Research Notes: Revisiting the Asian Financial Crisis

TRANSFORMATIONS: A REGION IN THE MAKING 182

By:

Vicente C. Reyes, Jr. Department of Political Science

National University of Singapore

TRANSFORMATIONS: BOOK REVIEW AND RESEARCH NOTES

RESEARCH NOTES:

REVISITING THE ASIAN FINANCIAL CRISIS CRITICAL REVIEW OF THE ECONOMIC PREMISES OF THE ARTICLE ENTITLED:

THE END OF THE ASIAN MYTH:

WHY WERE THE EXPERTS FOOLED?


Introduction and methodology

his study is primarily a critique of the article entitled: “The End of the Asian Myth: Why were the Experts Fooled?” written by Werner Baer, William Miles and Allen Morgan for the journal World Development Volume 27, Number 10 edition which

was issued in 1999. The critique will use standard econometric analyses to test the validity of the economic premises of the article and also test hypotheses that are derived from the premises of the article. The study sought to revisit and test some of the fundamental economic premises presented by the article mentioned above. This would be accomplished by undertaking cross-national econometric analyses of East Asian as well as Latin American countries. Specifically, the study addresses three core questions:

1.1.1 Using the premises of the article being critiqued, is it possible to create a model that approximates the neo-classical representation of national economies? 1.1.2 Holding everything constant, and once again using the premises of the article being critiqued, is there a significant difference between the economies of East Asia and Latin America? 1.1.3 What valuable lessons can be learned from the apparent misdiagnosis of the Asian Financial Crisis?

2. Theoretical Framework

2.1 Neo-Classical Economics

The article explicitly states that the overriding economic ideology of a considerable number of economists studying East Asia was Neo-Classical. It thus would be worthwhile to briefly discuss two of the most outstanding features of neo-classical economics.1 For the purposes of this study, these two features are (1) lack of government presence and (2) an open economy.2 The article claimed that many economists have erroneously attributed the phenomenal growth of East Asian economies with the region’s relatively open economies and small government presence. The lack of restrictions on trade and the easy entry of foreign capital to East Asia were used as indicators of open economies. The absence of direct government intervention and the lack of a government role in national economies (as

1 For this study Neo-Classical Economics would be assumed to adhere more closely to the American “Chicago” School. 2 It would be an oversimplification to simply say that these two features belong exclusively to the neo-classical economics school. It would also be an oversimplification to state that “open economies with no government” adhere to the neo-classical mode. The historical development of Classical Economics of Adam Smith to the later interpretations of David Hume, Jeremy Bentham, John Stuart Mill, Karl Marx, John Maynard Keynes and more recently Milton Friedman (to simply name a few) and to arbitrarily lump this rich tapestry of economic thought history into what is generally described as “Neo-Classical Economics” would be misleading; doing so would necessitate a different research paper altogether.

T


opposed to the example of Latin America) were used as the indicator of small government presence. The study clearly delineates an asserted difference between the more open economies of East Asia and the economies of Latin America which are characterized by heavy government presence. 2.2 Multiple Regression Analysis

Multivariable techniques examine the relationship amongst several variables using a correlation matrix or a variance-covariance matrix. Multiple regression and related discriminant analysis therefore use the correlation or variance-covariance amongst a set of variable to predict, explain or classify.(Schumacker et. al, 2002: 3)

The quotation above explains briefly the three main objectives of undertaking multiple regression analysis. Such is designed as statistical analyses to “predict, explain or classify” relationships among several variables. This is especially helpful for social scientists who wish to explain phenomena occurring in society which are non-experimental in nature. For this particular study, Multiple Regression Analysis will be most useful in explaining the possible relationships between economic phenomena (variables) in the countries covered in this research. The study used the premises of the article to identify the variables—both independent and dependent. The article claimed that the economic performance of a nation was determined by the amount of interaction with the government (strong presence or minimal presence) in economic affairs and the degree of openness (or on the contrary lack of openness) of the economy. The article reiterated the claim that East Asian economies were supposedly more open while Latin American economies were said to be heavily influenced by government intervention. The study identified economic performance as the dependent variable (DV) and the government presence and degree of openness of the economy as the independent variables (IV).

