(Luận văn) does economic growth affect environmental quality in some east asian countries

t to UNIVERSITY OF ECONOMICS STUDIES HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL ng hi THE HAGUE THE NETHERLANDS ep w n lo VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ad ju y th yi pl n ua al n va DOES INCOME PER CAPITA AFFECT CARBON DIOXIDE EMISSIONS IN SOME EAST ASIAN COUNTRIES? ll fu oi m at nh z z ht vb BY k jm PHAM THI THU HUYEN om l.c gm n a Lu MASTER OF ARTS IN DEVELOPMENT ECONOMICS n va y te re HO CHI MINH CITY, MAY 2012 i UNIVERSITY OF ECONOMICS STUDIES HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL THE HAGUE THE NETHERLANDS t to ng hi ep VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS w n lo ad ju y th yi DOES INCOME PER CAPITA AFFECT CARBON DIOXIDE EMISSIONS IN SOME EAST ASIAN COUNTRIES? pl n ua al n va A thesis submitted in partial fulfilment of the requirements for the degree of fu ll MASTER OF ARTS IN DEVELOPMENT ECONOMICS oi m at nh z z By vb ht PHAM THI THU HUYEN k jm om PHAM KHANH NAM l.c gm Academic Supervisor: n a Lu n va y te re HO CHI MINH CITY, MAY 2012 ii t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z ht vb k jm om l.c gm n a Lu n va y te re iii ACKNOWLEDGEMENT t to ng This thesis could not complete without considerable and kindly mental supports hi from my supervisor, Dr Pham Khanh Nam, who spent his valuable time to help me ep to find materials, books and super my schedule tightly He also response quickly as w soon as I asked for help and give comments, which partly contribute to the success n lo of this thesis From bottom of my heart, I sincerely thank him for all ad y th I grateful acknowledge to Prof., Dr Nguyen Trong Hoai, Dr Nguyen Van Ngai and ju Dr Phan Dinh Nguyen – Public Defense Committee for their important comments yi and explanations that support me to fulfill the thesis pl ua al This study also benefits greatly from the enthusiastic assistance of Dr Le Van n Chon, whom I got knowledge on econometric and suggested the suitable models for n va the study ll fu I would like to express my thanks for all students of MDE 16 for their unceasing oi m assistance and encouragement during the course I also show deep gratitude to the at accessing dataset and materials as well nh lectures, VNP staffs, library staffs for their helps in accumulating knowledge, z z Last but not least, it gives my deepest grateful to my family members, husband, vb ht managers and colleagues for their dear encouragement, give favorable times and jm opportunities to finish the M.A course as well as the thesis k gm I pledge to bear full responsibilities for errors, omissions and shortcomings of the om l.c study n a Lu n va y te re iv TABLE OF CONTENTS t to CHAPTER 1: INTRODUCTION ng 1.1 Problem Statement hi 1.2 Objectives of study ep 1.3 Research questions w 1.4 Scope of the research n lo 1.5 Structure of the thesis ad CHAPTER 2: EMPIRICAL AND THEORETICAL BACKGROUND y th 2.1 Theoretical background ju yi 2.2 Empirical review pl CHAPTER 3: RESEARCH METHODOLOGY 14 al ua 3.1 The conceptual framework 14 n 3.2 Variables 15 va n 3.2.1 Dependent environment variables 15 fu ll 3.2.2 Explanatory variables 17 m GDP per capita 17 oi nh Foreign Direct Investment (FDI) 17 at Trade openness 19 z z Population density 20 vb ht 3.3 Data……………………………………… ……… 22 k jm 3.4 The Econometric model 22 gm CHAPTER 4: DATA ANALYSIS 28 l.c 4.1 Carbon Dioxide Emissions and GDP for East Asian countries 28 4.2 Descriptive analysis 30 om 4.3 The Environment Kuznets curve (EKC) 32 a Lu 4.4 Determinants of pollution 40 y v te re REFERENCES 52 n 5.2 Limitation of the research and recommendations for further study 50 va 5.1 Main findings and policy implications 46 n CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 46 APPENDICES t to ng hi ep 57 B: Description of variables and data sources 71 C: Description of CO2 –GDP of six East Asian countries individually 74 D: Estimated results 78 E: Tests 82 A: