Vol. 40 No. 1 (2025): 79-vol. 40, núm. 1, enero-junio 2025
Articles

Determinants of corruption in Latin America: A country-level analysis with a Bayesian approach

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  • Héctor Flores Márquez,
  • Adrián Jiménez Gómez
Héctor Flores Márquez
Benemérita Universidad Autónoma de Puebla
Adrián Jiménez Gómez
Benemérita Universidad Autónoma de Puebla
DOI: https://doi.org/10.24201/ee.v40i1.e459

Published 2025-03-26

Keywords

  • Corruption,
  • Bayesian models,
  • Latin America,
  • IVBMA

How to Cite

Flores Márquez, H., & Jiménez Gómez, A. (2025). Determinants of corruption in Latin America: A country-level analysis with a Bayesian approach. Estudios Económicos De El Colegio De México, 40(1), 1–38. https://doi.org/10.24201/ee.v40i1.e459

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Abstract

Corruption is a social phenomenon that has a profound effect in the Latin American region, for this reason, it is proposed to find the causes that contribute to its development. The Bayesian Model Averaging with Instrumental Variables (IVBMA) methodology is used to find robust determinants of corruption in 19 Latin American countries. 23 regressors are considered with observations from 2013 to 2020. The IVBMA carries out 8,388,608 models, in order to extract the most robust determinants. It is revealed that institutional and economic elements are better predictors of corruption in the region.

