Performance Indicators for Quality with Adverse Selection, Gaming and Inequality Aversion

41 Pages Posted: 21 May 2008  

Michael Kuhn

Austrian Academy of Sciences - Vienna Institute of Demography

Luigi Siciliani

University of York

Date Written: April 2007

Abstract

Performance indicators are increasingly used to regulate quality in health care and other areas of the public sector. We develop a model of contracting between a purchaser (principal) and a provider (agent) under the following scenarios: a) higher ability increases quality directly and indirectly (through a lower marginal cost of quality); b) the provider can game the system (or misreport); c) the purchaser is averse to inequality. The main results are: 1) if ability has a direct impact on quality, quality effort is lower and distortions from informational rents are higher; under some conditions more able providers exert lower quality effort; 2) gaming or misreporting reduces the optimal level of quality effort but generally increases the level of quality targets (and measured quality). Pooling may be optimal for high types (or all types) if the benefit function is sufficiently concave and the cost of manipulation is at intermediate levels; 3) If patients receive a low benefit from health care because quality is low then higher inequality aversion increases the optimal level of quality and the associated transfer, and reduces quality dispersion.

Keywords: adverse selection, falsification, inequality aversion, moral hazard, performance, quality

JEL Classification: D82, I11, I18, L51

Suggested Citation

Kuhn, Michael and Siciliani, Luigi, Performance Indicators for Quality with Adverse Selection, Gaming and Inequality Aversion (April 2007). , Vol. , pp. -, 2007. Available at SSRN: https://ssrn.com/abstract=1135499

Michael Kuhn (Contact Author)

Austrian Academy of Sciences - Vienna Institute of Demography ( email )

Prinz Eugenstr. 8-10
Vienna, 1040
Austria

Luigi Siciliani

University of York ( email )

Heslington
York, YO10 5DD
United Kingdom

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