Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces

Article

Abstract

We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.

Keywords

Corruption Prediction Early warning system Neural networks Self-organizing maps 

JEL Classification

C45 D73 

Notes

Acknowledgements

We are grateful to Alisa Larson and Philip Jaggs for their comments on previous versions. All the remaining errors are the authors’ sole responsibility. We acknowledge the Spanish Ministry of Economy and Competitiveness for financial support (Project ECO2014-56102-P). This paper was prepared also within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program.

References

  1. Aidt, T. S. (2003). Economic analysis of corruption: A survey. The Economic Journal, 113(491), F632–F652.CrossRefGoogle Scholar
  2. Aidt, T. S. (2009). Corruption, institutions, and economic development. Oxford Review of Economic Policy, 25(2), 271–291.CrossRefGoogle Scholar
  3. Albornoz, F., & Cabrales, A. (2013). Decentralization, political competition and corruption. Journal of Development Economics, 105, 103–111.CrossRefGoogle Scholar
  4. Alt, J. E., & Lassen, D. D. (2003). The political economy of institutions and corruption in American states. Journal of Theoretical Politics, 15(3), 341–365.CrossRefGoogle Scholar
  5. Ambrey, C. L., Fleming, C. M., Manning, M., & Smith, C. (2016). On the confluence of freedom of the press, control of corruption and societal welfare. Social Indicators Research, 128(2), 859–880.  https://doi.org/10.1007/s11205-015-1060-0.CrossRefGoogle Scholar
  6. Benito, B., Guillamón, M.-D., & Bastida, F. (2015). Determinants of urban political corruption in local governments. Crime, Law and Social Change, 63(3–4), 191–210.CrossRefGoogle Scholar
  7. Besley, T., & Case, A. (1995). Does electoral accountability affect economic policy choices? Evidence from gubernatorial term limits. The Quarterly Journal of Economics, 110(3), 769–798.CrossRefGoogle Scholar
  8. Bouzid, B. N. (2016). Dynamic relationship between corruption and youth unemployment: Empirical evidences from a system GMM approach. World Bank Policy Research Working Paper: World Bank.Google Scholar
  9. Carboni, O. A., & Russu, P. (2015). Assessing regional wellbeing in Italy: An application of Malmquist–DEA and self-organizing map neural clustering. Social Indicators Research, 122(3), 677–700.CrossRefGoogle Scholar
  10. Cavoli, T., & Wilson, J. K. (2015). Corruption, central bank (in)dependence and optimal monetary policy in a simple model. Journal of Policy Modeling, 37(3), 501–509.  https://doi.org/10.1016/j.jpolmod.2015.03.012.CrossRefGoogle Scholar
  11. Chen, G., Jaradat, S. A., Banerjee, N., Tanaka, T. S., Ko, M. S., & Zhang, M. Q. (2002). Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data. Statistica Sinica, 12(1), 241–262.Google Scholar
  12. Clausen, B., Kraay, A., & Nyiri, Z. (2011). Corruption and confidence in public institutions: Evidence from a global survey. The World Bank Economic Review, 25(2), 212–249.CrossRefGoogle Scholar
  13. Cooray, A., & Schneider, F. (2013). How does corruption affect public debt? An empirical analysis. Working Paper: Department of Economics, Johannes Kepler University of Linz.Google Scholar
  14. D’Agostino, G., Dunne, J. P., & Pieroni, L. (2016). Government spending, corruption and economic growth. World Development, 84, 190–205.CrossRefGoogle Scholar
  15. Damania, R., Fredriksson, P. G., & Mani, M. (2004). The persistence of corruption and regulatory compliance failures: Theory and evidence. Public Choice, 121(3–4), 363–390.CrossRefGoogle Scholar
  16. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224–227.CrossRefGoogle Scholar
  17. de Figueiredo, J. N. (2013). Are corruption levels accurately identified? The case of U.S. states. Journal of Policy Modeling, 35(1), 134–149.  https://doi.org/10.1016/j.jpolmod.2012.01.006.CrossRefGoogle Scholar
  18. Del Monte, A., & Papagni, E. (2007). The determinants of corruption in Italy: Regional panel data analysis. European Journal of Political Economy, 23(2), 379–396.CrossRefGoogle Scholar
