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Identifying U.S. Merchandise Traders: Integrating Customs Transactions with Business Administrative Data

Author

Listed:
  • Fariha Kamal
  • Wei Ouyang

Abstract

This paper describes the construction of the Longitudinal Firm Trade Transactions Database (LFTTD) enabling the identification of merchandise traders - exporters and importers - in the U.S. Census Bureau’s Business Register (BR). The LFTTD links merchandise export and import transactions from customs declaration forms to the BR beginning in 1992 through the present. We employ a combination of deterministic and probabilistic matching algorithms to assign a unique firm identifier in the BR to a merchandise export or import transaction record. On average, we match 89 percent of export and import values to a firm identifier. In 1992, we match 79 (88) percent of export (import) value; in 2017, we match 92 (96) percent of export (import) value. Trade transactions in year t are matched to years between 1976 and t+1 of the BR. On average, 94 percent of the trade value matches to a firm in year t of the BR. The LFTTD provides the most comprehensive identification of and the foundation for the analysis of goods trading firms in the U.S. economy.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:wpaper:20-28
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    File URL: https://www2.census.gov/ces/wp/2020/CES-WP-20-28.pdf
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    References listed on IDEAS

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    1. 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.
    2. Christoph E. Boehm & Aaron Flaaen & Nitya Pandalai-Nayar, 2019. "Input Linkages and the Transmission of Shocks: Firm-Level Evidence from the 2011 Tōhoku Earthquake," The Review of Economics and Statistics, MIT Press, vol. 101(1), pages 60-75, March.
    3. Bethany DeSalvo & Frank F. Limehouse & Shawn D. Klimek, 2016. "Documenting the Business Register and Related Economic Business Data," Working Papers 16-17, Center for Economic Studies, U.S. Census Bureau.
    4. Christopher Ordowich & David Cheney & Jan Youtie & Andrea Fernández-Ribas & Philip Shapira, 2012. "Evaluating the Impact of MEP Services on Establishment Performance: A Preliminary Empirical Investigation," Working Papers 12-15, Center for Economic Studies, U.S. Census Bureau.
    5. Timothy Dunne & J. Bradford Jensen & Mark J. Roberts, 2009. "Producer Dynamics: New Evidence from Micro Data," NBER Books, National Bureau of Economic Research, Inc, number dunn05-1, June.
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    Cited by:

    1. Richard Beem & Christopher Goetz & Martha Stinson & Sean Wang, 2022. "Business Dynamics Statistics for Single-Unit Firms," Working Papers 22-57, Center for Economic Studies, U.S. Census Bureau.
    2. Handley, Kyle & Kamal, Fariha & Monarch, Ryan, 2024. "Supply chain adjustments to tariff shocks: Evidence from firm trade linkages in the 2018-2019 U.S. trade war," Economics Letters, Elsevier, vol. 244(C).
    3. Melissa Chow & Teresa C. Fort & Christopher Goetz & Nathan Goldschlag & James Lawrence & Elisabeth Ruth Perlman & Martha Stinson & T. Kirk White, 2021. "Redesigning the Longitudinal Business Database," Working Papers 21-08, Center for Economic Studies, U.S. Census Bureau.
    4. Basker, Emek & Kamal, Fariha, 2021. "Recall and response: Relationship adjustments to adverse information shocks," European Economic Review, Elsevier, vol. 139(C).

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    More about this item

    Keywords

    trade transactions; matching; machine learning;
    All these keywords.

    JEL classification:

    • F00 - International Economics - - General - - - General
    • F10 - International Economics - - Trade - - - General
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade

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