(with Murillo Campello and Gaurav Kankanhalli)
Big data on job vacancy postings reveal multiple dimensions of the impact of Covid-19 on corporate hiring. Firms disproportionately cut on hiring for high-skill positions (within-firm downskilling), with financially constrained firms reducing high-skill hiring the most. Applying machine learning to job-ad texts, we show that firms have skewed hiring towards operationally-core functions. New positions also take longer to fill, displaying greater flexibility regarding schedules and tasks. Financing constraints amplify pandemic-induced changes to the nature of positions firms seek to fill, with constrained firms’ new hires witnessing far greater adjustments to jobs 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.