Skill Signals in a Digital Job Search Market and Duration in Employment Gaps

Matthew Baird (), Paul Ko and Nikhil Gahlawat
Additional contact information
Matthew Baird: LinkedIn Corporation
Paul Ko: LinkedIn Corporation
Nikhil Gahlawat: LinkedIn Corporation

Journal of Labor Research, 2024, vol. 45, issue 3, No 4, 403-435

Abstract: Abstract With the rise of the digital job search market, new opportunities for signaling skills and competencies to employers have emerged. In this paper, we examine listed skills on individuals’ LinkedIn profiles in the United States between 2015 and 2021, both those members add themselves and skills for which they are endorsed from others in their network. We use an inverse probability weighted proportional hazards model with time varying covariates to estimate the relationship between listed skills on shortening employment gaps (time between jobs). We find that, for self-added and peer-endorsed skills respectively, an additional ten skills on the profile decreases median employment gap duration by about 0.7 and 0.4 months, from a median baseline of around 6 months gap. Individuals with no education listed on their profile have the largest benefit from listed skills in terms of reducing employment gaps. Disruptive tech and soft skills also are related to higher returns. Additionally, skills added during the employment break have a substantially stronger relationship than pre-existing added skills. More experienced workers have larger returns than less experienced workers, consistent with the hypothesis that these skills are otherwise difficult to signal to potential employers. These findings are consistent with online job markets’ use of technology offering more efficient ways to signal skills, shortening time to reemployment.

Keywords: Unemployment; Human capital; Signaling; Labor; Skills (search for similar items in EconPapers)
JEL-codes: I26 J24 J28 J64 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12122-024-09363-y

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