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Department of Pure Mathematics and Mathematical Statistics

Policy-relevant treatment effect estimation in marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to the market equilibrium. The standard way to address these challenges is to evaluate interventions via cluster-randomized experiments, where each cluster corresponds to an isolated market. This approach, however, cannot be used when we only have access to a single market (or a small number of markets). Here, we show how to identify and estimate policy-relevant treatment effects using a unit-level randomized trial run within a single large market. A standard Bernoulli-randomized trial allows consistent estimation of direct effects, and of treatment heterogeneity measures that can be used for welfare-improving targeting. Estimating spillovers--as well as providing confidence intervals for the direct effect--requires estimates of price elasticities, which we provide using an augmented experimental design. Our results rely on all spillovers being mediated via the (observed) prices of a finite number of traded goods, and the market power of any single unit decaying as the market gets large. We illustrate our results using a simulation calibrated to a conditional cash transfer experiment in the Philippines.

https://arxiv.org/abs/2109.11647

Further information

Time:

23Sep
Sep 23rd 2024
14:00 to 15:00

Venue:

MR11 (B1.39), CMS Pavilion B

Speaker:

Stefan Wager (Stanford University)

Series:

Statistics