Summary of empirical studies on different the EKC hypothesis w n lo ad ju y th yi pl ua al LIST OF FIGURES Figure 2.2 The Environmental Kuznets Curve in N-shape n Figure 2.1 The Kuznets Curve (1955) and the Environmental Kuznets Curve n va 14 ll fu Figure 3.1 Conceptual framework of the study 28 oi m Figure 4.1 CO2 emissions for the period of 1990-2007 29 Figure 4.3 The EKC shape for six East Asian countries 37 at nh Figure 4.2 CO2 emissions and GDP for the period of 1990-2007 z 48 z Figure 5.1 An overview of determinants on environmental quality ht vb k jm om l.c gm n a Lu n va y te re vi LIST OF TABLES t to ng hi Table 3.1 Summary of variables description 21 Table 4.1 Summary Statistic on the sample observations 30 Table 4.2 Correlation on the sample observations 31 Table 4.3 Estimated results for the EKC with different models 33 ep (without time trend) 34 w Table 4.4 A comparison of estimated results for the EKC with FEM n lo models ad 36 Table 4.6 Estimated results for EKC with FEM time effects 38 Table 4.7 Estimated results for FEM with extended model (time 41 ju y th Table 4.5 Estimated results for the EKC (N-shape) with FEM yi pl n ua al effects) n va ll fu oi m at nh z z ht vb k jm om l.c gm n a Lu n va y te re vii ABBREVIATIONS t to ng hi ep : Environmental Kuznets curve FDI : Foreign Direct Investment NGOs : Non-government organizations CO2 : Carbon dioxide emissions per capita : Gross Domestic Products GHG : Greenhouse gas OLS : Ordinary least square : Fixed Effects Model EKC w n GDP lo ad ju y th FEM REM yi X : Import M : Export NAFTA : The North American Free Trade Agreement Random Effects Model pl : n ua al n va ll fu oi m at nh z z ht vb k jm om l.c gm n a Lu n va y te re viii CHAPTER 1: INTRODUCTION 1.1 Problem Statement t to ng For many centuries, the environmental degradation has become the issue of life with hi economic development Since the industrial revolution began in 18th century, it was ep as a real problem Industrial revolution with the technological progress brought w great achievements, especially the worldwide usage of fossil fuels and coal in n lo various industries As a result, global economy has growth fast along with human ad who significantly consumed natural resources leading environmental quality is y th ju worsening seriously (Irina, 2008) This implies that economic growth may affect yi environmental quality pl ua al Every nation wishes to pursue targets of economic growth without damaging n environment This is not feasible when history shows that any country becomes an va industrial power, in the process of development, causing environmental damages, n ll fu which cannot solve for a short time In order to serve quick economic growth oi m targets, the policies of incomprehensive industrialization and urbanization with at nh massive exploitation of natural resources have caused environmental problems in these countries for recent years Consequently, the air pollutions and global z z warming have continuously increased by human’s uncontrolled activities in the vb ht development process For China case, environmental degradation is becoming jm serious problems, which not only affects strongly to domestic but also causes k gm international consequences Some specialists judged that environmental pollution is l.c creating challenges to government It is also a long-term burden on the Chinese om people and “China’s problem has become the world’s problem” (Kahn and Yardley, n a Lu 2007, p.2) cope with It becomes the common hot issues and needs co-operation of worldwide y economic growth, environmental pollution has been in red alarm in China and it te re countries because of its global matter Kim (1996) revealed that due to the quick n va In the current context, environmental problem is not for an individual country to crosses the boundaries to South Korea and Japan Especially, the global pollutants, in which carbon dioxide emissions are particular pollutant, causing global warming