Application 3 : Annotated Bibliography

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Copyright © 2014 by Academic Publishing House Researcher

Published in the Russian Federation European Journal of Economic Studies Has been issued since 2012. ISSN: 2304-9669 E-ISSN: 2305-6282 Vol. 13, Is. 3, pp. 147-160, 2015 DOI: 10.13187/es.2015.13.147

www.ejournal2.com UDC 33

Unemployment and Economic Growth of Developing Asian Countries: A Panel Data Analysis

1 Muhammad Imran

2 Khurrum S. Mughal 3 Aneel Salman

4 Nedim Makarevic 1 IQRA University, Islamabad Campus, Pakistan E-mail: [email protected] 2 COMSATS Institute of Information Technology, Islamabad E-mail: [email protected] 3 COMSATS Institute of Information Technology, Islamabad E-mail: [email protected] 4 Embassy of Bosnia and Herzegovina in Pakistan, Pakistan E-mail: [email protected]

Abstract This study presents the new regression estimates of the relationship between unemployment

and economic growth for 12 selected Asian countries over the period 1982-2011. Fixed effect and Pooled OLS techniques are used to analyze the panel data for measuring individual country effects, group effects and time effects while exploring the relationship between Unemployment rate and the Economic Growth. The results showed that higher unemployment rate has significant negative impact on GDP per capita growth (a proxy for economic growth). The results also investigated that economic growth seems to be significantly affected by traditional determinants such as Inflation (consumer price index), Population growth, Gross Capital Formation, Trade openness etc. Based on our results the author has concluded that reduction in unemployment rate would be a better option for more and sustained economic growth and also improving the welfare of the people.

Keywords: unemployment, economic growth, developing countries, panel data, fixed effect model.

1. Introduction Labor markets in Asia are characterized by pervasive unemployment and under-employment.

Asian countries vary in size and complexity. The nature, size and structure of population of Asia region have been changing qualitatively and quantitatively. From 7 most populous countries of the world, 6 of them (China, India, Brazil, Indonesia, Pakistan and Bangladesh) are located in Asia region. Economic growth, development and low level of unemployment are a dream that has become authenticity for some countries in the west, and also a few Asian countries like China, Japan, India and many other countries also. Man has constantly investigated to develop his material state through effectual use of resources, such as improving economic growth and low level of un-employment, price

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stability, stable currency value etc. Unemployment and Economic growth have been found to influence each other, but so far this aspect is normally neglected in studying the comparative analysis of developing Asian countries. Unemployment is a continuing concern of every economy and economic growth is driven by country’s structural changes. The structural changes can not contribute in economic growth if social costs of structural changes are high and one of them is persistent unemployment. Unemployment rate has negative consequences for the economic well-being of human being (Levine, 2012). According to ILO population report in 2012, the number of unemployed individuals in the world has increased by 4 million in 2012 with the total reaching to_197 million. This year it is expected that it will reach up to 5.1 million and further more 3 million people will be jobless in 2014. For over three decades there has been massive amount of exploration on both theoretical and empirical effects of unemployment on economic growth of developing countries but little more has been done to investigate the relative relationship between economic growth and unemployment in Asian countries like Bangladesh, Cyprus, India, Indonesia, Korea (south), Kuwait, Pakistan, Philippines, Sri-Lanka, Syria Arab, Thailand and Turkey.

After five years of world financial crisis, economic growth has decelerated with a rise in unemployment. Rises in unemployment rate of Asia is mainly due to increase in labor force. According the World Bank report in 2011, unemployment rates in 2011 was 5, 6 and 10 percent for Bangladesh, Pakistan and India respectively. According to Economic Survey of Pakistan in 2011-12, from now, in the past few years, industrial load shedding accounts for loss of 400,000 jobs in Pakistan. It is an economic reality that country’s qualitative and quantitative nature of workforce directly impacts its GDP per capita growth rate. Workforce of any country is not only a productive agent of goods and services but these also play a role in country’s purchasing power which in-turn is a fuel for economic growth. According to World Bank statistics in 2012, at the end of 1980 Asian countries unemployment was very low; however, in 2000s it started to increased and was high in 2011 and is still high today. The unemployment situation in Asia has become critical. There are misleading arguments that there is no negative relationship between unemployment and other economic indicators with economic growth because each indicator including rate of unemployment and Gross Domestic Product (GDP) are rising in the long run. Asia always presents highly contrasting economic images. Economic growth is a problem in Asia due to unemployment strain and other weak economic indicators lead by defective government policies and corruption. The degree to which persistent Unemployment influence the economic growth of Asia region needs to be investigated, especially in the period where there is decline in overall economic growth (real GDP growth per capita) of Asia region.

Economic growth is the main objective of every economy. It is a standard fact that countries with good economic conditions are operationally efficient. A survey of global financial and economic practices suggests that current economic conditions of Asia countries are not optimal. The author has critically reviewed some of important empirical researches to develop main objectives in the environment of Asian countries and further, to utilize it and to draw important conclusions and recommendations for policy making. Osinubi (2005) explore the possibility of relationship among unemployment, poverty and economic growth. The results have been found by using multi-equation model by collecting the time series data for 31 years from 1970 to 2000. He concluded that increase in employment will lead to increase the output and hence cause economic growth. On the other hand, a decrease in employment rate will decrease the output and then economic growth. Blanchard (2006) conducts the study about European unemployment on evolution of facts and ideas. From survey reports, he found that European Unemployment started to increase in 1970s; further increased in 1980s and it reached a plateau in 1990s and is still high. He considered the 30 years data from 15 European countries and found that total factor productivity growth started to decline.

