Perceptions of Corruption and Actual Corruption among Firms: To What Extent Can They be Reduced by Good Governance?

Jeff Nugent, USC professor of Economics at Dornsife College of Letters and Arts, teaches Development Economics.  Nugent researches institutions and development, the political economy of growth, small- and medium-sized manufacturing enterprises, contractual choice, old age security and fertility and regional (supranational) coordination of industrial development in developing countries.

Professor Nugent discussed his current research paper, “Perceptions of Corruption and Actual Corruption among Firms: To What Extent Can They be Reduced by Good Governance?”



Given the difficulties of measuring actual corruption, much attention in the literature has been devoted to perceptions of corruption. So too, in examining the determinants and effects of corruption, much attention has been given to the existence of various different kinds of regulations and the quality of governance. This paper attempts to distinguish between the effects of the various factors potentially affecting both perceptions of corruption and actual corruption reported by individual firms including different governance characteristics, regulations (various required permits) as well as firm and industry characteristics. Firm-level data for over 110,000 firms from 135 developing and transition countries taken from the Enterprise Surveys of the World Bank are used to estimate the various determinants of both perceptions of corruption and actual engagement in corruption (“gift-giving”) by firms. The results show both important similarities in, and differences between, the effects on firm perceptions of corruption, on the one hand, and actual corruption, on the other, of (a) six different Governance Indicators, (b) various different firm characteristics and especially (c) interactions between the two.  Implications for policy and further research are derived.


USC Price Bedrosian Center in collaboration with USC Price Department of Urban Planning & Spatial Analysis