Using Online Vacancy and Job Applicants' Data to Study Skills Dynamics

Fidel Bennett, Verónica Escudero, Hannah Liepmann and Ana Podjanin
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Ana Podjanin: ILO International Labour Organization

No 15506, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We assess whether online data on vacancies and applications to a job board are a suitable source for studying skills dynamics outside of Europe and the United States, where a rich literature has examined skills dynamics using online vacancy data. Yet, the knowledge on skills dynamics is scarce for other countries, irrespective of their level of development. We first propose a taxonomy that systematically aggregates three broad categories of skills – cognitive, socioemotional and manual – and fourteen commonly observed and recognizable skills sub-categories, which we define based on unique skills identified through keywords and expressions. Our aim is to develop a taxonomy that is comprehensive but succinct, suitable for the labour market realities of developing and emerging economies and adapted to online vacancies and applicants' data. Using machine-learning techniques, we then develop a methodology that allows implementing the skills taxonomy in online vacancy and applicants' data, thus capturing both the supply and the demand side. Implementing the methodology with Uruguayan data from the job board BuscoJobs, we assign skills to 64 per cent of applicants' employment spells and 94 per cent of vacancies. We consider this a successful implementation since the exploited text information often does not follow a standardized format. The advantage of our approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills bundles are the same across countries. To the best of our knowledge, we are the first to explore this approach in the context of emerging economies.

Keywords: skills taxonomy; skills dynamics; job board; online data; natural language processing (search for similar items in EconPapers)
JEL-codes: C81 J24 O33 O54 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2022-08
New Economics Papers: this item is included in nep-big, nep-edu, nep-lma and nep-pay
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Published - published in: Research in Labor Economics , 2024, 52B, 35-99.

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Related works:
Chapter: Using Online Vacancy and Job Applicants' Data to Study Skills Dynamics (2024)
Working Paper: Using online vacancy and job applicants’ data to study skills dynamics (2022)
Working Paper: Using Online Vacancy and Job Applicants' Data to Study Skills Dynamics (2022)
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