An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan
Maximilian Kasy (),
Soha Shami and
No 8535, CESifo Working Paper Series from CESifo
We introduce a novel methodology for adaptive targeted experiments. Our Tempered Thompson Algorithm balances the goals of maximizing the precision of treatment effect estimates and maximizing the welfare of experimental participants. A hierarchical Bayesian model allows us to adaptively target treatments at different groups. We implement our methodology in a field experiment. We examine the impact of three interventions designed to improve formal employment outcomes of Syrian refugees and local jobseekers in Jordan: one treatment to address liquidity constraints, one to address information frictions, and one to address challenges of self-control. Six weeks after being offered treatment, none of the interventions has a significant or meaningful impact on the probability that individuals are in wage employment; we estimate that our targeting algorithm had a positive but small effect on aggregate employment (approximately 1 percentage point). However, we find large employment effects of all treatments for refugees at the two-month follow-up, and suggestive evidence of four-month impacts for the cash grant; liquidity appears to be a key barrier to employment for refugees.
Keywords: adaptive experiments; refugees; job search (search for similar items in EconPapers)
JEL-codes: C93 J60 O15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ara, nep-exp and nep-lab
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Working Paper: An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_8535
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