(T (T

A STATISTICAL iiK^LYSIS OF TRj: Y:!:y:Z-y£^ Or VOLUME

-mx.-. CAP/CITY ON THE COST OF ( IMiT^^G COTTOu

CN THE HIGH Pl.AIHS 01 TE IAS

LOaiS SINGER GIA/^S, B , S .

AGRICULTURAL ECJOistOi-ilCG

S u b m i t t e d t o t h e G r a d u a t e F a c u l t } -o f T e x a s T e c h Vniver : - : i l :y m

P a r t i a l Uulf: . l l i - i \en^ c f t h e R e q u i r e n i e n t s toTi

t h e D e g r e e of

A p p r o v « a

/ ) ? - . . • " ' ' * / ' - • . > • • • • • • : / " ,

^05

ACKt^OWLEDGMSNTS

I am indebted to Dr. Mark L. Fowler

for his guidance as my committee chairman

and to the other members of my committee

who helped make this thesis a reality. This

research v/as financed by Cotton producers

Institute.

11

Ti^BLE OF C0NTENT3

ACKNOV^EDGMENTS ii

LIST OF TABLES V

LIST OF ILLUSTRATIONS vii

I. INTRODUCTION 1

The Problem 1

Objectives 4

Review of Literature . 5

Synthetic Cotton Gin Studies . . . 5

Statistical Cost Studies 8

II. TKEORSTICAL. CONSIDERATIONS 14

Short Run Costs 17

Long Run Costs 21

III. METHODOLOGY 26

Study Area and Sampling

Procedure 26

Source of Data 28

Analytical Procedures . 28

IV. FINDINGS 35

Sample Characteristics . . . 35

Volume and Capacity 35

Investmenc Costs . . . . . . . . . 37

Operating Costr Per Bale 39

Empirical K- Jsults of Gin Cr:st-

Outx^ut Relation^'lips 43 i i i

IV

The Model 43

Single-Battery Gins 47

TVo-Battery Gins 48

Three-Battery Gins 52

Four-Battery Gins . 59

All Gins 63

V . SUMMARY, CONCLUSIONS, A!!TD LIMITATIONS 6 7

Sumraary 67

Conclusions 71

Limitations 72

LIST OF REFERENCES 76

APPENDIX 79

LIST OF TABLES

Table Page

1. Mathematical Formulations of Cost-Output Relationships 15

2. Estimated Hourly Capacity, Seasonal Volume Ginned, for Sample Ginning Firms, 1963-64 through 1967-68 Seasons 36

3. Annual Volume Ginned, Estimated Seasonal Capacity, and Capacity Utilization for the Sample Ginning Firms, by Groups, 1963-64 through 1967-68 Seasons 38

4. Average Investment in Buildings, Machinery, Equipment and Land, Sample Ginning Firms, High Plains of Texas, 1963-64 through 1967-68, by Groups 39

5. Weighted Average Costs Per Bale for Sample Ginning Firms, by Item.s and Size Groups, High plains of Texas, 1963-64, through 1967-68 Seasons 41

6. Cost, Volum.e, Capacity Averages for all Gins and by Group Classifications . . 46

7. Estimated Annual Fixed Costs and Marginal Costs by Capacity per Hour for Single-Battery Gins, 1967 48

8. Estimated Total Fixed Costs per Bale, Total Variable Costs per Bale, and Total Cost per Bale for Selected Annual Capacities, by Hourly Total Gin plant Capacities, Single-Battery Gins, 1957 50

9. Estimated A>inual Fixed Costs and Marginal Costs by Capacity per Hour for Two-Battery Gins, 1967 5 3

V

vi

10. Estimated Total Fixed Costs per Bale, Total Variable Costs per Bale, and Total Cost per Bale for Selected Annual capacities, by Hourly Total Gin plant Capacities, Two-Battery Gins, 1967 54

11. Estimated Annual Fixed Costs and Marginal Costs by Capacity per Hour for Three-Battery Gins, 1967 . . . . 57

12. Estimated Total Fixed Costs per Bale, and Total Cost per Bale for Selected Annual capacities, by Hourly Total Gin plant Capacities, Three-Battery Gins, 1967 58

13. Estimated Annual Fixed Costs and Marginal Costs by Capacity per Hour for Four-Battery Gins, 1967 61

14. Estimated Total Fixed Costs per Bale, Total Variable Costs per Bale, and Total Cosr per Bale for Selected Annual Capaciii ies, by Hourly Total Gin plant Capacities, Four-Battery Gins, 1967 . > 62

LIST OF ILLUSTRATIONS

Figure Page

1. Fixed and variable proportions of total costs (panel A) and per bale costs (panel B) 19

2. Discontinuity of gin labor requirements 21

3. Short run total and average cost curves for four levels of ma cimum hourly output and the associated long run total cost curve and the economies of scale curve for gin plants with a specified number of batteries 24

4. The study area 27

5. Short run average cost curves for selected maximum gin plant hourly outputs and the economies of scale curve, single-battery gins, 1967 . , 51

6. Short run average cost curves for selected maximum gin plant hourly outputs and the economies of scale curve, two-battery gins, 1967 55

7. Short run average cost curves for selected maximum gin plant hourly outputs and the economies of scale curve, three-battery gins, 1967 60

8. Short run average cost curves for selec-ced maximum gin plant hourly outputs and the economies of scale curve, four-battery gins, 1967 64

9. Short run average cost curves end th-3 economies of scale curve, single-battery, two-battery, three-battery, and four-battery gins, 1967 66

vii

CKAPTFJ=v I

INTRODUCTION

The Problem

The cotton ginning industry has experienced some

major difficulties during the 1960's, particularly on the

High plains of Texas. Until recently, most of the High

Plains' cotton crop was hand-harvested, beginning sometime

in SeptemJoer and generally extending into December or

Janua2?y. Area gins, therefore, had approximately four

months to gin the crop. This method of harvesting allowed

sufficient time for existing gins to gin large annual

volum.es of cotton with equipment which produced a lov; rate

of hourly output.

During the latter part of the 1950's, however,

several changes began taking place vrhich were to have serious

effects on the cotton ginning industry. One of these changes

was the increasing cost and scarcity of seasonal laborers

necessary to harvest the cotton crop. This situation was

eased somewhat for a while by the importation of Mexican

Nationals dur-ing the fall to help with harvesting. But

labor from thj.s source was largely eliminated by Congress

in 1961. Under pressure from United States labor union.-::,

an amendment to the legislation regulating the Mexican

fcrm labor program v:as adopted, vrnich stopped the

importation of Mexican Nationals on a large scale bssis

(22). Almost immediately, cotton producers had to use

m.echanical harvesters to gather the major portion of their

crop.

Although some of the area crop is gathered by

mechanical ^'pickers," which usually make at least two

trips across the cotton field before the harvest is com­

pleted, most of the cotton crop is gathered by mechanical

»• strippers" which gather the entire crop in one trip.

In addition, because of the short growing season on the

High plains, the entire frost-free season is generally

needed for the cotton crop to have sufficient time to fully

mature. This means ordinarily none of the crop is gathered

until about two weeks after the first frost or killing

freeze of the season, and then it is all gathered as quickly

as possible to avoid the possibility of damaging weather

and deterioration in fiber quality.

The problemis created in the ginning industry as

a result of this method of harvesting are imrriediately

apparent. Instead of four months of a relatively even

flow of cotton being harvested and moved to area gins, there

is a two-month p iriod of harvesting with a large percentage

of the crop moved to gins in a tv/o-week period. Gins

become sv.-amped vdth cotton, creating a large backlog of

farm trailers on the gin yard. Growers are impatient to

get their trailers back as quickly as possible so tliat

their harvesting machines will not have to sit idle

awaiting the return of trailers. Because of the com­

petitiveness of the ginning business, ginners find it

necessary to comply with their customers' wishes; to

accomplish this, the number of bales ginned per hour must

be increased (in the absence of seed cotton storage). This

involves purchasing newer and faster machinery, which often

becomes obsolete before it is fully depreciated. Also,

stripped cotton brings with it more dirt, trash, green

bolls/ rocks, sticks, et cetera, than hand-pulled cotton.

This means that additional cleaning machinery must be

utilized and the entire plant is subjected to added wear

and breakdowns because of this foreign material.

Another major change which has affected the ginning

industry is decreased cotton allotments under government

acreage control programs. Although yields per acre have

generally increased, especially in the irrigated sectors

of the High plains, they have not increased enough to offset

tlie decrease in the number of acres planted to cotton.

Hence, the annual volume of cotton produced on the High

plains has declined in recent years.

The High plains ginning industry is, therefore,

characterized by large investments in miachinery and equip­

ment, short operating seasons, and 1O\T annual volumes of

output per gin. Each of these characteristics has con­

tributed to rising unit costs of gin operation. This is

a cause for serious concern by High plains cotton producers

as well as gin owners and operators, because increasing

costs of gin operation must be met by increasing charges

for gin services.

Objectives

Hie purpose of this study was to determine the

effects of annual volume of cotton ginned and gin capacity

on the costs of gin operations under present harvesting

methods and practices. It is generally accepted that by

increasing the annual volume of cotton ginned, and thereby

spreading fixed costs over more units of operation, that

the cost per bale of ginning will be reduced. It is not

generally known, however, how much costs can be reduced or

how much volume can be increased before reductions in per

bale costs become insignificant.

The specific objectives of this study were:

1. To determine the cost per bale of ginning

cotton associated with alternative volumes for specified

plant sizes, i.e., short-run plant cost curves; and

1

Annual gin capacity is the maximum number of bales which can be ginned during the harvesting season. This is determined by the gin's hourly capacity and the number of operating hours available during the season, adjusted by some specified percent of hourly capacity that can be attained throughout the season. Throughout this pajjer, gin plant hourly capacity, gin size, and scale of plant will be used synonomtously.

2. To determine the minimum cost per bale that

may be attained for alternative volumes when the size of

plant is permitted to vary, i.e., the economics of scale

curve.

Review of Literature

There are two general approaches to empirical

studies of firm costs—-the synthetic approach and the

analytic or statistical approach. The synthetic approach

involves studying the component parts of an operation and

then combining these parts into descriptions of present

or reorganized operations. Engineering data or data from

actual operating firms may be used. These data, combined

with input costs, give the cost of producing a specified

rate of output. The analytic approach involves subjecting

accounting cost data to statistical analysis, relating

costs to volume and other variables.

