Minimizing Learning Behavior in Repeated Real-Effort Tasks

20 Pages Posted: 1 Oct 2014 Last revised: 26 Mar 2018

See all articles by Volker Benndorf

Volker Benndorf

Heinrich Heine University Dusseldorf - Duesseldorf Institute for Competition Economics (DICE)

Holger Andreas Rau

University of Goettingen (Gottingen)

Christian Sölch

University of Erlangen-Nuremberg-Friedrich Alexander Universität Erlangen Nürnberg

Date Written: March 2018

Abstract

In this paper, we discuss learning behavior and the heterogeneity of subjects' ability to perform in real-effort tasks. Afterwards, we present a novel variant of Erkal et al.'s (2011) encryption real-effort task which aims to minimize learning behavior in repeated settings. In the task, participants encrypt words into numbers. In our variant, we apply a double-randomization mechanism to minimize learning and heterogeneity. Existing experiments with repeated real-effort tasks find a performance increase of 12-14% between the first and second half. By contrast, our task mitigates learning behavior down to 2% between the first and second half. The data show that subjects show a small heterogeneity in performance.

Keywords: Experimental Methods, Learning Behavior, Real-Effort.

JEL Classification: C90, C91

Suggested Citation

Benndorf, Volker and Rau, Holger Andreas and Sölch, Christian, Minimizing Learning Behavior in Repeated Real-Effort Tasks (March 2018). Available at SSRN: https://ssrn.com/abstract=2503029 or http://dx.doi.org/10.2139/ssrn.2503029

Volker Benndorf

Heinrich Heine University Dusseldorf - Duesseldorf Institute for Competition Economics (DICE) ( email )

Universitaetsstr. 1
Duesseldorf, NRW 40225
Germany

Holger Andreas Rau (Contact Author)

University of Goettingen (Gottingen) ( email )

Platz der Gottinger Sieben 3
Gottingen, D-37073
Germany

Christian Sölch

University of Erlangen-Nuremberg-Friedrich Alexander Universität Erlangen Nürnberg ( email )

Lange Gasse 20
Nuremberg, Bavaria 90403
Germany

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