Wang & Abrams (2007) constructed a simple model of government outlays, growth and unemployment, by taking data of 20 OECD countries during recent three decades started from 1970 to 1999. They examined that the negative relationship between unemployment and growth is due to another cause called government outlays. Adjemian et al. (2010) examine the relationship that how labor market institutions affect unemployment and then economic growth. The data set covers 183 European regions and period from 1980 to 2003. They show that high labor costs and trade union power lead to higher unemployment rate and lower economic growth rate. Ahmed et al. (2011) explore the relationship among unemployment and growth (GDP) of Nigerian Economy,

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by taking the secondary data for just 9 years from 2000 to 2008. They used regression techniques and showed that unemployment effect is 65.5 percent on the Nigerian GDP growth and there exist a negative relationship between unemployment and economic growth. Stephen (2012) explored the relationship between urban unemployment crisis on economic growth of Nigerian economy, also combining with inflation rate and investment level. Estimates showed that there exists a negative relationship between urban unemployment and economic growth. Stephen suggested that integrated vocational training programs and economic activities toward self-reliance and self- employment should be encouraged so that the unemployment rate can be minimized.

2. Data Description and Methodology The data set consists of the period 1982 to 2011, which is thirty (30) years. The observed data

was time series as well as cross sectional data, which is converted to Panel data/Pooled data. For this purpose we have already normalized the data for each country by using them as percentage of respective GDP in case the variable was in monetary terms. In our data set all the values of variables are presented, some of the observations were missing that have been attained by interpolation technique because missing values lower the quality of panel data.

Table 2.1: Descriptive statistic

Variables Mean Std. Dev. Minimum Maximum Observations

GDP_PC 3.0419 4.2349 -16.3 22.5 N = 360

UNEM 5.3052 3.4465 0.5 15.2 N = 360

INF 10.2711 14.5906 -3.0 88.1 N = 360

FDI 1.2536 1.7426 -2.8 10.5 N = 360

GCF 23.9313 6.1490 10.7 42.8 N = 360

TRD 63.3936 29.7340 12 150.3 N = 360

DCB 66.0208 47.2448 13.5 330.1 N = 360

PG 1.8522 0.9787 -2.8 5.4 N = 360

GS 25.4955 8.8311 6.7 64.7 N = 360

TNRR 8.0116 13.0090 0 63.7 N = 360

GFCE 12.5291 6.5161 4.1 76.2 N = 360

RIR 4.4180 6.2690 -24.6 46.2 N = 360 Total number of observations were 360 because there are twelve countries (n=12) and thirty

years’ time period (T=30). The mean value for unemployment is 5.3052 and the minimum value of the series is 0.5 and belongs to Kuwait for the year 1984-87 and 1984-1992. The maximum value of Unemployment15.2 belongs to Syria for the year 1997.

Graphical presentation for unemployment rate and economic growth are presented in Appendix Figure A2.1 and Appendix Figure A2.2. And Appendix Table A2.3 describes the matrix of correlation coefficients which shows that our studied data is free from the threat of high multicollinearity. Here GDP per capita growth is a dependent variable. GDP is a good measure of average real income in a country (Akbar et al, 2011).

The methodology adopted for this study is empirical and experimental. This research study has aim to examine whether unemployment has an impact on the economic growth of the selected twelve Asian countries. Now suppose variable factors of production only determine the output level in an economy, and the model presented by Tiwari & Mutascu in 2011 as follows:-

--- (i) Where, Y is output level (i.e. Per Capita GDP), L denotes the labor amount (measured by

Labor force of the country) and K denotes the capital (measured by Gross Capital Formation), it can be said that an increase in the amount of employed labor and capital will increase the output level of an economy. Then following above for our research study extended model after including the other explanatory variables, the model would be as follows:

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GDP_PCit = f (UNEMit, GCFit, PGit ,TRDit, RIRit, DCBit, INFit, GFCEit, TNRRit, GSit, FDIit)

…(ii) where

GDP_PCit = GDP per capita growth (annual %)

UNEMit = Unemployment, total (% of total labor force)

GCFit = Gross capital formation (% of GDP)

PGit = Population growth (annual %)

TRDit = Trade Openness (% of GDP)

RIRit = Real interest rate (%)

DCBit = Domestic credit provided by banking sector (% of GDP)

INFit = Inflation, consumer prices (annual %)

GFCEit = General government final consumption expenditure (% of GDP)

TNRRit = Total natural resources rents (% of GDP)

GSit = Gross savings (% of GDP)

FDIit = Foreign direct investment, net inflows (% of GDP)

Here, i show country effects in explanatory variables, and t shows time effects in explanatory

variables and the assumptions of is that , i.e. errors are independently identically distributed with zero mean and stable variances. Where i denote a particular country and t denotes a particular time.

The adopted methodology is distributed in four sections. First: - Group effects where all coefficients are constant across time and countries. Second: - Slope coefficient constant but intercept varies across countries. Third: - Slope coefficients constant but the intercept varies over countries as well as time. Fourth: - All coefficients (intercept and slope) vary across countries.

