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UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS INCOME INEQUALITY AND ECONOMIC GROWTH: PANEL DATA IN SOUTH EAST ASIA BY NGUYEN LE PHUONG LINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2016 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS INCOME INEQUALITY AND ECONOMIC GROWTH: PANEL DATA IN SOUTH EAST ASIA A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN LE PHUONG LINH Academic Supervisor: PROFESSOR TRAN TIEN KHAI HO CHI MINH CITY, November 2016 i ACKNOWLEDGEMENT Foremost, I would like to express my sincere gratitude toward my thesis advisor, Professor Tran Tien Khai for his valuable guidance and encouragement for my thesis His comments helped me for the writing of this thesis I would also like to thank Professor Truong Dang Thuy who gave me necessary guidance on data processing techniques and was willing to give me a hand whenever I have difficulties from my thesis I am gratefully indebted for his worthy advises Furthermore, I would also like to thank all lecturers, staff and my VNP 20 classmates at the Vietnam Netherlands Program Finally, I would like to send my deepest thanks toward my family members, my parents, my older sister and younger brother who have always been by my side, and encouraged me throughout my studying years and through the time of writing this thesis This achievement would not have been possible without them ii ABSTRACT This study has examined the impact of different levels of economic growth toward income inequality, applying the Kuznets reverted U-shape model and the Tribble S-curve model by employing the annual panel dataset from 1990 to 2012 at the country-level in the Southeast Asia New econometric technique Driscoll and Kraay is handled with the panel regression technique to correct for the autocorellation, heteroskedasticity and cross-section dependence The analysis finding reveals the significant relationship between income inequality and economic growth Furthermore, the empirical evidence also provides support for the existence of Kuznets inverted-U as well as inverted S-shaped curve in eight countries in Southeast Asia Key words: Income inequality, economic growth, Kuznets curve, S-curve, Southeast Asia, Panel data iii TABLE OF CONTENT Acknowledgement ii Abstract iii Table of content iv List of table v List of figures vi Chapter Introduction 1.1 Problem statement 1.2 Research objective 1.3 Research question 1.4 Research scope 1.5 Thesis structure Chapter Literature review 2.1 Arguments on the concept of Inequality 2.2 Theoretical literature 2.2.1 The Kuznets theory 2.2.2 Evolution derived from Kuznets theory 2.2.2.1 Kuznets theory in term of technology process 2.2.2.2 Kuznets theory in term of financial development 10 2.2.3 The S-curve 10 2.3 Empirical literature 13 2.3.1 Empirical papers support the growth-inequality relationship 13 2.3.2 Empirical papers against the growth-inequality relationship 14 2.3.3 Determinants of inequality 15 2.3.3.1 Structural shifts 15 2.3.3.2 Education and labor skills 16 2.3.3.3 Population growth 17 2.3.3.4 Institution 18 Chapter Research methodology 19 3.1 Data source 19 3.2 Research methodology 22 iv 3.2.1 Model specification 22 3.2.2 Measure of income inequality 23 3.2.2.1 The Lorenz curve 23 3.2.2.2 The Standardized World Income Inequality Database 24 3.2.3 Estimation strategy and correction model 26 3.2.3.1 The model of Pooled regression 26 3.2.3.2 The model of fixed effects estimation 27 3.2.3.3 The model of random effects estimation 28 3.2.3.4 The choice of panel regression model 29 3.2.3.5 Driscoll and Kraay standards and correction model 30 Chapter Empirical results 31 4.1 Overview of the income inequality in Southeast Asia 31 4.1.1 Current status 31 4.1.2 Pattern of change 32 4.2 The data descriptions 33 4.2.1 The descriptive statistic 33 4.2.2 The possible relationship by scatter 36 4.3 Panel data regression 37 4.3.1 Diagnosis analysis 37 4.3.2 Empirical results and analysis 39 Chapter Conclusion and policy implication 42 5.1 Concluding remarks 42 5.2 Policy implication 43 5.3 Limitations and directions for further research 44 Reference 45 Appendix A: Regression results 49 Appendix B: Results of Breusch-Pagan LM test 55 Appendix C: Results of Hausman test 56 Appendix D: Modified Wald Test for groupwise heteroskedasticity 58 Appendix E: Wooldridge test for autocorrelation in panel data 59 Appendix F: Pasaran test for Cross-section dependence 60 v LIST OF TABLES Table 3.1: Variable description 21 Table 4.1: Current level of income inequality in Southeast Asia 32 Table 4.2: Summary statistics 33 Table 4.3: Measures of GDP per capita and income inequality in Southeast Asia over 5-year period from 1990-2012 35 Table 4.4: Variance inflation factor (VIF) 37 Table 4.5: Model comparison 38 Table 4.6: Diagnostic problem 38 Table 4.7: Panel regression 41 vi LIST OF FIGURES Figure 2.1: The Kuznets’ model .8 Figure 2.2: Lewis’ dual sector model 16 Figure 3.1: The Lorenz’ curve 23 Figure 4.1: Gini coefficient index in Southeast Asian from 1980 to 2012 33 Figure 4.2: Relationship between income inequality and economic development .36 vii Chapter Introduction 1.1 Problem statement