Refugees must often combat the dual hardships of displacement and job uncertainty in their new country. The research team is implementing a new targeting method in a field experiment designed to help Syrian refugees and local job seekers in Jordan find work. This new targeting method is an algorithm that adaptively targets policy interventions, and balances the goals of 1) maximizing the precision of treatment effects with 2) maximizing the welfare of participants in the experiment.
The field experiment includes three types of support: a small, unconditional cash transfer (worth about one month of average monthly expenses); information to increase the ability to signal skills to employers; and a behavioral nudge to motivate refugees to look for jobs.
CEGA will fund the team to run a two-year follow-up survey. They will use this survey in three ways. First, the team will measure the impacts, two years later, for each of the different groups that received the interventions. Second, the team will compare whether the treatments impacted groups differently in the short-term versus two years later. Third, the team will use machine learning to probe whether modeling differences across treatment groups was successful or not. Using the two-year follow up data, the team can understand what makes treatment different by group, and quantify the additional benefits from targeting that the team would have realized had they used the “optimal” participant characteristics.
Once the long-term benefits of these new targeting methods are better understood, the implementation partner, the International Rescue Committee, is interested in scaling-up the method to other programs.
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