3. Results After conducting a panel data analysis represented by econometric models presented in the

methodology section, we see some interesting results. For choosing the best model between FEM and REM, Hausman test is used, which has favored FEM (Fixed Effect Model), detailed test results are presented in Appendix table A3.1. The results are distributed further in four sections.

3.1. Group effects where all coefficients are constant across time and countries The results for all coefficients constant across individual and/or time are presented in table

3.1. It is concluded that we cannot reject the null hypothesis that unemployment does not explain the GDP per capita growth (GDP_PC) and selected determinants considered enough in order to explain the economic growth. In Model-1a, in case of zero Unemployment Rate (UNEM), zero Gross Capital Formation (GCF), zero Population Growth (PG) and zero Trade Openness (TRD) for each country (from twelve selected countries) is expected to have 2.6970 GDP per capita growth (p<.0000).

Table 3.1: Results with OLS & Fixed Effect Model for period 1982-2011. DV is GDP per capita

growth (GDP_PC)

Model-1a Model-1b Model-1c Model-2a Model-2b Model-2c

(OLS) (OLS) (OLS) (Fixed Effect) (Fixed Effect) (Fixed Effect)

UNEM -0.1219** -0.1279** -0.0764* -0.1157** -0.1059* -0.0478*

GCF 0.1774*** 0.1578*** 0.1802*** 0.1613*** 0.1339*** 0.1528***

PG -1.3341*** -1.4711*** -1.6065*** -1.3246*** -1.4578*** -1.6287***

TRD -0.0124* -0.0067 -0.0198** -0.0109 -0.0061 -0.0198***

RIR 0.0130 0.0172 0.0571* 0.0611

DCB -0.1208** -0.0179** -0.0091* -0.0115***

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INF -0.0379** -0.0373** -0.0306** -0.0296**

GFCE 0.0887** 0.0847**

TNRR 0.0290 0.0270

GS 0.0021 0.0121

FDI 0.1470 0.1649

Intercept 2.6970* 4.2214** 3.0675* 2.9414** 4.1540** 2.9115*

F Test 27.24*** 17.69*** 12.51*** 25.2*** 16.69*** 11.88***

Adj. R2 .5132 .6218 .7113 .5415 .6372 .7215

Obs. 360 360 360 360 360 360

***, **, and * denote significance at 1%, 5% and 10% respectively.

And for 1 percent increase in unemployment rate (UNEM), the total GDP per capita growth

(GDP_PC) for selected countries is expected to decrease by 0.1219 percent, holding all other variables constant. In Model-1a unemployment rate (UNEM), population growth (PG) and trade openness (TRD) are negatively correlated to GDP per capita growth (GDP_PC) only Gross capital formation (GCF) is positively correlated. The signs of unemployment (UNEM) co-efficient are consistently negative across specifications and in all models it is statistically significant. Further the coefficient values of unemployment (UNEM) across specifications are nearly similar, ranging between 0,0478 and -.1279. A good-nees of fit measure Adjusted R2 is increasing with the addition of more regressors which means that the included variables are going to response more for better explanation of the model. Adjusted R2 of .7113 in Pooled OLS Model-1c means that this model accounts for 71 percent of the total variance in the GDP per capita growth (GDP_PC) rate of twelve selected countries and Adjusted R2 of .7215 in Fixed Effect Model with “with-in” effects mean that model accounts for 72 percent of total variances in the GDP per capita growth (GDP_PC) rate of selected Asian countries.

3.2. Slope coefficient constant but intercept varies across countries Appendix Table A3.2 presents the results by using Least Squre Dummy Variabel (LSDV) a

technique of Fixed Effect Model. Here we examine the fixed group effects by introducing group (country) dummy variables. The dummy variable c1 is set for Bangladesh and zero for other countries, similarly for other countries. There is no dummy for turkey as Turkey is a comparison country, in other words intercept for baseline in models are representing the intercept of Turkey. Akbar et al, in 2011 used Pakistan as a comparison country. LSDV fits the data better as Adjusted R2 increases from .5817 to .6671 and from .6671 to .7329. Each of c1-c11 dummy intercepts has deviation from its group specific intercept that is the baseline intercept (intercept for Turkey). These differences in country intercepts are due to the unique features of managerial talent or managerial style etc. after considering the Model-3a, we can write it in the equation form as follows:-

Bangladesh:

Cyprus:

India:

Indonesia:

Korea:

Kuwait: Pakistan:

Philippines:

Sri-Lanka:

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Syria:

Thailand:

Turkey: 3.3. Slope coefficients constant but the intercept varies over countries as well as

time Results for panel data models have been presented in Appendix table A3.3. The results

presented in Model-4c appear to be more robust and have higher value of adjusted R2 and making the prediction that 73% variances in economic growth are explained by the studied explanatory variables, country dummies and time dummies regressors. In all three models, individual time dummies were individually statistically significant as they include year’s 1983, 1985, 1986, 1987, 1989, 1990, 1992, 1995, 2000, 2003, 2004, 2006, 2007 and 2010 which suggest that GDP per capita growth have changed much over a time. Here, also some of the individual country effects were also statistically significant like as Indonesia, Korea (south), Kuwait, Philippines and Sri-Lanka. If all of these were statistically significant, then no reason for polling (Gujrati, 2003). The overall conclusion from the Appendix table A3.3 was that there was propound individual country effects and also individual time effects. In other words, the GDP per capita growth functions for twelve selected countries have changed due to explanatory variables effects, individual country effects and as well as time effects.

