Migrants and non migrants wage differentials in southeast provinces of vietnam

i; INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM VIETNAM- NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MIGRANTS AND NON-MIGRANTS WAGE DIFFERENTIALS IN SOUTHEAST PROVINCES OF VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAN THI HONG NHUNG -••u~••·,.·~ • -•~ • ' •• ;· , ' Academic Supervisor: NGUYEN HUU DUNG r ~·· E-~~; ~ti :.: i-~ : 11 ,'', ;I -, • '··••~->.H\;t t j i I I 'i ; I x::I J1 ~1 o6 HO CHI MINH CITY, June 2010 _._ ., .,- ,_., _ ,-,_,,-, - ~""~~~~ - CERTIFICATION I hereby certify that the substance of this thesis has not been submitted for any degrees and is not being currently submitted for any other degrees I also certify that, to the best of my knowledge, and any help received in preparing the thesis and all sources used have been acknowledged in the thesis Phan Thi Hong Nhung Date: June, 2010 iii ACKNOWLEDGEMENT This research is impossible completed without the valuable guidance, encouragement and advice from numerous individuals including Vietnam-Netherlands program lecturers, friends and my family members I would like to express my special thanks to all people to what they have done for my thesis completion First of all, I would like to send my deepest gratitude to my supervisor, Dr Nguyen Huu Dung who always gives valuable instructions, advice and comments during my completion of the thesis I also send my special thanks to Associate Professor Nguyen Trong Hoai for his lectures in econometrics, Mr Truong Thanh Vu, the lecturers of Vietnam-Netherlands project, for their kind help and instructions in data analysis by Stata software and Mr Le Cong Tam- MDE 12, Phan Thi Lien- MDE 14 for their support in the process of data mining Many especially respectful thanks are sent to my family for encouraging and providing me with an opportunity to pursue my desires in higher learning And finally, I would like to express my special thanks to my friends in MDE class 14 for their supportive friendship from the beginning day I joined the VNP program, and their continuous support during my research completion Above all, please sympathize for me and know that I would be so grateful for those who support me a lot in this thesis completion if I forget to mention their names IV - - - - - ABSTRACT This study examines the wage differentials between migrant workers and non-migrant workers Based on data from Vietnam Migration Survey in 2004, earnings equations with and without Instrumental Variable (IV) are estimated for migrant workers and nonmigrant workers From these results, the study compares the wage structure for migrant workers and non-migrant workers Oaxaca decomposition of the wage differentials of the two groups workers are carried out Results, which are controlled for observed characteristics and selection bias, indicate some main points The wage differentials between non-migrant and migrant workers are mostly due to the difference in structural factors Besides, there are differences in endowment factors v - ~ CONTENTS Certification ·· ··· ···· ·······.1 Acknowledgements ···· 11 Abstract 111 Content table 1v List of Tables viii List of Figures x List of Abbreviations xi CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 Thesis structure CHAPTER 2: LITERATURE REVIEW 2.1 Definition of migration 2.2 Reviews oftheoretical framework 2.2.1 Related to migration 2.2.1.1 Human capital theory 2.2.1.2 Harris- Torado model for migration 2.2.2 Related to wage determination and wage differentials VI 2.3 Previous empirical studies 12 2.4 Chapter remarks 18 CHAPTER 3: RESEARCH METHODOLOGY 3.1 Empirical model 19 3.1.1 Earnings function 19 1.2 Earnings differentials measurement 21 3.2 Definition of variables used in study 22 3.2.1 Dependent variable 22 3.2.2 Independent variables 23 3.3 