2.3 Questions and Hypothesis of the Study One of the main premises of the article was that the economic performance of a nation is determined by the combination of government presence and the degree of openness of the economy. The other main premise assumed that the economies of East Asia and Latin America—ceteris paribus- are different. Using the data provided in the article and expanding on these, the study undertook tests of hypotheses on both main premises. With statistical data from the World Development Report (focus on the year 1998), the study attempted to test two hypotheses: Given thirty (30) countries, fifteen (15) from Latin America and fifteen (15) from East Asia, is it possible to construct a model that adequately shows the significance of OPENESSS OF THE ECONOMY and GOVERNMENT ROLE IN THE ECONOMY


(Independent Variables) with the GROSS DOMESTIC PRODUCT (Indicator of Economic Performance and Dependent Variable for the study).

Hypothesis:

Creating a dichotomous variable called REGION, the study tested the hypothesis whether—ceteris paribus—the region (whether the country came from East Asia or Latin America) had a significant impact on the GROSS DOMESTIC PRODUCT of the country.

Ho : λREGION = 0 H1 : λREGION # 0

3. RESULTS OF THE TESTS OF HYPOTHESES

3.1 Results of Question

In order to fully address the question on whether it would be possible to construct a model that will represent a relationship between the performance of a nation’s economy and the dual factors of government presence and openness of the economy, several indicators were formulated. Studying the statistical tables from the World Development Indicators published by the International Monetary Fund (See Annex A), the study identified four indicators. For the dependent variable (DV) economic performance of a nation, the rate of growth of real Gross Domestic Product (GDP%) was selected. Five variables were selected to represent Independent Variables (IV) of the study. To represent government presence in the economy, the study selected the external foreign debt as a percentage of GDP (Extdbt%). The indicators for the openness of the economy were exports as a percentage of GDP (Exp%); gross domestic savings as a percentage of GDP (GDS%) and gross domestic investments as a percentage of GDP (GDI%). To signify whether the country belonged to the East Asian (represented by 1) or Latin American region (represented by 0) a dichotomous (dummy) variable REGION was likewise introduced. Table 1 below presents a graphical representation of the dependent variables and the independent variables of the study.

Null Hypothesis: There is NO IMPACT of the Dummy Variable REGION on GDP Performance

Alternative Hypothesis: There is an IMPACT of the Dummy Variable REGION on GDP Performance


Table 1: Multiple Regression Analysis with Variables of the Study

The purpose of hypothesis 1 is to come up with a model showing a significant mathematical relationship between the dependent and independent variables. The mathematical representation of the table above is presented below:

Multiple Regression Model Representation

GDP% = σ + γ1Extdbt% + γ2 Exp%+ γ3 GDS%+γ4GDI%+γ5 REGION+ Ζ

σ = CONSTANT γ1= Coefficient of Extdbt% γ2 = Coefficient of Exp% γ3 = Coefficient of GDS% γ4 = Coefficient of GDI% γ5 =Coefficient of REGION

Ζ = Random Disturbance Term

The results that would come from multiple regression analysis will help determine the value of the coefficients of the independent variables listed above.3 Table 2 below shows the results of the least squares operation that is the fundamental process used in multiple regression analysis.

3 The SPSS version 10 program was used for this study in the conduct of Multiple Regression Analysis

REGION GDI% GDS% Exp% Extdbt % GDP%

Region – whether from Asia or Latin

America (REGION)

Gross Domestic

Investments as % of GDP

(GDI%)

Gross Domestic

Savings as % of GDP (GDS%)

Exports as % of GDP (Exp%)

External Foreign Debt as % of GDP

( Extdbt %)

Dichotomous “ Dummy ” Variable

Openness of the Economy Government Presence in the

Economy

Independent Variables Dependent Variable

REGION GDI% GDS% Exp% Extdbt % GDP%

Region – whether from Asia or Latin

America (REGION)

Gross Domestic

Investments as % of GDP

(GDI%)

