Impacts of migration and migrant’s gender on children’s school enrollment and child work in viet nam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHIMINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACTS OF MIGRATION AND MIGRANT’S GENDER ON CHILDREN’S SCHOOL ENROLLMENT AND CHILD WORK IN VIET NAM BY VÕ THỊ THU HOÀI MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, APRIL 2014 i UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES THE HAGUE HO CHIMINHCITY VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACTS OF MIGRATION AND MIGRANT’S GENDER ON CHILDREN’S SCHOOL ENROLLMENT AND CHILD WORK IN VIET NAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By VÕ THỊ THU HOÀI Academic Supervisor Dr TRAN TIEN KHAI ii CERTIFICATION “I certify that the substance of this dissertation has not already been submitted for any degree and is not currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing this dissertation and all source used, have been acknowledged in this dissertation” VO THI THU HOAI iii ACKNOWLEDGEMENT This thesis would have not been fulfilled without special assistances from some individuals, group, family who have contributed to my studying process Foremost, I would like to express my sincere gratitude to my supervisor Dr Tran Tien Khai who continuously support of my M.A thesis by his patience, enthusiasm and immense knowledge His guidance helped me all the time to research and writing the thesis Secondly, I would like to thank Dr Truong Dang Thuy because of his dedicated support in research method during the time of this thesis All of his help strongly to find the solution and improves this paper Besides, my sincere thanks also go to Dr Nguyen Trong Hoai, Dr Pham Khanh Nam who supervised and motivated the Class MDE 17 to finish the course on time It is grateful to thank my classmates of MDE17 MDE18, VNP staffs for stimulating discussions, for the fun time we had together Last and not the least, I would like to express my grateful thank to my family who are always beside me, give me the birth and support me throughout my life April, 2014 VO THI THU HOAI Email: hoai.vtt@vnp.edu.vn iv ABSTRACT Accompany with development trend in over the world, migration flow plays important role in Vietnamese economy It contributes to the significant income and raises the living standard for household in the country, specially, in rural areas This paper tries to measure the impact of migration on children’s schooling enrollment and child work, addition to, determine how migrant’s gender matter in this impact The context is applied in rural areas in Vietnam with the dataset of VHLSS 2010 by Instrument variable method to deal with the problem of endogeneity of migration From the first stage of migrant indicator, it indicates that instrument historical migration network and number of male adults will impact on migrant indicator significantly and these instruments are the strong instruments The results show that the presence of migrant in the household will make the children take part in school more, at the same time make children work less Besides, not like others researches, gender of migrant and time of migrant using in household don’t have meaning with children’s welfare Keywords: migration, migrant’s gender, children’s school enrollment, child work v TABLE OF CONTENT ABSTRACT v CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research Objectives 1.3 Research question 1.4 Research scope 1.5 Structure of the research CHAPTER 2: LITERATURE REVIEW 2.1 Definition of key concept 2.2 Theoretical