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Burning Glass Technologies’ data use in policy-relevant analysis: An occupation-level assessment

Author

Listed:
  • Emile Cammeraat

    (OECD)

  • Mariagrazia Squicciarini

    (OECD)

Abstract

This work proposes an analysis of the statistical properties and distributional characteristics of Burning Glass Technologies’ (BGT) data on online job openings from platforms and companies, at the occupation level. BGT data are compared to official data on employment by occupation to assess their occupation-specific representativeness. This work further proposes weighting schemes aimed at making BGT-based analysis fully representative at the occupation and country levels, where appropriate.The analysis encompasses six economies – Australia, Canada, New Zealand, Singapore, the United Kingdom and the United States – for the period 2010-19. Overall, it finds that BGT data exhibit good statistical properties and are a useful source of timely information about labour market demand, especially for high-skill occupations and recruitment processes that are more likely to happen online.

Suggested Citation

  • Emile Cammeraat & Mariagrazia Squicciarini, 2021. "Burning Glass Technologies’ data use in policy-relevant analysis: An occupation-level assessment," OECD Science, Technology and Industry Working Papers 2021/05, OECD Publishing.
    • Handle: RePEc:oec:stiaaa:2021/05-en
    DOI: 10.1787/cd75c3e7-en
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    Cited by:

    1. Saerom (Ronnie) Lee & J. Daniel Kim, 2024. "When do startups scale? Large‐scale evidence from job postings," Strategic Management Journal, Wiley Blackwell, vol. 45(9), pages 1633-1669, September.
    2. Szabina Fodor & Ildikó Szabó & Katalin Ternai, 2021. "Competence-Oriented, Data-Driven Approach for Sustainable Development in University-Level Education," Sustainability, MDPI, vol. 13(17), pages 1-23, September.
    3. Elodie Andrieu & Malgorzata Kuczera, 2023. "Minimum Wage and Skills -Evidence from Job Vacancy Data," Working Papers 034, The Productivity Institute.
    4. Reuben Hurst & Saerom (Ronnie) Lee & Justin Frake, 2024. "The effect of flatter hierarchy on applicant pool gender diversity: Evidence from experiments," Strategic Management Journal, Wiley Blackwell, vol. 45(8), pages 1446-1484, August.
    5. David Evans & Claire Mason & Haohui Chen & Andrew Reeson, 2023. "An algorithm for predicting job vacancies using online job postings in Australia," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.

    More about this item

    Keywords

    Labour Demand; Occupations; Online Job Posting; Representativeness; Statistics;
    All these keywords.

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