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.
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Data sharing is not available given privacy restrictions.
Notes
We recognize this does not cover the full spectrum of employment situations, but focus on it as the motivating example for the conceptual framework.
For Fig. 3, the composition of workers in a given time period change depending on how far into the span it goes. Thus, at one month for non-employment spells, everyone in the sample is included in the average. By 12 months, only those who have had a non-employment spell lasting at least a year are included. To account for this, Appendix Fig. A.1 repeats the calculations, but limits the sample to employment gaps lasting at least one year so that the composition does not change. The trends are not meaningfully different.
Unfortunately, in our data set we are unable to recover the dates of individual endorsements for the same skill, as only the first endorsement timing is preserved.
Note that we performed the same exercise for self-added skills. As we would expect given the policy did not directly impact self-added skills, the first-stage F-statistics are much smaller, and the estimated second stage statistics as a result are much more volatile in the presence of weak instruments.
References
Abraham KG, Spletzer JR (2009) New evidence on the returns to job skills. Am Econ Rev 99(2):52–57. https://doi.org/10.1257/aer.99.2.52
Agrawal A, Horton J, Lacetera N, Lyons E (2015) Digitization and the contract labor market: a research agenda. In: Goldfarb A, Greenstein SM, Tucker CE (eds) Economic analysis of the digital economy. University of Chicago Press, Chicago, pp 219–250. https://doi.org/10.7208/9780226206981-011
Albert K (2017) The certification earnings premium: an examination of young workers. Soc Sci Res 63:138–149
Altonji JG, Pierret CR (2001) Employer learning and statistical discrimination. Q J Econ 116(1):313–350
Andersson F, Holzer HJ, Lane JI, Rosenblum D, Smith J (2024) Does federally funded job training work? Nonexperimental estimates of WIA training impacts using longitudinal data on workers and firms. J Human Resour 59(4):1244–1283
Baird M (2017) Labor supply estimation biases from disregarding nonwage benefits. Econ Inq 55(2):1064–1090
Baird M, Lara S (2024) Gender differences in skills: global differences in shares of types of skills (LinkedIn Economic Graph Research Note). https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/global-gender-differences-in-skill-types.pdf. Accessed 30 Jun 2024
Baird M, Bozick R, Zaber M (2022a) Beyond traditional academic degrees: the labor market returns to occupational credentials. IZA J Labor Econ 11(1). https://doi.org/10.2478/izajole-2022-0004
Baird MD, Engberg J, Gutierrez I (2022b) RCT evidence on Differential Impact of US Job Training programmes by Pre-training Employment Status. Labour Econ 75:102140
Belman D, Heywood JS (1991) Sheepskin effects in the returns to education: an examination of women and minorities. Rev Econ Stat 73:720–724
Bhuller M, Ferraro D, Kostøl AR, Vigtel TC (2023) The internet, search frictions and aggregate unemployment. National Bureau of Economic Research. https://www.nber.org/papers/w30911. Accessed 30 Jun 2024
Bitzan JD (2009) Do sheepskin effects help explain racial earnings differences? Econ Educ Rev 28(6):759–766
Card D (1999) The causal effect of education on earnings. Handb Labor Econ 3:1801–1863
Choi EJ (2023) Does the internet help the unemployed find jobs? Inf Econ Policy 62:101017
Chua PY, Kantenga K (2024) LinkedIn Hiring Rate (2; LinkedIn Economic Graph Technical Note). https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/linkedin-hiring-rate-methodology.pdf. Accessed 30 Jun 2024
Collier WJ (2005) The impact of demographic and individual heterogeneity on unemployment duration: a regional study. Appl Econ 37(2):133–153
Denzer M, Schank T, Upward R (2021) Does the internet increase the job finding rate? Evidence from a period of expansion in internet use. Inf Econ Policy 55:100900
Eriksson S, Rooth D-O (2014) Do employers use unemployment as a sorting criterion when hiring? Evidence from a field experiment. Am Econ Rev 104(3):1014–1039
Farber HS, Gibbons R (1996) Learning and wage dynamics. Q J Econ 111(4):1007–1047
Frederiksen A, Ibsen R, Rosholm M, Westergaard-Nielsen N (2006) Labour market signalling and unemployment duration: an empirical analysis using employer-employee data. IZA DP, No. 2132. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=904147. Accessed 30 Jun 2024