Gross Domestic

Savings as % of GDP (GDS%)

Exports as % of GDP (Exp%)

External Foreign Debt as % of GDP

( Extdbt %)

Dichotomous “ Dummy ” Variable

Openness of the Economy

Independent Variables Dependent Variable

Multiple Regression Analysis

Rate of Annual Growth Rate of GDP (GDP%)


Table 2: Model Summary of Multiple Regression Model

Table 2 above reveals key information about the interaction of the dependent and independent variables. The value of the R-square which, in the table above, is 0.774 means that about 78% of the variation in the dependent variable (GDP%) is caused by variance (changes) of the independent variables. Another way of interpreting this would be to say that variation in the dependent variable is explained 78% of the time by variances that occur in the independent variables. Another helpful statistic generated multiple regression analysis is used would be the Analysis of Variance (ANOVA) table. Table 3 below presents a summary of the ANOVA statistic for the given set of observations:

Table 3: ANOVA statistic

Two helpful pieces of data from Table 3 above provide more confidence in the results of the tests of hypothesis 1 for this study. These are the values for the significance level (Sig) and the F-statistic (F). The table above shows a value of 0.00 for the Sig and a high value of 13.032 for the F. These can be interpreted to mean that in the case of the Sig., a value of 0 signifies that the relationship between the dependent variable and the independent

ANOVA b

531.579 5 106.316 13.032 .000 a 155.000 19 8.158 686.579 24

Regression Residual Total

Model 1

Sum of Squares df Mean Square F Sig.

Predictors: (Constant), ASIA, GDI%, Extdbt%, Exp%, GDS% a. Dependent Variable: GDP% b.

A high F- stat means that the relationship between the variables is HIGHLY SIGNIFICANT

This figure says that the relationship between the Dep. Var. and the Ind. Var is NOT due to chance

Model Summary

.880a .774 .715 2.8562Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), REGION, GDI%, Extdbt%, Exp%,GDS%

a.

Model Summary

.880a .774 .715 2.8562Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), REGION, GDI%, Extdbt%, Exp%,GDS%

a.

Impressive R-square:78 % of the variation in the Dependent variable is caused by the variance of the IndependentVariables.


variable is NOT due to chance; and in the case of the F, a high value means that the relationship between the variables is STRONG.

Table 4 below, the table of coefficients provides more empirical proof in the results of the tests of hypothesis 1 for the study.

Table 4: Table of Coefficients

Two sets of values in the table of coefficients provide valuable information necessary for answering the tests of hypothesis 1 of the study. The significance (Sig.) level and the T-statistic (t) are extremely useful in assessing the strength of the relationships between the variables in a multi-variate model. Sig. levels that are closer to 0 mean that the coefficients of the variables are significant to the model. T values that are higher than absolute value 2 mean that the coefficients are useful predictors for the model. Table 4 above indicates that all the independent variables (not including the dummy variable REGION) have Sig. Levels that are close to 0 and also have T values that are greater than the absolute value of 2.

Is it now possible to state that there is a MODEL that can describe significantly the relationship between the dependent variable GDP% and the independent variables: Extdbt%, Exp%, GDS% and GDI%? One more test4 needs to be done in order to definitively check the robustness of the model—this is captured in Table 5 below, the Table of Collinearity Diagnostics.

4 There are plenty more statistical tests that can be done. However for the purpose of this paper, ANOVA, coefficients and collinearity tests were the ones deliberately chosen due to the constraints of time and space.

Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581

(Constant)Exp%Extdbt%GDS%GDI%REGION

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: GDP%a.

Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581


Model1

B Std. Error


Beta


t Sig.


T-stats that are higher than[2] say that the coefficients areUSEFUL predictors

Significance levels closerto 0 say that the coefficientsare significant to the model.


Table 5: Table of Collinearity Diagnostics

Multi-collinearity is the phenomenon where independent variables in a multiple regression model are highly correlated with each other. Such an occurrence dilutes the full explanatory, predictability and classificatory power of multiple regression analysis. With the presence of multi-collinearity, variances in the dependent variable may not be fully explained by independent variables. As a consequence, the conduct of collinearity tests that accompany multiple regression analysis is advised.

