Replication Data for: TIP for Tat: Political Bias in Human Rights Trafficking Reporting

Human trafficking affects millions of people globally, disproportionately harming women, girls, and marginalized groups. Yet one of the main sources of data on global responses to trafficking, the Trafficking in Persons (TIP) Report, is susceptible to biases because report scores are tied to political outcomes. The literature on human rights measurements has established two potential sources of bias. The first is the changing standards of accountability --- where more information and increased budgets change the standard to which countries are held over time. The second is political biases in reports, which are amended to comply with the interests of the reporting agency. In this paper, we examine whether either of these biases influence the TIP Reports. As opposed to other country-level human rights indicators, the State Department issues both narratives and scores which incentivizes attempts to influence the scores based on political interests. Using a supervised machine learning algorithm we examine how narratives are translated into scores, if scores are biased, and disentangle whether bias stems from changing standards or political interests. We find that the TIP Report scores are more influenced by political biases than changing standards.