The Algorithmic Assignment of Incentive Schemes

Saskia Opitz (), Dirk Sliwka (), Timo Vogelsang () and Tom Zimmermann ()
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Saskia Opitz: Faculty of Management, Economics and Social Sciences, Department of Corporate Development, University of Cologne, 50923 Cologne, Germany; and Max Planck Institute for Research on Collective Goods, 53113 Bonn, Germany
Dirk Sliwka: Faculty of Management, Economics and Social Sciences, Department of Corporate Development, University of Cologne, 50923 Cologne, Germany
Timo Vogelsang: Department of Accounting, Frankfurt School of Finance & Management, 60322 Frankfurt, Germany
Tom Zimmermann: Faculty of Management, Economics and Social Sciences, University of Cologne, 50923 Cologne, Germany

Management Science, 2025, vol. 71, issue 2, 1546-1563

Abstract: 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.

Keywords: randomized controlled trial; incentives; heterogeneity; treatment effects; selection; algorithm; machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:2:p:1546-1563

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