Predicting Worker Classification in the Gig Economy
As of 2015, 16 percent of workers in the United States considered themselves to be independent contractors or self-employed rather than employees.1 Given the tax and other advantages that can arise from not classifying workers as employees, it is likely that a significant proportion of those workers were not correctly categorized according to the applicable legal tests. Indeed, it is likely that many of those workers would be treated as employees if their statuses were formally contested. In a hand-collected data set of worker classification decisions in the tax context from 1927 to 2021, we found that more than half of the cases — 50.7 percent — were resolved in favor of finding workers to be employees.2
The issues involving worker classification go back decades. Given the rise of the gig economy, these issues have resurfaced with urgency, with Uber and other gig economy service providers making headlines for the past several years for allegedly misclassifying workers. The debate about whether Uber food delivery and ride-share workers are independent contractors (as Uber would have it) has thus far been concentrated in the employment law context. But the federal tax implications of worker classification are equally important. This article examines the worker classification issue in the gig economy in the realm of U.S. federal tax law.
Rev. Rul. 87-41, 1987-1 C.B. 296, sets out a 20-factor test that the IRS uses for worker classification. Each factor is associated with one of three categories of consideration: (1) behavioral control, (2) financial control, or (3) type of relationship. In any given set of real-world facts and circumstances, some factors may clearly weigh in favor of characterization as an employee or independent contractor, while other factors may not be so obvious in their influence. Moreover, it is difficult to gauge the relative importance of each of the 20 factors. In general, the factors are permitted to vary in importance based on all the surrounding circumstances. If a case goes to court, it is vital for the parties to know which factors to focus on to develop the most effective litigation strategy regarding the evidence and possible concessions as to various facts. Likewise, if a client requires advice on how to structure a contractual relationship with workers without risking unexpected tax liability, a tax professional should be able to point with confidence to the most relevant considerations and be able to provide an opinion about the most likely characterization for tax purposes.
Machine-learning techniques can help tax practitioners identify previously decided cases with similar facts and circumstances and, based on an analysis of all the case law, predict the outcome of a case if it were to go to court. Also, machine-learning tools permit tax practitioners to perform sensitivity analysis and scenario testing by changing each of the 20 IRS factors individually — recalculating the confidence in the predicted outcome each time. This scenario testing allows for an accurate estimate of the relevance of each factor based on a given set of circumstances. One such machine-learning tool, the Blue J Tax “worker classification” module, leverages a machine-learning model based on federal tax cases involving worker classification decided between 1927 and 2021. Over the entire database of decisions, the Blue J Tax worker classification machine-learning model has a 97 percent agreement rate with the courts in mapping facts and circumstances to court-determined worker classification outcomes.
In previous installments of Blue J Predicts, we examined the strengths and weaknesses of ongoing or recently decided tax cases, provided machine-learning-generated insights concerning key factors driving the courts’ decisions, and predicted how outcomes could change depending on competing findings of fact. In this month’s column, we look at employment cases involving workers in the gig economy that were decided in 2021. We take into account recent developments in the law that may point toward trends of how workers are classified as well as important changes to Uber’s contracts, and we predict how judges would likely classify Uber drivers for federal tax purposes in different scenarios.
1Lawrence F. Katz and Alan B. Krueger, “The Rise and Nature of Alternative Work Arrangements in the United States, 1995-2015,” 72(2) ILR Review 382-416 (2019).
2 In our hand-collected data set of 361 federal cases involving worker classification for tax purposes, there are 183 cases (50.7 percent) that found that the workers should be classified as employees and 178 (49.3 percent) that found that the workers should be classified as independent contractors.