Synthetic Cotton Gin Studies

Gin cost estimates were made by Covey and Hudson

for two production areas of Louisiana by using a "modified

economic-engineering approach" (7, p. 8). investment and

operating costs of four new cotton gins of different sizes

were used instead of generating cost data from a pure

economic-engineering approach. This study indicated that

if sufficient cotton is available for gin operation at or

near capacity, ginning costs per bale can be expected to

decrease as gin size or capacity is increased.

Campbell estimated ginning costs for single and

two battery cooperative gins in Texas and California (5)."

Cost and other data were obtained by personal surveys of

gins in the Lubbock area of Texas and the San Joaquin

Valley of California. Models representing different plant

sizes were then developed from this data, and per bale

costs of ginning cotton were estimated for various annual

volum.es ginned. Costs per bale were found to decrease

substantially as volume increased in both single and

multiple battery gins. As v uld be expected, single battery

gins had lov;er operating costs per bale than two-battei"y

gins when equal volumes were ginned.

Costs of ginning for gins with capacities of

eight, ten, and twelve bales per hour were estimated for

gins located on the High Plains, Rolling Plains, and Lower

Rio Grande Valley of Texas by Thompson and Ward using

synthesized model plant costs (22). Data for the analyses

vjere obtained by personal interviews with gin mianagers,

equipment manufacturers, accountants, and others associated

with the ginning industry. Model costs indicate that

A battery consists of a set of equ-iprnent asso­ciated with one press, i.e., each battery is an independent ginning unit.

economies of size tend to diminish rapidly with increases

in capacity.

Metcalf, et al., developed synthetic models of

gins with capacities of three, four, six, eight, nine,

and twelve bales per hour of machine picked cotton (14).

Rates per hour of ginning were held constant for each model,

and the length of ginning season varied to show the effects

of annual volume on the cost per bale of ginning. For each

model, increases in volume resulted in decreasing costs

per bale, with costs declining more rapidly in the lower

volume ranges than in the higher volume ranges. Although

ginning costs per bale were lower for the larger gins for

larger volumes, the difference in cost at maximum, seasonal

volumes for the various models was very small.

Costs of ginning on the High plains of Texas and

their relationship to volume and gin capacity have been

estimated by Wilmot, Shaw, and Looney (23). A sample of

thirty-six gins was drawn from a stratified population of

371 High Plains ginning firms. Gins were stratified into

four groups of equal size, based on estimated hourly

capacities, and nine gins were drawn at random from each

group, personnel at each gin were personally interviewed

to obtain cost data for the 1965-66 and 1966-67 ginning

seasons. The average total costs per bale increased sub­

stantially as average volumes decreased from the 1965-66

season to the 1966-67 season for all groups. The authors

8

concluded that plant capacity must be utilized fully to

achieve minimum operating costs and that "if these condi­

tions of declining production, rising costs, and inadequate

revenues continue unabated, more and more gins will even­

tually be forced to close" (23, p. 16). Inefficient use

of labor was cited as a major cause of high gin operating

costs. The authors pointed out the possible need for

several old inefficient gins to combine into one modern

plant in some areas to insure greater utilization of plant

capacity and more efficient use of gin labor.

Statistical Cost Studies

Some of the following studies refer to specific

activities not related to cotton ginning. However, the

theoretical concepts of cost-volume relationships and the

methods of estimating these relationships are applicable

to many areas of study, including gin cost-volume

relationships.

Brensike and Askew analyzed the relationship between

average unit costs and volume in the feed mill industry (3).

Information was obtained by personal interviews with mana­

gers of a stratified sample of feed mixing plants. Plants

were stratified by volume and location. Costs of operating

feed mixing firms under operating conditions as they existed

at the time of the study were obtained. In addition to

volume as a percentage of annual plant utilization the

authors hypothesized that unit operating costs were also

influenced by the age of plant and equipment and by

management.

The problem of variations in management skill in

attempting to determine cost-volume relationships of a

group of firms was also noted by Erdman (9). One plant,

according to Erdman, may have a smaller volume but have

lower average costs than other plants because of the ability

of the manager to use labor more efficiently or because of

his superior knowledge of machinery and equipment, et cetera.

Erdman suggested that plants be classified according to

management ability before attempting to determine cost-

output relations, although he admited that such a task

would be difficult.

Phillips estimated relationships between total feed

mixing costs and the degree of plant capacity used (17).

This study was based on the data from the Brensike and

Askew feed mill study (3). Feed mills in the midwest were

stratified by volume and sampled randomly. Actual peak

performance in the past, instead of rated plant output,

was used to determine plant capacity. A simple regression

of total mixing cost on. volume mixed was used to show the

relationship between output and costs during a single

production period (short run). A multiple regression model,

with some measure of capacity utilization, was used to

extend the analysis to both short- and long-run cost

10

functions. Total cost of feed mixing was taken to be

linearly related to plant capacity, while the volume

variable was used in exponential form with an exponant

of less than one.

Stallsteim^er, et i -/ examined some of the con­

sequences of using cross section data as a basis for

determining cost-volume relationships in the feed mill

industry (19). This study was a continuation of the

methodological inquiry of the Phillips' paper (17).

Several models v;ere developed illustrating various assump­

tions and hypotheses concerning cost-volume-capacity

relationships. Total annual costs of feed mixing was used

as the dependent variable in each case, with volume and

capacity as independent variables. As in the Phillips'

study, estimates of maximum annual capacity were made on

the basis of actual past peak perform.ances. The results

obtained from this analysis varied so widely (even though

most statistical tests of reliablity of the estimates were

acceptable for all models) that the authors concluded that

it was impossible to predict the effects of volume or

capacity on costs. Intercorrelation of volume and capacity

caused the coefficients of these variables to be unstable

and therefore unreliable as estimates.

Dietrich estimated the long run average coEt curve

for Texas and Oklahoma cattle feedlot operations (8}.

Four regression models were uscod, one linear and thjcee

11

non-linear, for analyzing economies of size in the feedlot

industry. All of the m.odels related cost per pound of gain

for various specified items of cost to the capacity of

individual feed lots. The log function, which assum.es

that costs per unit of output increase at a decreasing

rate as the size of plant increases, tended to fit the

data best, according to various statistical tests.

A study of North Carolina gin expenses was conducted

by Tussey and King (21) . Twenty-five gins w ere randomly

chosen from three groups that were stratified according

to number of gin stands operated per plant. The number

of gins chosen from each group represented a proportional

sample of the total nunriber of gins in each group in North

Cax-olina in 1957. Costs were divided into two broad groups:

out-of-pocket cash expenses, which v/ere further subdivided

into direct and indirect (or overhead) expenses, and

investments in durable goods. Direct cash expenses per

bale (labor, bagging and ties, repairs and servicing,

power, et cetera) v/ere found to be unrelated to volum.e of

cotton ginned. Total indirect (overhead) cash expenses

(office, insurance, taxes and licenses) had the expected 4

direct positive relationship v/ith plant size. Costs of

durable inputs were estimated by the "depreciation method,"

using estimated replacement costs of gin machinery and

interest on investment, and by the "capitalization method,"

wliich involved subtracting the present value of equipment

12

from the cost of the initial investment. Both laethods

resulted in an inverse relationship between fixed costs

per unit and annual volum e ginned. The authors concluded

that by necessity the numb>er of gins in North Carolina

would continue to decrease and volume per gin would

increase.

Paulson analyzed the costs of ginning in three

regions of Texas---the Blackland area, the High and Low

plains area, and the Gulf Coast area (15). Costs from more

than 1,200 records were used in developing various averages

and relationships between costs and volume for the three

regions. Total cost as a function of volume, plant invest­

ment, and number of saws per plant V7as computed by linear

regression equations. Average costs were derived from

these equations by dividing total cost by volume. The

author concluded that fixed costs primarily explain the

effects of volume on ginning cost per bale.

Paulson also developed standards for measuring

efficiency in the ginning business (16). Standard gin

income, standard cost of ginning, and standard volume of

ginning were developed for each of the three previously

mentioned regions of Texas and compared to actual gin

operations in these regions.

Thompson and Ward, in their study of cost relai ion-

ships of cotton gins on the Kiqh Plains, Rolling Plaii s,

and Lower Rio Grande Valley of Texas, obtained accounting

13

data from the Houston Bank for Cooperatives for gins in

each of these regions. Total ginning costs for single

and multiple battery plants seemed to have a linear rela­

tionship to volume of cotton ginned, with costs of multiple

battery plant operation being higher than those of single

battery plants for various volumes ginned. Total costs

per bale declined significantly when larger volumes of

cotton were ginned.

CHAPTER II

THEORETICAL CONSIDERATIONS

The major concepts of cost as used in economic

analysis are: total cost, total variable cost, total

fixed cost, average total cost, average variable cost,

average fixed cost, and marginal cost. Costs can be

further classified into "short run" and "long run." The

short run, as used in this study, refers to a specific

size or scale of plant capable of producing varying levels

of output per unit of time up to, but not exceeding, some

specified maximura rate of output. Because of the fixed

plant size, certain costs associated with the short run

remain constant in total, regardless of output. Long run

costs refer to the costs of production associated with

different scales of plant. All costs are variable in the

long run.

One of the basic principles underlying the nature

of cost output relationships as they exist in economic

theory is that of a unique functional relationship between

cost and the rate of output for a firm. Some matheraatical

forms of these cost-output relationships are summarized

in Table 1. It is assumed that the firm is a pure competi­

tor- in the purchase of resources, and, therefore, can

purchase any amount of a resource at a constant price per

14

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17

unit. All of these relationships and others presented in

this study are on an annual basis, unless otherwise

specified.

The general theoretical framework for this study

is contained in the principles of production economics

with specific application to marketing services provided

by cotton gins. The operation of a gin plant involves

the transformation of a raw product (seed cotton) into two

intermediate products—lint and seed. Seed cotton is

usually delivered to the gin by the grower in farm trailers,

averaging from tw o to four bales per trailer. The trailer

is weighed and identified as it arrives at the gin. It is

then parked on the gin yard to await ginning. In the

ginning process, the lint or fiber is separated from the

seed and burrs, cleaned, and pressed into five hundred

pound bales. It is then wrapped in bagging, secured by

steel bands, and usually loaded imjnediately on a gin-ovmed

truck for shipment to a cotton compress or warehouse. The

seed is usually conveyed to a storage house, loaded on a

gin-owned truck, and taken to a nearby oil mill for crushing.

Short Run Costs

The cost of providing this service is determined

by the functional relationship between output and the input

factors (assuming knov/n and constant resource prices) .

A given level of output can usually be produced by several

IS

factor combinations because of the substitutability of

input factors. Due to the nature of gin plant operation,

however, factor substitution in the short run is ver '

limited. The major types of resources used in ginning

cotton are labor, machinery, buildings, power, and raw

materials. The primary substitution possibilities are

between labor and machinery, and changes in machinery

generally involve changes in the scale of plant, v/hich

means most factor substitution possibilities are long run

considerations.