  19. Diaby, A., & Sylwester, K. (2014). Bureaucratic competition and public corruption: Evidence from transition countries. European Journal of Political Economy, 35, 75–87.CrossRefGoogle Scholar
  20. Dong, B., & Torgler, B. (2013). Causes of corruption: Evidence from China. China Economic Review, 26, 152–169.  https://doi.org/10.1016/j.chieco.2012.09.005.CrossRefGoogle Scholar
  21. European Commission (2014). EU Anti-corruption Report. Brussels: European Commission.Google Scholar
  22. Fan, C. S., Lin, C., & Treisman, D. (2009). Political decentralization and corruption: Evidence from around the world. Journal of Public Economics, 93(1–2), 14–34.  https://doi.org/10.1016/j.jpubeco.2008.09.001.CrossRefGoogle Scholar
  23. Ferejohn, J. (1986). Incumbent performance and electoral control. Public Choice, 50(1), 5–25.CrossRefGoogle Scholar
  24. Ferraz, C., & Finan, F. (2007). Electoral accountability and political corruption in local governments: Evidence from audit reports,” Working Paper: IZA.Google Scholar
  25. Fisman, R., & Gatti, R. (2002). Decentralization and corruption: Evidence across countries. Journal of Public Economics, 83(3), 325–345.  https://doi.org/10.1016/S0047-2727(00)00158-4.CrossRefGoogle Scholar
  26. Gerring, J., & Thacker, S. C. (2005). Do neoliberal policies deter political corruption? International Organization, 59(1), 233–254.CrossRefGoogle Scholar
  27. Goel, R. K., Nelson, M. A., & Naretta, M. A. (2012). The internet as an indicator of corruption awareness. European Journal of Political Economy, 28(1), 64–75.CrossRefGoogle Scholar
  28. Grechyna, D. (2012). Public corruption and public debt: Some empirical evidence.Google Scholar
  29. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417–426.  https://doi.org/10.1016/j.ejor.2015.05.050.CrossRefGoogle Scholar
  30. Habib, M., & Leon, Z. (2002). Corruption and foreign direct investment. Journal of International Business Studies, 33(2), 291–307.  https://doi.org/10.2307/3069545.CrossRefGoogle Scholar
  31. Hagenbuchner, M., & Tsoi, A. C. (2005). A supervised training algorithm for self-organizing maps for structures. Pattern Recognition Letters, 26(12), 1874–1884.CrossRefGoogle Scholar
  32. Hagenbuchner, M., Tsoi, A. C., & Sperduti, A. (2001). A supervised self-organizing map for structured data. In N. Allinson, H. Yin, L. Allinson, & J. Slack (Eds.), Advances in self organising maps (pp. 21–28). London: Springer.CrossRefGoogle Scholar
  33. Huysmans, J., Martens, D., Baesens, B., Vanthienen, J., & Van Gestel, T. (2006). Country corruption analysis with self organizing maps and support vector machines. In H. Chen, F.-Y. Wang, C. Yang, D. Zeng, M. Chau, & K. Chang (Eds.), Intelligence and security informatics (Vol. 3917, pp. 103–114)., Lecture notes in computer science Berlin: Springer.CrossRefGoogle Scholar
  34. International Monetary Fund. (2016). Corruption: Costs and mitigating strategies. Discussion note: IMF Fiscal affairs department and legal department.Google Scholar
  35. Ivanyna, M., Mourmouras, A., & Rangazas, P. (2010). The culture of corruption, tax evasion, and optimal tax policy. Working paper: International monetary fund.Google Scholar
  36. Ivanyna, M., & Shah, A. (2011). Decentralization and corruption: New cross-country evidence. Environment and Planning C: Government and Policy, 29(2), 344–362.CrossRefGoogle Scholar
  37. Jagric, T., Bojnec, S., & Jagric, V. (2015). Optimized spiral spherical self-organizing map approach to sector analysis—the case of banking. Expert Systems with Applications, 42(13), 5531–5540.  https://doi.org/10.1016/j.eswa.2015.03.002.CrossRefGoogle Scholar
  38. Kang, Q., Liu, S., Zhou, M., & Li, S. (2016). A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence. Knowledge-Based Systems, 104, 156–164.  https://doi.org/10.1016/j.knosys.2016.04.021.CrossRefGoogle Scholar
  39. Kaski, S., & Kohonen, T. (1994). Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7(6), 973–984.CrossRefGoogle Scholar
  40. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. New York: Wiley.Google Scholar