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References

  1. Acemoglu, D., S. Johnson, J.A. Robinson y P. Yared. 2008. Income and democracy, American Economic Review, 98(3): 808-842. DOI: https://doi.org/10.1257/aer.98.3.808
  2. Acemoglu, D. y T. Verdier. 1998. Property rights, corruption, and the allocation of talent: A general equilibrium approach, The Economic Journal, 108(450): 1381-1403. DOI: https://doi.org/10.1111/1468-0297.00347
  3. Apergis, N., O. Dincer y J. Payne. 2010. The relationship between corruption and income inequality in US States: Evidence from a panel cointegration and error correction model, Public Choice, 145(1-2): 125-135. DOI: https://doi.org/10.1007/s11127-009-9557-1
  4. Alesina, A., A. Devleeschauwer, W. Easterly, S. Kurlat y R. Wacziarg. 2003. Fractionalization, Journal of Economic Growth, 8: 155-194. DOI: https://doi.org/10.1023/A:1024471506938
  5. Arikan, G.G. 2004. Fiscal decentralization: A remedy for corruption?, International Tax and Public Finance, 11(2): 175-195. DOI: https://doi.org/10.1023/B:ITAX.0000011399.00053.a1
  6. Arin, K.P., E. Braunfels y G. Doppelhofer. 2019. Revisiting the growth effects of fiscal policy: A Bayesian model averaging approach, Journal of Macroeconomics, 62: 1-16. DOI: https://doi.org/10.1016/j.jmacro.2019.103158
  7. Barr, A. y D. Serra. 2010. Corruption and culture: An experimental analysis, Journal of Public Economics, 94(11): 862-869. DOI: https://doi.org/10.1016/j.jpubeco.2010.07.006
  8. Bhattacharyya, S. y R. Hodler. 2010. Natural resources, democracy and corruption, European Economic Review, 54(4): 608-621. DOI: https://doi.org/10.1016/j.euroecorev.2009.10.004
  9. Billger, S.M. y R.K. Goel. 2009. Do existing corruption levels matter in controlling corruption? Cross-country quantile regression estimates, Journal of Development Economics, 90(2): 299-305. DOI: https://doi.org/10.1016/j.jdeveco.2008.07.006
  10. Blażejowski, M., J. Kwiatkowski y J. Gazda. 2016. Bayesian model averaging in the studies on economic growth in the EU regions – Application of the Gretl BMA package, Economics and Sociology, 9(4): 168-175. DOI: https://doi.org/10.14254/2071-789X.2016/9-4/10
  11. Blażejowski, M., J. Kwiatkowski y J. Gazda. 2019. Sources of economic growth: A global perspective, Sustainability, 11(1): 1-14. DOI: https://doi.org/10.3390/su11010275
  12. Brademas, J. 2005. Civil Society and Corruption: Mobilizing for Reform, University Press of America.
  13. Braun, M. y R. Di Tella. 2004. Inflation, inflation variability, and corruption, Economics and Politics, 16(1): 77-100. DOI: https://doi.org/10.1111/j.1468-0343.2004.00132.x
  14. Brock, W A. y S.N. Durlauf. 2001. What have we learned from a decade of empirical research on growth? Growth empirics and reality, The World Bank Economic Review, 15(2): 229-272. DOI: https://doi.org/10.1093/wber/15.2.229
  15. Brosig-Koch, J., C. Helbach, A. Ockenfels y J. Weimann. 2011. Still different after all these years: Solidarity behavior in East and West Germany, Journal of Public Economics, 95(11): 1373-1376. DOI: https://doi.org/10.1016/j.jpubeco.2011.06.002
  16. Brunetti, A. y B. Weder. 2003. A free press is bad news for corruption, Journal of Public Economics, 87(7): 1801-1824. DOI: https://doi.org/10.1016/S0047-2727(01)00186-4
  17. Castañeda, V.M. 2016. Una investigación sobre la corrupción pública y sus determinantes, Revista Mexicana de Ciencias Políticas y Sociales, 61(227): 103-135. DOI: https://doi.org/10.1016/S0185-1918(16)30023-X
  18. Chen, L. y A.K. Aklikokou. 2021. Relating e-government development to government effectiveness and control of corruption: A cluster analysis, Journal of Chinese Governance, 6(1): 155-173. DOI: https://doi.org/10.1080/23812346.2019.1698693
  19. D'Agostino, G., J.P. Dunne y L. Pieroni. 2016. Corruption and growth in Africa, European Journal of Political Economy, 43: 71-88. DOI: https://doi.org/10.1016/j.ejpoleco.2016.03.002
  20. DeGroot, M.H. 1970. Optimal Statistical Decisions, Nueva York, McGraw Hill.
  21. De Viteri, A.S. y C. Bjørnskov. 2020. Constitutional power concentration and corruption: Evidence from Latin America and the Caribbean, Constitutional Political Economy, 31(4): 509-536. DOI: https://doi.org/10.1007/s10602-020-09317-3
  22. Dreher, A., C. Kotsogiannis y S. McCorriston. 2009. How do institutions affect corruption and the shadow economy?, International Tax and Public Finance, 16(6): 773-796. DOI: https://doi.org/10.1007/s10797-008-9089-5
  23. Durlauf, S.N., A. Kourtellos y C.N. Tan. 2012. Is God in the details? A reexamination of the role of religion in economic growth, Journal of Applied Econometrics, 27(7): 1059-1075. DOI: https://doi.org/10.1002/jae.1245
