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Measuring world governance: revisiting the institutions hypothesis


We consider the weighting scheme that results in a best-case scenario in the construction of the world governance indicators (WGIs), a proxy of institutional quality. To perform that, we use an approach that relies on consistent tests for stochastic dominance efficiency of a given index with respect to all possible indices constructed from a set of individual components. The test statistics and the estimators are computed using mixed integer programming methods. The results show that the equally weighted (fixed weights) composite WGI index is not the best-case scenario and that governance indicators at different years should be weighted differently. Furthermore, we revisit the institutions hypothesis in the empirical growth literature, where institutional quality is the main determinant of long-term development. We find that not only do institutions matter for economic development but also geography and macroeconomic policies do affect economic development directly.

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  1. 1.

    See, for example, Neumayer (2002), Easterly and Levine (2003), Beck and Laeven (2006), Easterly (2007), Durlauf et al. (2008) for the similar use of the WGIs.

  2. 2.

    See, for example, Dollar and Kraay (2003), Rodrik et al. (2004), Bulte et al. (2005), Harms and Lutz (2006); Aidt et al. (2008), Brunnschweiler (2008), Bosker and Garretsen (2009) for the similar use of the WGIs.

  3. 3.

    Space restrictions preclude us to present the best-case scenario WGIs for each year, but we present the best-case scenario for each governance indicator. The results for the best-case scenarios for each year are available upon request from the author.

  4. 4.

    Similar applications employed by Pinar et al. (2013) to construct the best-case scenario of Human Development Index and Agliardi et al. (2012) to construct the riskiest sovereign risk index for the emerging countries. The current study differs from the above mentioned ones in two ways. First, both Pinar et al. (2013) and Agliardi et al. (2012) examine the absolute levels of human development and sovereign risk, respectively. However, in the current paper, we examine the relative levels of governance indicators. In this case, we derive the least volatile governance indices and therefore these indices will be less subject to measurement error. Second, the current study examines the effect of the use of the proposed indices in the relevant literature as institutional quality proxies and provides further insights on how measurement could be an important issue.

  5. 5.

    As each governance indicator is bounded between \(-\)2.5 and +2.5, higher measured governance levels for more countries suggest a distribution that is negatively skewed and therefore having lower variability across countries and over time.

  6. 6.

    Each aggregate governance indicator is highly correlated with the other aggregate governance indicators at a given year. Moreover, each governance indicator outcomes at a given year is highly correlated with the following year’s outcomes of that indicator. The correlation coefficients, both the simple and the Spearman-rank ones, are very high (above 0.9) and very significant.

  7. 7.

    We have defined above \(\varvec{\lambda }\) and \(\varvec{\tau }\) to be different weighting vectors that are associated with different indices. In the discussion that follows, we use \(\varvec{\lambda }\) and \(\varvec{\tau }\) interchangeably with the index that they represent.

  8. 8.

    Test statistics are obtained for different governance levels considering all possible weight combinations which require mixed integer and linear programming for the first- and second-order SDE test statistics. See Sect. 4 of the ST for the mathematical formulation details.

  9. 9.

    We thank an anonymous referee for suggesting us to test for the first-stage best-case scenarios.

  10. 10.

    The next sections summarize the findings of the three studies when the best-case scenario governance indices are used as institutional proxies. However, detailed findings of the three studies are available upon request from the author.

  11. 11.

    To assess the robustness of the findings in the next sections, we also test whether the equally weighted governance indices are the best-case scenario indices or not for the full sample size which consists around 200 observations. We find that the equally weighted indices are not the best-case scenarios for all governance indices when the full sample size is used. Even though weights assigned to each year in the case of full sample size differ from the ones that are found with different sample sizes, the findings that are presented in the next sections do not change significantly when the best-case scenario governance indices for the full sample size are used as institutional proxies. Especially, the different results with the revisited papers still hold when the best-case scenario indices for the full sample size are used. Full sample size best-case scenario governance indices and detailed findings of three studies with those indices are available upon request from the author.

  12. 12.

    BG (2009) cited Rodrik et al. (2004) paper that the available language instruments for large sample do not pass over-identification test, whereas in the previous section, we find that the language instruments are valid for some of the best-case institutional quality indices. However, we continue with BG specification.


