Does provincial economic development in vietnam foster internal migration

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VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DOES PROVINCIAL ECONOMIC DEVELOPMENT IN VIETNAM FOSTER INTERNAL MIGRATION? o0o -Thesis Instructor: Dr Pham Khanh Nam Student: Võ Văn Hưng CLASS 20 Ho Chi Minh City, September 2015 CERTIFICATION “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 Van Hung Date: 14th September 2015 ACKNOWLEDGEMENT I would like to express my deepest gratitude to my instructors in Vietnam Netherlands Program, who helped me to achieve the knowledge through interesting lessons, useful assignment, utility seminars and new information during my master studying I greatly express my special thanks to Dr Pham Khanh Nam for all his academic recommendations through finishing thesis process I am grateful to all the staffs in the program have helped me to reach books and necessary documents during the learning process My thanks are also extended to all my classmates, who have companions and share learning experiences with me through over last years Mental support from my family is one of the most bolster for my effort to finish this program I would like to send my thanks and my love to all my family’s members ABBREVIATIONS PGDP Provincial Gross Domestic Product PCI Provincial Competiveness Index GSO General Statistics Office of Vietnam SEM Structural Equation Modeling FEM Fixed Effect Model REM Random Effect Model OLS Ordinary Least Squares VIF Variance Inflation Factor HCMC Ho Chi Minh City VND Vietnam Dong (Vietnamese Currency) ABSTRACT This paper investigates the impact of provincial economic development on internal migration in Vietnam based on the complex casual relationship between migration and economic development We examine that impact for further understanding about the determinants could lead to human migration decision beyond the difficulties at the new destinations Tabulations data about migration flows from GSO database in duration 2005-2013 and national statistical datasets from Statistical Yearbook about provincial development are used to analyze the impact of economic development on migration The quantitative results of Structural Equation Modeling (SEM) and Common Panel Methods indicated that the higher economic development provinces have higher immigration attractiveness Key Words: Migration, economic development, Vietnam, population, origin areas, destination areas, employment, jobs TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives and research questions 1.3 Scope of the study 1.4 Organization of the thesis CHAPTER 2: LITERATURE REVIEW 2.1 Concepts of migration .5 2.2 Migration theories 2.2.1 Harris – Todaro theory 2.2.2 Lee’s theory 2.2.3 Lewis’s theory 2.2.4 Other cognizance about migration 10 2.3 Empirical studies on migration 11 2.3.1 Economic development and other factors .11 2.3.2 Determinants of migration 13 2.3.3 Linkages between economic development and migration 14 CHAPTER 3: RESEARCH METHODOLOGY 3.1 Analytical framework and hypotheses 17 3.2 Estimation methods 21 3.2.1 Structural equation model (SEM) 21 3.2.2 Other common panel data methods 23 a Pooled OLS model 23 b Fixed effects model (FEM) .24 c Random effects model (REM) 24 3.2.3 Testing for appropriate models .25 a F-test for choosing FEM or OLS 25 b Breusch - Pagan LM-test for choosing REM or OLS 25 c Hausman test for choosing FEM or REM 26 3.3 Model specification 26 3.3.1 Structural equation model (SEM) 27 3.3.2 Common panel data techniques 29 3.3.3 Migration criteria 30 3.3.4 Instrument variable .31 3.4 Data sources 32 CHAPTER 4: EMPIRICAL RESULTS 4.1 Overview about inter-provincial migration in Vietnam 34 4.2 Descriptive statistics 37 4.3 Regression results 41 4.3.1 Results from simultaneous equation method (SEM) 41 4.3.2 Robustness test - common panel data methods 44 a Ordinary Least Squared Method (OLS) 44 b Fixed Effect Model (FEM) 47 c Random Effects Model (REM) 49 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 5.1 Main findings 53 5.2 Limitation of the study 54 5.3 Policy implications 54 5.4 Suggestions for further studies 55 References 56 APPENDIX 60 CHAPTER INTRODUCTION 1.1 Problem statement The modernization of the country is always an important work in the context of the economy increasingly dynamic and society moves deeper in integration with the world One of the top priorities for a sustainable development and