t to Carbon dioxide emissions have dramatically increased for recent century due to ng hi human economic development ep Measuring environmental-economic relationship is necessary for any country to w orient adequate economic- environmental policy The Environment Kuznets curve n lo (EKC) is reckoned as the best ruler in estimating this relationship since Grossman ad and Krueger made empirical study on the impact of the economic growth on y th ju environment in NAFTA countries in 1991 Under the EKC hypothesis, yi environmental quality changes with income levels It displays in a bell shape curve pl ua al Environment degrades with income levels in the first stage of development and then improves as soon as it passes a definite high-income level n va Researchers use the EKC hypothesis commonly to connect economic growth with n ll fu environmental reduction in both single countries and country groups with different oi m models Local pollutants and global pollutants are treated as dependent variables at nh Beside income, other factors are applied as explanatory variables Although the results are not the same for all studies, the economic growth links the environmental z z deterioration and vice versa However, this theory has still been debated for years vb ht because various empirical studies found ambiguous results on the EKC but it has jm meaning for policy makers to find the best solutions for development sustainability k gm (Dinda, 2004) om l.c In this study, the research concerns if fast economic growth affects the environmental quality in some East Asian countries, specifically six countries a Lu consist of three developing countries in Southeast Asia: Malaysia, Thailand, n Northeast Asia: Korea and Japan, using cross- country balanced panel data set for n va Vietnam and one developing country: China and two developed country in te re the period of 1990-2007 y n lo ad ju y th yi pl ua al Government expenditure n n va per capita  WIR: World Resources Institute oi m ll fu Notes: nh z  IEA: International Energy Agency at  GEMS: Global Environment Monitoring System z ht vb  EPA: US Environmental Protection Agency jm  OECD: Organization for Economic Cooperation and Development k  UNEP: United Nations Environment Programme Ozone Secretariat gm va  ORNL: Oak Ridge National Laboratory an  MARC: Monitoring and Assessment Research Center Lu  CCIW: Canada Center for Inland Waters om  BESD: World Bank’s Bank Economic and Social Database l.c  WDI: World Bank’s World Development Indicators n y te re  NBS: National Bureau of Statistics  FAO: Food and Agriculture Organization ac th  DOE: Malaysian Department of Environment si eg cd 70 jg hg n lo ad ju y th yi pl ua al Appendix B: Description of variables and data sources Variables Description Unit Definition Source n Carbon n va dioxide per capita Metric ton Those stemming from the burning of fossil fuels Carbon m ll fu CO2 and the manufacture of cement They include Information oi carbon dioxide produced during consumption of Center, nh emissions per solid, liquid, and gas fuels and gas flaring z vb Tennessee, ht k jm Domestic US gm In thousand The total output of a country’s economy divided by World the Bank’s United World Dollars midyear population in constant 2000 US Dollars, Development Indicator which allow for the comparison of GDP across om years without interference from the effects of Lu 2000 prices) l.c per (constant capita Oak States of America Gross Product Division, Ridge National Laboratory, z GDP Analysis Environmental Sciences at capita Dioxide an inflation on prices GDP is the sum of gross value va added by all resident producers in the economy n y te re plus any product taxes and minus any subsidies not included in the value of the products It is ac th si eg cd 71 jg hg n lo ad ju y th yi pl ua al calculated without making deductions for depreciation of fabricated assets or for depletion n n va and degradation of natural resources Foreign Direct In current