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AbstractWe present a simple model, illustrating how democracy may improve the quality of the economic institutions. The model further suggests that institutional quality varies more across autocracies than across democracy and that the positive effect of democracies on economic institutional quality increases in people's human capital. Using a panel data set that covers 150 countries and the period from 1920 to 2019, and different measures of economic institutional quality, we show results from fixed effect and instrumental variable regressions that are in line with the predictions of our model.

We compile biographical information on more than 5,000 Prussian politicians and exploit newly digitized administrative data to examine whether landowning and landless elites differ in the extent to which they support health infrastructure projects. Using exogenous variation in soil texture, we present results from 2SLS regressions, suggesting that the provision of health-promoting public goods improves with the political influence of the landless elite. We also provide evidence for two mechanisms: first, landless elites face a higher risk of strikes, and second, they have more economic benefits from improving the health of the poor. Finally, we illustrate that the relevance of these two channels differs for those health-related public amenities that improve the access to medical care and those that prevent the outbreak of infectious diseases.

We present a simple model that illustrates how democracy may improve the quality of economic institutions. The model further suggests that institutional quality varies more across autocracies than across democracy and that the positive effect of democracy on institutional quality is increasing in people's human capital. Using a new panel data set, covering 140 countries and the period from 1920 to 2015, and different measures of institutional quality, we present results from fixed effect and two-stage least squares regressions that confirm the predictions of our model.

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we largely expand the data coverage by providing democracy indicators for 186 countries in the period from 1919 to 2019.

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we largely expand the data coverage by providing democracy indicators for 186 countries in the period from 1919 to 2019.

We compile data for 186 countries (1919 - 2016) and apply different aggregation methods to create new democracy indices. We observe that most of the available aggregation techniques produce indices that are often too favorable for autocratic regimes and too unfavorable for democratic regimes. The sole exception is a machine learning technique. Using a stylized model, we show that applying an index with implausibly low (high) scores for democracies (autocracies) in a regression analysis produces upward-biased OLS and 2SLS estimates. The results of an analysis of the effect of democracy on economic growth show that the distortions in the OLS and 2SLS estimates are substantial. Our findings imply that commonly used indices are not well suited for empirical purposes.

We present a new aggregation method - called SVM algorithm - and use this technique to produce novel measures of democracy (186 countries, 1960-2014). The method takes its name from a machine learning technique for pattern recognition and has three notable features: it makes functional assumptions unnecessary, it accounts for measurement uncertainty, and it creates continuous and dichotomous indices. We use the SVM indices to investigate the effect of democratic institutions on economic development, and find that democracies grow faster than autocracies. Furthermore, we illustrate how the estimation results are affected by conceptual and methodological changes in the measure of democracy. In particular, we show that instrumental variables cannot compensate for measurement errors produced by conventional aggregation methods, and explain why this failure leads to an overestimation of regression coefficients.