 J.93 5.36 6.69

PCNT .o .o .o .o -.J .4 1.5 2.A 3.7 4.6

WM06 BASE 148.09 151.lJ 154.02 lSo.49 COMt' 155,51 159.13 162.48 165 • .J3 DIFF 7.42 s .oo 8.46 8.84 PCNT 5.0 5.3 s.s 506

BASE MEAN= 137.'J7 PCNT DifF MEANS= 2.6 AV ABS DIFF= 3•69 DIFF PCNT OF BASE I-IEAN= 2,7

IIIM07 BASE 23,70 21.'J9 19.84 l6,'i9 )8,96 20.10 21.08 22,26 23,50 ?4,39

COMI' 23.70 21.99 19.84 16.99 18. 'JO ~0.11 21.38 22.84 24.32 ?S.42

OIH .oo .oo .oo .oo -.06 .08 • 30 .sg .82 1.03

PCNT .o .o .o .o -.3 .4 1.4 2,6 3,5 4.2

IWM07 BASE 25.21 26.18 27 ,0 9 27.91 COMt' 2b,39 27.47 28,48 29.38 DIFF 1,18 l.t9 1, 39 1.46 PCNf 4.7 4,9 5, 1 s.2

BASE MEAN= 22.dO PcNT D!FF MEANS= 2.5 All ABS DIFF= •SA DIFF PCNT OF BASE MEAN= 2,6

lolM08 BASE 67.08 63.75 57.02 49 • '1-8 5!::, • 61 59,62 63.62 67,8A 72,36 75,91

COMP 67,08 63.15 57,02 49.98 55,56 59.70 63.89 1\8.39 7J.07 76.80

DIH .oo .oo .oo .oo -.06 ,08 .26 ,51 .70 ,89

PCNT .o .o • 0 .o -.1 • l ,4 .8 1.0 1.2

IIIM08 BASE 79,51 8).J9 87.34 9l .J6 COMP 80,51 84.49 Bd,53 92 0b3 DIFF 1,00 l , 1 0 l, 19 1.26 PCNT l • 3 l , 3 1,4 1•4

bA SE Mt.AN= 69.bO PCNT OJFF Mt.ANS= .1 AV ABS lJIFf= ,50 [) !Ff PCNT OF BASE MEAN= ,7

VARIABLE NAME 1972 l 97J 1974 1975 l'H6 1977 1978 }979 1980 !981 1982 1983 1984 1985

WM09 BASE 73.12 71. 94 10.03 69.72 75.35 11.01 79.66 Al 062 84.28 A5.92 COMP 73.12 71 • 94 10.03 69072 75.24 11.20 80.oB A2.45 85.33 87.25 DIH .oo .oo .oo .uo -.11 .19 042 083 1.05 1.33 PCNT .o .o .o .o -.1 .2 .s 1.0 1.2 1.s

WM09 BASE 87.89 89.95 92.00 94.UO COMP 89.30 91. so 93.62 9S.69 OIH 1.41 1.55 l .62 1.09 PCNT 1.6 1.1 1.8 1.a

BASE MEAN:: 80.89 PCNT OIFf Mf.ANS= .9 AV ABS Dlff= .13 Olff PCNT Of BASE MEAN= .9

WMlO BASE 56.84 62.09 60.65 53.62 61.45 67.09 10.59 74027 78.40 81.00 COMP 56.84 62.09 60.65 53.62 61.38 67.19 70.93 74.93 79.30 82093 OIFf .oo .oo .oo .oo -.01 .10 .34 066 .90 l • 13 PCNT .o .o .o .o -.1 .2 .s .9 l • l 1.4

WMlO BASE 84.85 88.20 91.59 95.00 COMP 86.11 89.56 93.05 96.54 DIH 1.26 1. :n l .46 l • 54 PCNT 1.s 1 .5 1.6 1•6

BASE MEAN= 73.32 PCNT Diff MEANS= •8 AV ABS Dlff:: ob3 Dlff PCNT Of BASE MEAN= .9

WMll BASE 46.94 45.66 43.93 41.25 44.}8 45.16 46.24 47.48 48.89 49.70

COMP 46.94 45.06 43.93 41.25 44.09 45.30 46.68 48.33 so.02 51.11

DIH .oo .oo .oo .oo -.10 .14 044 .as l • l 3 lo4l

PCNT .o .o .o .o -.2 .3 l • 0 1.8 2.3 208 WMll BASE so.s1 51.55 52.52 53.41

COMP 52.07 53.23 54.31 55.29 Dlff l .56 l .69 1.79 1.88 PCNT 3.1 3.3 3.4 3.5 I

BASE MEAN:: 47.67 PCNT DI Ff ME.ANS= 1•6 AV ABS Dlff= 078 Dlff PCNT Of BASE MEAN= 1.6 "' VI

"' I

WM12 BASE 4.99 5.03 5.12 4.88 ':>o47 5.81 6.01 6.3) 6.56 6.73

COMP 4.99 5.03 s.12 ... 88 5.47 5.01 &.00 6.33 6.60 6.77

Dlff .oo .oo .oo .oo -.oo .oo .01 003 .04 .04

PCNT .o .o .o .o - • l • l .2 .4 .5 .1

WM12 BASE 6.89 7.06 7.24 7.42 COMP 6.94 ,.12 7.29 7.47 Dlff .05 .05 .05 .06 PCNT .1 .1 .1 .0

BASE MEAN:: 6.11 PCNT Diff MEANS= .4 AV ABS Dlff= .02 DIFF PCNT OF BASE MEAN= .4

WM13 BASE 70.29 71.56 72.51 73.82 8 l .60 86.20 90.69 94.94 99.78 103.38

COMP 70.29 71.56 72.51 73.82 81.53 86.29 90.99 95.53 100.57 104.37

OIFf .oo .oo .oo .oo -.07 .10 .30 .59 .00 .99

PCNT .o .o oO .o -ol • l 03 06 .0 loO

WM13 BASE 107024 111.25 115027 ll9o27 COMP 108033 ll2o'+2 110052 120.57 Olff 1.09 1.17 lo25 1.Jo PCNT 1.0 1 o l l. l lol

BASE MEAN:: 92. 70 PCNT DJFf Mt:ANS= 06 AV ABS DIFF= o5c; OlfF PCNT OF BASE MEAN= .6

WM14 BASE 19.44 19.72 19066 19046 22.32 23.94 23.32 ?3o56 24036 24.65

COMP 19044 19.72 19.66 190'+6 22021 23.98 23o9l 24.62 25073 26.ll

Dlff oOO .oo oOO .oo - • l l .04 059 1.06 lo 38 1.46

PCNT oO oO oO .o -.5 .2 20s 4o'5 5.& 5o9

WM14 BASE c4o39 24068 2so21 2s.so COMP 25.82 25.97 26.43 26.66 DIFf lo43 1.29 1.21 1.16 PCNT 5.8 5.2 408 406

BASE MlAN= 22.87 PCNT DJFF MEANS= 3o0 AV ABS DIFf= 069 OIFF PCNT OF BASE MEAN= 3o0

VARIABLE NAME 1972 1973 1974 197:> 1976 1977 1978 )979 1980 1981 1982 1983 1984 198!:>

wM15 BASE 4.76 4.54 4.51 3.48 4.03 4.31 4.43 4.5n 4.62 4.68 COMP 4.76 4. :,4 4.51 3.48 4.03 4.30 4.44 4.53 4.b6 4.73 Dlff .oo .uo .oo . oo -.oo -.oo .02 .03 .05 .05 PCNl .o .o .o .o -.o -.1 .4 .7 1.0 I • 0

WMl!:> BASE 4. 71 4.77 4.84 4.90 COMP 4.75 4. 8 I 4.87 4.'13 DI ff .05 .04 .03 .03 PCNT 1.0 .8 .1 .6

BASE MEAN= 4.50 PCNT Dif f MtANS= .4 AV Al:lS Olff= .02 Diff PCNT Of BASE MEAN= .5

IIIMlb BASE 40.03 39.46 41.02 3l.b4 31.02 39.15 40. 36 41.32 42.61 43.76 COMP 40.03 39.'+6 41. 02 31.64 37.oo 39.ll 40.51 41.58 42.99 44.12 DI ff .oo .oo .oo .uo -.01 -.04 • 15 .26 .38 .37 PCNT .o .o .o .o -.o -.1 .4 .f> .9 .s

IIIMlb BASE 44.57 45.79 41.00 41:l.22 COMP 44.95 46. l 0 41.28 48.47 Dlff .38 .Jl .28 .25 PCNT .9 .1 .6 .5

BASE MEAN= 41.57 PCNT DI~ f MEANS= o4 AV ABS Olff= • 1 7 DIFF PCNT OF BASE MEAN= .4

wMI7 BASE 74.03 68.tl8 6'>.22 5].'.:>8 62.58 70.97 73.64 15.22 77.37 79.64 COMP 74.03 68.t!8 6'>.22 53.58 62.56 70.85 73.<H 75.92 78.45 80.76 Oiff .oo .oo .oo .oo -.02 -.12 .33 .11 1.01 l. 12

PCNT .o .o .o .o -.o -.2 .4 .9 1.4 1.4

wMl7 BASE. 80.94 82.97 85.26 87.39 COMP 82.08 83.93 86. 1 l 88.14 Oiff 1.14 .96 .84 .76 PCNT 1.4 1.2 l.O .9 I

BASE MEAN= 74.12 PCNT D!FF ME.ANS= .7 AV ABS OlFF= .so OIH PCNT OF BASE MEAN= .1 N V,

"" I

111418 BASE 71.75 64.00 56.62 43. ·10 59.71 73.81 11.03 78.89 81.69 P3.61

COMP 71. 75 64.00 56.62 43.70 59.68 73.67 77.47 79.85 83.15 85 .14

OlFf .oo .oo .oo .oo -.03 - .14 .43 .96 1.45 1.53

PCNl .o .o • 0 • 0 -.o -.2 .6 1 . 2 1.8 1.a

wM18 BASE 84.25 85.75 a1.e2 89.82 COMP 85.80 87.07 88.96 90.o5 Olff 1.55 1.31 1 • l 5 1.03 PCNT 1.8 1.5 I• 3 Id

BASE ME.AN= 74 • 18 PCNT DI ff Mt.ANS= .9 AV ABS OlFF= 061'\ DIH PCNT OF BASE MEAN= .9

WMl 9 BASE 100.46 10 I. 77 8'>.90 74.35 101.47 !J0.21 139.o5 146.10 155 04b lf:>3.43

COMP 100.46 101.77 85. 90 74 • 35 101.37 129.84 140.21 148.84 159.76 ll':,8.09

Dlff .oo .oo .oo .uo -.10 -.37 1.16 2.74 4.30 4.66

PCNT .o .o .o .o -.1 -.3 .8 l.9 2.a 2.9

WM19 BASE 162.80 174.'+3 182.10 189.58 COMP I(,"/ .56 l?tl.58 185.78 192.94 OIH 4.76 4.15 J.68 3.36 PCNT 2.9 2.4 2.0 1 • 8

BASE ME.AN= 136.22 PCNT DIFF MEANS= 1.s AV ABS UIFF= 2.09 OIFF PCNT Of BASE MEAN;: 1.5

1111420 BASE 19.37 19.49 19.20 I 8. 72 19.83 19.95 20.10 ?0.24 ?0.50 20.64

COMP 19.37 l 9 • 49 11-J.20 l ~ • / 2 19.78 19.99 20.32 ?0.67 21.09 ?1.34

DlFF .oo .oo .oo .oo -.05 .04 - ~2 .44 .59 • 71

PCNT .o .o .o .o -.2 .2 l • l 2. J 2.9 3.4

wM20 BASE. 20.66 20.80 20.95 21.us COMP 21 .42 21. '.:>8 21.15 2 l .t!6 DlFf .76 .78 .so .81 PCNT 3.7 3.1 3.8 3•H

tlASE Mt.AN= 20 • l l 1-'CNT Oif F" MtA.NS:c l. A AV ABS DIFF= .37 OIFF" PCNT OF RASE. MEAN= 1 .8

VARIABLE NAME 1972 1973 1974 1975 1976 1971 1978 1979 1980 1981 1982 1983 1984 1985

WM21 BASE 41.38 37.00 38.12 32. 90 34.06 34.55 34.91 35.30 35.85 36.55 COMP 41.38 31.00 38.12 32.90 34.05 34.56 34.94 35.34 35.91 36.62 Olff .oo .oo .oo .oo -.01 .01 .o3 .os .06 .01 PCNT .o .o .o .o -.o .o • 1 .1 .2 .2

WM2l BASE 37.28 38.06 38.91 39.83 COMP 37.36 38.14 39.oo 39.92 Olff .08 .08 .09 .09 PCNT .2 .2 .2 .2

BASE MEAN: 36.76 PCNT OIFF MEANS= • 1 AV ABS DIFF= .04 OIFF PCNT OF BASE MEAN= • l

WM22 BASE 91.82 92.72 91 . 48 89.b8 91.84 92.85 93.55 94.32 95.20 95.80 COMP 91.82 92.72 91.48 89.68 91.78 92.92 93.80 94.80 95.84 96.58 Olff .oo .oo .oo .oo -.06 .01 .24 .47 .63 .78 PCNT .o .o .o .o -.1 .1 .3 .s .1 .0

WM22 BASE 9b.36 97.03 97.69 98.Jl COMP 97.21 97.94 98.64 99.JO Dlff .85 .91 .95 • 98 PCNT .9 .9 1.0 1.0

BASE MEAN= 94.19 PCNT DIFF M£ANS= .4 AV ABS DIFF= .43 0 IfF PCNT OF BASE MEAN= .5

WM23 BASE 84.45 87.84 85. 76 81.39 89.38 94.77 98.25 lOl.77 105 • 73 lOA.82 COMP 84.45 87od4 85.76 8 1. 39 89.27 94.85 9B.ss 102.39 106.53 109.79 Olff .oo .oo .oo . oo - • 10 .00 .31 .62 .80 .97 PCNT .o .o .o .o - .1 • 1 .3 • 6 .8 .9

WM23 BASE 111.96 l 15.32 118. 76 122 • c:'.3 COMP 112.99 116 . 41 119.88 123.39 DIFF l.03 1.09 1.12 1 . 16 PCNT .9 .9 . 9 .9 I

BASE MEAN= l00.46 PCNT DIFF MEANS= .5 AV ABS 0 IF"F= .52 DIFF PCNT OF BASE MEAN= .s "' "' .p. I

WM24 BASE 44.66 47.33 48.17 47.82 52.79 54.95 56. 85 59.07 61.66 63.34 COMP 44.66 47.33 48.17 47.82 52.61 55.17 57.63 60.59 63.72 65.90

Olff .oo .oo .oo .oo - • l 9 .22 .77 1.53 2.0b 2.56 PCNT .o .o .o .o -.4 .4 1.4

2 ·" 3.J 4.0

WM24 BASE 64.88 66.85 68.8) 70 0b7 COMP 67.68 69.86 12.01 74.00 OIFF 2.81 3.01 3.18 J.33 PCNT 4o3 4.5 4.6 4.7

BASE MEAN= 57.70 PCNT Diff Ml::ANS= 2.4 AV ABS Dlff= 1•40 OIH PCNT OF BASE MEAN= 2.4

WM25 BASE 43.15 45.99 42.97 40.<+3 45.28 49.13 51.28 53.39 55.92 57.98

COMP 43.15 45.'il9 42.97 40.43 45.25 49.16 51.34 sJ.53 Sti.10 58.21

Olff .oo .oo .oo .uo -.02 .04 007 • 14 .17 .23 PCNT .o .o .o .o -.o .1 • 1 .3 .3 .4

WM25 BASE 60.13 62.37 64.66 67.02 COMP 60.38 62.b5 64.96 67.34 Olff .25 .28 .30 .32 PCNT .4 .5 .5 .s

BASE MEAN= 52.d4 PCNT DIFF Ml:.ANS= ·2 AV ABS DIFF= .13 nIFF PCNT OF BASE MEAN= .2

WM26 BASE 11.97 13.02 12.02 12.12 14 • 15 14.99 15.44 16.00 16. 71 11.20

COMP 11.97 13.02 12.02 12.12 1<+.07 15.03 15.66 lbo4S 17.29 17.89

DIFf .oo .oo .oo .oo -.07 .04 .22 .45 .58 .69

PCNT .o .o .o .o -.5 .J 1.s 2.8 3.5 4.0

WM26 BASE 17.57 10.10 18.66 l 'ii. l 8 COMP 18.29 18.83 19 ... 1 19.95 OIFF .12 • 74 .15 . 11 PCNT 4. 1 4. l 4.0 4.0

BASE MEAN= 15.57 PCNT DIFF Mt.ANS= 2.2 AV ABS Dlff: •36 OIFF PCNT OF BASE MEAN= 2.3

VARIABLE NAME. 1972 1973 1'174 1975 l 'H6 1977 l 'H8 )979 1980 1981 1982 198J 1984 1985

wM27 BASE 409.46 429.JJ 417.59 4JO .Ol 499.90 555.50 592.45 6Jl .l<i 675.55 705.79 ·COMI' 409.4t, 429.J3 417.59 4JO.Ol 498.67 55t,. 71 598.19 642.54 691.26 7?4.82 OIH .oo . oo .oo .oo -1.23 1 • 21 s. 74 \l.JQ 15.71 19.03 PCNT .o .o .o .o -.2 .2 1.0 1.11 2.3 2.1

wM27 bASE. 731.25 7 6 I• ':>2 792.91 1:123 .48 COMP 751 • 9 I 783.20 815.60 847.ll OIH 20.66 21.68 22.69 2J.o3 PCNT 2.e 2.e 2.9 2.9

BASE MEAN= 1>03.99 PCNT OIFF Mt::ANS= I • 7 AV ABS DIFF= 10.21 OlfF PCNT OF BASE MEAN= 1.1

wM28 BASE 2.50 2."15 2.65 2.J9 2.82 3.0J 3.20 3.38 3.57 3.73 COMP 2.50 2. 75 2.o5 2.39 2o82 J.04 J.21 3.41 3.61 J.77 OIH oOO .oo .oo .oo -oOO oOO .01 .03 004 .05 PCNT oO oO .o .o -.1 .2 .4 .8 I o 0 lo2

liiM2il BASE 3088 4.05 4o22 4o39 COMP J.93 4. 10 '+o28 4.45 OIFF .05 .05 .06 .06 PCNT t.J 1.3 1.4 1.4

BASE MEAN= 3.33 PCNT OJFF ME.ANS= .7 AV ABS OIFF= 001 OIFF PCNT OF BASE MEAN= .8

wM2'>1 1:iASE 30056 28oJ9 20.95 25.92 32.68 37.09 35oA6 35.54 37.34 ]8048 COMP 30.56 28.J9 20.95 25.92 32.52 36.87 37 o 14 38.17 4 l o06 42038 OIFF .oo .oo .oo .oo -ol6 -.22 lo28 2.63 3o72 3o89

PCNT oO .o oO .o -.5 - .6 306 7.4 10.0 10.1

liM2Y BASE 37.40 37048 38.65 39031 COMP 4lo22 40.74 4lo5I 4lot18 OIH 3.82 3o26 2.86 2.58 PCNT 10.2 0.1 7.4 606 I