Although many countries in Asia have witnessed a significant growth in current decades as well as the shrinkage of poverty, the GDP per capita growth rate in South East Asia was approximately 6% in 2007, they also have accompanied a high income inequality level According to Zhuang, Kanbur, and Maligalig (2014), there are 12 among 30 countries in Asia, which indicated an increase in inequality (worsening in Gini index) during the 1990s and the 2000s, including PRC, India and Indonesia, where have the highest population of the region Besides that, many of the Asian countries also experienced the rapid spread in expenditure share of top 5% and 1% income classes, implying that the advantage groups in the society is getting richer in a much faster way Eventually, in the universal point of view, although gini coefficient in developing Asia is still in an average level 28-51, compared to the income inequality in Latin America the Caribbean and Sub-Saharan Africa, 45-60 and 30-66 respectively, the wider gap of income inequality in those Asian countries is such a considerable issue while the inequality in some Latin America has been remedied over recent years There are several reasons why economic growth should be concerned with inequality On the economic point of view, the inequality is not purely a political object but also considered as an important factor, which matters for growth and poverty in long run Some evidences show that countries with higher level of inequality will hinder the economic growth, thus leading to the increasing level of poverty as well as contributing to the higher inequality degree of education, health and so on Furthermore, inequality is an important factor leading to crime, social unrest or violent conflict There is empirical evidence that income inequality weakened the investment and growth by filling up social discontent, increased political instability (Alesina and Perotti,1996) In fact, the most concerning object in all economic debates is the development of the economy as a whole or the economic growth particularly On the other hand, inequality or income distribution can negatively affect the physical and human capital accumulation, which is the driving force to growth Therefore, it is obvious that poor income distribution may impede the economic growth in the long run Eventually in order to improve the living standard and optimize social welfare as a whole, it is important to look into the effects of economic growth on income distribution A desirable economic growth will reduce the income disparity as well as lower poverty rate leading to a stable society From the current situation of the economy and society, inequality is one of the most worthy issues in economics, yet very few studies are paid certain concern about the effect of economic growth on income inequality, especially for a particular region such as South East Asia, where the inequality is increasing recently, which may worsen economic growth in the long run There have been some empirical studies regarding to the relationship between growth and income inequality It is generally believed that the level of the economic growth is cited as a remedy for improving the poor situation (Adam, 2004; Dollar &Kraay, 2002) However, given evidence that current pattern of growth and globalization are raising income distribution gap, which hinders the poverty reduction rate (Ravallion, 2001) According to Kuznets (1955), growth plays an important growth to inequality by the concept of reverted U-shaped relationship In the initial phase of development, increasing economic growth will lead to the increase in inequality, but in latter stage, the inequality will reduce with the upward speed of growth For those reasons listed, this research will go further to examine the relationship between economic growth and inequality nexus focusing in Southeast Asian region combining of countries from 1990 to 2012, the period of new technologies and institutional transition associated with rapid economic growth The result is able to suggest some policy implications for the decision-making process Even though there have been a number for publications in public domain, this study is different with other previous studies conducted in the following key areas: (i) explore the effect of economic growth on income inequality in a new context of sub-region - Southeast Asia - where encountering the increasing income inequality while achieving a faster and higher level of economic growth; (ii) because of the shortage of income inequality data over a long time period, most of empirical studies about growth and inequality have relied on cross-section regression as a framework for their analysis, and have not had further researches for particular regions, this study will apply the Driscoll and Kraay estimation in Conceicao, P., & Galbraith, J K (2001) Toward a new Kuznets hypothesis: Theory and evidence on growth and inequality Inequality and industrial change: A global view, 139-160 Cowell, F A (1995) Measuring income inequality Deininger, K., & Squire, L (1998) New ways of looking at old issues: inequality and growth Journal of development economics, 57(2), 259-287 Desbordes, R., &Verardi, V (2012).Refitting the Kuznets curve Economics Letters, 116(2), 258-261 Dollar, D., &Kraay, A (2002) Growth is Good for the Poor Journal of economic growth, 7(3), 195-225 Donaldson, J A (2008) Growth is good for whom, when, how? 