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Fuzzy firm name matching: Merging Amadeus firm data to PATSTAT

Author

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  • Leon Bremer

    (Vrije Universiteit Amsterdam)

Abstract

When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps, namely (1) cleaning, (2) similarity scoring, (3) a decision rule based on supervised machine learning, and (4) group identification using community detection. The algorithm is applied to merging firms in the Amadeus Financials and Subsidiaries databases, containing firm-level business and ownership information, to applicants in PATSTAT, a worldwide patent database. For the application the algorithm vastly outperforms an exact string match by increasing the number of matched firms in the Amadeus Financials (Subsidiaries) database with 116% (160%). 53% (74%) of this improvement is due to cleaning, and another 41% (50%) improvement is due to similarity matching. 18.1% of all patent applications since 1950 are matched to firms in the Amadeus databases, compared to 2.6% for an exact name match.

Suggested Citation

  • Leon Bremer, 2023. "Fuzzy firm name matching: Merging Amadeus firm data to PATSTAT," Tinbergen Institute Discussion Papers 23-055/VIII, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20230055
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    File URL: https://papers.tinbergen.nl/23055.pdf
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    References listed on IDEAS

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    5. Michele Pezzoni & Francesco Lissoni & Gianluca Tarasconi, 2014. "How to kill inventors: testing the Massacrator© algorithm for inventor disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 477-504, October.
    6. Eugenie Dugoua & Marion Dumas & Joëlle Noailly, 2022. "Text as Data in Environmental Economics and Policy," Review of Environmental Economics and Policy, University of Chicago Press, vol. 16(2), pages 346-356.
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    More about this item

    Keywords

    Fuzzy name matching; supervised machine learning; name disambiguation; patents;
    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
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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