Optimal Disability Assistance When Fraud And Stigma Matter

Laurence Jacquet

No 1098, Working Paper from Economics Department, Queen's University

Abstract: I study the optimal redistributive structure when individuals with distinct productivities also differ in disutility of work due to either disability or distaste for work. Taxpayers have resentment against inactive benefit recipients because some of them are not actually disabled but lazy. Therefore disabled people who take up transfers are stigmatized. Their stigma disutility increases with the number of non-disabled recipients. Tagging transfers according to disability characteristics decreases stigma. However, tagging is costly and imperfect. In this context, I show how the level of the per capita cost of monitoring relative to labour earnings of low-wage workers determines the optimality of tagging. Under mild conditions, despite their stigma disutility, inactive and disabled people get a strictly lower consumption than low-wage workers. The results are valid under a utilitarian criterion and a criterion which does not compensate for distaste for work.

Keywords: Tagging; Disability benefit; Fraud; Stigma (search for similar items in EconPapers)
JEL-codes: H21 H53 I3 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2006-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1098.pdf First version 2006 (application/pdf)

Related works:
Working Paper: Optimal disability assistance when fraud and stigma matter (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1098

Access Statistics for this paper

More papers in Working Paper from Economics Department, Queen's University Contact information at EDIRC.
Bibliographic data for series maintained by Mark Babcock ().