Income and democracy: a bivariate copula approach

References

• D. Acemoglu, S. Naidu, P. Restrepo, and J.A. Robinson, Democracy does cause growth. J. Polit. Econ. 127 (2019), pp. 47–100.
   Web of Science ®Google Scholar
• D. Acemoglu, J. Simon, J.A. Robinson, and P. Yared, Income and democracy. Am. Econ. Rev. 98 (2008), pp. 267–293.
   Web of Science ®Google Scholar
• D. Acemoglu, J. Simon, J.A. Robinson, and P. Yared, Re-evaluating the modernization hypothesis. J. Monet. Econ. 56 (2009), pp. 1043–1058.
   Web of Science ®Google Scholar
• R. Adcock, and D. Collier, Measurement validity: A shared standard for qualitative and quantitative research. Am. Polit. Sci. Rev. 95 (2001), pp. 529–546.
   Web of Science ®Google Scholar
• D.W.K. Andrews, Chi-square diagnostic tests for econometric models: Introduction and applications. J. Econom. 37 (1988), pp. 135–156.
   Web of Science ®Google Scholar
• Z.F. Arat, Democracy and economic development: Modernization theory revisited. Comp. Polit. 21 (1988), pp. 21–36.
   Web of Science ®Google Scholar
• R.J. Barro, Determinants of democracy. J. Polit. Econ. 107 (1999), pp. S158–S183.
   Web of Science ®Google Scholar
• R.J. Barro, and J.W. Lee. A new data set of educational attainment in the world, 1950–2010, J. Dev. Econ. 104 (2003), pp. 184–198.
   Google Scholar
• J. Benhabib, A. Corvalan, and M.M. Spiegel, Income and democracy: Evidence from nonlinear estimations. Econ. Lett. 118 (2013), pp. 489–492.
   Web of Science ®Google Scholar
• C.L. Britt, M. Rocque, and G.M. Zimmerman, The analysis of bounded count data in criminology. J. Quant. Criminol. 34 (2018), pp. 591–607.
   Web of Science ®Google Scholar
• C.A. Cameron, T. Li, P.K. Trivedi, and D.M. Zimmer, Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts. Econom. J. 7 (2004), pp. 566–584.
   Google Scholar
• C.A. Cameron, and P.K. Trivedi, Regression Analysis of Count Data, Econometric Society Monograph No. 53, 2nd ed., Cambridge University Press, 2013.
   Google Scholar
• G. Casper, and C. Tufis, Correlation versus interchangeability: The limited robustness of empirical findings on democracy using highly correlated datasets. Polit. Anal. 11 (2002), pp. 1–11.
   Web of Science ®Google Scholar
• M. Cervellati, F. Jung, U. Sunde, and T. Vischer, Income and democracy: comment. Am. Econ. Rev. 104 (2014), pp. 707–719.
   Web of Science ®Google Scholar
• U. Cherubini, E. Luciano, and W. Vecchiato, Copula Methods in Finance, Wiley, Chichester, 2004.
   Google Scholar
• S. Chib, and R. Winkelmann, Markov chain Monte Carlo analysis of correlated count data. J. Bus. Econ. Stat. 19 (2001), pp. 428–435.
   Web of Science ®Google Scholar
• D.G. Clayton, A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65 (1978), pp. 141–151.
   Web of Science ®Google Scholar
• M. Coppedge, J. Gerring, S.I. Lindberg, S.E. Skaaning, and J. Teorell, V-Dem Comparisons and Contrasts with Other Measurement Projects, Varieties of Democracy (V-Dem) Project, 2016.
   Google Scholar
• S. Demarta, and A.J. McNeil, The t copula and related copulas. Int. Stat. Rev. 73 (2005), pp. 111–129.
   Web of Science ®Google Scholar
• Z. Elkins, Gradations of democracy? Empirical tests of alternative conceptualizations. Am. J. Pol. Sci. 44 (2000), pp. 287–294.
   Web of Science ®Google Scholar
• P. Embrechts, and A. McNeil, Modelling Dependence with Copulas and Applications to Risk Management, Elsevier, Amsterdam, 2003.