The hypothesis that substitution possibilities

are nonexistent or., at best,- quite limited in the short

run leads to the logical conclusion that each unit of ou'cput

will contain fixed proportions of variable inputs. This

m.eans that total variable costs will increase linearly as

volume increases (given the previous assurnptions of resource

prices) and that short run marginal and average variable

cost v;ill be constant and equal. Under this condition,

savings in unit costs or production as a result of larger

volumies of output in the short run com.e entirely from the

spreading of fixed costs over additional units of oucput

(see Figure 1).

The implied assumption of the preceding hypothes:s

that variable cost is a continuous function of output

requires some modification when applied to the cottcn

ginning industry because of the tende' .cy of gin labor

V CQ —

•d 0) X

•H P4

0) r-^

^ JO

•r-{

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19

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0)

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$ Total Annual Cost

20

costs to remain fixed over considerable ranges of output

and change by jumps, discon-cinuously, at various levels of

output. iMost High Plains' gins keep at least one man

working in the gin plant on a year-round basis. However,

when the first load of cotton arrives at the gin, this

man must have additional help to gin the cotton. This

requires hiring at least part of a crew, even though very

little cotton may be ginned for some time. Additional

labor may be added, possibly one man at a time as more

cotton is ginned, until finally a full crew has been hired

and the plant can operate at its maximum rate of output.

In Figure 2, output per day of one to OA bales can be

ginned by OW laborers. An increase in daily volume from

OA to OB requires OX laborers. As daily volume continues

increasing up to the gins daily capacity CD, the num.ber

of laborers required continues upward in this stepwise

manner until a full crew of OZ laborers is required.

Because of the discontinuous supply of seed cottor*

that is moved to the gin during a normal ginning season,

the gin plant does not operate continuously. The amount

of gin labor, however, is not decreased as it was added,

as the supply of cotton- decreases or stops during the

season. If a ginner expects to have a crew available for

ginning when there is cotton to gin, he must ke^p them

on the payroll during intervals when the gin is not

operetinq. The iTiore time that crev s are on the payroll

21

m U 0) u o

o u 0)

13

(0

O EH

X

W

0 B D Output/day

Fig. 2.—Discontinuity of gin labor requirements.

22

and not ginning cotton during the ginning season, the

higher will be the average cost of labor per bale. Con­

versely, given some specified norm.al length of ginning

season, as annual volume increases, the total gin labor

per bale will decrease, and if gin labor is classified as

a variable cost, total cost will increase at a decreasing

rate. This would mean that short run marginal costs

decrease as volume increases instead of remaining constant,

as previously hypothesized.

Gin labor, however, cannot be defined as a com­

pletely fixed or variable cost of gin operation. The

minimum crew size that is necessary to operate a plant,

along with some minimum number of working hours per week,

determines the fixed cost of gin labor for any specified

length ginning season. Crew size and working hours are

otherwise somewhat flexible, varying v/ith the actual and

anticipated flow of seed cotton to the gin.

Long Run Costs

Gin plant hourly capacity can be increased in the

long run by increasing the output per battery or by in­

creasing the number of batteries. Output per battery can

be increased by adding more machinery or by replacing

existing machinery with higher capacity equipment. The

increase in labor and power requirements associated with

increases in hourly battery capacity \. ould be expected to

23

be relatively smaller than the added output, thus reducing

variable costs per bale. Increasing hourly plant capacity

by increasing the number of batteries, however, would mean

adding completely different lines of eqjipment, vrith

separate operating crews. The addition of a second battery

with the same basic machinery and capacity as the first

battery, therefore, would double the total plant capacity

but would not be expected to affect the variable input

requirements per bale.

AJI increase in the hourly capacity per battery

would shift the plant total cost curve upward, but its

slope (mtarginal cost) would decrease. The addition of

another batter '', under the conditions described above,

would result in an upward parallel shift of the gin's

total cost curve. Its slope (marginal cost) would remain

the same.

The effects of changes in the scale of plant on

gin costs are illustrated in I'igure 3, for gin plants with

a specified number of batteries capable of ginning four

different rates of maximum hourly outputs. The four total

cost curves show the properties of increasing fixed costs

and decreasing marginal costs as capacity per hour is

increased. Long run total costs therefore increase at a

decreasing rate as the hourly capacity per battery is

increased, where volume ginned equals capacity (V--Ks) .

24

4J CQ O

o

c i H (0 4J O EH

0 rH (0

m JH

ft • M CQ

o

O

TC4 J LRTC

VjL=Ksi V2=Ks . V3=KS3 V . = K s ,

Annual Volume

Fig. 3.--Short run total and averaqe ccst curves for four levels of maxiraura hourly output and i.he associated long run total cose curve and the econo:vi-s of scale curve for g m plants v/.ith a specified num.ber of batteries-.

25

The long run total and corresponding average cost

curves are discontinuous, consisting of the relevant por­

tions of each of the short run total and average cost

curves. The long run cost curves would be smooth sloping

curves if the changes in scale were infinitely small.

Changes in gin cost-output relationships as the

result of adding to the number of batteries would be

similar to those illustrated for a fixed number of batteries

The short run total cost curves, hov;ever, would be parallel

and the reduction in per bale costs (AC1-A4) v/ould be less

as plant capacity increased (where V=Ks).

I I I

CHAPTER III

METHODOLOGY

Study Area and Sampling Procedure

The study area includes approximately the southern

two-thirds of Texas Crop Reporting Districts 1-N and 1-S

(Figure 4) . A list of cooperative gins in the area served

by the Lubbock Cotton Classing Office was obtained from

that office. Cooperative gins were chosen for the study

because of the similarity of bookkeeping methods, the

generally readily available information from individual

gins, and the availability of gin cost data from the Houston

Bank for Cooperatives.

A stratified random sample of thirty gins was

drawn from a population of ninety gins. All ninety gins

were arranged in ascending order by estimated hourly capa­

city based on data relating to the gin stand complexes on

file with the classing office. The population was divided

"The physical capacity of a cotton gin depends primarily on the number of stands and saws . . . A gin stand is one ginning 'unit' containing a cylinder of fromi 70 to (178) saws. In the ginning operation, the saws pull the lint through the narrow openings which are too narrow to let the seed through, thereby separating the seed and lint" (21, p. 10),

Mr, Roy Baker, U,S,D,A. ginning specialist at Lubbock personally aided in computing the capacity est.i mates

26

p'.. ^. r^. rt.

27

F i g . 4.'—The Study Area.

28

into four groups, with each group representing roughly

25 percent of the total combined capacity of the ninety

gins. The number of gins selected from each group was such

that their combined capacity would represent about 25 per­

cent of the total capacity of the thirty gins in the

sample. This method of sampling was to insure a uniform

representation of the different size gins based on annual

volume ginned.

Source of Data

Data relating to the volume of cotton ginned and

the various components of costs were obtained from the

sample gin firms by personal interviews. These items were

taken direct from the audit reports for each of the five

years 1963-1967. A list of fixed assets and the purchase

price of each, including land, v/as also taken from the

audit reports for each year. Similar, though less detailed,

data were obtained from the Houston Bank for Cooperatives

for these and other High plains gins.

Analytical Procedures

4

Cost-output relationshjps for High Plains cotton

gins were estimated by the method of ordinary lea.~t squares

regressio?:. The short run involves estimating the effects

of plant utilization on costs. Given the assumptions of

constant resource prices and constant proportions of

29

variable inputs per unit of output, increases in output

in the short run result in decreases in costs per

unit of output over the entire range of output. Savings

in unit costs of output as volume increases would result

entirely from the spreading of fixed costs over m.ore units

of output. A model of the following type would reflect

this relationship: TC = a + bv, v/here total cost (TC)

is a linear function of volume (V) and average total cost

(ATC = a/v + b) would approach marginal cost (MC = b =

average variable cost) as volume becanr e very large.

The long run average cost curve can be estimated

by two methods: (1) stratification of the plants by size

and regressing total cost on volume for each strata, or

(2) by using a multiple regression model with some

measure (s) of plant capacity in addition to volumwC as

independent variables.

Gin capacity per hour, as noted in the theoretical

section of this paper, is determdned by two factors:

(1) the output capacity per battery, and (2) the number

of batteries per plant. It is therefore necessary to have

some measure of the effects of each of these variables on

costs to have any meaningful estimate of cost-output

relationships in the long run. Also, for time series

data, such as those used in this study, some m.easure of

the effects of time on costs is needed to adjust for

30

inflationary changes in resource prices during the period

of the analysis.

Several approaches were used in an atTiempt to

develop m.eaningful cost-output relationships for High

Plains gins. There were many false starts and reformula­

tions of the algebraic forms of the equations used. Some

models v/ere good "fits" in terms of various statistical

tests only to prove inadequate when subjected to logical

or economic criteria, on the other hand, models which

seemed to be logical descriptions of the relationships

being tested often had unacceptable statistical tests of

reliability. A high degx'ee of correlation between some

of the independent variables (miulticollinearity) was a

problem throughout the analysis.

Gins were str£itified into groups according to the

number of batteries per plant and total plant hourly

capacity, by individual years and all years included in

the analysis, in attempts to test hypotheses concerning

the nature of the relationships between various causal

factors and gin costs.

Hypotheses about the short run dictated a model

in v/hich short run marginal costs are constant for any

given scale of plant. Long run marginal costs, hov:evei,

were hypothesized to decrease vrith increases in plant

scale. Inclusion of both of these characteristics into

one model reauires at least one interaction variable

31

between volume and capacity, such that m.arginal cost is

constant for a given capacity and decreases as capacity

increases.

Since gin capacity can be increased by increasing

the output per battery or by adding batteries, the effects

of each type of capacity increase on costs must be isolated.

Increasing capacity by the first method affects marginal

costs as v/ell as the level of fixed costs, while the

addition of similar size batteries affects only the latter.

The incorporation of these two variables into the model,

with each affecting costs as hypothesized presented some­

what of a problem since the addition of batteries to a

plant simultaneously adds to the total hourly capacity.

Therefore, to separate the effects of the two types of

expansion in scale on costs, hourly capacity per battery

and total plant hourly capacity were used in the model.

The algebraic form of the model that was developed

as representative of the cost-output relationships of High

Plains gin firm.s is given by:

TC = a + bj V + b2VK]3 + ^3^p " 4^ "*' ^5*^

Where: TC = Total annual cost of ginning.

V = Annual gin volume in running bales.