  24. Eicher, T.S. y D. J. Kuenzel. 2016. The elusive effects of trade on growth: Export diversity and economic take‐off, Canadian Journal of Economics, 49(1): 264-295. DOI: https://doi.org/10.1111/caje.12197
  25. Eicher, T.S., C. Henn y C. Papageorgiou. 2012. Trade creation and diversion revisited: Accounting for model uncertainty and natural trading partner effects, Journal of Applied Econometrics, 27(2): 296–321. DOI: https://doi.org/10.1002/jae.1198
  26. Eicher, T.S., A. Lenkoski y A.E. Raftery. 2009. Bayesian model averaging and endogeneity under model uncertainty: An application to development determinants, Working Paper No. 94, Center for Statistics and the Social Sciences University of Washington.
  27. Elbahnasawy, N.G. y C.F. Revier. 2012. The determinants of corruption: Cross‐country panel data analysis, The Developing Economies, 50(4): 311-333. DOI: https://doi.org/10.1111/j.1746-1049.2012.00177.x
  28. Epstein, G.S. y I.N. Gang. 2019. Inequality, good governance, and endemic corruption, International Tax and Public Finance, 26(5): 999-1017. DOI: https://doi.org/10.1007/s10797-019-09542-z
  29. Estrella, A. y F.S. Mishkin. 1998. Predicting US recessions: Financial variables as leading indicators, Review of Economics and Statistics, 80(1): 45-61. DOI: https://doi.org/10.1162/003465398557320
  30. Fernández, C., E. Ley y M.F. Steel. 2001. Benchmark priors for Bayesian model averaging, Journal of Econometrics, 100(2): 381-427. DOI: https://doi.org/10.1016/S0304-4076(00)00076-2
  31. Fisman, R. y E. Miguel. 2007. Corruption, norms, and legal enforcement: Evidence from diplomatic parking tickets, Journal of Political Economy, 115(6): 1020-1048. DOI: https://doi.org/10.1086/527495
  32. Freille, S., M. E. Haque y R. Kneller. 2007. A contribution to the empirics of press freedom and corruption, European Journal of Political Economy, 23(4): 838-862. DOI: https://doi.org/10.1016/j.ejpoleco.2007.03.002
  33. Garay, L.J. y E. Salcedo. 2018. El Gran Libro de la Corrupción en Colombia, Bogotá, Planeta.
  34. Gatti, R. 2004. Explaining corruption: Are open countries less corrupt?, Journal of International Development, 16(6): 851-861. DOI: https://doi.org/10.1002/jid.1115
  35. Glass, A. y X. Wu. 2002. Does corruption discourage foreign direct investment and innovation?, documento sin publicar.
  36. Gnimassoun, B. 2015. The importance of the exchange rate regime in limiting current account imbalances in sub-Saharan African countries, Journal of International Money and Finance, 53: 36-74. DOI: https://doi.org/10.1016/j.jimonfin.2014.12.012
  37. Grove, W.A., A. Hussey y M. Jetter. 2011. The gender pay gap beyond human capital: Heterogeneity in noncognitive skills and in labor market tastes, Journal of Human Resources, 46(4): 827-874. DOI: https://doi.org/10.1353/jhr.2011.0003
  38. Gupta, S., H. Davoodi y R. Alonso-Terme. 2002. Does corruption affect income inequality and poverty?, Economics of Governance, 3(1): 23-45. DOI: https://doi.org/10.1007/s101010100039
  39. Hamdi, H. y A. Hakimi. 2020. Corruption, FDI, and growth: An empirical investigation into the Tunisian context, The International Trade Journal, 34(4): 415-440. DOI: https://doi.org/10.1080/08853908.2019.1699481
  40. Hoeting, J.A., D. Madigan y A.E. Raftery. 1997. Bayesian model averaging for linear regression models, Journal of the American Statistical Association, 92(437): 179-191. DOI: https://doi.org/10.1080/01621459.1997.10473615
  41. Hoeting, J. A., D. Madigan, A.E. Raftery y C.T. Volinsky. 1999. Bayesian model averaging: A tutorial, Statistical Science, 14(2): 382-401. DOI: https://doi.org/10.1214/ss/1009212519
  42. Horváth, R. 2013. Does trust promote growth?, Journal of Comparative Economics, 41(3): 777-788. DOI: https://doi.org/10.1016/j.jce.2012.10.006
  43. Iwasaki, I. y T. Suzuki. 2012. The determinants of corruption in transition economies, Economics Letters, 114(1): 54-60. DOI: https://doi.org/10.1016/j.econlet.2011.08.016
  44. Jeffreys, H. 1998. The Theory of Probability, Oxford, Oxford University Press. DOI: https://doi.org/10.1093/oso/9780198503682.001.0001
  45. Jetter, M. y C.F. Parmeter. 2015. Trade openness and bigger governments: The role of country size revisited, European Journal of Political Economy, 37: 49-63. DOI: https://doi.org/10.1016/j.ejpoleco.2014.11.001
  46. Jetter, M. y C.F. Parmeter. 2018. Sorting through global corruption determinants: Institutions and education matter-not culture, World Development, 109: 279-294. DOI: https://doi.org/10.1016/j.worlddev.2018.05.013
