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.
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A December 2014 survey by the Spanish Center for Sociological Research showed that 63.9% of Spanish citizens cited corruption as the country’s major problem.
The Nomenclature of Territorial Units for Statistics classification is a hierarchical system for dividing up the economic territory of the European Union.
Some of these measures are under study or are in the process of being implemented.
Aidt, T. S. (2003). Economic analysis of corruption: A survey. The Economic Journal, 113(491), F632–F652.
Aidt, T. S. (2009). Corruption, institutions, and economic development. Oxford Review of Economic Policy, 25(2), 271–291.
Albornoz, F., & Cabrales, A. (2013). Decentralization, political competition and corruption. Journal of Development Economics, 105, 103–111.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Cooray, A., & Schneider, F. (2013). How does corruption affect public debt? An empirical analysis. Working Paper: Department of Economics, Johannes Kepler University of Linz.
D’Agostino, G., Dunne, J. P., & Pieroni, L. (2016). Government spending, corruption and economic growth. World Development, 84, 190–205.
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.
Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224–227.
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.
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.
Diaby, A., & Sylwester, K. (2014). Bureaucratic competition and public corruption: Evidence from transition countries. European Journal of Political Economy, 35, 75–87.
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.
European Commission (2014). EU Anti-corruption Report. Brussels: European Commission.
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.
Ferejohn, J. (1986). Incumbent performance and electoral control. Public Choice, 50(1), 5–25.
Ferraz, C., & Finan, F. (2007). Electoral accountability and political corruption in local governments: Evidence from audit reports,” Working Paper: IZA.
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.
Gerring, J., & Thacker, S. C. (2005). Do neoliberal policies deter political corruption? International Organization, 59(1), 233–254.
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.
Grechyna, D. (2012). Public corruption and public debt: Some empirical evidence.
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.
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.
Hagenbuchner, M., & Tsoi, A. C. (2005). A supervised training algorithm for self-organizing maps for structures. Pattern Recognition Letters, 26(12), 1874–1884.
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.
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.
International Monetary Fund. (2016). Corruption: Costs and mitigating strategies. Discussion note: IMF Fiscal affairs department and legal department.
Ivanyna, M., Mourmouras, A., & Rangazas, P. (2010). The culture of corruption, tax evasion, and optimal tax policy. Working paper: International monetary fund.
Ivanyna, M., & Shah, A. (2011). Decentralization and corruption: New cross-country evidence. Environment and Planning C: Government and Policy, 29(2), 344–362.
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.
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.
Kaski, S., & Kohonen, T. (1994). Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7(6), 973–984.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. New York: Wiley.
Kaufmann, D., & Bellver, A. (2005). Transparency: Initial empirics and policy applications. Policy research working paper. Washington: World Bank.
Kaymak, T., & Bektas, E. (2015). Corruption in emerging markets: A multidimensional study. Social Indicators Research, 124(3), 785–805.
Knack, S., & Azfar, O. (2003). Trade intensity, country size and corruption. Economics of Governance, 4(1), 1–18.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.
Kohonen, T. (1993). Physiological interpretation of the self-organizing map algorithm. Neural Networks, 6(6), 895–905.
Kohonen, T. (2001). Self-organizing maps. Berlin: Springer.
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.
Kunieda, T., Okada, K., & Shibata, A. (2014). Corruption, capital account liberalization, and economic growth: Theory and evidence. International Economics, 139, 80–108.
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.
Leeson, P. T., & Sobel, R. S. (2008). Weathering corruption. Journal of Law and Economics, 51(4), 667–681.
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.
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.
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.
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.
Lo, Z., & Bavarian, B. (1993). Analysis of convergence properties of topology preserving neural networks. IEEE Transactions on Neural Networks, 11, 207–220.
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.
Mauro, P. (1998). Corruption and the composition of government expenditure. Journal of Public Economics, 69(2), 263–279.
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.
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.
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.
Nour, M. A. (1994). Improved clustering and classification algorithms for the Kohonen self-organizing neural network. Unpublished Ph.D. dissertation: Kent State University.
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.
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.
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.
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.
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.
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.
Pieroni, L., & d’Agostino, G. (2013). Corruption and the effects of economic freedom. European Journal of Political Economy, 29, 54–72.
Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter? Journal of Development Economics, 86(1), 91–111.
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.
Rende, S., & Donduran, M. (2013). Neighborhoods in development: Human development index and self-organizing maps. Social Indicators Research, 110(2), 721–734.
Saha, S., & Gounder, R. (2013). Corruption and economic development nexus: Variations across income levels in a non-linear framework. Economic Modelling, 31, 70–79.
Salinas-Jiménez, M. M., & Salinas-Jiménez, J. (2007). Corruption, efficiency and productivity in OECD countries. Journal of Policy Modeling, 29(6), 903–915.
Stockemer, D., & Calca, P. (2013). Corruption and turnout in Portugal—a municipal level study. Crime, Law and Social Change, 60(5), 535–548.
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.
Tavits, M. (2007). Clarity of responsibility and corruption. American Journal of Political Science, 51(1), 218–229.
Transparency International (2009). Global corruption report. Corruption and the private sector. Cambridge: Cambridge University Press. Transparency International. Ernst & Young.
Transparency International (2016). Global Corruption Barometer 2015/16: Transparency International.
Treisman, D. (2000). The causes of corruption: A cross-national study. Journal of Public Economics, 76(3), 399–457.
Treisman, D. (2002). Decentralization and the Quality of Government. UCLA manuscript.
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.
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.
Wu, Y., & Zhu, J. (2011). Corruption, anti-corruption, and inter-county income disparity in China. The Social Science Journal, 48(3), 435–448.
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.
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.
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López-Iturriaga, F.J., Sanz, I.P. Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces. Soc Indic Res 140, 975–998 (2018). https://doi.org/10.1007/s11205-017-1802-2
- Early warning system
- Neural networks
- Self-organizing maps