   Google Scholar
• P. Embrechts, A. McNeil, and D. Straumann, Correlaion and Dependence in Risk Management: Properties and Pitfalls, Cambridge University Press, Cambridge, 1999.
   Google Scholar
• M.J. Frank, On the simultaneous associativity of F(x,y) and x+y − F(x,y). Aequ. Math. 19 (1979), pp. 194–226.
   Google Scholar
• E.W. Frees, J.F. Carriere, and E.A. Valdez, Annuity valuation with dependent mortality. J. Risk. Insur. 63 (1996), pp. 229–261.
   Web of Science ®Google Scholar
• E.W. Frees, and E.A. Valdez, Understanding relationships using copulas. N. Am. Actuar. J. 2 (1998), pp. 1–25.
   Google Scholar
• C. Genest, and J. Nešlehová, A primer on copulas for count data. ASTIN Bull. 37 (2007), pp. 475–515.
   Web of Science ®Google Scholar
• C. Genest, B. Remillard, and D. Beaudoin, Goodness-of-fit tests for copulas: A review and a power study. Insur. Math. Econ. 44 (2009), pp. 199–213.
   Web of Science ®Google Scholar
• C. Genest, and L.P. Rivest, Statistical inference procedures for bivariate archimedean copulas. J. Am. Stat. Assoc. 88 (1993), pp. 1034–1043.
   Web of Science ®Google Scholar
• E. Glaeser, G. Ponzetto, and A. Shleifer, Why does democracy need education? J. Econ. Growth 12 (2007), pp. 77–99.
   Web of Science ®Google Scholar
• S. Grønneberg, and N.L. Hjort, The copula information criteria. Scand. J. Stat. 41 (2014), pp. 436–459.
   Web of Science ®Google Scholar
• E.J. Gumbel, Distributions des valeurs extremes en plusieurs dimensions. Publ. Inst. Stat. Univ. Paris 9 (1960), pp. 171–173.
   Google Scholar
• J. Hausman, B.H. Hall, and Z. Griliches, Econometric models for count data with an application to the patents-R&D relationship. Econometrica 52 (1984), pp. 909–938.
   Web of Science ®Google Scholar
• B. Heid, J. Langer, and M. Larch, Income and democracy: Evidence from system GMM estimates. Econ. Lett. 116 (2012), pp. 166–169.
   Web of Science ®Google Scholar
• M. Hernández-Alava, and S. Pudney, bicop: A command for fitting bivariate ordinal regressions with residual dependence characterized by a copula function and normal mixture marginals. Stata J. 16 (2016), pp. 159–184.
   Web of Science ®Google Scholar
• J. Högström, Does the choice of democracy measure matter? Comparisons between the two leading democracy indexes, freedom house and polity IV. Gov. Oppos. 48 (2013), pp. 201–221.
   Web of Science ®Google Scholar
• D. Huard, G. Evin, and A.C. Favre, Bayesian copula selection. Comput. Stat. Data Anal. 51 (2006), pp. 809–822.
   Web of Science ®Google Scholar
• R.W. Jackman, On the relation of economic development to democratic performance. Am. J. Pol. Sci. 17 (1973), pp. 611–621.
   Web of Science ®Google Scholar
• H. Joe, Families of m-variate distributions with given margins and m(m-1)/2 bivariate dependence parameters, in Distributions with Fixed Marginals and Related Topics, Vol. 28, L. Rüschendorf, B. Schweizer and MD. Taylor, eds., Institute of Mathematical Statistics, Hayward, CA, 1996. pp. 120–141.
   Google Scholar
• H. Joe, Dependence modeling with copulas, in Monographs on Statistics and Applied Probability 134, A.J Mcneil, ed., CRC Press, 2015.
   Google Scholar
• S.A. Klugman, and R. Parsa, Fitting bivariate loss distributions with copulas. Insur: Math. Econ. 24 (1999), pp. 139–148.
   Web of Science ®Google Scholar
• E.H. Lee, Copula analysis of correlated counts. Advances in Econometrics, 34, in Bayesian Model Comparison, Jeliazkov I., Poirier, D.J., eds., Emerald Group Publishing Limited, Bingley, pp. 325–348.
