Parental absence and child labor in vietnam

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS PARENTAL ABSENCE AND CHILD LABOR IN VIETNAM BY NGUYEN NGOC MINH THU MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2017 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS PARENTAL ABSENCE AND CHILD LABOR IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN NGOC MINH THU Academic Supervisor: NGUYEN HOANG BAO HO CHI MINH CITY, November 2017 ACKNOWLEDGEMENT I would first like to thank my thesis supervisor Dr Nguyen Hoang Bao of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics I much appreciate for all his dedication, his guiding, supporting, and patience to my study He has set a great example of a teacher, researcher, especially for me I would like to express my gratitude to the VNP officers who were involved in my thesis process by updating thesis schedule and providing good condition for my research process Without their passionate participation, the thesis process could not have been successfully conducted Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Nguyen Ngoc Minh Thu Ho Chi Minh City, November 2017 ABSTRACT Child labor is defined as a task which had took out the childhood from children; have an ability to affect negatively on child physical and mental development These tasks also obstruct child schooling by disposing child from the chance of learning at school or force them to leave school early In a worst form, child labor contains child who serve as slaves, has been splitting with their parents, high opportunity to get serious illness and have to protect themselves on the street (International Programme on the Elimination of Child Labour (IPEC) ILO (2013) also estimated about one – third of the child labors in Vietnam, or around 569,000 children have to work around 42 hours per week in 2012 and this will have a bad influence on child schooling or children have to stop completely schooling time for work This paper uses a panel data built up from VHLSS 2012 with descriptive statistics of parents and children characteristics to determine whether parent’s absence is a main reason for child labor We apply Probit and OLS regression, as well as Heckman selection model, to examine the impacts of parents’ characteristics on the probability of child working and working hours The main finding of this study emphasize that parent’s absence is not a key factor lead to child labor It is economic factors and poverty that play an important role in leading child labor status and child working hours CONTENTS ACKNOWLEDGEMENT ABSTRACT CHAPTER 1: INTRODUCTION 1.1 Child labor 1.2 Parents time for children 11 1.3 The Impaction of Parents on child labor 12 1.4 Research objective 12 1.5 Structure of the thesis 13 CHAPTER 2: LITERATURE REVIEW 14 2.1 Review of theory: 14 2.2 Effect of parents on children’s skill development 19 2.3 Effect of Parents on children education 20 2.4 Effect of parents on children’s labour 20 2.5 Effect of poverty on child’s labor 21 CHAPTER 3: RESEARCH METHODOLOGY 22 3.1 Conceptual framework: 22 3.2 Child labor status (working or not) – logit model 24 3.3 Child working hours – multiple linear regression model 25 3.4 The logit model Error! Bookmark not defined 3.5 Heckman selection model 27 CHAPTER 4: EMPIRICAL RESULTS 28 4.1 Overview of child labor problem in Vietnam 28 4.2 Data: 30 4.3 Empirical results 41 CHAPTER 5: CONCLUSION 47 5.1 Conclusion 47 5.2 Policy implications: 47 REFERENCE 49 LIST OF TABLES Table 1: Variable description of child working or not regression 24 Table 2: Variable description of child working hour regression 26 Table 3: Descriptive Statistics of children’s variable 31 Table 4: Descriptive Statistics of father’s variables 32 Table 5: Descriptive Statistics of mother’s variable 33 Table 6: Frequency of social-demographic characteristics 34 Table 7: Child’s working general information 35 Table 8: Parents living in family characteristics by child labor status 39 Table 9: Parents income characteristics by child labor status 40 Table 10: Parents illness characteristics