In Table 5 above, the values that are worth inspecting are the condition indexes. Scores of 13 reveal problems, the independent variables may be suffering from multi-collinearity. A score of 30 above in the condition index reveals major problems of multi-collinearity. Studying the condition index above, presents a situation where the model DOES NOT have problems of multi-collinearity.

At this point, it is possible to say that Hypothesis 1 can be answered positively—there is a model that represents a significant relationship between the dependent variable GDP% and the independent variables: Extdbt%, Exp%, GDS% and GDI%.

Inspecting Table 4 again, the Table of Coefficients, we can identify the coefficients of the independent variables and from there formulate the model:

Collinearity Diagnosticsa

4.638 1.000 .00 .01 .01 .01 .00 .01.656 2.660 .00 .29 .03 .02 .00 .10.406 3.378 .01 .21 .00 .02 .02 .36.190 4.943 .01 .01 .94 .10 .00 .12

7.985E-02 7.621 .10 .48 .02 .85 .08 .403.055E-02 12.321 .87 .00 .01 .00 .89 .01

Dimension123456

Model1

EigenvalueCondition

Index (Constant) Extdbt% Exp% GDS% GDI% REGIONVariance Proportions


A score of 13 reveal problems w/regardto multi-collinearity. A score of 30 + revealsmajor problems.The condition indexes for this model DO NOThave problems of multi-collinearity.

Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581


Model1

B Std. Error


Beta


t Sig.


Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581


Model1

B Std. Error


Beta


t Sig.



Recalling the multiple regression model, formulated at an earlier part of this paper :

We extract the values of the coefficients of the independent variables and come up with the estimated model below:

The statistical tests that have been conducted on the proposed regression equation above represent a significant relationship. This relationship is apparent among the data set of fifteen countries from East Asia, and another fifteen from Latin America for the year 1998—between the dependent variable economic performance of a country—represented as percentage change in the GDP (GDP%) with the independent variables of government presence in the economy—represented as percentage of external debt to GDP (Extdbt%) and openness of the economy—represented as exports as a percentage of GDP (Exp%); gross domestic savings as a percentage of GDP (GDS%); gross domestic savings as a percentage of GDP (GDI%) and the dummy variable REGION—that represents whether a given country comes from East Asia or Latin America. Recalling the purpose of multiple regression analysis: “explaining, predicting and classifying” the model above accomplishes the first two. The model explains the relationship between the dependent variable and the independent variables through the coefficients of the variables. Figure 1 below which is the Normal Plot comparing the observed probabilities of the Dependent Variable with that of the expected values reveal the predictive power of the model:

GDP% = - 1.101 + - 0.034 Extdbt % + - 0.085 Exp% + - 0.252 GDS% + 0.519 GDI% + 0.906 REGION + z

Question 1: Yes, it is possible to find a MODEL that represents a significant relationship between GDP; the ROLE of the Govt . and OPENNES

SSS of the economy.

GDP% = α + γ1 Extdbt% + γ2 Exp% + γ3 GDS% + γ4 GDI% + γ5 REGION + z


Figure 1: Observed versus Expected values of the Dependent Variable

Normal P-P Plot Reg. Stand. Resid.

Dependent Variable: GDP%

Observed Cum Prob

1.00.75.50.250.00

Expe

cted C

um P

rob

1.00

.75

.50

.25

0.00

As can be gleaned from the observed and expected values; these are “closely bunched together” which would mean the model has strong predictive power. Moreover, with knowledge of the coefficients of the model, further extrapolations on possible values could be made with the use of simple algebraic computations. 4. Results of Hypothesis 1 Multiple regression analysis serves to “explain, predict and classify.” The first two have already been utilized. In testing hypothesis 2 the third purpose of multiple regression analysis—classifying—is now put to good use. Would it be possible to classify the nations under this study into two clear divisions, namely countries belonging to East Asia and those belonging to Latin America? Previously, the study was able to determine that a model representing a significant relationship between dependent variable—economic performance of a country—and independent variables—government presence and openness of the economy—is quite possible to achieve given a set of data. This part of the study proceeds to test hypothesis 2, whether or not ceteris paribus—the REGION—the country as East Asian or Latin American makes an impact on economic performance. In order to fully test hypothesis 2 it is necessary to recall the salient values found in Table 4.


