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What's The Best Way To Help Refugees Land A Job? (NPR)
January 18, 2018
From the article:
It wouldn't make any sense to send a French-speaking refugee to a German-speaking town in Switzerland.
But under Switzerland's current system of placing refugees, that's a situation that can easily happen. This problem isn't unique to Switzerland, and it's not the only kind of mismatch that might happen.
The solution, says a new study from Stanford University's Immigration Policy Lab and ETH Zurich, is the creation of an "algorithm" — in layman's terms, the set of rules given to a computer that will enable it to reach a specific goal. The algorithm described in the study, published online Thursday in the journal Science, uses data to predict where a refugee — or one person in a family of refugees — has the best chance of getting a job.
...And as new data is added to the algorithm, it adapts to changing conditions, the researchers say. For example, if an agency adds data that shows newly-resettled refugees aren't getting jobs in a certain city, the algorithm will be less likely to recommend they be placed there.
Cindy Huang, a senior policy fellow at the Center for Global Development who wasn't involved in the study, says this algorithm is an example of how innovation can help vulnerable people. (One of the study's co-authors, Jeremy Weinstein, is a non-resident fellow at CGD.) And it's an improvement on other ideas she's seen that involve attempts to use existing technology, like e-learning platforms, to help refugees — but that aren't cost-effective because they weren't designed with refugees in mind.
"What the study shows is that you can improve employment outcomes, which are critical to longer-term integration," she says. "More refugees should be resettled, but this is a way to do more with the number that have already been accepted into a country."
But since the findings from the algorithm are based on historical data, she cautions that it's still unproven in a practical setting.
"To validate the findings and see how it works in the messy world, the next step is a trial to see how it performs in the field," Huang says.