literature 2.3 Empirical literature 12 2.4 Conceptual framework 16 CHAPTER 3: METHODOLOGY 18 3.1 Endogeneity problem 18 3.2 Endogeneity of migration 18 3.3 Estimated equation 20 3.3.1 Validity of Instrument variable: 20 3.3.2 IVs methods 20 3.3.3 Estimated equation: 24 3.3.4 Method to run IVs regression 26 3.4 Data 28 3.4.1 Source of data 28 3.4.2 Variables description and measurement: 29 CHAPTER 4: OVERVIEW OF MIGRATION IN CASE OF VIETNAM 36 4.1 Socio-economic setting and migration in Vietnam 36 4.1.1 Migration aboard 36 4.1.2 Internal migration 37 4.2 Characteristics of migrant 39 vi 4.2.1 International migrant 39 4.2.2 Internal migrant 44 CHAPTER 5: EMPERICAL ANALYSIS 46 5.1 Descriptions of variables 46 5.2 Estimation results 51 5.3 Interpretation of results 57 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 63 6.1 Conclusions 63 6.2 Recommendations: 64 6.3 Limitations 65 REFERENCE 67 APPENDIX 70 vii LIST OF FIGURES Figure 2.1: Conceptual framework about impact of migration and migrant’s gender on children’s welfare 1717 Figure 3.1: Histogram of expenditure per capita a year 31 Figure 3.2: Histogram of natural logarithm of expenditure per capita a year 31 Figure 4.1: International migration trend from 2000 to 2010 (Department of oversea database) 40 Figure 4.2: Number of male and female international migrant from 2006-2010 (IOM) 41 Figure 4.3: Main destinations of international migrant from 2000-2010 (IOM) 42 Figure 4.4: Structure of international migration labor of Viet Nam from 2006-2010 (IOM, 2011) 43 viii LIST OF TABLES Table 3.1: Description and measurement of variables 32 Table 5.1: T-test between household with non-migrant and household with migrant 48 Table 5.2: T-test between household with male migrant and household with female migrant 50 Table 5.3: Factors affecting migration indicator in the household (results of first stage regression of Instrumental variables) 53 Table 5.4: Factors affecting children’s school enrollment and child work (results of second stage regression of Instrumental variables) 56 ix ABBREVIATIONS BLUE: Best linear unbiased estimates DID: Difference in difference GSO: General statistic office IOM: International Organization for Migration IVs: Instrumental variables OLS: Ordinary least square PSM: Propensity score matching methods VHLSS: Vietnam Household Living Standard Survey UN: United Nations UNDP: United Nations Development Programme US: United Stages x REFERENCE Acosta, P (2011) “Female Migration and Child Occupation in Rural El Salvador”.Forthcoming in Population Research and Policy Review Acosta, P (2006) “Labor Supply, School Attendance, and Remittances from International Migration: The Case of El Salvador”.World Bank Policy Research Working Paper 3903 Böcker A 1994 Chain Migration over Legally Closed Borders: Settled Migrants as Bridgeheads and Gatekeepers Netherlands' Journal of Social Sciences 30:87-106 Cox Edwards, A and M Ureta 2003 International Migration, Remittances and Schooling: Evidence from El Salvador Journal of Development Economics 72 (3): 429–61 Cuong,(2008).