Gee LK, Jones J, Burke M (2017) Social networks and labor markets: how strong ties relate to Job finding on Facebook’s Social Network. J Labor Econ 35(2):485–518. https://doi.org/10.1086/686225
Gibbons R, Katz LF (1991) Layoffs and Lemons. J Labor Econ 9(4):351–380. https://doi.org/10.1086/298273
Hall RE, Mueller AI (2018) Wage dispersion and search behavior: the importance of Nonwage Job values. J Polit Econ 126(4):1594–1637. https://doi.org/10.1086/697739
Heller SB (2014) Summer jobs reduce violence among disadvantaged youth. Science 346(6214):1219–1223
Hudomiet P (2015) Four Essays in Unemployment, Wage Dynamics and Subjective Expectations. [PhD Thesis]. https://deepblue.lib.umich.edu/handle/2027.42/113598. Accessed 30 Jun 2024
Hungerford T, Solon G (1987) Sheepskin effects in the returns to education. Rev Econ Stat 69(1):175–177. https://doi.org/10.2307/1937919
Ingram SJ (2019) Occupational licensing and the earnings premium in the United States: updated evidence from the current population survey. Br J Ind Relat 57(4):732–763
Jaeger DA, Page ME (1996) Degrees matter: new evidence on sheepskin effects in the returns to education. Rev Econ Stat 78:733–740
Katz LF, Roth J, Hendra R, Schaberg K (2022) Why do sectoral employment programs work? Lessons from WorkAdvance. J Labor Econ 40(S1):S249–S291
Kuhn P, Mansour H (2014) Is internet job search still ineffective? Econ J 124(581):1213–1233
Kuhn P, Skuterud M (2004) Internet job search and unemployment durations. Am Econ Rev 94(1):218–232
LinkedIn (2019) LinkedIn Language Matters Report. https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions-lodestone/body/pdf/Linkedin-Language-Matters-Report-FINAL2.pdf. Accessed 30 Jun 2024
Majid S, Liming Z, Tong S, Raihana S (2012) Importance of soft skills for education and career success. Int J Cross-Disciplinary Subj Educ 2(2):1037–1042
Monge-Naranjo A, Sohail F (2015) The composition of long-term unemployment is changing toward older workers. The Regional Economist.
Mora Rodríguez JJ, Muro J (2015) On the size of sheepskin effects: a meta-analysis. Economics: The Open-Access, Open-Assessment E-Journal 9(2015-37):1–18
Pallais A (2014) Inefficient hiring in entry-level labor markets. Am Econ Rev 104(11):3565–3599
Rajkumar K, Saint-Jacques G, Bojinov I, Brynjolfsson E, Aral S (2022) A causal test of the strength of weak ties. Science 377(6612):1304–1310
Sharone O (2017) LinkedIn or LinkedOut? How social networking sites are reshaping the labor market. In: Emerging conceptions of work, management and the labor market, vol 30. Emerald Publishing Limited, pp 1–31. https://www.emerald.com/insight/content/doi/10.1108/S0277-283320170000030001/full/html?fullSc=1andutm_source=TrendMDandutm_medium=cpcandutm_campaign=Equality%252C_Diversity_and_Inclusion%253A_An_International_Journal_TrendMD_0andWT.mc_id=Emerald_TrendMD_0. Accessed 30 Jun 2024
Wheeler L, Garlick R, Johnson E, Shaw P, Gargano M (2022) LinkedIn(to) Job opportunities: experimental evidence from Job Readiness Training. Am Econ J: Appl Econ 14(2):101–125. https://doi.org/10.1257/app.20200025
Zhu TJ, Fritzler A, Orlowski J (2020) Jobs, Skills and Migratino Trends Methodology & Validiation Results. World Bank Group. https://documents1.worldbank.org/curated/en/827991542143093021/pdf/World-Bank-Group-LinkedIn-Data-Insights-Jobs-Skills-and-Migration-Trends-Methodology-and-Validation-Results.pdf. Accessed 30 Jun 2024
Zide J, Elman B, Shahani-Denning C (2014) LinkedIn and recruitment: how profiles differ across occupations. Empl Relations 36(5):583–604
Acknowledgements
We would like to thank Rand Ghayad, Karin Kimbrough, Anne Trapasso, Chris Grant, Caroline Liongosari, and Virginia Roode for feedback on this paper, as well as members of the Economic Graph Research & Insights team and the LinkedIn causal inference reading group.
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Matthew Baird led the project, designing the research plan, coding the analysis, and taking primary responsibility for the writing of the paper. Paul Ko and Nikhil Gahlawat both assisted with each of these stages (research design, coding help, and writing of the paper) and provided quality assurance reviews of the code.
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Baird, M., Ko, P. & Gahlawat, N. Skill Signals in a Digital Job Search Market and Duration in Employment Gaps. J Labor Res 45, 403–435 (2024). https://doi.org/10.1007/s12122-024-09363-y
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DOI: https://doi.org/10.1007/s12122-024-09363-y