K^ = Gin capacity per hour per battery

(assumes same capacity for all batteries

associated with one gin plant).

32

Kp = Total gin plant capacity per hour

(BK ) .

B = Number of batteries per gin firm.

T = Time in years (1963 = 1) .

Short run or individual cost functions are derived

from the multiple regression equation by specifying the

year and alternative levels of capacity and number of

batteries. Total annual cost is then expressed as a func­

tion of annual volume up to but not exceeding annual plant

capacity, or:

TC = a + b5T + b4E + b3Kp + (b^ + 1D2K^)V

where the bar means that the variable i s held constant at

some specified level. Annual plant capacity (Ks) is

defined by specifying the number of batteries, hourly

capacity per battery, and the length of ginning season,

i,e., iCs = B Kb H E or ics = Kp H E since BKb = Kp. "H"

refers to the numl>er of operating hours in the season and

"E" refers to the estimated percentage of the rated hourly

capacity that can be attained throughout the season.

The short run average (per bale) cost is obtained

by dividing the total cost equation by volume, or

ATCg = a/v + b5T/V + h^B/V T b3Kp/V + b-j + b2K)3

= ^a + b5T + b4B - b3Kp)A[j + (b^ + ^2^-^:^^

= AFC + AVC

33

where ATCg is average total cost, AFC is average fixed cost,

which decreases as volumie (V) increases, and AVC is

average variable cost which is constant for a specified

scale of plant. Marginal cost is given by the derivative

of total cost with respect to volume. In this model it

is constant and equal to average variable cost (AVC) for

a specified scale of Dlant. That is, >5C = —^- = bi -t-V

^2^b' "where ^2 is expected to be negative as explained

previously in chapter II.

The long run average cost curve, or the "economies

of scale curve" is the locus of least cost points of the

average cost curves for the individual plants. These

points occur where seasonal volumes of the various scales

of plant are equal to their seasonal capacities.

The long run total and average cost functions are

obtained from the model as follows:

1, Specify a year (T) and obtain,

TC = a + b5T + b^V + b2VK^ t b3Kp + b4B

= (a + b5T) + (b V + b2VKb + b3Kp + b4B) - (a -r b^T) + V(b^ + b^K^ + b3 ^ + b4 - ) B

V V

2, Substitute'seasonal capacity as defined above

for K. and K^ in the equation and obtain: D P ^ K K^

_ S _ j3

TC = (a + b^T) -)- V(b3 + b2 BHE + 3 H£V + 4 y

34

3. Set seasonal capacity eqjal to volume and the

long run total cost function becomes:

Kg KG B

TC = (a + b^T) -f v(b^ - b2 iSS 4- b3 iiS; - 4 ^ )

^s ^3 B = a + be-T -r v{b; + b2 BHE + flE + b^ Kg )

4. The long run average cost function is:

ATCL = (a + b^D/V + (b^ + b2 SHE ^ HE + H ^s )

Long run marginal cost (LR lC) is given by the

derivative of the long run total cost (LRTC) function V7ith

respect to volumie (V) , or:

PI^TC 3[a + b5T + V(bi + b2 BHE + KE + ^4 K 3 LRMC = ?V

^ ^ B_ = b^ + b2 BHE + KE + b4 Kg

The nature of the individual short run total and

average cost functions and their relationships to the

long run functions are graphically illustrated in Figure 2

Chapter II.

CHAPTER IV

FUHDINGS

Sample Characteristics

Volume and Capacity

The average annual volume of cotton ginned by the

sample gins during the period of analysis (1963-64 through

1967-68) w as 7,477 bales oer gin, rangina from, an average

of 4,227 bales for the gins in group I to 8,757 bales for

the gins in group rv (Table 2).

The average ginning capacity for gins in the

sample (for the same period) was nineteen bales per hour,

ranging from eleven bales per hour for girs in group I to

thirty-three bales per hour for gins in group IV (Table 2).

Annual capacity was defined in Chapter III as:

K = BKj HE or

Kg = K ,HE

The ginning efficiency rate (E) v.'as assumed to be

85 percent. The maximum number of hours in the ginning

season (H) was estimated as follov/s:

H = 295- - 1068 0.28

where: 299 = the nuir.rer of operating hours available in

the peak two-week ginning period (24, p. 21)

35

36

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0.28 = percent of total crop ginned in the peak

two-week ginning period.

Annual capacity per gin (as defined above)

averaged 17,246 bales for the entire sample, ranging from

approximiately 10,000 bales for gins in group I to 30,000

bales for gins in group IV. Average capacity utilization

for all gins in the sample was 42 percent for the study

period, ranging from 52 percent for gins in group I to

35 percent for gins in group III (Table 3).

Investment Costs

Total investment in land, buildings and equipment

for all gins in tlie sample averaged $532,573.82 per gin

for the 1963-67 period, ranging from $318,220.06 for the

gins in group I to $882,102.57 for the gins in group IV

(Table 4). Investment in buildings and equipment averaged

$512,790.44 per gin in the sample, ranging from $308,807.06

per gin for the gins in group I to $842,617.56 per gin for

the gins in group IV. Land investment averaged $19,783.38

per gin for the sample, ranging from $9,413.00 for the

gins in group I to $39,485.01 for the gms in group TV.

• Based on five-year average, 1965-67, for countie, in crop Reporting Districts 1-N and 1-S.

38

TABLE 3

ANNUAL VOLUME GIITOED, ESTIMATED SEASONAL CAPACITY, AND CAPACITY UTILIZATION FOR

THE SAMPLE GINNING FIRMS, BY GROUPS, 1 9 6 3 - 6 4 THROUGH 1 9 6 7 - 6 8 SEASONS

Group By Season

1963-64 1 2 3 4

Totals 1964-65

1 2 3 4

Totals 1965-66

1 2 3 4

Totals 1966-67

1 2 3 4

Totals 1967-68

1 2 3 4

Totals 1963-64 through 1967-68

1 2 3 4

Totals

^Estimated by:

Seasonal Volume Ginned

68,044 58,671 64,934 60,262 251,911

68,484 65,095 57,633 59,388 250,600

82,385 68,984 68,902 64,371 284,642

50,666 34,707 36,195 34,959 156,527

54,956 41,268 31,603 35,028 162,855

•

324,535 268.725 259,267 254,008

1,106,535

Kp(0.S5){^'

Seasonal Capacity^

108,921.6 129,798.2 152,490.2 119,813.8 511,023.8

128,890.6 129,798.2 152,490.2 119,813.8 530,992.8

128,890.6 129,798.2 152,490.2 119,813.8 530,992.8

128,890.6 129,798.2 140,690.4 119,813.8 519,193.0

128,890.6 129,798.2 140,690.4 119,813.8 519,193.0

624,484.0 648,991.0 738,851.4 599,069.0

2,611,395.4

299/ ^ '0.28).

Volume as % of Capacity

62.5 45.2 42.6 50.3 49.3

53.1 50.2 37.8 49.6 47.2

63.9 53.1 45.2 53.7 53.6

39.3 26.7 25.7 29.2 30.0

42.6 31.8 22.5 29.2 31.4

52.0 41.4 35.1 42.4 42.4

39

H m

K

to C

o

o M O

ro

O

CN

O « O

o u o

ra ^ fd

rH r-{ 0 P

S'

(

C! t

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m

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• o c\ r-

CN rH in

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i H 00

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v

308,807.06

525,495.31

706

> i - p D"> ^ c:

0) -H. « OJ

to Q) '.> C H

^ r-{ -I-<

ri pu

0) a

-H rC u d "^

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a -H

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00 OI

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27

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318,220.06

545,689.85

734

ent

E 4J m 0) > c; H

r-{

r? A-> 0 H

40

Operating Costs Per Bale

Total operating cost averaged $24.29 per bale for

all sample gins during the 1963-67 seasons (Table 5).

Total costs averaged $24.03, $24.22, $25.69, and $23.43

per bale for the gins in groups I, II, m , and IV,

respectively, for the period.

Labor was the highest single cost of gin operation,

averaging $7.16 per bale during the study period for all

gins in the sample. This represented 30 percent of the

total average operating costs. Average labor costs for

group I, II, III, and TV were $7.24, $7.31, $7.40, and

$6.70 per bale, respectively, representing 31 percent,

30 percent, 30 percent, and 28 percent of total cost per

bale for the gins in each group. Gin and repair labor

made up the greater portion of total per bale labor costs,

averaging $4.99 per bale for the sample during the period,

which represented 70 percent of the total labor costs.

Gin and repair labor for each of the groups (I-IV)

averaged $4.73, $5.07, $5,28, and $4.95 per bale respec­

tively, and represented 65 percent, 69 percent, 71 percent,

and 74 percent of the total labor cost per bale for the

gins in each group.

The salary of gin managers averaged $1.26 per bale

for the sample during the period, which represented

18 percent of total labor costs. Gin managers' salary

finr- •

41

in

a

m c o

M O CM

00

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m ^ rH CN

rH rH rH 00 ro Tj" CN rH rH

rH rH

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v O O r H C J i C D — < i n r o O C O C N C 3

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0 , 0 <T» m

VO

c^ r» c^

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in CM CM .

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ro CO C^ r » •^ CN ro rH o o r^ m c> r*

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^ -. — r= > y " y j j -t - ; t 0 j - , 3 C

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42

for each of the groups averaged $1.65, $1.17, $1.18, and

$0.96 per bale for groups I through IV, respectively.

This was 23 percent, 16 percejit, 16 percent, and 14 percent

of total labor cost per bale, office labor made up the

remainder of labor costs.

Depreciation was the second largest cost per bale

of gin operation, averaging $5.11 per bale for the sample

during the 1963-67 study period, which represented 21

percent of total costs per bale. Depreciation for each of

the groups I through 17 was $4.68, $4.97, $5.78, and $5.13

per bale, respectively. This represented 19 percent,

21 percent, 23 percent, and 22 percent of total costs per

bale for each of the groups during the period.

Reparis and supplies, bagging and ties, and power

requirements made up the major portion of the remiainder

of operating costs. ComJbined, they averaged $7.28 per

bale for all sample gins during the study period. This

represented 30 percent of the average total costs per bale.

These costs for each of the groups in the sample were

$7.09, $7.43, $7.09, and $7.54 per bale, xvhich represented

29 percent, 31 percent, 2S percent, and 32 percent of

average total operating- costs per bale, respectively.

