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In-demand skills: a shield against automation—evidence from online job vacancies

Author

Listed:
  • Oleš, Tomáš

    (Department of Economic Policy, Faculty of Economics and Finance, Bratislava University of Economics and Business, Bratislava, Slovakia)

Abstract

"This paper investigates how in-demand skills, advertised wages, and occupational exposure to automation co-evolve in Slovakia’s online labor market. Drawing on data covering nearly the full universe of online job vacancies posted in Slovakia in 2022, the analysis extracts skills from unstructured text and maps them into fifteen conceptual categories spanning cognitive, socio-emotional, and manual domains. These categories account for a sizeable share of wage variation, with several linked to notable premia or penalties. Automation risk is gauged through a novel Europe-specific measure of exposure to AI and machine learning, software, and robotics, constructed by matching patent text to task-level occupational descriptions in a shared semantic space. First, the analysis examines whether the conditional number of in-demand skills differs across firms that are more tilted toward adoption of automation technologies, proxied by the occupational structure of their labor demand. The evidence reveals a non-monotonic pattern: vacancies posted by firms more exposed to AI and software list more skills, whereas those concentrated in robotics-exposed roles list fewer; across technologies, skill demand peaks at intermediate adoption levels, forming a clear hump shape. Analysis of skill composition and automation exposure shows that bundles demanding abstract and manual abilities—people and project management, software-specific, financial, hand–foot–eye coordination—are correlationally associated with lower exposure, while clusters featuring routine cognitive, customer-service, and social or character skills align with higher exposure, indicating complementarity. Estimates of average treatment effects confirm a negative association between abstract and manual skills and automation exposure, supporting the view that such capabilities act as a shield against automation. Routine and socio-emotional skills, by contrast, remain concentrated in highly exposed occupations, consistent with their complementary role in tasks that evolve alongside new technologies." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Oleš, Tomáš, 2026. "In-demand skills: a shield against automation—evidence from online job vacancies," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 60, pages 1-005.
    • Handle: RePEc:iab:iabjlr:v:60:i::p:a005
    DOI: 10.1186/s12651-026-00424-6
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    References listed on IDEAS

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    1. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    2. David J. Deming, 2017. "The Growing Importance of Social Skills in the Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1593-1640.
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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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