Timely, forward-looking indicators of wage growth are rare. This paper uses data from 70 million online job postings to construct monthly estimates of annual growth in advertised wages in the US, UK, France, Germany, Ireland, Italy, the Netherlands, and Spain from 2019 to 2022. We show that the online data closely overlaps with the distribution of wages from representative surveys and broadly follows aggregate wage trends from national statistical sources. In Europe, growth in advertised wages after the pandemic was below headline inflation but in line with core inflation. In the US, it more closely followed headline inflation. Our tracker led survey-based estimates of growth for all workers by around six months and was a reliable leading indicator of industry and regional-level wage growth for the period we analyzed. In several countries, growth in our wage tracker after the pandemic was significantly stronger for lower-paid workers. We show that this can be explained by tighter labor markets in sectors where labor demand grew exceptionally strongly in 2022.
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," forthcoming at Research in Labor Economics. Abstract. This paper presents new evidence on anti-competitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011- 2023 (partial), I report new information on franchise brands’ use of inter-firm non-solicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee non-compete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anti-competitive clauses began to appear in FDDs in 2018. While non-solicitation and no hire clauses have declined in use, the use of non-competes remained stable over time. While prior evidence on anti-competitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this paper spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora. 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. CRAML software is at https://github.com/sjmeis/CRAML_Beta/. To acccess the text files used, I request you complete a short form. You may also use this form to notify the author of any issues/ questions. PDFs uploaded to DocumentCloud are available by searching documentcloud.org: +user:peter-norlander-103369 +access:public . 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: Stephen Meisenbacher for development of the CRAML software, document collection, and computational assistance; Patricia Tabarani, Chloe Clark, Kayleigh Currier, Zach Nelson, and Damian Orozco for research assistance; Kate Bahn, Michael Lipsitz, Ioana Marinescu, Eric Posner, Todd Sorensen, Evan Starr, Spencer Weber Waller, David Weil, and two anonymous reviewers and the editors for feedback on earlier versions of the manuscript; participants at the 86th Midwest Economic Association Annual Meeting, the 74th Annual Labor and Employment Relations Association, 42nd Annual Strategic Management Society, the 82nd Academy of Management, and at Michigan State University School of Human Resources and Labor Relations. Errors are mine.