BASE MEAN= 34040 PCNT OJFF Mt.ANS= 4o9 AV ABS DIFF= lo74 OIFF PCNT OF RASE MEAN= 5. l N V, V, I

wM30 BASE 22.89 23o29 22.75 20 • '>18 24 048 25079 25.97 20.26 26.98 21.22 COMP 22.89 23o29 22o75 20098 24.39 25.82 26.29 26.91 27.80 28ol0

OIH .oo oOO .oo .oo -o09 .02 .33 .64 .82 .88

PCNT .o .o .o .o -.4 • 1 l • 3 2.4 3.0 3.2

liM30 l:lASE 27.29 27.56 27.97 28oJO COMP 28.13 2eoJ2 20.f,6 21:1. 'i/4 OJFF .84 .75 .69 .o4 PCNl 3ol 2.1 2.5 2.3

BASE MEAN: 25.55 PCNT OJf F ME.ANS= l•S AV Al:iS UIFF= •41 f)}FF PCNT OF BASE MEAN= }.6

wM31 BASE }3.44 llo62 9.13 6.44 10. 17 l0.81 10.74 J0.46 10.32 9.87

COMP 13.44 11 .02 9. 13 6.44 10.10 10.ao 10.ao 10.57 I0.47 10.01

OIFF oOO .oo .oo .oo -.01 -.oo .06 • I l .14 • I 5

PC"'4T oO .o .o .o -.1 -.o .s 1.0 1.4 1.5

liiMJl BASE 9.44 9.05 8.71 8oJ6 COMP 9 058 9 o l 8 8.82 8046 DIFF ol4 .1 2 • I l oiO PCNl 1.5 1.4 l • 3 1.2

BASE Mt:AN= 9.YO PCNl DJFF ME.ANS= .7 Al/ Al:lS OIFF= • 0 7 OIFF PCNT OF RASE MEAN= .1

wM32 BASE 380.0l 377 .50 389.30 345.'>14 407.81 436.22 453o65 468.}S 484.50 495066

COMP 380.0l 377.50 JBY.30 J45.94 401010 436.24 454oll 469.07 485.71 496.98

DIH .oo .oo oOO .oo - • 11 .02 .46 .92 l. 21 t.]2

PCNT .o .o .o .o -.o .o • I .? .2 .]

wM32 BASE. 510 .06 524062 539 . 94 5 55.50 COMP 511.34 525.t!O 54i.04 550 0 ':>6 DIH l • 28 I • l 8 i • 1 0 1.06 PCNT 03 . 2 • ?. .2

BASE MEAN= 454. 92 PCNT 0 l • F Mt.ANS= ~ I AV Al:lS DIFF= •6 ? OIFF PCNT OF RASE. MEAN= • 1

VARIABLE NAME 1972 197 J 1974 1975 19 76 1977 1978 1979 1980 1981 19 82 1983 1984 1985

WM33 BASE 4 5 .32 46.67 4b.30 44.bO 5 2. 77 57.67 57.61 58.07 60.34 6 l .85 COMP 45.32 46.b7 46.30 44ob0 52.46 57.67 58.93 60.7? 63.87 65. 72 DIFF .oo .oo .oo .oo -.31 -.oo l • 32 2.66 3.53 3.86 PCNT .o .o .o .o -.6 -.o 2.3 4. 6 So9 602

IIIM33 BASE 62.01 63.15 6'+.90 66.36 COMP 65.84 66ob6 68.18 69.50 DIFF 3.83 J. 5 2 J.28 3.14 PCNT 6.2 s.6 s.1 4.7

BASE MEAN= 56026 PCNT OJfF MEANS= 3.2 AV ABS l}lff= lo82 OlfF PCNT OF BASE MEAN= 3.2

IIIM34 BASE 59.14 62.57 61.76 58.c:!9 64.53 66.92 68.33 69080 71086 73031 COMP 59.14 620 5 7 61076 58029 64.31 67.05 68096 71.07 73.45 75.19 DIFF .oo oOO .oo .oo -.22 .13 .63 l .26 lo 60 l.A7 PChT oO .o .o .o -o3 o2 .9 1.8 2.2 2.6

WM34 BASE 74.38 75083 77.36 78.ilO COMP 7bo29 77076 79030 80.76 DIFF lo91 lo93 l 094 lo 96 PCNT 206 2.5 2.5 2os

BASE MEAN= 68078 PCNT Diff Mc.ANS= 1.4 AV ABS D lFF= 096 Olff" PCNT Of BASE MEAN= 1.4

WM35 BASE 28.28 32.52 32.47 28042 JI.OJ 32.33 330 3 1 34062 36.0l 37.20

COMP 28.28 32052 32.47 28.'+2 31.01 32.35 33.38 34077 36.20 37.44

DIH .oo .oo .oo .oo -.02 .02 .01 .14 .19 .24

PCNT .o .o .o .o -.1 • l .2 .4 .s .6 WM35 BASE 38.25 39040 40055 41.,2

COMP 38.52 39.69 40086 42004 DIFF .27 029 .JI .32 PCNT .1 .7 .8 •8 I

BASE MEAN= 34.72 PCNT Dlff" Mt.ANS= .•4 AV ABS DIFF= •13 Olff PCNT OF BASE MEAN= .4 .., VI

"' I

WM36 BAS!:: 23.29 28.04 28.81 2s.10 33.00 35.35 36.92 38.44 40.63 4} .99

COMP 23.29 28.04 28.81 2s.10 32.76 35.49 37.40 39.45 41.84 43.45

DtH .oo .oo .oo .oo -.24 .13 .48 l • 0 l 1.21 1.46 PCNT .o .o .o .o -.7 .4 1.3 2.6 3.0 3.5

IIIM36 BASE 43.46 45.07 46.74 48.42 COMP 44.94 46.6-1 48.32 50.04 DIH 1.48 1.53 l.57 l .b2 PCNT 3.4 3.4 3.4 3.3

BASE MEAN= 36.80 PCNT OJFF Mt:ANS= 2.0 AV ABS DIFF= .17 Dlff" PCNT OF BASE MEAN= 2.1

IIIM37 BASE 41. 37 52.94 so.so 44 • .Jl 57.53 62.69 66.45 70065 75.45 78.61

COMP 41.37 52.94 so.so 44.31 57.38 62.77 66082 71.44 76.45 79.80

Dlff .oo .uo .oo .uo -. l 5 .08 .37 .79 l. 0 l l. l 9 PCNT .o .o .o .o -.3 • l .6 l. l 1.3 1.s

WM37 BASE 82.20 86.07 90.11 94.c:!3 COMP 83.41 87.30 91.36 95.50 OIFF 1.21 1.23 1.25 1.21 PCNT 1.5 1.4 1.4 1.3

BASE MEAN= 68.08 PCNT OJFF ME.ANS= .9 AV ABS DIFF= o6J DIFF PCNT OF BASE MEAN= .9

1,MJ8 BASE 40.62 41.49 4 l. l 8 38.57 43.85 47.21 49.J!! c;1.20 53.34 54.95

COMP 40.62 41.49 41. 18 38.57 43.73 47.25 49.67 c;l.82 54.13 55.88

Dlff .oo .oo .oo .oo -.11 .04 • ;>9 06? 080 093

PCNT o0 .o .o .o -.3 • l .6 1.2 1.5 1.7

1,M38 BASE 56.50 58.22 60.07 61.92 COMP 57044 59015 61.00 62oil6 Dlff .94 .93 o9 ] o 94 PCNT lo7 1•6 1.s 1.5

BASE MEAN= 49.89 PCIIIT Diff Mt.ANS= . 9 AV ABS UIFF= .47 Dirr PCNT OF BASE MEAN= .9

vAWIABLE NAME 1972 1973 1974 1975 1'176 1977 197A )979 19AO I 981 1982 1983 1984 198:>

iliM3.,, 8ASE 958.41 1222.10 1339.90 1044 . 50 1248.30 1195 • 10 )266.40 1259 .30 1305.30 1341.10 COMP 9!:>8.41 1222.10 1339.90 1044.:>0 1164.20 1229.80 1401.90 1555.20 1655.20 1765.30 DI ff .oo .oo .oo .oo -&4.10 34.70 135.so 295.90 349.90 4 24.20 PCNT .o .o .o .o -6.7 2.9 10,7 23.'i 26.8 31.6

iliM39 BASE 1380.50 1420.30 1461.so 1=>03.70 COMP 1805.10 1864.00 1920.60 1'08.90 Olff 42'+.60 '+43.70 459.10 '+75.20 PCNT Jo.a 31.2 :)l .4 31•6

BASE MEAN= 1281.89 PCNT Olff ME.ANS= 16•5 AV Al:lS Dlff= ;,23.35 nlfF PCNT OF BASE MEAN= 17.4

iliH40 BASE 188.64 1A6.79 188.93 190.''7 21 'I. l 8 235.84 245.61 2sa.10 211.22 279.90 COMP 188.64 1A6.79 188.93 190.97 219.04 2J5.95 246.10 259.69 212.53 2A I .49 DIH .oo .oo .oo .oo -.14 • l 1 .49 .9Q 1.31 1.59 PCNT .o .o .o .o -.l .o . 2 • 4 . :, ,6

iliMltO BASt:: 291.10 302.50 314.04 J25.~5 COMP 292,79 J04.26 315.85 J27, <+ l Olff 1.69 1, 16 l.81 1,86 PCNT .6 • 6 .6 ,6

BASE MEAN= 249,93 PCNT DifF Ml:ANS= ,3 AV ABS OlfF= • 84 Dlff POH Of BASE MfAN= ,3

iliMlt 1 BASE. l 11 .68 113,08 11 ..1. 89 !11,40 117. 15 107,18 ll4. 15 115,81 118.53 123,32 COMP 111,68 113,08 113.89 111,40 117,13 107.23 114.18 115.83 118.59 l23,lt9 Olff .oo .oo ,00 .oo -.02 ,05 ,03 ,02 • 06 .11

PCNT .o .o .o .o -.o .o .o .o • l • l iliHitl BASE 125,77 128,62 131,18 133,76

COMP 12b.05 129,01 131,65 134,29 DIFf .28 ,J9 ,47 • 53 PClliT .2 .3 .4 ,4 I

BASE ME.AN= 118,'17 PCNT DIFF ME.ANS= • I AV ABS OlfF= • 14 OIFF PCNT Of BASE MEAN= • l "' V, .... I

iliHlt2 BASE 62,14 72,23 69,78 65 ,"'l 78,51 89,08 95.29 103,21 110. 12 1)6.24

COMP 62, 14 72,23 6 9 ,7 8 65,91 78 • 29 89,21 95.95 104.57 111,90 118,39

Dlff .oo .oo .oo .uo -.22 , 13 ,66 1, 3Fi 1,78 2. 15

PCNT .o .o .o .o -.3 • 1 .1 1. 3 1,6 1.8

iliMlt2 BASE 121. 74 12 7. 85 134,22 140,66 COMP 123,99 130,18 136,62 143.15 Dlff 2,25 2,33 2.<+o 2,'+9 PCNT 1,8 1.0 1 • ,q 1,8

BASE MEAN= 99,07 PCNf DIF F ME.ANS= I • I Av ABS Dlff: 1.13 DIFf PCNT Of BASE MEAN= 1. 1

WM43 BASE 145.46 153,23 146,25 1 34, 16 148,t,9 H,0.93 167.13 173,0Q 180.18 lf\4,87

COMP 145.46 153,23 14 6 • 25 !34, I 6 14d.41 161.19 168,20 175.lFi 182.84 1A8 .05

DIFF .oo .oo .oo .uo -.20 ,26 1.01 2.01 2.66 3 ,18

PCNT .o .o .o .o -.2 .2 .6 1.2 1,5 1,7

wH43 ElASE. 189,62 195,27 20 I• 2 l <'.07.14 COMP 192.94 198,67 204.66 210 .62 Dlff 3,32 3.40 3,45 J.48 PCt.T 1,8 1,7 l • 7 1 • 7

BASE ME.AN= 110.:.2 PCNf OJF f MEANS= ,9 AV AbS DIFF= 1.65 nlFF PCNT OF BASE MEAN= 1.0

wH44 BASt:: 32,95 32,54 30,8A 27,86 32.05 34,69 37,os 39.61 42.44 44.70

COMP 32.95 32,54 30.88 27,d6 31,93 34,81 37.53 40,61 43.84 46.49

DIFF .oo .oo .o o .oo -.12 .12 .48 1.00 1,40 1. 79

PCNT .o .u .o .o - .4 .3 1 • 3 2 .5 3.J 4,0

wM4'+ BASE 46.88 49. 33 51,78 5 4.!3 CO MP 48. 9 I 51. 56 54. I 8 56. lo Ol ff 2 .0 3 2.23 2 ,4 0 2.57

NI 4. J 4, 5 4,f.. 4,7 Ill\ ,t M AN = 9 . 78 PClli T 0 It F Mt ANS= 2 ,<; Av At; IJ H , a l. 0 l 0 1• p Nf 0 FI A M AN •

VARIABLE NAME 1972 1973 1'174 1975 1976 1977 1978 )979 1980 )98) 1982 1983 1984 1985

wM45 BASE 61.31 64.02 64.71 63.17 69.68 72.55 74.J6 76.45 78.93 f\0.65 COMP 61.31 64.02 64 • 71 63.17 69.42 72.78 75.Jl 78035 81.51 ~3.85 DI ff .oo .oo .oo .oo -.26 .23 .95 1.91 2.5t1 3.20 PCNT .o .o .o .o -.4 .3 1.3 2.5 J.3 4.0

WM45 BASE 82.11 84.00 85.90 87.o4 COMP 85.60 87. 71 89.78 91.66 OIFf 3.49 3.71 3.88 4.02 PCNT 4.3 4.4 4.5 406

BASE MEAN= 74.68 PCNT Diff Mt:ANS= 2.3 Av ABS DIFF= 1.13 Dlff PCNT Of BASE MEAN= 2.3

wH4b BASE 4.05 J.92 3.61 3.15 3.38 3 • 4 l J.42 3.43 3.45 J.44 COMP 4.05 3.92 3.61 3. 15 3.36 3.'+2 3.47 3.53 3.58 3.61 Dlff .oo .oo .oo .oo -.01 .01 .o5 .10 .14 .16 PCNT .o .o .o .o -.4 .4 1.6 3.o 3.9 4.8

WM46 BASE 3.42 3.41 3.39 3. 36 COMP 3.60 J.60 3.59 3.56 Dlff .18 .19 .19 .19 PCNT 5.2 5.5 5.7 5.9

BASE" MEAN= 3.'+9 PCNT Diff ME.ANS= 2.5 AV ABS OJFf= .09 Dlff PCNT Of BASE MEAN: 2.5

WM47 BASE 43.51 46.15 46.96 46.07 52.09 55.12 57.82 61.36 65.18 67.97 COMP 43.51 46.15 46.96 46.07 51 .85 55.35 58.11 63.15 67.67 11.15 DJ ff .oo .oo .oo .oo -.24 .23 • "19 l • 79 2.48 3.18

PCNT .o .o .o .o -.5 .4 1.5 2.9 308 4.7 WM4+7 BASE 70.68 73.97 11.22 80.38

COMP 74.27 77.90 81.46 84.89 Dlff 3.59 J.93 4.24 4.51 PCNT 5. l 5.3 5.5 5•6 I

BASE MEAN= 60.32 PCNT Diff MEANS= 2.9 AV ABS Dlff= l • 79 OIFF PCNT Of BASE MEAN= 3.0 "' VI 00 I

WM4tl BASE 199.99 152.25 108.99 l 08 • 92 17'1.14 228.34 222.51 208.29 219.20 236.18 COMP 199.99 152.25 108.99 108.92 1 79. 14 221.91 233. 77 235.99 262.74 281.68 Dlff .oo .oo .oo .oo .oo -6.43 ll.26 77.10 43.5'+ 45.50 PCNT .o .o .o .o .o -2.8 s.1 13.3 19.9 19.3

wM4tl BASE 216.30 204 • 17 213.87 216.27 COMP 261.47 241.50 243.92 241.4 7 Dlff 45.17 36.73 JO.OS 25.20 PCNT 20.9 17.9 14.l 11 • 7

BASE MEAN: 193.93 PCNT Diff MEANS= 9o5 AV ABS DJFF= 19040 l'llff PCNT Of BASE MEAN= 10.0

wH49 BASE 178.73 169.57 179.80 166.00 192. 72 215.13 203.80 198.49 205.55 208.66 COMP 178. 73 169.57 179.80 166.00 191.40 215.03 209.50 210.39 221.27 226.02 Dlff .oo .oo .oo .oo -1.32 - • 10 5.10 )l.90 15.72 17.36 PCNT .o .o .o .o -.7 -.o 2.8 6.0 7.6 8.3

wH49 BASE 208.56 212.52 216.37 221.12 COMP 225.75 228.59 231.15 235.84 Dlff 17.19 16.07 14.78 14.12 PCNT 8.2 706 6.8 604

BASE MEAN= 198.40 PCNT Diff MEANS= 4o0 AV ABS OIFf= 8• l 6 Dlff PCNT Of BASE MEAN= 4. 1

wH5O BASE 35.56 34.09 Jl.70 30.06 36.47 42.68 36.8'o 16.65 37.86 36.97

COMP 35.56 34.09 31.70 30.06 36.ll 43.07 38.99 39.76 41.16 39.88

Dlff .oo .oo .oo .uo -.37 .40 2.16 3.11 3.29 2.91

PCNT .o .o .o .o -1 .o .9 5.9 a.5 8.7 7.9

wHSO BASE. 34.78 35.19 30.00 35.62 COMP 37.24 37.07 37.69 37.19 Dlff 2.46 1 .88 1 .69 1.57 PCNT 7.1 5.3 4 • 7 4.4

BASE MEAN= 35.75 PCNT Diff ME.ANS: 308 AV ABS Dlff:: 1•42 Olff PCNT Of BASE MEAN= 4.0

IIARIABLE NAME 197 2 1973 1-;7 4 197:> I 976 1977 197A 1979 19AO 1981 1982 1983 198 4 198~

IIIM51 BASE. 186.27 19 0.23 183. 86 170 . '+l 19 0.5 0 195.13 192.93 191.25 193.57 192.A4 COMP 186.27 190.23 18 3 . 86 170.41 189.33 195.87 196. 95 198.77 202.47 202.14 DIH .oo .oo .oo .oo -1.t 7 .74 4 • 02 7.5? 8.90 9.30 PCNT .o .o .o .o -.6 .4 2. 1 3.9 4.6 4.8

wM51 BASE. 190.58 190.64 192.03 19c.J8 COMP 198.88 l97.o9 198.06 197.66 OIH 8.30 7.05 6.03 s. c:'8 PCNT 4.4 3.7 3.1 2.1

BASE MEAN= l.~9.47 PCNT DIF F Mt.AN S= 2 • I All ABS DIFF= 4-17 OIFF PCNT OF BASE MEAN= 2.2

IIIM52 BASt. 32.70 33. 91 3J.4l 32.06 35.59 36.39 37.16 38.83 40.26 41. l 8 COMP 32.70 33.91 33.41 32.06 35.47 36.53 37.60 39.63 41.39 42.67 OIH .oo .oo .oo .uo -.12 .14 .43 .a1 l • l 3 t.49 PCNT .o .o .o .o -.3 .4 1.2 2. I 2.0 3.6

iiM52 BASE 4<'.19 43.47 44.54 4:,.!:>0 COMP 43.93 45.42 46.66 47.74 DIH l. 74 l • 95 2. I 2 2.24 PCNT 4 • l . 4.5 4 • 8 4.9

