Tommy Krieger
Doctoral Student, Chair of Political Economy, University of Konstanz



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

K. Gründler and T. Krieger (2019): Should we care (more) about Data Aggregation? Evidence from the Democracy-Growth-Nexus. CESifo Working Paper Series No. 7480. [Link to Paper] (R&R Journal of Applied Econometrics)

E. Brox and T. Krieger (2019): Birthplace Diversity and Team Performance. ZEW Discussion Paper No. 19-020. [Link to Paper] (Under review)

T. Krieger (2019): Democracy and Institutional Quality: Theory and Evidence. [Link to Paper(Under review)


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.

Machine Learning Index

During my doctoral studies, I developed (together with Klaus Gründler, ifo Munich) a new democracy index which is based on a machine learning technique for pattern recognition. The current version of my index is available for 186 countries and covers the period from 1919 to 2016. [Download]


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 Robert Alan Dahl (1971), I define Political Participation as the right of citizens to elect their political leaders and representatives. To operationalize this aspect of democracy, I use different measures of voter turnout.

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.



A key challenge in producing a democracy index is to find an aggregation rule that transforms the observable regime characteristics in an index. The standard procedure is to weight the regime characteristics and then to apply a multiplicative or additive aggregation function (Gary Goertz, 2006). My research suggests that the standard approach creates non-randomly biased democracy indices (for details, see here). As an alternative, I propose an aggregation method that is based on a machine learning tool for pattern recognition, known as Support Vector Machines (for details, see here). A major advantage of my approach is that it puts the aggregation problem into a non-linear optimization problem and thus avoids simple assumptions about the functional relationship between the regime characteristics and the degree of democratization.


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.

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