  1. Aidt T, Dutta J, Sena V (2008) Governance regimes, corruption and growth: theory and evidence. J Comp Econ 36:195–220

    Article  Google Scholar 

  2. Acemoglu D, Johnson S, Robinson JA (2001) The colonial origins of comparative development: an empirical investigation. Am Econ Rev 91:1369–1401

    Article  Google Scholar 

  3. Agliardi E, Agliardi R, Pinar M, Stengos T, Topaloglou N (2012) A new country risk index for emerging markets: a stochastic dominance approach. J Emp Financ 19:741–761

    Article  Google Scholar 

  4. Anderson G (1996) Nonparametric tests of stochastic dominance in income distributions. Econometrica 64:1183–1193

    Article  Google Scholar 

  5. Arndt C, Oman C (2006) Uses and abuses of governance indicators. Organization for Economic Cooperation and Development (OECD) Development Center Study, Paris

  6. Barrett GF, Donald SG (2003) Consistent test for stochastic dominance. Econometrica 71:71–104

    Article  Google Scholar 

  7. Bawa VS, Bodurtha JN Jr, Rao MR, Suri HL (1985) On determination of stochastic dominance optimal sets. J Financ 40:417–431

    Article  Google Scholar 

  8. Beach C, Davidson R (1983) Distribution-free statistical inference with Lorenz curves and income shares. Rev Econ Stud 50:723–735

    Article  Google Scholar 

  9. Beck T, Laeven L (2006) Institution building and growth in transition economies. J Econ Growth 11:157–186

    Article  Google Scholar 

  10. Bloom DE, Sachs JD (1998) Geography, demography, and economic growth in Africa. Brookings Pap Econ Act 2:207–295

    Article  Google Scholar 

  11. Bosker M, Garretsen H (2009) Economic development and geography of institutions. J Econ Geogr 9: 295–328

    Google Scholar 

  12. Brunnschweiler CN (2008) Cursing the blessings? Natural resource abundance, institutions, and economic growth. World Dev 36:399–419

    Article  Google Scholar 

  13. Bulte EH, Damania R, Deacon RT (2005) Resource intensity, institutions and development. World Dev 33:1029–1044

    Article  Google Scholar 

  14. Davidson R, Duclos J-Y (2000) Statistical inference for stochastic dominance and for the measurement of poverty and inequality. Econometrica 68:1435–1464

    Article  Google Scholar 

  15. Diamond J (1997) Guns, germs, and steel: the fates of human societies. W.W. Norton, New York

    Google Scholar 

  16. Dollar D, Kraay A (2003) Institutions, trade and growth. J Monet Econ 50:133–162

    Article  Google Scholar 

  17. Durlauf SN, Kourtellos A, Tan CM (2008) Are any growth theories robust? Econ J 118:329–346

    Article  Google Scholar 

  18. Engerman SL, Sokoloff KL (1997) Factor endowments, institutions, and differential paths of growth among new world economies: a view from economic historians of the United States. In: Haber S (ed) How Latin America fell behind. Stanford University Press, Stanford, pp 260–304

    Google Scholar 

  19. Easterly W, Levine R (2003) Tropics, germs, and crops: how endowments influence economic development. J Monet Econ 50:3–39

    Article  Google Scholar 

  20. Easterly W (2007) Inequality does cause underdevelopment: insights from a new instrument. J Dev Econ 84:755–776

    Article  Google Scholar 

  21. Fishburn PC (1974) Convex stochastic dominance with continuous distribution functions. J Econ Theory 7:143–158

    Article  Google Scholar 

  22. Frankel JA, Romer D (1999) Does trade cause growth? Am Econ Rev 89:379–399

    Article  Google Scholar 

  23. Gallup JL, Mellinger AD, Sachs JD (1998) Geography and economic development. NBER Working Paper No. 6849

  24. Glaeser EL, La Porta R, Lopez-de-Silanes F, Shleifer A (2004) Do institutions cause growth? J Econ Growth 9:271–303

    Article  Google Scholar 

  25. Hall RE, Jones C (1999) Why do some countries produce so much more output per worker than others? Q J Econ 114:83–116

    Article  Google Scholar 

  26. Harms P, Lutz M (2006) Aid, governance and private foreign investment: some puzzling findings for the 1990s. Econ J 116:773–790

    Article  Google Scholar 

  27. Horvath L, Kokoszka P, Zitikis R (2006) Testing for stochastic dominance using the weighted McFadden-type statistic. J Econom 133:191–205

    Article  Google Scholar 

  28. Kaufmann D, Kraay A, Zoido-Lobaton P (1999) Aggregating governance indicators. World bank Policy Research Working Paper 2195

  29. Kaufmann D, Kraay A, Zoido-Lobaton P (2002) Governance matters II—updated indicators for 2000/01. World Bank Policy Research Working Paper 2772

  30. Kaufmann D, Kraay A, Mastruzzi M (2004) Governance matters III: governance indicators for 1996, 1998, 2000, and 2000. World Bank Econ Rev 18:253–287

