Using Machine Learning for Measuring Democracy: An Update

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 8903

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Index Numbers and Aggregation; Leading indicators ; Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access ; Institutions and the Macroeconomy ; Capitalist Systems: Political Economy
• Subject: data aggregation ; democracy indicators ; machine learning ; measurement issues ; regime classifications ; support vector machines

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gründler, Klaus ; Krieger, Tommy
• Event: Veröffentlichung
• (who): Center for Economic Studies and Ifo Institute (CESifo)
• (where): Munich
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/235273
• Last update: 10.03.2025, 11:43 AM CET