Social Indicators Research

, Volume 142, Issue 1, pp 311–341 | Cite as

Modelling Corruption Perceptions: Evidence from Eastern Europe and Central Asian Countries

  • Giorgio d’AgostinoEmail author
  • Luca Pieroni


This work proposes a multidimensional framework that is based on a latent class model to identify various types of corruption and to outline their importance. A dataset of Eastern European and Central Asian countries is used to identify four groups of corrupt activities, which go beyond the usual classification of corruption into administrative and political corruption. Our estimates are validated by means of a direct administrative corruption index that is derived from the same dataset and also by a comparison with the corruption perception rankings that are published by Transparency International. The potential of the proposed approach is illustrated with an application to the relationship between firms’ competitiveness and the latent classes of corruption that we have identified.


Corruption Eastern Europe and Central Asian economies Latent class models Multidimensional item response theory Firm competitiveness 

JEL Classification

C51 C52 D22 D73 


  1. Ades, A., & Di Tella, R. (1997). The new economics of corruption: A survey and some new results. Political Studies, 45(3), 496–515.Google Scholar
  2. Ades, A., & Di Tella, R. (1999). Rents, competition, and corruption. American Economic Review, 89(4), 982–993.Google Scholar
  3. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In 2nd international symposium on information theory (pp. 267–281). Budapest: Akademiai Ki- ado.Google Scholar
  4. Anderson, J. H., & Gray, C. W. (2006). Anticorruption in transition 3: Who is succeeding... and Why?. Washington DC: World Bank.Google Scholar
  5. Andrews, R. L., & Currim, I. S. (2003). A comparison of segment retention criteria for finite mixture logit models. Journal of Marketing Research, 40, 235–243.Google Scholar
  6. Banfield, J. D., & Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49(3), 803–821.Google Scholar
  7. Bardhan, P. (1997). Corruption and development: A review of issues. Journal of Economic Literature, 35(3), 1320–1346.Google Scholar
  8. Bardhan, P. (2006). The economist’s approach to the problem of corruption. World Development, 34(2), 341–348.Google Scholar
  9. Bartolucci, F. (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72(2), 141–157.Google Scholar
  10. Bartolucci, F., d’Agostino, G., & Montanari, G. E. (2010). An investigation of the discriminant power and dimensionality of items used for assessing health condition of elderly people. Technical Report. arXiv:1008.3268v1 [stat.AP].
  11. Bartolucci, F., Montanari, G. E., & Pandolfi, S. (2012). Item selection by an extended latent class model: An application to nursing homes evaluation. SSRN: Technical Report.Google Scholar
  12. Biernacki, C., & Govaert, G. (1998). Choosing models in model-based clustering and discriminant analysis. Institut National de Recherche en Informatique et en Automatique: Technical Report.Google Scholar
  13. Bliss, C., & Di Tella, R. (1997). Does competition kill corruption? Journal of Political Economy, 105(5), 1001–1023.Google Scholar
  14. Bock, R. (1997). A brief history of item response theory. Educational Measurement: Issues and Practice, 16, 21–23.Google Scholar
  15. Cappellari, L., & Jenkins, S. P. (2006). Summarising multiple deprivation indicators. ISER Working Paper Series 2006-40. Institute for Social and Economic Research.Google Scholar
  16. Clarke, G. R. G., & Xu, L. C. (2002). Ownership, competition, and corruption: Bribe takers versus bribe payers. Policy Research Working Paper Series 2783. The World Bank.Google Scholar
  17. d’Agostino, G., Dunne, J. P., & Pieroni, L. (2016a). Government spending. Corruption and Economic Growth, World Development, 84, 190–205.Google Scholar
  18. d’Agostino, G., Dunne, J. P., & Pieroni, L. (2016b). Corruption and growth in Africa. European Journal of Political Economy, 43, 71–88.Google Scholar
  19. Delavallade, C. (2011). What drives corruption? Evidence from north African firms. Working Papers 244. Economic Research Southern Africa.Google Scholar
  20. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Google Scholar
  21. Dimitrova-Grajzl, V., Grajzl, P., & Guse, A. J. (2012). Trust, perceptions of corruption, and demand for regulation: Evidence from post-socialist countries. Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), 41(3), 292–303.Google Scholar
  22. Dreher, A., Kotsogiannis, C., & McCorriston, S. (2007). Corruption around the world: Evidence from a structural model. Journal of Comparative Economics., 35(3), 443–466.Google Scholar
  23. Faye, O., de Laat, J., & Zulu, E. (2009). Poverty dynamics and mobility in nairobi’s informal settlements. APHRC: Technical Report.Google Scholar
