Demographic factors and economic growth the bi directional causality in south east asia

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DEMOGRAPHIC FACTORS AND ECONOMIC GROWTH: THE BI-DIRECTIONAL CAUSALITY IN SOUTH EAST ASIA BY VO TAN THANH DIEP MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2015 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DEMOGRAPHIC FACTORS AND ECONOMIC GROWTH: THE BI-DIRECTIONAL CAUSALITY IN SOUTH EAST ASIA A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By VO TAN THANH DIEP Academic Supervisor: PROFESSOR NGUYEN TRONG HOAI HO CHI MINH CITY, December 2015 i DECLARATION “I certify the content of this dissertation has not already been submitted for any degree and is not being currently submitted for any other degrees I certify that, to the best of my knowledge, any help received in preparing this dissertation and all source used, have been acknowledged in this dissertation.” Signature Vo Tan Thanh Diep Date: ii ACKNOWLEDGEMENT Foremost, I would like to express my sincere gratitude to my supervisor, Professor Nguyen Trong Hoai for his professional knowledge, perceptive guidance and for giving me valuable opportunities His guidance really helped me for the direction of the research and writing of this thesis In addition my advisor, I would like acknowledge the love from my family to me over the last 24 years A special thank is to my parents for their support throughout my life, to my sister and my relation in Ho Chi Minh City for valuable support during my studies Furthermore, I would also like to thank all lecturers and staff at the Vietnam Netherlands Program and my VNP 20 classmates Most of all, a special thanks go to my better haft – Nguyen Son Kien - for the motivation, encouragement and affectionate care that he bring to my life iii ABSTRACT This study has demonstrated new evidence sustaining the idea that variation in demographic factors is an important determinant of growth in per capita income Using an annual panel dataset from 1990 to 2013 at the country-level in the Southeast Asia, this study is conducted to analyze the following key areas in comparing with current literature First, the determination of the impact of a number of the demographic factors on the economic growth by using a various aspect of demographic factors, including: population growth, life expectancy, and age structure Second, the interpretation of the bi-directional causality among: (i) the population growth and the economic growth; and (ii) the life expectancy and the economic growth Furthermore, the two new econometric techniques, Driscoll and Kraay estimation, and structural equation model, in parallel with the panel regression technique are applied It is noticeable about the following key contribution, including: (i) the specification of the various aspects of demographic factors on the economic growth is analyzed in the new context (Southeast Asia) where most countries have experienced the demographic transition, and have received the demographic dividend; and (ii) the worth analysis of the bi-directional causality has been recognized since it is one of the first in its line of current literature that confirms the inverse effect of the economic growth on population growth, and life expectancy simultaneously Key words: Demographic transition, economic growth, population growth, life expectancy, age structure, Southeast Asia, Panel data, SEM iv TABLE OF CONTENT Declaration ii Acknowledgement iii Abstract iv Table of content v List of tables viii List of figures ix Chapter Introduction 1.1 Problem statement 1.2 Research objective 1.3 Research questions 1.4 Research scope 1.5 Thesis structure Chapter Literature review 2.1 Theoretical literature 2.1.1 2.1.1.1 Demographic factors 2.1.1.2 Demographic Transition 2.1.2 Demographic factors and economic growth 2.1.2.1 The perspective of Malthusian Regime 2.1.2.2 The perspective of Post-Malthusian 10 2.1.2.3 The perspective of Modern Growth Regime 11 2.1.3 2.2 Key concepts Demographic transition and economic growth 13 2.1.3.1 The labor supply mechanism 13 2.1.3.2 The savings mechanism 14 2.1.3.3 The Human capital mechanism 14 Empirical studies 15 2.2.1 Population growth and age structure 15 2.2.2 Life expectancy 18 2.2.3 Bi-directional causality 19 2.2.4 Determinants of economic growth, population growth, and life expectancy 20 2.2.4.1 Economic growth 20 2.2.4.2 Population growth 21 2.2.4.3 Life expectancy 21 v 2.3 Hypothesis construction and conceptual framework 22 2.3.1 Hypothesis construction 22 2.3.1.1 Demographic factors and economic growth 22 2.3.1.2 Two-way relationship 24 2.3.2 Conceptual framework 25 Chapter Research methodology 27 3.1 Data 27 3.2 Model specification 29 3.2.1 Model specification for one way effects 29 3.2.2 Model specification for the bi-directional causality 30 3.3 Research methodology 32 3.3.1 Models of panel data regression 32 3.3.1.1 The model of Pooled regression 32 3.3.1.2 The model of fixed effects estimation 32 3.3.1.3 The model of random effects estimation 34 3.3.1.4 Driscoll and Kraay standard errors and panel models 34 3.3.2 The structural equation model (SEM) 36 3.3.2.1 The causal effect and mediate