Recall that the hypothesis stated that if the coefficient of the dummy variable region is not equal to 0, then the null hypothesis can be rejected and the alternative hypothesis can be accepted. Closer inspection of the model below and especially of the coefficient of the dummy variable region reveals a value of 0.907. But a more careful scrutiny of the significance level of the coefficient shows a value of .581 for the variable REGION. This figure says that the variable is NOT SIGNIFICANT and thus the null hypothesis that γ5 = 0 cannot be rejected. As was said previously, what the model in fact says is that the REGION—whether the nations come from East Asia or Latin America—has an indeterminate impact on the nation’s economic performance (based on the data set used for the analysis). The implication of such a figure is that there is NO CLEAR DIFFERENCE between economies of East Asia and Latin America (again based on the data set used for the analysis).

GDP% =

- 1.101 + - 0.034 Extdbt

% + - 0.085 Exp%

+ - 0.252 GDS%

+ 0.519 GDI%

+ 0.907 REGION + z Recalling the Multiple Regression Formula:

Ho : γ 5 = 0

H1 : γ 5 = 0

Ho : γ 5 = 0

H1 : γ 5 = 0 Accept

NO, the REGION has an impact on GDP

Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581


Model1

B Std. Error


Beta


t Sig.


Coefficientsa

-1.101 2.454 -.449 .659-8.53E-02 .024 -.472 -3.516 .002-3.48E-02 .010 -.431 -3.408 .003

-.252 .065 -.596 -3.857 .001.519 .102 .588 5.065 .000.907 1.614 .085 .562 .581


Model1

B Std. Error


Beta


t Sig.

Dependent Variable: GDP%a. Measures the effect of the Ind. Variables on the Dep. Variables.

Measures the effect of the dummyvariable on the Dependent variable


The statistical tests support the main arguments that have been purported by the article. Conventional wisdom that pointed out a difference between economies of East Asia and Latin America—claiming that the former were more open while the latter was characterized by heavy government intervention—is flawed. 5. Lessons Learned Reviewing the premises presented by the article and recalling the results of the tests of hypothesis conducted by the study, two valuable lessons are put forth: 5.1 The argument of the article about the need to conduct inductivisim—studying individual differences that occur within Asian economies—deserves serious consideration. The results of the tests of hypothesis state that a nation’s economic performance is significantly related to the degree of government presence and openness of the economy. However, as the article pointed out, individual phenomena (not usually seen in macroeconomic figures) still need to be seriously looked into in order to better understand the economic conditions of the region. Although the formula seems to present a neat fit; the empirical evidence as presented by the article presents a contrasting picture. Government intervention and rigidities in the economy—not clearly apparent in macroeconomic figures—may have had a serious impact on the developments of the economies in the region. 5.2 For policy analysts and serious students of economics of development, the results of the tests of hypothesis point to an absence of a clear difference between the East Asian and Latin American economies. Consequently, in the search for solutions to economic problems that plague these nations, a careful analysis of the experience of other regions—with the objective of “learning from mistakes approach”—may be the most logical and efficacious path. Careful comparative, historical, and empirical studies are in order. 6. Conclusion and Areas for Further Research

The paper answered three core questions: (1) Using the premises of the article being critiqued, is it possible to create a model that approximates the neo-classical representation of national economies? (2) Holding everything constant, and once again using the premises of the article being critiqued, is there a significant difference between the economies of East Asia and Latin America? (3) What valuable lessons can be learned from the apparent misdiagnosis of the Asian Financial Crisis? In addressing these questions, the paper addressed a fundamental question of model-building and also tested a hypothesis both of which were derived from the premises of the article. The study proposes that further studies be conducted in order to deepen knowledge of the nature of the Asian financial crisis and to avoid a possible recurrence. Case Studies on the instances of government intervention and the rigidities of the economies of East Asia could shed new light on possible reasons that could be attributed to the crisis. Likewise, the study suggests longitudinal studies be conducted to compare the economic histories of