3.4. All coefficients (intercept and slope) vary across countries Appendix Table A3.4 presents the estimated GDP per capita growth where all the studied co-

efficients vary across countries. In our models the differential slope coefficients were different for different countries. For unemployment rate (UNEM), the relationship for GDP per capita growth (GDP_PC) and unemployment (UNEM) is negative for all countries which is showing that with increase in unemployment (UNEM) the GDP per capita growth (GDP_PC) will be lowers. Some of the differential slope coefficients are also statistically significant (Gross capital formation in Kuwait, Gross capital formation in Turkey, Population growth in Kuwait, Real interest rate in Syria, Domestic credit provided by baking sector in Cyprus, Inflation in Korea (south), Inflation in Syria, Gross savings in Turkey, Foreign Direct investment in Kuwait and Foreign direct investment in Turkey, we can say that the variable introduced in the model influences the GDP per capita growth rate.

The relationship between Inflation (INF) and GDP per capita growth (GDP_PC) also presents the mix nature. Some countries have positive slope differential and some countries have negative slope differential. In last, the relationship for foreign direct investment (FDI) and GDP per capita growth (GDP_PC) have also mix nature for slope differential intercepts.

Limitations In terms of policy implications, the issues that are central in the exploration of the

unemployment should also be investigated, which will also be closely linked with the question of reduced unemployment. Although analysis presented and empirical models constructed for research are as complete and comprehensive as possible but still there are some limitations causing further suggestions for future research. First:

– Analysis covers only twelve (12) Asian countries thus the results only presents the realities of twelve selected countries only. Second;

– Main explanatory variable is unemployment rate that have different causes for different countries which needs to be explored in depth.

4. Conclusion We have used a panel data of twelve selected developing countries from Asia to capture in

time and country effects of unemployment rate on economic growth. Considering our data set of twelve countries between 1982 and 2011 periods, we have consistently found that high unemployment causes the decrease in economic growth in all models. Research study first presents

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the importance of unemployment phenomenon toward economic growth. As we saw the unemployment is very heatedly discussed in national as well as international level. Growth of Asian developing countries is influenced by unemployment rate, especially among some Asian countries namely India, Indonesia, Sri-Lanka, and Thailand which have the highest unemployment rates when compared with other studied countries from Asia region. The above discussion clearly makes Kuwait, India and Turkey at the top but Pakistan, Sri-Lanka and Thailand at last in order while comparing for economic growth. All in all, the research study supports the view that there is some scope for developing countries in order to correcting and maintaining the economic development indicators, so the economic growth would be sustainable. Research conclusion underlines the importance of unemployment rate to the economic growth, both on global and as well as on local level. Hence, the conclusion indicates that increased unemployment rate decrease the economic growth rate in the long-run.

References: 1. Adjemian, S., Langot, F., & Rojas, C. Q. (2010). How do Labor Market Institutions affect the link

between Growth and Unemployment: The case of the European Countries. The European Journal of Comparative Economics, 7(2), 347-371.

2. Akbar, A., Imdadullah, M., Aman. U. M., & Aslam. M. (2011). Determinants of Economic growth in Asian countries: a panel data perspective. Pakistan journal of social sciences, 32(1), 145-157.

3. Blanchard, O. (2006). European Unemployment: the Evolution of facts and ideas. Economic Policy in Great Britain. 5-59.

4. Gujarati, D. N. (2003). Basic Econometrics. 3ra Edition, Mc Grawhill International Editions, Economic Series.

5. Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, Vol.46, 69-85. 6. International Labor Office (ILO) (2012). Global Employment Trends. The Challenge of a Jobs

Recovery. 7. Labor Force Survey of Pakistan 2010-11. Government of Pakistan, Islamabad. 8. Levine, l. (2012). Economic Growth and Unemployment rate. Congressional Research Service. 7-

5700. 9. Osinubi, T. S. (2005). Macroeconometric Analysis of Growth, Unemployment and Poverty in

Nigeria. Pakistan Economic and Social Review, XLIII(2), 249-269. 10. Stephen, B. A. (2012). Stabilization Policy, Unemployment Crises and Economic Growth in

Nigeria. Universal Journal of Management and Social Sciences, 2(4), 55-63. 11. Tiwari, A. K., & Matascu, M. (2011). Economic growth and FDI in Asia: A panel-data approach.

Economic analysis and policy, Vol. 41. 12. The World Bank (2012). World Development Report. World Bank, Washington, DC 13. Wang, S., & Abrams, B. A. (2007). Government Outlays, Economic Growth and Unemployment: A

VAR Model. Working paper, University of Delaware, New York.