mechanism 36 3.3.2.2 The simultaneous (non-recursive) structural equation model 37 3.3.2.3 The logic of SEM 39 Chapter 4: Empirical results 40 4.1 Overviews of demographic transition in Southeast Asian 40 4.2 Data description 42 4.2.1 Descriptive statistic 42 4.2.2 The possible relationship by scatter 44 4.2.3 Correlation 45 4.2.4 Demographic factors by deciles 46 4.3 Panel data regression 47 4.3.1 Diagnostic analysis 47 4.3.2 One-way direction - Driscoll and Kraay estimation 49 4.4 The bi-directional causality estimation 52 Chapter Conclusions and policy implication 56 5.1 Concluding remarks 56 5.2 Policy implication 58 vi 5.3 The limitation and directions for further research 61 Reference 62 vii LIST OF TABLES Table 3.1: Variable descriptions 28 Table 4.1: Summary statistic .43 Table 4.2: Pairwise correlations 45 Table 4.3: Demographic variables by GDP per capita deciles 46 Table 4.4: Variance inflation factor (VIF) 47 Table 4.5: Model comparison .48 Table 4.6: Diagnostic problem 48 Table 4.7: Panel regression 51 Table 4.8: SEM regression 54 viii LIST OF FIGURES Figure 2.1: Life Cycle Income and Consumption Figure 2.2: The process of demographic transition and population growth Figure 2.3: Population growth and food supply Figure 2.4: Population growth and economic growth, in period of 1300-2000 10 Figure 2.5: The exogenous growth model 12 Figure 2.6: The endogenous growth model 13 Figure 2.7: Conceptual framework .26 Figure 3.1: The causal effect and mediate mechanism 37 Figure 3.2: The non-recursive mechanism 38 Figure 3.3: The logic of SEM .39 Figure 4.1: Demographic factors during the period of 1960-2013 .41 Figure 4.2: The orientation of age structure in Southeast Asian countries 41 Figure 4.3: The population pyramids in Southeast Asia .42 Figure 4.4: Relationship between economic growth and demographic factors 44 ix CHAPTER CONCLUSIONS AND POLICY IMPLICATION 5.1 Concluding remarks Using an annual panel dataset from 1990 to 2013 at the country-level in the Southeast Asia (classified by United Nations, 2015), this study is conducted to analyze the following key areas in comparing with current literature First, the determination of the impact of a number of the demographic factors on the economic growth by using a various aspect of demographic factors, including: population growth, life expectancy, and age structure Second, the interpretation of the bi-directional causality among: (i) the population growth and the economic growth; and (ii) the life expectancy and the economic growth Furthermore, the two new econometric techniques, Driscoll and Kraay estimation, and structural equation model, in parallel with the panel regression technique are applied Moreover, the noticeable contribution of this study are: (i) the specification of the various aspects of demographic factors on the economic growth is analyzed in the new context (Southeast Asia) where most countries have experienced the demographic transition, and have received the demographic dividend (Bloom and Finlay, 2009); and (ii) the worth analysis of the bi-directional causality has recognized since it is the first one in its line of current literature that confirms the inverse effect of the economic growth on population growth, and life expectancy simultaneously Findings from this study provides the empirical evidence of significant effect on the economic growth from the three key following areas First, there is the positive effect of the population growth which is consistent with the empirical results from Azomahou and Mishra (2008) and Bloom and Williamson (1998) Second, the life expectancy presents a substantial boost to the economy which is verified in the papers of Barro and Lee, (1994); Gallup and Sachs, (2000); and Ashraf et al., (2008) Third, the significant results of age structure are presented, including: (i) the negative effect dependence population (proxied by population 0_14, and population 65) and the dependence working-age structure (proxied by dependence young, and dependence old); and (ii) the positive effect of working age P a g e | 56 population (proxied by population 15_64) The former is quite compatible with Bloom et al (2010) in negative effect of both young and old dependence on short-term growth, while the later has been confirmed in the argument of Crenshaw et al., (1997), and Azomahou and Mishra (2008) that the improvement in participation rate of labor force would directly lead to a raise in economic growth These finding could infer that the old population is becoming a pressure in Southeast Asia in future unless the government don’t make a suitable pension policy and health care system In addition, this study has demonstrated the significant inverse effects of the economic growth on the two following demographic factors First, the negative effect of GDP per capita on the population growth are present which is reflected by the impact of different population policy control in the Southeast Asia Second, the GDP per capita affects positively on the life expectancy In general, these findings have filled the gap of current literature by confirms the significant presence of the bi-directional causality between the