by child labor status 40 Table 11: Regression result 43 LIST OF FIGURES Figure 1: Child labor affects the nation, source: Srinivas Viswanathan, 2014 Figure 2: Global Incidence of Child Labor, 2000 to 2012, source: Our World in Data Figure 3: Incidence of Child Labor between SEA countries, 2000 to 2012, source: Our World in Data 10 Figure 4: Incidence of working children who work only in five SEA countries 11 Figure 5: The relationship of household, parent and child characteristics to child labor 23 Figure 6: Vietnam child population by age group and gender, source: Vietnam National Child Labor survey, 2012 29 Figure 7: Frequency of child labor status by gender 36 Figure 8: Frequency child labor status by ratio of educated child in household 37 Figure 9: Percentage working status of child by first child in household status 37 Figure 10: Frequency of child labor status by child’s reasoning to hospital 38 Figure 11: Percentage working status of child by father living together status 38 Figure 12: Percentage working status of child by mother living together status 39 Figure 13: Frequency of father education level by child labor status 41 Figure 14: Frequency of mother education level by child labor status 41 CHAPTER 1: INTRODUCTION 1.1 Child labor Cigno and Rosati (2005) has announced that children all over the world are related to a large number of activities which could be able classified as work These activities include a large range of activities, from less harm for children as home activities to the most damaging job as soldiering or prostitution ILO (2013) has estimated in World Report that 265 million cases of child labor have been found, which is nearly 17 per cent of children all over the world According to this report, the highest number of child labor happened in Sub-Saharan Africa (Ospina & Roser, 2014) Most of child labor cases has found in developing countries where children often work under manufacturing or agriculture factors According to ILO’s Statistical Information and Monitoring Program, agriculture is the sector in which child labor could be found the most Edmonds and Pavcnik (2005) has estimated that in Nepal, 85% of child labor is in agriculture, Cambodia 73% and in Morocco 84% Figure 1: Child labor affects the nation, source: Srinivas Viswanathan, 2014 Viswanathan (2014) presented the figure that child labor is undesirable because of its negative affect on the nation as a whole As indicated in Figure 1, child labor would disturb child schooling time and also their cognitive development This would affect child’s professional expertise and their soft skill which will lead to poor wage later in their working age Low wage caused bad working environment and poor labor standards These conditions will lately cause poor income or even unemployment when children grow up Figure presented child labor aged 5-17 which has been reduced from 23 per cent on 2000 to 17 per cent on 2012 (Our World in Data, n.d.) From 2000 till 2012, the number of child labor around 78 million which has reduced almost one-third The ratio of reducing in girl labor as 40 per cent while the ratio falling in boy labor is 25 per cent From 2008-2012, the reducing in child labor is greater than 2000-2008, among of that, the Asia and Pacific region has contributed the large number of reducing on fours year 2008-2012 (Making progress against child labor- Global estimates and trends 2000-2012, 2013) Figure 2: Global Incidence of Child Labor, 2000 to 2012, source: Our World in Data Together with South Sudan and Suriname, Vietnam has been listed in 74 countries which have serious problem of child labor by the US labor Department (List of goods produced by child labor and forced labor, 2016) In the same report, most of Southeast Asian countries such as Malaysia, Indonesia, Cambodia, and Thailand are included in the list Our World in Data (n.d.) has shown as Figure the Incidence of Child labor figure a trend of child labor which aged from 7-14, however the figure not show all the result at the same 10 time so that the dot line presents as: Cambodia