Predicting Compliance: Leveraging Chat Data for Supervised Classification in Experimental Research

Hausladen, Carina I., Martin Fochmann, and Peter Mohr. "Predicting compliance: Leveraging chat data for supervised classification in experimental research." Journal of Behavioral and Experimental Economics (2024): 102164.

20 Pages Posted: 2 Mar 2023 Last revised: 27 Jan 2024

See all articles by Carina Ines Hausladen

Carina Ines Hausladen

ETH Zürich - Department of Humanities, Social and Political Sciences (GESS)

Martin Fochmann

Free University of Berlin

Peter Mohr

Free University of Berlin (FUB)

Date Written: February 26, 2023

Abstract

Behavioral and experimental economics have conventionally employed text data to facilitate the interpretation of decision-making processes. This paper introduces a novel methodology, leveraging text data for predictive analytics rather than mere explanation. We detail a supervised classification framework that interprets patterns in chat text to estimate the likelihood of associated numerical outcomes. Despite the unique advantages of experimental data in correlating textual and numerical information for predictive modeling, challenges such as limited sample sizes and potential data skewness persist. To address these, we propose a comprehensive methodological framework aimed at optimizing predictive modeling configurations, particularly in small experimental behavioral research datasets. We also present behavioral experimental data from a preregistered tax evasion game (n=324), demonstrating that chat behavior is not influenced by experimenter demand effects. This establishes chat text as an unbiased variable, enhancing its validity for prediction. Our findings further indicate that beliefs about others' dishonesty, lying attitudes, and risk preferences significantly impact compliance decisions.

Keywords: chat data; supervised classification; experimental research; tax evasion; compliance

JEL Classification: C55; C92; D83

Suggested Citation

Hausladen, Carina Ines and Fochmann, Martin and Mohr, Peter, Predicting Compliance: Leveraging Chat Data for Supervised Classification in Experimental Research (February 26, 2023). Hausladen, Carina I., Martin Fochmann, and Peter Mohr. "Predicting compliance: Leveraging chat data for supervised classification in experimental research." Journal of Behavioral and Experimental Economics (2024): 102164., Available at SSRN: https://ssrn.com/abstract=4371333 or http://dx.doi.org/10.2139/ssrn.4371333

Carina Ines Hausladen (Contact Author)

ETH Zürich - Department of Humanities, Social and Political Sciences (GESS) ( email )

Stampfenbachstrasse 48
Zürich, 8006
Switzerland

Martin Fochmann

Free University of Berlin ( email )

Thielallee 73
Accounting and Taxation
Berlin, 14195
Germany

Peter Mohr

Free University of Berlin (FUB) ( email )

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