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

Fariha Kamal and Wei Ouyang

Working Papers from U.S. Census Bureau, Center for Economic Studies

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.

Keywords: trade transactions; matching; machine learning (search for similar items in EconPapers)
JEL-codes: F00 F10 F14 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2020-09
New Economics Papers: this item is included in nep-bec and nep-int
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://www2.census.gov/ces/wp/2020/CES-WP-20-28.pdf First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:20-28

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