“ Do Foreign Remittances Matter to Poverty and Inequality? Evidence from Vietnam.”Economics Bulletin, vol 15, No 1, p 1-11 Dang, N.A (2001) “Rural labor out-migration in Vietnam: a multi-level analysis”, in migration in Viet Nam- Theoretical Approaches and Evidence from a Survey( Transport Communication Publishing House) Gujarati, D.N (2003) Basic Econometrics, 4thed, United States: Gary Burke Hanson, G H and C Woodruff 2003 “ Emigration and educational attainment in Mexico.” Mimeograph, University of California at San Diego IOM (2010) World Migration Report 2010, http://www.publications.iom.int Khandker, S.R., Gayatri B.Koolwal and HussainA.Samad (2010), “Handbook on Impact Evaluation”, The World bank, Washington, D.C 67 Lee, E (1966) “A theory of migration” Demography Journal of Political Economy,Vol 93, No 5, pp 901 - 918 Lewis, W A (1954) Economic development with unlimited supplies of labour.The manchester school, 22(2), 139-191 McKenzie, D., &Rapoport, H (2007) Network effects and the dynamics of migration and inequality: theory and evidence from Mexico Journal of development Economics, 84(1), 1-24 Mansuri, G (2006) Migration, School Attainment and Child Labor: Evidence from rural Pakistan Washington DC: World Bank Merrell J Tuck-Primdahl, and Indira Chand (2013) “Migration and Remittances” Retrieved May, 18th, 2013 from http://web.worldbank.org Nguyen,T and Purnamasari, R (2011) “Impacts of international migration and remittances on child outcomes and labor supply Indonesia.”Policy Research Working” Paper 5591, World Bank, Washington, D.C Nguyen V.P (2011), “International remittances and household welfare in VietNam from VHLSS 2006 and VHLSS 2008”, Unpublished M.A Thesis, VietnamNetherlands Project for M.A in Development Economics Pfeiffer, L and J.E Taylor (2008).“Gender and the Impacts of International Migration: Evidence from Rural Mexico” in A Morrison, M Schiff, and M Sjoblom (eds) The International Migration of Women The World Bank, Washington, DC Rodrigue, J.-P., Comtois, C., Slack, B (2009) “The Geography of Transport Systems.”London, New York: Routledge ISBN 978-0-415-48324-7 68 Todaro, Michael P 1969 "A model of labor migration and urban unemployment in less- developed countries." The American Economic Review 59: 138-48 Todaro and Smith (2003)“ Economic development” 8th edition, 2003, Pearson Addsion Wesley UNDP, (2009) “Human Development Report 2009” Overcoming Barriers: Human Mobility and Development UN, (2010), “Internal migration: Opportunities and Challenges for socio-economic development in Vietnam” UN, (2003) Millennium Development Goals: Closing the Millennium Gaps, The United Nations inViet Nam, Ha Noi WD Pfau and TL Giang (2006) “The growing role of international remittances in the Vietnamese economy: evidence from the Vietnam (Household) living standard surveys” paper presented at the Conference on Global Movements in the Asia Pacific, Ritsumeikan Asia-Pacific University (APU), Oita, Japan, Nov 17-18, 2006 IOM report, Retrieved June, 13th, 2013 from www.iom.int.vn World Bank, 2012, Brief on Global Migration and Remittances Cameron, Instrumental variable” Retrieved http://cameron.econ.ucdavis.edu/e240a/ch04iv.pdf 69 Oct, 7th, 2013 from APPENDIX First stage: Source SS df MS Model Residual 17.0210733 281.986962 19 3589 895845965 078569786 Total 299.008035 3608 082873624 H_migrant Coef HH_school hh_age hh_gender h_csize h_child Migra_net No_male_ad ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons -.0013607 0029358 -.0373879 -.0118164 -.0255892 3261748 -.0273846 021067 -.0530749 0017336 0395091 -.0000161 0086968 -.0002939 -.0033 -.0017292 0201267 0226411 -.0689023 -.1215405 Std Err .0014497 0005025 012939 0061626 0086148 0397905 00786 0097175 0282063 0020135 0310541 0000192 0314887 0407713 01016 0358992 0173684 0128396 0514309 1191304 t -0.94 5.84 -2.89 -1.92 -2.97 8.20 -3.48 2.17 -1.88 0.86 1.27 -0.84 0.28 -0.01 -0.32 -0.05 1.16 1.76 -1.34 -1.02 Number of obs F( 19, 3589) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.348 0.000 0.004 0.055 0.003 