(with Murillo Campello and Gaurav Kankanhalli)
Journal of Financial and Quantitative Analysis, Forthcoming
Big data on job postings reveal multiple facets of the impact of Covid-19 on corporate hiring. Firms disproportionately cut new hiring for high-skill positions, with financially constrained firms reducing skilled hiring the most. Applying machine learning methods to job-ad texts, we find that firms have skewed their hiring towards operationally-core functions. New positions display greater flexibility regarding schedules and tasks. While job posting levels show signs of recovery starting in late-2020, changes to job descriptions and skill profiles persist through early-2022. Financial constraints amplify these changes, with constrained firms’ new hires witnessing greater adjustments to job roles and employment arrangements.
(with Daniel Ferrés and Gaurav Kankanhalli)
Using the 2010 prosecution of U.S. technology firms engaging in anti-poaching agreements as a shock, we study the impact of labor market collusion on corporate hiring and innovation. During the collusive period, cartel firms displayed elevated job posting rates relative to comparable firms that were not party to these agreements. Occupation-level tests show that the effects were amplified in job roles critical to the firms’ operations. Textual analysis of job-ad descriptions provides evidence that cartel firms enjoyed greater bargaining power in the hiring process, with workers being offered lower flexibility, non-wage benefits, and training opportunities. Notably, cartel firms exhibited superior innovative capabilities over the collusive period, while the dissolution of the agreements led to a curtailment in their innovation output. Our results reveal important linkages between firms’ anti-competitive conduct in labor markets and their innovation and market valuations.