The Targeted Assignment of Incentive Schemes

Abstract

A central question in designing optimal policies concerns the assignment of individuals with different observable characteristics to different treatments. We study this question in the context of increasing workers' performance by using targeted incentives based on measurable worker characteristics. To do so, we ran two large-scale experiments. The key results are that (i) performance can be predicted by accurately measured personality traits, (ii) a machine learning algorithm can detect such heterogeneity in worker responses to different schemes, and (iii) a targeted assignment of schemes to individual workers increases performance in a second experiment significantly above the level achieved by the single best scheme.

Keywords: Randomized Controlled Trial, Incentives, Heterogeneity, Treatment Effects, Selection, Algorithm

JEL Classification: C21, C93, M52