  47. Jong-Sung, Y. y S. Khagram. 2005. A comparative study of inequality and corruption, American Sociological Review, 70(1): 136-157. DOI: https://doi.org/10.1177/000312240507000107
  48. Karl, A. y A. Lenkoski. 2012. Instrumental variable Bayesian model averaging via conditional Bayes factors, ArXiv Preprint, arXiv:1202.5846.
  49. Kass, R.E. y A.E. Raftery. 1995. Bayes factors, Journal of the American Statistical Association, 90(430): 773-795. DOI: https://doi.org/10.1080/01621459.1995.10476572
  50. Kaufmann, D., A. Kraay y M. Mastruzzi. 2004. Governance matters III: Governance indicators for 1996, 1998, 2000, and 2002, World Bank Economic Review, 18(2): 253-287. DOI: https://doi.org/10.1093/wber/lhh041
  51. Knutsen, C.H., A. Kotsadam, E.H. Olsen y T. Wig. 2017. Mining and local corruption in Africa, American Journal of Political Science, 61(2): 320-334. DOI: https://doi.org/10.1111/ajps.12268
  52. Kolstad, I. y A. Wiig. 2016. Does democracy reduce corruption?, Democratization, 23(7): 1198-1215. DOI: https://doi.org/10.1080/13510347.2015.1071797
  53. Koop, G. 2003. Bayesian Econometrics, Chichester, John Wiley and Sons Ltd.
  54. Koop, G., R. León-Gonzalez y R. Strachan. 2012. Bayesian model averaging in the instrumental variable regression model, Journal of Econometrics, 171(2): 237-250. DOI: https://doi.org/10.1016/j.jeconom.2012.06.005
  55. Kutan A.M., T.J. Douglas y W.Q. Judge. 2007. Does Corruption Hurt Economic Development?: Evidence from Middle Eastern, North African and Latin American Countries, Illinois, University at Edwardsville.
  56. LaPalombara, J. 1994. Structural and institutional aspects of corruption, Social Research, 61(2): 325-350.
  57. La Porta, R., F. Lopez de Silanes, A. Shleifer y R. Vishny. 1999. The quality of government, Journal of Law, Economics, and Organization, 15(1): 222-279. DOI: https://doi.org/10.1093/jleo/15.1.222
  58. Lederman, D., N. Loayza y R. Reis. 2001. Accountability and corruption: Political institutions matter, Working Paper Series No. WPS-2708, World Bank Group. DOI: https://doi.org/10.1596/1813-9450-2708
  59. Leff, N. 1964. Economic development through bureaucratic corruption, American Behavioral Scientist, 8(3): 8-14. DOI: https://doi.org/10.1177/000276426400800303
  60. Lenkoski, A., A. Karl y A. Neudecker. 2014. IVBMA: Bayesian instrumental variable estimation and model determination via conditional bayes factors. R package version, 1, 05, https://CRAN.R-project.org/package=ivbma.
  61. León-Gonzalez, R. y T. Vinayagathasan. 2015. Robust determinants of growth in asian developing economies: A Bayesian panel data model averaging approach, Journal of Asian Economics, 36: 34-46. DOI: https://doi.org/10.1016/j.asieco.2014.12.001
  62. Levy, S. y M. Walton. 2009. No Growth Without Equity? Inequality, Interests, and Competition in Mexico, Washington DC, World Bank Publications. DOI: https://doi.org/10.1596/978-0-8213-7767-3
  63. Ley, E. y M.F.J. Steel. 2009. On the effect of prior assumptions in Bayesian model averaging with applications to growth regression, Journal of Applied Economics, 24(4): 651–674. DOI: https://doi.org/10.1002/jae.1057
  64. Lima, M.S.M. y D. Delen. 2020. Predicting and explaining corruption across countries: A machine learning approach, Government Information Quarterly, 37(1): 101407. DOI: https://doi.org/10.1016/j.giq.2019.101407
  65. Lipton, D., A. Werner y C. Gonçalves. 2017. Corrupción en América Latina: un balance, https://www.imf.org/es/Blogs/Articles/2017/09/21/corruption-in-latin-america-taking-stock
  66. Liu, C. y J.L. Mikesell. 2014. The impact of public officials, corruption on the size and allocation of US state spending, Public Administration Review, 74(3): 346-359. DOI: https://doi.org/10.1111/puar.12212
  67. López, N.M. 1997. Corrupción, ética y democracia, en F.J. Laporta y S. Álvarez (coords.), La Corrupción Política, Alianza.
  68. López-Iturriaga, F.J. y I.P. Sanz. 2017. Predicting public corruption with neural networks: An analysis of Spanish provinces, Social Indicators Research, 140: 1-24. DOI: https://doi.org/10.2139/ssrn.3075828
  69. Lui, F. 1985. An equilibrium queuing model of Bribery, Journal of Political Economy, 93(4): 760-781. DOI: https://doi.org/10.1086/261329
  70. Madigan, D., J. York y D. Allard. 1995. Bayesian graphical models for discrete data, International Statistical Review, 63: 215-232. DOI: https://doi.org/10.2307/1403615
  71. Masanjala, W.H. y C. Papageorgiou. 2008. Rough and lonely road to prosperity: A reexamination of the sources of growth in Africa using Bayesian model averaging, Journal of Applied Econometrics, 23(5): 671-682. DOI: https://doi.org/10.1002/jae.1020
