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]
K. Gründler and T. Krieger (2018): Machine Learning Indices, Political Institutions, and Economic Development. CESifo Working Paper Series No. 6930. [Link to Paper]
Work in Progress
T. Krieger: Democracy and Institutional Quality: Theory and Evidence. [Link to Paper]
K. Gründler and T. Krieger: Should we care about Data Aggregation? Evidence from the Democracy-Growth-Nexus. [Link to Paper]
E. Brox and T. Krieger: Birthplace Diversity and Team Performance.
K. Gründler and T. Krieger: Political Regimes and Economic Preferences.
Machine Learning Index
Conceptualization and Operationalization
I use a concept of democracy that includes three core aspects of democracy: Political Participation, Political Competition, and Freedom of Speech.
Following Adam Przeworski (1991), I define the political process as competitive if individuals with different party affiliations compete in public elections for political support. To operationalize this defintion of Political Competition, I use various election outcomes and information from the Varieties of Democracy Database.
Following the Universal Declaration of Human Rights (1948), I define that citizens enjoy the Freedom of Opinion if they can freely choose their sources of information and can express their political views even when these views are not compatible with the political views of the government. I operationalize this aspect of democracy through two expert-based indices from the Varieties of Democracy Database.
Dahl, Robert Alan (1971). Polyarchy: Participation and Opposition. Yale University Press.
Goertz, Gary (2006). Social Science Concepts. A User's Guide. Princton University Press.
Przeworski, Adam (1991). Democracy and the market. Political and economic reforms in Eastern Europe and Latin America. Cambridge University Press.