JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1180 10.6339/25-JDS1180 Data Science in Action BEACON: A Tool for Industry Self-Classification in the Economic Census https://orcid.org/0000-0002-7832-932X DumbacherBrianbrian.dumbacher@census.gov1∗ WhiteheadDaniel1 JeongJiseok1 PfeiffSarah1 U.S. Census Bureau, Washington, DC 20233, United States Corresponding author. Email: brian.dumbacher@census.gov. 20251742025232429448 Supplementary Material

The supplementary material consists of a Python program that implements a simplified version of BEACON. All of the methodological components are present, but the full text cleaning algorithm cannot be shared for confidentiality reasons. Likewise, the confidential data sources used by BEACON cannot be shared. The public data sources that are part of BEACON’s training data are available at the references cited. See https://github.com/uscensusbureau/BEACON for additional files and documentation.

19720242032025 2025 This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. International copyright, 2025, U.S. Department of Commerce, U.S. Government. Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China. Open access article.2025 Open access article under the CC BY license.

Business Establishment Automated Classification of NAICS (BEACON) is a text classification tool that helps respondents to the U.S. Census Bureau’s economic surveys self-classify their business activity in real time. The tool is based on rich training data, natural language processing, machine learning, and information retrieval. It is implemented using Python and an application programming interface. This paper describes BEACON’s methodology and successful application to the 2022 Economic Census, during which the tool was used over half a million times. BEACON has demonstrated that it recognizes a large vocabulary, quickly returns relevant results to respondents, and reduces clerical work associated with industry code assignment.

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