43

Empirical Results of Gin Cost-Output Relationships

The Model

T^e effect of volume ginned and gin capacity on

total annual ginning costs was estimated by ordinary least

squares regression and resulted in the following equation:

TC = 10,048.38 + 14.75V - 0.192VKt> + 2325.90K^ +

11,052.77B (1.41)

R2 = .89 (9.33) (-1.44) (3.26) +

3,428.92T (3.03)

where: TC = total annual cost of ginning

V = annual gin volume in running bales

Kvj = gin capacity per hour per batteiry (assumes

same capacity for all batteries associated

with one gin plant)

K = total gin plant capacity per hour (BK] )

B = number of batteries per gin firm

T = time in years (1963=1)

The numb>ers in parentheses belov7 the coefficients

are t-ratios. All but two of the coefficients are signi-

ficantly different from zero at the 1 percent probability

level. The coefficients of VK^ and B are insignificant at

the 10 percent probability level. They are significant

at the 20 percent probability level. These variables

were retained in the analysis, however, because of logical

44

considerations. Also, as pointed out by Anderson and

quoted by Heady,

Even if the evidence against the regression coefficient being different from zero is slight, the best estimate of its size is still that obtained from the data. Indeed it is most unlikely that the true value of the coefficient is exactly zero (12, p. 211).

The coefficient of determination (R^) indicates

that 89 percent of the variations in annual total gin costs

are explained by the independent variables. The simple

correlation coefficients are as follows:

V VK ^ Kp B T

VKy .88

Kp .62 .72

B .64 .48 .82

T -.34 -.28 -.03 -.04

TC .91 .80 .75 .77 -.19

There were seventy-four gins with a total of 356

observations used in estimating the parameters of the

equation. Forty-four gins with 208 observations were added

to the original sample of thirty gins with 148 observations.

This was done to increase the number of observations and

range of magnitude for the various factors included in the

equation and, hopefully, to provide a meaningful estimate

of the relationships among the variables.

The additional gins were among the population of

High Plains cooperative gins from, which the original sample

45

was drawn, and they represent a wide range of hourly

capacities, number of batteries, and annual volumes ginned

during the five year period of analysis, cost and volum.e

data for these gins were obtained from the Houston Bank

for Cooperatives. The distribution of all gins used in

estimating the parameters of the model regression equation,

by estimated average hourly and seasonal capacities and

annual volumes ginned, is shown in Table 6.

For estimates of both short run and long run gin

cost-output relationships, time is held constant at

five == 1967, which was the last year included in the

analysis. This was done so that the estimated costs would

be more representative of the present time.

Four groups of short run cost curves were derived

from the model. Each of these groups or famj.lies of cost

curves show the cost-output relationships associated with

one, two, three, and four battery gins at different levels

of volume up to various specified annual capacities. The

economies of scale curve is formed by the locus of points

where volume equals capacity for the selected short run

cost curves. 4

The maximum output per battery for High plains

gins, based on the sample data, is approximately twenty

bales per hour. Therefore, cost-output relationships

have been computed from the model for annual volumes up

to the estimated annual capacity which can be obtained

46

• J r->

cn z M

u

w a z po w <

< &.

M C

^§

K -a

ta X o O >

(0 o o

vo

l.

9« o

f C

ap.

Avg

. a

s a

Avg

. o>o -P << • 3 o a z

Ave

rage

A

nnua

l C

ap.

>i 0 rH • tJ) SH ac 3 13 3

cr-c, > 3 iH < U 0

a.

0 -H o 01 3 E 3 3 3 U C r-i

c e o

C> rJ C *J 3

3 0 > U VH < O

JJ c rH '.: c r j O 3 3 o u c CP C rH > < c < -p

o

Z O XI o

o <« c 2 O '•!

c o

-H JJ

C 3 3 V Q -r* U >JH C3 -r!

cs n 3

tn CM CN in

• ^

CO

r-i I

CO

rH

in

ro CN

f

CO

CN

o o

ro CN

CN I

CN

CO

CN

rH

CD

O fO

GO

I o m

CO

in

(O

GO

I

o CM

o CM

o CN

—i r-i •*

^ •

O

VO rH 3\ , * P»

CN •* CO «

<?

ro tn 0% ^

CN

CM o CM ^ CO

o CO •

UO CM

vO in •

\0 CN

O rH •

VO CM

m r» •

•^f

CM

c GO 1

m CM

in

r-f CO Oi

r ^ vO CM r-i

r-i

r-i

CO VO •

ro ro ro ^

<^ CO rH

c CO

^ c •

ro O fO %

rH CO CM

-i VO

m CM «

CM i-i CD « r» CO CM

r o

CO ro •

vO CO CM ^ o Ov rH

VO •n ro

o ro

00 (*>

H

• *

n a 3 O u o

47

from a gin plant having a specified number of batteries,

each of which can gin up to twenty bales per hour. This

results in four major discontinuous sections of the long

run cost curves.

The actual range of seasonal volumes, by number

of batteries per plant, ginned during the 1963-64 through

1967-68 seasons by gins used in the analysis are as

follows:

Single-battery gins: 441-10,215 bales

Two-battery gins: 1,459-17,330 bales

Three-battery gins: 2,785-31,400 bales

Four-battery gins: 9,954-23,828 bales

Single-Battery Gins

Short run cost curves for single-battery gins were

derived from the model by specifying the number of batteries

and then expressing total annual costs as a function of

annual volume up to but not exceeding annual capacity for

specified levels of hourly output capacity. The long run

total cost function is obtained by equating volume and

seasonal capacity in the equation. The average cost function

is obtained by dividing the total cost function by volume.

Estimates of annual fixed costs and m.arginal

costs for single battery gins for various hourly capacities

are given in Table 7. Estimated costs per bale at various

annual volumies for selected maximum rates of hourly output

48

w

II ® 1 r * t 3

taxi'^ JJ '-1 0

3 —' - H C)

u > O (0 <

a c

- rJ

> w

0 01 O ' J J 3 M

O t k i 0 u 3 u u

> 2 <

•oJ] X -H

C-t

3 3 n C JJ

IS

r-i i J - :

C 3 JsJ C 2 , - ' -< -

'J

JJ c 3 S \ r-i JJ3l

u a r-H 3 JKi 3 a,-' JJ 3

o u tH

^ ' ^ ^ r H > 1 V( JJ >1 3 -H U o y o : : : 3 j j

a JJ 3 3 u ca

s

CTiff>OrHCNrOrOr!'L">'-Or~-r>OOC>OrHrH O r ^ ' V O ' S ' C N O C O v O ' ^ C N O O O v O r r r O r H C '

r O r O n r O n r O C N C N C M C ^ i C M r H r H — r H r - ! 0 r H r H r - > r H r H r H r H r H r H r H r H r H r H - H r - l r H r H

i n ' ^ ' i ' r O r ' i r o C N C N C M r H r H O O C vOin^rocMrHOOcor ' -OuO'cro r O O O i - ( r - . r ^ C i f O s O C N C D * r O r o m C r i r o o c o - i - ^ o c r ^ r H - r r ^ C0rH'C-c: r - ( - r r r - i - i -Tr -O' r - r»c

O vO • c rH O <^

o CN r~

-^ r C3

r O \ O C O O r O L O r ^ C c ^ J ^ r - c r ' r H « - o c O > - ' ro ro r i •«;• <>f TT •^ I..-) L- uO in L.O 'O 3 C O r-

rHCOO'^r-iavr-TrfNOCDin.'^i^SvOvr ro ro m -0 O r» CD C O C •-( <J ro r> v- uO •-0 in .-o CN rH o c CO CO r o ir Tf -o CN i-i

I r o % :

H 3

ro - uO vO r- CC C O O -H OJ ro T* O O r« CD r-lrHrHrHrHr-4-Jr^r-t

'j'invor*a<3^0rHfM'^^invOr~aoc>o r H r H r H r H r H r H r H r H r H r H C M

• « ; p i n v O r - C O C 7 v O . - < f M r O ' * u O v O r - J 5 C ^ O r H r H rH —I rH rH r-! r J - ( r-i r n

n Qi

-H

u (U

JJ JJ 3 Xi U-i

0

u ii>

"i 3 C

3 r "

-;-i

>. ^

'0 O

- H r J

C - r 4

JJ

^ 3 E > i iH O

J J JJ 3

,Q

tH y cu S JJ • H

u 3 Ou 3 U

> i r H U 3

S i —

i\ '

• .#- X <N

• o \ ON

cr\ CN

*»^ —. i n 30

• o - 04

X 1!

9) >i

•• >> Xi

•o 0 +J (0

r-<

3 y

r H 3

^ • * ^

• ~ l

•• '" 3

r - l

n 0)

- H

u 4) -P

a j j X

o o in C>1 CO CM

-f-

»« r-i

—" T^ r-

• (N UO

o

•-> r-i

+ ^-^ vn ^.^ CM

a> •

CJ 00 r CM

g ^ ^ CJ

^

r o

H-

JJ CO

E .•'

u

r f j t

CD ^

i, j o «

TO O 0

JJ Ij

£] •»-< » _ •

'_-\ u 6:

r l

II

•»-(

u u^ c-<

to ^ V-i

0 S.4 •

a >, A JJ

E -n 3 O C (5

c. Tl O 0 O

• H UJ u -H C y rg i r H

c- cu 'A

> i

a r-^ u JJ 3

0 C f ! ^

- r l

^ .r- 3 - J £ » - ' -^4

X « .•?

r- ir vC'

c. Ti r-< O

- H fc< VM C - t

l«H U

o Ir" C

i l 7^

- r i f3

.~. tn i n - ( * h . ^

C 1 ' J ^ u o ';3

-C t ; ? o

•

^ CM

r H

• o

1

in t-"

• ^ r-^

II

u > < ll

2

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r-» .P

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UH

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»/ =

-^ J J W

V J "Ci

49

are given in Table 1 of the appendix. Total fixed costs

per bale, total variable costs per bale, and total costs

per bale at estim.ated annual capacities are given for

single battery gins in Table 8,

The economies of scale curve for single battery-

gins decreases from $19.83 for a seven bale per hour gin

operating at maximum seasonal capacity (6,354 bales) to

$14.82 for a twenty bale per hour gin operating at maximum

seasonal capacity (18,154 bales). This represents a net

reduction of $5.01 in the cost per bale of gin operations.

Fixed costs per bale decrease from $6.42 to $3.91 and

variable costs per bale decrease from $13.41 to $10.91 as

the annual capacity of ginning increases from 6,354 bales

to 18,154 bales. The reduction in total costs per bale

over the range of capacity output was the result of

(1) spreading fixed costs over relatively greate.r volumes

of output and (2) more efficient use of tlie variable inputs

as was previously explained in chapter II.

Selected short run average cost curves and the

associated economies of scale curve for single-battery

gins are illustrated in Figure 5.

