IDEAS home Printed from https://ideas.repec.org/p/tiu/tiutil/4392ac65-4fb6-4e9a-a92d-5da46339c7a9.html
   My bibliography  Save this paper

Data Science for Institutional and Organizational Economics

Author

Listed:
  • Prüfer, Jens

    (Tilburg University, TILEC)

  • Prüfer, Patricia

    (Tilburg University, TILEC)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Prüfer, Jens & Prüfer, Patricia, 2018. "Data Science for Institutional and Organizational Economics," Discussion Paper 2018-011, Tilburg University, Tilburg Law and Economic Center.
    • Handle: RePEc:tiu:tiutil:4392ac65-4fb6-4e9a-a92d-5da46339c7a9
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/25399042/TILEC_2018_011.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Claude Ménard & Mary M. Shirley, 2018. "A Research Agenda for New Institutional Economics," Post-Print hal-02046587, HAL.
    3. 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.
    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. 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.
    6. Jens Prüfer & Christoph Schottmüller, 2021. "Competing with Big Data," Journal of Industrial Economics, Wiley Blackwell, vol. 69(4), pages 967-1008, December.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    8. 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.
    9. Claude Ménard & Mary M. Shirley (ed.), 2018. "A Research Agenda for New Institutional Economics," Books, Edward Elgar Publishing, number 17960.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    3. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    4. Giacomo Calzolari & Leonardo Felli & Johannes Koenen & Giancarlo Spagnolo & Konrad O. Stahl, 2021. "Relational Contracts and Trust in a High-Tech Industry," CRC TR 224 Discussion Paper Series crctr224_2021_316, University of Bonn and University of Mannheim, Germany.
    5. Plinio Limata, 2020. "Blockchain and Institutions (I): trust and (de)centralization," CERBE Working Papers wpC35, CERBE Center for Relationship Banking and Economics.
    6. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    7. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    8. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    9. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    10. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    11. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    12. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    13. Ortiz-Riomalo, Juan Felipe & Koessler, Ann-Kathrin & Engel, Stefanie, 2021. "Inducing perspective-taking for prosocial behaviour in natural resource management," Journal of Environmental Economics and Management, Elsevier, vol. 110(C).
    14. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    15. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    16. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    17. John Gathergood & Neale Mahoney & Neil Stewart & Jörg Weber, 2019. "How Do Individuals Repay Their Debt? The Balance-Matching Heuristic," American Economic Review, American Economic Association, vol. 109(3), pages 844-875, March.
    18. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    19. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    20. Arthur Blouin & Julian Dyer, 2021. "How Cultures Converge: An Empirical Investigation of Trade and Linguistic Exchange," Working Papers tecipa-691, University of Toronto, Department of Economics.

    More about this item

    Keywords

    data science; maching learning; institutions; text analysis;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tiu:tiutil:4392ac65-4fb6-4e9a-a92d-5da46339c7a9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/research/institutes-and-research-groups/center-ar/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.