0.000 0.000 0.030 0.060 0.389 0.203 0.400 0.782 0.994 0.745 0.962 0.247 0.078 0.180 0.308 = = = = = = 3609 11.40 0.0000 0.0569 0.0519 2803 [95% Conf Interval] -.004203 0019505 -.0627564 -.0238989 -.0424796 2481605 -.0427951 0020147 -.108377 -.0022142 -.0213764 -.0000537 -.0530407 -.0802312 -.0232199 -.0721141 -.0139262 -.0025325 -.1697391 -.3551106 0014815 003921 -.0120194 0002662 -.0086988 404189 -.0119741 0401193 0022271 0056813 1003946 0000214 0704344 0796433 0166199 0686558 0541796 0478147 0319345 1120295 Check the presence of endogeneity with indicator of migrant (Durbin Wu Hausman test) 70 Source SS df MS Model Residual 45.930266 422.186564 21 3587 2.18715552 11769907 Total 468.11683 3608 129744132 enroll Coef H_migrant HH_school hh_age hh_gender h_csize h_child h_migrant1 h_migrant2 u ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons 684646 0145662 -.00565 051001 0358219 0808212 -.0101002 -.0051451 -.6948715 0479791 1422478 -.0065459 2460112 -.0000538 0298674 0081153 0109753 -.0794204 0192147 -.0163804 2254961 -.0122219 Std Err .1366295 0017775 0006819 0161834 0077737 0112407 0380532 0086156 1358398 012229 0326512 0024729 0391167 0000236 0385595 0498864 0123627 0439108 0213875 0159257 0638928 1474158 t 5.01 8.19 -8.29 3.15 4.61 7.19 -0.27 -0.60 -5.12 3.92 4.36 -2.65 6.29 -2.28 0.77 0.16 0.89 -1.81 0.90 -1.03 3.53 -0.08 71 Number of obs F( 21, 3587) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.000 0.002 0.000 0.000 0.791 0.550 0.000 0.000 0.000 0.008 0.000 0.023 0.439 0.871 0.375 0.071 0.369 0.304 0.000 0.934 = = = = = = 3609 18.58 0.0000 0.0981 0.0928 34307 [95% Conf Interval] 4167667 0110812 -.006987 0192715 0205807 0587824 -.0847083 -.0220371 -.9612025 0240026 078231 -.0113944 1693179 -.0001 -.0457333 -.0896933 -.0132632 -.1655129 -.0227182 -.0476048 1002262 -.3012491 9525252 0180512 -.0043131 0827305 0510632 10286 0645079 0117468 -.4285404 0719557 2062645 -.0016974 3227044 -7.57e-06 1054682 1059238 0352139 0066722 0611475 0148439 350766 2768052 Source SS df MS Model Residual 50.1827763 361.116415 21 3587 2.38965601 100673659 Total 411.299191 3608 11399645 c_labor Coef H_migrant HH_school hh_age hh_gender h_csize h_child h_migrant1 h_migrant2 u ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons -.3140513 -.0094732 0051109 -.0055104 -.0691097 -.0804541 -.0211241 0025609 3745503 -.0672138 -.0954618 0064329 -.4027465 -.0000227 0643001 -.0132967 -.0111418 -.0266574 -.0017181 -.0006509 -.1300057 1.113803 Std Err .1263618 0016439 0006307 0149672 0071895 0103959 0351935 0079681 1256315 01131 0301974 0022871 0361771 0000218 0356618 0461374 0114336 0406109 0197802 0147289 0590913 1363375 t -2.49 -5.76 8.10 -0.37 -9.61 -7.74 -0.60 0.32 2.98 -5.94 -3.16 2.81 -11.13 -1.04 1.80 -0.29 -0.97 -0.66 -0.09 -0.04 -2.20 8.17 Number of obs F( 21, 3587) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.013 0.000 0.000 0.713 0.000 0.000 0.548 0.748 0.003 0.000 0.002 0.005 0.000 0.298 0.071 0.773 0.330 0.512 0.931 0.965 0.028 0.000 = = = = = = 3609 23.74 0.0000 0.1220 0.1169 31729 [95% Conf Interval] -.5617994 -.0126963 0038744 -.0348554 -.0832055 -.1008366 -.0901254 -.0130617 128234 -.0893886 -.1546677 0019487 -.4736763 -.0000654 -.0056192 -.103755 -.0335589 -.1062802 -.0404997 -.0295287 -.2458616 8464965 -.0663031 -.0062501 0063474 0238347 -.0550138 -.0600715 0478773 0181834 6208666 -.0450391 -.0362559 010917 -.3318168 00002 1342195 0771615 0112752 0529653 0370635 028227 -.0141498 1.38111 The coefficient of predicted residual u from the first stage is significant, it proves that H_migrant