Two-Battery Gins

The short run and long run cost curves for two-

battery gins were derived from the model using the same

procedure as vnth single-battery gins. Estimates oi" fixed

50

-. C4

^

a S ' a CQ

»-» W tJ EH H CO '_) o < o t«

W r j r/3 <S U3

§ ^ ^ H 3 w « 2: U r-< 2 < 0 > < C Ov

, r . < - <

^ 3 ' ^ . £-• r-" CH io 0 U 2 S tH a ^ H

00 J J 0 ^ic :u

»4 -Z "Z -^ 5 5 c. S 2 < = 0 C3 =H E-« Cn EH

S :. r ^ cu J t-. 1

3 0 a ! 0:1 C3 EH J EH 0 CO CS >• :2 0 3 J .-( cj A =: 00 ^ 3 Q =H 0 y J/J 13 ?< 0 ^ H U >< Cn a

TA

L

OT

AL

0 EH EH ^ Q

a§ H

M EH to a

u >-i ScJl a 0 JJ tn r-l 0 JJ a EH M Q

0 0

0) r-i Xi U a 0 •H CJV

0 7: 0 > JJ r H

in ,-3 rH 0 C3

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X a;

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1

r O v O C r » O v O r ^ O v O r O r o ^ O O C M

o o r H i n r H v o r ^ J O u o c M C i v o c o o o o

O <7> CO CO r~ r- o vc vo in UO in* in TT r-*r-ir-ir-i,~ir-ir-i^r-{r-'. r-i,-{r-ir-i

r H C M r o r o • » s • ^ n > o r - t ^ o O ^ ^ O r H r H r r CN o 00 o • ? CM o CO vo •<3' .--) r-i cn

ro ro PO c i CM CM c»j c j r-i r-ir-ir-ir-t, .-^r-{,-\^r-i

rH r-l ^ rH O

CN -y r« r» rsi r*j '^* c. o u' i n o C7< rH • ^ C ^ ! n ( N 0 3 3 0 • ^ r o 0 4 r - i O C ^ C ^

• * • • • • # • • • • • » » v O i n u o u O i n T i ' ^ T ? < > 5 t ' T ^ r o r o

• * — t c ? i r - r f r v i o c 3 i n r o r H u i o v o r ^ c o £ r i o o r - » c N r o ro CN rH o o CD crj r- o '_- •*

:D vo r f - 1 Tj' L-> ro CM r-l

v O r - - C O C » < ? » O r H C N r O ^ - L ' < O r - C O r H r H r H r H r H r - j r H r H r H

r - C D O O r H C N r O T - L D rH r H rH rH rH —I

o (^ 3 cr> o rH -H i-f rH CM

0) r H

Xi (0 £H

E o u

0) rH

Xi EH

c O

MH

w ItJ

tn J.' » O o

TO

X lu

f3 3 C C (0

(tJ JJ o

JJ

D» c

•H TO • r «

>

-H

^ ! ^ Xi JJ

•o o •p cu 3 n cu u E OrH

>)? c

IH

CU

03 JJ CR o u

c;

C I

c

> TO C

CJ

c

u c

JJ CT

o

O

c c JJ

u-i

c E 3 CO

\ ^ CJ";

•DX

0) r-i (0

u ft 4J CO

o

1 9 . 1 6

1 6 . 5 6 1 4 , 8 2

SRAC (8 b a l e s / h r . ) SRAC2 (14 b a l e s / h r . )

(20 bales/hr.)

IJ .AC

Ksi=7,261 KS2=12,708 Ks3=18,154

Annual Volum.e (bales)

Fig. 5.—Short run average cost curves for selected maximiUim gin ,plant hourly outputs and the economies of scale curve, single-battery gins, 1967.

52

and miarginal costs for hourly capacities from. 14(7) to

40(20) bales per plant (battery) are given in Table 9

(assuming equal battery capacities). per bale costs at

various annual volumes for specified miaximum rate of hourly

output are given in Table 2 of the appendix. Total costs

per bale at volumes equal to specified annual capacities

for two-battery gins are given in Table 10.

Total costs per bale for two-battery gins operating

at seasonal capacity decrease from $18.77 to $14.46 as

plant capacity increases from 14 bales per hour to 40 bales

per hour. There vjas a net reduction of $4.31 in variable

costs per bale. Fixed costs, however, only decreased by

$1.81 per bale over the range of increasing miaximum output.

This is approximately $0.70 per bale less than the decrease

in fixed costs associated with the 7-14 bale per hour

capacity range of single-battery gins. The reason for this

is that greater outlays of capital are necessary for two-

battery gins to achieve the same relative increases in

maximum output as single-battery gins.

Selected short run average cost curves and the

economies of scale curve for two-battery^ gins are shov n

in Figure 6.

Thr e e - B a 11 erY_Gi_ns

The short run and long run costs for three-battery

g ins were derived frora the model using the sarr.c procedure

53

(0 EH to o o a X H fa

Z

9

5 vo

6 to 0. z

M

>• u C l M >i

^s cu H -S FH U <

a > • CO o

w EH to o o

EH (0 H

rH T3l W ^ - s 4J fl -H tn -H o O «j > o o <

> - ' >-i <o 4) n C C J J •H a M Of iH 0 tH 0) u to >

z < n h 10

'Otj l

•n u EH CM

rH ^^ ITJ 3 n C JJ

ss CJ

> 1 ^ j ^ N

3 CJ - * c n n C CUJii •< a • - '

j j

c

rH rj ' T l ,

4J r j ^

3 04>J

j J > i © -n M OI CJ 0) rt IT3 JJ iH CUJJ © r a n 5 c a

&

CQ 0)

rH (0

n

c^3^0 r -1 r • l r o roT^ ' _ovOt * • r - c ^c O » H - H C ^ r - ^ 0 ' t c ^ o o o • o • ^ ^ c M O a O ' T • f * ) r ^ J ^

r o r o r o r o r O f o C M r v j r H r H r H r H r H r H r H r H

CN CN rH .-I , - rH rH O r H r H r H r H r - ' r H ^ - H

O C T i X O O r o r f i n O r - i r o C D C J ^ O i r O r» in ro rH o r~ LO ?<•; rH 00 vo '-0 :~

O r H r o ^ n ^ ^ C O O t ^ ; • r • . o ^ • f f l r H r ^ C> • * c . •^ o ^ O LO c; o O Ll rH '.. r H 3 T r r H r * « r « - » r - ' « r O r - r o o ,"

lO 00 r*. O CO .3

tn vo X rH O I-" ro cr> o

• ^ O O r O C O C M r ^ C M O r H - O O t n O ' r : c r > ^

in inovor - r~ooooc^cr>oo—(- --^ c^

rH r- c-1 00 ro 00 •>* cv Tf o in o o . - . o CN r . o r - c o c M r o u ^ o x O r - i r o r r r r ^ C ' O r j o o o r ^ i n r o r H C ' r ^ ' O - ' T C N O c : • C T T ' ^

r ^ o o c M ^ v o c o O r H r o i n r - o c C N ^ O r H r H r H r H r H r H C N C M C N O ^ C N — r O r O r O

O O O C N r f v O C O O C N ^ - v O C D O C M T r v O C O O r H r H r H r H r H C M C M C M C M C M C O r O r - r O r O r r

^ l n v O t " • c o c » 0 ' - ( C N r o T J ' l n v £ s r - o o c ^ o r^t-ir-ir-ir-ir-ir-ir-r-ir-tl>i

<« 0

M C ^ £ 3 C

0) / : JJ

> i

^ •0 rj

-H —1

a, •H 4J r-t 3 E

N u 0

JJ •p

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u 0) a > i JJ - H

10

a nj u > i

r H l4 3 0 x: • o'ffl D» Xi tO>i >H 0) >• <:

^

m 0

batter!

• . ^ 00 CM

• o \ o 0^ CN ^-r

^ in CO •

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JJ R}

r H 3 U

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u 0 D i o:

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+ ^-^ CM •*^

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•:J c 10

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number

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54

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S K < I CU J tH <

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si g H i ^ 1

to H

I j

•*.» W r H 0 .p rj EH yi a

0 o

0 r-i

Xi U ra o •rl c ; \ r3 n i' > - P - H

7* j ^ -H d =: CJ CJ

JJ 0 EH

•o V u X 0

•-1 (l> r H JJ r H

a 'J: rj JJ 0 s 0 'o

CH

j J

c O > i

r H i ^

U — rH .-; a rJ CUii 3 •0 — c u 5

JJ

c r H JJ 3 cu -H 0 — .

0 ~ cu rH .T >: lO fii U ' 4J f3 (1)

0 u a

t

n u

j rH 0 Q

1

1

0) 0

r-i IT}

a

1

I 1 1 f 1

r > - r o r - i n o o i . o ~ ' r o . ^ j ' f - « M L " 0 ' 0 t * * c N t ^ r o O j 3 , ^ o r ^ T i > c N ~ r-'^

C D C O t ^ r > - O O O v O i n i n L O r r ^ « » f r-tr-it-ir-lr-ir-ir-i,-ir-ir-i,-ir-,-ir-i

vOr^•^CMT?0^-.0^«C^w-N^.- C rf

ro o r^ m ro >-M o C-. CO r- r- r uo LO

uoin^Ti>-.*T--c-ororOrOr^rOro

CO ro 00 rt C» •^ O u'l rJ o rH r CM t ocMf i invOCDO—If*" , T j o r - o o r ^ i n r o r H O r - o - t r r M o c o r •^ro

C N ^ v O C O C i — ' r O u O r - C v O o T T V O

r H r l r H r H r H C N v>i C M C M C M r O .— f O f O

r H C M r o r o ^ L . o c r ~ r » c o c » C r H r - i ! • ^ • C M O C O v O - ^ r i O C O v O r r r - r H O ;

r O f > r O C M C M r < C N e N r H r H r H . — r H O ' r - i ^ r - l r - i r ^ ^ t - ^ ^ ^ r - i r - i , - , - ' , - )

T f v O C O O C N ' d ' v O C O O C ^ J T - ^ r C O O r - i r H r H C M C M C M C M C N r O r O f O r ^ m rf

<

Xi o EH

E o u

^

Cjv

0) r H

n EH

E o u

VJ

fe H

en JJ (A o (J

"O

o

a 3 ha

c <0

iJ o JJ

c •o •rt

> - H

-a ^ > ' ;a -p -rt

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o r-t

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to ^

55

0)

CQ

U (D Oi

•P m o u

SRACi (16 bales/hr.) SRAC2 (28 bales/hr.)