Datasource 27 3.3.1 Introduction ofthe data set 27 3.3.2 The reliability of the data set 28 3.3.3 Detail ofdata 28 3.4 Estimation strategy 30 Chapter remarks 31 CHAPTER 4: OVERVIEW OF MIGRATION IN HO CHI MINH CITY, BINH DUONG AND DONG NAI PROVINCE 4.1 A profile of migrants and non-migrants in HoChiMinh City, Dong Nai and Binh Duong 32 4.1.1 Age structure 32 4.1.2 Gender 33 4.1.3 Education level 34 VII 4.1.4 Type of activity 35 4.1.5 Earnings 36 4.1.6 Duration of residence 38 4.1.7 Type ofoccupation 39 4.2 Earnings ofworkers 40 4.2.1 Monthly average income classified by gender and regions .40 4.2.2 Monthly average income classified by types of occupation and regions 41 4.2.3 Monthly average income classified by sectors and regions 42 4.2.4 Monthly average income classified by education levels and regions 43 4.2.5 Monthly average income classified by characteristics of migrant and non-migrant workers 44 4.3 Chapter remarks 46 CHAPTER 5: DETERMINANTS OF EARNINGS AND WAGE DIFFERENTIALS OF MIGRANTS AND NON-MIGRANTS 5.1 Determinant of earnings 5.1.1 Estimation results of model with OLS and 2SLS 5.1.2 The effect of year since migration on migrant's earnings (model 2) 50 5.1.3 Determinants of earnings for migrants and non-migrants (model 1) 52 5.2 Wage differentials with Oaxaca's method 55 5.3 Charter remarks 57 CHAPTER 6: CONCLUSION AND POLICY IMPLICATION 6.1 Conclusion 59 6.2 Policy implications 61 viii 6.3 Limitation ofthe research 63 REFERENCES APPENDICES IX • LIST OF TABLES Table 3.1: Definitions and notations of variables Table 4.1: Distribution of age structure by current residence, migrant status and gender (%) Table 4.2: Distribution of sex ratio by age structure current residence and migrant status (%) Table 4.3: Distribution of the obtained highest education level by current residence, migrant status and gender (%) Table 4.4: Distribution of the types of activity by current residence and migrant status(%) Table 4.5: Monthly average earnings (thousand VND) by current residence, migration status, age group and gender Table 4.6: Distribution of duration residence of migrants m current residence by household registration status Table 4.7: Distribution of types of occupation by current residence, migrant status Table 4.8: Monthly average income classified by gender and regions Table 4.9: Monthly average income classified by types of occupation and regions Table 4.10: Monthly average income classified by sectors and regions Table 4.11: Monthly average income classified by education levels and region Table 4.12: Monthly average income classified by characteristics of migrant and nonmigrant worker Table 5.1: Estimation results of model with OLS and 2SLS Table 5.2: Testing for endogeneity Table 5.3: The correlation matrix between the explanatory variable and its instrument Table 5.4: The effect of year since migration on earnings for migrants (model2) X Table 5.5: Estimating results ofmodell for migrants and non-migrants Table 5.6: Earnings differentials between migrants and non-migrants by Oaxaca's method xi 6.3 Limitation of the research This research while providing some significant conclusions on the earnings differentials between migrants and non-migrants workers in HCMC, Binh Duong and Dong Nai, it is also subject to some limitations Firstly, data set used in this study is extracted from Vietnam National Migration Survey in 2004, which is conducted for migrant objectives who have resided in the household in the study area for one month and over, but only within the five years (from 1999 to 2004) The data shows that the duration of residence from several months to three years of migrants in HCMC, Binh Duong and Dong Nai are 66.10%, which is a rather big number The reality proves that a period of time from several months to three years so that a migrant can settle a stable life in