Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/36321
Title: Hyperbolic Punishment Functions
Authors: al-Nowaihi, Ali
Dhami, S.
First Published: Dec-2012
Publisher: De Gruyter
Citation: Review of Law and Economics, 2012, 8 (3), pp. 759-787 (22)
Abstract: All models in Law and Economics use punishment functions (hereafter, PF) that incorporate a trade-off between probability of detection, p, and punishment, F. Suppose society wishes to minimize the total costs of enforcement and damages from crime, T( p,F). For a given p, an optimal punishment function (OPF) determines an F that minimizes T( p,F). A popular and tractable PF is the hyperbolic punishment function (HPF). We show that the HPF is an OPF for a large class of total cost functions. Furthermore, the HPF is an upper (lower ) bound for an even larger class of punishment functions. If the HPF cannot (can) deter crime, then none (all ) of the PF’s for which the HPF is an upper (lower ) bound can deter crime. Thus, if one can demonstrate that a particular policy is ineffective (effective) under the HPF, there is no need to even compute the OPF. Our results should underpin an even greater use of the HPF. We give illustrations from mainstream and behavioral economics.
DOI Link: 10.1515/1555-5879.1570
ISSN: 1555-5879
Links: http://www.degruyter.com/view/j/rle.2012.8.issue-3/1555-5879.1570/1555-5879.1570.xml?format=INT
http://hdl.handle.net/2381/36321
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2012, Walter de Gruyter GmbH. Deposited in accordance with the publisher's Repository Policy available on the SHERPA/RoMEO website.
Appears in Collections:Published Articles, Dept. of Economics

Files in This Item:
File Description SizeFormat 
hyperbolic-punishment-function.pdfPost-review (final submitted)271.04 kBAdobe PDFView/Open
1555-5879.1570.pdfPublished (publisher PDF)980.46 kBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.