  41. Kaufmann, D., & Bellver, A. (2005). Transparency: Initial empirics and policy applications. Policy research working paper. Washington: World Bank.Google Scholar
  42. Kaymak, T., & Bektas, E. (2015). Corruption in emerging markets: A multidimensional study. Social Indicators Research, 124(3), 785–805.CrossRefGoogle Scholar
  43. Knack, S., & Azfar, O. (2003). Trade intensity, country size and corruption. Economics of Governance, 4(1), 1–18.CrossRefGoogle Scholar
  44. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.CrossRefGoogle Scholar
  45. Kohonen, T. (1993). Physiological interpretation of the self-organizing map algorithm. Neural Networks, 6(6), 895–905.CrossRefGoogle Scholar
  46. Kohonen, T. (2001). Self-organizing maps. Berlin: Springer.CrossRefGoogle Scholar
  47. Kong, D. T., & Volkema, R. (2016). Cultural endorsement of broad leadership prototypes and wealth as predictors of corruption. Social Indicators Research, 127(1), 139–152.CrossRefGoogle Scholar
  48. Kunieda, T., Okada, K., & Shibata, A. (2014). Corruption, capital account liberalization, and economic growth: Theory and evidence. International Economics, 139, 80–108.CrossRefGoogle Scholar
  49. Kuo, R. J., Ho, L. M., & Hu, C. M. (2002). Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers & Operations Research, 29(11), 1475–1493.CrossRefGoogle Scholar
  50. Leeson, P. T., & Sobel, R. S. (2008). Weathering corruption. Journal of Law and Economics, 51(4), 667–681.CrossRefGoogle Scholar
  51. León, C. J., Araña, J. E., & de León, J. (2013). Correcting for scale perception bias in measuring corruption: An application to Chile and Spain. Social Indicators Research, 114(3), 977–995.CrossRefGoogle Scholar
  52. Li, H., Gong, T., & Xiao, H. (2016). The perception of anti-corruption efficacy in China: An empirical analysis. Social Indicators Research, 125(3), 885–903.CrossRefGoogle Scholar
  53. Li, X., & Juhola, M. (2014). Country crime analysis using the self-organizing map, with special regard to demographic factors. AI & Society, 29(1), 53–68.CrossRefGoogle Scholar
  54. Li, X., & Juhola, M. (2015). Country crime analysis using the self–organising map, with special regard to economic factors. International Journal of Data Mining, Modelling and Management, 7(2), 130–153.CrossRefGoogle Scholar
  55. Lo, Z., & Bavarian, B. (1993). Analysis of convergence properties of topology preserving neural networks. IEEE Transactions on Neural Networks, 11, 207–220.CrossRefGoogle Scholar
  56. Lucchini, M., & Assi, J. (2013). Mapping patterns of multiple deprivation and well-being using self-organizing maps: An application to swiss household panel data. Social Indicators Research, 112(1), 129–149.CrossRefGoogle Scholar
  57. Mauro, P. (1998). Corruption and the composition of government expenditure. Journal of Public Economics, 69(2), 263–279.CrossRefGoogle Scholar
  58. Moreno, D., Marco, P., & Olmeda, I. (2006). Self-organizing maps could improve the classification of Spanish mutual funds. European Journal of Operational Research, 174(2), 1039–1054.CrossRefGoogle Scholar
  59. Nguyen, T. H. (2006). Tax corruption, public debt and the policy interaction in emerging economies. Paper presented at the Vietnam Development Forum (VDF), Tokyo, Japan.Google Scholar
  60. Nguyen, T. T., & van Dijk, M. A. (2012). Corruption, growth, and governance: Private vs. state-owned firms in Vietnam. Journal of Banking & Finance, 36(11), 2935–2948.  https://doi.org/10.1016/j.jbankfin.2012.03.027.CrossRefGoogle Scholar
  61. Nour, M. A. (1994). Improved clustering and classification algorithms for the Kohonen self-organizing neural network. Unpublished Ph.D. dissertation: Kent State University.Google Scholar
  62. Nwabuzor, A. (2005). Corruption and development: New initiatives in economic openness and strengthened rule of law. Journal of Business Ethics, 59(1–2), 121–138.CrossRefGoogle Scholar
  63. Olken, B. A. (2007). Monitoring corruption: Evidence from a field experiment in Indonesia. Journal of Political Economy, 115(2), 200–249.  https://doi.org/10.1016/j.jpubeco.2009.03.001.CrossRefGoogle Scholar