demographic factors (population growth, and life expectancy) and the economic growth P a g e | 57 5.2 Policy implication From the conclusion remarks, it is evident that the variation in population factors, for instance population growth, age structure, and life expectancy, clearly related to economic growth process in Southeast Asia during the two recent decades Therefore, this section will provide some key policies and programs which related to demographic factors and their impact on economic growth Population growth Policy Since the population growth was the main subject of demographers for centuries, the policy on population growth have been concerned as an important policy in most of the countries in Southeast Asia According to World Population Policies Database (WPPD henceforth), it is clear that there was an effort of Southeast Asian governments to stabilize population growth Particularly, in the low income countries such as Cambodia, Lao PDR, and Vietnam with the relatively high population growth, the governments have been applying the lower population growth policy in order to reduce pressures on national resources, and prepare for the employment and basic social services of all citizens On the contrary, the governments in higher income countries like Malaysia, Thailand have been satisfactory with their population growth and maintaining their population Moreover, the countries with highest income - Singapore – have been put effort into raising their population growth since the proportion of population over 65 in Singapore considerably rose by about 5% in the period of 1990-2013 (1) The success of Singapore could reflect through the fact that its population growth have significantly improved in recent times Furthermore, Thailand and Malaysia should learn by experience of Singapore since there was a moderate reduction in their population growth and fraction of population under 15 during the past decade Besides, the low-income countries should consider the situation of Thailand or Malaysia to capture full benefit from demographic transition in the next period In conclusion, the governments should observe the experience of other countries and concern different policies due to the stage of demographic transition in each countries P a g e | 58 Age structure Policy Nevertheless, the population growth itself could not provide the general picture of population status, hence the policymakers should concern more about the age structure as an important signal for the variation in population From the results of this study, the expansion of population, especially the working age group has undoubtedly accelerated the growth of GDP per capita in Southeast Asia during the past two decades However, the reduction in the share of young people implies that these countries could face the shrink in working age population in the future; hence it will depress economic growth in the region Consequently, there could be certain tradeoffs between the cost for current population burden and future benefit from huge working age group, and policymakers should consider carefully these costs and benefits of in order to maintain the sustainable growth and ensure the benefit for the next generation Moreover, the inevitable consequences of the larger working age in present period is the aging population in future The dawn of this issue in Southeast Asian countries was marked by the increase in the share of old people associated with the improvement in life expectancy As suggested by Bloom et al (2010), the negative impact of the increase in fraction of old population just exist in short run, yet not in the long run However, Eggleston and Fuchs (2012) claimed the new demographic transition with the longevity could lead to a difficulty for policymakers to maintain the positive relationship between life expectancy and income per capita since almost the increase in life expectancy in recent times come from the decline in mortality of population over 65 As a results, the portion of working time to life expectancy will decrease, hence reduce the income per capita Indeed, the aging population could affect a large range of the economy, such as the economic performance, savings rate, employment, and health Although the proportion of population over 65 in Southeast Asia just increased by 1.6%, the negatively remarkable impact of this change on GDP per capita may reflect the incipient social security systems in this region Therefore, governments should adopt some measures to address this phenomenon and endeavor some policies to deal with the coming of an aging population in the next period The sustainable pension system and the improvement in statutory retirement age were some considered P a g e | 59 decisions that Southeast Asian government should endeavor to prepare for the potential risk from aging population Health Policy The pronounced contribution of health to economic growth process was confirmed by the significantly positive impact of life expectancy on GDP per capita in Southeast Asia On the other hand, the significant relatively small effect of output per capita on life expectancy may reflect the incipient health services in this area In