LRAC

Ksi=12,708 KS2=25,415 Ks3-36,307

Annual Volume (bales)

Fig. 6.—Short run average cost curves for selected m.aximum gin plant hourly outputs and the economies of scale curve, tv70-batter ^ gins, 1967.

56

as with single- and two-battery gins. Estimates of fixed

and marginal costs for hourly capacities from 21(7) to

60(20) bales per plant (battery) are given in Table 11

(again assuming equal battery capacities). Per bale costs

at various annual volumes for specified rates of hourly

output are given in Table 3 of the appendix. Fixed,

variable, and total costs per bale at volumes eq[ual to

specified annual capacities are given in Table 12.

Total costs per bale for three-battery gins when

operating at seasonal capacity decrease from $19.42 to

$14.33 as the seasonal capacity of ginning increases from

19,061 bales to 54,461 bales. This is a net reduction of

$4,09 in the cost per bale of ginning. Since the maxim.um

hourly output per battery again varied from seven to twenty

bales per hour, variable costs per bale ranged from $13.41

to $10.91, for a net decline of $2.51 per bale over the

range of output. Fixed costs per bale decreased from $5.01

to $3,42, showing a net decrease of $1.59 per bale, which

is $0.22 per bale less than the decrease in average fixed

costs for two-battery gins over the same range of output

per battery. Larger outlays for the same proportional

increases in output is again the reason for this decline

in the net reduction of fixed costs per bale associated

with increases in the scale of plant.

SBTT^':

57

5 2i o < D Cv

C -H

w = ^ '" 3 ^.rf " • " fc^

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< <

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r o fO r o r o r^ r o CN CM <^I .-N C l —I r-J r— rH rH O

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58

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^ I r- -o -? CN c i r-i Tf .^; —t —i r-i c -H ' f— r H r - ( — i r - — I f — ^ r - l - ^ r - ( ^ - ! — 0 • 3 !

— ' O c ^ a c N r ' r c c ^ c r r ^ ^ o r - rr r J ^ .- - r - r^ 0 _->

—I ' 0 r j ..^ TT ^

LO T? T - f •-;- I**/ "^ ."^ "^ ro r

•—' ^ r-« O ro 2 C ro ,0 O r j i.- CO r^ O CO O ^ v.-" r^ C M - r 5 . ' — .-^ .?

c r- LO -1 o \2 '7 -H :3 u-i •"' o r~ •«? c\ rH Tf r~ o r j L- 23 o .-o r. r ^ ^ rH CM rJ C>l CM ro m ro "T • ^ T -T •-"- i n

»-. !

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E 0 u

rH •!? r- O fo J C' CN uo CO •-• - r r-- o Cl CJ f J ro ro 'O -T <• "T V _0 i-O j ^ O

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V

c u

o

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59

Selected short run average cost curves and the

economies of scale curve for three-battery gins are

illustrated in Figure 7.

Four-Battery Gins

The short run and long run costs for four-battery

gins were derived from the model using the procedure

described for single-battery gins. Estimates of fixed and

marginal costs for hourly capacities from 28(7) to 80(20)

bales per plant (battery) are given for four-battery gins

in Table 13 (assuming equal battery capacities). Per bale

costs at various annual volumes for specific rates of

hourly output are given in Table 4 of the appendix. Fixed,

variable, and total costs per bale at volumes equal to

specified annual capacities are given in Table 14.

Total costs per bale for four-battery gins operating

at seasonal capacity decrease from $18.24 to $14.27 as the

volume of ginning is increased from a maximum of 25,415

bales to a maximum of 72,614 bales. This is a net reduction

of $3.97 per bale over the range of output. Variable co5/!:s

per bale again ranged from $13.41 to $10.91 since the range

of capacity per battery is the same—seven to twenty bales

per hour. Fixed costs per bale decreased from $4.83 to

$3.36 over the range of maximum output levels, lor a net

reduction of $1.47 per bale. This is $0.12 per bale less

CO

SRAC

)

1 (24 bales/hr.) SRAC2 (42 bales,/hr.) ii SRAC3 (60 bales/hr.)

fU CQ

s

ft

CO o o

LRAC

Ksi=21,784 KS2=38,123 KS3=54,461

Annual Volume (bales)

Fig. 7.—Short run average cost curves for selected maximum gin plant hourly outputs and the economies of scale curve, three-battery gins, 1967

61

u

CS r-5 CS-3 ^ D ^

2-* CO r :

ro '^ Zt

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•^ =- 5H

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than the net reduction in fixed costs per bale for three-

battery gins over the same range of maximum battery output.

Selected short run cost curves and the economies

of scale curve for four-battery gins are illustrated in

Figure 8.

All Gins

Ginning costs per bale decrease with increases in

volume for any given hourly rate of battery capacity and

specified number of batteries. This is the result of

constant variable costs per bale and the spreading of

fixed costs over greater volum.es of output. As the hourly

capacity per battery is increased, costs per bale decrease

at maximum outputs irrespective of the number of batteries

(1-4) that a plant may operate. However, the reduction in

per bale costs, as battery capacity increases, diminishes

as the number of batteries per plant increases. This is

because the change in fixed costs relative to the change

in volume, as battery capacity is increased, becomes larger

as the number of batteries per gin increases.

For gin plants with batteries capable of producing

twenty bales per hour, the maximum seasonal capacities

(as defined in this paper) for single-battery, two-battery,

three-battery, and four-battery gins are 18,154, 36,307,

54,461, and 72,614 bales, respectively. The average cost

per bale at volumes equal to each of the above seasonal

64

0) fH Id m u ft

o o

SRACi (32 bales/hr.) (56 bales/hr.)

(80

LRAC

Ksi=29,046 KS2=50,830 Ks3=72,614

Annual Volume (bales)

Fig. 8.—Short run average cost curves for selected m.aximum gin plant hourly outputs and the economdes of scale curve, four-battery gins, 1967,

65

capacities for each size gin (by number of batteries)

are $14.82, $14.46, $14.33, and $14.27, respectively.

Variable costs per bale are the same for each of these

minimum cost points ($10.91). Fixed costs per bale at

each of these points are $3.91 for single-battery gins,

$3.54 for two-battery gins, $3.42 for three-battery gins,

and $3.36 for four-battery gins.

The long run or economics of scale curve and

selected short run average cost curves for gins with one

to four batteries per plant are illustrated in Figure 9.

66

9> r-{

to

u

99 o o rH (0 -p o

18,154 K s , l - B

36,307 Ks,2-B

54,461 Ks,3-B

72,614 Ks,4-B

Annual Volume (bales)

Fig. 9.—Short run average cost curves and the economies of scale curve, single-battery, two-battery, three-battery, and four-battery gins, 1967.

CHAPTER V

SUMMARY, CONCLUSIONS, AND LIMITATIONS

Summary

The High Plains cotton ginning industry has

experienced some major difficulties during the past decade.

A sudden change in harvesting methods has been one of the

major factors affecting gin operations. A major portion

of the cotton crop is now being harvested with mechanical

"strippers" instead of hand laborers as in the past. This

has resulted in a shorter ginning season and higher levels

of foreign material mixed with the cotton. Growers are

impatient to get their cotton trailers back from the gin

and/ because of the competiveness of the ginning industry,

ginners must comply with their customers' wishes. This

m.eans that in the absence of seed cotton storage, expensive,

high capacity machinery m.ust be added to the gin plant.

This problem has been further intensified by a decline in

the overall volum.e of cotton produced on the High Plains

as a result of decreased cotton allotments under government

acreage control programs. The High Plains ginning industry

is, therefore, characterized by large investments in

machinery and eqtiipment, short operating seasons, and low

annual volumes of output per gin. Each of these factors

has contributed to rising unit costs of gin operation.

67

68

The objective of this study was to determine the

effects of annual volume of cotton ginned and gin capacity

on the costs of gin operations under present harvesting

methods on the High Plains of Texas. Specifically, the

objectives were:

1. To deternvine the cost per bale of alternative

volumes for specified plant sizes, i.e., short run plant

cost curves; and

2. To determine the minimum cost per bale that

may be attained for alternative volumes when the size of

plant is permitted to vary, i.e., the "economies of scale"

curve.

The nature of cost-output relationships are deter­

mined by the underlying relationships between resources

and output. Resource substitutions are limited in the

short run, but offer some possibilities for reducing costs

per bale in the long run. Because of limited short run

resource substitution possibilities, it was hypothesized

that variable unit costs would be constant for a given

scale of- plant (assuming constant resource prices).

Increasing substitution possibilities in the long run,

however, would be expected to reduce variable costs per

unit where gins can operate at or near output capacity.

Given some specified length of ginning season, gin

capacity can be increased by increasing the hourly capacity

per battery or by increasing the number of batteries. The

69

first method affects the level of total fixed costs and

variable costs per unit of output. The second method of

increasing capacity affects only total fixed costs if the

additional battery has the same basic machinery and capa­

city as the existing battery(s).

To test these hypotheses, a stratified sample of

gins was randomly drawn from a list of High Plains coopera­

tive gins. The sample was drawn from four groups strati­

fied on the basis of total gin plant hourly capacity.

These gin firms were interviewed to obtain volume and cost

data for the five year period, 1963-67.

Gin cost-volume relationships were estimated by the

least squares regression method. The algebraic form of

the model which was developed as being representative of

the High Plains ginning industry?- is given by:

TC = a + b^ V + b2 VK , + b3 Kp + b4 B + bs T

where: TC = annual total cost of ginning

V = annual volume of cotton ginned

Kvj = hourly capacity per battery (assumes same

capacity for all batteries associated with

one gin plant)

K = total gin plant hourly capacity (BK^)

B =•• number of batteries per plant

T = time in years (1963=1)

70

Short run total cost functions are derived from

the model by setting Kj^, ic, B, and T constant at some level

and varying volume. The average cost function is obtained

by dividing the total cost equation by volume.

The long run total cost curves are obtained by

setting volume equal to annual capacity for various scales

of plant. Annual or seasonal capacity is defined in this

study as: Kg = KpHE, where Kg is seasonal capacity, Kp

is defined above, H is the estimated available hours of

gin operation per season and E is the ginning efficiency

rate.

The effects of volume and capacity on total annual

gin costs were estimated by the following regression

equation:

TC = 10,048.38 + 14.75V - 0.192Vk^ + 2325.90k +

11,052.77B + 3428.92T

R2 =: .89 (9.33) (-1.44) (3.26)

(1.41) (3.03)

All but two of the coefficients (Vk ^ and B) are

highly significant. Additional gins were added to the

original sample to increase the number of observations

and range of magnitudes for the various factors included

in the equation so that a meaningful relationship among

the variables might be obtained.

