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Squeezing More Out of Your Data: Business Record Linkage with Python

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  • John Cuffe
  • Nathan Goldschlag

Abstract

Integrating data from different sources has become a fundamental component of modern data analytics. Record linkage methods represent an important class of tools for accomplishing such integration. In the absence of common disambiguated identifiers, researchers often must resort to ''fuzzy" matching, which allows imprecision in the characteristics used to identify common entities across dfferent datasets. While the record linkage literature has identified numerous individually useful fuzzy matching techniques, there is little consensus on a way to integrate those techniques within a single framework. To this end, we introduce the Multiple Algorithm Matching for Better Analytics (MAMBA), an easy-to-use, flexible, scalable, and transparent software platform for business record linkage applications using Census microdata. MAMBA leverages multiple string comparators to assess the similarity of records using a machine learning algorithm to disambiguate matches. This software represents a transparent tool for researchers seeking to link external business data to the Census Business Register files.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:wpaper:18-46
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    File URL: https://www2.census.gov/ces/wp/2018/CES-WP-18-46.pdf
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    References listed on IDEAS

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    1. Andrea Tancredi & Brunero Liseo, 2015. "Regression analysis with linked data: problems and possible solutions," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 19-35.
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    Cited by:

    1. Joseph Staudt & Yifang Wei & Lisa Singh & Shawn Klimek & J. Bradford Jensen & Andrew Baer, 2019. "Automating Response Evaluation for Franchising Questions on the 2017 Economic Census," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 209-227, National Bureau of Economic Research, Inc.
    2. Fariha Kamal & Wei Ouyang, 2020. "Identifying U.S. Merchandise Traders: Integrating Customs Transactions with Business Administrative Data," Working Papers 20-28, Center for Economic Studies, U.S. Census Bureau.
    3. John Cuffe & Sudip Bhattacharjee & Ugochukwu Etudo & Justin C. Smith & Nevada Basdeo & Nathaniel Burbank & Shawn R. Roberts, 2019. "Using Public Data to Generate Industrial Classification Codes," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 229-246, National Bureau of Economic Research, Inc.

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