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Measuring the impacts of COVID-19 on job postings in Australia using a reweighting-estimation-transformation approach

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  • Kailing Shen

    (Australian National University)

  • Bledi Taska

    (Burning Glass Technologies)

Abstract

We propose a reweighting-estimation-transformation (RWET) approach to estimate the impacts of COVID-19 on job postings in Australia. Contrary to the commonly used aggregation-based method on counting data, our approach can be used in a relatively ‘thin’ market, such as Australia. In a thin market, the number of job postings is relatively small, and the share of empty cells increases substantially when aggregating the data into finer categories. Using Australian job postings collected by Burning Glass Technologies and the RWET approach, our empirical evidence shows that the overall labour demand in Australia as of July 2020 is slowly recovering from its lowest 45 per cent dip at the beginning of May. Our results also suggest that the impacts of the pandemic are relatively evenly distributed across skill levels, but vary substantially across states, industries and occupations. Our findings of the dynamics on the demand side of the labour market suggest that skill-targeted policies might not be as effective as policies targeted at the state and industry levels to facilitate economic recovery.

Suggested Citation

  • Kailing Shen & Bledi Taska, 2020. "Measuring the impacts of COVID-19 on job postings in Australia using a reweighting-estimation-transformation approach," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 23(2), pages 153-171.
  • Handle: RePEc:ozl:journl:v:23:y:2020:i:2:p:153-171
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    References listed on IDEAS

    as
    1. Forsythe, Eliza & Kahn, Lisa B. & Lange, Fabian & Wiczer, David, 2020. "Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims," Journal of Public Economics, Elsevier, vol. 189(C).
    2. Nir Jaimovich & Henry E. Siu, 2009. "The Young, the Old, and the Restless: Demographics and Business Cycle Volatility," American Economic Review, American Economic Association, vol. 99(3), pages 804-826, June.
    3. repec:wyi:journl:002164 is not listed on IDEAS
    4. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    5. Peter Kuhn & Kailing Shen, 2013. "Gender Discrimination in Job Ads: Evidence from China," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 287-336.
    6. Hilary Hoynes & Douglas L. Miller & Jessamyn Schaller, 2012. "Who Suffers during Recessions?," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 27-48, Summer.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Hoehn-Velasco, Lauren & Silverio-Murillo, Adan & Balmori de la Miyar, Jose Roberto, 2021. "The long downturn: The impact of the great lockdown on formal employment," Journal of Economics and Business, Elsevier, vol. 115(C).
    2. Shen, Kailing & Zhu, Yanran, 2023. "Labor Force Transition Dynamics: Unemployment Rate or Job Posting Counts?," IZA Discussion Papers 16373, Institute of Labor Economics (IZA).
    3. Zeng, Xiangquan & Chu, Shuai & Chen, Xuan, 2022. "China's Labor Market Demand in the Shadow of COVID-19: Evidence from an Online Job Board," IZA Discussion Papers 15201, Institute of Labor Economics (IZA).
    4. Maciej Berk{e}sewicz & Herman Cherniaiev & Robert Pater, 2021. "Estimating the number of entities with vacancies using administrative and online data," Papers 2106.03263, arXiv.org.
    5. Zeng, Xiangquan & Chu, Shuai & Chen, Xuan, 2022. "China's Labor Market Demand in the Shadow of COVID-19: Evidence from an Online Job Board," GLO Discussion Paper Series 1074, Global Labor Organization (GLO).

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    More about this item

    Keywords

    job posting; COVID-19; thin market;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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