has the rapid reducing from the beginning of around 60 per cent to 11.5 per cent of child labor in 2012, Vietnam also have a large decreasing from more than 20 per cent on 2006 to 10.9 per cent in 2012, Thailand: 15.1% of child labor in 2005-2006, Philippines has a small different from 2001 to 2011 as their number of child labor in beginning is already low around 12% on 2001 to 9% on 2011 Lastly, Indonesia have a stable trend on child labor from 2000 to 2009 and drop incredibly on 2010 as 3.7 per cent Figure 3: Incidence of Child Labor between SEA countries, 2000 to 2012, source: Our World in Data Even the overall cases of child labor reduce; the percentage of children who work only increased in Vietnam and Indonesia in Figure Vietnam has the percentage of children who work only increased from 15.9 per cent in 2011 to 19 per cent in 2012 For Indonesia the figure is 11 per cent in 2009 and increases to 44.4 per cent in 2010 Child labor in others countries such as Thailand and Philippines remain unchanged, or slightly increased as Cambodia 44 Mother Income (million/year) Father Working Hours (hours/month) Mother Working Hours (hours/month)  -0.04*** (0.01) -0.00 (0.00) 0.00 (0.00) -0.00 -0.00 0.00 -4.85 (4.15) 2.07*** 0.54 1.12** (0.5) Cost Father In Patient Cost (million/year) Mother In Patient Cost (million/year) Child School Cost (million/year) -0.01 (0.01) -0.03 (0.04) -0.73*** (0.12) -0.00 -0.00 -0.02 -5.28 (13.17) 27.78 (21.77) -116.54*** (37.42) The regression shows that parents living with children does not affect child labor, both the probability of participating in paid work and the working hours Most of the variables significant are economic ones, indicating that it is economic conditions that drive children to work This is in line with Kaur (2002), which presented that parent income is the most driving force child labor Though children might join the nonpaid job or not well paid job, they still contribute to the stability of household financial Which mean, their nonpaid job could save more time for their parents in working also Moreover, father working hours have strong significant to labor status more than mother working hours regard to father income higher than mother Table 12: Heckman selection regression results Heckman selection model Seletion equation (Working or not) Coefficients Standard errors  dy/dx Regression equation (Work hours per year) Coefficients Standard errors Household characteristics Father is house head First child Ratio of educated children in households No of Child in Household 0.248* (0 128) 0.082 0.828*** (0.069) -1.705*** (0.101) 0.275 0.289*** (0.034) -134.224 (120.501) -0.566 -591.364*** (140.681) 0.096 37.112 (36.470) 45  Parent characteristics Father Age (year) Mother Age (year) Father living In-house (month/year) Mother living In-house (month/year)  -0.014* (0.008) 0.014* (0.008) 0.034 (0.056) 0.004 (0.095) -0.004 0.004 0.011 0.001 Child characteristics Child Gender -0.008 (0.056) -0.002 Child age Subsidy for Child Education (million/year) Out-patient Times (per year) In-patient Times (per year) 0.242*** (0.048) -0.110*** (0.026) 0.080 -12.085 (26.231) -23.299 (87.296) 0.102 (0.102) Parent Working Father Income (million/year) Mother Income (million/year) Father Working Hours (hours/month) Mother Working Hours (hours/month)  67.932 (52.652) 47.019*** (14.676) 68.864*** (11.673) Child wage (thousand VND/ hour)  -34.055 (63.885) 98.736 (73.442) -0.008*** (0.002) -0.019*** (0.004) -0.0006 (0.0005) 0.0002 (0.0004) -0.002 -0.187 (2.474) -0.006 -3.125 (4.458) -0.000 0.000 2.087*** (0.536) 1.101** (0.497) Cost Father In Patient Cost (million/year) Mother In Patient Cost (million/year) Child School Cost (million/year) -0.004 (0.000) -0.009 (0.017) -0.309*** (0.038) -0.001 -0.003 - 4.015 (13.003) 28.189 (21.386) -86.870* (48.263) It is also stated in the research of Saga (1975) that the parent’s characteristics such as education, occupation, income status would have related to children labor In this research, we 46 look further some laws or programs have a strong significant to child