BASE MEAN= 38.J7 PCNT DifF Mt.ANS= 2. 2 All ABS DIFF= .07 [)!FF PCNT OF AASE MEAN= 2.3

IIIM53 BASE 229.21 238.J2 234.02 c.22.76 248.10 c60.96 210.15 2AO • Pl 292.38 300.57 COMP 229.c:'l 230.:n 234.02 <'.22.76 247.28 col • 81 273.75 287.3? 302.05 312.39 DIH .oo .uo .oo .oo -.02 .BS 3.60 7 • 14 9.67 11.82 PCNT .o .o .o .o -.3 .3 1.3 2.s 3.3 3.9

wMSJ BASE. 307.38 316.14 325.13 J33.J5 COMP 320.28 329.16 339.3 3 J4b.05 DIH 12.90 13.62 14.20 14. 70 PCNT 4.2 4.3 4.4 4.4 I

BASE MEAN= 275.62 PCNT DifF ME.ANS= 2. 3 All ABS Ulff= 6•38 O!FF PCNT OF BASE MEAN= 2.3 "' V,

"' I

1111454 BASE 73.91 70.23 62.73 53.16 58.52 6 I .4 7 63.Sl 6 6.58 69.68 12.00 COMP 73.91 70.23 62.7 3 53.16 58. 34 61.66 64.60 68.15 71.84 74.70 DIH .oo .oo .oo .vo -.11 .19 .79 1.57 2.16 2.11 PCNT .o .o .o .o -.3 .3 1.2 2.4 3.1 3.8

1111454 BASE 74.09 76.61 79.0 8 8 1.38 COMP 77.13 79.90 82.58 85.05 OIH 3.04 3.29 3.50 3.67 PCNT 4 .1 4.3 4.4 4.5

BASE MEAN= 68.80 PCNT D! Ff Mt.ANS= 2. 2 All ABS OIFF= 1 • 5 I f1!FF PCNT OF BASE. MfAN = 2.2

1111455 BASE. 186.54 l9J.-;9 192.31 181.93 199.27 205.02 2 08.Sl 212.91 218.55 2? l .40

COMP 186.54 193.99 l 92. 3 I 181. 93 198.26 205.87 212.13 2 20.0 6 228.03 232.94

OIFF .oo .uo .oo .oo -1.01 .as 3.62 7. I 5 9.48 \l.54 PCNT .o .o .o .o -.s • 4 l • 7 3 • 4 4.3 s.2

iiM55 BASE 22 3 .10 226.21 2 29.0S 231 .04 COMP 235.58 239.Jl 242.61 c.44.94 OIFF 12.48 \3.10 13.56 13.90 PCNT 5.6 5.13 5.9 6•0

BASE MEAN= 209.27 PCNT DifF Mt:ANS= 2.9 Al/ ABS DIFF= 6•19 D IFF PCNT OF BASE MEAN= 3.0

wMC BASE 3343.60 3313.llO 3138.SO 2tl4~ 0 tlO 3 144.10 3284.90 3404.AO 3548 .10 3713.80 382 0.50

COMP 334 3. 60 3313.llO 3138.c;o 2tl45.t10 3132.30 3300.70 3461 .60 36 6 0.70 3869.40 401 6 .20

OIH .oo .uo .oo .oo - I l. 80 IS.BO 56.AO l 12.60 155 . 60 1'15.70

PCNT •. o .o .o .o - .4 .s 1.7 3. 2 4.2 5. l

wMC BASE 3915.70 4040.10 4162.60 4c74.IO COMP 4134.50 4277.4.0 441:':i.40 4::>4-0 .uo DIH 218.BO 237.30 t52.80 L65.90 PCNT 5.6 5.9 6.1 6·2

HASE MEAN= 3567.83 PCNl DIFF Mt.AN S= J.o All ABS DIFF= !OB.79 OIFF PCNT Of BASE Mf AN= 3.0

IIARIABLE NAME 1n2 1973 1974 197!:> \976 1977 1978 1979 1980 )981 1982 1983 1984 l 98!:>

WM If IX BASE. 981.84 1062.30 1087.40 1052.20 1220.00 1314.20 1359.60 1403.20 1473.40 15?1.SO COMP 981.84 1062.JO 1087.40 1052.20 1210.10 1318.70 1389.50 1465.50 1556.10 1619.30 Dlff .oo .uo .oo .oo -9.30 4.50 29.90 62.30 82.70 Q7.80 PCNT .o .o .o .o -.0 .3 2.2 4.4 s.o 6.4

WMifIX BASE. 1552.80 1599.10 1653.30 1102.00 COMP 1654.50 1701.90 1756.90 ltl07.lO DIFf 101.70 102.so 103.60 105.10 PCNT 6.5 6e4 o.3 6.2

BASE MEAN= l35o.53 PCNT DifF MEANS= 3.6 All AijS DIFF= 49.98 OlfF PCNT OF BASE MEAN= 3.7

WMI IN\/ BASE. .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo COMP .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo PCNT .o .o .o .o .o .o .o .o .o .o

WMllN\I BASE .oo .oo .oo .oo COMP .oo .oo .oo .oo Dlff .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= .oo PCNT DifF ME.ANS= .o AV ABS DIFF= .oo OIFf PCNT Of BASE MEAN= .o

WMSLEOU BASE 180.67 178.74 l 8 l. 28 189.72 182.96 182.71 199.40 203.0S 205.36 210.s2 COMP !B0.67 178.74 181.28 189.72 183.33 181 .IH 197.34 202.12 207.0l 215.53

Dlff .oo .oo .oo .oo .37 -.84 -2.06 -.93 1.65 s.01

PCNT .o .o .o .o .2 -.5 -1 .o -.5 .0 2.4 WMSLEOU BASE 216.97 218.42 220.50 225.42

COMP 225.oa 228.76 232.06 i:'.37.91 Dlff 8.11 10.34 11.48 12.49 PCNT 3.1 4.7 5.2 5.5 I

BASE MEAN= 199.70 PCNT DIFF MEANS= le6 All ABS DIFF= 3o81 OlfF PCNT Of BASE MEAN= 1.9 .., "' 0 I

WMSLOTH BASE 320.64 329.•+l 307.86 303.30 365.64 330.20 312.70 377.39 39£,.82 405.36 COMP 320.64 329.41 307.86 303.30 365.15 333.08 311.63 369.04 388.69 403.85

Dlff .oo .oo .oo .oo -.49 2.88 -1.01 -8.35 -8.13 -1 .51 PCNT .o .o .o .o -.1 .9 -.3 -2.2 -2.0 -.4

WMSLOTH BASE 431.70 464.37 475.47 484.73 COMP 439. 71 481.38 500.12 ~14.21 Olff 8.01 11.01 25.25 29.48 PCNT 1.9 3.7 5.3 6d

BASE MEAN= 378.97 PCNT DIFF Mt.ANS= 1.2 All ABS DlfF= 7.30 Olff PCNT OF BASE MEAN= 1.9

WMEX BASE • 06 .06 .06 .01 .01 .01 .01 .07 .01 .01

COMP .06 .06 .06 .01 .01 .01 .01 .01 .01 .01

Dlff .oo .oo .oo .oo .oo .oo .oo .oo .oo .oo

PCNT .o .o .o .o .o .o .o .o .o .o

WMEX BASE .01 .01 .01 .01 COMP .01 .01 .07 .01 DIFf .oo .oo .oo .oo PCNT .o .o .o .o

BASE MEAN= .01 PCNT DifF Mt.ANS= .o All ABS Dlff= .oo OlfF PCI\JT Of BASE MEAN= .o

WMTOT BASE 9923.00 10262.00 10086.00 9228.30 10548.00 11021.00 11404.nO )1794.00 12315.00 12675.00

COMP 9923.00 10262.00 10000.00 9~28.30 10431.00 11005.00 11681.oo 12374.00 13061.00 135113.00

Dlff .oo .oo .oo .oo -111.00 58.00 211.00 sao.oo 746.00 908.00

PCNT .o .o .o .o -1.1 .5 2.4 4.9 6. l 1.2

WMTOT BASE 12969.00 13359.00 13760.00 14!34.00 COMP 13922.00 14355.00 14791.oo 15197.00 Dlff 953.00 996.00 1031.00 1063.00 PCNT 7.3 7.5 1.5 7.5

BASE MEAN= 116 77 ... 5 PCNT DifF ME.ANS= 4.o AV ABS DIFF= 480.64 OIFF PCNT Of BASE MEAN= 4. l

19 72 WA SHIN G I ON IN PUT-OUTPUT TAl:JLE. 2 3 4 5 b 7 8 9 10 l 1 12 13 14 15

l FIELD CROP l '+. 2 3 . 5 b 7 .2 . 9 .o .o • o .o 3 1. 3 S . 5 I • .o .o .o . o

2 \/EGET ABLE~ .o J . O 2. 4 .o .o .o . 2 I OJ. 7 .o 3 .1 4. l . o . o .o .o

3 LIi/ESTOCK .o .o 32.0 .o .o l Ob.2 <;8.b • l .o •O 2 . 5 • l • I .o • 0

4 OTHER AGFd .o I. 0 .6 J.b .o .o .o .o .o .o • l .o .o .o . A 5 FISHING .o • 0 • 0 .o • l .o .o 36.4 .o .o ,0 .o .o .o .o

6 MEAT PROD .o . o .o .o • e. 7 .8 .o .6 s.i .o .8 .o .o .o .o 7 DAIRY PROD .o .o .o .o • l .3 32.3 1.8 .o .o 1.8 .o .o .o .o 8 CANNING .o • 0 .u .o • l .o .9 2.7 1.3 .o ,9 .o .o .o .o

9 GRAIN MILL .o . o 32. 8 .o . o .2 .o 3,6 8.1 .o 8,8 .o .o .o .o

10 BEi/ER AGES .o . o . o • 0 .2 .o • l .o .o 14.4 .9 .o .o .o .o

11 OTHER FOOO .o • 0 3 ,4 .o .2 1. 5 l. 4 b.9 2.2 6.e 8,0 .o .o .o .o 12 TEXTILES • l • l .o .o ,5 .o .o .o .o .o .o .o • 1 .o • l 13 APPAREL 1.9 . o .o .o .o .o .o .o ,3 .2 .o .o .5 .o .o 14 MINING .3 . 2 . o . o .o .o .o .o .o .o .s .o .o 1.0 .o 15 FORESTRY .o .o • 0 . o .o .o .o .u .o .o .o .o .o .o 9.3

16 LOGGING .o . o • 0 . o .o .o .o .o .o .o .o .o .o .o .6 17 SAWMILLS .o • 0 .o .o .o .o .o .o .o .o .o .o .o .3 • l 18 PLY'-'OOD .u .o .o .o .o .o .o .o .o .o .o .o .o .o .o 19 OTHER WOOD .o l. O .o .o .o .2 .o .s .o .2 • l .o .o .o .o 20 FURNITURE .o • 0 • 0 . o .o .o .o .o .o .o .o .o .o .o .o

21 PULPMILLS .o .o • 0 .o .o .o .o .o .o .o .o .o .o .o .o

22 PAPER MILL .o . 3 .J .o .o .o • 0 .o .o .o .4 .o 1.8 .o .o

23 PAPBD MILL .2 1. 0 .o .o .o J.8 7 .4 10,b 2.3 7.9 4 .o .o • l ,2 .o 24 PRINTING .o .o . o • 0 .o ,3 .o ,6 .o ,6 ,5 .o .o .o .2 25 INDUS CHEM 12.4 9 ,7 2, 4 l, b • l .3 . 2 .2 .o • 1 .1 .o .o .3 ,7

26 OTHER CHEM .o . o .8 • I .o .2 • l .o .o .o .o .o .o .o • l

27 PETROLEUM 3.1 l • ? l. 7 l , I 2.1 . 3 .6 .3 • l • l ,3 .o • l .5 .3

28 GLASS .o .o .o .o .o .3 .o s .2 .o l 6. o • 4 .o .o .o .o

29 CEMENT • l . 2 • l .o .o • l .o .o ,2 .o .o .o .o 1,5 .4

30 FERR METAL • 4 .o • 1 • I . o .o .o .o .o .o .o .o .o .2 .o

31 NONFERMETL .o • 0 • 0 .o • l .o .o .o .o ,O .o .o .o .o .o

32 ALUMINUM • l . o . o . o . o • 0 .o .o .o .o .o .o .o .o .o

33 HEAVY METL .o • 0 .o .o .o .o .o .o .o ,0 .o .o .o .o .3

34 LIGHT METL ,4 . 3 . 3 .o . o • 4 .o 17,4 l.4 43.4 ,6 .o .o • l .o 35 NONE.LC EQP .o .o • 0 .o .o .o .o .o .o .o .o .o .o .o .o

36 MACH TOOL .o .o .o .o • l ,4 .o .s .o • l .o .o .o .3 .o

37 INDUS EOP .o . o . o • 0 • 0 ,c .o .6 ,0 .o .o .o • l .o .o

38 ELEC MACH .o . o .o . o .o . 2 .o .o .o .o .o .o .o .o .o

39 AEROSPACE .o • 0 . o . o . o .o .o .o .o ,O .o .o .o .o .o

40 MOTOR\/EH .o . o . o . o . o .o .o .o .o • l .o .o .o .o .o

41 -SHIP BLDG .o . o .o . o 2 . 3 .o .o .o .o ,0 .o .o .o .o .o

42 OTHER MFG .o • I .o . o • I • I . 3 ,4 ,0 .3 ,2 .o .o ,2 • l

43 TRANS SERII J .7 2 . 9 6 0 6 , 5 I, 3 b. 5 l. I l 4 .9 3,7 4. f, l, 7 • l .2 ,5 2.9

44 ELEC co 1. 9 1.1 1,4 . 2 • I . 9 l • I 2 ,4 , 6 ,8 4.0 ,2 .o • l .o

45 GAS co .o .o .o . o .o .5 .9 4, l . o ,9 4,0 .2 .o • l .o

46 0TH UTILS 2.3 1. 2 l • 4 1.0 . o . 2 • l • 4 .o .2 ,3 .o • l .2 .o 47 CO,.MUNICAT 1 , 3 I. 9 1.J , 6 • l ,9 • 6 1.3 .3 ,S ,7 .2 ,6 ,2 .7

48 RES BLD .u .o .o • 0 • 0 .o .o .o .o ,0 .o .o .o .o .o

49 NONRES BLO .o • 0 .o • 0 .o .o .o .o .o ,0 .o .o .o .o .o

50 HIGHWAY S .o . o .o , (/ • 0 .o .o .o ,0 .o .o .o .o .o .o 51 0TH STRUCT .o . o .v . o . o .o .o .o .o .o .o .o .o .o .o

52 MAINTENANC 4, 7 2 , Q 2 .4 ,4 .o . 2 .2 .2 .o • I ,2 .o • l ,4 2,0

53 TRADE 13 .8 14 . A 11. I 2.4 3, 3 :, • l 5,5 )/l ,5 4,4 9.3 5,2 .2 l , 7 1.0 3,5

54 FIN INS RE 3 .4 1. 8 1, 6 , 3 ,1 .7 1. 6 1. 2 • 4 1,5 1,3 .o .5 .1 .5

55 SERI/ICES 17,l 6 .4 4. ':i 2. 0 , 8 J. 7 6, 2 11.2 5,3 4,2 4,9 • l 1.5 1,6 1.2

56 SUBTOTAL 81.l 54,5 l 74. 9 l:,, O I J • e. 141 , 4 159.4 24b.5 67.o 120,9 8 9 ,0 l • l 7,6 9,4 23.6

57 IMPORTS sv.J 22 . 0 6 3 . 4 '1. 8 3, 8 l 2 <l.6 23.7 6 7.1 73, l 56, 1! 46,9 5.0 70.3 19.4 4,8

58 \/ALU': ADD 235,1 2 77 . ?. 9 5. 2 3:,. ] 23 . A S4 ,7 41, 7 187 .<; 46,4 142, 2 76,8 10.0 63,8 47.7 2?8.8

59 TOT OUTPUT 366.5 3!:> 3 . a 33 3 . 5 6U. 0 4V. 8 324 ,7 ?24, 8 501.5 186. 5 320,o 2 12,8 16,l 141,7 76,6 257.2

-~·· .. ·~-·, ·•-•--· . -- •~-- .. ~.

1472 WA SHlNGION INf'Ul-OUf.PUT T ABlt. lb 17 l~ 19 t!.O t!. l 22 23 24 2':> 26 27 2-8 29 30

I FIELD CROP .o .o • ti . o .o .u .o .o .o .o .o .o .o .o .o 2 VEGETABLES . o . o • 0 . o .o .o • 0 .o .o .-0 .o .o .o .o .o 3 LIVESTOCK . o . o • 0 .o . o • 0 • 0 .o .o .o .o .o .o .o .o 4 OTHER AGRI .o .o . o . o .o • 0 . o .o . o . o .o .o .o .o .o 5 FISHING .o • 0 • 0 . o • 0 .u . o .o .o .o .o .o .o .o .o 6 MEAT PROD .o • 0 • 0 . o . o • 0 . o .o . o .o .o .o .o .o .o 7 DAll<Y PROD .o . o .o .o . o .u . o .o .o .o .o .o .o .o .o 8 CANNING .o . o .o . o . o .o • 0 .o . o .o .o .o .o .o .o 9 GRAIN MILL .o .o 3 • 0 • 0 • 0 .o .o . o .o .o .o .o .o .o

10 BEVERAGES .o . o • 0 . o .o • 0 • 0 .o .o .o .o .o .o .o .o 11 OTHf.R FOOD .o . o . o . o .o . o .o .o . o 06 .o .o .o .o .o 12 TEXTILES .o .o .o .o .2 .o . o .o . o . o .o .o .o .o .o 13 APPAREL .o .o . o . o . o .o . o .o . o .o .o .o .o .o .o 14 MINING .o . o .o .o .o • l • l .2 .o .2 .o • l l.4 14.6 .o 15 FORESTRY 177 .4 5s.o 4. l .o . o . o .o .o .o .o .o .o .o .o .o 16 LOGGING 48 • 7 184.4 6 =, • 9 4 • fl . o IJ.J 12. 8 12.0 .o .o .o .o .o .o .o 17 SAWMILLS 5.5 33 . 5 12.3 3J.h t!.. 7 24 .5 7.8 7 . 6 .o . o .o .o .o .o .2

18 PLYWOOD 3 . 6 '•. 9 11 • 9 ~.3 . 2 (. 7 .o . o .o .o .o 2.1 .o .o .o 19 OTHER WOOD • l l l • 0 . 3 '+. 3 I• 0 .o • l .4 . o . \ • I • l • l .2 • I 20 FURNITURE .o . o .o . o • 4 .o . o .o .o .o .o .o .o .o .o

21 PULPMILLS .u . o .2 . o .o 1.0 13.9 24.8 .o 1.2 .o .o .o .o .o

22 PAPER MILL .o .o .2 • l l.5 • l .9 32.8 11.5 • l .u .o .o .o .o 23 PAPBD MILL • l l. 3 . 9 . 5 .4 . 6 7.4 lb.4 .2 l.8 .2 .3 1.4 1.2 .o

24 PRINTING • l • 0 2 • l .o .o • l • l 3.l • l .2 .o .o • l • I 25 INDUS CHEM .2 . o .2 • 0 . o ~ .7 7. l I• 4 .o 10.2 .1 1.3 .o .o .3