УДК 33

Безработица и экономический рост в развивающихся азиатских странах: панель анализа данных

1 Махаммад Имран

2 Харум Магал 3 Анель Салман

4 Недим Макаревик 1 IQRA университет, Исламабад Кампус, Пакистан E-mail: [email protected] 2 КОМСАТС Институт информационных технологий в Исламабаде E-mail: [email protected] 3 КОМСАТС Институт информационных технологий в Исламабаде E-mail: [email protected] 4 Посольство Боснии и Герцеговины в Пакистане, Пакистан E-mail: [email protected]

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Аннотация. В этом исследовании представлены новые оценки регрессии взаимосвязи безработицы и экономического роста для 12 выбранных стран Азии за период 1982-2011 годы. Фиксированный эффект и объединенные МНК методы используются для анализа панельных данных, оценивающих отдельные страновые эффекты, групповые эффекты и временные эффекты, исследуя взаимосвязь между уровнем безработицы и экономическим ростом. Результаты показали, что более высокий уровень безработицы оказывает значительное негативное влияние на ВВП на душу населения (аппроксимация процессов экономического роста). Результаты также указывают, что на экономический рост, похоже, оказывают существенное воздействие такие традиционные детерминанты как инфляция (индекс потребительских цен), рост численности населения, валовое накопление капитала, степень открытости торговли и т.д. На основе полученных результатов автор пришел к выводу, что снижение уровня безработицы будет благоприятным фактором для поддержания устойчивого экономического роста и повышения благосостояния людей.

Ключевые слова: безработица, экономический рост, развивающиеся страны, панельные данные, модель с фиксированными эффектами.

Appendix

Appendix Figure A2.1 Graphical presentation of Unemployment rate of 12 selected countries

0 5

1 0

1 5

0 5

1 0

1 5

0 5

1 0

1 5

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

Bangladesh Cyprus India Indonesia

Korea Kuwait Pakistan Philippines

Sri Lanka Syrian Arab Thailand Turkey

U n e

m p lo

y m

e n t ra

te , (%

o f to

ta l la

b o

r fo

rc e

)

Years

Figure A2.2 Graphical presentation of GDP per capita growth of 12 selected countries

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-2 0

-1 0

0 1

0 2

0 -2

0 -1

0

0 1

0 2

0 -2

0 -1

0

0 1

0 2

0

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

Bangladesh Cyprus India Indonesia

Korea, Rep. Kuwait Pakistan Philippines

Sri Lanka Syrian Arab Thailand Turkey

G D

P p

e r

c a

p it a

g ro

w th

( a n

n u

a l %

)

Years

Table A2.3

Matrix of Correlation Coefficients

GDP_PC UNEM GCF PG TRD RIR DCB INF GFCE TNRR GS FDI

GDP_PC 1

UNEM -0.0927* 1

GCF 0.4039* -0.1384* 1

PG -0.4010* -0.1043* -

0.4689* 1

TRD -0.0238 -0.1151* 0.1396* -0.2051* 1

RIR 0.1055* -0.1513* 0.1521* 0.0139 -0.0457 1

DCB -0.0232 -0.2916* 0.0574 -0.1838* 0.5810* -0.0171 1

INF -0.1354* 0.2617* -0.0943 -0.0070 -0.2761* -0.2756* -0.2526* 1

GFCE 0.0776 -0.2748* -0.0511 -0.0254 0.4265* -0.0112 0.4206* -0.1151* 1

TNRR 0.0932 -0.2425 -0.2758 0.4662 0.2134 -0.0689 -0.0694 -0.1596* 0.4222* 1

GS 0.1114* -0.4381* 0.2660* 0.1384* 0.2487* 0.0908 -0.0449 -0.2459* 0.1388* 0.5466* 1

FDI 0.0060 0.0230 0.0475 -0.2044* 0.4319* -0.0230 0.6283* -0.1728* 0.0629 -

0.2126* -

0.2139* 1

1. Source: World Bank Development Indicators, Economic surveys of selected respective countries.

2. * denote significance at 5%.

Table A3.1 Hausman specification test answers for best model by comparing the Fixed Effect Model and

Random Effect Model. The Hausman test results for all models are presented in Appendix Table

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A3.1: Hausman test results for all models

Sr. No Model Hausman test value Significant or not Sig.

1 Model-2a 0.021 Significant

2 Model-2b 0.029 Significant

3 Model-2c 0.000 Significant

4 Model-3a 0.001 Significant

5 Model-3b 0.000 Significant

6 Model-3c 0.009 Significant

7 Model-4a 0.004 Significant

8 Model-4b 0.031 Significant

9 Model-4c 0.011 Significant

10 Model-5a 0.010 Significant

11 Model-5b 0.000 Significant

12 Model-5c 0.002 Significant

If Hausman test value <0.05 then statistically significant.

