Data science for institutional and organizational economics

Jens Prüfer and Patricia Prüfer
Authors registered in the RePEc Author Service: Jens Prüfer

Chapter 28 in A Research Agenda for New Institutional Economics, 2018, pp 248-259 from Edward Elgar Publishing

Abstract: To what extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This chapter briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. The authors 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. They conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory.

Keywords: Economics and Finance; Law - Academic; Politics and Public Policy (search for similar items in EconPapers)
Date: 2018
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Related works:
Working Paper: Data Science for Institutional and Organizational Economics (2018)
Working Paper: Data Science for Institutional and Organizational Economics (2018)
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