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Working paper SWIID Version 5.0, October 2014 Sarel, M M (1997) How macroeconomic factors affect income distribution: the crosscountry evidence (No 97-152) International Monetary Fund Thornton, J (2001) The Kuznets inverted-U hypothesis: panel data evidence from 96 countries Applied Economics Letters, 8(1), 15-16 Tribble Jr, R (1996) The Kuznets-Lewis process within the context of race and class in the US economy International Advances in Economic Research, 2(2), 151-164 Tribble, R (1999) A Restatement of the S‐Curve Hypothesis Review of Development Economics, 3(2), 207-214 Vanhoudt, P (2000) An assessment of the macroeconomic determinants of inequality Applied economics, 32(7), 877-883 Wan, G., Lu, M., & Chen, Z (2006) The inequality–growth nexus in the short and long run: Empirical evidence from China Journal of Comparative Economics, 34(4), 654-667 Zhuang, J., Kanbur, R., & Maligalig, D (2014) Asia’s income inequalities Inequality in Asia and the Pacific, 21 48 APPENDIX A: REGRESSION RESULTS Estimated results of model Figure A.1 -1: The pooled OLS regression Figure A.1 -2: The Fixed effects regression 49 Figure A.1 -3: The Random effects regression Figure A.1 -4: The Fixed effects with Driscroll and Kraay regression 50 Estimated results of model Figure A.2 -1: The pooled OLS model Figure A.2 -2: The Fixed effects regression 51 Figure A.2 -3: The Random effects regression Figure A.2 -4: The Fixed effects with Driscroll and Kraay regression 52 Estimated results of model Figure A.3 -1: The pooled OLS regression Figure A.3 -2: The Fixed effects regression Fixed-effects (within) regression Group variable: id Number of obs Number of groups = = 150 R-sq: Obs per group: = avg = max = 18.8 22 within = 0.3843 between = 0.0106 overall = 0.0011 corr(u_i, Xb) F(16,126) Prob > F = -0.9579 gini Coef lngdp lngdp2 lngdp3 agrshare urbanshare unemploy indgdp open infcon cl edu_index grosscapital lnlife pop1564 popden lbftatio _cons 151.7549 -16.25375 5935861 -.0019033 246781 3477276 3070275 0050022 -.0264478 -.0467129 -6.683381 0956713 -59.83237 -.4075144 0013305 104397 -186.9621 59.81164 6.98464 2715721 1075811 1482009 1689413 1080996 0106442 0309345 0393843 9.847156 0382905 18.03902 2084825 0016635 1326233 140.5923 sigma_u sigma_e rho 11.209966 1.7932725 97504774 (fraction of variance due to u_i) F test that all u_i=0: Std Err F(7, 126) = t 2.54 -2.33 2.19 -0.02 1.67 2.06 2.84 0.47 -0.85 -1.19 -0.68 2.50 -3.32 -1.95 0.80 0.79 -1.33 5.06 53 P>|t| = = 0.012 0.022 0.031 0.986 0.098 0.042 0.005 0.639 0.394 0.238 0.499 0.014 0.001 0.053 0.425 0.433 0.186 4.92 0.0000 [95% Conf Interval] 33.38944 -30.07615 0561529 -.2148031 -.0465043 0133978 0931015 -.0160623 -.0876664 -.1246533 -26.17061 0198956 -95.53106 -.8200952 -.0019615 -.1580606 -465.1901 270.1204 -2.431352 1.131019 2109966 5400662 6820574 5209534 0260667 0347707 0312276 12.80385 171447 -24.13368 0050663 0046224 3668546 91.26589 Prob > F = 0.0000 Figure A.3 -3: The Random effects regression Figure A.3 -4: The Fixed effects with Driscroll and Kraay regression 54 APPENDIX B: RESULTS OF BREUSCH – PAGAN LM TEST Figure B.1: Breusch – Pagan LM Test for Pooled OLS and REM of model Figure B.2: Breusch – Pagan LM Test for Pooled OLS and REM of model 55 Figure B.3: Breusch – Pagan LM Test for Pooled OLS and REM of model APPENDIX C: RESULTS OF HAUSMAN TEST Figure C.1: Hausman Test for FEM and REM of model 56 Figure C.2: Hausman Test for FEM and REM of model Figure C.2: Hausman Test for FEM and REM of model 57 APPENDIX D: MODIFIED WALD TEST FOR GROUPWISE HETEROSKEDASTICITY Figure D.1: Modified Wald Test for groupwise Heteroskedasticity of model Figure D.2: Modified Wald Test for groupwise Heteroskedasticity of model Figure D.3: Modified Wald Test for groupwise Heteroskedasticity of model 58 APPENDIX E: WOOLDRIDGE TEST FOR AUTOCORRELATION IN PANEL DATA Figure D.1: Wooldridge Test for Autocorrelation of model Figure D.2: Wooldridge Test for Autocorrelation of model Figure D.3: Wooldridge Test for Autocorrelation of model 59 APPENDIX F: PASARAN TEST FOR CROSS-SECTION DEPENDENCE Figure F.1: Pasaran test for cross-section dependence for model Figure F.2: Pasaran test for cross-section dependence for model Figure F.3: Pasaran test for cross-section dependence for model 60 ... conducted in the following key areas: (i) explore the effect of economic growth on income inequality in a new context of sub-region - Southeast Asia - where encountering the increasing income inequality. .. high income inequality level According to Zhuang, Kanbur, and Maligalig (2014), there are 12 among 30 countries in Asia, which indicated an increase in inequality (worsening in Gini index) during... inverted-U as well as inverted S-shaped curve in eight countries in Southeast Asia Key words: Income inequality, economic growth, Kuznets curve, S-curve, Southeast Asia, Panel data iii TABLE OF CONTENT
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