Neural Network Analysis of the Employee Classification Problem for Tax Purposes

Abstract

Since 1987 the U.S. Internal Revenue Service has relied on twenty common law factors for guidance in determining whether a worker is an emplyoee or an independent contractor. This study presents new evidence on the task of simplifying that complex classification problem. Neural network methodology is used to classify workers using data obtained from Private Letter Rulings issued by the Internal Revenue Service from 1988 through a portion of 1993, a data set not previously used for this purpose. The model is highly accurate in correctly classifying workers as either employees or independent contractors. The overall prediction success rate using sample data was 97.2 percent and drops to 91.4 percent when a holdout sample was used. These findings are robust for each of the years in the study. For comparison purposes, classification results using logistic regression are also included. Results from both methodologies are identical