  72. Mauro, P. 1995. Corruption and growth, The Quarterly Journal of Economics, 110(3): 681-712. DOI: https://doi.org/10.2307/2946696
  73. Melgar, N., M. Rossi y T.W. Smith. 2010. The perception of corruption in a cross-country perspective: Why are some individuals more perceptive than others?, Economia Aplicada, 14(2): 183-198. DOI: https://doi.org/10.1590/S1413-80502010000200004
  74. Mirestean, A. y C.G. Tsangarides. 2016. Growth determinants revisited using limited information Bayesian model averaging, Journal of Applied Econometrics, 31(1): 106-132. DOI: https://doi.org/10.1002/jae.2472
  75. Mo, P. 2001. Corruption and economic growth, Journal of Comparative Economics, 29(1): 66-79. DOI: https://doi.org/10.1006/jcec.2000.1703
  76. Moral-Benito, E. 2012. Determinants of economic growth: A Bayesian panel data approach, Review of Economics and Statistics, 94(2): 566-579. DOI: https://doi.org/10.1162/REST_a_00154
  77. Nielsen, F. y A. Alderson. 1995. Income inequality, development, and dualism: Results from an unbalanced cross-national panel, American Sociological Review, 60(5): 674-701. DOI: https://doi.org/10.2307/2096317
  78. Nieto, N. 2021. Corruption and inequality: A dangerous cocktail in Mexico, México Interdisciplinario, 10(20): 48-65.
  79. Pellegrini, L. y R. Gerlagh. 2004. Corruption’s effect on growth and its transmission channels, Kyklos, 57(3): 429-456. DOI: https://doi.org/10.1111/j.0023-5962.2004.00261.x
  80. Persson, T., G. Tabellini y F. Trebbi. 2003. Electoral rules and corruption, Journal of the European Economic Association, 1(4): 958-989. DOI: https://doi.org/10.1162/154247603322493203
  81. Policardo, L. y E. Carrera. 2018. Corruption causes inequality, or is it the other way around? An empirical investigation for a panel of countries, Economic Analysis and Policy, 59: 92-102. DOI: https://doi.org/10.1016/j.eap.2018.05.001
  82. Pulido, N.R., A.C. Poveda y J.E.M. Carvajal. 2020. Corruption and institutions: An analysis for the Colombian case, Heliyon, 6(9): e04874. DOI: https://doi.org/10.1016/j.heliyon.2020.e04874
  83. Ríos, V. y W.D. Wood. 2018. The Missing Reform: Strengthening the Rule of Law in Mexico, Washington DC, Woodrow Wilson Center Press.
  84. Sandholtz, W. y W. Koetzle. 2000. Accounting for corruption: Economic structure, democracy, and trade, International Studies Quarterly, 44(1): 31-50. DOI: https://doi.org/10.1111/0020-8833.00147
  85. Seldadyo, H. y J. de Haan. 2006. The determinants of corruption: A literature survey and new evidence, comunicación presentada en la conferencia de la European Public Choice Society, Turku, Finlandia, Turku School of Economics.
  86. Serra, D. 2006. Empirical determinants of corruption: A sensitivity analysis, Public Choice, 126(1-2): 225–256. DOI: https://doi.org/10.1007/s11127-006-0286-4
  87. Sheryazdanova, G., R. Nurtazina, B. Byulegenova y I. Rystina. 2020. Correlation between e-government and corruption risks in Kazakhstan, Utopía y Praxis Latinoamericana, 25(7): 41-48.
  88. Shleifer, A. y R. W. Vishny. 1993. Corruption, The Quarterly Journal of Economics, 108(3): 599-617. DOI: https://doi.org/10.2307/2118402
  89. Stanig, P. 2015. Regulation of speech and media coverage of corruption: An empirical analysis of the Mexican press, American Journal of Political Science, 59(1): 175-193. DOI: https://doi.org/10.1111/ajps.12110
  90. Tanzi, V. 1998. Corruption around the world: Causes, consequences, scope, and cures, IMF Working Paper No. 1998/063. DOI: https://doi.org/10.5089/9781451848397.001
  91. Treisman, D. 2000. The causes of corruption: A cross-national study, Journal of Public Economics, 76(3): 399-457. DOI: https://doi.org/10.1016/S0047-2727(99)00092-4
  92. Van Rijckeghem, C. y B. Weder. 1997. Corruption and the rate of temptation: Do low wages in the civil service cause corruption?, IMF Working Paper No. 1997/073.
  93. Villoria, M. y A. Ramírez. 2013. Los modelos de gobierno electrónico y sus fases de desarrollo: un análisis desde la teoría política, Gestión y Política Pública, 22(SPE): 69-103.
  94. Zellner, A. 1971. An Introduction to Bayesian Inference in Econometrics, Hoboken, John Wiley and Sons.
  95. Zellner, A. 1986. On assessing prior distributions and Bayesian regression analysis with g-prior distributions, en P. Goel y A. Zellner (eds.), Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, Amsterdam, Elsevier.
  96. Zeugner, S. y M. Feldkircher. 2009. Benchmark priors revisited: On adaptive shrinkage and the supermodel effect in Bayesian model averaging, IMF Working Paper No. 2009/202. DOI: https://doi.org/10.5089/9781451873498.001