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Crime, Punishment, and the Market for Offenses
Open accessAbstract
Crime is a subject of intense emotions, conflicting ideologies. However, economists have generally explained it as a reflection of individual choice and equilibrating market forces. Two major themes of the literature are outlined: the evolution of a ‘market model’ to explain the diversity of crime across time and space, and the debate about the usefulness of ‘positive’ versus ‘negative’ incentives. Systematic analyses generally indicate that crime is affected on the margin by both positive and negative incentives; there are serious limitations to the effectiveness of incapacitation and rehabilitation; and optimal enforcement strategies involve trade-offs between narrow efficiency and equity considerations.
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Citations
Cited by
Angrist, J. D., & Pischke, J.-S. (2010) · Journal of Economic Perspectives · doi:10.1257/jep.24.2.3 · Cited by 1,566
Alves, L. G. A., Ribeiro, H. V., Lenzi, E. K., & Mendes, R. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069580 · Cited by 154
Earned income tax credit and crime
Lenhart, O. (2021) · Contemporary Economic Policy · doi:10.1111/coep.12522 · Cited by 14
Metadata
- Authors
- Ehrlich, I.
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 1996-02-01
- Volume
- 10
- Issue
- 1
- Pages
- 43-67
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W2051528258
- Cited by
- 714
- References
- 0
- FWCI
- 26.17
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Crime, Punishment, and the Market for Offenses. https://app.cochat.ai/papers/10.1257/jep.10.1.43
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- Ehrlich, I. (1996). Crime, Punishment, and the Market for Offenses. Journal of Economic Perspectives. https://doi.org/10.1257/jep.10.1.43
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