26 OTHER CHEM .2 .7 3.2 . 2 .2 .o .2 l.5 ·2 08 l.7 • I .o .2 • I 27 PETROLEUM 2 . 2 l • 3 . 2 .2 • l 8 .1 5.2 .4 • l 0 6 • l 3.7 • l 1.5 • l 28 GLASS .o .o .o ·" .o .o .o .o .o • l .o .o .o .o .o 29 CEMENT .o .o .o .o .o . o .o .o . o .o .o • I .o 28.8 .2

30 FERR METAL . 2 • l • l • l .o • l . 2 • l . o . o .o .2 .o .o 1.6

31 NONFERMETL .o . o .o • 0 .o . 2 . 3 .o .o .o .o • I .o .o .1

32 ALUMINUM .o . o .o . 5 • I . o .o .o .o .o .o • l .o .o .o 33 HEAVY METL .o . 8 • l • l .o .2 .3 .o .o . o .o .3 .o .o • I 34 LIGHT METL I. 3 .4 • l ob • l . 4 • 4 .o .o l • 3 .5 • I .o .2 • l

35 NONELC EQP .2 . o . o .o .o . o .o .o .o .o .o .o .o .o .o 36 MACH TOOL 2 . '=> . 0 . 3 • l • l .o .o .o .o .o .o .o .o .1 1.s

37 INDUS EOP .o . 9 . 3 . 8 . o . z .9 .3 .o . 3 .o .3 .o • I .o 38 ELEC MACH .o . 2 • l .2 .o .u .o .o .o .3 .o .o .o .o .o

39 AEROSPACE .o . o .o .o .o .o .o .o .o .o .o .o .o .o .o

40 MOTORVEH .o .o .o .o . o .o .o .o . o .o .o .o .o .o .o

41 SHIP BLDG .o • l . o . o .o .o . o .o . o .o .o .o .o .o .o 42 OTHER MFG . 2 .o • l .5 z. o • 0 .o • I .o .4 • I • I .o .o • I

43 TRANS SERV 3.6 21:l. 7 14 .6 ,., . 5 6 . 6 IO. l Il.6 4.o 3.s . s 4.3 .4 9.0 I.A

44 ELEC co .o .6 l.3 . o .o J.2 3.1 b . 4 . 5 4.4 .2 2.6 .3 2.8 2.1

45 GAS co .o . 6 l.3 .o . o 8 .2 b • 0 4. 2 .o 1.1 .3 9.1 .7 3.5 l. 6

46 0TH UTILS .o . 9 • l • l . o t!.. J . 5 1.2 • l .4 .o .8 .o .2 • 1

47 COMMUN I CAT . 9 .9 .6 1. 0 • 4 . 3 .9 I. 3 2.9 . 9 .3 .6 .o 1.0 .4

48 RES BLD .o .o .o . o .o .o .o .o .o .o .o .o .o .o .o

49 NONI-IES BLD . o . o . o • 0 .o .o .o .o .o .o .o .o .o .o .o 50 HIGHWAYS .o .o .o .o .o .o .o .o .o . o .o .o .o .o .o

51 0TH STRUCT .o . o . o . o . o .o .o .o . o .o .o .o .o .o .o

52 MAINTENANC 1.2 2. I 2 . 3 . 2 .7 l.9 I. 7 .4 l.7 • l 1.6 • l 1.0 .2

53 TRADE 10.3 22 . l 12.7 lU. 6 l.9 J.6 6.6 l l • 6 3.7 3.5 . 7 l.7 .3 3.8 s.2

54 FIN INS RE 4.5 4.4 l • I 1 . 3 • 4 .a l • 7 1. 5 lol .9 .2 4.3 • l I• I .5

55 SERVICES 14 • 6 13.7 <+. 7 '+. 4 2 • 0 <+ . S b.O 9 . 2 8 . 6 13.2 1.5 2.6 .3 .1 2.1

56 SUBTOTAL c:.71 .=i 369. l I Jt:l. 4 8 1,.4 l '+ • ':> 9b . 2 94.3 l4b.9 36.J 54.7 7.5 36.4 5.3 10.0 20.0

57 IMPORTS 40.0 7<+ . 0 71. l:l I 00. ':> 19 .4 41 .4 91 . A 84 . S 44.7 43.2 12.0 409.5 2.5 30.6 22.9

58 VALUE ADO 251.3 3b4.8 150 . 9 9'-J . 'I 36 . t, 79.2 l 4 l • o 184 .5 158.6 160 .4 16.0 110 .o l7 .1 85.e 72.3

59 TOT OUTPUT 561:l. 8 8 08.0 36 l. 0 2Al . 7 10.~ 2lb . b 327.2 42U 0 9 239 . 6 258.) 35.5 555.9 24.9 186.4 I I '5. 2

197 2 WA Sh I N·G I ON I ~! l-'Ul-OUTl-'UT TABLE. Jl 3 2 :n J4 JS J6 37 J!l 39 40 41 42 4) 44 45

l FIELD CROP .o • 0 .o • 0 .o .(J .o .o .o .o .o .o .o .o .o 2 VEGETABLES .o .o .u .o .o .o .o .o .o .o .o .o .2 oO .o 3 LIVE.STOCK .o • 0 • 0 .o .o .o .o .o .o .o .o .o .o oO .o 4 OTHER AGRI .o . o • 0 . o .u .o . o .o .o .o .o oO oO ol .o 5 FISHING • 0 . o Q .o .o • u . o .o . o .o .o .o .o oO .o 6 ME.Al PROO .o • 0 .o . o .o .o .o .o • I .o .o .4 lo4 o7 .o 7 OAil<Y PROO .o . o • 0 . o • 0 .o .o .o .o .o .o .o .4 o4 .o 8 CANNING .o . o .o . o .o .o .o .o oO .o .o .o .4 o l .o 9 GRAIN MILL .o . o .o . o • 0 .o .o .o .o .o • l .o .2 oO .o

10 BEVERAGES .o .o .o • 0 • 0 • 0 • 0 .o • 1 .o .o .o .5 oO .o 11 OTHER FOOD .o .o • 0 • 0 .o .o .o • 0 • 1 .o .o • 1 .9 loO .o 12 TEXT ILES .o .o • u • 0 .o .u .o .o .o .o • l .o o l ol .o 13 APPAREL .4 • 0 • 0 • 0 .o .o .o .o .o .o .2 .o o l .o .o 14 MINING .6 • 0 . o . o .o .o .o .o .4 .o .o oO .2 l7o7 .o 15 FORESTRY .o .o • 0 .o .o .o .o .o .o .o .o oO .o ol .o 16 LOGGING • l .o • 0 .o .o .o .o .o .o .o .o .o .o oO .o 17 SAWMILLS .o .o .o • 0 • I .o • 1 oO o4 08 2.2 .2 o2 loO .o 18 PLYWOOD .o • 0 .o .o .o • 0 • 0 .o .o .A l. l .o .o oO .o 19 OTHER WOOD • l . J • 0 . 2 .1 oO .2 .2 .3 ·2 • 1 .3 o2 oO .o 20 FURNITURE • 0 .o .o • 0 .o .u .o .o .2 .o 1. l oO oO oO .o 21 PULP"41LLS .o • 0 .o .o .o .o .o .o oO .o .o .o .o .o .o 22 PAPER MILL .o • 0 .o . o .o .o .o .o • I o I • 1 • 4 .3 o I .o 23 PAPBO MILL .a . 2 • 0 .4 .o .o • 0 .4 3.3 ·2 • 1 .4 .2 lo4 • 1

24 PRINTING .o • 1 • 0 • 0 .2 .o • 1 • 1 . 3 .2 • 1 • 1 .8 lo2 .3 25 INDUS CHEM • 1 .2 • 0 .4 • I • l • l • 1 • I o4 .1 • l .2 .o .o 26 OTHER CHEM • I .4 3 .4 o2 .u • I .2 1 • I .2 .6 1.1 .a o2 .o 27 PET~OLEUM .5 3.0 . 2 • I • 1 • I • I .o 2o0 .3 1.2 .2 38.9 06 .2 28 GLASS .o .o .o • 0 .o .o .o .o .a .o .a .o .o .o .o 29 CE.MENT • 1 . 3 .o . 3 • I .3 • I .o . 2 .o • l .3 • I • 1 .a 30 FERH METAL .4 . s 10.3 3. 2 '+. J ic. 8 2.7 .3 .A 8 • 1 .9 .o • 1 o l .o 31 NONfERMETL • l I . 8 .4 .6 . o .o • I .o .o o4 09 • l .o • 1 .o 32 ALUMINUM l.J 13 3 .7 6.0 2. 2 .o • l 1.3 .6 o4 .9 o l • 4 .o .o .o 33 HEAVY METL .u .2 5.J .3 • l .1 . 9 • l 2.2 06 1.2 .o .o o l .o 34 LIGHT HETL • l .4 1.5 1.4 . 6 • l .4 .9 2.1 .9 1.6 2.0 • 1 lo4 • l 35 NONELC EQP .o .o • 0 • 0 .9 .o .o .o • 0 .o l • 0 .o oO o l .o 36 11ACH TOOL .v 1.9 I .6 J.o 2.1 "· 3 J.l 1.3 606 .9 .6 .8 .5 • l .o 37 IN('US EQP • 1 .ii .4 .o . 2 .o 3.5 .5 I.A • l • l o2 .o o4 .o 38 EU:C MACH .o . 5 .o .o .4 .o .7 1.5 2.2 .o .6 .2 .o o3 .o 39 AE.ROSPACE .o .o • 0 • 0 .u .o .o 2.5 11.5 .o .o .o 1.5 .o oO

40 MOTORVEH • 0 . o .o .o .4 .o .o .o .o I.a .o .o .2 .o .o 41 SHIP 8LDG .o .o • 0 • 0 .o .o .o .o • l .o 4.4 .o 1.7 oO .o 42 OTHER MFG • 1 .o • 0 . 2 . 2 .4 .4 1.5 3.8 .4 .6 4.4 • l • l • l 43 TRANS SEkll 1 .... 12. 3 I• J • '1 .4 .4 .4 .3 .6 06 2.4 2.2 97.2 3ol .3

. 44 ELEC co • 7 36.3 .6 • g .4 .3 .4 .4 4o 3 1.4 .9 .5 6.8 13600 .5

45 GAS co l • 9 3.9 . 2 . 9 . 2 • l .I .o 2.0 .4 • l • I .4 .4 75.0

46 0TH UTILS .o .4 • 1 .2 • I .o .1 ol 1.3 .2 .2 • I • 4 o2 • l 47 COMMUNICAT • l . 9 2 . 2 l • l . A . -;, 1.4 .9 8.4 2.6 1.2 1.2 11 • 4 3.0 .s 48 RES RLD .o • 0 • 0 • 0 . o • C, .o .o .o .o .o .o .o .o .o 49 NONl<ES BLD • 0 • 0 • 0 • 0 . o .o .o • 0 .o .o .o .o .o .o .o 50 HIGHWAYS .o . o . o . o .o .o .o .o .o .o .o .o .o .o .o 51 0TH STRUCT .o • 0 • 0 .o .o .o . o .a .o .o .o .o .o .o .o 52 MAINTENANC .o I. O . J • I . M .o . 2 .2 .9 • I .9 .3 6. I o5 • I

53 TF<AOE od 5.6 2.1 1.4 l. 3 1.s 2.8 2 . I 4oh 2.9 9.7 3.2 15.2 1.9 .5

54 FIN INS RE .o J.2 . 8 . 5 .4 .3 .5 .4 J.8 • f, l. l .0 16.0 2o7 .8

55 SERI/ICES • tl 4.1 2. t, c.H 2. 2 I • ':> 2.2 1.6 61 .4 2.c; 5.5 3.4 29.5 11.1 1.5

56 SUBTOTAL 1 u .J 211 • 'J 36. 9 21 . 8 16. 9 1 '+ • l 22.0 16.2 134.1 28.c; 42.4 23.6 232.7 19208 AO.O

57 IMPORTS I J • '+ 380.0 4':>.3 5'-J. 1 .cd• J 2.J . J 41 .4 41) .6 95804 18806 I 11 • 7 62-l 145.5 32o9 6 l .3

58 II AL UE ADD 30.6 2'>7.1 67 ·" 6.J.l 40 .4 4b 0 J 64. I 5 f • 0 944.7 78.9 281.B I 11. 0 934.2 345.9 75.2

59 TOT OUTPUT 5<+.J 849 .1 149. 7 14'+.0 8'>.6 1:d.6 121. s 1 IJ. 9 2037.2 296.o 435.9 196.7 1312.4 571 .6 216. <;

1972 WASHINGTON INPUT-OUTPUT T Al:ILE 46 4 7 4R 49 50 51 '=>2 53 54 55 SUBTOT CONSMP INIIEST INV CH SL mu

l FIELD CROP .o • 0 • 0 .o .o .o .o .·o .o .o 124 .1 3.4 .o 3.0 .3 2 VEGUABLES .o • 0 .u . u .o • 0 .o .o .o ·8 147.5 29.4 .o .o .3 3 LIVl:STOCK .o . o .o • 0 .o .o .o .o .o .o ?39.b 68.5 .o .8 • 0

4 OTHER AGRI .o .o • 7 .3 • j .o .o .o .o 2. l 9.6 20.9 .o .o .o 5 F ISHJNG .o . o . o .o .o .o .o .o .o .o 36.5 1.9 .o .o .o 6 MEAT PROD .o . o .o • 0 .o .o • 0 .o .o • f, 17. 7 257.0 .o 6.9 .2

7 OAl~Y PROD .o .IJ .o .o .o .o .o .o .o l.o 38. I 151 .2 .o .3 .5

8 CANNING .o • 0 .o .o .o .o .o .o .o 1.3 1.1 105.3 .o -3.} .2

9 GRAI"' MILL .o • 0 • 0 .o . o .o .o .o .o .1 54.8 15.9 .o 2.3 .3

10 BEVERAGES .o .o .o . o .o .o .o .o .o I.A 18.0 79.0 .o .3 .o 11 OTHER FOOD .o • 0 • 0 • 0 • 0 .o .o • 4 .o 1.2 34 • 7 83.9 .o 3.7 .? 12 TEXTILfS .o .o • 0 .o .o .o .o .o .o .o 1.4 1.7 .o • l .o 13 APPAREL • 0 • 0 • l .o • 0 • 0 • 0 .5 • I I.A 6.1 24. 7 .o 5.2 .o 14 MINING .o .o I. 8 1.8 10.3 b.l 1.9 .o .o • l 59.6 1.2 .o .o .2

15 FORESTRY .o .o • 0 • 0 .o • u .o • 0 .o .o ?45.9 .6 .o .o .o 16 LOGGIN& .o • 0 • 0 .o .o • I • 0 .o .o .o 342.7 .o l. 0 5.9 .o 17 SAWMILLS .o .o 36.7 2.2 .3 d .ti 1.3 1.2 .o .o l 83 .6 3.9 3.1 -3.6 .2

18 PLYWOOD .o .o 18.2 :, • 0 1.9 b.O .5 .0 .o .o 74.0 .9 .o .2 • l

19 OTHER WOOD .o .o 21.9 4 . 3 .2 5.4 .5 .4 .o .2 55.0 2.5 6.3 .9 • l

20 FURNITURE .o • l 1.7 .5 .o • I .o .4 • I .o 4 .b 28.6 1.0 • I 2.5

21 PULPMILLS .o .o .o • 0 .o • 0 .o .o .o .o 41 • l .o .o 21 .8 .o 22 PAPER MILL .o l.6 • 0 .o .o •. o .o 15.8 1.0 .5 10.0 8. l .o -3.0 .6

23 PAPbO MILL • l .2 .3 .3 .o • I .o 4.4 5.5 2.5 90.3 15.6 .o -2.0 .7

24 PRINT ING .3 2.s .o • I .o .o .o 72.3 29.1 39.8 154.0 47.5 .o 1.2 1.5

25 INDUS CHEM .2 .o .o • I .o .J .o I • l .o 2.2 66.3 .o .o 2.6 .2

26 OTHER CHEM .o • l .9 . :, .3 .6 l • l 2.5 .2 2.2 24.5 3.2 .o -.9 .3

27 PETROLEUM 1.0 .6 5 .0 6.2 I I • 0 IJ.4 3. 7 11. I 2.1 5.1 143.0 151.2 .o 15.5 5.A

28 &LASS .o .o .o .o .o .o .o .o .o .o 22.0 .o .o -.1 • l

29 CEMENT .o • l 42.8 41. 0 14.7 32.7 3.7 .2 • 0 .3 169.2 5.1 .o 1.2 .2

30 FERR METAL . o • l 3.2 J.7 .4 2d.tl l .5 .o .o .o 75.7 • l .o 1.2 .o 31 NONFERMETL .o • l l .o • ',I .u c .4 • I .o .o .o 9.8 .o .o -.5 .o 32 ALUMINUM .o .o 1.7 I. 7 .o 4. 3 • l .o .o .o 155.6 • I 2.4 -18.8 .o 33 HEAVY METL .o • l 12.s 26.2 s .s 22.6 2.3 .7 .o • l 83. 9 .4 23. l -.1 .4

34 LIGHT METL .o .o I. 7 1.5 • I I • l .3 I• 0 .o .2 89.9 .4 8.9 -.2 .1

35 NONELC EQP .o • 0 .o • I .o • 0 • 0 .o .o .o 2.3 .2 6.3 - • l .2

36 MACH TOOL .o .o .3 .) • l .4 .o 1.0 . o 1.0 42.7 1.9 1.2 1.3 l • l

37 INDUS EQP .o • I .z . 2 .o • 0 .o .o .o •6 14.2 .o 12.1 • l .6

38 ELEC MACH .o 2 .5 1.5 I • 8 • I 1.3 .4 • l .o • I 15.2 .5 2.5 4.6 • l

39 AEROSPACE .o • 0 .o .o .o . o .o .o .o • 0 21.5 .o .o -211.3 .o 40 "°OTORVEH .o .o • l .o • I • I • 0 .o • 0 .o 2.8 13.9 l3.7 20.0 .o 41 SHIP BLOG .o .o .o . o .o .u .o . o .o .o 8.6 6.9 .5 .4 .o 42 · OTHER MFG • 0 .3 3. 0 2.8 .3 1 . 5 .4 4 • 2 2 • l l 4 .4 46.7 7.6 5.o 3.4 1.4

43 TRANS SER\/ .4 2 . C/ 11. 8 tl . 6 1.2 11.8 2 . I 29.J 9.5 I 7. 6 383.9 J97.6 10.2 -2.8 10.1

44 ELEC co 2.2 l • 7 .6 .7 .2 .6 .o 43.5 14 • 9 2 7.9 325.6 )93.9 .o .o 5.2

4S GAS co .2 • I • I . o . o .u .o '>.9 1 .5 7.4 154.8 54.9 .o .o 3 .7

46 0TH UTILS 12.3 .5 .2 .3 . 2 • 0 . o b.9 3.2 J.) 44.3 l IO. l .o .o 4 • I

47 COMMUN I CAT l. I l • C/ J .l J. ) I• l J . S .4 4:>.4 30.J 92.c:; 242.6 226.3 .o .o 7.A

48 RES BLD .o .o .o .o . o .o .o .o .o .o .o .o 7bl.7 .o .o 49 NONRES bLD .o .o .o • 0 .o .o • 0 .o .o .o .o .o 322.9 .o 110.5

so HIGHWAYS .o • 0 .o . o .o . o .o .o .o .o .o .o .o .o .o

51 0TH STRUCT .o .o .o . o .o .o • 0 .o .o .o .o .o 322.9 .o .o 52 1-!AINTENANC l.6 2. l .2 • I • I • l • 0 1s.2 19.8 9.2 87.l 49.6 .o .o 3.4

