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Data science for institutional and organizational economics

In: A Research Agenda for New Institutional Economics

Author

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  • Jens Prüfer
  • Patricia Prüfer

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.

Suggested Citation

  • Jens Prüfer & Patricia Prüfer, 2018. "Data science for institutional and organizational economics," Chapters, in: Claude Ménard & Mary M. Shirley (ed.), A Research Agenda for New Institutional Economics, chapter 28, pages 248-259, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:17960_28
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    2. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.

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    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • K0 - Law and Economics - - General

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