Short run average cost curves and the associated

economies of scale curve were estimated for single-battery.

71

two-battery, three-battery, and four-battery gins. Ginning

costs per bale decreased with increases in volume for any

given hourly rate of battery capacity and specified number

of batteries. As the hourly capacity per battery was

increased, costs per bale decreased at maximum seasonal

volumes regardless of the number of batteries per plant.

The reduction in per bale costs associated with increasing

battery capacity, however, diminished as the number of

batteries per plant was increased.

Conclusions

The effects of volume on costs for a particular

size or scale of plant was determined from the regression

model by specifying the number of batteries and maximum

output per battery and then varying volume up to the

specified plant capacity. Costs per bale for all scales

of plant, as estimated by the regression model, decreased

rapidly in the lower ranges of output, but declined slowly

as volume exceeded 75 percent of the estimated plant capa­

city (see tables in the appendix for a detailed presentation

of the effects of plant utilization on ginning costs).

There were considerable economies of scale asso­

ciated with increases in the output capacity per battery,

but only negligible decreases in costs per bale as the

scale of plant v/as expanded by increasing the number of

batteries. This was as expected since by expanding output

72

by increasing the maximum output per battery, both fixed

and variable costs per bale decrease. Fixed costs per

bale decrease as capacity increases (at volume ginned

equal to capacity) because the increases in volume are

relatively greater than the increases in the total fixed

costs. Variable costs per bale decrease because of more

efficient use of the variable resources (labor and power^

particularly) . Increasing gin capacity by adding to the

number of batteries per plant also reduces fixed costs

per bale as explained above^ but to a lessor extent. Th-is

is the result of larger capital outlays necessary for

the same relative increases in output. There are no savings

in variable costs per bale if the additional battery(s)

has the same basic type of machinery and output capacity.

TPhis is the expected result of adding similar, but sepa­

rate lines of machinery which requires separate crews for

its operation.

Limitations

The prim.ary weaknesses of this paper can be divided

into three general areas: (1) the cost data, (2) estimates

of both hourly and seasonal capacity, and (3) probleT.s

related to the regression model.

The data used for this study were taken from gin

audit reports, and accounting cost classifications are

often unsuitable for economic analysis. Often, costs not

73

associated with the actual gin operation v/ere included in

the audits. Sometimes these costs could be determined and

then separated from other costs. It was not infrequent,

however, for such costs to be "lumped" together with the

actual costs of gin operations, making it impossible to

separate them without referring to sources other than the

audit reports. Examples of such costs were dues collected

from customers and paid to various organizations such as

the National Cotton Council and Cotton Producers Institute.

Compress charges were also sometimes included as a part

of gin costs.

Costs associated with transporting cotton to the

compress and seed to the oil mill were also generally

included as costs of gin operation. Most gins consider

trucking costs as a part of gin operation; therefore,

relatively few gins had separate breakdowns of these costs

in their audits. Several attempts vrere made to statisti­

cally estim.ate costs associated with trucking and other

non-gin operation costs which could not always be detenr.ined

from the audit reports. The results were largely unsatis­

factory; therefore, most of these costs had to be included

in the analysis.

An effort was also made to separate costs as

reported in the audits into fixed and variable crests. This

effort was also unsuccessful because of the numerous

aggregations of fixed and variable costs into one cost

74

classification. Part of this problem was the result of

the many different methods of reporting costs by various

accounting firms and by different accountants within the

same firm.

The audit breakdown of fixed assets and their costs

was also inadequate for economic analysis purposes. A

more detailed presentation was needed in most instances

if depreciation is to be standardized for all firms in the

sample. This would provide a more realistic estimate of

the cost of gin operations associated with purchasing and

maintaining m.achinery and equipment.

Estimating gin capacity was the second major problem

area of the study. Computations of hourly battery capacity

solely on the basis of the gin stand comiplex (number and

size of saws and manufacturer) is, at best, a hazardous

undertaking. There are many other factors that affect the

actual physical capacity of a gin plant. Some of these

factors are the quality and condition of the cotton being

ginned and the capacity of burr extractors, stick machines,

lint cleaners, the press and other machinery used in the

ginning process.

Seasonal capacity is also difficult to estimate.

The length of ginning season and the ginning efficiency

rate are based on past averages, but may vary greatly

from year to year. Changes are taking place so rapidly

that past averages may be meaningless by the timie tl ey ere

75

computed. The weather is a significant factor in deter­

mining the length of ginning season and condition of the

cotton, as well as total volume ginned. One of the years

included in the analysis was a particularly bad year in

so far as weather was concerned. This probably caused

some distortion in the final results of the analysis.

One solution to this problem would be to increase the

number of years from which data are collected.

The third major weakness of this study was the

problems directly related to the regression model. Many

forms of equations were tested in an effort to develop a

meaningful relationship among the variables. The results

of only one equation were given in this report. Inter­

correlation in the variables was a problem with many of

the equations, inclxiding the regression model presented

in this paper. The coefficient of VKb was not significant

and intercorrelation was probably a factor in this result.

The use of ordinary least squares regression may not be

adequate for an analysis of the type that was attempted

in this paper.

The assumption that K]3=Kp/B, and therefore equal

for each battery in a multiple-battery plant is another

weakness of the regression model. Some m.ethod of measuring

the effects of unequal battery sizes, within a gin plant,

on the costs of gin operation should be included in the

model.

LIST OF REFERENCES

(1) Abel, Martin E. and Waugh, Frederick V. "Relationships Between Group Averages and Individual Observations." Agricultural Economics Research, U.S. Department of Agriculture, Vol. XVIII, No. 4 (October, 1966), pp. 105-115.

(2) Anderson, R. F. Costs of Assembling and Ginning Cotton in Georgia Related to Size of Gin. Georgia Experiment Station, University of Georgia, Bulletin N.S. 153, March, 1966.

(3) Brensike, John V. and Askew, William R. costs of Operating Selected Feed Mills as Influenced By Volume, Services, and Other Factors. U.S. Departmient of Agriculture. Agricultural Marketing Service, Marketing Research Report 79, 1955.

(4) Bressler, R. G., Jr. "Research Determination of Economies of Scale." Journal of Farm Economics, Vol. XXVII, No. 3 (August, 1945), pp. 526-539.

(5) Campbell, John D. Costs of Ginning Cotton By Cooperatives at Sinqle-Gm and Two-Gin Planrs, California and Texas, 1962. U.S. Department of Agriculture, Farmer Cooperative Service, Marketing Research Report No. 640, January, 1964,

(6) Cotton Economic Research Committee. The Texas Cotton Ginning Industry. University of Texas, Research Report No. 73.

(7) Covey, Charles D. and Hudson, James F. Cotton Gin Efficiency as Related to Size, Location, and Cotton production Density ij.-: Louisiana. Agricultural Experiment Station, Louisiana State University and Agricultural and Mechanical College. Bulletin No. 577, December, 1963.

4

(8) Dietrich, Raym.ond A. Costs and Economies of Size jn Texas - Oklahoma Cattle Feedlot Operations. Texas A & M University," B - 1083. May, 1969.

(9) Erdman, H. E. "Interpretation of Variations in Cost Data for a Group of Individual Farms," Journal of FariT'. Economics, Vol. XXVI, No. 2 (May, 1944) , ""pp, 388-391.

76

77

(10) French, B. C ; Sammet, L. L.; and Bressler, R. G. "Economic Efficiency in Plant Operations with Special Reference to the Marketing of California Pears." Hilgardia, vol. XXIV, No. 19, 1956.

(11) Johnston, j. Statistical Cost Analysis. New York: McGraw-Hill Book Company, Inc., 1960.

(12) Heady, Earl O. and Dillon, John L. Agricultural Production Functions. Ames Iowa: Iowa State University Press, 1961, p. 211.

(13) Mathia, Gene A. and Hammond, Leigh H. "Measuring Economic Efficiency: An Application to Apple Marketing Facilities." Proceedings, Marketing Section, Association of Southern Agricultural Workers, 64th Annual Convention. New Orleans, Louisiana, February 1, 1967, pp. 195-212.

(14) Metcalf, AlonzoV., et al. Assembling, Storing, and Ginning Cotton in the Mississippi Delta. Agricultural Experiment Station, University of Missouri, Research Bulletin 878, Southern Cooperative Series Bulletin No, 99, January, 1965.

(15) Paulson, W. E. Cost and Profit of Ginning Cotton in Texas. Texas Agricultural Experiment Station, Agricultural and Mechanical College of Texas, Bulletin No. 506, January, 1942.

(16) . Efficiency as Applied to Cotton Ginning Business. Texas Agricultural Experiment Station, Agricultural and Mechanical College of Texas, Bulletin No. 654, August, 1944.

(17) Phillips, Richard. "Empirical Estimates of Cost Functions for Mixed Feed Mills in the Midwest." Agricultural Economics Research, U.S. Department of Agriculture, Vol. VIII, No. 1, January, 1956.

4

(18) Spencer, Milton H.- Managerial Economics. 3rd ed. Homicwood, Illinois: Richard D. Irwin, Inc., 1968.

(19) Stollsteimer, J. F.; Bressler, R. G.; and Boles, J. W. "Cost Functions From Cross - Section Data - Fact or Fantasy," Agricultural Economics Research, U.S. Department of Agriculture, Vol. XIII, No. 3, July, 1961, pp. 79-88.

78

(20) Thoaipson, Russell G. and Ward, J. M. An Economic Analysis of Cotton Gin Plants - High Plains, ] 2J-AiB2 plains, and Lower Rio Grande Valley of Texas. Texas A & M University, Texas Agricultural Experiment Station, B - 1020, July, 1964.

(21) Tussey, W. Glenn and King, Richard A. Costs of Ginning Cotton in North Carolina - 1957. North Carolina State College, Department of Agricultural Economics, Agricultural Economics Information Series No. 72, November, 1959.

(22) U.S. Statutes at Large, Vol. LXXV, "Laws and Concurrent Resolutions Enacted During the First Session of the Eighty-Seventh Congress of the United States of America, 1961, Reorganization plans. Amendment to the Constitution, and ProclamatioJis, Public Law 87-345. Washington, U.S. Government Printing Office, p. 761.

(23) Wilmiot, Charles A.; Shaw, Dale L.; and Looney, Zolon M. Cotton Gin Operating Costs In West Texas. U.S. Department of Agriculture, Econoraic Research Service, Marketing Research Report No. 831, November, 1968.

(24) , et_ al_. Engineering and Economic Aspects of Cotton Gin Operations. . . . Mid South, V7esc Texas, Far West. U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No, 116, July, 1967.

APPENDIX

79

80

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