53 TRADE .7 2 .6 52 .3 21.8 d . 2 2':i . I 7 .4 3~ • I 13.? 45.3 479.6 2 469.8 83.3 -6.3 -2.3

54 FIN INS RE l.3 3.2 4."> 4. 7 1.6 4 • ., .6 4do8 106.5 41 .4 289.9 759. I .o .o 7.4

55 SERVICES J.O 19 .7 25 .5 2J . 9 S.7 2c .3 2 .3 200.) 112.7 135.9 850.2 1718.J .o .o 9.3

56 SUBTOTAL 24.2 43.o 253 0 d 170.H 69 . 8 20 f . "> 3 0.3 :,52 .4 351. 7 466.8 5907.6 6922.6 1588.9 -149.B 1911.3

57 IMPORTS 4. I 43.S 200,0 l7 d .7 J':i . 6 l 8b 0 J 32.7 22"1 .2 73 .ci 186.5 5096.2 ]343.6 981.1:l .o IFHl. 7

58 VALUE AD D 13J. 8 44 1:> . 0 307.9 24:> . 3 9J 0 b JJd.':i 9 5 . 2 3321.6 1026.A ?083.) )4989.5 1588 .9 .o .o 94) .6

59 TOT OUTPUT 162.l :, 32 . '> 761.1 :,94.',1 j 99 . 0 732.3 15 tl . J 4103.2 1452.4 27 36.5 25994.l IJA55.l 2570.7 -149.8 13?0.5

1972 WASt1INGION INPUT-OUTPUT TABLE SL 0TH f:.XP OR T TOTAL

l FIELD CROP .7 2J4. H 3M, .3 2 VEGETABLES 1.2 17 5 .5 3 5 3 . 9 3 LIVESTOCK .6 24.0 3 33 0 5 4 OTHER AGRI .2 29.c; 60.2 5 FISHING .o 2 .4 4 0 .8 6 MEAT PROO .9 42. 0 324.7 1 OAl~Y PROD 3.7 31. I 224 0 9 8 CANNING 1.0 390. S 5 0 1.6 9 GkAIN MILL .7 112. S 186.5

10 BEVERAGES • I 2 2 2 . 6 32 0.0 11 OTHER FOOD 1.0 8 9 .2 21 2 . 1 12 TEXTILES .o 12 . 9 16.l 13 APPAREL • l 105. 6 141.7 14 MINING l. l 14.4 76 . 5 15 FORESTRY .o 10.7 25 7.2 16 LOGGING .o 2 19. I 568.7 17 SAWMILLS .1 6 20 . 2 8 08. I 18 PLYWOOD .7 2 85. I 36 1.0 19 OTHER WOOD .5 2 16 .4 28 1.7 20 FURNITURE .5 33 . I 7 0.4 21 PULPMILLS .o 15 3. 9 2 16 . 8 22 PAPER MILL .4 251.l 32 7. 2 23 PAPBO MILL 1.2 31 5 . 2 4 2 1. 0 24 PRINTING .3 35 . 0 2 3 9 .5 25 INDUS CHEM 1. 9 18 7. 4 258 .4 26 OTHER CHEM . 0 1.1 35 . 6 27 PETROLEUM 6.1 234. 5 556 . 1 28 GLASS .o 3 .0 2s . o 29 CEMENT 1.8 8 . 8 186 . 3 30 FER~ METAL .2 38.0 115. 2 31 NONF ER METL .o 4 5 .2 54 . 5 32 ALUMINUM • 1 70 9 . 6 849 .0 33 HEAIIY METL 1.7 40.4 149.8 34 LIGHT METL • l 4 5, 0 144. 2 35 NONELC EQP 1.5 7 5 , I 85 ,5 36 MACH TOOL ,2 35 ,3 83 ,7 37 INDUS EQP .2 99 ,7 12 7,5 38 ELEC MACH .o 91 , 0 11 3 , 9 39 AEROSPACE .o 222 7, 0 20 37. Z 40 MOTORIIEH . o 245 . 5 295 . 9 41 SHI P BLOG 12 .3 4 07. 3 4 36.0 42 OTHER MFG 1, 8 13 0. 7 196 . 6 43 TRANS SER\/ 9 ... 703,5 1312. 5 44 ELEC co 4.& 4 2 . 1 571. 6 4 5 GAS co 2 .1 I • 2 2 16.7 4 6 0 TH UTILS 1. s 2 . 0 l bc:',0 4 7 COMMUN I CAT 24, 9 30 . 6 5 32 ,2 48 PE S BLO .o . o 7 6 1, 7 49 NONKES BLO 119. l 22 ,4 5 94. 9

0 HIGHWAY S 199.0 • 0 199 . 0 ) Of H TRUC T l 5<,. I 250 , 3 73 2 . 3

- MAI NT ENANC I !l. 5 . o 158 0 b TR 11 D · l O. 1 106 tl , 9 4103 . l t IN IN RE lb. 5 3 7 9 . J 145 2, 2

' RVJ . 31.3 127 .5 2 73 6 , 6 VI.HO A b4 0.3 10885 , H c5993 . 8

JMf' OA J2 0.6 • 1 992J. O V~L t ADU / 0 7 .6 93S.7 19 163 . 9

T U PU l t>b f:1 . 6 1 I 82 1, 5 ':>508 0, 7

l 9RO lolASH!N<.,I ON i ►JPUT -OUT PUT TABLE 2 l 4 5 t:, 7 8 9 10 11 12 13 14 15

l FIELD CROP 20.2 5 .1 99.'-J 1.2 • 0 .o .o .o 4 l. 2 8.4 1.9 .o .o .o .o

2 VEGETABLES .o 4.4 3.b . o . o . o .2 15b. 7 .o 4.A 44.2 .o .o .o .o 3 LIV~STOCK . o . o 4b. l . o .o 13J.2 124.6 .2 .o .o 3.0 • l .2 .o .o 4 OTHER AGR I .o 1. 2 .d '+ . 2 .o • 0 . o • 0 .o .o • l .o .o .o 1.0

5 FISHING .o • 0 . o . o • I .o . o 4:>.9 .o .o .o .o .o .o .o 6 MEAT PROD .o .o . o .o . 2 o.7 • 0 .e 5.A .o .9 .o .o .o .o

7 DAIRY PROD • 0 .o .o • l) • l .J 34. 7 2 . l .o .o 1.8 .o .o .o .o 8 CANNING .o . o .u .o • 1 .o l • l 3 .6 1.6 .o l • l .o .o .o .o 9 GRAIN MIL L .o • 0 42.9 . o . o .2 . o '+. 7 9.4 .o 9.7 .o .o .o .o

10 BEVERAGES .o . o • 0 . o . 2 .o • I .o .o 18.9 1.0 .o .o .o .o

11 OTHER FOOD .o .o 4.4 .o .2 l • 7 1.6 8. 9 2.5 9.o 8.8 .o .o .o .o

12 TEXT}LES • l • l • 0 .o .6 .o .o .o .o .o .o .o • l .o • l 13 APPAREL 2.3 .o .u . o .o .o .o .o .4 . 3 .o .o .7 .o .o

14 MINING .4 .1 .o • 0 .o .o • 0 .o .o .o .5 .o .o 1.3 .o 15 FORESTRY .o .o .o .o . o .o • 0 .o .o .o .o .o .o .o 11.4

16 LOGGING .o . o .o .o .o .o .o .o .o .o .o .o .o ·• 0 .7

17 SAWMILLS .o .o • 0 .o .o .o • 0 .o .o .o .o .o .o .4 • l 18 PLYlolOOD .o .o .o . o .o .o .o .o .o .o .o .o .o .o .o 19 OTHER lolOOO .o 2.0 • 0 .o .o .4 .o l • l .o .5 .2 .o .o .o .o

20 FURNITURE .o .o .o .o .o • 0 • 0 .o .o .o .o .o .o .o .o 21 PULPMILLS .o .o .o .o .o .o .u .o .o .o .o .o .o .o .o

22 PAPER MILL .o .4 .4 .o .o .o .o .o .o .o .4 .o 2.s .o .o 23 PAPBD MILL .2 l. 2 .o .o • 0 ... 0 7.9 lt:'..7 2.4 9.A 4. 1 .o • 1 .2 .o 24 PRINTING .o .o • 0 .o .o • 4 .o .8 .o .A .5 .o .o .o .2

25 INDUS CHEM 17 .5 l 4. o 3.5 2.0 • l .4 . 2 .3 .o .2 .9 .o .o .4 .9

26 OTHER CHEM .o .o l • 1 • l .o .2 • l .o .o .o .o .o .o .o • 1

27 PETROLEUM 5.2 2.2 3.0 I. 7 4. 3 .5 l • 0 .5 .2 .2 .4 .o .2 .9 .5

28 GLASS .o .o .o .o .o • 4 .o 1.0 .o 22.3 .4 .o .o .o .o 29 CEMENT • l .3 • l .o .o • l .o .o .3 .o .o .o .o 2.1 .s

30 FERR METAL .s • 0 • l • l .o .o .o • 0 .o .o .o .o .o .2 .o 31 NONfERMETL .il • 0 • 0 .o • l .o .o .o .o .o .o .o .o .o .o 32 ALUMINUM • l .o .o .o .o .o .o .o .o .o .o .o .o .o .o 33 HEAVY METL .o .o .o .o .o .o .o .o .o .o .o .o .o .o .4

34 LIGHT METL .s .4 .4 .o .o .5 .o 22.8 1.1 58.6 • b .o .o • 1 .o 35 NONELC EQP .o .o .o .o .o .o .o . o .o .o .o .o .o .o .o 36 MACH TOOL .o .o .o .o • l .5 .o .8 .o ·2 .o .o I .o .4 .o

37 INDUS EQP .o .o • 0 • 0 • 0 .4 • 0 l. 3 .o .o .o .o .2 .o .o 38 ELEC MACH .o .o • 0 .o .o .J .o .o .o .o .o .o .o .o .o 39 AEROSPACE .o .o • 0 . o .o .o .o .o .o .o .o .o .o .o .o

40 MOTORVEH .o .o • 0 . o .o • 0 .o .o .o .2 .o .o .o .o .o

41 SHIP BLOG .o .o • 0 .o 2.8 .o .o .o .o .o .o .o .o .o .o

·42 OTHER MFG .o • l .o .o • l • l .4 ob .o .s .2 .o .o .3 • l

43 TRANS SERV 4. 8 3.7 8.8 .6 1.6 7.6 1. 3 l <; • 7 4.3 6.3 1.9 • 1 .3 .6 3.4

44 ELEC co 2.4 1.5 l. 9 .2 • 1 I• ll 1. J J • l •A l • l 4.5 .3 .o • l .o 45 GAS co .o .o • u .o • 0 .:, 1. 0 5. 0 .o l • l 4.1 .3 .o .1 .o

46 0TH UTILS 3.2 1.7 2.0 l • 3 .o .2 • l .6 .o .3 .3 .o .2 .3 .o

47 COMMUN I CAT 2 .0 3 .0 2.2 . 9 . 2 1.3 .9 2.2 • 5 08 1.0 .3 l • 0 .3 1. 0

48 RES BLD .o • 0 .u .o .o .o .o .o .o .o .o .o .o .o .o

49 NONRES BLD .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o

so HIGHlolAYS • 0 .o .o • 0 .o .o .o .o .o .o .o .o .o .o .o 51 0TH STRUCT .o .o • 0 .o .o • 0 .o .o .o .o .o .o .o .o .o

52 MAINTENANC b • l 3.8 3.2 . 5 .o .2 .2 . 3 .o .2 .2 .o • 1 .s 2.4

53 TRADE 20 .ii 22.8 18.4 J.2 4.7 b .9 7.6 28.7 b.;> 14.9 6.9 .3 2.9 1.s 4.9

54 FIN INS RE 4 • 4 2.4 2.2 • 4 .9 . 8 l. 9 l • 7 .5 2-1 1.5 .o .1 .9 . 6

55 SERVICES 2::..0 9.6 6.9 t:'..6 1.2 4. 9 8.3 10.9 1.2 6.5 6. 3 .2 2.5 2.3 1.6

56 SUBTOTAL 115. 7 80.4 251.8 1~ . o lo.1 l 7'=' • 7 194.5 ]49.2 8408 167.9 10,. 5 1.6 11.7 13.3 30.2

57 IMPORTS 55.4 21.s 72 .3 9 . 9 J.O l4J.6 23.5 72.4 84.3 78.4 48.9 6.6 99.8 24.4 4.6

58 VALUE ADO 306.2 369.3 129.4 41. 3 29. 7 t,4 0 7 49.7 252.6 56.o 197.o 88.4 13.3 91.2 62.2 2A0.4

59 TOT OUTPUT 471.3 471. 2 453.:> 7lJ.2 50.9 384. l 267.7 674.1 225.o 44J • . 1 ?44.7 21.5 202.1 99.8 315.3

1980 WASHINGlON INPuf-ouTPuT TAHLE. 16 17 18 19 20 21 2? 23 24 25 26 27 28 29 30

l FIELD CROP oO oO oo oO oO oO oO .o .o .o oO .o .o .o .o 2 \/EGE TABLES oO oO .o oO .o oO oO .o .o .o .o .o .o .o .o 3 LIVESTOCK .o .o .o 0 0 .o 0 0 0 0 .o oO .o .o .o .o .o .o 4 OTHE.R AGRI oO oO oO .o .o oO oO .o .o .o .o .o .o .o .o 5 FISHING oO oO oo oO .o oO .o .o .o .o .o .o .o .o .o 6 MEAT PROD .o .o .o oO .o .o .o .o .o .o .o .o .o .o .o 7 DAIRY PROD .o .o .o oO .o .o .o .o .o .o .o .o .o .o .o 8 CANNING .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 9 GRAIN MILL .o .o .4 .o .o .o .o .o .o .o .o .o .o .o .o

10 BEVERAGES .o .o .o .o .o .o .o oO .o .o .o .o .o .o .o 11 OTHER FOOD .o .o .o .o .o .o .o .o .o .9 .o .o .o .o oO

12 TEXTILES .o .o .o .o .3 oO oO .o .o .o .o .o .o .o oO

13 APPAREL .o oO .o .o .o oO .o .o .o .o .o .o .o .o oO

14 MINING oO .o oO .o oO o l • l 02 .o .3 .o .2 2.1 21.1 .o 15 FORESTRY 216.8 1005 s.o oO .o .o oO .o .o .o .o .o .o .o .o 16 .LOGGING 59.8 23706 80.4 tl.O oO lJoO l3o8 l 5 o l oO .o oO oO .o .o oO

17 SAWMILLS 607 42.7 14.9 550J 3.1 23o7 803 '-Jo4 oO oO .o oO oO oO o3

18 PLYWOOD '+o9 7.0 l 6 0 l 17 o l 03 tlo3 oO oO oO oO .o 3o9 oO .o oO

19 OTHER WOOD .2 22 o 7 06 1106 lo7 .o .2 08 oO .2 .2 .3 .3 .4 o2

20 FURNITURE oO oO .o oO o4 0 0 oO .o oO .o .o .o .o .o .o 21 PULPMILLS oO • 0 3 oO oO loO 1505 32 0 l oO 1.7 oO oO .o .o oO

22 PAPER MILL .o oO 02 .2 1.6 o l 09 3b.6 15.4 • 1 .o .o .o .o .o

23 PAPBD MILL • 1 1.5 1.0 . ., .4 . ', 7.1 18.5 .3 2o3 03 .4 1.8 1.5 .o 24 PRINTING • 1 .o .3 .2 oO .o • l • 1 4.5 .2 .3 .o .o .1 • l 25 INDUS CHEM · .3 .o .J .o oO l0o2 8.3 1.9 .o 15.2 1 • l 2.4 .o .o .4

26 OTHER CHEM .3 .9 4.1 .3 .J oO .2 2.0 .3 l • l 2o7 .2 .o .3 • 1

27 PETROLEUM 3.5 201 03 .4 02 1u.2 1o3 .6 .2 1.2 .2 800 o2 2.1 o2

28 GLASS .o .o oo oO .o .o .o .o .o .2 .o .o .o .o oO

29 CEMENT oO .o .o oO oO oO oO oO oO .o oO 02 oO 43.7 03

30 FERR METAL 02 o l o l 02 oO o l 02 o l oO oO oO 03 oO .o lo9

31 NONFERMETL oO 0 0 .o oO oO o2 03 oO oO oO oO o2 oO .o o l

32 ALUMINUM oO .o 0 0 loO o2 oO oO oO .o oO oO 02 oO .o • o· 33 HEAIIY METL .o l • l • l .2 .o .2 .3 .o . o .o oO .s oO .o .r 34 LIGHT METL lo 5 oS • l 1.0 • l .4 04 .o .o 1.8 .7 .2 .o .3 • l

35 NONE.LC EQP .3 .o .o .o .o .o .o .o .o .o .o .o .o .o .o 36 MACH TOOL 3.3 l. l .4 .2 o l .o .o .o .o .o .o .o .o .2 2.0

37 INDUS EQP oO l. 7 .6 2.0 .o .J l.4 .s .o .7 .o .7 .o .2 .o 38 ELfC MACt-t .o .3 • l .4 .o .o .o .o .o .5 .o .o .o .o .o 39 AEROSPACE .o .o .o .o .o .o .o .o .o .o .o .o .o .o oO

40 MOTORVEH .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 41 SHIP BLDG .o • l .o .o .o .o .o .o .o .o .o .o .o .o .o 42 OTHER MFG .3 .o • l .9 c.5 .o .o • l .o .1 .2 .2 .o .o o l

43 TRANS SERI/ 4.3 3600 l7o3 1205 05 b 03 10.7 l4o3 5.6 5. l 01 6.9 .6 12.3 2o2

44 ELEC co oO .7 1.6 .o oO 3oO 3.3 7.9 .1 6.o .3 4.3 .s 3.8 3.3

45 GAS co .o .1 l. 5 .o .o '. 2 5.8 '+08 .o 9.6 .4 13.6 .8 4o4 1.8

46 0TH UTILS .o l. 2 • l .2 .o 2.4 .6 1.6 • 1 • f, .o 1.3 .o .3 0 1

47 COMMUN I CAT 1.3 l.4 .9 2.0 .5 .J l • 2 2.0 4o9 l.4 oS 1 • 1 .o 1.1 06

48 RES BLD .o .o .o .o oO .o .o .o oO .o .o .o .o .o .o 49 NONRES BLD .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 50 HIGHWAYS .o .o .o .o .o .o .o .o .o .o oO .o .o .o .o 51 0TH STRUCT .o .o .o oO .o • 0 0 0 oO oO .o .o oO oO .o oO

