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Political Corruption and Institutional Stability


This article is the first to statistically examine the reciprocal relationship between formal political institutions and political corruption. We argue that political corruption is an informal institution that allows nondemocratic leaders to build political support, act as a substitute for liberalizing concessions in the formal institutions of the state, and thereby extends the longevity of non-democratic regimes. Yet, whereas high corruption level will prevail in nondemocratic regimes, we expect the electoral constituency in democratic regimes to have the formal power to curb political corruption. We demonstrate that these expectations hold by estimating a dynamic multinomial regression model on data for 133 countries for the 1985–2008 period. Our model shows that high-corruption autocracies and hybrid regimes are more stable than their low-corruption counterparts, but that low-corruption democracies are more stable than high-corruption ones. For autocratic and hybrid regimes, the stability is due both to corruption making the formal institutions more resistant to democratization and that the formal institutions prevent reductions in corruption. Consistent democracies, on the other hand, are able to reduce corruption and become more stable as a result.

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  1. 1.

    Our classifications of democratic, authoritarian, and hybrid regimes are based on the SIP measure of democracy (Gates et al. 2006). The measure ranges from 0 (full autocracy) to 1 (full democracy), and the cutoff points for hybrid regime and democracy are set at 0.15 and 0.80, respectively. The Polity project shows the same trends (see

  2. 2.

    The data on corruption are from the PRS Group (2006). Both the democracy and corruption variable are normalized to range from 0 to 1 in Fig. 1.

  3. 3.

    Some of this increase in perceived corruption may reflect that the International Country Risk Guide (ICRG) now set higher standards for how public affairs are to be conducted than in the 1980s, but the figure still indicates no corruption-reducing effect of democracy at the global level.

  4. 4.

    See also Grzymala-Busse (2010).

  5. 5.

    See Gerring and Thacker (2004) and Treisman (2007) for excellent reviews.

  6. 6.

    The term clientelism is sometimes discussed as distinct from the phenomenon of political corruption by being explicitly confined to the electoral arena and involving a broader distribution of rents. Many researchers, however, see political corruption and rent seeking as an integral part of the concept of clientelism (see, for example, Kitschelt 2000; Keefer 2007; Bratton 2007).

  7. 7.

    For more on informal institutions and patronage/political corruption as a subtype thereof, see Lauth (2000), Grzymala-Busse (2010), and Helmke and Levitsky (2004, 2006).

  8. 8.

    Scholars have proposed a range of labels for hybrid regimes, including “inconsistent,” “semidemocracy,” “electoral authoritarianism,” or “semi-authoritarianism.” We use the label hybrid regimes from Levitsky and Way (2002)and Diamond (2002). Most of the hybrid regimes in our sample are electoral authoritarian systems, but some display another mix of democratic and autocratic traits, for example, combining strong democratic institutions with severe restrictions on suffrage. We expand on our discussion of these regime categories below.

  9. 9.

    Indeed, a large political base dilutes the value of the tangible reward for each client and, in turn, reduces the recipient’s obligations to support the leader.

  10. 10.

    The dataset is available at

  11. 11.

    Changes to political institutions often take time and are associated with a period of turmoil. The Polity project codes these transition periods with a set of transition codes (−77, −88, −99). Such periods may last for several years. To take such transitions into account when observing countries annually, we replace transition codes with the regime type observed immediately before the transition.

  12. 12.

    We have data at a finer temporal resolution than the year for both the corruption (quarterly series) and democracy indicators (in principle, daily series). We assign the value of the last observation within the year as the annual observation.

  13. 13.

    For more information about the data and coding, see The data are available from

  14. 14.

    The Maddison GDP data are measured in 1990 International Geary-Khamis dollars. To reduce missingness, we have interpolated data as well as supplemented with GDP data from World Bank (2011) and Gleditsch (2002). For details, see Dahl et al. (2014).

  15. 15.

    The transition probability corresponding to cell j,i is the column proportions, or the probability of observing a state j at t given that a country was in state j at t − 1.

  16. 16.

