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Tax compliance and social desirability bias of taxpayers: experimental evidence from Indonesia

Published online by Cambridge University Press:  17 May 2021

Endra Iraman Open the ORCID record for Endra Iraman [Opens in a new window] ,
Yoshikuni Ono  and
Makoto Kakinaka
Endra Iraman*
Affiliation:
Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529, Japan
Yoshikuni Ono
Affiliation:
Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo 169-8050, Japan
Makoto Kakinaka
Affiliation:
Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529, Japan
*
*Corresponding author. E-mail: endraira0576@gmail.com
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Abstract

Identifying taxpayers who engage in noncompliant behaviour is crucial for tax authorities to determine appropriate taxation schemes. However, because taxpayers have an incentive to conceal their true income, it is difficult for tax authorities to uncover such behaviour (social desirability bias). Our study mitigates the bias in responses to sensitive questions by employing the list experiment technique, which allows us to identify the characteristics of taxpayers who engage in tax evasion. Using a dataset obtained from a tax office in Jakarta, Indonesia, we conducted a computer-assisted telephone interviewing survey in 2019. Our results revealed that 13% of the taxpayers, old, male, corporate employees, and members of a certain ethnic group had reported lower income than their true income on their tax returns. These findings suggest that our research design can be a useful tool for understanding tax evasion and for developing effective taxation schemes that promote tax compliance.

Keywords

list experimentsocial desirability biassurveytax compliancetax evasion

Information

Type
Research Article
Information
Journal of Public Policy , Volume 42 , Issue 1 , March 2022 , pp. 92 - 109
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Supplementary material: Link

Iraman et al. Dataset

Dataset

https://doi.org/10.7910/DVN/QW1JXG
Link