52 MAINTENANC lo5 206 02 05 o3 01 2o0 2ol 06 2o4 o l 2.1 o2 lo4 .3

53 TRADE l '+ o 6 32.8 17.9 20.2 2.5 '+ • l 8.3 lbo9 6ol 5.s 1.2 3.3 .s 6.1 7o4

54 FIN INS RE s.s 5.7 2. l 2 o l 04 .8 l.9 lo9 1.6 1.2 .3 7.3 .2 1.s .7

55 SERI/ICES 20o0 19.8 6.'+ tl.3 206 '+ 0 9 1.2 13.l 13o9 20.5 2.s 4.8 .s 1 • 1 3.8

56 SUBTOTAL J45oJ 49009 173.4 14~ o 3 I ti. 0 9 / o 9 10505 18'+08 S'+oO 79o4 1108 62o7 1.1 10300 26o4

57 IMPORTS 42.6 1104 81. 7 I 5::. 05 2oos 3::..b 95.2 10507 6lo7 55o9 16o7 6 7506 3.6 37.3 ?.7 oO

58 VALUE ADD Jo7.o 467.8 18302 16::. o2 4 l 06 7 f o 0 1s200 237.9 22605 22lo7 23o5 102.2 24.5 ll 908 90 o 1

59 TOT OUTPUT 694.9 1036.1 43803 <+65. 9 80 o I 21Uob 35207 52t1.4 342oi? 35700 51 o 9 920.5 35.7 200.2 143.5

1980 WASHINGION INPUT-OUTPUT TABLt:: 31 32 33 J4 JS J6 37 38 39 40 41 42 ~3 44 45

l FIELD CROP .o .o .o .o .o .o .o .o oO oO .-o oO o-0 oO oO

2 VEGETABLES .o .o .o oO .o oO .o oO oO .o oO oO o3 oO .o 3 LIVEsTocic; .o .o .o oO oO oO oO oO oO oO oO oO oO oO oO

4 OTHER AGRI oO .o .o .o .o .o .o .o ,0 ,0 ,o oO oO o l .o 5 FISHING .o .o • u .o .o .u .o .o oO oO oO oO oO oO oO

b MEAT PROO oO .o ,0 .o oO .o .o .o •l oO oO 0_6 lo9 o9 oO

7 OAJRY PROO oO oO .u .o oO .o oO oO oO oO oO oO oS o5 oO

8 CANNING oO .o .o .o .o .o .o oO .o .o oO oO .6 ol .o 9 GRAIN M-ILL .o .o .o .o .o .o .o .o .o .o o l oO o3 oO oO

10 BEVERAGES oO oO ,0 .o oO .o .o .o • l oO .o .o ,1 oO .o 11 OTHER FOOD .o .o ,0 .o .o .o • 0 .o • l oO ,0 o2 1,2 lo2 .o 12 TEXTILES .o .o ,0 .o .o .o ,o .o oO oO • 1 ,0 • l ol .o 13 APPAREL .3 ,o .o .o .o .o .o .o oO oO ,3 oO o l oO .o 14 MINING ,5 .o .o .o oO .o .o oO 06 oO oO oO o3 24o3 .o 15 FORESTRY .o .o ,0 .o .o oO .o .o oO oO ,0 oO oO ol oO

16 LOGGING • l .o .o ,0 .o .o oO .o .o oO oO oO oO oO oO

17 SAWMILLS .o oO oO .o • l .o • t'. .o 06 1,2 2,4 o3 .3 1.3 .o 18 PLYWOOD .o ,o ,0 ,o oO .u .u .o oO lo3 lo4 .o ,o .o .o 19 OTHER WOOD ,2 ,1 ,0 ,4 o2 .o .6 ,4 .1 .4 02 08 .4 oO .o 20 FURNITURE oO .o .o .o .o .o .o .o .3 ,0 1 0 l .o .o .o oO

21 PULPMILLS .o .o .o .o .o .o ,o .o .o .o oO oO oO oO .o 22 PAPER MILL oO .o .o .o .o .o .o .o • l o l 0 1 06 o4 • 1 oO

23 PAPBO · MILL .o .2 .o ,5 .o .o oO .5 4 • l .2 • 1 .6 .2 lo6 • l

24 PRINTING oO • l .o .o .3 .o .2 • l ,5 o3 • l .2 lo2 lo6 .3

25 INDUS CHEM • l .3 .o .6 • l .2 .2 o l .2 06 .9 o2 o3 o0 oO

26 OTHER CHEM • l ,6 .4 o5 .3 .o .2 .3 l. 6 .3 01 3.3 .o .3 oO

27 PETROLEUM .5 5.3 .3 .2 .2 .2 .2 oO 306 .5 1.8 .4 71.8 1.0 .3

28 GLASS .o .o ,0 .o .o oO .o oO oO .o oO .o oO .o .o 29 CEMENT • l ,4 ,0 .4 • l 06 ,2 .o o3 oO ,2 o5 .2 o l .o 30 FERR METAL ,J ,6 13,9 3.8 5.4 4,8 5,2 ,4 1.0 11.2 .9 oO • l • l oO

31 NONFERMETL • l 2,5 .5 ,8 .o oO ,2 .o .o .5 loO o2 .o ol oO

32 ALUMINUM 1.2 223,0 10,3 3o3 .o .2 3,2 1.0 o7 1.6 .2 .8 .o .o .o 33 HEAVY METL oO ,3 8,3 ,4 • l l,4 1.9 • l 3o3 l • O 1,5 .o oO • 1 .o 34 LIGHT METL • l ,5 2,1 1,8 ,8 .2 ,9 1.2 3.7 1.3 1,7 3.5 • l 1,8 • l

35 NONE.LC EQP oO .o .o .o 1,3 .o .o .o .o .o 1.2 .o oO o l .o 36 MACH TOOL o0 2,8 2,5 4,l J,O ti.6 6,8 l,9 9,9 1.4 ,1 1.6 08 • l .o 37 INDUS EOP • l 1,7 ,8 .o ,4 .o 11, 0 l, l 3,7 ,2 .2 05 o0 08 .o 38 ELEC MACh .o ,8 ,0 .o .6 .o 1,6 2o4 3.5 .o ,8 o4 o0 .5 .o 39 AEROSPACE .o .o • 0 • 0 .o .o .o 3,4 24,2 .o .o .o 2.2 o0 .o 40 MOTORVEH .o .o • 0 .o ,5 oO .o .o ,O 2,7 .o o0 o3 . • 0 .o 41 SHIP BLOG .o .o .- .o .o • 0 .o .o .o • l .o 4,8 o0 2o4 ,0 ,0

42 OTHE.R MFG • 1 .o • 0 ,3 ,3 .8 ,8 2.3 5,8 ,6 ,8 8.8 .2 • l • l

43 TRANS SERV lo5 16,7 l,8 l , l ,5 .7 ,1 ,4 ,7 08 2,5 4o0 134.9 4.0 .3

44 ELEC co .6 49,2 ,8 l, l ,5 ,6 ,1 ,5 !:>08 2,0 ,9 loO 9.4 111.0 ,6

45 GAS co 1.4 4,9 ,2 l, 0 ,2 o2 .2 .o 2o5 o5 • l o l 05 .5 78,6

46 0TH UTILS .o ,6 , l ,3 • l .o ,2 • l 1.9 .3 ,3 o2 .6 .3 • l

47 COMMUN I CAT o l 1.5 3,1 l. 7 l. 3 2.0 3.4 1.5 l4o0 4o5 1.6 2.5 19.5 4o7 .1

48 RES BLD oO .o • 0 .o .o .o .o oO .o .o .o .o .o .o .o 49 NONkES BLD .o .o .o • 0 .o .o .o oO .o .o .o .o oO o0 .o 50 HIGHWAYS .o .o .o .o .o oO .o .o oO .o .o .o .o o0 .o

51 0TH STRUCT • 0 .o • 0 .u .o .o .o .o .o .o .o .o .o .o .o 52 MAINTENANC .o l ,4 .4 • l l , l .o .4 .3 1.2 • l 1.0 o5 8,6 .1 • l

53 TRADE. • 8 9,0 4. !:, 2. l 2.0 J.3 6.6 J.3 7o4 4.9 12.3 608 24.9 2.9 .1

54 FIN INS RE .o 4,4 1,2 ,1 .5 .6 l • l .6 5.3 .9 1.2 lo5 22.9 3.6 .9

55 SERVICES • 7 6.4 4.5 J.C, 3.2 3,2 5.o 2.5 95.3 4.o 6.8 1.0 46.9 26.5 }.9

56 SUBTOTAL 8.7 334,2 56,3 29. l 2J,5 27,6 51.4 24.5 202.9 43o5 50.2 46,9 355.2 257.4 84.8

57 IMPORTS 10.3 484,5 60,3 7 l. 9 36,0 41) .6 75.4 5J.3 1305.J 27lo2 !18,5 llOol 180.2 42.4 78o9

58 VALUE ADO 24.6 355,6 95.6 7tl.6 5J.3 8'+.5 128.5 7tl. l 1304.J 11404 308.5 203.4 1322.8 459.4 87.l

59 TOT OUTPUT 4J.6 1174,3 212,3 l 7'-J, 5 I 12 .8 l 52, I:! 255,4 155.9 2012.5 429.1 411.2 360.4 1s5s.2 759.2 2'51l.9

l--i .""- ('l ""Sti !Nl; l Ot,,, l•,P t1l- UUl f->u T T ArlLt:. .. t . .. 7 4 ~ 4-, :,l) :> l :> ? :, J ., .. ',', <.Ul:l TO T CONSMP INVFST INV CH S I ~ r,u

I FIELD CR OP • 0 , (', , LI , l ' • 0 , I,) . o , (1 • 0 , 0 I 77 ,9 5 , 2 • 0 4,7 ,4

2 \/EGE TABLES . o , (1 • l) • lJ • u , u • u • 0 • 0 I, 4 ? 15 ,6 45,A .o .o , 4

3 LIVt:.STOC"- .o • 0 l' • \J • 0 • u • 0 . o • 0 • 0 10 7 ,4 91, 0 .o c; ,h • n 4 OTHt:.w AGRI .o . o l. 0 . J • J • u • LI • 0 • 0 J.? 12 . 2 28,1 .o .o .n 5 FISHING .o . (1 • 0 • [! • u • u , l1 • 0 • 0 , (1 46,0 2,5 .o ,0 .n t, MEAT PROO • 0 • 0 • u • LI • 0 , () , (1 • 0 . () , q 20 , !l 321> ,7 .o ,l . ?

7 OAil<Y PR OD • 0 • 0 • u ,ti • 0 • i.) • (I .o , 0 I, 4 41. 4 1sg, 2 .o • I . c,

e- CANNING .o • 0 , (I . ,1 . o • u • 0 • 0 , 0 2 .0 10 . 2 J4 5 , 8 .o ) , ) , ?

9 GR AIN MILL • 0 • 0 • 0 o V • 0 • 0 . ,1 . o .o I , fl t,!l,7 2 1,1 .o • I • 3

10 BlVt:.l<AGES .u • 0 • l) , ll • u • 0 • 0 • 0 . o 2 , t, 23 ,t, 109, 3 .o I , 5 • 0

11 CTHtR FOOD .o • 0 • 0 • 0 . o • u , () • 6 , 0 I , H 43, I JI0,5 .o , b . ?

12 TEXT ILES . o ,(l • 0 . o • 0 • 0 • 0 .o • 0 , 0 1, 6 2,2 .o • I .o 13 APPA l<t::L • 0 • 0 • I • 0 • 0 • 0 • 0 • 7 • I 2 , h 7,9 32,5 .o I, 0 .o

14 ,.INING • 0 , (1 2 . ':, 2 , 4 l'+,S , ... 2 . 7 . o , O , ? 82 ,0 I. 7 • 0 • I , 2

15 FORt:.SHH • 0 , <) • u . ,1 • 0 • 0 • u • 0 , 0 • 0 303,8 , A .o • 0 .o It, LOG\JJNG • 0 • l' • 0 , () • 0 , 2 . (J .o , 0 • 0 4?8.7 .o I , 6 3,4 • 0

17 SAIIMJLLS • 0 , () '+6 . 4 L • ~ o4 I u, c I • t' I, 9 , 0 , 0 ?3 t>,9 5 ,4 4,7 2,7 .?

18 PLY•OOD • 0 • 0 2 t> . '-J 1 . 2 c • ci / , o , t' I • '+ , O • 0 107,3 1,5 .o ,8 • I 19 OTHE R wO OD • 0 • 0 ➔ o . 7 "·" ... l V • 2 I, I l, U , 0 • c; JI 7 ,4 5 , 5 I'>, 7 I•?. .?

20 FURNJTURf .o , I c , I ·" , (, ·" • 0 . 6 • I • 0 5,5 3t,,5 I, 5 ,? ?, 7

21 PULPMILLS . o • 0 • 0 • 0 • 0 • 0 • 0 .o • 0 ,0 50 , b .o .o ,? .o 22 PAPE'- MILL • 0 c • '+ • 0 • C • 0 • u • 0 23 , J I, 3 ,7 89,9 10,5 .o • l • 7

23 PAPt:l C MILL • I .3 • 4 . 3 • 0 • I • 0 o , 2 t,. ii 3,5 I 04, !l 19,4 .o ,7 ,7

24 PM IN T ING ... '+ , 2 • 0 , l • 0 • 0 • 0 11 '> , 3 40 ,7 62, A ?3 7, I t,t, . 8 .o I , I I ,A

25 INDUS CHEM . J • 0 • 0 , I • 0 ... • 0 l,8 • 0 3,7 90, 6 .o .o I• 3 , 3

2b OTHE R CHEM • 0 . 2 I. 3 • 7 , 5 • I I • t, '+ , I • 3 3,6 36,0 4,7 .o .2 • 4

2 7 Pf. TI-I OLEU~ 1,8 I. J r' , b I u . <+ J~.6 2U , 4 6 . 6 22 , !l J , 8 10 .J ?50 ,0 ?72,9 .o 2,1 A,A

28 GL.O SS . o • 0 • 0 • (1 • 0 • 0 • 0 • 0 • 0 • 0 30,3 .o .o .? • I

29 CEMt:tH • 0 . 2 t,J , o 5 7 , 7 2 1.9 41 . 7 5 , 5 ,4 .o ,5 ?40 .J 1.1 .o . 6 .)

JO FERR METAL • 0 , l '+, U ... . s • t, Jc,! c . O . o . n . a 95. I . 2 .o .2 • 0

31 NONFEt<METL • 0 • 2 I • J I. 3 . o c. • ..; , I • 0 . a • 0 12 ,6 • 0 .o -.1 .o 32 ALUMINUM . ,) • J 2 , 7 2 , 7 • 0 o . I . 2 . o • 0 , 0 ?58 ,7 .2 4,5 6. I • 0

33 t-<EAYY METL • 0 • 2 1,; . u 3 I. 0 d , 2 2 d , d 3 .'+ I, 2 . o . 2 118,J .5 39,0 . 5 .c;

34 LIGHT METL • 0 . u 2 . c i . f"I .1 l • 3 ... j. 5 • 0 . ) 120.1 .5 13,3 , 6 , I

35 NONEL C EOP • u • 0 , (J , l • 0 • u • 0 • 0 • 0 . o 3,0 .3 10.4 . s . 2

36 "'ACH TOOL . u • 0 . 5 • 4 • I . ':> • 0 l. 7 • 0 I I, 9 68 .6 2.q 2 .1 ,8 l ,4

37 INDU S EQP • 0 . 2 ... . 3 • 0 • 0 • 0 .o • 0 I. 4 32.8 . o 30,3 2,2 I • I

JE< E.LEC MA (>; • 0 '+ , M 2 , 3 c , 7 , I l • 7 , b • I • 0 ,? 24,7 . 8 4,5 , f, • I

39 Af.ROSPACE • 0 • 0 • u , t1 • 0 .u .o • 0 .o . n 29 .8 .o .o 20 • I • 0

40 "40T 0R'JEi-t • 0 • 0 • I • ('1 • I ·" • 0 .o • 0 • 0 4. I 19.2 21, 2 ,9 • 0

41 SHIP BLD G • 0 • n • 0 • C • 0 • u • 0 • 0 • 0 . o 10,2 9.5 .8 I. 3 • 0

42 OTHtR MF G .o . ':, .. ... ~. () . 5 ,: • LI , 6 I , J 3,2 24,7 75,7 I I. 6 !l ,S 2.9 I , fl

43 TRAN S SER'/ , 6 ... 7 I ~ • .J I v • c, -; • 6 lJ , O c • c< 4:> , I 12 , R 26,A c; I J, I 254,8 15.4 I • 4 12,?

44 ELEC co J. il 2 . 7 ·" . -i . 2 , t, • 0 6ti , 9 20 . I 42 .4 442 ,0 263 .1 .o .o c;. q

45 GAS co • J • I , I . o • 0 • 0 . c, d , J 1.g I O. l I 74 ,6 68.7 .o .o 3 , q

46 0TH UTIL S Id,':, . ~ • J . ": , J , (i • 0 I 1 .4 ... 7 5, l 65,4 157.1 .o .o s . o

47 COMMUN I CAT 1.-; J . 7 ... . ~ ':> . ,? l , Y 4 , ', • 7 f, :, . 7 49 , p, 172.7 4) 1,5 )49 .6 .o .o I I. 0

48 !<ES BLO • 0 , ll • 0 , l l • 0 , \J • 0 • 0 • 0 • 0 .o .o 1001.1 .o • 0

49 NONRE.S BLD • 0 , (1 • u • Ll • 0 , l) • 0 .o • 0 ,I) . o .o 422,6 .o IAfi ,2

50 t-<IGHWAY S .o • n • l) , ,\ • n • 0 • 0 • 0 • 0 • 0 • 0 .o .o .o .o 51 0 TH STRUC T • 0 • 0 , u • C • u • u • 0 • 0 . o . o • 0 .o 422.6 .o .o 52 MAINT EN ANC 2 . J J . 4 J • I , I , c' • 0 2J , 9 2 7.J 14,4 123.2 68.6 • 0 .o 3 ,g

53 Tt<AO!:. l • l :, • 0 t<v , I 4 ! • ~ l J , u J :, , l 11.-, 71. 0 21 . I 81. c; 754,0 1959 .2 148,8 J . I -3,2

54 FIN 1"45 i<E i , 9 S . 3 ,, • u 0 . 2 2 . 2 :, , r, , t< 7 7, 4 148 ,1 t,4,g 418 ,0 l l 15,5 .o .o A,7

55 SEFh' I CES 4 , ,:1 J o , 2 .J 7,'J J- . ..... "·" 2-, . J J.':, 35J . 9 17 4 ,7 23 1. s 1370,6 ?h 7 6 , 5 • 0 .a 12.1

':,6 sue r o TAL 37 , 0 7 t, , 7 J b I . c ( '+ 0 . :, l ut> , J Zt,o , Y 4 7 . I 93:, , 4 5 It> • il 197 , n 8<;3 4,6 I nc:; 02 .4 2175 .2 73, 2 270,0

57 IMP Of< TS J , 4 eS . 2 2 l " . c cO:, . ', 3 I , .:; I '-l .> , b .. o . J 29t. , '+ f, 9 . 7 218,c; t,52 5,4 3713 , 8 1 .. 7),4 .o 205,4

5e- II ALUE AD D 191 . J 7 3 I, <+ '- l, I• -' J I I . 2 I C" . I j~J . t, JJi:: . O 52 I !l, 0 I 4 I'+. 7 JcJ7 . q 2 14 23 , 8 ?2 00.3 . o .o 10%.0