a big city as HCMC or in Binh Duong, Dong Nai province is not long enough Therefore, we can not recognize the effect of "Ysm" variable (the number of years elapsed since an immigrant's arrival in a new residential place) on earnings function for migrants Secondly, the data resource for migration is almost only conducted by GSO, so this thesis can not use other resources to descriptive statistic in order to objectively provide a general picture about a profile of migrants in HCMC and two provinces, Binh Duong and Dong Nai - 63 REFERENCES Arrow, K.J (1973), "The Theory of Discrimination", Discrimination in Labor Markets, Princeton: Princeton University Press, p 3-33 Aslund, & Rooth, D (2007), "Do When and Where Matter? 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China Center for Economic Research, Peking University • ·-3- APPENDICES APPENDIX 1: Distribution of monthly average earnings and correlation matrix Distribution of monthly average earnings (wage) before transforming into logarithm form Distribution of monthly average earmngs (wage) after transforming into logarithm form t ~ 'Q) • -4- Correlation matrix of the variables in model school exp2 exp gen sec OCC2 OCC1 · :3chool 1.0000 exp -0.3984 0.0000 1.0000 exp.2 -0.3882 0.0000 0.9689 0.0000 1.0000 gen -0.0019 0.9433 0.1143 0.0000 0.1070 0.0001 1.0000 OCC1 0.3226 0.0000 -0.1464 0.0000 -0.1189 0.0000 -0.0330 0.2274 1.0000 OCC2 0.0109 0.6902 -0.0705 0.0098 -0.0849 0.0019 0.0593 0.0299 -0.1951 0.0000 1.0000 sec 0.1652 0.0000 -0.0342 0.2111 -0.0371 0.1742 0.0529 0.0528 2 62 0.0000 0.0246 682 1.0000 According to correlation matrix table, correlation between variables is very weak The purpose of this statistical description is not to express completely the result but gain the insights into the nature of relationships APPENDIX 2: Descriptive statistic analysis 2.1 Mean of wage classified by gender Group Statistics WAGE GEN 695 Mean 1371640 Std Deviation 1480684.343 Std Error Mean 56165.559 646 1663745 1616212.617 63589.035 N Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means • Sig F WAGE Equal variances assumed Equal variances not assumed 184 668 Sig (2-tailed) Mean Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper -3.454 1339 001 -292104.29 84571.235 -458011 -126198 -3.443 1305.494 001 -292104.29 84841.825 -458546 -125663 t df -5- ANOVA WAGE Between Groups Within Groups Sum of Squares 9.57E+13 3.14E+15 Total 3.23E+15 1337 Sig .000 F 13.583 Mean Square 3.189E+13 2.348E+12 df 1340 2.2 Mean of wage classified by occupations Multiple Comparisons Dependent Variable: WAGE LSD (I) OCCODE (J) OCCODE 1.00 2.00 3.00 2.00 1.00 3.00 3.00 1.00 2.00 * The mean difference IS Mean Difference U-J) Std Error -47416.26 212099.220 491298.54(*) 211530.278 47416.26 212099.220 538714.80(*) 85705.246 -491298.54{*) 211530.278 -538714.80(*) 85705.246 s1gmficant at the 05 level 95% Confidence Interval Sig .823 020 823 000 020 000 Lower Bound -463499.76 76331.15 -368667.24 370583.40 -906265.92 -706846.20 Upper Bound 368667.24 906265.92 463499.76 706846.20 -76331.15 -370583.40 AN OVA WAGE Between Groups Sum of Squares 9.57E+13 Within Groups Total df Mean Square 3.189E+13 3.14E+15 1337 2.348E+12 3.23E+15 1340 F 13.583 Sig .000 2.3 Mean of wage classified by sectors Group Statistics WAGE SEC N 1217 Mean 1531380 Std Deviation 1604084.343 Std Error Mean 45981.370 124 1325637 907539.408 81499.445 -6- Independent Samples Test Levene's Test for E:qualitv of Variances F WAGE Equal variances assumed Equal variances not assumed t-test for Equality of Means Sig .016 5.792 df t 1.405 1339 2.199 211.599 Mean Sia (2-tailed) Difference Std Error Difference 160 205743.35 146413.56 95% Confidence Interval of the Difference Lower Upper -81481.6 492968.3 029 205743.35 93575.883 1282.968 390203.7 2.4 Mean of wage classified by migration status (female) Group