The net inflows of investment to acquire a lasting World oi US Dollars Bank, Global interest in or management control over an Development Finance nh Investment m ll fu FDI at enterprise operating in an economy other than that z z of the investor It is the sum of equity capital, vb reinvested earnings, other long-term capital, and ht Trade In US A share of the sum of exports (X) and imports (M) World intensity Dollars of goods and services in GDP It is calculated by accounts data and OECD (constant formula: (X+M)/GDP) an va Population density is calculated by mid-year Food and Agriculture n population divided by land area in square Organization and World y te re density Persons/km2 national National Accounts data Lu Population om 2000 prices) POP Bank l.c TRADE gm payments k jm short-term capital, as shown in the balance of kilometers Population bases on the de facto Bank ac th si eg cd 72 jg hg n lo ad ju y th yi pl ua al definition of population, which counts all residents regardless of legal status or citizenship, except for n n va refugees not permanently settled in the country of m ll fu asylum and who are generally considered part of the population of their country of origin Land area oi nh is a country's total area, excluding area under at inland water bodies, national claims to continental z z shelf, and exclusive economic zones In most cases, vb the definition of inland water bodies includes major ht k jm rivers and lakes and definition are from World Bank’s World Development Indicator Available l.c Data gm Source: om http://data.worldbank.org/indicator an Lu va n y te re ac th si eg cd 73 at jg hg t to Appendix C: Description of CO2 –GDP of six East Asian countries individually ng hi 10.2 ep Japan's CO2-GDP w 10 lo ad 1995 2004 1994 2003 9.8 y th 2007 ju 1998 2005 2002 2000 pl 2006 2001 ua al 9.6 yi CO2 emissions per capita n 1996 1997 1992 va 9.4 n 1991 1993 n 1990 1999 fu 34000 36000 ll 38000 GDP per capita 40000 42000 oi m at nh z South Korea's CO2-GDP z 2007 10 vb 2004 l.c 1995 1998 om 1994 a Lu 1993 2006 gm 1996 1999 2005 k 1997 2001 2000 jm n 1992 n va 1991 CO2 emissions per capita ht 2002 2003 1990 8000 10000 12000 GDP per capita 14000 16000 y te re 6000 th 74 t to ng China's CO2-GDP hi 2007 2005 w n 2004 lo ad y th 2003 ju 2002 1996 1997 1995 2001 1998 19992000 1994 1993 1992 1991 1990 yi pl CO2 emissions per capita ep 2006 n ua al va 500 n 1000 GDP per capita 1500 2000 ll fu oi m nh Malaysia's CO2-GDP at 2007 z 2005 2006 z vb 2004 1995 om 1999 1994 l.c 1993 gm 1998 k 1996 1997 2001 2002 2000 jm ht 1992 a Lu 1991 n 1990 3000 4500 5000 y te re 3500 4000 GDP per capita n 2500 va CO2 emissions per capita 2003 th 75 t to ng Thailand's CO2-GDP hi ep 2006 2004 2007 2005 2003 n 3.5 w 2002 lo 2001 1997 1996 1999 2000 1995 1994 yi 2.5 ju y th ad 1998 pl 1993 n ua n 1.5 va 1990 al 1992 1991 1500 2000 GDP per capita ll fu 2500 oi m at nh z Vietnam's CO2-GDP vb 1.4 z 2007 ht 2005 2006 2003 om l.c 2002 gm k jm 1.2 2004 2001 a Lu 2000 1998 1999 1997 n va 1996 300 500 600 y 400 GDP per capita te re 200 n 1995 1994 1993 1992 1990 1991 th 76 t to ng South Korea China Malaysia Thailand Vietnam ep 10 hi Japan w n lo ad ju pl 10 yi n ua al n va ll fu CO2 y th 2000 2005 1990 1995 oi 1995 m 1990 2000 2005 1990 1995 2000 2005 at nh year Graphs by state z z k jm ht vb om l.c gm n a Lu n va y te re th 77 t to Appendix D: Estimated results ng hi  Estimated results for the EKC with FEM (with time effects) ep xtreg CO2 GDP_k GDP_ksq i.t, fe Number of obs Number of groups w Fixed-effects (within) regression Group variable: state 108 Obs per group: = avg = max = 18 18.0 18 n = = lo R-sq: ad within = 0.8892 between = 0.9200 overall = 0.9176 ju y th corr(u_i, Xb) F(19,83) Prob > F = 0.1294 = = 35.04 0.0000 yi Coef ua 0579473 0010011 n 6639978 -.0119696 t P>|t| [95% Conf Interval] 11.46 -11.96 0.000 0.000 5487429 -.0139608 7792527 -.0099784 0.99 1.51 2.48 3.28 4.84 5.56 5.74 3.83 3.53 4.64 4.99 5.21 6.33 7.20 7.00 6.95 7.45 