The Algorithmic Assignment of Incentive Schemes

• 成果类型: Article
• 署名作者: Opitz, Saskia; Sliwka, Dirk; Vogelsang, Timo; Zimmermann, Tom
• 署名单位: University of Cologne; Max Planck Society; Frankfurt School Finance & Management; University of Cologne
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03362
• 发表日期: 2025
• 关键词: Randomized controlled trial; incentives; Heterogeneity; treatment effects; selection; algorithm; Machine Learning

摘要

The assignment of individuals with different observable characteristics to different treatments is a central question in designing optimal policies. We study this question in the context of increasing workers' performance via targeted incentives using machine learning algorithms with worker demographics, personality traits, and preferences as input. Running two large-scale experiments, we show that (i) performance can be predicted by accurately measured worker characteristics, (ii) a machine learning algorithm can detect heterogeneity in responses to different schemes, (iii) a targeted assignment of schemes to individuals increases performance significantly above the level of the single best scheme, and (iv) algorithmic assignment is more effective for workers who have a high likelihood to repeatedly interact with the employer or who provide more consistent survey answers.