K. Gründler and T. Krieger (2016): Democracy and growth: Evidence from a machine learning indicator. European Journal of Political Economy, Volume 45 (Supplement), 85-107. [Link to Paper]
Working Paper (under review)
1.) K. Gründler & T. Krieger (2020). Should we care (more) about data aggregation? Evidence from Democracy-Indices. CESifo Working Paper Series No. 7480.
Aggregation tools transform multidimensional data into indices. To investigate how the design of an aggregation process influences regression results, we build democracy indices that differ regarding their scale and aggregation function. Our results show that the shape of the aggregation function significantly affects OLS and 2SLS estimates since different shapes produce systematically different index values for regimes at the lower and upper end of the democracy spectrum. We also find that dichotomous indices produce significantly smaller OLS estimates than continuous indices because of greater measurement uncertainty. Whether continuous and dichotomous indices cause different 2SLS estimates depends on their design.
2.) E. Brox & T. Krieger (2019). Birthplace diversity and team performance. ZEW Disczssion Paper No. 10-020.
We present a simple model to illustrate how birthplace diversity may affect team performance. The model assumes that birthplace diversity increases the stock of available knowledge due to skill complementarities and decreases efficiency due to communication barriers. The consequence of these two opposing effects is a hump-shaped relationship between birthplace diversity and team performance. To verify this prediction, we exploit self-collected data on the first division of German male soccer. Our data set covers 7,028 matches and includes information about 3,266 players comming from 98 countries. We propose two different instrumental variable approaches to identify the effect of birthplace diversity on team performance. Our findings suggest that an intermediate level of birthplace diversity maximizes team performance.
3.) K. Gründler & T. Krieger (2020). Using Machine Learning for measuring democracy. An Update. Mimeo.
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 and the period from 1919 to 2019.
Working Paper (in preparation for submission)
1.) T. Krieger (2020). Democracy and institutional quality. Theory and Evidence. Mimeo.
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 democracies and that the postitive 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 regression that confirm the predictions of our model.
2.) T. Krieger (2020). Elite structure and the provision of health-promoting public goods. Mimeo.
Work in Progress
K. Gründler and T. Krieger: Do democratic transitions really increase interpersonal trust?
T. Krieger: Landed elites and the provision of human-capital promoting public goods. Evidence from Prussia.
T. Krieger: Non-monetary benefits from office holding.