Table A3.2 Results with Fixed Effect Model for period 1982-2011. DV is GDP per capita growth

(GDP_PC)

Model-3a Model-3b Model-3c

(Fixed Effect) (Fixed Effect) (Fixed Effect)

UNEM -0.0139** -0.0919** -0.0755* GCF 0.2634*** 0.2448*** 0.2637*** PG -1.6982*** -1.8264*** -1.7092*** TRD -0.0188 -0.0157 -0.0160 RIR 0.0131 0.0198 DCB -0.0179** -0.0171** INF -0.0733*** -0.0695*** GFCE 0.0583 TNRR 0.0939 GS -0.0242 FDI -0.0263 c1( Bangladesh) -2.5255 -2.8014* -2.3472 c2(Cyprus) -0.4755 -0.8217 -0.8895 c3(India) -0.0052 -2.7381* -2.9286* c4(Indonesia) -2.7986* -3.5749** -4.2766** c5(Korea) -2.0614* -3.9480** -3.5129** c6(Kuwait) 3.4611** 2.4294 -3.8314 c7(Pakistan) 1.3720 -1.3164 -1.3736 c8(Philippines) -0.2636** -2.9141** -2.8032** c9(Sri-Lanka) -0.4139** -2.8794** -2.7710** c10(Syria) 0.2709 -1.9280 -3.9371** c11(Thailand) -0.8235 -3.0770** -2.8632 Intercept(baseline) 0.9216 2.0549** 2.8666*

for Turkey F Test 8.62*** 8.68*** 7.15*** Adj. R2 .5817 .6671 .7329 Obs. 360 360 360

***, **, and * denote significance at 1%, 5% and 10% respectively.

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Table A3.3

Results with Fixed Effect Model for period 1982-2011. DV is GDP per capita growth (GDP_PC)

Model-4a Model-4b Model-4c

(Fixed Effect) (Fixed Effect) (Fixed Effect) UNEM -0.0121** -0.0574** -0.0606* GCF 0.2449*** 0.2179*** 0.2452*** PG -1.7085*** -1.8327*** -1.8128*** TRD -0.0104 -0.0088 -0.0091 RIR 0.0621* 0.0677* DCB -0.0123* -0.0150 INF -0.0607** -0.0582** GFCE 0.0288 TNRR 0.0922 GS 0.0230 FDI 0.0208 c1 ( Bangladesh) 0.7721 -2.3213 -2.1547 c 2 (Cyprus) 0.1480 -1.2652 -1.6489 c 3 (India) 0.3765 -2.1168 -2.4829 c4 (Indonesia) -0.6980* -3.1904** -4.1328** c5 (Korea) -0.9773* -3.4135** -3.2971* c6 (Kuwait) 3.1942** 0.4200** -3.4118** c7 (Pakistan) 1.4910 -0.7393 -0.8302 c8 (Philippines) -0.5230* -2.8496** -2.8049** c9 (Sri-Lanka) -0.6208* -2.6955** -2.6957** c10 (Syria) 0.1767 -1.5119 -3.3900 c11 (Thailand) -0.9464 -3.1337* -3.3883 t2 (1983) 3.0664** 3.0262** 2.9891** t3 (1984) 1.5313 1.9425 1.9555 t4 (1985) 0.9723 0.9397 1.0027 t5 (1986) 2.5660* 2.2876 2.5937 t6 (1987) 3.7237** 4.0869** 4.3635** t7 (1988) 3.7575** 4.2176** 4.5024** t8 (1989) 3.5211** 3.6466** 3.7726** t9 (1990) 4.8532*** 5.2528*** 5.2276*** t10 (1991) 1.4859 1.9191 1.9554 t11 (1992) 3.0041** 3.1187** 3.1027** t12 (1993) 2.0082 2.1318 2.1521 t13 (1994) 1.7217 2.0490 2.0970 t14 (1995) 2.9422** 3.3744** 3.4093** t15 (1996) 1.7791 2.1739 2.1770 t16 (1997) 0.8383 1.1100 1.1675 t17 (1998) -1.5393 -0.8454 -0.5613 t18 (1999) 1.2500 1.3962 1.6320 t19 (2000) 3.1279** 3.2325** 3.2710** t20 (2001) 0.1276 0.2056 0.2888 t21 (2002) 2.1627 2.1802 2.3257 t22 (2003) 3.1952** 3.1735** 3.2556** t23 (2004) 3.8936** 4.0151** 4.0469** t24 (2005) 3.3942** 3.6274** 3.4600** t25 (2006) 2.9080* 3.0924** 2.9498* t26 (2007) 2.6135* 2.7415* 2.6087* t27 (2008) 0.3916 1.0841 0.7452

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t28 (2009) -1.2535 -1.3039 -1.2148 t29 (2010) 2.9952** 3.3571* 3.4178** t30 (2011) 1.5965 2.0812 2.0644 Intercept(combined) (Turkey + 1982)

1.4220 2.8408 2.0526

F Test 4.70*** 5.04*** 4.65*** Adj. R2 .5915 .6711 .7388 Obs. 360 360 360 ***, **, and * denote significance at 1%, 5% and 10% respectively.

Table A3.4 Results with Fixed Effect Model for period 1982-2011. DV is GDP per capita growth

(GDP_PC)