Statistics WAGE MIGRANT 560 Mean 1503036 Std Deviation 1603667.442 Std Error Mean 67767.318 135 826592.59 504917.849 43456.409 N Independent Samples Test Levene's Test for Eaualitv of Variances F WAGE Equal variances assumed t-test for Eaualitv of Means 000 Equal variances not assumed Mean Difference Std Error Difference Lower Upper 4.841 693 000 676443.12 139728.46 402101.2 950785.0 8.403 652.782 000 676443.12 80503.844 518365.4 834520.8 SiQ 32.474 SiQ (2-tailed) 95% Confidence Interval of the Difference df t 2.5 Mean of wage classified by migration status (male) Group Statistics WAGE MIGRANT 482 Mean 1772488 Std Deviation 1656776.593 Std Error Mean 75464.106 164 1344146 1448672.294 113122.3 N Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed Equal variances not assumed 13.775 SiQ .000 !-test for Equality of Means t df 95% Confidence Interval of the Difference SiQ (2-tailed) Mean Difference Std Error Difference Lower UpQ_er 2.949 644 003 428341.21 145242.33 143135.5 713547.0 3.150 318.954 002 428341.21 135983.40 160803.5 695879.0 2.6 Mean of wage classified by migration status (private sector) - 7- Group Statistics • MIGRANT WAGE Mean N 927 290 1664466 1105966 Std Deviation Std Error Mean 1696468.098 1169004.515 55719.323 68646.309 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed t-test for E_g_uali.!Y of Means 37.566 df t Sl9c 000 Equal variances not assumed 5.231 1215 6.317 700.373 95% Confidence Interval of the Difference Upper Lower Mean Sig (2-tailed) Difference Std Error Difference 000 556500.50 106776.61 349013.1 767967.9 000 66413.566 364913.1 732067.9 556500.50 2.7 Mean ofwage classified by migration status (public sector) Group Statistics MIGRANT WAGE N Mean 115 Std Deviation 1331122 1255556 938066.004 343187.671 Std Error Mean 87475.106 114395.9 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed t-test for ~uali1Y_ of Means Siq 3.495 t 064 Equal variances not assumed df Mean Sig._12-tailecJ} Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 240 122 611 75566.16 315336.22 -548677 699809.7 525 19.620 606 75566.18 144008.03 -225203 376335.1 2.8 Mean of wage classified by migration status (professionals occupation) Group Statistics MIGRANT WAGE N Mean 48 1868750 1011111 -8- Std Deviation Std Error Mean 1294038.088 613278.983 186778.3 204426.3 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed Equal variances not assumed t-test for Equality of Means Sig .034 4.715 Std Error Difference Mean Sig (2-tailed) Difference df t 95% Confidence Interval of the Difference Upper Lower 1.937 55 058 857638.89 442748.57 -29649.1 1744927 3.097 24.076 005 857638.89 276904.79 286230 1429047 2.9 Mean of wage classified by migration status (staffs occupation) Group Statistics MIGRANT WAGE N 495 124 Std Error Mean Std Deviation Mean 2036586.919 1601078.894 1902937 1292984 91537.775 143781.1 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed 14.404 Sig .000 Equal variances not assumed !-test for Equality of Means df t Mean Sig (2-tailed) Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 3.103 617 002 609953.50 196578.98 223908.5 995998.5 3.579 233.369 000 609953.50 170446.99 274142.0 945765.0 2.10 Mean of wage classified by migration status (elementary job) Group Statistics MIGRANT WAGE Mean Std Deviation Std Error Mean 1330071 979518.07 1069312.677 654573.474 48062.031 50804.755 N 495 166 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed Equal variances not assumed 27.392 Sig .000 !-test for Equality of Means I df Mean Sig (2-tailed) Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 3.980 659 000 350552.63 88079.866 177601.6 523503.6 5.012 467.437 000 350552.63 69936.270 213124.2 487981.0 • -9- 2.11 Mean of wage classified by migration status (HCMC) Group Statistics MIGRANT WAGE N 346 98 Std Error Mean 71601.697 46841.812 Std Deviation 1331868.548 463710.281 Mean 1568249 1045306 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed Equal variances not assumed !