order to preserve the positive impact of life expectancy on income per capita and prevent the pressure from aging population, the measures to improve life expectancy should more focus on the child and working-age health As suggested from SEM results, the developed immunization programs for children could be a direct method to improve health, hence the medical care for infants and children should be concerned as a paramount objective to increase the survival change of child to complete the working age Moreover, the evidence also suggest that the health of woman should be more concerned due to the fact that the participant of female have direct relation with population growth and the lower fertility could lead to an increase in life expectancy Although there is an insignificant relationship between health and human capital in this study, education is still considered as an important factor in health development Particularly, the benefit from the increase in life expectancy could be maximized through the education system and the educated people seem to be more healthy (Bloom et al., 2003) Therefore, the improved health program should consider the impact of education as the complementary method to capture the full benefit of human capital to income growth P a g e | 60 5.3 The limitation and directions for further research Although this study is conducted to analyze the various aspects of demographic factors to the economic growth, there is several related issue should be considered in the further research First, there could be a several elements can affect the economic growth, but these have not been presented in this study due to missing data problem Second, the time span in this study is over two decade, but it should be concerned in longer (some papers argued about 60 years) It means that there could be a case of nonlinear phenomena, or other effects of macro-factors, if the study can approach more data set Third, this study can consider a several new econometric technique, including: (i) nonparametric estimation; (ii) Var; or (iii) instrumental variables, to test and robust the study’s findings The emergence of analyzing 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Root MSE P>|t| 0.004 0.000 0.000 0.258 0.000 0.006 0.040 0.119 0.000 0.000 Model P a g e | 67 = = = = = = 216 10 2117.67 0.0000 0.7809 0.6395 [95% Conf Interval] 1394658 6.05819 3.241826 -.0086809 0044119 -.0311109 0170682 -.2151696 -.4910884 -45.44477 5519508 9.537991 4.508212 0026415 0079164 -.0068334 566057 029078 -.4035853 -31.98502 Regression with Driscoll-Kraay standard errors Method: Pooled OLS Group variable (i): id maximum lag: lngdp Coef pop0_14 pop65 lnlifebase fe_ma trade gcf dum cl lndensitybase _cons -.1059098 -.5327396 4.088387 0063166 0099676 -.0092686 2849647 0027989 -.2961979 -2.416459 Drisc/Kraay Std Err .0058659 0783167 5048692 0053514 0004673 0055275 1468837 0267684 0454516 2.003408 t -18.06 -6.80 8.10 1.18 21.33 -1.68 1.94 0.10 -6.52 -1.21 Number of obs Number of groups F( 9, 9) Prob > F R-squared Root MSE P>|t| 0.000 0.000 0.000 0.268 0.000 0.128 0.084 0.919 0.000 0.258 = = = = = = 216 10 3441.87 0.0000 0.8232 0.5744 [95% Conf Interval] -.1191793 -.7099042 2.946294 -.0057891 0089105 -.0217725 -.0473094 -.0577553 -.3990167 -6.948483 -.0926403 -.355575 5.230481 0184224 0110248 0032354 6172387 0633531 -.1933791 2.115565 Model Regression with Driscoll-Kraay standard errors Method: Pooled OLS Group variable (i): id maximum lag: lngdp Coef depo_ratio depy_ratio lnlifebase fe_ma trade gcf dum cl lndensitybase _cons -.3219621 -.0221325 4.406163 0081531 0100834 -.0092869 427901 0112998 -.2839611 -6.419655 Drisc/Kraay Std Err .0468335 0018643 4914765 0050537 0005041 0055412 1223229 0225394 0428255 2.111333 t -6.87 -11.87 8.97 1.61 20.00 -1.68 3.50 0.50 -6.63 -3.04 Number of obs Number of groups F( 9, 9) Prob > F R-squared Root MSE P>|t| 0.000 0.000 0.000 0.141 0.000 0.128 0.007 0.628 0.000 0.014 Model P a g e | 68 = = = = = = 216 10 1933.93 0.0000 0.8269 0.5684 [95% Conf Interval] -.4279067 -.0263498 3.294366 -.003279 0089431 -.021822 1511873 -.0396879 -.3808391 -11.19582 -.2160174 -.0179152 5.51796 0195853 0112237 0032482 7046148 0622875 -.1870831 -1.643488 Regression with Driscoll-Kraay standard errors Method: Pooled OLS Group variable (i): id maximum lag: lngdp Coef pop15_64 lnlifebase fe_ma trade gcf dum cl lndensitybase _cons 0583743 4.891064 -.0141455 0093171 -.0173553 0848371 0570614 -.5056741 -12.81881 Drisc/Kraay Std Err .0126863 5201905 0028953 0004891 0056524 1761342 0589788 0319125 1.942476 t 4.60 9.40 -4.89 19.05 -3.07 0.48 0.97 -15.85 -6.60 Number of obs Number of groups F( 8, 9) Prob > F R-squared Root MSE P>|t| 0.001 0.000 0.001 0.000 0.013 0.642 0.359 0.000 0.000 216 10 453.65 0.0000 0.7400 0.6950 [95% Conf Interval] 0296758 3.714312 -.0206951 0082106 -.0301419 -.3136062 -.076358 -.5778651 -17.213 Appendix B Bi-directional causality regressions P a g e | 69 = = = = = = 0870728 6.067817 -.007596 0104236 -.0045687 4832805 1904808 -.4334831 -8.424625 Structural equation model Estimation method = ml Log likelihood = -4623.7497 Coef Number of obs OIM Std Err z P>|z| = 184 [95% Conf Interval] Structural lngdp