Should We Care (More) About Data Aggregation? Evidence from the Democracy-Growth-Nexus

Abstract

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.