The Normative Analysis of ‘Tagging’ Revisited : Dealing with Stigmatization
AbstractShould income transfers be conditional upon personal characteristics of the potential recipients (the so-called “tagging”) or should they only be tied to reported incomes ? This question is addressed in a partial equilibrium setting distinguishing two types of jobs and a distribution of worker types. In a system with tagging, there is clear evidence that the assessment of the eligibility of applicants ceates stigmatization. By assumption, the intensity of stigma is exogenously distributed. Then, tagging is always suboptimal under a Rawlsian criterion. With a utilitarian criterion, the analysis shows that tax/transfer systems with and without tagging can solve the first-order optimality conditions. A numerical analysis suggests that tagging can only be recommended if the distribution of the intensity of stigmatization relative to earnings is highly concentrated on low values. However, this is only a necessary condition. Tagging is never optimal if the dispersion of abilities among the ‘high-ability people’ is too large or too narrow.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES) in its series Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) with number 2003030.
Date of creation: 01 Dec 2003
Date of revision:
tagging; optimal taxation; welfare programs; stigmatization;
Other versions of this item:
- Laurence Jacquet & Bruno Van der Linden, 2006. "The Normative Analysis of Tagging Revisited: Dealing with Stigmatization," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 62(2), pages 168-198, June.
- H2 - Public Economics - - Taxation, Subsidies, and Revenue
- I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-04-04 (All new papers)
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- Robin Boadway, 2012. "Recent Advances in Optimal Income Taxation," Hacienda Pública Española, IEF, vol. 200(1), pages 15-39, March.
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