    The steady-state probabilities or the “stationary probability distribution” is the distribution across the six outcomes that emerge if the transition probability matrix is repeated many times. As such, it accounts for how all possible transitions affect this distribution. It can be generated by multiplying the matrix of transition probabilities with itself a high number of times. The steady-state distribution can also be obtained by solving a system of linear equations; see Taylor and Karlin (1998, p. 247).

  17. 17.

    The multinomial logit model with six categories for the dependent variable requires the estimation of a large number of parameters. We refrain from including additional variables to avoid over-fitting of the model. We have also estimated a model with a set of region dummies, reported briefly in Appendix A.2.

  18. 18.

    The estimate of −2.055 for “oil” in the equation for low-corruption democracy (equation 5), for instance, means that the odds for oil economies of being in the low-corruption democracy state relative to the reference outcome is exp(−2.055) = 0.12 times that of nonoil economies. Or, nonoil economies are about eight times more likely to be low-corruption democracies than the reference outcome. Given the constraints on the model, the reference outcome in this case is either high-corruption autocracy, high-corruption hybrid, or low-corruption autocracy.

  19. 19.

    We set parameters for control variables to 0 if they were not significant at the 10 % level, and parameters for lagged dependent variables to zero if their p values were higher than 0.50.

  20. 20.

    We used a random-number generator to change one arbitrarily selected country-year observation to a transition from low-corruption inconsistent to high-corruption autocracy and one from low-corruption democracy to high-corruption autocracy. These two artificial observations adds unbiased noise to the estimation, but allows the estimation of two parameters modeling the log odds of “empty-cell transitions.” All other parameters estimating the log odds of empty-cell transitions were constrained to be equal to these, so that the estimates reflect the “average” log odds of such transitions. The large negative estimate we obtain is a good approximation to the low-probability event of changing corruption level and regime type in the same year. Without this adjustment, the parameters corresponding to empty cells are estimated to be very large negative numbers, corresponding to relative risks approaching zero, with very large standard errors. The presence of such poorly defined estimates also hurt the precision of other parameters in the model.

  21. 21.

    The matrix of predicted transition probabilities and corresponding 90 % confidence intervals are reported in Appendix Table 11. Because of a limitation in the Clarify software, we simplified the model reported in Table 1 and constrained the empty-cell parameters ex ante to have a value of −12.74.

  22. 22.

    Figures 5, 6, and 7 report the same distributions for other control variables.

  23. 23.

    For autocracies, the mean difference was 0.041, the standard deviation 0.027, the t value 1.54, and the one-sided p value 0.062.

  24. 24.

    These conclusions are not restricted to our “typical case,” a middle-income nonoil economy. Appendix Tables 6 and 7 report the same tests for low-income and oil-producing countries. We obtain very similar results for autocracies and hybrid regimes for all four combinations of income level and oil. The only exception is that low-income or oil-producing high-corruption democracies are more stable than their low-corruption counterparts.

  25. 25.

    The comparison of transition probabilities in Tables 8 and 9 for other combinations of income level and oil show that Hypothesis 2 A has some support, but Hypothesis 1 A and 3 A do not.

  26. 26.

    The results shown in Tables 8 and 9 show that this tendency is stronger in low-income oil economies.

  27. 27.

    Table 1 shows that there are many more observations of transitions underlying these probabilities—e.g., 18 from low-corruption to high-corruption autocracy and 47 from low-corruption to high-corruption democracy.


  1. Acemoglu D, Robinson JA. De facto political power and institutional persistence. AEA Papers and Proceedings. 2006a;May(2):325–330. Available at

  2. Acemoglu D, Robinson JA. Economic origins of dictatorship and democracy. New York: Cambridge University Press; 2006b.

    Google Scholar 

  3. Acemoglu D, Robinson JA, Verdier T. Kleptocracy and divide and rule: a model of personal rule. J Eur Econ Assoc. 2004;2(2–3):162–92.

    Article  Google Scholar 

  4. Adzera A, Boix C, Payne M. Are you being served? Political accountability and quality of government. J Law Econ Org. 2003;19(2):445–90.