modern society is to reduce social inequality and eliminate poverty Along with the existence and development of the country, leaders, policy makers and scientists always seek for better solutions and improve the policies in order to match those requirements However, in a most natural way, it has never been a simple task and can be done in the short - term to get the immediate results but it is a difficult and long - term process Vietnam never could be an exception case in suffering the negative effect from development process These negative effects are the huge factors causing inequality, poverty, stagnation, unkempt for the social and economic development not only in the present but also for future generations One downside is that people often together rushed to the area where the development which is generally higher than other areas, as an allusion proverb “lua thoc dau bo cau theo do” in Vietnamese It is easily to espy the phenomenon of rural people leave their fields for the big cities to find jobs The nature of that phenomenon can be interpreted as the migrant attempt to come to higher advantage destinations with the expectation of improving their lives (De Jong & Fawcett, 1981) However, the out-control migration cause the imbalance in population density such as overload status at the large city or the slow growth rate in rural areas since the lack of labor In addition, Migration leads to many serious social problems For instance, mass-migration could create environmental pollution because of the inadequate infrastructure (Bilsborrow, 1992) and theft could also be a painful problem for downtown citizen (Deshingkar, 2006) because the migrants fall into deadlock situation There are enormous difficulties for any migrant when they have to resettle with many different conditions at the new destinations, but in reality a huge amount of migrants still migrate day by day because of these motives: economic motives, social motives and residental satisfaction motives (De Jong & Fawcett, 1981) Along with many factors can affect on the internal migration such as distance among provinces, difference in population density, education facilities, recreation facilities, living standard (Dang, Goldstein & McNally, 1997), cost of moving (Greenwood, 1975), et cetera; Difference in provincial economic development is also a factor contributes to the migration activities since the province has higher development index will more attract migrants than others In other words, high provincial development leads to the expectation in improving migrant living conditions and becomes an economic motive in migration flows This study aims to examine the impact of provincial economic development on internal migration to interpret whether high economic development provinces attract migrants and whether the encouragement of economic development – an instrument for redistribution population density? Finding out this linkage support the argument that Government could intervene migration flow via encourage development tools in order to avoid the backward contexts had mentioned above Throughout the history of the country, Vietnam has had numerous internalmigration flows with different scales, from the early primitive times, through thousands feudal years until today Many of them are forced migrations or reluctant migrations due to Government’s policy or natural circumstances such as migrations in 1954 and 1975 or the migration by policy of "new economic zone" However, the forced migrations have its certain limitation such as the unexpected from those forced to change their habitat This phenomenon leads to the reactions against this compulsion, for example the resistance, escapade or fake declared information As the arguments stated above, the consequence of residential overcrowding in one area raises the stagnation in the economic development process This goes against the expectations of migrants with the purpose of improving their living, which included economic factors, the ambivalence is extremely clear Therefore, in order to limit the downside that caused by these consequences, policy makers need to find solutions to redistribute population density Recently, along with the development and integration, spontaneous migration almost completely replaced the forced migration, the issuing macroeconomic policies so that people could freely to migrate by their own decisions but still within the control of the government is the necessary and urgent policies Stemming from the above practical requirements, we carried out the study “Does provincial economic development in Vietnam foster internal migration?” 