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An algorithm for predicting job vacancies using online job postings in Australia

Author

Listed:
  • David Evans

    (CSIRO)

  • Claire Mason

    (CSIRO)

  • Haohui Chen

    (CSIRO)

  • Andrew Reeson

    (CSIRO)

Abstract

Timely and accurate statistics on the labour market enable policymakers to rapidly respond to changing economic conditions. Estimates of job vacancies by national statistical agencies are highly accurate but reported infrequently and with time lags. In contrast, online job postings provide a high-frequency indicator of vacancies with less accuracy. In this study we develop a robust signal averaging algorithm to measure job vacancies using online job postings data. We apply the algorithm using data on Australian job postings and show that it accurately predicts changes in job vacancies over a 4.5-year period. We also show that the algorithm is significantly more accurate than using raw counts of job postings to predict vacancies. The algorithm therefore offers a promising approach to the timely and reliable measurement of changes in vacancies.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01562-9
    DOI: 10.1057/s41599-023-01562-9
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    References listed on IDEAS

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    Cited by:

    1. David Evans & Claire Mason & Haohui Chen & Andrew Reeson, 2024. "Accelerated demand for interpersonal skills in the Australian post-pandemic labour market," Nature Human Behaviour, Nature, vol. 8(1), pages 32-42, January.

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