59 TOT OUTP UT ,: 3 I • I ,:i 7 -l . l l O ~ I • I ( t,'I . ? ?. 1 c . 3 d50 , J 2 1-. . J 1:, .. 4:, , H 200 1. 2 L. 253,4 )64~) . & l h4 16,5 Jt,48 ,5 73.2 I 571 , 4

19>10 1o1ASHINGIQ11, I NPU T-OUfl-'UT fABLI:. SL OT'" E XPu~ r TOTAL

I FIE.LO C~GP 1.0 288 . 2 4 77. 4 ? VE.Gl:.TA6LE.S l • 7 20 7.7 '+ 7 l. <' 3 LIVESTOCK .d 4d.7 453.S 4 OTHER AGRI . 2 29 . 5 10 . 2 5 FISHING .o 2 .4 50."1 6 I-IE.AT PRJO I.I JS . o 384. l 1 OAII-IY PKOD 4.4 32 . 0 267. t:>

8 CANNING I. J 513.4 b74.<' 9 GRAIN Mill .9 13 ... 0 225 .1

10 tlE.VERAGES • l 308.6 443.1 11 OTHER FOOD 1,2 89.2 244 .8 12 TEXTILES .o 17. 5 2 1.4 13 APPAREL • l 16 l. I 202 .b 14 ~lNlNG 1.4 14 .<+ 9Y.8

15 FORESTRY .o I u . 7 31:,.3

16 LOGGING .o 261.2 694 . 9 17 SAWMILLS . 9 785 . 5 !03b.J 18 PLYWOOD I • 0 327. 7 438 .<+ 19 OTHER WOOD l • 0 324 . 9 4b5.9 20 FURNITURE .b 33 . I 80.l 21 PULPMILLS .o 1 b O. 1 210.9 22 PAPER MILL .5 2s1.1 JS2.8 23 .PAPBO MILL l • 4 40 1. 2 52d.2 24 PRINTING • 4 3S.'l 342.2

25 INDUS CHEM 2.6 26c'.3 357.1

26 OTHl:.R CHEM l • I 9.6 52.0 27 PETROLEUM 10.3 376.4 920.5 28 GLASS .o 5 . 0 35.6 29 CEMENT 2,5 8.8 260 .2 30 FERR METAL ,2 4 7 ,6 143,3 31 NONfERME.TL • 0 31,1 43.6 32 ALUMINUM • l 90<+ . 8 l l 7 4. 4 33 HEAVY ME.TL 2,4 so.a 212,0

34 LIGHT METL • 1 45.0 179.7 35 NONE.LC EQP c'. 0 9b .4 112.8 36 MACH TOOL ,3 76.7 152.8 37 INDUS E.OP • 4 18 8 . 3 255 .1 38 E.LE.C MACH .o 125 .4 156.l 39 AEROSPACE .o 2762.7 28 le'. b 40 MOTORVEH .u 3 1:lJ. 7 42Y.l 41 SHIP BLDG 15,9 4]9,4 4 7 7, 1

42 OTHE.R MFG 2,5 2':,7, 1 360, 1 43 TRANS SERV I 1,8 104 9 , 2 1857,9 4"4 E.LEC CO b.l 42. I 759,2 45 GAS co 2 ,4 l , ? 25 0 .8 46 0TH UTILS 2,0 2 . 0 231,5 47 COMMUNJCAT 38,6 42,9 673,b 48 RES BLD • 0 . o 100 1.1 49 NONRES BLD 140,S 20,0 769, 3 50 HIGHWAYS 272,3 • 0 272 , 3 51 0TH STRUCT 173, 7 260 , 0 85b,3

52 MAINTENANC 23.9 • 0 2 19.6 53 TR AllE. l S, I 1568,8 6445,8 54 FIN INS RE 21 , 5 437 . 5 2 001, 2

55 SE.RI/ICES 4':>,4 14 1:l , 1 4253,J

56 SUBTOTAL 81 J. 5 1411 5 ,4 36484 , 0 57 IMPORTS 396,8 • I 12315 ,l

58 VALUE ADD Yl2,7 810,<+ cb503 ,2

59 TOT OUTPUT 212:i.1 14%5,9 /5302.3

1985 WASt1!1'lGTON i~JPUf-OUT PU T TABLE 2 l 4 5 6 7 8 9 10 l I 12 13 14 15

l FIELD Cf<OP 22. l 6.0 l 0'>. 7 I• J .o .o .u .o 45.A 9.A 2.0 .o .o .o .o 2 \IE.GET ABLES .o 5 . ?. J. 8 • 0 .o .u .] lBc.8 .o 5.6 47.3 • 0 .o .o .o 3 LIi/ESTOCK .o • 0 49. 2 • 0 .o 14::>. 0 127.5 .2 .a .o 3.3 .2 .2 .a .o 4 OTHER AGRI .o l .4 .H 4.] .o .u .o .o .a .o • l .o .o .o l • 0 5 FISHING • 0 . o • 0 .o • I .o .o 4tl.4 .o .o .o .o .o .o .o 6 MEAT PROD .o .o .o . o .3 <;. ':, • 0 .9 6.4 .o 1.0 .o .o .o .o 7 DAlf<Y PROD .o .o .o • 0 • I .3 32. I 2.3 .o .o 1.8 .o .o .o .o 8 CANNING .o .o .o .o • I .o l • l 4. c 1.1 .o l. l .o .o .o .o 9 GRAIN MILL .o .o 45.3 . o .o .2 .o ':>.4 10.J .o 10.3 .o .o .o .o

10 1:!Ellt.RAGES .o .o .o .o .2 .o • l .o .o 2 l. I l • 0 .o .o .o .o

11 OTHER FOOD .o .o 4.b .o .J 1 • 8 l.6 l U, l 2.1 10.J 9.2 .o .o .o .o

12 TEXTILES • l • I .o .o .6 .o .o .o .a .a .o .o .2 .o • I

13 APPARlL 2.6 .o .o .o .o .u .o .o .4 .4 .o .o .0 .o • 0

14 MINING • 4 .3 .o • 0 .o • 0 .o .o .o .o .6 .o .o 1.4 .o 15 FORES TRY .o • 0 .o • (J .o .u • 0 .o .o .o .o .o .o .o 12.s

16 LOGGING .u • 0 .u .o .o .o .o .o .o .o .o .o .o .o .8

17 SAWMILLS .o .o .o • 0 • 0 .o • 0 .o .o .o ,0 .o .o .4 • I 18 PLYIIIOOD .o • 0 • 0 .o • 0 • 0 .o .o ,0 ,0 .o .o .o .o .o 19 OTHER WOOD .o 2.6 .o • 0 .o .':, .o 1.4 ,0 .1 ,2 .o .o .o .o

20 f URN IT URE • 0 .o • 0 • 0 .u .o .o .o .o .o .o .o .o .o .o

21 PULPMILLS .o . o .o .o .o .o • 0 .o .o ,0 .o .o .o .o ,0

22 PAPER MILL .o .4 ,4 .o .o • 0 • 0 .o .o .o • 4 .o 2.B .o .o

23 PAPl:lD MILL ,2 l,4 ,0 .o .o .. • l 7,7 14, l 2.6 10.9 4,2 .o • l .2 .o

24 PRINTING .u .o .o .o .o • 4 .o l • 0 .o .9 .6 .o .o .o .3

25 INDUS CHEM 20.1 l -, • 3 3.9 2.2 .2 .4 .] ,4 .o ,? 1,0 .o .o .s I. 1

26 OTHER CHEM .o .o 1.2 • l .o ,3 • l .o .o .o .o .o .o .o • l

21 PETROLEUM 6.0 2 • h 3,4 1,9 4. H . ':, I.I .1 • ;> .2 .5 .o .2 .9 .5

28 GLASS .o • 0 ,0 .o .o .4 .o d.3 .o 26.4 .5 .o .o .o .o

29 CE.ME.NT • I .3 .2 .o • 0 • I .o .o .3 .o .o .o .o 2.3 .6

30 FERR METAL ,5 .o • I • l .o .o .o .o .o .o .o .o .o .2 .o 31 NONFE.RMETL .o .o • 0 • 0 • I .o .o .o .o .o .o .o .o .o .o 32 ALUMINUM .2 .o • 0 .o .o .o .o .o .o .o .o .o .o .o .o

33 HEAIIY ME.TL .o .o .o .o .o .o .o .o .o .o .o .o .o .o ,4

34 LIGHT ME.TL . ':, • 4 • 4 .o .o .5 • 0 20.0 1.8 66.8 .6 .o .o • l .o 35 NONl:.LC EQP .o .o .o • (J .o .o .o .o .o .o .o .o .o .o • 0

36 MACH TOOL .o • 0 ,0 • 0 • l ob .o .9 .o .2 .o .o .o .5 .o

37 INDUS EOP .o • 0 .o .o .o .4 .o l.5 .o .o .o .o .3 .o .o

38 ELEC MACH .o • 0 .o .o .o • J • 0 .o .o .o .o .o .o .o .o 39 AEROSPACE .o .o • 0 .o .o • 0 .o .o .o .o .o .o .o .o .o

40 MOTORIIEH .o .o .o .o .o .o .o .o .o .2 .o .o .o .o .o

41 SHIP BLOG .o .o .o .o J.o .o .o .o .o .o .o .o .o .o .o

42 OTHER MFG .o .2 .o .o .2 .2 • 4 .0 .o .1 .3 .o .o .3 .2

43 TRANS SERI/ ::; • 2 4,4 9,4 .6 I• 7 d,J I, 3 22.9 4.8 7.4 2.0 .2 .3 .1 3.8

44 t::LEC co c.5 l, 7 I • '-J .2 • l l. l 1,3 J.5 .s 1.2 4.6 .3 .o • 1 .o

45 GAS co .o • 0 .o • 0 • 0 .5 1.0 5.6 .o l • 3 4.2 .3 .o • I .o 4b 0TH UTILS J,6 2 . I 2.-z 1 ,4 .o .J • I .1 • 0 .4 .4 .o .2 .3 .o

47 COMMUN I CAT 2.s 3.9 2. ':, l. 0 .2 l • ':, l, O 2.8 .6 l • o 1.2 .4 l.4 .4 I•;>

48 RES BLD .o .o • (J • 0 .o .o • 0 .o ,0 .o .o .o .o .o .o 49 NONHES BLD .o • 0 .o .o .o .o .o .o .o .o .o .o .o .o .o

so HIGHl!,AYS .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 51 0TH STRUCT • 0 .o .o .o .o .o .o .o .o .o .o .o .o .o .o

52 MAINTENANC o.6 4.5 J,4 .s .o ,J ,2 .3 .o .2 .2 .o .2 .s 2.1

53 HlAOE 2<'. • d 26,7 l 9. 7 J.4 ':,. 0 I• S 7.8 3J.S ti.9 11.4 7,4 .4 3.4 1.6 5.4

54 FIN INS RE '+ • b 2. 7 2.2 .4 .9 .9 I • 8 1.9 .6 2.3 1.5 .o .8 .9 .6

55 SERVICES 21.J l I, 2 ., ... "2 • H 1.2 ':,,3 8.4 }9,7 s.o 7.6 6.b .2 2.9 2.5 1.8

56 SUBTOTAL 127.9 9 5 .4 268.b 2'.l .4 I 'i,2 l 91 , l 195,2 400.2 93.9 193.1 l 14 • 0 1.8 13.9 14.0 33.2

57 IMPORTS 59.4 2.i.9 77.3 lV.J 3.o 156,S 27.9 91.4 94.o 95.o 53.4 7,4 119.3 2s.5 4.9

58 1/ALUE ADD 33':,. 2 43 2 ,4 138,l 4..1, 7 31, 2 7U,4 50.9 294,6 62.2 230•4 94.6 15. l 109.0 65.3 306,6

59 T01 OUTPUT :>,:'.<'.. 5 SSI. 'l <o 84. I 74 ... ':,J.4 41&.o ?7J.9 78b,2 250.1 518.5 262.0 24.3 242,3 104.A 344.7

1985 wASHJNGTON INPUT-OUTPUT TABLE 16 l 7 18 19 20 cl 22 23 24 25 26 27 28 29 30

l FIELD CROP .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 2 VEGETABLES .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 3 LIVE.STOCK .o .o .u .o .o .o .o .o ,0 ,O .o .o .o ,0 .o 4 OTHER AGRI .o • 0 ,0 .o .o .o .o .o ,0 ,0 .o .o .o .o .o 5 FISHING .o • 0 • 0 .o .o • 0 • 0 .o ,0 .o .o .o .o .o .o 6 MEAT PROO .o • 0 ,0 .o .o ,o .o .o .o ,0 .o .o .o .o .o 1 DAIRY ?ROO .o .o .o .o .o .o .o .o .o .o .o .o .o .o .o 8 CANNING .o .o .o .o .o ,o .o .o .o .o .o .o .o .o .o 9 GRAIN MILL .o .o ·" .o .o .o .o .o .o .o .o .o .o .o .o

10 BEVERAGES .o .o .o .o .o .u • 0 .o .o .o .o .o .o .o .o 11 OTHER FOOD .o .o .o .o .o .o .o .o .o 1.0 .o .o .o .o .o 12 TEXTILES .o .o .o .o ,3 .o .o .o ,0 .o .o .o .o .o .o 13 APPARE.L .o ,o .o .o .o .o .o .o .o . () .o .o .o .o .o 14 MINING .o .o ,0 .o .o • l • I .3 .o ,4 ,0 .2 2,5 22.1 .o 15 FORESTRY <'.38,7 76,4 5,4 .o .o .o .o .o ,0 .o .o .o .o .o .o 16 LOGGING 6<+.3 251.0 85.0 9.7 .o 14 .4 13.9 lb.6 .o .o .o .o .o .o .o 17 SAWMILLS 7.4 46.3 16. l 6tl. 3 3.2 20.!l 8.6 lU.6 .o .o .o .o .o .o .3

18 PLYWOOD 5.6 8.0 18.3 22.2 .3 '7.9 .o .o .o .o .o 4.9 .o .o .o 19 OTHER wooo .2 27.l • 7 15,8 2.0 .o .2 1.0 .o .3 .3 .3 .3 .5 .2

20 FURNITURE .o .o .o .o .4 .o .o .o .o .o .o .o .o .o .o 21 ·PULPMILLS .o .o ,J .o .o l • l 15.9 36.2 .o 2.0 .o .o .o .o .o 22 PAPER MILL .o • 0 .2 ,2 l. 7 • I .9 42.0 11.0 .2 .o .o .o .o .o 23 PAPBO MILL • l l.S 1.0 .9 .4 .5 1.0 19.8 .3

2 ·" .3 .5 2.0 1.5 .o

24 PRINTING • l .o .3 .2 .o .o • l • l 5.2 ·2 .4 .o .o .2 • l

25 INDUS CHEM .3 .o .3 .o .o l t'.. l 9.0 2.3 .o 19.? 1.4 3.0 .o .o .5

26 OTHER CHEM .3 l. 0 4.5 .4 .3 .o .2 t'..3 .4 1.4 3.2 .2 .o .3 .2

27 PETROLEUM 4.0 2.4 • 4 .b .2 l<'..2 7.9 .1 .2 1.5 .2 10.2 .3 3.1 .2

28 GLASS .o .o .o .o .o .o .o .o .o .2 .o .o .o .o .o 29 CEMENT .o .o .o .o .o .o .o .o .o .o .o .2 .o 48.4 .3

30 FERR METAL .2 • l • l .2 .o • l .2 • l .o .o .o .3 .o .o 1.9

31 NONFERMETL • 0 .o .o .o .o • <' .3 .o .o .o .o .2 .o .o • l

32 ALUMINUM .o .o .o 1.2 .2 .o .o .o .o .o .o .2 .o .o • 0

33 HEAVY METL .I) 1, 2 • l .2 .o • <' .3 .o .o .o .o .6 .o .o .2

34 LIGHT METL 1.6 .5 • l l • l • 1 .4 .4 .o .o 2.1 .0 .2 .o .3 • l

35 NONE.LC EOP .3 • 0 .o .o .o .o .o .o .o .o .o .o .o .o .o 36 MACH TOOL 3. b 1.2 • 4 .2 .2 .o .o .o .o .o .o .o .o .2 2.1

37 INDUS EOP .o l, 9 • 6 2.5 .o .3 1,5 .6 .o .A .o .9 .o .2 .o 38 ELEC MACH .o , 3 ,2 .5 .o .o ,o .o .o .6 .o .o .o .o .o 39 AEROSPACE .o .o • 0 .o .o • u .o .o .o .o .o .o .o .o .o 40 MOTORIIEH .o .o .o • 0 .o .o .o .o .o .o .o .o .o .o .o ~l SHIP BLOG .o • l ,0 .o .o .o .o .o .o .o .o .o .o .o .o 42 OTHER MFG ,3 .o ,2 1,2 2,8 .o .o .2 ,0 .9 .2 .2 .o .o ,2

43 TRANS SERI/ ... 7 39,0 18,B 15,4 ,b 7. I 11,0 16.l 6.4 6.2 .s 8.4 ,1 13•2 2.3

44 ELEC co .o .8 I • 6 .o .o .J.2 3.2 8 .5 .a 6.9 .3 5.0 .5 3,9 3.2

45 GAS co .o ,7 1,5 .o • 0 1.1 5.7 5.2 .o 11.0 .4 15.7 1.0 4.5 l , A

46 0TH UTILS .o I, 3 • l .2 .o ~.1 .7 1.9 .2 .A .o 1 • 7 .o .3 .2

47 COMMUN I CAT 1.6 1. 6 I , I t'.. 7 .6 .4 l ,4 c..5 6.2 1.9 .1 1.5 .o 2.0 .7

48 RES BLO .o • 0 • 0 .u .o .o .o .o .o .o .o .o .o .o .o 49 NONRES E!LO .o • 0 .o .o .o .o • 0 .o .o .o .o .o .o .o .o 50 HIGHiiAYS .o .o • u • 0 .o .o .o .o .o .o .o .o .o .o .o 51 0TH STRUCT .o .o .o .o • 0 .o .o .o .o .o .o .o .o .o .o 52 MAINTENANC 1.6 2,Y .3 ,6 .3 .7 2 • l <' ,4 .1 2,9 .2 3.2 .2 1.5 .3

53 TRADE lb,O 35,5 19,4 25 ,0 2.6 .. ,6 8,5 19.0 1.0 6.7 1.4 4,0 ,6 6,6 1.1

54 FIN INS RE 5.d 5, q <' .2 t'.,5 ,4 ,8 1.8 2.1 1.1 l.4 ,3 8.4 .2 1.6 .1

55 SERIIJCES 2t'., 0 21,5 7,0 lU,2 t'..7 5.b 7 ·" l '+ • 7 15.9 2'+.6 2.9 5.8 .6 lo2 3.'I

56 SUBTOTAL :nY.O 52!:I. 3 186.7 I At'., 0 19.l l l l • 6 l 08, 3 20<+.9 61 .a 95.5 13.8 76,0 8.9 112.2 21.2

57 IMPORTS 48,2 87,4 89,H 18'1 ,6 21 • I 3'1.H 98.3 l Zt'. .2 10.1 67.n 19.2 823.5 4.4 39.3 ?8.3

58 1/ALUE. ADD 338,l 50t>,8 l 'ltl. 5 c.04, l 4J.4 BI .2 156.5 267.9 259.4 266.3 21.2 222.0 29.l 129.3 93.7

59 TOT OUTPUT 765, 3 1122,5 475.0 ':, 7':J, 7 8J .6 238.6 163,0 595.0 391.9 428.9 60.2 1121.s 42.4 200.0 149.2

l FIELD C~OP 2 VEGETABLES 3 LIVESTOCK 4 OTHER AGRI 5 FISHING 6 MEAT PROO 7 OAlf-lY PROO 8 CANNING 9 GRAIN MILL

10 BEVERAGES 11 OTHER FOOD 12 TEXTILES 13 1WPAREL 14 MINING 15 FORl:.STR'r' 16 LOGGING 17 SAWMILLS 18 PLYWOOD 19 OTHER WOOO 20 FURNITURE 21 PULPMILLS 22 PAPl:.R MILL 23 PAPt:IO MILL 24 PRINTING 25 INDUS CHEM 26 OTHER CHEM 27 PETROLEUM 28 GLASS 29 CEMt.NT 30 F ERk MET AL 31 NONF'ERMETL 32 ALUMINUM 33 HEAIIY METL 34 LIGHT METL 35 NONELC EQP 36 MACH TOOL 37 INDUS EQI-' 38 ELEC MACH 39 AEROSPACE 40 MOTORVEH 41 SHIP BLDG 42 OTHER MFG 43 TRANS SERV 44 ELEC CO 45 GAS CO 46 0TH UTILS 47 COMMUNICAT 48 RES BLD 49 NONRES BLD 50 HIGHillA'r'S 51 0TH STRUCT 52 ,_.AINTENANC 53 TRAUE 54 FIN INS RE 55 SERVICES 56 SUBTOTAL 57 IMPORT S Sf; 1/ALUE AO() 59 101 oun-•u r

JI

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1 • 0 .o • l .o .o • 1 .o .o .o .o • l

1.2 • 4

1 • 0 .o • l .o .o .u .u .o .6 .o .6

7.0 o.'+

I 'I. o 35 . 2

3?.

