Data Science for Institutional and Organizational Economics

Research output: Working paperDiscussion paperOther research output

Abstract

To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory.
LanguageEnglish
Place of PublicationTilburg
PublisherTILEC
Number of pages12
Volume2018-011
StatePublished - 3 May 2018

Publication series

NameTILEC Discussion Paper
Volume2018-011

Fingerprint

science
economics
social science
empirical method
text analysis
social institution
political institution
research method
empirical research
governance
learning

Keywords

  • data science
  • maching learning
  • institutions
  • text analysis

Cite this

Prüfer, J., & Prüfer, P. (2018). Data Science for Institutional and Organizational Economics. (TILEC Discussion Paper; Vol. 2018-011). Tilburg: TILEC.
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Prüfer, J & Prüfer, P 2018 'Data Science for Institutional and Organizational Economics' TILEC Discussion Paper, vol. 2018-011, TILEC, Tilburg.

Data Science for Institutional and Organizational Economics. / Prüfer, Jens; Prüfer, Patricia.

Tilburg : TILEC, 2018. (TILEC Discussion Paper; Vol. 2018-011).

Research output: Working paperDiscussion paperOther research output

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Prüfer J, Prüfer P. Data Science for Institutional and Organizational Economics. Tilburg: TILEC. 2018 May 3, (TILEC Discussion Paper).