Predictable Exodus: Startup Acquisitions and Employee Departures

45 Pages Posted: 12 Oct 2018 Last revised: 4 Sep 2019

Date Written: September 1, 2019

Abstract

This paper investigates the effectiveness of startup acquisitions as a hiring strategy. Unlike conventional hires who choose to join a new firm on their own volition, most acquired employees do not have a voice in the decision to be acquired, much less by whom to be acquired. Startup acquisitions therefore provide an empirical setting in which non-founding employees are quasi-randomly assigned to a new employer. I argue that the lack of worker choice lowers the average match quality between the acquired employees and the acquiring firm, leading to elevated rates of turnover. Using comprehensive employee-employer matched data from the US Census, I document that acquired workers are significantly more likely to leave compared to regular hires. This effect is more pronounced among high-earning individuals. Moreover, I demonstrate that these departures can be largely predicted ex-ante. Leveraging population data on career histories, I construct a measure of “startup affinity” for each target and acquiring firm based on pre-acquisition employment patterns, and show that this strongly predicts post-acquisition worker retention. Lastly, an analysis of serial acquirers suggests that firms learn over time how to effectively retain employees from startup acquisitions.

Keywords: Entrepreneurship, Mergers and Acquisitions, Human Capital, Startups

JEL Classification: L26, G34, J63, M50

Suggested Citation

Kim, J. Daniel, Predictable Exodus: Startup Acquisitions and Employee Departures (September 1, 2019). Available at SSRN: https://ssrn.com/abstract=3252784 or http://dx.doi.org/10.2139/ssrn.3252784

J. Daniel Kim (Contact Author)

The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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