variable has endogeneity Test the weak instrument in endogeneity (F-test) Ho: Migra_net = No_adult=0 F( 2, 3589) = 41.56 Prob> F = 0.0000  (Prob>F) F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.000 0.002 0.000 0.000 0.827 0.304 0.000 0.000 0.008 0.000 0.023 0.433 0.873 0.384 0.069 0.370 0.304 0.000 0.948 3609 19.49 0.0000 0.0980 0.0930 34305 [95% Conf Interval] 4300342 011055 -.0070047 0192444 0206381 0590034 -.4359151 -.1343681 0238045 0778707 -.0113889 1690457 -.0000997 -.0453697 -.0898549 -.0134636 -.1659132 -.0227501 -.047597 0997994 -.2985775 Test heteoroskedasticity: Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of enroll chi2(1) = 299.45 Prob> chi2 = 0.0000 73 = = = = = = 9647768 0180263 -.0043312 0826143 0511056 1030731 3485396 0419039 0717489 2058869 -.0016982 3224191 -7.34e-06 1058041 1057491 0349815 0062677 0611078 0148462 3503739 2794684  (Prob>chi2) F R-squared Root MSE enroll Coef H_migranthat HH_school hh_age hh_gender h_csize h_child h_migrant1 h_migrant2 ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons 6974055 0145407 -.0056679 0509293 0358719 0810383 -.0436878 -.0462321 0477767 1418788 -.0065435 2457324 -.0000535 0302172 0079471 0107589 -.0798227 0191789 -.0163754 2250867 -.0095546 Robust Std Err .1383205 0018564 0007272 0165834 007464 0113142 2210929 0548929 0120854 0328379 0026639 0361444 0000283 0428184 0487293 0123013 0402236 0224291 0162537 0744061 1524216 t 5.04 7.83 -7.79 3.07 4.81 7.16 -0.20 -0.84 3.95 4.32 -2.46 6.80 -1.89 0.71 0.16 0.87 -1.98 0.86 -1.01 3.03 -0.06 P>|t| 0.000 0.000 0.000 0.002 0.000 0.000 0.843 0.400 0.000 0.000 0.014 0.000 0.059 0.480 0.870 0.382 0.047 0.393 0.314 0.003 0.950 = = = = = 3609 19.72 0.0000 0.0980 34305 [95% Conf Interval] 4262109 0109009 -.0070937 0184156 0212377 0588553 -.4771682 -.1538566 0240817 077496 -.0117665 1748667 -.000109 -.0537337 -.0875927 -.0133593 -.1586861 -.0247961 -.0482427 0792042 -.3083962 9686001 0181804 -.0042422 0834431 050506 1032213 3897926 0613923 0714717 2062617 -.0013206 3165981 2.00e-06 114168 103487 0348772 -.0009594 0631539 015492 3709692 289287 Wald Test: Ho: Big_road = Electro = Market = Ele_school = Jun_school = high_school = h_migrant1 = h_migrant2 = 74 F( 8, 3588) = 0.89 Prob> F = 0.5274  (Prob>F) >α, accept Ho, chosen restricted model Restricted model of children school enrollment Linear regression Number of obs F( 12, 3596) Prob > F R-squared Root MSE enroll Coef H_migranthat HH_school hh_age hh_gender h_csize h_child ln_exp_pc pro_females hh_work Com_agri Enterprise Health_cen~r _cons 6566228 0145136 -.0055998 050138 036233 0801911 0477566 1408543 -.0063986 2506184 -.0000545 2284622 -.0347447 Robust Std Err .1298049 0018444 0007227 016532 0074555 0112639 0120359 0327204 0026533 0357727 0000281 0752969 1388772 t 5.06 7.87 -7.75 3.03 4.86 7.12 3.97 4.30 -2.41 7.01 -1.94 3.03 -0.25 Model of child work 75 P>|t| 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.016 0.000 0.053 0.002 0.802 = = = = = 3609 32.06 0.0000 0.0962 34302 [95% Conf Interval] 4021242 0108974 -.0070167 017725 0216155 0581068 0241588 0767019 -.0116008 1804815 -.0001095 0808333 -.3070307 9111213 0181298 -.0041829 0825509 0508504 1022754 0713545 2050067 -.0011965 3207553 6.14e-07 3760911 2375413 Source SS df MS Model Residual 49.6334868 361.665704 20 3588 2.48167434 100798691 Total 411.299191 3608 11399645 c_labor Coef H_migranthat HH_school hh_age hh_gender h_csize h_child h_migrant1 h_migrant2 ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons -.3578305 -.0094148 0051182 -.0063883 -.068921 -.0804735 0995505 0569401 -.0666796 -.0941939 006496 -.4021378 -.0000233 0646675 -.012618 -.0112969 -.0255435 -.001772 -.0007531 -.1318827 1.108516 Std Err .1262085 0016454 000631 0149564 0071909 0104012 1851449 0416033 0113157 030214 0022872 0361988 0000218 0356796 0461659 0114339 0406377 0197919 0147377 0591399 1364288 t -2.84 -5.72 8.11 -0.43 -9.58 -7.74 0.54 1.37 -5.89 -3.12 2.84 -11.11 -1.07 1.81 -0.27 -0.99 -0.63 -0.09 -0.05 -2.23 8.13 Number of obs F( 20, 3588) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.005 0.000 0.000 0.669 0.000 0.000 0.591 0.171 0.000 0.002 0.005 0.000 0.285 0.070 0.785 0.323 0.530 0.929 0.959 0.026 0.000 = = = = = = 3609 24.62 0.0000 0.1207 0.1158 31749 [95% Conf Interval] -.6052781 -.0126408 003881 -.0357122 -.0830195 -.1008664 -.2634493 -.0246283 -.0888655 -.1534323 0020117 -.47311 -.0000661 -.0052869 -.1031321 -.0337144 -.1052188 -.0405766 -.0296482 -.247834 8410303 -.1103828 -.0061889 0063553 0229357 -.0548224 -.0600807 4625503 1385085 -.0444938 -.0349554 0109803 -.3311655 0000194 1346219 0778961 0111206 0541317 0370325 028142 -.0159315 1.376002 Test heteroskedasticity: Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of c_labor chi2(1) = 558.00 Prob> chi2 = 0.0000  (Prob>chi2) F R-squared Root MSE c_labor Coef H_migranthat HH_school hh_age hh_gender h_csize h_child h_migrant1 h_migrant2 ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r _cons -.3578305 -.0094148 0051182 -.0063883 -.068921 -.0804735 0995505 0569401 -.0666796 -.0941939 006496 -.4021378 -.0000233 0646675 -.012618 -.0112969 -.0255435 -.001772 -.0007531 -.1318827 1.108516 Robust Std Err .1307565 0016332 0006537 0152633 0066665 0105247 2301667 0554682 0107979 0309208 0024124 0348088 0000217 0335456 0407382 0114679 03632 0198015 0150997 0691506 1362491 t -2.74 -5.76 7.83 -0.42 -10.34 -7.65 0.43 1.03 -6.18 -3.05 2.69 -11.55 -1.07 1.93 -0.31 -0.99 -0.70 -0.09 -0.05 -1.91 8.14 P>|t| 0.006 0.000 0.000 0.676 0.000 0.000 0.665 0.305 0.000 0.002 0.007 0.000 0.283 0.054 0.757 0.325 0.482 0.929 0.960 0.057 0.000 Wald test: Ho: hh_gender=h_migrant1=h_migrant2=Enterprise= Ele_school= Jun_school= high_school=0 F( 9, 3588) = 0.68 Prob> F = 0.7303  Accept Ho, chosen restricted model 77 = = = = = 3609 25.24 0.0000 0.1207 31749 [95% Conf Interval] -.614195 -.012617 0038365 -.0363139 -.0819916 -.1011085 -.3517202 -.0518123 -.0878502 -.1548179 0017662 -.4703847 -.0000658 -.0011028 -.0924904 -.0337811 -.0967536 -.0405954 -.0303579 -.2674612 8413827 Electro= -.101466 -.0062127 0063998 0235374 -.0558504 -.0598386 5508212 1656925 -.045509 -.0335698 0112258 -.3338908 0000192 1304379 0672544 0111873 0456665 0370514 0288517 0036957 1.37565 Market= Restricted model of child work Linear regression Number of obs F( 11, 3597) Prob > F R-squared Root MSE c_labor Coef H_migranthat HH_school hh_age h_csize h_child ln_exp_pc pro_females hh_work Com_agri Big_road Health_cen~r _cons -.3252663 -.0095975 005057 -.0689722 -.080671 -.0696312 -.0925498 0062602 -.4027374 0629992 -.1203738 1.076948 Robust Std Err .1176566 0016249 000642 0066309 0104337 0106842 030559 0023881 034157 0333571 069041 1285849 t -2.76 -5.91 7.88 -10.40 -7.73 -6.52 -3.03 2.62 -11.79 1.89 -1.74 8.38 Check multicollinearity 78 P>|t| 0.006 0.000 0.000 0.000 0.000 0.000 0.002 0.009 0.000 0.059 0.081 0.000 = = = = = 3609 45.09 0.0000 0.1190 31739 [95% Conf Interval] -.5559466 -.0127834 0037983 -.0819729 -.1011276 -.0905788 -.1524646 0015781 -.4697065 -.0024015 -.2557373 8248416 -.0945859 -.0064117 0063158 -.0559715 -.0602145 -.0486835 -.032635 0109424 -.3357684 1284 0149896 1.329055 H_migr~t