  24. Ferraz, C., & Finan, F. (2008). Exposing corrupt politicians: The effects of Brazil’s publicly released audits on electoral outcomes. Quarterly Journal of Economics, 123(2), 703–745.Google Scholar
  25. Gauthier, B., & Reinikka, R. (2001). Shifting tax burdens through exemptions and evasion—An empirical investigation of Uganda. Policy Research Working Paper Series 2735. The World Bank.Google Scholar
  26. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.Google Scholar
  27. Goodman, L. A. (1978). Analysing qualitative/categorical data log-linear models and latent-structure analysis. Boston: Addison-Wesley Pub. Co.Google Scholar
  28. Gorodnichenko, Y., & Sabirianova-Peter, K. (2007). Public sector pay and corruption: Measuring bribery from micro data. Journal of Public Economics, 91(5–6), 963–991.Google Scholar
  29. Hellman, J. S., Jones G., & Kaufmann, D. (2000). Seize the state, seize the day: State capture, corruption and influence in transition. Technical Report 2444. World Bank Policy Research Working Paper.Google Scholar
  30. Hellman, J. S., Jones, G., & Kaufmann, D. (2003). Seize the state, seize the day: State capture and influence in transition economies. Journal of Comparative Economics, 31(4), 751–773.Google Scholar
  31. Herzfeld, T., & Weiss, C. (2003). Corruption and legal (in)effectiveness: An empirical investigation. European Journal of Political Economy, 19(3), 621–632.Google Scholar
  32. Johnson, S., Kaufmann, D., & Zoido-Lobaton, P. (1999). Corruption, public finances, and the unofficial economy. Policy Research Working Paper Series 2169. The World Bank.Google Scholar
  33. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2007). Measuring corruption : Myths and realities. In Africa region findings & Good practice infobriefs (no. 273). Washington, DC: World Bank.Google Scholar
  34. Knack, S., & Keefer, P. (1995). Institutions and economic performance: Cross-country tests using alternative measures. Economics and Politics, 7, 207–227.Google Scholar
  35. Knack, S. (2007). Measuring corruption: A critique of indicators in Eastern Europe and Central Asia. Journal of Public Policy, 27(3), 255–291.Google Scholar
  36. Kuklys, W. (2004). Measuring standard of living in the UK—An application of sen’s functioning approach using structural equation models. Papers on strategic interaction 2004–11. Max Planck Institute of Economics, Strategic Interaction Group.Google Scholar
  37. Lambsdorff, J. G. (2007). The institutional economics of corruption and reform: Theory, evidence and policy. Cambridge: Cambridge University Press.Google Scholar
  38. Mauro, P. (1995). Corruption and growth. Quarterly Journal of Economics, 110(3), 681–712.Google Scholar
  39. Magidson, J., & Vermunt, J. K. (2001). Latent class factor and cluster models, bi-plots, and related graphical displays. Sociological Methodology, 31, 223–264.Google Scholar
  40. Mocan, N. (2008). What determines corruption? International evidence from microdata. Economic Inquiry, 46(4), 493–510.Google Scholar
  41. Nowak, R. (2001). Corruption and transition economies. Technical Report. Presented to the preparatory seminar for the 9th OCSE economic forum. BucharestGoogle Scholar
  42. Olken, B. A. (2009). Corruption perceptions vs. corruption reality. Journal of Public Economics, 93(7–8), 950–964.Google Scholar
  43. Pieroni, L., & d’Agostino, G. (2013). Corruption and the effects of economic freedom. European Journal of Political Economy, 29(C), 54–72.Google Scholar
  44. Rasch, G. (1961). On general laws and the meaning of measurement in psychology. Berkeley Symposium on Mathematical Statistics and Probability, 4, 321–333.Google Scholar
  45. Reinikka, R., & Svensson, J. (2006). Using micro-surveys to measure and explain corruption. World Development, 34(2), 359–370.Google Scholar
  46. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.Google Scholar
  47. Scott, J. C. (1972). Comparative political corruption. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  48. Seligson, M. A. (2006). The measurement and impact of corruption victimization: Survey evidence from Latin America. World Development, 34(2), 381–404.Google Scholar
  49. Svensson, J. (2003). Who must pay bribes and how much? Evidence from a cross section of firms. The Quarterly Journal of Economics, 118(1), 207–230.Google Scholar
  50. Svensson, J. (2005). Eight questions about corruption. Journal of Economic Perspectives, 19(3), 19–42.Google Scholar
  51. Tanzi, V. (1998). Corruption around the world: Causes, consequences, scope, and cures. IMF Working Papers 98/63. International Monetary Fund.Google Scholar
  52. Warren, E. M. (2004). What does corruption mean in a democracy? American Journal of Political Science, 48(2), 328–343.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EconomicsRoma Tre UniversityRomeItaly
  2. 2.Department of Political ScienceUniversity of PerugiaPerugiaItaly

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