CHILD LABOR SUPPLY AND HUMAN TRAFFICKING RISK An Empirical Analysis of Trafficking Survivors in the Mekong Delta Lien H Tran* Diep Vuong** Phuong Thao Le*** * Tran is a Visiting Fellow at the Center for Vietnamese Studies at Temple U. and an economist at the U. S. Federal Trade Commission. The views expressed here are my own. ** Vuong is President and Co-Founder of Pacific Links Foundation. *** Le is Assistant Professor, School of Public Health, New York U.
Why should we be concerned? Presence of very young female children in the commercial sex trade in Southeast Asia in the last two decades raises concerns These trade networks can destabilize local communities, reduce educational opportunities and worsen labor market outcomes for female children. 2
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Identifying victims Estimating the size of this population is fraught with difficulty because of identification issues: A person is considered to be a trafficked victim when there is an element of force, fraud, or coercion into situations that do not fit the original description or promise (United Nations, 2000). An alternative view is that certain children and young women made the choice to work in the sex trade, and they would lose income if prohibited from working. 4
Why concern with age at time of being trafficking? Abuse of young children is likely to cause long-lasting trauma and permanent reduction in life long earning capability that could have been obtained through accumulation of human capital through education. 5
The data ADAPT program Data are collected on returnees at the point of service at locations where Pacific Links operates shelters under the ADAPT program: Long Xuyen at the border of Vietnam and Kampuchea Lao Cai at the border of Vietnam with China 6
Research question To better understand why certain trafficked victims were so young at the time of being trafficked, we estimate a statistical relationship between age at time of being trafficked and own sociodemographic characteristics as well as characteristics of parents 7
Summary statistics for returnees in the Mekong Delta Variable. Obs Mean Std. Dev. Min Max Age_at_time_trafficked 77 17.05 3.772 11 33 Est Year_trafficked 77 2006.792 1.921 1998 2011 Own_edu 77 4.480 2.958 0 11 Orphan 77 0.286 0.4547 0 1 Employ_search 77 0.636 0.420 0 1 8
Summary statistics for returnees in the Mekong Delta (cont d) Variable Obs Mean Std. Dev. Min Max Own_age 77 22.26 4.219 15 37 Father_age 77 50.000 7.2511 33 71 Mother_age 77 47.753 0.38 33 64 Mother_age_yr_trafficked 77 42.545 6.8509 29 60 Father_age_yr_trafficked 77 44.792 6.9421 29 67 9
Model 1 Number of obs = Source SS df MS F( 8, 68) = 44.82 Model 909.33965 8 113.667456 Prob > F = 0.0000 Residual 172.452557 68 2.53606702 R-squared = 0.8406 Total 1081.79221 76 14.234108 Adj R- squared = 0.8218 77 Root MSE = 1.5925 Age_at_time_beingtrafficked Coef. Std. Err. t P> t [95% Conf. Interval] Own_age 0.7862912 0.053894 14.59 0.000 0.6787474 0.8938350 Own_edu 0.0861132 0.0639745 1.35 183.000-0.0415460 0.2137720 Norphan_father_age 0.0893817 0.069106 1.29 0.200-0.0485171 0.2272805 Father_edu 0.099158 0.0802127 1.24 0.221-0.0609040 0.2592200 Norphan_mother_age -0.0390403 0.073225-0.53 0.596-0.1851585 0.1070779 Mother_edu 0.2023772 0.1176132 1.72 0.090-0.0323163 0.4370706 Orphan 2.301195 1.89115 1.22 0.228-1.4725350 6.0749250 Employ_search -1.043645 0.392969-2.66 0.010-1.8278020-0.2594877 Constant -3.268892 1.616117-2.02 0.047-6.4938030-0.0439815 10
Model 2 Number of obs = 77 Source SS df MS F( 8, 68) = 48.53 Model 920.563042 8 115.07038 Prob > F = 0.0000 Residual 161.229165 68 2.37101714 R-squared = 0.851 Total 1081.79221 76 14.234108 Adj R-squared = 0.83334 Root MSE = 1.5398 Age_at_time_beingtrafficked Coef. Std. Err. t P> t [95% Conf. Interval] Own_age 0.7586173 0.0506686 14.97 0.000 0.6575097 0.8597249 Own_edu 0.0746422 0.0619235 1.21 0.232-0.0489242 0.1982000 Father_yr_trafficked- 0.0970748 0.0665592 1.46 0.149-0.0357421 0.2298916 _non orphan Father_edu 0.0882648 0.0777199 1.14 0.260-0.0668228 0.2433525 Mother_yr_trafficked- -0.004759 0.0713482-0.07 0.947-0.1471321 0.1376141 _non orphan Mother_edu 0.2220029 0.1131644 1.96 0.054-0.0038131 0.4478190 Orphan 3.890603 1.630865 2.39 0.020 0.6362627 7.1449420 Employ_search -0.9558398 0.3820441-2.5 0.015-1.7181970-0.1934827 Constant -4.234119 1.460795-2.9 0.005-7.1490890-1.3191490 11
Main findings Father s educational attainment does not have an effect, but mother s educational attainment has a positive and significant effect on age of being trafficked suggesting that higher mother s educational attainnment delays the age of child being trafficked The search for employment coefficient is negative and statistically significant, suggesting that searching for employment is a a significant factor in the trafficking of younger children in search of employment 12
Caveats Small sample Data collected at point of service, so there is a possibility of sample bias 13
Still.. Preliminary results suggest: Importance of mother s educational attainment for children outcomes Household labor supply decisions play an important role in exposing children to trafficking risk in rural labor markets 14
Reference United Nations. Protocol to Prevent, Suppress and Punish the Trafficking in Persons, Especially Women and Children, Supplementing the United Nations Convention against Transnational Organized Crime (2000). 15