-test for Eaualitv of Means Sig .000 18.128 df t Sig (2-tailed) Mean Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 3.819 442 000 522942.43 136923.39 253840.7 792044.2 6.112 425.984 000 522942.43 85562.599 354765.0 691119.9 2.12 Mean of wage classified by migration status (Binh Duong province) Group Statistics MIGRANT WAGE 310 Mean 2026661 Std Deviation 2300735.825 Std Error Mean 130673.0 57 1330702 2348018.815 311002.7 N Independent Samples Test Levene's Test for Equality of Variances !-test for Eaualitv of Means ' F WAGE Equal variances assumed Sig 4.388 Equal variances not assumed 037 I df 2.092 365 2.063 77.082 Mean Sig (2-tailed) Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 1849.061 1350070 042 695959.54 337339.75 4241.733 1367677 037 695959.54 332629.34 2.13 Mean of wage classified by migration status (Dong Nai province) Group Statistics • WAGE MIGRANT 386 Mean 1360518 Std Deviation 1082269.257 Std Error Mean 55086.055 144 1067639 663932.959 55327.747 N < ' - 10- Independent Samples Test Levene's Test for Equality of Variances Equal variances assumed 11.295 Equal variances not assumed Mean Difference Std Error Difference Lower Upper 528 002 292879.25 96343.579 103615.5 482143.0 3.751 415.408 000 292879.25 78074.535 139408.8 446349.7 t 001 Sig (2-tailed) 95% Confidence Interval of the Difference 3.040 Sig F WAGE t-test for Equality of Means df 2.14 Mean of wage classified by migration status (illiterate+ primary) Group Statistics I MIGRANT WAGE N Mean Std Deviation Std Error Mean 103 50 1220582.52 774000.00 1021583.570 524135.829 100659.620 74124.000 Independent Samples Test Levene's Test for Equality of Variances F WAGE Equal variances assumed Equal variances not assumed 10.806 Sig .001 t-test for Equality of Means t df Sig (2-tailed) Mean Difference Std Error Difference 95% Confidence Interval of the Difference Upp_er Lower 2.907 151 004 446582.52 153597.63 143104.5 750060.6 3.572 150.496 000 446582.52 125006.91 199587.3 693577.7 2.15 Mean of wage classified by migration status (secondary education) ' Group Statistics MIGRANT WAGE Mean Std Deviation Std Error Mean 1545498 987301.59 1570819.505 447217.911 86340.049 39841.338 N 331 126 Independent Samples Test Levene's Test for Equality of Variances F WAGE • Equal variances assumed Equal variances not assumed 30.854 Sig .000 t-test for Equality of Means t df Sig (2-tailed) Mean Difference Std Error Difference 95% Confidence Interval of the Difference Lower Upper 3.926 455 000 558196.90 142168.43 278808.7 837585.1 5.870 433.597 000 558196.90 95089.096 371304.0 745089.8 • • - 11 - • 2.16 Mean of wage classified by migration status (high school) Group Statistics MIGRANT WAGE N 552 109 Mean 1738332 Std Deviation 1767956.288 1429633 1757523.472 Std Error Mean 75249.217 168340.2 Independent Samples Test Levene's Test for EquaiHy of Variances F WAGE Equal variances assumed Equal variances not assumed 5.116 t-test for Equality of Means Mean Difference Std Error Difference Lower U_l)J)_er 659 096 308698.49 185127.23 -54811.8 672208.8 1.674 154.265 096 308698.49 184393.26 -55563.2 672960.2 t 024 Sig (2-tailed) 1.667 Sig df 95% Confidence Interval of the Difference 2.17 Mean of wage classified by migration status (college/university/higher) Group Statistics MIGRANT WAGE N 56 14 Mean 1771429 935714.29 Std Deviation 1391756.808 614700.856 Std Error Mean 185981.3 164285.7 Independent Samples Test Levene's Test for !;_quality_of Variances F WAGE Equal variances assumed Equal variances not assumed 3.937 Sig .051 t-test for l;g_uality of Means Sig (2-tailedl_ Mean Difference Std Error Difference Lower U_l)J)_er 2.185 68 032 835714.29 382533.62 72380.332 1599048 3.368 48.748 001 835714.29 248150.86 336971.2 1334457 t