female~t t_migr~t HH_sch~l H_migrant female_it t_migrant HH_school hh_age hh_gender h_csize h_child c_labor enroll Migra_net No_male_ad ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r 1.0000 0.7170 0.6775 0.0130 0.1265 -0.0842 -0.0685 -0.0874 0.0467 -0.0199 0.1639 -0.0533 0.0776 -0.0004 -0.0452 0.0695 0.0051 0.0100 0.0302 0.0464 0.0338 0.0324 0.0338 -0.0227 1.0000 0.4849 -0.0004 0.1043 -0.0181 -0.0520 -0.0713 0.0304 -0.0217 0.1173 -0.0038 0.0456 -0.0190 -0.0429 0.0508 -0.0013 -0.0087 0.0163 0.0459 0.0156 0.0194 0.0190 0.0069 1.0000 -0.0010 0.0911 -0.0408 -0.0598 -0.0491 0.0421 -0.0286 0.1049 -0.0376 0.0301 -0.0148 -0.0605 0.0293 0.0251 0.0044 0.0157 0.0478 0.0214 0.0293 0.0317 -0.0131 79 1.0000 -0.1136 0.0183 -0.1359 -0.1251 -0.1413 0.1802 0.0732 -0.0163 0.4054 -0.0235 0.1491 0.0756 0.0264 0.0477 0.0655 0.0757 0.0779 0.0683 0.0793 0.0236 hh_age hh_gen~r 1.0000 -0.1569 -0.0780 -0.1563 0.1564 -0.1394 0.1014 0.2706 0.0576 0.0206 -0.4328 -0.0217 0.0468 -0.0110 0.0522 0.0819 0.0309 0.0134 0.0073 -0.0120 1.0000 0.1128 0.0673 -0.0078 0.0328 0.0431 0.2637 -0.0782 -0.2001 0.1457 -0.0448 -0.0643 -0.0375 -0.0414 -0.0386 -0.0292 0.0065 -0.0296 0.0230 h_csize 1.0000 -0.1075 -0.0786 0.0007 -0.0790 0.0177 -0.2659 0.0164 0.0672 -0.0993 -0.0226 0.0008 -0.0834 -0.0700 -0.0745 -0.0272 -0.0344 0.0025 h_child c_labor enroll Migra_net No_male_ad ln_exp_pc pro_females hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r hh_work Com_agri Enterprise Big_road Electro Market Ele_school Jun_school high_school Health_cen~r h_child c_labor 1.0000 -0.0804 0.0679 -0.0502 -0.0071 -0.2577 0.0612 0.0265 -0.0104 -0.0105 -0.0079 -0.0498 -0.0494 -0.0367 -0.0355 0.0066 -0.0410 1.0000 -0.6217 -0.0338 0.1665 -0.1005 -0.0525 -0.0396 -0.2136 -0.0114 0.0144 -0.0123 -0.0254 -0.0249 -0.0163 -0.0113 -0.0358 enroll Migra_~t No_mal~d ln_exp~c pro_fe~s 1.0000 0.0392 -0.2206 0.1072 0.0587 0.0411 0.1589 -0.0493 0.0303 0.0211 0.0259 -0.0044 0.0226 0.0055 0.0596 1.0000 -0.0391 0.0714 0.0043 -0.0398 0.1637 -0.0011 0.0043 0.0814 0.1750 0.0880 0.0367 0.0010 -0.0234 1.0000 -0.0135 -0.3435 -0.0575 -0.2264 -0.0112 -0.0308 -0.0139 0.0173 -0.0064 0.0015 0.0173 -0.0068 hh_work Com_agri Enterp~e Big_road Electro 1.0000 -0.0274 -0.0001 0.0345 -0.0063 -0.0149 0.0083 0.0390 0.0264 -0.0176 1.0000 0.0913 -0.0179 0.0136 0.0473 0.0392 1.0000 -0.0342 0.0066 0.0527 0.0499 0.0196 0.0081 -0.0319 0.0542 1.0000 -0.0132 0.0316 0.0835 0.0371 0.0284 0.0458 -0.1366 1.0000 0.0901 0.0706 -0.0228 0.0285 0.0480 0.0458 1.0000 -0.0380 0.0468 0.0741 0.1204 0.0301 0.1031 0.1178 0.1037 0.0329 0.0555 0.0087 1.0000 -0.0357 0.0132 -0.0080 0.0043 0.0257 -0.0236 0.0176 0.0065 0.0062 -0.0191 Market Ele_sc~l 1.0000 0.1235 0.1465 0.1341 -0.0524 1.0000 0.4248 0.0555 -0.0138 Jun_sc~l high_s~l Health~r Jun_school high_school Health_cen~r 1.0000 0.1011 0.0088 1.0000 -0.0479 1.0000 There is no big correlation between the variables in the data, so we exclude the impact of multicollinearity in the model 80 81 ... the impact of migration and gender of migrant on children’s school enrollment and child work like below: Migration School enrollment Child work Migrant’s gender School enrollment Child work Figure... as Cuong (2008), Pfau and Long (2006) research about migration and remittance before Meanwhile impact of migration and migrant’s gender on children’s school enrollment and child work in Vietnam... the impact of migration on children’s schooling enrollment and child work, addition to, determine how migrant’s gender matter in this impact The context is applied in rural areas in Vietnam with