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The Antecedents and Effects of Corruption – A Reassessment of Current (Empirical) Findings

Eugen Dimant presents his survey of empirical literature on measuring corruption using both micro and macro data, as well as on the antecedents of corruption.

Corruption has fierce impacts on economic and societal development and is subject to a vast range of institutional, jurisdictional, societal and economic conditions. Research indicates that corruption’s predominantly negative effects have arisen to a massive trans-border threat while creating high obstacles to sustainable and prospective development. Although being exceedingly rampant in poor countries, corruption has become a vicious threat to western countries as well.

Formerly, corruption was widely assumed impossible to measure. Nowadays, thanks to efforts of various institutions, estimates that are more precise have been identified, setting the foundation to carry out long overdue analyses of corruption’s impact on economy and society going beyond simple correlation analyses. Approaches that are more sophisticated now allow for a better understanding of the antecedents and effects of corruption, explaining why corruption has more detrimental effects on some countries than on others.

In a recent paper, I surveyed older and most recent empirical literature on measuring corruption using both micro- and macro data, as well as on the antecedents and the effects of corruption (although I will not talk about the latter in the present blog).

Measuring Corruption

Let us first turn to the prolonged use of macro data. Presently, five distinct measures of corruption have gained public and scholarly attention in the past years and are widely used in cross-country comparisons: the Business Environment and Enterprise Performance Surveys (BEEPS) conducted by the World Bank & European Bank for Reconstruction and Development, the well-known Corruption Perceptions Index (CPI) by Transparency International and the International Country Risk Guide (ICRG) created by the Political Risk Services (PRS) Group are all based on entrepreneurial and country expert surveys. Only the Control of Corruption Index (CCI), again created by the World Bank, and the Index of Economic Freedom (EFI) by the Heritage Foundation are based on a broader range of sources. However, all indices suffer from a similar set of deficiencies: since corruption is hard to be measured directly, the construction of an index that relies on beliefs and perceptions is subject to profound bias and measurement errors (see Apaza (2009), Chatterjee & Ray (2012), Donchev & Ujhelyi (2014)). What is more, there is some suspicion that such indices are overly unfavorable of the non-Western countries. These indices were created in developed Western countries and “particular interest groups try to measure corruption in their own justifiable way” (Ahmed & Ullah, 2014, p. 112).

Rather than solely relying on subjective perception-based data, corruption could also be measured using other more objective micro data. This approach has gained scholarly attention and is likely to be the most promising approach in the future to tackle the issue of measuring corruption. Several studies exist that analyzed corruption on a country-level using e.g. audit reports (Ferraz & Finan, 2011), combinations of local and aggregate data (Chatterjee & Ray, 2012), administrative data (Fazekas, et al., 2013), panel data on corruption convictions in combination with public sector wages (Alt & Lassen, 2014) or household spending and public sector assets (Gorodnichenko & Peter, 2007). Although difficult to obtain, the use of micro data allows overcoming the shortcomings associated with cross-country macro data. These efforts should be strengthened in the future.


Name / Authors

Covered Countries

Covered Years


Business Environment and Enterprise Performance Surveys (BEEPS)

29 (Focus on Eastern European and Asian countries)[1]

1999, 2002, 2005, 2008

Corruption Perceptions Index (CPI)

(Transparency International (2014))

1995 – 2014 (annually)

Control of Corruption Index (CCI)

215 (Kaufmann et al. (2014))

1996-2013 (annually)

International Country Risk Guide (ICRG)

140 (PRS Group (2014))

1980-2014 (monthly)

Index of Economic Freedom (EFI)

186 (cf. The Heritage Foundation (2014))

1984-2014 (annually)


Alt & Lassen (2014)

USA (covering 48 states)


Lin & Yu (2014)

13 (East and Southeast Asian countries)


Fazekas et al. (2013)



Chatterjee & Ray (2012)

18 (European countries)


Ferraz & Finan (2011)


Varying (audit reports from 2004 for 496 municipalities)

Gorodnichenko & Peter (2007)


1991, 1997-2003

Antecedents of Corruption

A wide range of factors are predictive of the spread of corruption. Over the past decades, research indicated that corruption is mediated by a variety of economic, legal and sociological factors such as abundance of resources (cf. Ades & Di Tella (1999)), burdensome bureaucracy (cf. Tanzi (1998)), lack of competition (cf. Shleifer & Vishny (1993)), political instability (cf. Treisman (2000)), poverty (cf. Gupta et al. (2002)), religion (cf. La Porta et al. (1999)), and wage inequality (cf. Rijckeghem & Weder (2001)).

In addition, latest research also added the extent of internet and eGovernment (cf. Andersen et al. (2011)), human development (cf. Sims et al. (2012)), as well as selective migration (cf. Dimant et al. (forthcoming)) to the picture (for an exhaustive list of empirical research on the antecedents of corruption please see the paper).