0.324 0.135 0.015 0.002 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.1959095 -.0946032 0970354 2569161 5736071 728706 7705638 3718645 3137616 5518541 6269665 6821147 9314298 1.144923 1.124321 1.143311 1.299798 5856144 6887796 8829897 1.048011 1.373213 1.539446 1.588722 1.175156 1.125115 1.379045 1.458168 1.524977 1.785433 2.018972 2.016871 2.06008 2.245867 3998581 1.763923 at z z k jm om l.c 0.002 gm 3.16 ht 342909 vb (fraction of variance due to u_i) 90.79 Prob > F = 0.0000 n F(5, 83) = a Lu F test that all u_i=0: nh 99329817 33965371 89531395 oi sigma_u sigma_e rho m 1.08189 ll _cons 1964655 1969328 1975792 1988715 201011 2038099 205675 2019376 2039644 2079456 2089539 2118853 2146859 2197253 224376 2304645 23783 fu 1948524 2970882 4900125 6524635 9734099 1.134076 1.179643 7735103 7194385 9654495 1.042567 1.103546 1.358431 1.581947 1.570596 1.601695 1.772832 n va t 10 11 12 13 14 15 16 17 18 Std Err al GDP_k GDP_ksq pl CO2 va n y te re th 78 t to ng  Estimated results for Fixed Effects Model for the EKC (without time hi trend) ep Fixed-effects (within) regression Group variable: state Number of obs Number of groups w R-sq: n lo within = 0.7408 between = 0.8248 overall = 0.8026 ad y th corr(u_i, Xb) yi Coef 1.015438 -.0139572 -.6543284 Obs per group: = avg = max = 18 18.0 18 Std Err .0633616 0013608 3550032 t P>|t| 16.03 -10.26 -1.84 = = 142.90 0.0000 [95% Conf Interval] 0.000 0.000 0.068 8897304 -.0166569 -1.358645 1.141146 -.0112574 0499879 n ua al 4.0852984 4732019 98676093 (fraction of variance due to u_i) fu F(5, 100) = 57.78 ll F test that all u_i=0: n va sigma_u sigma_e rho pl GDP_k GDP_ksq _cons 108 F(2,100) Prob > F = -0.9275 ju CO2 = = Prob > F = 0.0000 oi m nh at  Estimated results for Fixed Effects Model for the EKC (with time trend) z Number of obs Number of groups = = 108 Obs per group: = avg = max = 18 18.0 18 vb R-sq: z Fixed-effects (within) regression Group variable: state k jm ht within = 0.8470 between = 0.8999 overall = 0.8953 F(4,98) Prob > F = -0.3254 Coef GDP_k GDP_ksq t tsq _cons 7062673 -.0121102 1080351 -.0013725 8341525 0620134 0010808 029436 0014857 35558 sigma_u sigma_e rho 1.1688066 3672266 91015417 (fraction of variance due to u_i) Std Err t 5832036 -.014255 0496203 -.0043208 1285156 8293309 -.0099654 1664499 0015758 1.539789 n a Lu n va 0.000 0.000 0.000 0.358 0.021 [95% Conf Interval] om 11.39 -11.21 3.67 -0.92 2.35 P>|t| 135.65 0.0000 l.c CO2 Prob > F = 0.0000 th 79 77.97 y F(5, 98) = te re F test that all u_i=0: = = gm corr(u_i, Xb) t to ng  Estimated results for Fixed Effects Model for the EKC (N- shape) hi Number of obs Number of groups 108 within = 0.9132 between = 0.8868 overall = 0.8789 Obs per group: = avg = max = 18 18.0 18 F(3,99) Prob > F = = y th ep Fixed-effects (within) regression Group variable: state w n R-sq: lo ad corr(u_i, Xb) = -0.8597 = = 347.19 0.0000 ju yi pl CO2 Coef 2.354925 -.1036598 0012989 -1.643681 sigma_u sigma_e rho 2.3976862 27521337 98699621 1023851 0064458 0000926 2181913 n ua al GDP_k GDP_ksq GDP_kcb _cons Std Err P>|t| 23.00 -16.08 14.02 -7.53 0.000 0.000 0.000 0.000 [95% Conf Interval] 2.151771 -.1164496 0011151 -2.07662 2.55808 -.09087 0014827 -1.210743 n va fu (fraction of variance due to u_i) ll oi m F test that all u_i=0: t F(5, 99) = 130.83 Prob > F = 0.0000 at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 80 t to ng  Estimated results for extended model (with time effects) hi xtreg CO2 GDP_k GDP_ksq FDI TRADE POP i.t, fe ep Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 Fixed-effects (within) regression Group variable: state w n lo within = 0.9085 between = 0.2285 overall = 0.2581 ad corr(u_i, Xb) ju y th pl 8598346 -.0136298 1.51e-12 1.074421 -.0271261 n ua al GDP_k GDP_ksq FDI TRADE POP Coef yi CO2 Std Err .0728483 001112 3.05e-12 4148518 0069955 t = = P>|t| 36.11 0.0000 [95% Conf Interval] 11.80 -12.26 0.49 2.59 -3.88 0.000 0.000 0.623 0.011 0.000 7148618 -.0158428 -4.57e-12 2488395 -.0410475 1.004807 -.0114168 7.58e-12 1.900002 -.0132046 0.97 1.60 2.59 3.08 4.27 5.01 5.21 3.79 3.15 3.54 4.45 4.37 4.96 4.68 4.14 4.27 4.05 0.333 0.114 0.011 0.003 0.000 0.000 0.000 0.000 0.002 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.1857972 -.0718898 1132948 2134714 4715764 6414839 7008006 383878 2599943 3868645 5992325 6069706 8007609 8246186 7109646 756876 7691215 5415635 661781 8595189 9907744 1.295159 1.486367 1.566128 1.233281 1.154673 1.379653 1.56825 1.623859 1.873413 2.045379 2.024354 2.080056 2.258566 2.839058 7.206166 at z z Prob > F = 0.0000 n 38.25 a Lu F(5, 80) = om (fraction of variance due to u_i) l.c gm 0.000 k jm 4.58 ht 1.097228 vb F test that all u_i=0: nh 3.7548662 31433471 99304074 oi sigma_u sigma_e rho m 5.022612 ll _cons 1827481 1843335 1874875 195296 2069237 2122754 2174119 2134111 2247864 2494364 2434639 2554914 269502 306714 3299868 3324467 3742203 fu 1778832 2949456 4864069 6021229 8833677 1.063925 1.133464 8085796 7073336 8832588 1.083741 1.115415 1.337087 1.434999 1.367659 1.418466 1.513844 n va t 10 11 12 13 14 15 16 17 18 F(22,80) Prob > F = -0.5643 va n y te re th 81 t to Appendix E: Tests ng hi  F-test for Overall Significant ep regress CO2 GDP_k w Source SS n lo Model Residual ad Total 544.712647 829395721 1176.51185 107 10.9954378 Coef ju yi 8834554 -.0179444 1.532143 P>|t| 25.73 -20.14 11.16 = = = = = = 108 656.76 0.0000 0.9260 0.9246 91071 [95% Conf Interval] 0.000 0.000 0.000 8153783 -.0197109 1.259949 9515325 -.016178 1.804337 n ua n va GDP_ksq GDP_k GDP_ksq FDI df MS POP at SS TRADE nh CO2 656.76 0.0000 oi 2, 105) = Prob > F = m F( ll fu GDP_k = GDP_ksq = Source Number of obs F( 2, 105) Prob > F R-squared Adj R-squared Root MSE t 0343336 0008909 1372764 al regress Std Err pl ( 1) ( 2) MS 105 y th test GDP_k df 1089.42529 87.0865507 CO2 GDP_k GDP_ksq _cons GDP_ksq Total 1176.51185 107 10.9954378 GDP_k GDP_ksq FDI TRADE POP _cons 1.024592 -.0206309 2.54e-11 5564778 -.0031355 8620072 test GDP_k GDP_ksq FDI Std Err t P>|t| 108 595.76 0.0000 0.9669 0.9653 61797 [95% Conf Interval] k Coef = = = = = = jm CO2 ht 227.511866 381887417 vb 102 z 1137.55933 38.9525165 z Model Residual Number of obs F( 5, 102) Prob > F R-squared Adj R-squared Root MSE 9530095 -.0224149 1.87e-11 2621181 -.0047418 3480965 1.096174 -.0188469 3.20e-11 8508376 -.0015292 1.375918 om 0.000 0.000 0.000 0.000 0.000 0.001 l.c a Lu TRADE 28.39 -22.94 7.58 3.75 -3.87 3.33 gm 0360888 0008994 3.35e-12 1484046 0008098 2590935 POP n GDP_k = GDP_ksq = FDI = TRADE = POP = Constraint dropped y 733.34 0.0000 th 4, 102) = Prob > F = te re F( n 1) 2) 3) 4) 5) va ( ( ( ( ( 82 t to ng  Hausman test hi ep w n lo ad ju y th yi pl ua al n Coefficients (b) (B) fixed sqrt(diag(V_b-V_B)) S.E n va (b-B) Difference oi m at nh z z jm ht vb gm 124949 001751 3.36e-12 7100419 012461 0363856 0532033 0753208 1161564 162219 1801806 1947367 1773579 209837 2761494 2621323 290391 3233541 4002936 4449011 4541187 5157746 om l.c -.2465091 0093982 -8.49e-12 1.381469 -.0208594 -.0438445 -.0051637 0375003 0116303 -.0327483 -.0148531 0066442 0632255 0554278 -.0856756 0368059 0383851 -.0216337 -.1846714 -.2456082 -.2555609 -.2111153 k 1.106344 -.023028 1.00e-11 -.3070483 -.0062667 2217277 3001093 4489065 5904926 916116 1.078778 1.12682 7453541 6519058 9689344 1.046935 1.07703 1.358721 1.61967 1.613268 1.674027 1.724959 ll 8598346 -.0136298 1.51e-12 1.074421 -.0271261 1778832 2949456 4864069 6021229 8833677 1.063925 1.133464 8085796 7073336 8832588 1.083741 1.115415 1.337087 1.434999 1.367659 1.418466 1.513844 fu GDP_k GDP_ksq FDI TRADE POP _It_2 _It_3 _It_4 _It_5 _It_6 _It_7 _It_8 _It_9 _It_10 _It_11 _It_12 _It_13 _It_14 _It_15 _It_16 _It_17 _It_18 Test: Ho: difference in coefficients not systematic n va y te re chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 59.93 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) n a Lu b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg th 83 t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 84