NAICS Code Prediction Using Supervised Methods1

Abstract

When compiling industry statistics or selecting businesses for further study, researchers often rely on North American Industry Classification System (NAICS) codes. However, codes are self-reported on tax forms and reporting incorrect codes or even leaving the code blank has no tax consequences, so they are often unusable. IRS’s Statistics of Income (SOI) program validates NAICS codes for businesses in the statistical samples used to produce official tax statistics for various filing populations, including sole proprietorships (those filing Form 1040 Schedule C) and corporations (those filing Forms 1120). In this paper we leverage these samples to explore ways to improve NAICS code reporting for all filers in the relevant populations. For sole proprietorships, we overcame several record linkage complications to combine data from SOI samples with other administrative data. Using the SOI-validated NAICS code values as ground truth, we trained classification-tree-based models (randomForest) to predict NAICS industry sector from other tax return data, including text descriptions, for businesses which did or did not initially report a valid NAICS code. For both sole proprietorships and corporations, we were able to improve slightly on the accuracy of valid self-reported industry sector and correctly identify sector for over half of businesses with no informative reported NAICS code.

Keywords

• naics codes
• tax compliance
• cart
• random forests
• data linkage
• machine learning