Impact Brief: An AI Solution for Refugee Resettlement
Stanford political scientist Jens Hainmueller recalls the chilling moment when he realized that his own profession had an important role to play in the global refugee crisis.
His hometown in Germany had seen a rash of violent attacks on refugee shelters. He began thinking about how the country could help refugees succeed while easing social tensions. The first thing needed—and the thing most lacking—was data and evidence about what works and what doesn’t.
He co-founded the Immigration Policy Lab (IPL) at Stanford to address that need. Soon after the lab was established in 2016 he took a team to Washington, D.C., to convene a workshop with people whose work is shaping the country’s response to the refugee crisis.
Gathered in the room, many of them meeting for the first time, were members of the State Department’s Bureau of Population, Refugees, and Migration; the Department of Health and Human Services; and the various nonprofits that place refugees around the country and help them adjust. It was frustrating, they agreed, to have so much responsibility over the course of refugees’ lives but then know so little about how they are faring and whether the system is working. The placement officers, who decide where to send incoming refugees, debated whether cities, rural areas, or mid- sized towns were best. But they weren’t drawing on hard data. Just anecdotal evidence backed by different reasoning.
The first thing that was needed, he thought—and the thing most lacking—was data and evidence about what works and what doesn’t.
By the end of the meeting, the group had given Hainmueller and his IPL colleagues a mission: Tell us how we can improve the process by which refugees are allocated to locations around the country. “The United States runs the world’s largest resettlement program, bringing refugees into the country and giving them a chance to escape persecution and start over,” Hainmueller says. “But how can we, through the best possible policies, ensure that refugees become productive citizens who can contribute to society and the U.S. economy?”
Back at Stanford, Hainmueller delved into the available data, which notes whether refugees were employed 90 days after arriving in the country. His analysis soon revealed that certain locations worked better for certain kinds of refugees than others.
73: Projected percentage increase of employment among asylum seekers in Switzerland
Depending on one’s background, one place might especially reward one’s strengths; meanwhile, traits that could be a liability in one place become less detrimental in another. With that insight, he realized the potential for an algorithm to apply the data’s lessons to incoming refugees. He called in Kirk Bansak, a grad student and IPL’s data scientist, and together they worked out the math of the problem, translating it into code. Would the algorithm work? Hainmueller and Bansak didn’t need the placement officers to actually use it to find out. Just as one would backtest an investment strategy, they’d run the algorithm on historical data. To train it for use in the United States, they used data on more than 30,000 refugees placed by a major resettlement agency from 2011 to 2016. Then they asked the algorithm to assign optimal locations for refugees who arrived at the end of 2016.
The gains were striking: Compared to the actual historical outcomes, the median refugee was projected to be more than twice as likely to find a job within 90 days. When they repeated the test with data from Switzerland, the gains were even greater. Had asylum seekers in that sample been assigned to the algorithm-identified location, their projected employment rate would have been 73% higher, an increase from 15 to 26%.
BIG IMPACT, SMALL FOOTPRINT
With those results, the IPL team found government partners eager to test the algorithm in the real world. Switzerland was the first to launch a pilot program, and there is strong interest in running similar tests in the United States and the Netherlands. In Switzerland, the pilot program will place half the refugees with the guidance of the algorithm and half randomly, and compare their employment rates over the next several years.
The algorithm’s potential is all the more remarkable when compared with other interventions— like language instruction and job training—that, while essential, can be costly, logistically challenging, and difficult to scale.
“Algorithmic assignment is a rare policy reform that could deliver improved outcomes at almost no cost,” Hainmueller says.
It can also improve over time. Because the algorithm constantly mines updated data on refugee outcomes, it will respond to changing conditions at each resettlement location in order to optimize matches.
And it can easily be adapted for use in any country with some customization of the software.
“Our goal was to develop a tool that not only worked well but was also practical from a real-world implementation standpoint,” Bansak says.
“By improving an existing process using existing data, our algorithm avoids the financial and administrative hurdles that can often impede other policy innovations.”
The team looks forward to seeing the algorithm implemented in other parts of the world where governments are struggling to respond to migration’s challenges, like South America and Africa. They’re also working on incorporating refugees’ preferences into the algorithm, as well as adapting it to give immigrants recommendations on where to settle.
Already, the project is taking its place in a broader innovation agenda linking Stanford with groups around the world bringing technology to the global refugee crisis.
Hainmueller is optimistic: “At Stanford, we’re known for our ingenuity. If we can marshal some of that in service to refugees and the countries working to support them, we can make a difference.”
This issue brief describes how teams of researchers and leaders in government, business, and nonprofits can work together to generate new ideas, insights, and solutions to make progress on social problems. The Immigration Policy Lab was awarded funding in the second round of Stanford Impact Labs Start-Up Impact Lab Funding. This brief was written prior to the launch of Stanford Impact Labs to show how new evidence and insights developed jointly by scholars and external practitioners can inform policies and programs to improve lives.
Stanford Impact Labs invests in highly motivated teams of researchers and practitioners from government, business, nonprofit organizations, and philanthropy. These teams—impact labs—work together on social problems they choose and where practical progress is possible. With financial capital and professional support from Stanford Impact Labs, they can rapidly develop, test, and scale new solutions to social problems that affect millions of people worldwide.
Learn more about the work Stanford Impact Labs is investing in at impact.stanford.edu.