Using Public Data to Generate Industrial Classification Codes

Statistical agencies face increasing costs, lower response rates, and increased demands for timely and accurate statistical data. These demands on agency resources reveal the need for alternative data sources, ideally data that are cheaper than current surveys and available within a short time frame. Textual data on public-facing websites present an ideal data source for certain US Census Bureau statistical products. We identify such data sources and argue that they may be well suited for classification tasks such as industrial or occupational coding. Using these data sources allows statistical agencies to provide more accurate, more timely data for lower costs and lower respondent burden compared to traditional survey methods, while opening the door for new and innovative statistical products. We explore how public data can improve the production of federal statistics, using the specific case of using website text and user reviews, gathered from Google Places API, to generate North American Industrial Classification System (NAICS) codes for approximately 120,000 single-unit employer establishments. Our approach shows that public data is a useful tool for generating NAICS codes. We also find challenges, and provide suggestions for agencies implementing such a system for production purposes.