labor such as: subsidy with the opposite impact on child labor status Table 12 presents the estimation results from the Heckman selection model Hypothesis testing results indicate that the two equations (selection and regression equations) are not correlated (see Appendix) and as a result, estimating the two equations separately does not result in biased estimates The results of Heckman selection model presented in Table 12 is very consistent and close to those from the Probit and OLS regression presented in Table 11 47 CHAPTER 5: CONCLUSION 5.1 Conclusion This study examines the leading role of parent’s absence on child labor in Vietnam based on dataset of VHLSS 2012 The regression analysis emphasizes three main findings as: Parents’ absence not effect on child labor even on the decision of pushing child to the labor market or not and child working hours Instead of parents’ absence, parents’ income are factors play a leading role on the child labor with father or mother income have a same highest significant to child labor status This result also has been presented by Cigno and Rosati (2005) as child labor elasticity with poverty because all the child activities as schooling, free time would be trade off with a cost Beside parents’ characteristic, children’s characteristics such as age, health condition (out-patient time) and household characteristic such as ratio of educated children, no of child in household also have a strong significant with child working hour This finding has been stated in the same research as young as children have to work, as high as percentage of children would have an bad effect on health both in present or in later- their adult life Although parents’ absence is not the leading role cause child labor but these are definite a key factor on children development All decisions related to children consumptions, daily activities as schooling or working are taken by their parents since young children are dependent agents (Cigno & Rosati, 2005) On the other hand, this study might not have a full dataset and this study just only consider the situation on one year 2012 Even 2012 is the year have full information or child labor report from ILO and some organization but the analysis based on a long period might help to predict a trend of child labor better 5.2 Policy implications: Child labor rate of Vietnam in 2012 is lower than the average point of the world (ILO, 2014) since all the policies on poverty alleviation help to control the poverty – key factor caused child labor Policies for a broad-based poverty decrease include the expansion of income earning 48 opportunities, develop human capital and reduce risk… (Vietnam Academy of Social Sciences – VASS, 2011) Beside policies for poverty reducing, programs target to children such as easy school access will help to keep maintaining and increase child education time These program might include also schooling fee, education materialize and easy way to go to school in daily A program to secure child education against the risk of household poverty is needed In addition, programs and projects which increase the awareness of community about the negative of child labor will help parents understand and take decision better for children long term development 49 REFERENCE International Programme on the Elimination of Child Labour (IPEC) (n.d.) What is child labor Retrieved from http://ilo.org/ipec/facts/lang en/index.htm Esteban Ortiz-Ospina and Max Roser “Child labor” Retrieved from https://ourworldindata.org/child-labor/ Srinivas Viswanathan Child labor presentation Retrieved from https://www.slideshare.net/srinisrg/chiild-labour Family undergoing major shifts in Viet Nam, shows first-ever nationwide survey on the family (2008, June 26) Retrieved from: https://www.unicef.org/vietnam/media_16537.html Children the losers in modernisation (2008, June 27) Retrieved from Vietnam News: http://vietnamnews.vn/society/177910/children-the-losers-in-modernisation.html Speech at the launch of the Survey of the Family in Viet Nam by Ms Maniza Zaman, Deputy Representative of UNICEF (2008, June 26) Retrieved from: http://www.un.org.vn/en/unicef-speeches/549-speech-at-the-launch-of-the-survey-of-thefamily-in-viet-nam-by-ms-maniza-zaman-deputy-representative-of-unicef.html List of goods produced by child labor or forced labor (2016) Bureau of international labor affairs United States department of labor Marking progress against child labor- Global estimates and trends 2000-2012.