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Machine Learning Based Linkage of Company Data for Economic Research: Application to the EBDC Business Panels

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  • Valentin Reich

Abstract

This article presents a comprehensive approach to probabilistic linkage of German com pany data using Machine Learning and Natural Language Processing techniques. Here, the long-running ifo Institute surveys are linked to fnancial information in the Orbis database by addressing the unique challenges of company data linkage, such as corporate structures and linguistic nuances in company names. Compared to a previous linkage, the approach achieves improved match rates and is able to re-evaluate existing matches. This article contributes best practice advice for company data linkage and serves as a documentation for the resulting research dataset.

Suggested Citation

  • Valentin Reich, 2024. "Machine Learning Based Linkage of Company Data for Economic Research: Application to the EBDC Business Panels," ifo Working Paper Series 409, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    • Handle: RePEc:ces:ifowps:_409
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    References listed on IDEAS

    as
    1. Zeno Enders & Franziska Hünnekes & Gernot Müller, 2022. "Firm Expectations and Economic Activity," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2396-2439.
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    3. Kilian Huber, 2018. "Disentangling the Effects of a Banking Crisis: Evidence from German Firms and Counties," American Economic Review, American Economic Association, vol. 108(3), pages 868-898, March.
    4. Jamie C. Moore & Peter W. F. Smith & Gabriele B. Durrant, 2018. "Correlates of record linkage and estimating risks of non‐linkage biases in business data sets," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1211-1230, October.
    5. Link, Sebastian & Peichl, Andreas & Roth, Christopher & Wohlfart, Johannes, 2023. "Information frictions among firms and households," Journal of Monetary Economics, Elsevier, vol. 135(C), pages 99-115.
    6. Abowd, John M. & Vilhuber, Lars, 2005. "The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 133-152, April.
    7. John Cuffe & Nathan Goldschlag, 2018. "Squeezing More Out of Your Data: Business Record Linkage with Python," Working Papers 18-46, Center for Economic Studies, U.S. Census Bureau.
    8. Sebastian Link, 2018. "Harmonization and Interpretation of the ifo Business Survey's Micro Data," CESifo Working Paper Series 7427, CESifo.
    9. Anna Gumpert & Henrike Steimer & Manfred Antoni, 2022. "Firm Organization with Multiple Establishments [“Organizing Offshoring: Middle Managers and Communication Costs]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 1091-1138.
    10. Michele Peruzzi & Georg Zachmann & Reinhilde Veugelers, 2014. "Remerge- regression-based record linkage with an application to PATSTAT," Bruegel Working Papers 852, Bruegel.
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    More about this item

    Keywords

    record linkage; company data; orbis; survey data;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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