Meta-Analysis of Human Trafficking in the United States: Economic, Demographic, and Sociological Drivers
David J. Corliss, PhD | September 20 | 9:00-10:00 AM
Topic: Research | Knowledge Level: Intermediate, Advanced
Meta-analysis is a method for combining multiple independent studies on the same subject or question, producing a single large study with increased accuracy and enhanced ability to detect overall trends and smaller effects. This presentation applies meta-analysis to human trafficking data. There are now a number of localized studies for one state or a metropolitan area which can be combined using meta-analysis. In this study, state-level data from the National Human Trafficking Resource Center is combined with economic, demographic, and other data sources to develop a statistical model to predict metropolitan areas with the greatest human trafficking risk - not all of which have been found by law enforcement at this time. Factors driving high levels of trafficking are found to include several economic factors, including poverty rates, GINI index, and recent homelessness. Yet, even after taking economic factors into account, race is found to be a risk factor, and the percentage of the population held in slavery at the 1860 census is found to be a statistically significant predictor of the rate slavery victim reports today. These risk factors are applied to socio-economic data of states and metropolitan areas today, identifying areas at highest risk of human trafficking today and leading to the development of data-driven mitigation strategies such as training emergency room workers to recognize indicators of human trafficking victims.
· Share research indicating metropolitan levels with high but undetected levels of human trafficking.
· Describe meta-analysis as an important methodology for human trafficking researchers.
· Demonstrate how organizations fighting human trafficking can partner with volunteer statistical researchers to improve outcomes.