The Algorithmic Assignment of Incentive Schemes

67 Pages Posted: 17 Apr 2022 Last revised: 25 Jan 2024

See all articles by Saskia Opitz

Saskia Opitz

University of Cologne; Max Planck Institute for Research on Collective Goods

Dirk Sliwka

University of Cologne - Faculty of Management, Economics and Social Sciences; IZA Institute of Labor Economics

Timo Vogelsang

Frankfurt School of Finance & Management

Tom Zimmermann

University of Cologne

Date Written: October 23, 2023

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

JEL Classification: C21, C93, M52

Suggested Citation

Opitz, Saskia and Sliwka, Dirk and Vogelsang, Timo and Zimmermann, Tom, The Algorithmic Assignment of Incentive Schemes (October 23, 2023). Available at SSRN: https://ssrn.com/abstract=4077778 or http://dx.doi.org/10.2139/ssrn.4077778

Saskia Opitz

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Max Planck Institute for Research on Collective Goods ( email )

Kurt-Schumacher-Str. 10
D-53113 Bonn, 53113
Germany

Dirk Sliwka

University of Cologne - Faculty of Management, Economics and Social Sciences ( email )

Richard-Strauss-Str. 2
Cologne, D-50923
Germany

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

Timo Vogelsang (Contact Author)

Frankfurt School of Finance & Management ( email )

Sonnemannstraße 9-11
Frankfurt
Germany

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
373
Abstract Views
1,350
Rank
149,849
PlumX Metrics