(2013) International Labor Office Result of nation-wide survey on the family in Viet Nam 2006.(2008) Ministry of culture, sports and tourism Nguyen Thi Lan Huong “Vietnam national child labor survey 2012- Main findings.” ILO Cataloguing in Publication Data (2014) Alessandro Cigno and Furio Camillo Rosati "The Economics of Child Labour." 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between Child Labour and Mothers’ Work: The Case of India” Iza (2007) Franc¸ois Charles Wolff , Maliki “ Evidence on the impact of child labor on child health in Indonesia, 1993–2000” Economics and Human Biology (2008) Anokhi Parikh and Elisabeth Sadoulet “The Effect of Parents’ Occupation on Child Labor and School Attendance in Brazil” University of Oxford, Department of Economics (2005) 52 APPENDIX Appendix 1: The summary statistics of numerical variables Variable Obs Mean Std Dev Min Max Edu 6026 4.32 2.85 11 NoofChild 6026 1.72 0.8 Edu1 5354 6.98 3.73 12 WorkDays1 5352 20.34 7.2 30 YIncome1 5344 15218.15 27687.78 394097 PTimes1 5301 0.95 2.95 81 Edu2 5887 6.12 3.92 12 WorkDays2 5886 19.39 8.86 30 YIncome2 5883 7296.5 18477.4 328413 PTimes2 5878 1.37 2.98 48 Appendix 2: The summary statistics of category and dummy variables Labor LivingF Freq Percent Cum 5,392 89.48 89.48 634 10.52 100 Total 6,026 100 749 12.43 12.43 5,277 87.57 100 Total 6,026 100 53 LivingM Firstchild RatioofEC 154 2.56 2.56 5,872 97.44 100 Total 6,026 100 1,929 32.01 32.01 4,097 67.99 100 Total 6,026 100 266 4.41 4.41 1/4 0.07 4.48 1/3 23 0.38 4.86 1/2 185 3.07 7.93 2/3 111 1.84 9.77 3/4 37 0.61 10.39 4/5 0.08 10.47 5,395 89.53 100 Total 6,026 100 54 STATA RESULTS The regression results of OLS reg labor Edu LivingF LivingM NoofChild RatioofEC firstchild Edu1 WorkDays1 YIncome1 PTimes1 Edu2 W > orkDays2 YIncome2 PTimes2 note: LivingM omitted because of collinearity Source SS df MS Model Residual 103.336817 370.57238 13 5140 7.94898593 072095794 Total 473.909197 5153 09196763 labor Coef Edu LivingF LivingM NoofChild RatioofEC firstchild Edu1 WorkDays1 YIncome1 PTimes1 Edu2 WorkDays2 YIncome2 PTimes2 _cons 0174438 0650914 0373695 -.4400898 0743853 -.0032023 -.0003837 -3.05e-07 -.0013888 -.0121073 0002469 -2.34e-07 -.0008629 3671005 Std Err .0014702 0604138 (omitted) 0058423 0174882 0103951 0014304 0006152 1.68e-07 0013567 0014249 0005073 2.44e-07 0014426 0653533 t Number of obs F( 13, 5140) Prob > F R-squared Adj R-squared Root MSE P>|t| = = = = = = 5154 110.26 0.0000 0.2181 0.2161 26851 [95% Conf Interval] 11.86 1.08 0.000 0.281 0145615 -.0533455 020326 1835282 6.40 -25.16 7.16 -2.24 -0.62 -1.82 -1.02 -8.50 0.49 -0.96 -0.60 5.62 0.000 0.000 0.000 0.025 0.533 0.069 0.306 0.000 0.626 0.337 0.550 0.000 0259161 -.4743742 0540065 -.0060065 -.0015898 -6.35e-07 -.0040486 -.0149007 -.0007476 -7.13e-07 -.003691 2389802 048823 -.4058054 0947641 -.0003981 0008223 2.44e-08 0012709 -.0093139 0012415 2.44e-07 0019651 4952208 55 The regression results of Probit * WORKING OR NOT: THE LOGIT MODEL logit labor FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1 /// > Monthhours1 Monthhours2 Subsidy firstchild RatioofEC NoofChild gender OPTimes IPTimes SchCost,r Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: log log log log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = = = = -1701.4775 -1477.4399 -1307.4596 -1235.7539 -1230.5131 -1230.4843 -1230.4843 Logistic regression Number of obs Wald chi2(19) Prob > chi2 Pseudo R2 Log pseudolikelihood = -1230.4843 labor Coef FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1 Monthhours1 Monthhours2 Subsidy firstchild RatioofEC NoofChild gender OPTimes IPTimes SchCost _cons 0764581 -.0146663 -.0143955 -.0425462 -.0228134 