New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector

This repository holds data, replication files, and a machine learning classifier to detect no poach clauses for "New Evidence on Employee Noncompete, No Poach, and No Hire Agreements in the Franchise Sector."

The process to create the machine learning classifier from unstructured text is described in "Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)" with Stephen Meisenbacher.

Replication materials are released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License at . CRAML software is at https://github.com/sjmeis/CRAML_Beta/. I am grateful for support from the Economic Security Project Anti-Monopoly Fund; Loyola Rule of Law Institute; Loyola Quinlan School of Business; and Loyola University Chicago. I thank: Patricia Tabarani, Chloe Clark, Zach Nelson, and Damian Orozco for research assistance; Stephen Meisenbacher, Laura Zbella, Angelica Vaca, Denise DuVernay, DocumentCloud, Michael Morisy, Mitchell Kotler, and Kiera Murray for research support; Chris Erickson, Eric George, Ioana Marinescu, Eric Posner, Marshall Steinbaum and participants at the 86th Midwest Economic Association, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, and 82nd Academy of Management conferences for comments on earlier drafts. Errors are mine.

Abstract. This paper presents new evidence about the prevalence, source, scope, content, and variety of anti-competitive behavior in the labor market. Drawing from a text corpus of 17,785 franchise disclosure filings, I find that 26% of filings from January 2011-August 2022 contained an employee noncompete clause that requires franchisees to bar employees from working for a competitor after leaving. Further, 44% contained a non-solicitation clause barring recruitment between firms, and 25% contain a no hire clause. Using new open-source, replicable methods to classify unstructured text, this paper also publicly releases: the text corpus, the software used to analyze the data, a knowledge base of rules to detect anti-competitive clauses, and an open-source machine learning classifier to detect no poach clauses. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights a large and representative sample of previously hidden inter-firm contracts that block employee mobility and describes tools that can automatically identify future unseen instances.