Henry Sauermann (ESMT Berlin)

“Can Early-Stage Startups Hire Talented Scientists and Engineers? Ability, Preferences, and Employee Job Choice”

Abstract. Early-stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet, these startups compete with large technology firms to hire the best workers. Theories of ability sorting predict that high ability workers will choose jobs in large established firms that offer complementary assets and higher pay, leaving low ability workers to take lower-paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enables early-stage startups to hire talented workers who have a distinct taste for a startup work environment, even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school to industrial R&D occupations, we overcome common challenges in studying job choice by observing both ability and ex ante stated preferences years prior to first-time employment. We find that higher ability workers are more likely to join early-stage startups, despite receiving significantly lower pay than their established firm counterparts. Supplementary analyses show that preferences predict who applies to startup jobs, while ability predicts who receives startup job offers. We also show that since many more individuals apply to startup jobs than there are openings, startups can “cherry pick” the most talented workers. Despite resource constraints and greater risk of failure, startups are indeed able to hire talented workers with a taste for startup employment at lower pay.