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t> ,4

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2 , 9 254,5

. J , b • 0

3 . 2 l. q

. 9

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19 , I 5),4

5 ,4 .7

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l. 6 I O • .l

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J7H · " 555 . 5 '+u S . tJ

J 33'1 . Y

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.o • 0 • 0 • 0 .o • 0 . o .o • 0 .o • 0 . o • 0 .u .o .o .o .o .o .o • 0 .o .o • 0 .s ,4 • 0 .o

14,4 ,b

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• 0 2,7

• '-J

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4.4

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.... 5 • 0 .o .o . o . o . 3

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c. l .o .o . o . o .I

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J 9t>. I

1985 wASHINGlON INl-'Ul-OUfPUT lABLt. JS J6 37 38 39

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6.0 .o . o . 2 . 9

1. 5 J.4 .s .1 .o . 6 .o .4 . 6 .6 .3 . 2

l • 7 .o .o .o • 0

l. 3 <' .4

. 6 J.7

21.1 41. 7 t, I. h

I JU • 4

.o • 0 .o .o .u .o .o • u .o .u .o .o • 0 .o .o .o • (i

• 0 .o .o .o .o .u .o .J .o . 3 .o .1

i:,. ~

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l • l .'I .b . 2 • 0

c'. • . ,

• 0 .o .o .o .o

J.-;

ob J.o

3<:: .9 4tl .4

I OU .ll lA<'.. I

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.ll • 0 .o .o .o • 0 .o .o .o .o .o • 0 .o .o .o .2 .o .8 .o .o .o .o .2 . 3 . 2 .3 .o . 2

6.2 . 2

4.0 2 .4

I • 1 .o

8.5 13.7

2 .1 .o .o • 0

l • l . 9 .8 . 2 .2

4. 7 .o .o .o .o .4

8 , 2 I, 3 6,2

64,6 94,2

160,9 ) 19,7

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1.4 .o

c.3 I, 2 c,8 4, 0

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l. 9 .o .o .o .o .3

J,9 .6

c'.,9 2-1. o h I. 9 91. 2

I 8<'. . I

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.o

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.o • I .2 .o .o .1 .o .o .1 .o .9 .3 .o • I

4,4 .5 .2

1,9 4,3

.o ,4

I , 1 .o ,8

),8 4 • l

.o 11,4 ... 3 4,1

27,9 .o .2

7. J .9

6,3 2.1 2.2

17,A • o .o .o .o

1,4 8,5 5,9

109,9 235,5

1503,7 1504,0 3243,2

40

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.o

.o

.o

.o

.o

.o

.o

.o

.o

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.o

.o

.o

.o

.o 1,4 1,7 ,6 .o .o • l .3 .3 .1 .3 .1 .o .o

12,A .1

1,9 l • 2 l,S .o

l • 7 .2 .o .o

3,2 .o ,8 .9

2.2 .s .3

5,9 • o • .o .o .o .2

5,9 l • l '+ ,R

5<' • l 325,S 137 •? 514,9

41

.o

.o

.o

.o

.o

.o

.o

.o • I .o .o • l .3 .o .o .o

2.8 1.6 .3

1.2 .o • l • l .2

l • l ,8

2.1 .o .2

l • O l • l .2

l • 7 1,9 l • 4

.8

.2 ,9 .o .o

5,4 .9

2,9 1.0

• l .3

2.0 .o .o .o .o

l • l 14. 0 1.3 7,6

57,1 133,8 348,9 C,39,7

42

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.8

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.o

.2

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4,3 .6 .o .1 .o .2

1.0 .o

4.4 .o

2. I ,6 ,5 .o .o .o

12.2 5.1 1.2

.2

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.o

.o

.o

.o

.6 A,8 l,R 9,0

61,6 140,7 ?.62,0 464,3

43

.o ,4 .o .o .o

2.2 .6 .1 ,3 .8

1,4 .2 .2 .3 .o .o .3 .o .6 .o .o .5 .3

1.4 .4 .o

138,8 .o .2 .2 .o .o .o .2 .o .9 .o .o

2.s .3

2.9 .2

158,7 10.5

.6

.7 25,3

.o

.o

.o

.o 10.1 29.3 25.6 ss.2

422.5 201.1

1555,6 2185,2

44

.o

.o

.o • l .o .9 .s • I .o .o

l • 3 • l .o

25,8 • l .o

1,4 .o .o .o .o • l

1,7 1,7 .o .3

l, l .o .2 • l • l .o • I

1,9 .2 • l .9 .c; .o .o .o • ?.

4,2 179.0

.5

.3 5,5

.o

.o

.o

.o

.1 3,1 3.6

28,?. 264.7 54, I

4AA,5 R07,3

45

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o

.o • l .4 .o .o .3 .o .o .o .o .o .o • l .o .o .o .o .o .o .o • I ,4 .6

7q,1 • I ,R .o .o .o .o • l .7 .9

2.0 85,R A7 .6 Q2,3

265,7

1985 WASnJNGION lNPUl-OUlPUT TA8LE. 46 47 4A 49 !:>O !:>I 5? !:>3 54 55 SUBTOT CONSMP INVEST INV CH SL EOU

l FIELD CROP .o • 0 .o . o .o .u .o .o .o .o 193.7 5.9 .o 2.6 .4

2 \IE.GET ABLES .o .o .o .o .o .u .o .o .o l.6 ?47.0 52.2 .o .o .4

3 LIVE.STOCr< .o • 0 • 0 • 0 .o .o .o .o .o .o 325.6 93.B .o 1.0 .o 4 OTHER AGRI .o .o 1.0 .3 .3 .o .o .o .o 3.5 12.B 31 .6 .o .o .o 5 FISHING .o • 0 .u .o .o .o .o .o .o .o 48.5 2.5 .o .o .o

6 MEAT PROD .o .o .o .o .o .o .o .o .o 1.0 23.2 363.2 .o .2 .2

7 DAIRY PwOD .o • 0 .o .o .o .o .o .o .o l.4 39. l 195.0 .o .o .s B CANNING .o .o .o .o .o .o .o .o .o 2.) 11.3 166.2 .o 3.2 .3

9 GRAIN MILL .o .o .o .o .o .o .o .o .o 1.2 73.5 23.B .o • l .4

10 BEVERAGES .o .o .o .o .o .o .o .o .o 2.R 26. l 124.6 .o 1.1 .o 11 OTHER FOOD .o • 0 • 0 • 0 .o .o .o .1 .o 2. (l 47.4 123.5 .o .4 .2

12 TEXTILES .o .o 0 .o .o • 0 .o .o .o .o 1.8 2.4 .o • l .o 13 APPAREL .o .o • l .o .o .o .o .9 .2 3.n 9. l 37.4 .o .R .o 14 MINING .o .o c'.5 i.6 14.4 I 0 4 3.1 .o .o .2 86.4 1.9 .o .o .3

15 FORESTRY • 0 .o .o .o .o • 0 .o .o .o .o 333.l .9 .o .o .o 16 LOGGING .o .o • 0 .o .o • <' .o .o .o .o 456.0 .o l.B 2.0 .o 17 SAWMILLS .o • 0 49.6 J.c' .4 l U. 3 2.0 i.2 .o .o 263.2 6.1 5.4 1.9 .3

18 PLYWOOD .o • 0 29.0 d.J J.o ti. 3 .9 1.7 .o .o 123.7 1.8 .o .6 .2

19 OTHER WOOD .o .o 52.'1 lU. 8 .4 11. 3 1.4 1.3 .o .6 139.6 7.0 20.0 1.0 .2

20 FURNITURE .o .2 2.0 .ti .o • 1 .o .1 • 1 .o 5.6 39.6 1.6 • l 2.8

21 PULPMILLS .o • 0 • 0 .o • 0 .o .o .o .o .o 55.5 .o .o .3 .o

22 PAPER MILL .o 2 .'I • 0 .o .o .o .o 2b• 0 1.4 •B 99.1 11.5 .o • l .1

23 PAPBD MILL • l .3 .4 .3 .o • 1 • 0 ti.a 1.3 3.7 111.8 21.0 .o .6 .8

24 PRINTING .5 5.J • 0 • 1 .o .o .o 135.5 46.7 72.6 276.4 77.3 .o .0 2.0

25 INDUS CHEM .4 .o .o • 1 .o • 4 .o ,: .2 .o 4.4 107.9 .o .o 1.3 .3

26 OTHER CHEM .o .2 1.3 .8 .5 .7 1.9 4.8 .3 4.2 41. 5 s.s .o .2 .4

27 PETROLEUM <'. 2 1.6 9.3 12.0 20.4 21 .6 a.o 21.1 4.5 12.3 293.3 327.0 .o 1.4 10.2

28 GLASS .o .o .o .o .o .o .o .o .o .o 35.8 .o .o .2 • l

29 CEMENT .o . 2 64.8 64 .'-l 22.2 4J.o 6.5 .4 .o .6 259.3 9.0 .o .3 .3

30 FERR METAL .o . 2 3. <; '+. 7 .5 30.7 2. l .o .o .o 98.8 .2 .o • l .o

31 NONFEkMETL .o .2 l. 3 1. 4 .o c'. 9 .2 .o .o .o 13.7 .o .o -.3 .o 32 ALUMINUM .o .o 2.8 t.9 • 0 c:, • l .2 .o .o .o 293.S .2 5.3 4.5 .o 33 HEAIIY METL .o . 2 I 8. 5 40. 6 8.1 2';. 0 4.0 I. 4 .o .2 126.3 .6 45. l .3 .6

34 LIGHT METL .o .o 2.2 2.0 .1 1.2 .4 1. 7 .o .3 135.2 .s l 5 • l .4 • l

35 NONE.LC EOP .o .o .o • l • 0 • 0 .o .o .o .o 3.5 .4 12.3 .4 .3

36 MACH TOOL .o .o .5 .4 • l .5 .o 2.0 .o 1J.5 79. l 3.3 2.4 • f, l • t,

37 INDUS EOP .o .3 .4 .4 .o .o .o .o .o 1.6 38.7 .o 35. l 1.a 1. 2

38 ELEC MACH .o 6.1 c' .4 J.o • l 1.1 .7 .2 .o •2 28.B .9 5.4 .5 .2

39 AEROSPACE .o • 0 .o .o .o .o .o .o .o .o 34.4 .o .o 18.3 .o

40 MOTORVEH .o .o • l .o • l .2 .o .o .o .o 4.7 21.9 24.6 .a • 0

41 SHIP BLDG .o .o .o .o .o • 0 • 0 .o .o .o 11.6 10.9 .9 1.2 • 0

42 OTHER MFG .o .7 4.8 '+.8 .6 c. l • 7 9 • 1 3.9 30.3 94. l 14.2 10.6 2.8 2.2

43 TRANS SERV .6 5.1:l 15.7 l l • 9 ,i. ':, lJ.7 3.2 52.2 14•5 30.S 580.8 276.3 17.B l • l 13.4

44 ELEC co 3.3 3. l .0 .9 .2 • t, .o 73.7 2106 45.9 466.5 285.3 .o .o 6.2

45 GAS co .3 . 2 • I .o .o .o • 0 'l. 2 2.0 ll .2 183.3 74.5 .o .o 4.0

46 0TH UTILS 21. 8 1 • 1 .3 .s .3 .o • 0 )3.5 S.4 5.9 76.5 179.1 .o .o 5.7

47 COMMUNJCAT c'.4 s.o 5 ,6 6. J 2.0 ~.4 . 8 10'1.5 62ol 211.0 543.0 418.8 .o .o 13.3

48 RES BLO .o .o .o . o .o ,(J .o .o .o .o .o .o 1034.0 .o .o

49 NONRES BLD • 0 • 0 .o • 0 .o .o • 0 .o .o .o .o .o 412.S .o 2?9.9

so HIGHWAYS .o • 0 ,0 . o .o .o .o .o .o .o .o .o .o .o .o

51 0TH 5TRUCT .o .o .o .o .o .o .o .o .o .o .o .o 412,5 .o .o 52 MAINTENANC 2.6 4.2 .3 • l • l .2 • 0 21.1 JO.a 16,4 140.2 78.2 .o .o 4.3

53 TRADE I. 3 6.2 82.2 4!:>.-, 12 .9 Jti. J 13.7 Ac.2 2308 92.7 f!J9.J 4513.6 172.3 2.4 -3.5

54 FIN INS RE 2.() 6.3 S.8 6.4 <'. 0 :i.3 .9 a:i.2 159.2 10.1 451 .4 1271. 7 .o .o 9.2

55 SERI/ICES :, .5 44.8 Jt:! .9 Jd.2 d. 7 2'i,':, 4 .1 40'1.6 197.4 210.3 1556.6 305 l .4 .o .o )3.4

56 SUBTOTAL 43.0 95. l J99.8 274.5 lQb.9 210.<; 54. 8 1087.8 581•2 92406 9547.0 11933.0 2234.7 56.S 323-1

57 IMPORTS 3.4 t:!O .4 216.3 ;>21 • 7 3:,. 6 l9c.4 4':>.5 JJJ.4 Bl .4 231,0 7446.9 4274.l 1702.6 .o 2?5.4

58 IIALUE ADD cl9.5 904.6 41 tl. 0 J4tJ.3 120.6 39tl.<' l 51. 6 6039.tl 1598,1 3684.6 24417 0 8 2508.4 .o .o 1207.7

59 TOT OUTPUT c6:i.8 1080.l 1034.0 H44 05 2t>'-J . 2 dt,!.:i 252,0 7461. 0 2260.8 4840.2 41411.9 1A715.6 3937.J 56.5 17<i6.2

l '-11:l'i WASHINGTON I NPU T- OUT PUT TA BLE. '.>L 0 TH t.XPORT TOT o!, L

1 FIEL D CR OP j. 2 J l fJ . fl 522 . b 2 VEGt.TABLE S 2. l 250 .1 55 l. 8 3 LIVt. S lOCK j • 0 6? . ll 48 4 . 2 4 OTHt.R AGRI . 3 24 . 5 74 • Z 5 FISHING .o 2 . 4 53 . 4 6 MEAT PROO l • 3 30 . 0 4\tl 0 l 7 DAIR Y PROD 4. 9 34 . b z 1 ... 1 8 CANNING 1. b b03 . 6 78ti . 2 9 GRAIN MILL l. 0 15 1. 2 250 . 0

10 BEV ER AGES • l 366 . 4 51 tl, J 11 OTHt. R FOOD l . 5 b9 . Z 262 , 2 12 TEXT ILES .o co. o 2 4 . J 13 APPAR EL • 1 194 , 9 <'4 2 . J 14 MINI NG l , ti l 4, 4 l 04 . 8 15 FORE. STRY .o 10 ,7 344, 7 16 LOGGIN G .o 304,9 76', , 5 17 SAIIIMILLS l • l tl44,7 11zz. , 18 PLYIIIOOD 1. 3 347,4 4 75 , 0 19 OTHE. R WO OD 1 • 4 40 6 . J 5 75 , 5 20 FURNITUR E .7 33 . 1 bJ,5 21 PULP MILL S .o 182 , 9 2 3 1:1 , 7 22 PAPER MILL ,b 251 .1 363 . l 23 PAPtlO MILL l. 6 45 9 . 0 594 , 8 24 PRINTING .5 35 , 0 392 . o 25 INDUS CHEM j .4 3 16 . 3 429 . <' 26 OTHE R CHEM l • 4 10,9 59 . 9 27 PETROLEUM 1), 3 47f,. 2 l 12 l , 4 28 GLA SS . o h . 3 42 , 4 29 CEME.NT 3 . l 'l . fl 280 . il 30 FERk MET AL . J 4 9 .7 l 4-, . l 31 tl!ONF ER METL • 0 2 I. 7 35 .l 32 ALUMINUM . 2 1036 . J 1340 . 0 33 HEAVY METL 2 . 9 S5.9 23 l . 7 34 LIGH T METL • l 4 5 . 0 l 96 . 4 35 NON E.L C £ OP 2. 6 11 0. q 130 . 4 36 MACH TOOL . 3 94 . >l 182 . 1 37 INDU S EQP . s 24 2 . J 3 19 . b 38 E. LEC ~lACH . o 146 . 3 l8Z . l 39 AERO SP ACE .o 3 190 . S 3243.2 40 MOT OR VEH .o 462 . 9 5 14 . '>

4 1 SHIP BLDG 19 . 5 4 95. 7 539 . 1:1 42 OTHER MF G J. 4 33 7. 4 464 . 7 43 TRA NS SERV 14 . 6 128 1. 2 2 185 . 2 44 ELE C co T. l 4 2 . I 807 . 2 45 GA S co <' .1:1 I. 2 26S . 8 46 0 TH UTIL S <' . 5 2 . 0 265 • 8 47 COMMUN I CAT Sc .4 5 2. 5 1oeo . o 48 RES BLD . o . o 1034.0 49 NONRES BLD 182 .l 20 . 0 844 • 5 50 HIGHWAYS <' 69 .2 • 0 269 . 2 SI 0 TH STRUCT 189 .0 260 . 0 8 6 1 . s 52 MAINTENAN C 24 .4 . o 25i'. .I 53 TRAU E l ti . 6 19 18 . 7 746 1 .4 54 FIN INS RE 2s. 2 50 3.4 2260 . -. 55 SERVICES 55. 9 162 . 8 484 0 .1 56 SUBT OTAL c,23 .... 16394. 0 '+ 141,: . 4 5 7 JMPORl S 4 84 , 7 • I 14 133 . 'I

8 VA LUE ADO l l 2 J . 9 tlb6 . 0 0 l <''• . 0 q TO T OUTP UT 2532 . 5 1 7 ,! 0 . l r,',h l (J. I