  64. Olken, B. A. (2009). Corruption perceptions vs. corruption reality. Journal of Public Economics, 93(7–8), 950–964.  https://doi.org/10.1016/j.jpubeco.2009.03.001.CrossRefGoogle Scholar
  65. Olszewski, D. (2014). Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems, 70, 324–334.  https://doi.org/10.1016/j.knosys.2014.07.008.CrossRefGoogle Scholar
  66. Ortega, B., Casquero, A., & Sanjuán, J. (2016). Corruption and convergence in human development: Evidence from 69 countries during 1990–2012. Social Indicators Research, 127(2), 691–719.  https://doi.org/10.1007/s11205-015-0968-8.CrossRefGoogle Scholar
  67. Pellegata, A., & Memoli, V. (2016). Can corruption erode confidence in political institutions among European countries? Comparing the effects of different measures of perceived corruption. Social Indicators Research, 128(1), 391–412.CrossRefGoogle Scholar
  68. Pieroni, L., & d’Agostino, G. (2013). Corruption and the effects of economic freedom. European Journal of Political Economy, 29, 54–72.CrossRefGoogle Scholar
  69. Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter? Journal of Development Economics, 86(1), 91–111.CrossRefGoogle Scholar
  70. Rehman, H. U., & Naveed, A. (2007). Determinants of corruption and its relation to GDP (A panel study). Journal of Political Studies, 12(2), 27–59.Google Scholar
  71. Rende, S., & Donduran, M. (2013). Neighborhoods in development: Human development index and self-organizing maps. Social Indicators Research, 110(2), 721–734.CrossRefGoogle Scholar
  72. Saha, S., & Gounder, R. (2013). Corruption and economic development nexus: Variations across income levels in a non-linear framework. Economic Modelling, 31, 70–79.CrossRefGoogle Scholar
  73. Salinas-Jiménez, M. M., & Salinas-Jiménez, J. (2007). Corruption, efficiency and productivity in OECD countries. Journal of Policy Modeling, 29(6), 903–915.CrossRefGoogle Scholar
  74. Stockemer, D., & Calca, P. (2013). Corruption and turnout in Portugal—a municipal level study. Crime, Law and Social Change, 60(5), 535–548.CrossRefGoogle Scholar
  75. Swiderski, B., Kurek, J., & Osowski, S. (2012). Multistage classification by using logistic regression and neural networks for assessment of financial condition of company. Decision Support Systems, 52(2), 539–547.  https://doi.org/10.1016/j.dss.2011.10.018.CrossRefGoogle Scholar
  76. Tavits, M. (2007). Clarity of responsibility and corruption. American Journal of Political Science, 51(1), 218–229.CrossRefGoogle Scholar
  77. Transparency International (2009). Global corruption report. Corruption and the private sector. Cambridge: Cambridge University Press. Transparency International. Ernst & Young.Google Scholar
  78. Transparency International (2016). Global Corruption Barometer 2015/16: Transparency International.Google Scholar
  79. Treisman, D. (2000). The causes of corruption: A cross-national study. Journal of Public Economics, 76(3), 399–457.CrossRefGoogle Scholar
  80. Treisman, D. (2002). Decentralization and the Quality of Government. UCLA manuscript.Google Scholar
  81. Treisman, D. (2007). What have we learned about the causes of corruption from ten years of cross-national empirical research? Annual Review of Political Science, 10, 211–214.CrossRefGoogle Scholar
  82. Van Rijckeghem, C., & Weder, B. (2001). Bureaucratic corruption and the rate of temptation: Do wages in the civil service affect corruption, and by how much? Journal of Development Economics, 65(2), 307–331.CrossRefGoogle Scholar
  83. Wu, Y., & Zhu, J. (2011). Corruption, anti-corruption, and inter-county income disparity in China. The Social Science Journal, 48(3), 435–448.CrossRefGoogle Scholar
  84. Zheng, W.-W., Liu, L., Huang, Z.-W., & Tan, X.-Y. (2017). Life satisfaction as a buffer of the relationship between corruption perception and political participation. Social Indicators Research, 132(2), 907–923.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of Business and EconomicsUniversity of ValladolidValladolidSpain
  2. 2.Higher School of EconomicsMoscowRussia

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