    Article  Google Scholar 

  5. Arriola LR. Patronage and political stability in Africa. Comp Polit Stud. 2009;42(10):1139–362.

    Article  Google Scholar 

  6. Bates RH. States and markets in tropical Africa: the political basis of agricultural policy. Series on social choice and political economy. Berkeley: University of California Press; 1981.

    Google Scholar 

  7. Bayart JF. The state in Africa: the politics of the belly. New York: Longman; 1993.

    Google Scholar 

  8. Bratton M. Formal versus informal institutions in Africa. J Democr. 2007;18(3):96–110.

    Article  Google Scholar 

  9. Bratton M, Van de Walle N. Neopatrimonial regimes and political transitions in Africa. World Polit. 1994;46(July):453–89.

    Article  Google Scholar 

  10. Bueno de Mesquita B, Smith A, Siverson RM, Morrow JD. The logic of political survival. Cambridge: MIT Press; 2003.

    Google Scholar 

  11. Chabal P, Daloz J-P. Africa works: disorder as political instrument. Oxford: James Currey; 1999.

    Google Scholar 

  12. Dahl R. Democracy and its critics. New Haven: Yale University Press; 1989.

    Google Scholar 

  13. Dahl M, Gates S, Hegre H, Strand H. Why waves? Global patterns of democratization, 1820–2008. Typescript, PRIO; 2014. Available at

  14. Darden K. The integrity of corrupt states: graft as an informal state institution. Polit Soc. 2008;36(1):35–60.

    Article  Google Scholar 

  15. Diamond L. Thinking about hybrid regimes. J Democr. 2002;13(2):21–35.

    Article  Google Scholar 

  16. Eckstein H. Authority patterns: a structural pattern for inquiry. Am Polit Sci Rev. 1973;67(4):1142–61.

    Article  Google Scholar 

  17. Englebert P. Pre-colonial institutions and post-colonial states and economic development in tropical Africa. Polit Res Q. 2000;53(7):7–36.

    Article  Google Scholar 

  18. Epstein DL, Bates R, Goldstone J, Kristensen I, O’Halloran S. Democratic transitions. Am J Polit Sci. 2006;50(3):551–69.

    Article  Google Scholar 

  19. Fearon JD, Laitin DD. Ethnicity, insurgency, and civil war. Am Polit Sci Rev. 2003;97(1):75–90.

    Article  Google Scholar 

  20. Gandhi J, Przeworski A. Cooperation, co-optation and rebellion under dictatorships. Econ Polit. 2006;18(1):1–26.

    Article  Google Scholar 

  21. Gates S, Hegre H, Jones MP, Strand H. Institutional inconsistency and political instability: polity duration, 1800–2000. Am J Polit Sci. 2006;50(4):893–908.

    Article  Google Scholar 

  22. Gerring J, Thacker SC. Political institutions and corruption: the role of unitarism and parliamentarism. Br J Polit Sci. 2004;34:295–330.

    Article  Google Scholar 

  23. Gleditsch KS. Expanded trade and GDP data. J Confl Resolut. 2002;46(5):712–24.

    Article  Google Scholar 

  24. Grafton RQ, Rowlands D. Development impeding institutions: the political economy of Haiti. Can J Dev Stud. 1996;XVII(2):261–77.

    Article  Google Scholar 

  25. Grzymala-Busse A. The best laid plans: the impact of informal rules on formal institutions in transitional regimes. Stud Comp Int Dev. 2010;45(3):311–33.

  26. Gurr TR. Persistance and change in political systems, 1800–1971. Am Polit Sci Rev. 1974;68(4):1482–504.

    Article  Google Scholar 

  27. Helmke G, Levitsky S. Informal institutions and comparative politics: a research agenda. Perspect Polit. 2004;2(4):725–40.

    Article  Google Scholar 

  28. Helmke G, Levitsky S. Informal institutions and democracy. Baltimore: Johns Hopkins University Press; 2006.

    Google Scholar 

  29. Huntington SP. Political order in changing societies. New Haven: Yale University Press; 1968.

    Google Scholar 

  30. Huntington SP. The third wave: democratization in the late twentieth century. Norman: University of Oklahoma Press; 1991.