    Article  Google Scholar 

  31. Kaufmann D, Kraay A, Mastruzzi M (2005) Governance matters IV: governance indicators for 1996–2004. World Bank Policy Research Working Paper 3630

  32. Kaufmann D, Kraay A, Mastruzzi M (2006) Governance matters V: aggregate and individual governance indicators for 1996–2005. World Bank Policy Research Working Paper 4012

  33. Kaufmann D, Kraay A, Mastruzzi M (2007a) Governance matters VI: aggregate and individual governance indicators for 1996–2006. World Bank Policy Research Working Paper 4280

  34. Kaufmann D, Kraay A, Mastruzzi M (2007b) The worldwide governance indicators project: answering the critics. World Bank Policy Research Working Paper 4149

  35. Kaufmann D, Kraay A, Mastruzzi M (2008) Governance matters VII: aggregate and individual governance indicators for 1996–2007. World Bank Policy Research Working Paper 4654

  36. Knack S (2006) Measuring corruption in Eastern Europe and Central Asia: a critique of the cross-country indicators. World Bank Policy Research Working Paper 3968

  37. Kopa M, Chovanec P (2008) A second-order stochastic dominance portfolio efficiency measure. Kybernetika 44:243–258

    Google Scholar 

  38. Kopa M, Post T (2011) A general test for portfolio efficiency. SSRN Working Paper. SSRN.

  39. Kuosmanen T (2004) Efficient diversification according to stochastic dominance criteria. Manage Sci 50:1390–1406

    Article  Google Scholar 

  40. Kurtz MJ, Schrank A (2007) Growth and governance: models, measures and mechanisms. J Polit 69: 538–554

    Google Scholar 

  41. Linton O, Whang, Y-J (2012) Testing for the stochastic dominance efficiency of a given portfolio. CEMMAP Working Paper No. CWP27/12

  42. Mankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Q J Econ 107:407–437

    Article  Google Scholar 

  43. Manly BFJ (1994) Multivariate statistical methods: a primer. Chapman & Hall, London

    Google Scholar 

  44. Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman A, Giovanni E (2005) Handbook on constructing composite indicators: methodology and user guide. OECD Statistics Working Paper 2005/3

  45. Neumayer E (2002) Is good governance rewarded? A cross-national analysis of debt forgiveness. World Dev 30:913–930

    Article  Google Scholar 

  46. North DC (1990) Institutions, institutional change and economic performance. Cambridge University Press, New York

    Book  Google Scholar 

  47. Pinar M, Stengos T, Topaloglou N (2013) Measuring human development: a stochastic dominance approach. J Econ Growth 18:69–108

    Article  Google Scholar 

  48. Post T (2003) Empirical tests for stochastic dominance efficiency. J Financ 58:1905–1932

    Article  Google Scholar 

  49. Post T, Versijp P (2007) Multivariate tests for stochastic dominance efficiency of a give portfolio. J Financ Quant Anal 42:489–516

    Article  Google Scholar 

  50. Rodrik D, Subramanian A, Trebbi F (2004) Institutions rule: the primacy of institutions over geography and integration in economic development. J Econ Growth 9:131–165

    Article  Google Scholar 

  51. Sachs JD (2003) Institutions matter, but not for everything. Financ Dev 40:38–41

    Google Scholar 

  52. Sachs JD, Warner AM (1997) Fundamental sources of long-run growth. Am Econ Rev 87:184–188

    Google Scholar 

  53. Scaillet O, Topaloglou N (2010) Testing for stochastic dominance efficiency. J Bus Econ Stat 28:169–180

    Article  Google Scholar 

  54. Wong W-K, Phoon K, Lean H (2008) Stochastic dominance analysis of Asian hedge funds. Pacific-Basin Financ J 16:204–223

    Article  Google Scholar 

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This research is part of author’s PhD thesis at the Department of Economics of the University of Guelph. The author wishes to thank his PhD supervisor Thanasis Stengos, and committee members, Michael Hoy, Ege Yazgan, Alex Maynard, Ilias Tsiakas, and James Amegashie for their helpful suggestions and comments on improving the paper. The author is also grateful to anonymous referees for their constructive comments and suggestions.

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Correspondence to Mehmet Pinar.

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Pinar, M. Measuring world governance: revisiting the institutions hypothesis. Empir Econ 48, 747–778 (2015).

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  • World governance indicators
  • Institutions
  • Geography
  • Openness
  • Nonparametric stochastic dominance
  • Mixed integer programming

JEL Classification

  • C12
  • C13
  • C14
  • C15
  • O1
  • O57