Public Sector Pay and Corruption: Measuring Bribery from Micro Data

Yuriy Gorodnichenko () and Klara Sabirianova Peter ()
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Yuriy Gorodnichenko: University of California, Berkeley
Klara Sabirianova Peter: University of North Carolina, Chapel Hill

No 1987, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This study is the first to provide a systematic measure of bribery using micro-level data on reported earnings, household spending and asset holdings. We use the compensating differential framework and the estimated sectoral gap in reported earnings and expenditures to identify the size of unobserved (unofficial) compensation (i.e., bribes) of public sector employees. In the case of Ukraine, we find that public sector employees receive 24-32% less wages than their private sector counterparts. The gap is particularly large at the top of the wage distribution. At the same time, workers in both sectors have essentially identical level of consumer expenditures and asset holdings that unambiguously indicate the presence of non-reported compensation in the public sector. Using the conditions of labor market equilibrium, we develop an aggregate measure of bribery and find that the lower bound estimate of the extent of bribery in Ukraine is between 460 mln and 580 mln U.S. dollars (0.9-1.2% of Ukraine’s GDP in 2003).

Keywords: Ukraine; wage differentials; public sector; corruption; wage; bribery (search for similar items in EconPapers)
JEL-codes: J3 J4 O1 P2 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2006-02
New Economics Papers: this item is included in nep-cis, nep-dev, nep-lab and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Published - published in: Journal of Public Economics, 2007, 91 (5-6), 963-991

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Related works:
Journal Article: Public sector pay and corruption: Measuring bribery from micro data (2007)
Working Paper: Public Sector Pay and Corruption: Measuring Bribery from Micro Data (2006)
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