Abstract
Existing research points to a democratic advantage in public good provision. Compared to their authoritarian counterparts, democratically elected leaders face more political competition and must please a larger portion of the population to stay in office. This paper provides an impartial reevaluation of the empirical record using the techniques of global sensitivity analysis. Democracy proves to have no systematic association with a range of health and education outcomes, despite an abundance of published empirical and theoretical findings to the contrary.
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Notes
Ross (2006) shows that once missing data problems are accounted for, there is no significant relationship between democracy and infant mortality rates. The expenditures literature has two reported findings that contradict the democratic advantage argument. Mulligan et al. (2004) consider the relationship between regime type and public policy broadly defined, and find that there are not major differences between autocracies and democracies in terms of education spending.
The discussion here follows closely that of Hegre and Sambanis (2006).
Sala-I-Martin 1997, 179. Technically, the appropriate method for calculating the cumulative distribution function is still under debate. If the estimates appear normally distributed, Sala-I-Martin recommends taking a weighted mean of the estimates and their variances. The idea behind the weighting scheme is to give more weight to estimates from models with better fit, under the assumption that they are more likely to be the “true model.” This weighting is not strictly necessary, and it has recently been criticized on the grounds that goodness-of-fit metrics are generally incomparable across models with different numbers of covariates and observations (Gassebner et al. 2013). For this reason, I will calculate the simple unweighted means for the estimates.
The standard approach is to calculate the mean \( {\overline{\beta}}_{\mathrm{m}} \) and variance \( {\overline{\sigma}}_{\mathrm{m}}^2 \), and then compute the cumulative distribution function using the standard normal approximation. Yet, given that my estimates are unweighted, the simpler method is to just examine the empirical distribution and avoid the normal approximation entirely.
Lake and Baum (2001) actually include GNP per capita, not the logged GDP per capita, but graphical analysis shows that the logged model better approximates a linear relationship. Deacon (2009) also moves back and forth between various income and logged income measures. The replication analysis will employ logged GDP per capita.
To calculate the total number of models, we can employ the combinations formula, n! / (r!(n – r)!), where n is the total number of unique elements, and r is the number of unique elements in the combination. For this analysis, there are 21 possible models that have 2(r) out of the 7(n) concepts.
This measure, which is a sum of five weighted ordinal scores on governance sub-measures (competitiveness of participation, regulation of participation, competitiveness of executive, openness of executive recruitment, constraints on executive), has been strongly criticized as including redundant components, omitting key aspects of democracy, and relying on a “convoluted aggregation rule” that has no theoretical justification. As a consumer of these indices, it is difficult to grasp what a unit change in a country’s Polity score means in terms of reforms on the ground.
In their cross-section analysis, Lake and Baum (2001) examine the two years that have the highest data coverage from each decade (1970, 1975, 1985, 1987, 1990, and 1992). Deacon (2009) presents cross-section findings from data from 1989, Moon and Dixon (1985) aggregate a quality of life index over 1970–1975, and Franco et al. (2004) rely on data from 1998.
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Acknowledgments
This manuscript was written and revised while the author received research support from Yale University, Princeton University, and the NSF Graduate Research Fellowship Program. Thanks to Allan Dafoe, Thad Dunning, Susan Rose-Ackerman, Ken Scheve, Lily Tsai, and Michael Weaver for their helpful comments on various aspects of the manuscript. Thanks also to the editorial team at SCID and the three anonymous reviewers for their support of the manuscript and constructive feedback. Any remaining errors are my own.
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Truex, R. The Myth of the Democratic Advantage. St Comp Int Dev 52, 261–277 (2017). https://doi.org/10.1007/s12116-015-9192-4
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DOI: https://doi.org/10.1007/s12116-015-9192-4