Matching State Business Registration Records to Census Business Data

J. Daniel Kim and Kristin McCue

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

Abstract: We describe our methodology and results from matching state Business Registration Records (BRR) to Census business data. We use data from Massachusetts and California to develop methods and preliminary results that could be used to guide matching data for additional states. We obtain matches to Census business records for 45% of the Massachusetts BRR records and 40% of the California BRR records. We find higher match rates for incorporated businesses and businesses with higher startup-quality scores as assigned in Guzman and Stern (2018). Clerical reviews show that using relatively strict matching on address is important for match accuracy, while results are less sensitive to name matching strictness. Among matched BRR records, the modal timing of the first match to the BR is in the year in which the BRR record was filed. We use two sets of software to identify matches: SAS DQ Match and a machine-learning algorithm described in Cuffe and Goldschlag (2018). We find preliminary evidence that while the ML-based method yields more match results, SAS DQ tends to result in higher accuracy rates. To conclude, we provide suggestions on how to proceed with matching other states’ data in light of our findings using these two states.

Pages: 14 pages
Date: 2020-01
New Economics Papers: this item is included in nep-big
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https://www2.census.gov/ces/wp/2020/CES-WP-20-03.pdf First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:20-03

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