0222973 -.0085472 -.0345084 4403358 -.0012132 0005022 3908649 1.571144 -2.923389 5282748 -.023959 -.2217683 2024208 -.7282065 -1.739144 Robust Std Err .1253734 1556533 0043613 0112466 0151183 0155094 0152994 0442163 2355231 0008612 0007073 0882219 1394234 1988984 0628953 1066443 0705196 2097731 1223696 2.049114 z 0.61 -0.09 -3.30 -3.78 -1.51 1.44 -0.56 -0.78 1.87 -1.41 0.71 4.43 11.27 -14.70 8.40 -0.22 -3.14 0.96 -5.95 -0.85 P>|z| 0.542 0.925 0.001 0.000 0.131 0.151 0.576 0.435 0.062 0.159 0.478 0.000 0.000 0.000 0.000 0.822 0.002 0.335 0.000 0.396 Note: 18 failures and successes completely determined = = = = 5140 590.32 0.0000 0.2768 [95% Conf Interval] -.1692692 -.3197411 -.0229435 -.0645892 -.0524448 -.0081006 -.0385335 -.1211708 -.021281 -.0029011 -.000884 2179532 1.297879 -3.313222 4050023 -.232978 -.3599841 -.208727 -.9680465 -5.755333 3221855 2904086 -.0058476 -.0205032 006818 0526952 0214391 052154 9019526 0004747 0018884 5637765 1.844408 -2.533555 6515473 1850601 -.0835525 6135685 -.4883664 2.277045 56 Probit regression – Marginal effect * Marginal effects mfx Marginal effects after logit y = Pr(labor) (predict) = 03521325 variable FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1* Monthh~1 Monthh~2 Subsidy firstc~d* Ratioo~C NoofCh~d gender OPTimes IPTimes SchCost dy/dx 0025975 -.0004983 -.0004891 -.0014454 -.000775 0007575 -.0002904 -.0011724 0127448 -.0000412 0000171 013279 0447298 -.0993171 0179472 -.000814 -.0075342 0068769 -.0247396 Std Err .00426 00529 00016 00036 00053 00054 00052 00148 00596 00003 00002 0036 00576 01398 00299 00362 0025 00714 00285 z 0.61 -0.09 -3.08 -4.03 -1.47 1.41 -0.56 -0.79 2.14 -1.40 0.71 3.68 7.77 -7.11 6.01 -0.22 -3.02 0.96 -8.67 P>|z| [ 0.542 0.925 0.002 0.000 0.141 0.159 0.578 0.428 0.032 0.162 0.477 0.000 0.000 0.000 0.000 0.822 0.003 0.336 0.000 -.005761 -.010858 -.0008 -.002149 -.001808 -.000297 -.001314 -.004071 001064 -.000099 -.00003 006216 033444 -.12671 012094 -.007916 -.012425 -.007126 -.030334 95% C.I ] 010956 009862 -.000178 -.000742 000258 001812 000734 001727 024426 000017 000064 020342 056016 -.071924 023801 006288 -.002643 020879 -.019145 (*) dy/dx is for discrete change of dummy variable from to X 11.894 11.9691 14.896 7.41303 42.3623 39.5152 281068 248845 905253 151.925 139.143 157017 676848 932101 1.73074 1.48074 659728 038327 1.51521 57 Heckman regression > > > > > * HECKMAN TWO STEPS *heckman hour wage Edu Age NoofChild RatioofEC firstchild FMonths1 FMonths2, /// *select(labor = Edu FMonths1 FMonths2 NoofChild RatioofEC firstchild Edu1 YIncome1 Edu2 YIncome2) t wostep * HECKMAN MAXIMUM LIKELIHOOD heckman hour wage Age NoofChild RatioofEC FMonths1 FMonths2 YIncome1 YIncome2 /// IPCost1 IPCost2 head1 gender OPTimes IPTimes SchCost Monthhours1 Monthhours2, /// select(labor = FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1 /// Monthhours1 Monthhours2 Subsidy firstchild RatioofEC NoofChild gender OPTimes IPTimes SchCost) Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -5343.4507 -5343.3488 -5343.3309 -5343.3308 Heckman selection model (regression model with sample selection) Log likelihood = -5343.331 Coef Std Err z Number of obs Censored obs Uncensored obs = = = 5140 4612 528 Wald chi2(17) Prob > chi2 = = 195.24 0.0000 P>|z| [95% Conf Interval] hour wage Age NoofChild RatioofEC FMonths1 FMonths2 YIncome1 YIncome2 IPCost1 IPCost2 head1 gender OPTimes IPTimes SchCost Monthhours1 Monthhours2 _cons 68.86435 47.01981 37.11222 -591.3648 -34.05514 98.73628 187771 -3.12588 -4.015432 28.18058 -134.2243 67.93223 -12.08578 -23.29997 -86.8703 2.087788 1.101699 -357.978 11.67312 14.67699 36.47017 140.6817 63.88521 73.44211 2.474505 4.458748 13.00344 21.38496 120.5015 52.65287 26.33132 87.29647 48.26382 5365496 4978577 1177.211 5.90 3.20 1.02 -4.20 -0.53 1.34 0.08 -0.70 -0.31 1.32 -1.11 1.29 -0.46 -0.27 -1.80 3.89 2.21 -0.30 0.000 0.001 0.309 0.000 0.594 0.179 0.940 0.483 0.757 0.188 0.265 0.197 0.646 0.790 0.072 0.000 0.027 0.761 45.98546 18.25344 -34.36801 -867.0958 -159.2679 -45.20762 -4.66217 -11.86487 -29.50171 -13.73317 -370.403 -35.26551 -63.69421 -194.3979 -181.4657 1.03617 1259157 -2665.27 91.74323 75.78619 108.5924 -315.6338 91.15756 242.6802 5.037712 5.613104 21.47084 70.09433 101.9543 171.13 39.52266 147.798 7.725053 3.139406 2.077482 1949.314 labor FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1 Monthhours1 Monthhours2 Subsidy firstchild RatioofEC NoofChild gender OPTimes IPTimes SchCost _cons 0348146 0047545 -.0082402 -.0194015 -.0141481 014396 -.0042819 -.0092398 2481089 -.0006424 0002099 2423634 8284861 -1.704879 289697 -.0089399 -.1103979 101797 -.309821 -1.050929 0565642 0957421 002237 0041409 0080905 0083643 0091667 0170884 1281336 0005191 000462 04898 0698555 1008426 0349293 0563454 0267785 1012589 0382082 1.27805 0.62 0.05 -3.68 -4.69 -1.75 1.72 -0.47 -0.54 1.94 -1.24 0.45 4.95 11.86 -16.91 8.29 -0.16 -4.12 1.01 -8.11 -0.82 0.538 0.960 0.000 0.000 0.080 0.085 0.640 0.589 0.053 0.216 0.650 0.000 0.000 0.000 0.000 0.874 0.000 0.315 0.000 0.411 -.0760492 -.1828965 -.0126248 -.0275174 -.0300051 -.0019976 -.0222482 -.0427324 -.0030284 -.0016598 -.0006957 1463644 6915719 -1.902526 2212368 -.1193749 -.1628828 -.0966668 -.3847077 -3.555861 1456785 1924056 -.0038557 -.0112856 0017089 0307897 0136844 0242527 4992461 0003749 0011155 3383625 9654004 -1.507231 3581572 1014952 -.0579129 3002608 -.2349342 1.454002 /athrho /lnsigma -.194902 6.372894 202084 0401657 -0.96 158.67 0.335 0.000 -.5909793 6.29417 2011754 6.451617 rho sigma lambda -.192471 585.7503 -112.7399 1945978 23.52705 116.9489 -.5305996 541.4065 -341.9556 1985047 633.7261 116.4757 LR test of indep eqns (rho = 0): chi2(1) = 0.76 Prob > chi2 = 0.3846 58 Heckman regression- Marginal effects * Marginal effects, the selection (labor) margins, dydx(FMonths1 FMonths2 YIncome1 YIncome2 Age1 Age2 IPCost1 IPCost2 head1 /// > Monthhours1 Monthhours2 Subsidy firstchild RatioofEC NoofChild gender) predict(psel) atmean Conditional marginal effects Model VCE : OIM Number of obs = 528 Expression : Pr(labor), predict(psel) dy/dx w.r.t : NoofChild RatioofEC FMonths1 FMonths2 YIncome1 YIncome2 IPCost1 IPCost2 head1 gender Monthhours1 Monthhours2 Age1 Age2 Subsidy firstchild at : wage = 8132828 (mean) Age = 13.1875 (mean) NoofChild = 1.929924 (mean) RatioofEC = 6964331 (mean) FMonths1 = 11.95455 (mean) FMonths2 = 11.97538 (mean) YIncome1 = 4.840604 (mean) YIncome2 = 1.55983 (mean) IPCost1 = 2464621 (mean) IPCost2 = 1497803 (mean) head1 = 9469697 (mean) gender = 1.458333 (mean) OPTimes = 3333333 (mean) IPTimes = 0435606 (mean) SchCost = 4912841 (mean) Monthhours1 = 137.1136 (mean) Monthhours2 = 127.6326 (mean) Age1 = 42.53598 (mean) Age2 = 40.51894 (mean) Subsidy = 320089 (mean) firstchild = 7727273 (mean) dy/dx NoofChild RatioofEC FMonths1 FMonths2 YIncome1 YIncome2 IPCost1 IPCost2 head1 gender Monthhours1 Monthhours2 Age1 Age2 Subsidy firstchild 0963421 -.5669768 011578 0015812 -.0027404 -.0064522 -.001424 -.0030728 0825114 -.0029731 -.0002137 0000698 -.0047051 0047876 0806007 2755225 Delta-method Std Err .0120884 0396257 0188143 0318397 0007485 0013938 0030491 005684 0426972 0187393 0001727 0001537 002695 0027867 0164842 0252304 z 7.97 -14.31 0.62 0.05 -3.66 -4.63 -0.47 -0.54 1.93 -0.16 -1.24 0.45 -1.75 1.72 4.89 10.92 P>|z| 0.000 0.000 0.538 0.960 0.000 0.000 0.640 0.589 0.053 0.874 0.216 0.650 0.081 0.086 0.000 0.000 [95% Conf Interval] 0726492 -.6446419 -.0252973 -.0608235 -.0042074 -.0091839 -.0074001 -.0142133 -.0011736 -.0397014 -.0005522 -.0002314 -.0099873 -.0006743 0482923 2260718 1200349 -.4893118 0484533 0639858 -.0012733 -.0037205 0045521 0080677 1661964 0337552 0001249 000371 000577 0102494 1129091 3249732 ... street and children working in unhealthy situation and help 70% of those return back and living with their families 4.1 Overview of child labor problem in Vietnam Child laborers defined by Vietnam. .. ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS PARENTAL ABSENCE AND CHILD LABOR IN VIETNAM. .. Our World in Data Even the overall cases of child labor reduce; the percentage of children who work only increased in Vietnam and Indonesia in Figure Vietnam has the percentage of children who