Working Paper: CEPR ID: DP15359
Authors: Simon Quinn; Stefano Caria; Grant Gordon; Maximilian Kasy; Soha Shami; Alex Teytelboym
Abstract: We introduce a novel adaptive targeted treatment assignment methodology for field 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. We implement our methodology in Jordan, testing policies to help Syrian refugees and local jobseekers to find work. The immediate employment impacts of a small cash grant, information and psychological support are close to zero, but targeting raises employment by 1 percentage-point (20%). After four months, cash has a sizable effect on employment and earnings of Syrians.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| small cash grant (F35) | employment impacts (J68) |
| information provision (L86) | employment impacts (J68) |
| psychological support (D91) | employment impacts (J68) |
| targeted interventions (I24) | employment rates (J68) |
| cash intervention (E41) | employment (J68) |
| cash intervention (E41) | earnings (J31) |
| liquidity constraints (E41) | labor market access (J48) |
| information interventions (L86) | job search intensity (J68) |
| behavioral nudge interventions (D91) | employment rates (J68) |
| treatment (M53) | positive effects on Jordanian participants (I25) |