Paper reference

Eugen Dimant. The Antecedents and Effects of Corruption - A Reassessment of Current (Empirical) Findings. Munich Personal RePEc Archive (2014).


Ades, A. & Di Tella, R., 1999. Rents, competition, and corruption. American Economic Review, 89(4), pp. 982-994.

Ahmed, S. & Ullah, G., 2014. Global Corruption Hoax: Politicization of the Concept of Corruption and the Issues of Corruption Measurement Indices. Journal of Economics and Sustainable Development, 5(7), pp. 108-113.

Alt, J. & Lassen, D., 2014. Enforcement and Public Corruption: Evidence from the American States. Journal of Law, Economics, and Organization, 30(2), pp. 306-338.

Andersen, T., Bentzen, J., Dalgaard, C.-J. & Selaya, P., 2011. Does the Internet Reduce Corruption? Evidence from U.S. States and across Countries. The World Bank Economic Review, 25(3), pp. 387-417.

Apaza, C., 2009. Measuring Governance and Corruption through the Worldwide Governance Indicators: Critiques, Responses, and Ongoing Scholarly Discussion. PS: Political Science and Politics, 42(1), pp. 139-143.

Chatterjee, I. & Ray, R., 2012. Does the evidence on corruption depend on how it is measured? Results from a cross-country study on microdata sets. Applied Economics, Volume 44, pp. 3215-3227.

Dimant, E., Krieger, T. & Redlin, M., forthcoming. A crook is a crook...But is he still a crook abroad? On the effect of immigration on destination-country corruption. German Economic Review.

Donchev, D. & Ujhely, G., 2014. What Do Corruption Indices Measure?. Economics & Politics, 26(2), pp. 309-331.

Fazekas, M., Tóth, I. & King, L., 2013. Anatomy of grand corruption: A composite corruption risk index based on objective data, Budapest, Hungary: Corruption Research Center Budapest.

Ferraz, C. & Finan, F., 2011. Electoral Accountability and Corruption: Evidence from the Audits of Local Governments. American Economic Review, Volume 101, pp. 1274-1311.

Gorodnichenko, Y. & Peter, K., 2007. Public sector pay and corruption: Measuring bribery from micro data. Journal of Public Economics, Volume 91, pp. 963-991.

Gupta, S., Davoodi, H. & Alonso-Terme, R., 2002. Does corruption affect income inequality and poverty?. Economics of Governance, Volume 3, pp. 23-45.

Kaufmann, D., Kraay, A. & Mastruzzi, M., 2014. WGI 2014 Interactive > Home. [Online]
Available at:
[Accessed 16 12 2014].

La Porta, R., Lopez-De-Silanes, F., Schleifer, A. & Vishny, R., 1999. The quality of government. Journal of Law, Economics and Organization, Volume 15, pp. 222-279.

Lin, M.-W. & Yu, C., 2014. Can Corruption Be Masured? Comparing Global Versus Local Perceptions of Corruption in East and Southeast Asia. Journal of Comparative Policy Analysis, 16(2), pp. 140-157.

PRS Group, 2014. ICRG | The PRS Group. [Online]
Available at:
[Accessed 16 12 2014].

Rijckeghem, C. & Weder, B., 2001. Corruption and the rate of temptation: Do low wages in the civil service cause corruption?. Journal of Development Economics, 65(2), pp. 307-331.

Shleifer & Vishny, 1993. Corruption. The Quarterly Journal of Economics, 108(3), pp. 599-617.

Sims, R., Gong, B. & Ruppel, C., 2012. A contingency theory of corruption: The effect of human development and national culture. The Social Science Journal, Volume 49, pp. 90-97.

Tanzi, V., 1998. Corruption around the world - Causes, consequences, scope, and cures. IMF Staff Papers, 45(5), pp. 559-594.

The Heritage Foundation, 2014. 2014 Index of Economic Freedom | The Heritage Foundation. [Online]
Available at:
[Accessed 16 12 2014].

Transparency International, 2014. Corruption Perceptions Index 2014. [Online]
Available at:
[Accessed 16 12 2014].

Treisman, D., 2000. The causes of corruption: A cross-national study. Journal of Public Economics, Volume 76, pp. 399-457.

World Bank, 2010. EBRD-World Bank Business Environment and Enterprise Performance Suvey (BEEPS). [Online]
Available at:,,contentMDK:22609753~pagePK:146736~piPK:146830~theSitePK:258599,00.html
[Accessed 16 12 2014]. 

[1] In 2008 the methodology of the report was changed and Albania and Croatia were added to the sample. Comparisons to earlier years may be problematic (World Bank, 2010). 

[2] This list is not exhaustive but rather highlights the more recent micro-based approaches to measuring corruption. Further studies can be found in Fazekas et al. (2013, p. 5).

Author : Eugen Dimant

09 Apr 2015

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