Charlotte S. Alexander, an assistant professor of legal studies in the department of Risk Management and Insurance at the J. Mack Robinson College of Business with a secondary appointment at the College of Law, has been awarded a grant by the U.S. Department of Labor to study federal district court misclassification decisions along with her colleague Mohammad Javad Feizollahi at the Robinson College of Business.
The purpose of the project is to understand how courts distinguish between employees and independent contractors, and the factors influencing their decisions. Alexander is principal investigator; Feizollahi, an assistant professor of business analytics, is co-principal investigator.
Misclassification refers to employers’ practice of improperly classifying employees as independent contractors. This distinction is important, because only employees get the benefit of employment and labor law protections, such as the right to the minimum wage and overtime, protections against job discrimination, workers’ compensation in case of occupational illness or injury, family and medical leave.
Workers may file suit to challenge their classification, and many have done so, particularly “gig” workers who are classified as independent contractors by companies such as Uber, Lyft, and TaskRabbit. However, employment and labor statutes do a poor job distinguishing between employees and independent contractors. This lack of clarity is further compounded because judges’ classification opinions have not been studied in a rigorous and systematic way.
“The law itself provides little help, and we lack crucial information about how judges actually deploy the employee-independent contractor distinction in the cases before them,” Alexander said.
The 24-month project, funded at $247,745, will study all U.S. District Court opinions addressing misclassification in cases filed in 2008 through 2015 (the first year for which complete data is available through the most recent calendar year when the proposal was submitted).
The project will use the tools of big data analytics to examine court opinions collected from the Free Law Project’s RECAP archive and the federal courts’ Public Access to Court Electronic Records (PACER) system. Text mining and content analysis will explore plaintiffs’ misclassification win/loss rates; legal tests used by courts; factors exerting greatest influence on judges’ decisions; and other worker, employer and litigation variables associated with plaintiff misclassification wins and losses.
“This research will illuminate the ways in which federal district courts draw the line between employee and independent contractor, a distinction that will come under increasing pressure as the structure of work continues to change in today’s gig economy,” Alexander said.
The project is one of nine Labor Research and Evaluation Grants awarded by the U.S. Department of Labor to support university-based research on workforce policies and programs.