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Nowcasting the Australian Labour Market at Disaggregated Levels

Author

Listed:
  • Samuel Shamiri
  • Leanne Ngai
  • Peter Lake
  • Yin Shan
  • Amee McMillan
  • Therese Smith
  • Kishor Sharma

Abstract

Detailed labour market and economic data are often released infrequently and with considerable time lags between collection and release, making it difficult for policy‐makers to accurately assess current conditions. Nowcasting is an emerging technique in the field of economics that seeks to address this gap by ‘predicting the present’. While nowcasting has primarily been used to derive timely estimates of economy‐wide indicators such as GDP and unemployment, this article extends this literature to show how big data and machine‐learning techniques can be utilised to produce nowcasting estimates at detailed disaggregated levels. A range of traditional and real‐time data sources were used to produce, for the first time, a useful and timely indicator—or nowcast—of employment by region and occupation. The resulting Nowcast of Employment by Region and Occupation (NERO) will complement existing sources of labour market information and improve Australia's capacity to understand labour market trends in a more timely and detailed manner.

Suggested Citation

  • Samuel Shamiri & Leanne Ngai & Peter Lake & Yin Shan & Amee McMillan & Therese Smith & Kishor Sharma, 2022. "Nowcasting the Australian Labour Market at Disaggregated Levels," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(3), pages 389-404, September.
    • Handle: RePEc:bla:ausecr:v:55:y:2022:i:3:p:389-404
    DOI: 10.1111/1467-8462.12464
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    References listed on IDEAS

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    4. Kim Nguyen & Gianni La Cava, 2020. "Start Spreading the News: News Sentiment and Economic Activity in Australia," RBA Research Discussion Papers rdp2020-08, Reserve Bank of Australia.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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