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Data science for institutional and organizational economics

In: A Research Agenda for New Institutional Economics

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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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    References listed on IDEAS

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    1. Claude Ménard & Mary M. Shirley (ed.), 2018. "A Research Agenda for New Institutional Economics," Books, Edward Elgar Publishing, number 17960.
    2. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    3. Jens Prüfer & Christoph Schottmüller, 2021. "Competing with Big Data," Journal of Industrial Economics, Wiley Blackwell, vol. 69(4), pages 967-1008, December.
    4. Raj Chetty & John N. Friedman & Emmanuel Saez, 2013. "Using Differences in Knowledge across Neighborhoods to Uncover the Impacts of the EITC on Earnings," American Economic Review, American Economic Association, vol. 103(7), pages 2683-2721, December.
    5. Rios-Morales, Ruth & Gamberger, Dragan & Smuc, Tom & Azuaje, Francisco, 2009. "Innovative methods in assessing political risk for business internationalization," Research in International Business and Finance, Elsevier, vol. 23(2), pages 144-156, June.
    6. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    7. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    8. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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    Cited by:

    1. 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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    More about this item

    Keywords

    Economics and Finance; Law - Academic; Politics and Public Policy;
    All these keywords.

    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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