    Google Scholar 

  31. Hyden G. African politics in comparative perspective. New York: Cambridge University Press; 2006.

    Google Scholar 

  32. Jaggers K, Gurr TR. Transitions to democracy: tracking democracy’s third wave with the Polity III data. J Peace Res. 1995;32(4):469–82.

    Article  Google Scholar 

  33. Johnston M. The political consequences of corruption. A reassessment. Comp Polit. 1986;18(4):459–77.

    Article  Google Scholar 

  34. Johnston M. Syndromes of corruption: wealth, power, and democracy. Cambridge: Cambridge University Press; 2005.

    Book  Google Scholar 

  35. Keefer P. Clientelism, credibility, and the polity choices of young democracies. Am J Polit Sci. 2007;51(4):804–21.

    Article  Google Scholar 

  36. King G, Tomz M, Wittenberg J. Making the most of statistical analyses: improving interpretation and presentation. Am J Polit Sci. 2000;44(2):347–61.

    Article  Google Scholar 

  37. Kitschelt H. Linkages between citizens and politicians in democratic politics. Comp Polit Stud. 2000;33(6–7):845–79.

    Article  Google Scholar 

  38. La Porta R, Lopez-de Silanes F, Shleifer A, Vishny R. The quality of government. J Law Econ Org. 1999;15(1):222–79.

    Article  Google Scholar 

  39. Lauth H-J. Informal institutions and democracy. Democratization. 2000;7(4):21–50.

    Article  Google Scholar 

  40. Lederman D, Loayza NV, Soares RR. Accountability and corruption: political institutions matter. Econ Polit. 2005;17(1):1–35.

    Article  Google Scholar 

  41. Lemarchand R. Political clientelism and ethnicity in tropical Africa: competing solidarities in nation-building. Am Polit Sci Rev. 1972;66(1):68–90.

    Article  Google Scholar 

  42. Levitsky S, Way LA. The rise of competitive authoritarianism. J Democr. 2002;13(2):52–65.

    Google Scholar 

  43. Levitsky S, Way LA. Competitive authoritarianism. Hybrid regimes after the Cold War. Cambridge: Cambridge University Press; 2010.

    Book  Google Scholar 

  44. Maddison A. Contours of the world economy 1–2030. Essays in macroeconomic history. Oxford: Oxford University Press; 2007.

    Google Scholar 

  45. Manzetti L, Wilson CJ. Why do corrupt government maintain public support. Comp Polit Stud. 2007;40(August):949–70.

    Article  Google Scholar 

  46. Medina LF, Stokes S. Clientelism as political monopoly. Unpublished manuscript. 2002.

  47. Montinola GR, Jackman R. Sources of corruption: a cross-country study. Br J Polit Sci. 2002;32:142–70.

    Article  Google Scholar 

  48. Nyblade B, Reed SR. Whoe cheats? Who loots? Political competition and corruption in Japan, 1947–1993. Am J Polit Sci. 2008;52(4):926–41.

    Article  Google Scholar 

  49. O’Donnell G. Another institutionalization: Latin America and elsewhere. Kelllogg Institute Working paper No. 222. 1996.

  50. O’Donnell G, Schmitter P, Whitehead L. Transitions from authoritarian rule. Baltimore: Johns Hopkins University Press; 1986.

    Google Scholar 

  51. Persson T, Tabellini G, Trebbi F. Electoral rules and corruption. J Eur Econ Assoc. 2003;1(4):958–89.

    Article  Google Scholar 

  52. PRS Group. International country risk guide: the political risk rating. East Syracuse: PRS Group; 2006.

    Google Scholar 

  53. Przeworski A. Democracy and the market: political and economic reforms in Eastern Europe and Latin America. Cambridge: Cambridge University Press; 1991.

    Book  Google Scholar 

  54. Przeworski A, Alvarez ME, Cheibub JA, Limongi F. Democracy and development. Political institutions and well-being in the world, 1950–1990. Cambridge: Cambridge University Press; 2000.

