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Cited by 300

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Extrinsic and Intrinsic Motivations for Tax Compliance: Evidence from a Field Experiment in Germany

Open access
Dwenger, Nadja, Kleven, Henrik, Rasul, Imran, Rincke, Johannes·2016·American Economic Journal: Economic Policy

Abstract

We study extrinsic and intrinsic motivations for tax compliance in the context of a local church tax in Germany. This tax system has historically relied on zero deterrence so that any compliance at baseline is intrinsically motivated. Starting from this zero deterrence baseline, we implement a field experiment that incentivized compliance through deterrence or rewards. Using administrative records of taxes paid and true tax liabilities, we use these treatments to document that intrinsically motivated compliance is substantial, that a significant fraction of it may be driven by duty-to-comply preferences, and that there is no crowd-out between extrinsic and intrinsic motivations. (JEL C93, D64, H26, H71, K34, Z12)

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Metadata

Authors
Dwenger, N., Kleven, H., Rasul, I., & Rincke, J.
Venue
American Economic Journal: Economic Policy
Published
2016-08-01
Volume
8
Issue
3
Pages
203-232
ISSN
  • 1945-7731 (ISSN-L)
  • 1945-774X
OpenAlex
W2341092366
Cited by
300
References
38
FWCI
70.29
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Extrinsic and Intrinsic Motivations for Tax Compliance: Evidence from a Field Experiment in Germany. https://app.cochat.ai/papers/10.1257/pol.20150083
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Dwenger, N., Kleven, H., Rasul, I., & Rincke, J. (2016). Extrinsic and Intrinsic Motivations for Tax Compliance: Evidence from a Field Experiment in Germany. American Economic Journal: Economic Policy. https://doi.org/10.1257/pol.20150083

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.