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Social Control, Trade Openness and Human Trafficking

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Abstract

Objective

Human trafficking has generated growing concern among both policy makers and researchers. However, research has been hampered by a lack of valid data and appropriate methods. Our study attempts to improve understanding of this issue by developing a macro-level social disorganization perspective which suggests that trade openness may be an important vector of human trafficking such that countries in transition between high and low levels are likely to face major challenges in controlling trafficking and will therefore be especially likely to experience high rates. Our analysis is based on United Nations panel data containing 163 time points for 43 countries from 2003 to 2008 where there is full information across the variables of interest.

Methods

The study first relies on semi-parametric fixed effects regression estimators to determine the “true” functional form of the relationship between trade openness and human trafficking. Next, we utilize random and fixed effects regression analysis and negative binomial regression analysis to assess the existence of an inverted U-relationship between trade openness and human trafficking.

Results

Consistent with our theoretical prediction, the spline approximation of the relationship between trade openness and human trafficking rates exhibits a clear inverted-U. The random and fixed effects regression results support the same conclusion. The turning point is estimated to be 1.995 and two sensitivity analyses confirm this finding through a parametric and a nonparametric bootstrap method with replications. Finally, using negative binomial and fixed effects negative binomial regressions, we again confirm that there is an inverted-U relationship between trade openness and human trafficking counts.

Conclusions

In line with a macro-level social disorganization perspective we argue that countries with relatively weak social control will have high rates of crime and deviance. We operationalize social control in terms of the openness of a country’s trade to the international community and as expected we find a curvilinear relationship between levels of trade openness and human trafficking.

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Notes

  1. They are Cambodia, China, Laos, Myanmar, Thailand and Vietnam.

  2. Before we take the log of human trafficking rates we added 1 to the human trafficking counts of all the countries. The addition of 1 is trivial given that the mean count is 182.

  3. Available at https://www.ceicdata.com/en.

  4. The status of Singapore as an outlier on trade openness and growth has been noted previously by Frankel and Romer (1999:385), who examine the relationship between trade openness and growth in 150 countries, conclude that “Singapore is a large outlier that has an extremely high actual share given its size”.

  5. We justify our decision to include the economic stress index on two grounds. First, high inflation rates at the country of destination means that each unit of local currency buys fewer goods and services, resulting in the erosion of net purchasing power and making the country of destination less attractive to both migrants and natives. And second, high unemployment at the country of destination means that there are many native citizens actively seeking work, again making destination countries less appealing for both migrants and natives. We expect both components of the economic stress index (inflation and unemployment) to be inversely related to human trafficking rates.

  6. We regard level of democratization as conceptually distinct from trade openness. Japan provides a clear example. The country obtained the highest score (10) in the POLITY index but its trade openness (0.25) is the lowest among all the countries in the data set. Other countries that scored high (>9) on the level of democratization but low (<0.5) in terms of trade openness include India, Italy, Greece, Spain and the United Kingdom.

  7. It is generated using the Stata command xtsemipar lnhtr2 lnprotmus2 lnelf1 polity2 lnmis1_02, nonpar(open) spline knots1(1(2)3) ci cluster(countryid).

  8. The centering transformation involves three steps: (1) We subtract the mean of \({\text{OPEN}}_{\text{it}}\) from each value; (2) We call this new variable “mopen” and we then create “mopensqr” by squaring mopen; (3) We rerun the three regression models and the results are displayed in Table 5 of Appendix.

  9. In Stata, we use the commands rreg and mmregress (user written) to perform the two robust regressions in columns 5 and 6 in Table 2.

  10. In Stata, we use the command qreg to perform the quantile regression at the median. The results are reported in column 4 of Table 2.

  11. We also find that excluding the outlier Singapore in our estimation models does not alter the main findings. Results are available upon request.

  12. The turning point is computed as argext = b[opensqr]/2_b[open].

  13. As we observed in Fig. 1, the log of the human trafficking rate is indeed quite normally distributed.

  14. For this analysis we used the Stata command wherext open opensqr, boot rep(1000) kdensity seed(123).

  15. Here we used the Stata command bs "regress lnhtr2 lnprotmus2 lnelf1 polity2 lnmis1_02 open opensqr" "(−0.5*_b[open]/_b[opensqr])", rep(1000).

  16. This can be done with the Stata command nbreg.

  17. This can be done with the Stata command xtnbreg with the fixed effects option.

  18. We again find that removing the outlier Singapore in the negative binomial models do not affect the main findings. Results are available upon request.

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Acknowledgments

We are indebted to Sara Heller, James Finkenauer, Thomas Loughran, James Lynch and John MacDonald for their helpful comments on prior drafts.

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  1. University of Maryland, College Park, MD, USA

    Bo Jiang & Gary LaFree

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Correspondence to Bo Jiang.

Appendix

Appendix

See Tables 4 and 5.

Table 4 43 countries (163 time points) included in the analysis
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Table 5 Estimated coefficients for linear regression models after centering, human trafficking rates for 43 countries, 2003–2008
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Jiang, B., LaFree, G. Social Control, Trade Openness and Human Trafficking. J Quant Criminol 33, 887–913 (2017). https://doi.org/10.1007/s10940-016-9316-7

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