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Hiring through Startup Acquisitions: Preference Mismatch and Employee Departures

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September 2018

Written by: J. Daniel Kim

Working Paper Number:

CES-18-41

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. The lack of worker agency may result in a preference mismatch 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. By constructing a novel peer-based proxy for worker preferences, I show that acquired employees who prefer to work for startups ' rather than established firms ' are the most likely to leave after the acquisition, lending support to the preference mismatch theory. Moreover, these departures suggest a deeper strategic cost of competitive spawning: upon leaving, acquired workers are more likely to found their own companies, many of which appear to be competitive threats that impair the acquirer's long-run performance.

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Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords. :
employee, employ, merger, acquisition, entrepreneurial, venture, entrepreneur, entrepreneurship, acquirer, competitor, hiring, workforce, startup firms, hire, earner, startups employees

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Bureau of Labor Statistics, Center for Economic Studies, Ordinary Least Squares, National Bureau of Economic Research, Columbia University, Harvard University, Quarterly Journal of Economics, Journal of Political Economy, American Economic Review, University of Chicago, MIT Press, Princeton University Press, Longitudinal Business Database, Employer Identification Numbers, Journal of Economic Perspectives, Department of Homeland Security, North American Industry Classification System, Longitudinal Employer Household Dynamics, Business Dynamics Statistics, Guzman and Stern, Hypothesis 2

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