1.2 Research objectives and research question The research is constructed to quantify the relationship between provincial economic development and migration flows across provincial level in Vietnam by specific objectives as follow: - To have a further understanding about the role of income for migrant’s decision - To interpret how urbanization impact on migration decision and to identify the migrant’s perception of urbanization for their moving motivation - To study the impact of industrial structure on internal migration and propose policy implications for the future establishing of industrial areas Research questions The transference of human is natural and undeniable in the history of mankind all over the world In order to recognize above realities, it is deserved to assert that migration has possibility to fetch both opportunities and challenges for development In spatial contexts and specific time, the interaction between migration and development will be different In a specific context of Vietnam, by using secondary data and analytical models, the goal of this study is to find relevant answers for these questions below: - Do people tend to move to higher income provinces within Vietnam? - Whether the establishing of industrial park could be seen as a good instrument for entice people to appointed destinations? - Do Migrants choose high urbanization rate areas for their resettlement purpose in Vietnam? Sjaastad, L A (1962) The costs and returns of human migration The journal of political economy, 80-93 Sriskandarajah, D (2005) Migration and development A paper prepared for the Policy Analysis and Research Programme of the Global Commission on International Migration Global Commission on International Migration, September 59 APPENDIX The correlation matrix among variables corre urban income in_struct density pgdp edu health pop (obs=482) urban urban income in_struct density pgdp edu health pop 1.0000 0.4205 -0.4298 0.4137 0.5183 -0.3692 -0.0098 0.3698 income in_str~t 1.0000 -0.1397 0.2610 0.5820 -0.2540 -0.0945 0.1992 1.0000 0.2043 -0.1249 0.0267 0.0565 -0.0104 density pgdp edu health pop 1.0000 0.7336 -0.4707 -0.1168 0.7525 1.0000 -0.3377 -0.0509 0.7974 1.0000 0.6603 -0.4257 1.0000 -0.1738 1.0000 60 SEM regression result Structural equation model Estimation method = ml Log likelihood = -25894.334 Coef Number of obs OIM Std Err z P>|z| = 463 [95% Conf Interval] Structural urban F R-squared Adj R-squared Root MSE P>|t| 0.033 0.001 0.000 0.043 0.095 0.000 0.251 = = = = = = 497 132.20 0.0000 0.6181 0.6135 15901 [95% Conf Interval] -175.7601 2.837012 0804412 -23258.46 -426.9203 4.554085 -12678.37 -7.263587 11.77006 1815947 -351.2734 5280.875 9.86386 3321.701 Result from model by pool OLS method reg in_mig in_struct density pgdp edu health pop Source SS df MS Model Residual 2.0912e+11 1.1501e+11 475 3.4853e+10 242121632 Total 3.2412e+11 481 673853088 in_mig Coef in_struct density pgdp edu health pop _cons -75.99597 15.1619 0510512 -6522.622 2950.735 8.514016 -8883.129 Std Err 12.65141 2.485184 0183724 5695.853 1451.862 1.124378 3329.149 t -6.01 6.10 2.78 -1.15 2.03 7.57 -2.67 63 Number of obs F( 6, 475) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.006 0.253 0.043 0.000 0.008 = = = = = = 482 143.95 0.0000 0.6452 0.6407 15560 [95% Conf Interval] -100.8556 10.27859 0149499 -17714.81 97.86746 6.304645 -15424.81 -51.13632 20.04522 0871524 4669.563 5803.602 10.72339 -2341.449 Result from model by pool OLS method reg in_mig urban income in_struct density pgdp edu health pop Source SS df MS Model Residual 2.1506e+11 1.0906e+11 473 2.6883e+10 230571861 Total 3.2412e+11 481 673853088 in_mig Coef urban income in_struct density pgdp edu health pop _cons 313.8702 -97.24901 -36.21534 11.16243 075221 940.3558 622.6858 7.841474 -13125.93 Std Err 64.35439 42.79937 14.65264 2.567677 02611 6006.728 1490.666 1.294849 4206.638 t 4.88 -2.27 -2.47 4.35 2.88 0.16 0.42 6.06 -3.12 64 Number of obs F( 8, 473) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.024 0.014 0.000 0.004 0.876 0.676 0.000 0.002 = = = = = = 482 116.59 0.0000 0.6635 0.6578 15185 [95% Conf Interval] 187.4143 -181.3494 -65.00766 6.116968 023915 -10862.82 -2306.461 5.297106 -21391.94 440.326 -13.14858 -7.42302 16.2079 126527 12743.53 