df 2.18 Frequency table of Ysm variable I Ysm Freq Percent Cum 37 42 39 71 85 12 12.94 14.69 13.64 24.83 29.72 4.20 12.94 27.62 41.26 66.08 95.80 100.00 286 100.00 Total 95% Confidence Interval of the Difference - 12- APPENDIX 3: Results of regression models 3.1 Regression model! without IV (n=l,341) reg lnW school exp exp2 gen occl occ2 sec, robust Linear regression 1341 Number of obs F( 7, 1333) = Prob>F = R-squared = RootMSE = 49.69 0.0000 0.1744 I 61824 e ~ ~ • lnW School Exp Exp2 Gen Occl Occ2 Sec cons Coef .0333081 0271899 -.0002411 1994539 4051597 3105636 -.1936372 12.98916 Std Err .0056119 005541 000138 0346891 089649 0350806 0525437 0746244 T 5.94 4.91 -1.75 5.75 4.52 8.85 -3.69 174.06 P>t 0.000 0.000 0.081 0.000 0.000 0.000 0.000 0.000 [95% Conf .0222989 01632 -.0005118 1314027 2292912 2417444 -.2967146 12.84277 Interval] 0443173 0380599 0000296 2675052 5810281 3793829 -.0905599 13.13555 3.2 Regression model! with IV (n=1,341) ivreg lnW (school= edufather edumother) exp exp2 gen occl occ2 sec, robust Number of obs F( 7, 1333) Prob>F R-squared RootMSE Instrumental variables (2SLS) regression ,,, ' Coef lnW 0490209 School 0282227 Exp -.0002294 Exp2 1950938 Gen 3364757 Occl 3074005 Occ2 -.2104206 Sec 12.82821 cons Instrumented: school Std Err .0083781 0055865 0001398 0346545 0882617 0353177 0515749 1004505 t 5.85 5.05 -1.64 5.63 3.81 8.70 -4.08 127.71 - 13- P>t 0.000 0.000 0.101 0.000 0.000 0.000 0.000 0.000 [95% Conf .0325853 0172634 -.0005037 1271106 1633288 2381161 -.3115975 12.63115 = = = = = 1341 47.80 0.0000 0.1697 62 Interval] 0654566 039182 0000448 2630771 5096227 3766849 -.1092437 13.02527 Instruments: • exp exp2 gen occ occ2 sec edufather edumother 3.3 Results of regression model for non-migrants ivreg InW (school= edufather edumother) exp exp2 gen occl occ2 sec ifmigrant= =0, robust Number of obs F( 7, 1034) Prob>F R-squared RootMSE Instrumental variables (2SLS) regression Coef .0348226 School 022941 Exp -.0000908 Exp2 1537358 Gen 4459725 Occl 317627 Occ2 -.2557655 Sec 13.04659 cons Instrumented: school Instruments: exp exp2 InW Std Err .0090647 0065401 0001613 0406935 0987967 0412234 0572307 1110216 t 3.84 3.51 -0.56 3.78 4.51 7.71 -4.47 117.51 P>t 0.000 0.000 0.574 0.000 0.000 0.000 0.000 0.000 = 1042 = 36.44 = 0.0000 = 0.1721 = 64013 Interval] 05261 0357745 0002258 233587 6398373 3985181 -.143464 13.26444 [95% Conf .0170352 0101075 -.0004074 0738846 2521077 2367358 -.368067 12.82874 gen occ1 occ2 sec edufather edumother 3.4 Results of regression model for migrants ivreg InW (school= edufather edumother) exp exp2 gen occl occ2 sec ifmigrant =1, robust Number of obs F( 7, 291) Prob> F R-squared RootMSE Instrumental variables (2SLS) regression , Std Err Coef .0199791 0548046 School 008651 0440211 Exp 0002082 -.0008563 Exp2 0541626 3828654 Gen 1755666 0939477 Occl 0543328 1896443 Occ2 0948772 1542538 Sec 2161188 12.58281 cons Instrumented: school Instruments: exp exp2 gen occ occ2 InW t 2.74 5.09 -4.11 7.07 0.54 3.49 1.63 58.22 P>t 0.006 0.000 0.000 0.000 0.593 0.001 0.105 0.000 [95% Conf .0154827 0269946 -.001266 2762654 -.2515935 0827093 -.0324787 12.15746 sec edufather edumother - 14 - = = = = = 299 17.91 0.0000 0.2862 46349 Interval] 0941266 0610475 -.0004466 4894654 439489 2965794 3409863 13.00817 ... migrant and non- migrant workers in Ho Chi Minh, Binh Duong and Dong Nai •!• To investigate the determinants of earnings of migrants and non -migrants •!• To examine factors contributing to migrant and. .. DETERMINANTS OF EARNINGS AND WAGE DIFFERENTIALS OF MIGRANTS AND NON -MIGRANTS 5.1 Determinant of earnings 5.1.1 Estimation results of model with OLS and 2SLS 5.1.2 The effect of year... 31 CHAPTER 4: OVERVIEW OF MIGRATION IN HO CHI MINH CITY, BINH DUONG AND DONG NAI PROVINCE 4.1 A profile of migrants and non -migrants in HoChiMinh City, Dong Nai and Binh Duong