Model-5a Model-5b Model-5c

(Fixed Effect) (Fixed Effect) (Fixed Effect) UNEM -0.0731** -0.1736** -0.2109***

GCF 0.2947*** 0.3170*** 0.3508** PG -0.3653* -0.0032* -0.2158*** TRD 0.0147* 0.0391** 0.0605** RIR -0.0305 -0.0269 DCB -0.0722*** -0.0783** INF -0.0723** -0.0840 GFCE 0.0938 TNRR 0.1157 GS -0.0687 FDI -0.3975 c1 ( Bangladesh) -54.9464 -54.9464 -54.9464 c2 (Cyprus) 28.3735 28.3735 28.3735 c3 (India) -8.4057 -8.4057 -8.4057 c4 (Indonesia) -24.1146 -24.1146 -24.1146 c5 (Korea) -12.7651 -12.7651 -12.7651 c6 (Kuwait) -21.0859 -17.7851 -14.5294 c7 (Pakistan) -24.1161 -21.1258 -19.1920 c8 (Philippines) -22.9338 -19.2020 -17.5053 c9 (Sri-Lanka) -23.7749 -20.2757 -15.4650 c10(Syria) -23.7749 -17.6983 -15.3115 c11 (Thailand) -21.9153 -15.6808 -13.2670 c1UNEM ( Bangladesh) -0.8446 -1.5191 -1.1287 c2 UNEM (Cyprus) -0.6047 -0.8515 -0.8888 c3 UNEM (India) -0.2002 -0.4470 -0.4843 c4 UNEM (Indonesia) -0.0094 -0.2374 -0.2747 c5 UNEM (Korea) -1.4889 -1.2421 -1.2048 c6 UNEM (Kuwait) -0.8561 -1.1030 -1.1403 c7 UNEM (Pakistan) -0.0072 -0.0525 -0.1116 c8 UNEM (Philippines) -0.2078 -0.2676 -0.3267 c9 UNEM (Sri-Lanka) -0.7810 -0.8407 -0.8999 c10 UNEM (Syria) -0.0352 -0.0244 -0.0836 c11 UNEM (Thailand) -0.4508 -0.3916 -0.3319 c12 UNEM (Turkey) -0.3013 -0.2416 -0.1824 c1GCF ( Bangladesh) 0.5269 0.5492 0.5830 c2 GCF (Cyprus) 0.0646 0.0423 0.0085 c3 GCF (India) 1.3662 1.3439 1.3101 c4 GCF (Indonesia) 0.0658 0.0434 0.0096 c5 GCF (Korea) 0.0735 0.0959 0.1297 c6 GCF (Kuwait) 0.7392** 0.7615** 0.7953** c7 GCF (Pakistan) 0.6310 0.5555 0.5167 c8 GCF (Philippines) 0.0450 0.0304 0.0693 c9 GCF (Sri-Lanka) 0.2139 0.2894 0.3282

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c10 GCF (Syria) 0.1227 0.0472 0.0083 c11 GCF (Thailand) 0.0736 0.1491 0.1879 c12 GCF (Turkey) 1.2357*** 1.1311*** 1.3501*** c1PG ( Bangladesh) -3.2355 -3.5976 -3.3850 c2 PG (Cyprus) 0.6811 0.3190 0.5316 c3 PG (India) -4.6530 -5.0151 -4.8025 c4 PG (Indonesia) -2.8331 -3.1952 -2.9826 c5 PG (Korea) 3.2162 2.8541 3.0667 c6 PG (Kuwait) -3.2426** -3.6047*** -3.3921*** c7 PG (Pakistan) -1.1203 -1.1932 -1.2964 c8 PG (Philippines) -2.4704 -2.5433 -2.6466 c9 PG (Sri-Lanka) -2.0235 -2.0964 -2.1997 c10 PG (Syria) 3.5426 3.6155 3.7187 c11 PG (Thailand) 8.6917 8.7646 8.8679 c12 PG (Turkey) 3.4735 3.5464 3.6497 c1TRD ( Bangladesh) -0.0035 -0.0280 -0.0493 c2 TRD (Cyprus) 0.2562 0.2317 0.2104 c3 TRD (India) 0.0412 0.0168 -0.0045 c4 TRD (Indonesia) -0.1193 -0.1437 -0.1651 c5 TRD (Korea) -0.0820 -0.1065 -0.1278 c6 TRD (Kuwait) 0.1677 0.1432 0.1218 c7 TRD (Pakistan) 0.0097 0.0279 -0.0197 c8 TRD (Philippines) 0.1728 0.1350 0.1432 c9 TRD (Sri-Lanka) 0.0980 0.0603 0.0684 c10 TRD (Syria) 0.1987 0.1609 0.1691 c11 TRD (Thailand) 0.1835 0.1457 0.1539 c12 TRD (Turkey) 0.0776 0.0398 0.0480 c1RIR ( Bangladesh) 0.1289 0.1595 0.1559 c2 RIR (Cyprus) -0.3636 -0.3330 -0.3366 c3 RIR (India) 0.0764 0.1070 0.1034 c4 RIR (Indonesia) 0.3510 0.3816 0.3780 c5 RIR (Korea) -0.5785 -0.5480 -0.5515 c6 RIR (Kuwait) 0.0490 0.0795 0.0760 c7 RIR (Pakistan) 0.0415 0.0581 0.0444 c8 RIR (Philippines) 0.0210 0.0376 0.0239 c9 RIR (Sri-Lanka) -0.1271 -0.1104 -0.1241 c10 RIR (Syria) 0.3616** 0.3782** 0.3645** c11 RIR (Thailand) -0.0909 -0.0742 -0.0879