3551.833 10.38584 -4859.919 Result from model by FEM xtreg in_mig urban density pgdp edu health pop, fe Fixed-effects (within) regression Group variable: province2 Number of obs Number of groups = = 497 63 R-sq: Obs per group: = avg = max = 7.9 within = 0.2947 between = 0.6065 overall = 0.5577 corr(u_i, Xb) F(6,428) Prob > F = -0.9770 = = in_mig Coef urban density pgdp edu health pop _cons 190.2177 31.68295 -.2475056 -4630.607 6113.935 78.4641 -120273.1 122.9007 15.34816 0212722 9030.046 1266.075 9.564696 11105.56 sigma_u sigma_e rho 73537.88 7226.8822 99043455 (fraction of variance due to u_i) F test that all u_i=0: Std Err t 1.55 2.06 -11.64 -0.51 4.83 8.20 -10.83 F(62, 428) = 65 28.36 P>|t| 0.122 0.040 0.000 0.608 0.000 0.000 0.000 29.81 0.0000 [95% Conf Interval] -51.34643 1.515814 -.2893166 -22379.36 3625.437 59.66448 -142101.3 431.7818 61.85009 -.2056945 13118.15 8602.433 97.26372 -98444.87 Prob > F = 0.0000 Result from model by FEM xtreg in_mig income density pgdp edu health pop, fe Fixed-effects (within) regression Group variable: province2 Number of obs Number of groups = = 497 63 R-sq: Obs per group: = avg = max = 7.9 within = 0.4256 between = 0.5986 overall = 0.5524 corr(u_i, Xb) F(6,428) Prob > F = -0.9866 = = in_mig Coef income density pgdp edu health pop _cons 389.5499 53.14346 -.405755 -10928.11 2248.123 98.35146 -142612.2 38.86488 13.9593 0245181 8159.962 1170.351 8.762794 10257.31 sigma_u sigma_e rho 98337.651 6521.9433 99562066 (fraction of variance due to u_i) F test that all u_i=0: Std Err t 10.02 3.81 -16.55 -1.34 1.92 11.22 -13.90 F(62, 428) = 66 40.08 P>|t| 0.000 0.000 0.000 0.181 0.055 0.000 0.000 52.85 0.0000 [95% Conf Interval] 313.1601 25.70615 -.4539459 -26966.7 -52.228 81.128 -162773.2 465.9397 80.58076 -.357564 5110.475 4548.474 115.5749 -122451.2 Prob > F = 0.0000 10 Result from model by FEM xtreg in_mig in_struct density pgdp edu health pop, fe Fixed-effects (within) regression Group variable: province2 Number of obs Number of groups = = 482 63 R-sq: Obs per group: = avg = max = 7.7 within = 0.3197 between = 0.5962 overall = 0.5550 corr(u_i, Xb) F(6,413) Prob > F = -0.9808 = = in_mig Coef in_struct density pgdp edu health pop _cons -56.1361 34.26454 -.2785607 -2357.575 7045.905 87.20347 -127663.6 34.18615 15.41284 0221343 9024.853 1206.996 9.612042 11434.29 sigma_u sigma_e rho 81511.167 7197.5538 99226318 (fraction of variance due to u_i) F test that all u_i=0: Std Err t -1.64 2.22 -12.59 -0.26 5.84 9.07 -11.16 F(62, 413) = 67 29.15 P>|t| 0.101 0.027 0.000 0.794 0.000 0.000 0.000 32.35 0.0000 [95% Conf Interval] -123.3366 3.967136 -.3220706 -20097.95 4673.283 68.30885 -150140.2 11.06445 64.56194 -.2350507 15382.8 9418.527 106.0981 -105186.9 Prob > F = 0.0000 11 Result from model by FEM xtreg in_mig urban income in_struct density pgdp edu health pop, fe Fixed-effects (within) regression Group variable: province2 Number of obs Number of groups = = 482 63 R-sq: Obs per group: = avg = max = 7.7 within = 0.4326 between = 0.5900 overall = 0.5501 corr(u_i, Xb) F(8,411) Prob > F = -0.9873 = = in_mig Coef urban income in_struct density pgdp edu health pop _cons -257.1457 417.0454 -47.93839 56.85476 -.4245064 -8920.383 2797.499 102.55 -142766.9 121.785 46.50165 31.60603 14.40836 0264106 8316.409 1242.287 9.068355 10610.91 sigma_u sigma_e rho 102001.75 6589.5776 99584385 (fraction of variance due to u_i) F test that all u_i=0: Std Err t -2.11 8.97 -1.52 3.95 -16.07 -1.07 2.25 11.31 -13.45 F(62, 411) = 68 33.88 P>|t| 0.035 0.000 0.130 0.000 0.000 0.284 0.025 0.000 0.000 39.16 0.0000 [95% Conf Interval] -496.5449 325.6347 -110.068 28.5315 -.4764231 -25268.39 355.4707 84.72385 -163625.3 -17.74645 508.4562 14.19124 85.17802 -.3725898 7427.621 5239.527 120.3761 -121908.5 Prob > F = 0.0000 12 Result from model by REM xtreg in_mig urban density pgdp edu health pop, re Random-effects GLS regression Group variable: province2 Number of obs Number of groups = = 497 63 R-sq: Obs per group: = avg = max = 7.9 within = 0.1881 between = 0.6653 overall = 0.6139 corr(u_i, X) Wald chi2(6) Prob > chi2 = (assumed) in_mig Coef urban density pgdp edu health pop _cons 403.7091 14.27687 -.0762968 1136.121 3290.375 15.94456 -33701.26 91.8202 5.002836 0134389 7678.078 1232.969 2.403505 5836.968 sigma_u sigma_e rho 12661.966 7226.8822 75428352 (fraction of variance due to u_i) Std Err z 4.40 2.85 -5.68 0.15 2.67 6.63 -5.77 69 P>|z| 0.000 0.004 0.000 0.882 0.008 0.000 0.000 = = 