   Google Scholar
• S.M. Lipset, Some social requisites of democracy: Economic development and political legitimacy. Am. Political Sci. Rev. 53 (1959), pp. 69–105.
   Web of Science ®Google Scholar
• A.W. Marshall. Copulas, marginals, and joint distributions. Lecture Notes-Monograph Series. 28 (1996), pp. 213–222.
   Google Scholar
• A.W. Marshall, and I. Olkin, Multivariate distributions generated from mixtures of convolution and product families, in Topics in Statistical Dependence, IMS Lecture Notes-Monograph Series, 16, Block H.W., Sampson A.R., Savits T.H., eds., Institute of Mathematical Statistics, Hayward, CA, 1990. pp. 371–393.
   Google Scholar
• M.G. Marshall, and K. Jaggers, Polity iv project: Dataset users’ manual – political regime characteristics and transitions, 1800–2006, Available at https://home.bi.no/a0110709/PolityIV_manual.pdf, 2006.
   Google Scholar
• P. McCullagh, and J.A. Nelder, Generalized Linear Models, Chapman & Hall, New York, 1989.
   Google Scholar
• D.E. Neubauer, Some conditions of democracy. Am. Political Sci. Rev. 61 (1967), pp. 1002–1009.
   Web of Science ®Google Scholar
• S.M. Paleologou, Income and democracy: The modernization hypothesis re-visited via alternative non-linear models. Empir. Econ. 48 (2015), pp. 909–921.
   Web of Science ®Google Scholar
• E. Papaioannou, and G. Siourounis, Economic and social factors driving the third wave of democratization. J. Comp. Econ. 36 (2008), pp. 365–387.
   Web of Science ®Google Scholar
• D. Pemstein, S.A. Meserve, and J. Melton, Democratic compromise: A latent variable analysis of ten measures of regime type. Polit. Anal. 18 (2010), pp. 426–449.
   Web of Science ®Google Scholar
• J. Prieger, A flexible parametric selection model for non-normal data with application to health care usage. J. Appl. Econom. 17 (2002), pp. 367–392.
   Web of Science ®Google Scholar
• A. Przeworski, M. Alvarez, J.A. Cheibub, and F. Limongi, Democracy and Development: Political Institutions and Material Well-being in the World, 1950–1990. Cambridge University Press, New York, 2000.
   Google Scholar
• A. Sklar, Fonctions de Répartition a n Dimensions et Leurs Marges. Publ. Inst. Stat.Univ. Paris 8 (1959), pp. 229–231.
   Google Scholar
• A. Sklar, Random variables, joint distributions, and copulas. Kybernetica 9 (1973), pp. 449–460.
   Google Scholar
• A. Skrondal, and S. Rabe-Hesketh, Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models, CRC Press, Boca Raton, 2004.
   Google Scholar
• M. Smith, Modelling selectivity using archimedean copulas. Econom. J. 6 (2003), pp. 99–123.
   Google Scholar
• S. Treier, and S. Jackman, Democracy as a latent variable. Am. J. Pol. Sci. 52 (2008), pp. 201–217.
   Web of Science ®Google Scholar
• P.K. Trivedi, and D.M. Zimmer, Copula modeling: An introduction for practitioners. Found. Trends Econom. 1 (2007), pp. 1–111.
   Google Scholar
• P.K. Trivedi, and D.M. Zimmer, A note on identification of bivariate copulas for discrete count data. Econometrics 5 (2017), pp. 1–11.
   Google Scholar
• S. Vandenberghe, N.E.C. Verhoest, and B. De Baets, Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 Min rainfall. Water Resour. J. 46 (2010), pp. 1–17.
   Web of Science ®Google Scholar
• World Bank, World Development Indicators, 2017. http://data.worldbank.org/data-catalog/world-development-indicators
   Google Scholar
• R. Winkelmann, Econometric Analysis of Count Data, 5th ed., Springer-Verlag, Berlin, 2008.
   Google Scholar
• D.M. Zimmer, and P.K. Trivedi, Using trivariate copulas to model sample selection and treatment effects: Application to family health care demand. J. Bus. Econ. Stat. 24 (2006), pp. 63–76.
   Web of Science ®Google Scholar