During an event at AAAS headquarters, statisticians Davina Durgana and Paul Zador discussed techniques for gathering data on human trafficking prevalence. This information can then be used to aid government and law enforcement efforts. | Stephen Waldron/AAAS
Statisticians are increasingly applying their mathematical skills to estimate the prevalence of human trafficking in the United States and a pair of them presented several potential techniques to improve how data is collected and analyzed during a presentation at AAAS on 16 March.
Davina Durgana and Paul Zador, both statisticians working in the anti-trafficking field, walked through strategies that could provide investigators a better idea of the number of trafficking victims in a given geographical area.
The presentation was co-organized by AAAS, the Washington Statistical Society and Statistics Without Borders, a group that offers trained volunteers capable of using statistics and data science to help solve problems.
Jessica Wyndham, interim director of the AAAS Scientific Responsibility, Human Rights and Law Program, noted the world’s largest general scientific organization has a long history of applying science and technology to address human rights concerns.
“We are pleased to be able to co-host this event highlighting the value of rigorous statistical methods in determining human trafficking prevalence,” Wyndham said, “and hope that this work will contribute to the design of effective programs to address human trafficking and support those affected.”
Durgana is a senior statistician with the Walk Free Foundation, an organization which tracks and reports on human trafficking, as well as aiming to influence government action and policy related to the problem. Her work focuses on supporting the efforts of law enforcement groups and nonprofit organizations by using statistical models to monitor human trafficking. In January, Durgana was recognized by Forbes magazine as one of the nation’s 30 brightest innovators under the age of 30 in the science category and the accompanying article summarized her research work as “math vs. slavery.”
During her presentation, Durgana said understanding the prevalence of human trafficking can play a critical role in helping those ensnared by human trafficking.
“Having better data on prevalence will allow policymakers and others to have informed ways of measuring interventions and figuring out how effective policy can be made,” she said.
Durgana highlighted several statistical techniques, including one known as multiple systems estimation that has been used to estimate war casualties. The process involves examining multiple lists of trafficking victims, usually gathered by governments or nongovernmental organizations.
Researchers identify people who appear on more than one list, a method that can help statisticians understand how likely a victim is included on more than one list within a certain time frame. Such data, Durgana said, help researchers then estimate the number of unlisted victims or the unknown population.
The method, Durgana said, requires high levels of government infrastructure.
“We need recordkeeping, we need these lists to exist,” she said, “and we need a government that is capable of analyzing and matching these lists.”
Durgana said the technique has shown promise in the United Kingdom and the Netherlands. She noted that the process is easier to employ in those countries than in the United States because the U.S. government is larger and more complex.
Durgana said another statistical method known as Respondent-Driven Sampling can be effective in gauging levels of human trafficking in an area. A 2014 article published by the Royal Statistics Society described RDS as a “widely used method for sampling from hard-to-reach human populations.”
The process involves targeting a small group of trafficking victims and asking them to refer researchers to people from their personal networks who could also be victims. It is an “expensive and difficult undertaking,” said Durgana, noting that it is generally effective on a smaller scale when the cost of such an analysis is lowest.
Another proposed technique that Durgana noted as potentially useful is aggregating data from statewide estimates of human trafficking and using that data to generate a national estimate.
She cited a recent study conducted by researchers from the University of Texas at Austin where researchers examined existing databases and gathered information through interviews and online-based surveys. The team found, at the time of the report’s publication in December 2016, that there were an estimated 313,000 human trafficking victims in Texas. Durgana cited Texas as an interesting example because of its large and diverse population and noted that efforts are currently underway to conduct similar research in Florida.
Durgana emphasized such estimates are immediately valuable to those working to fight human trafficking in a given state, from law enforcement officials to nongovernmental organizations.
Zador, a senior statistician with the research corporation Westat, has designed surveys used to estimate human trafficking in countries like India and Guatemala and analyzed the resulting data.
He said different methods are useful for different populations and that researchers must consider local culture and customs when investigating a specific region or group for potential trafficking victims. “‘Trafficking’ and the other terms we are using here have local definitions that may be very different than what you and I might accept,” Zador said.
Durgana agreed that much work still needs to be done in the development and testing of survey questions, adding that “we’re still learning as we go along.”
Theresa Harris, a project director with AAAS’ Scientific Responsibility, Human Rights and Law program, introduced Durgana at the beginning of the event.
“When scientific researchers, NGOs and impacted communities work together, they can build increasingly accurate and effective datasets needed to develop solutions to social challenges,” Harris said.
[Associated image: Stephen Waldron/AAAS]