195.70 0.0000 [95% Conf Interval] 223.7448 4.471496 -.1026366 -13912.63 873.8008 11.23378 -45141.51 583.6734 24.08225 -.049957 16184.88 5706.95 20.65534 -22261.02 13 Result from model by REM xtreg in_mig income density pgdp edu health pop, re Random-effects GLS regression Group variable: province2 Number of obs Number of groups = = 497 63 R-sq: Obs per group: = avg = max = 7.9 within = 0.2567 between = 0.5910 overall = 0.5493 corr(u_i, X) Wald chi2(6) Prob > chi2 = (assumed) in_mig Coef Std Err z income density pgdp edu health pop _cons 187.2755 18.63197 -.1249037 814.3932 2470.674 18.31686 -29025.75 39.61928 4.872776 017484 7604.27 1276.334 2.38371 5585.759 sigma_u sigma_e rho 11151.656 6521.9433 74513498 (fraction of variance due to u_i) 4.73 3.82 -7.14 0.11 1.94 7.68 -5.20 70 P>|z| 0.000 0.000 0.000 0.915 0.053 0.000 0.000 = = 203.48 0.0000 [95% Conf Interval] 109.6231 9.08151 -.1591718 -14089.7 -30.89505 13.64488 -39973.63 264.9278 28.18244 -.0906357 15718.49 4972.244 22.98885 -18077.86 14 Result from model by REM xtreg in_mig in_struct density pgdp edu health pop, re Random-effects GLS regression Group variable: province2 Number of obs Number of groups = = 482 63 R-sq: Obs per group: = avg = max = 7.7 within = 0.2057 between = 0.6400 overall = 0.6024 corr(u_i, X) Wald chi2(6) Prob > chi2 = (assumed) in_mig Coef in_struct density pgdp edu health pop _cons -81.24489 22.29229 -.0837568 -849.7024 4860.718 15.74508 -24916.07 24.02343 5.369721 0141084 7855.039 1230.053 2.506244 5866.425 sigma_u sigma_e rho 12830.085 7197.5538 76062392 (fraction of variance due to u_i) Std Err z -3.38 4.15 -5.94 -0.11 3.95 6.28 -4.25 71 P>|z| 0.001 0.000 0.000 0.914 0.000 0.000 0.000 = = 178.96 0.0000 [95% Conf Interval] -128.33 11.76784 -.1114087 -16245.3 2449.857 10.83294 -36414.06 -34.15984 32.81675 -.0561048 14545.89 7271.578 20.65723 -13418.09 15 Result from model by REM xtreg in_mig urban income in_struct density pgdp edu health pop, re Random-effects GLS regression Group variable: province2 Number of obs Number of groups = = 482 63 R-sq: Obs per group: = avg = max = 7.7 within = 0.2333 between = 0.6585 overall = 0.6214 corr(u_i, X) Wald chi2(8) Prob > chi2 = (assumed) in_mig Coef urban income in_struct density pgdp edu health pop _cons 227.8549 157.4284 -55.02182 19.84327 -.1225855 4246.737 2208.191 16.88592 -30846.25 102.9276 44.17028 25.00604 5.279713 0179725 7665.721 1316.07 2.392575 5964.156 sigma_u sigma_e rho 11137.02 6589.5776 7406925 (fraction of variance due to u_i) Std Err z 2.21 3.56 -2.20 3.76 -6.82 0.55 1.68 7.06 -5.17 72 P>|z| 0.027 0.000 0.028 0.000 0.000 0.580 0.093 0.000 0.000 = = 220.77 0.0000 [95% Conf Interval] 26.12043 70.85623 -104.0328 9.495226 -.1578109 -10777.8 -371.2589 12.19656 -42535.78 429.5893 244.0006 -6.010888 30.19132 -.08736 19271.27 4787.641 21.57528 -19156.72 16 Breusch – Pagan LM test for choosing appropriate model between REM and OLS Breusch and Pagan Lagrangian multiplier test for random effects in_mig[province2,t] = Xb + u[province2] + e[province2,t] Estimated results: Var in_mig e u Test: sd = sqrt(Var) 6.74e+08 4.34e+07 1.24e+08 25958.68 6589.578 11137.02 Var(u) = chibar2(01) = Prob > chibar2 = 677.48 0.0000 17 Hausman test for choosing appropriate model between REM and FEM Coefficients (b) (B) fixed random urban income in_struct density pgdp edu health pop -257.1457 417.0454 -47.93839 56.85476 -.4245064 -8920.383 2797.499 102.55 (b-B) Difference 227.8549 157.4284 -55.02182 19.84327 -.1225855 4246.737 2208.191 16.88592 -485.0005 259.617 7.083427 37.01149 -.301921 -13167.12 589.308 85.66408 sqrt(diag(V_b-V_B)) S.E 65.09603 14.53925 19.32974 13.40617 0193522 3224.807 8.747036 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 187.18 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 73 ... of provincial economic development on internal migration to interpret whether high economic development provinces attract migrants and whether the encouragement of economic development – an instrument... investigates the impact of provincial economic development on internal migration in Vietnam based on the complex casual relationship between migration and economic development We examine that impact for... relationship among economic factors and in- migration Urbanization Income Provincial Competitiveness Index Economic factors Industrial structure Population density Immigration Provincial GDP Education
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