c12 RIR (Turkey) -0.1588 -0.1421 -0.1558

c1DCB ( Bangladesh) 0.0041 0.0763 0.0493 c2 DCB (Cyprus) 0.0084* 0.0806** 0.0867** c3 DCB (India) -0.0426 0.0295 0.0356 c4 DCB (Indonesia) -0.0065 0.0656 0.0717 c5 DCB (Korea) 0.1825 0.2547 0.2608 c6 DCB (Kuwait) 0.0132 0.0854 0.0915 c7 DCB (Pakistan) 0.2041 0.2149 0.2129 c8 DCB (Philippines) -0.0472 -0.0365 -0.0384 c9 DCB (Sri-Lanka) -0.1266 -0.1158 -0.1178 c10 DCB (Syria) -0.0150 -0.0042 -0.0062 c11 DCB (Thailand) -0.0990 -0.0882 -0.0901 c12 DCB (Turkey) -0.0085 0.0022 0.0002 c1INF ( Bangladesh) 0.1370 0.2093 0.2174 c2 INF (Cyprus) -0.9583 -0.8866 -0.8779 c3 INF (India) -0.0179 0.0543 0.0624 c4 INF (Indonesia) -0.0758 -0.0035 0.0045 c5 INF (Korea) -1.0790** -1.0066** -0.9986** c6 INF (Kuwait) 0.0101 0.0824 0.0905 c7 INF (Pakistan) -0.1435 0.0815 0.0666 c8 INF (Philippines) -0.2306 -0.0054 -0.0203 c9 INF (Sri-Lanka) -0.0043 0.2208 0.2059 c10 INF (Syria) 0.2134** 0.4385*** 0.4236***

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c11 INF (Thailand) -0.0828 -0.6577 -0.6726 c12 INF (Turkey) -0.0788 0.1462 0.1313 c1GFCE ( Bangladesh) -0.6576 -0.6576 -0.5637 c2 GFCE (Cyprus) 0.0460 0.0460 0.1399 c3 GFCE (India) -2.1654 -2.1654 -2.0716 c4 GFCE (Indonesia) -1.9960 -1.9960 -1.9022 c5 GFCE (Korea) -1.4322 -1.4322 -1.3384 c6 GFCE (Kuwait) -0.1443 -0.1443 -0.0505 c7 GFCE (Pakistan) 0.0719 0.0719 0.0244 c8 GFCE (Philippines) 0.2346 0.2346 0.1871 c9 GFCE (Sri-Lanka) 0.5419 0.5419 0.4944 c10 GFCE (Syria) -0.4025 -0.4025 -0.4500 c11 GFCE (Thailand) -0.5555 -0.5555 -0.6030 c12 GFCE (Turkey) -0.6995 -0.6995 -0.7470 c1TNRR ( Bangladesh) -0.1338 -0.1338 -0.1813 c2 TNRR (Cyprus) -18.5468 -18.5468 -18.4311 c3 TNRR (India) -1.7412 -1.7412 -1.6254 c4 TNRR (Indonesia) 0.6234 0.6234 0.7391 c5 TNRR (Korea) 15.2901 15.2901 15.4058 c6 TNRR (Kuwait) -0.0901 -0.0901 0.0255 c7 TNRR (Pakistan) 0.3168 0.3168 0.2711 c8 TNRR (Philippines) 2.1654 2.1654 2.1197 c9 TNRR (Sri-Lanka) -3.1801 -3.1801 -3.2250 c10 TNRR (Syria) 0.0636 0.0636 0.0179 c11 TNRR (Thailand) -0.6454 -0.6454 -0.6911 c12 TNRR (Turkey) -6.2859 -6.2859 -6.3316 c1GS ( Bangladesh) 0.2087 0.2087 0.1399 c2 GS (Cyprus) 0.2162 0.2162 0.2849 c3 GS (India) 1.6726 1.6726 1.6039 c4 GS (Indonesia) 0.0918 0.0918 0.1606 c5 GS (Korea) 0.6190 0.6190 0.6878 c6 GS (Kuwait) 0.1335 0.1335 0.2023 c7 GS (Pakistan) 0.3779 0.3779 0.3195 c8 GS (Philippines) 0.0162 0.0162 0.0421 c9 GS (Sri-Lanka) 0.0479 0.0479 0.0104 c10 GS (Syria) 0.1864 0.1864 0.2448 c11 GS (Thailand) 0.4317 0.4317 0.3733 c12 GS (Turkey) -1.2835** -1.2835** -1.3419** c1FDI ( Bangladesh) -1.5925 -1.5925 1.9901 c2 FDI (Cyprus) -0.0512 -0.0512 0.3462 c3 FDI (India) -0.0434 -0.0434 0.3541 c4 FDI (Indonesia) 0.1206 0.1206 0.5182 c5 FDI (Korea) 2.3491 2.3491 2.7467 c6 FDI (Kuwait) 10.1035*** 10.1035*** 9.7059*** c7 FDI (Pakistan) -0.4627 -0.4627 -0.4343 c8 FDI (Philippines) 0.3960 0.3960 0.3676 c9 FDI (Sri-Lanka) 0.3151 0.3151 0.2867 c10 FDI (Syria) -0.1073 -0.1073 -0.1357 c11 FDI (Thailand) -0.6333 -0.6333 -0.6618 c12 FDI (Turkey) -1.9384 -1.9384* -1.9668* Intercept (baseline) 4.1160** 6.8959*** 6.1994** F Test 2.77*** 3.20*** 3.09*** Adj. R2 .6478 .6937 .7549 Obs. 360 360 360

***, **, and * denote significance at 1%, 5% and 10% respectively.

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