Modern Slavery: An Empirical Study on Human Trafficking

In spite of the efforts of the international community human trafficking continues to be a problem. Within the thousands of people trafficked yearly, women and children are disproportionately represented. Although the statistics on the number of victims are not completely reliable, an empirical study is necessary to better understand the causes of human trafficking. Using observational data gathered from the International Organization for Migration as the dependent variable, the study focused on whether economic, political, or sociological factors impacted the number of victims. My argument is that these factors impact individual vulnerability of which traffickers take advantage.

The dependent variables are as follows:

1. Number of trafficked victims by country of origin
2. Number of trafficked victims by country of destination
3. Ranking of country by number of trafficked victims (Origins) 
4. Ranking of country by number of trafficked victims (Destination)

Considering that human trafficking is a black market activity the statistics available for the activity are not completely reliable. To compensate for the reliability of the numbers rankings were used along with the raw numbers.

The independent variables and their expected relationship to human trafficking are as follows:

Economic Factors:

• GDP per capita: negativePoltical:

Political Factors: 

• Failed State Index: positive
•  Polity IV : negative
•  Trafficking In persons Report: positive

Sociological Factors:

• Number of refugees by country of residence: positive
• Male/female secondary school enrollment ratio : negative
• Human Development Index: negative

Panel:

• Country (154 countries)
• Year (2006-2011)

Although HDI measures wealth of a country as well, it is included in sociological factors because it also measures quality of life of in a country. While country was used as a fixed effect, year was excluded because there is not a significant difference over the years. The TIP report rankings were not included in the model but ran as an implicit interaction with all other independent variables. My main interest in the research lies with whether displacement (measured by the number of refugees) creates a vulnerability in people that traffickers use to their benefit.

Table 1

Table 1 shows that as GDP per capita of countries increases the number of victims that end up in those countries increases while the number of victims originating from those countries decreases.  However, for the number of victims originating from countries, the results were the opposite. While independently GDP was found to be not statistically significant, the tests show that when interacted with the number of refugees in residence it was statistically significant in models for all dependent variables except when TIP report scores were low. This suggests that for countries that have weak laws against human trafficking low GDP countries have more trafficked victims originating from their country as number of refugees increase and high GDP countries have less trafficked victims originating from their country as number of refugees increase.

The research shows that the number of refugees residing in the country matters for countries with low GDP as well as a high TIP report ranking. Since dealing with human trafficking can be costly, countries with low GDP can focus on either strengthening their anti-trafficking laws, or implement protective measures for refugees to prevent.