Latin America and East Asia as both regions go through periods of boom and bust. In the end, a more profound knowledge of the economies of the region would prove truly beneficial. ■


Annex A

Cross National Data for East Asian Economies and Latin American Economies for the Year 1998

12345678910111213141516171819202122232425262728293031

A B C D E F G H ICountry GDP% Exp% Infl% Extdbt% FDI% GDS% GDI% REGIONCHINA 7.80 21.94 -0.84 16.63 4.62 42.34 38.80 1.00INDO -13.21 53.91 57.64 176.49 -0.36 36.48 13.98 1.00SOKOR -6.69 49.47 7.51 44.52 1.71 34.46 21.17 1.00MYS -7.50 114.36 5.27 65.29 2.98 48.48 26.69 1.00PHL -0.48 56.79 9.70 69.98 3.49 13.63 20.17 1.00THA -10.17 58.62 8.07 79.71 6.53 36.22 20.26 1.00TWN 4.57 47.82 1.68 n/a n/a 26.01 24.91 1.00HKG -5.14 127.49 2.83 n/a n/a 30.11 29.68 1.00SGP 0.40 n/a -0.27 n/a 6.64 52.43 32.80 1.00LAO 3.97 3.70 90.98 199.09 3.57 13.37 24.90 1.00KHM 1.00 34.06 14.78 77.68 4.21 5.50 15.01 1.00BRN 1.00 n/a n/a n/a n/a n/a n/a 1.00VNM 5.80 n/a n/a 82.25 7.25 21.30 28.71 1.00MMR 4.95 n/a 51.49 n/a n/a 10.23 n/a 1.00JAP -2.82 n/a 0.65 n/a 0.09 28.70 n/a 1.00ARG 3.90 10.40 0.92 49.49 2.23 17.40 19.88 0.00BRA 0.15 7.43 3.20 30.57 4.12 18.62 21.28 0.00CHL 3.41 27.53 5.11 49.13 6.37 22.37 26.54 0.00MEX 4.80 31.25 15.93 39.69 2.72 22.23 24.35 0.00VEN -0.67 20.03 35.78 39.64 4.71 20.23 19.62 0.00COL 0.60 13.93 20.36 33.12 2.99 13.53 19.60 0.00ECU 0.55 25.29 36.10 80.05 4.13 17.95 24.66 0.00URY 4.49 21.93 10.81 36.83 0.74 14.86 15.79 0.00PER 0.30 11.96 7.25 52.88 3.34 18.88 24.29 0.00GTM 5.15 18.60 6.97 24.41 3.49 9.31 15.98 0.00CRI 6.18 48.97 11.67 39.08 4.41 17.58 28.69 0.00BOL 4.75 33.15 7.67 72.94 11.24 10.78 19.96 0.00PRY -0.42 44.97 11.53 26.62 2.28 6.00 21.00 0.00NIC 4.05 39.10 13.04 298.77 8.65 -8.98 33.43 0.00PAN 4.14 33.80 0.63 77.60 12.91 23.02 32.78 0.00

Source: Global Development Finance & World Development Indicators 2002


Annex B: Bibliography

Baer, Werner, William R. Miles and Allen B. Morgan. “The End of the Asian Myth: Why

were the Experts Fooled?” World Development. Vol. 27, No. 10 pp 1735-1747 Sandström, Sven. The East Asia Crisis and the Role of the World Bank. Statement to

the Bretton Woods Committee. Washington, D.C. Februay 13, 1998. Schumacker, Randall., Robert Mount and Michael Monahan. Multiple Regression and

Discriminant Analysis Revisited. Paper presented at the American Educational Research Association. Louisiana, April 2002.

Yin, R.K. Case Study Research, Design and Methods, 2nd ed. Newbury Park: Sage Publications, 1994.

Research Notes: Revisiting the Asian Financial Crisis

Documents

Transcript of Research Notes: Revisiting the Asian Financial Crisis