    Book  Google Scholar 

  55. Robinson JA, Verdier T. The political economy of clientelism. CEPR Working Paper 3205, February. 2002.

  56. Rose-Ackerman S. Corruption. A study in political economy. New York: Academic; 1978.

    Google Scholar 

  57. Rose-Ackerman S. Corruption and government: causes, consequences and reform. Cambridge, United Kingdom: Cambridge University Press; 1999.

    Book  Google Scholar 

  58. Ross M. Does oil hinder democracy. World Polit. 2001;53(April):325–61.

    Article  Google Scholar 

  59. Sanhueza R. The hazard rate of political regimes. Public Choice. 1999;98:337–67.

    Article  Google Scholar 

  60. Schedler A. The menu of manipulation. J Democr. 2002;13(2):36–50.

    Article  Google Scholar 

  61. Schedler A. The logic of electoral authoritarianism. In: Schedler, editor. Electoral authoritarianism: the dynamics of unfree competition. Boulder: Lynne Rienner Publishers; 2006.

  62. Shleifer A, Vishny RW. Corruption. Q J Econ. 1993;108(August):599–617.

    Article  Google Scholar 

  63. Smith IO. Election boycotts and hybrid regime survival. Comp Polit Stud. 2014;47(5):743–765.

  64. Tavits M. Clarity of responsibility and corruption. Am J Polit Sci. 2007;51(1):218–29.

    Article  Google Scholar 

  65. Taylor HM, Karlin S. An introduction to stochastic modeling. 3rd ed. Academic Press: San Diego; 1998.

    Google Scholar 

  66. Thompson MR, Kuntz P. After defeat: when do rulers steal elections? In: Electoral authoritarianism. Colorado: Lynne Rienner Publishers; 2006. p. 113–28.

    Google Scholar 

  67. Treisman D. The causes of corruption: a cross national study. J Public Econ. 2000;76(3):399–457.

    Article  Google Scholar 

  68. Treisman D. What have we learned about the causes of corruption from ten years of cross-national empirical research. Annu Rev Polit Sci. 2007;10:211–44.

    Article  Google Scholar 

  69. Vanhanen T. A new dataset for measuring democracy, 1810–1998. J Peace Res. 2000;37(2):251–65.

    Article  Google Scholar 

  70. Warren ME. What does corruption mean in a democracy? Am J Polit Sci. 2004;48(2):328–43.

    Article  Google Scholar 

  71. Weingast BR. The political foundations of democracy and the rule of law. Am Polit Sci Rev. 1997;91(2):245–63.

    Article  Google Scholar 

  72. Wintrobe R. The tinpot and the totalitarian: an economic theory of dictatorship. Am Polit Sci Rev. 1990;84(3):849–72.

    Article  Google Scholar 

  73. Wintrobe R. The political economy of dictatorship. Cambridge: Cambridge University Press; 1998.

    Book  Google Scholar 

  74. World Bank. World development indicators. Washington, DC: World Bank; 2011.

    Google Scholar 

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Correspondence to Hanne Fjelde.



Graphs of Steady-State Distributions

Fig. 5

Distribution of predicted steady-state distribution, low-income nonoil producers, 1997–2000

Fig. 6

Distribution of predicted steady-state distribution, low-income oil producers, 1997–2000

Fig. 7

Distribution of predicted steady-state distribution, middle-income oil producers, 1997–2000

Additional Regression Results

Table 6 Comparing predicted steady-state proportions
Table 7 Comparing predicted probabilities in same state (supplementing Table 3)
Table 8 Probability of transition between selected regime types by level of corruption (supplementing upper half of Table 4)
Table 9 Stability of high-corruption state versus low-corruption state (supplementing lower half of Table 4)
Table 10 Comparing predicted probabilities of increase/decrease in corruption (supplementing Table 5)

Estimated Transition Matrices

Table 11 Predicted transition probabilities, median income nonoil-exporting country, 1985–2008

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Fjelde, H., Hegre, H. Political Corruption and Institutional Stability. St Comp Int Dev 49, 267–299 (2014).

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  • Corruption
  • Regime type
  • Institutional stability
  • Regime change
  • Informal institutions