Predicting Skill Shortages in Labor Markets: A Machine Learning Approach

Dawson, Nik; Rizoiu, Marian-Andrei; Johnston, Benjamin; Williams, Mary-Anne

doi:10.2139/ssrn.3681132

Cited by 3 publications

(2 citation statements)

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“…More recent attempts have also aimed at predicting occupational skill shortages based on a series of underlying labour market indicators (e.g. Dawson et al, 2020;OECD, 2017). 11 We also acknowledge however the notable challenges of the EIBIS measure, such as the fact that it is not clear if the lack of availability of staff with the right skills refers mostly to firms' external recruitment difficulties or inhouse skill gaps affecting the proficiency of existing workers.…”

Section: The European Investment Bank Investment Surveymentioning

confidence: 99%

Corporate Training and Skill Gaps: Did COVID-19 Stem EU Convergence in Training Investments?

Pouliakas

Wruuck

2022

SSRN Journal

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Section: The European Investment Bank Investment Surveymentioning

confidence: 99%

Corporate Training and Skill Gaps: Did COVID-19 Stem EU Convergence in Training Investments?

Pouliakas

Wruuck

2022

SSRN Journal

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“…Intuitively, occupations experiencing high posting volatility are difficult to predict. Subsequent work showed these indicators to be predictive of labour shortages in the Australian Labour Market (Dawson et al, 2020a). In the present research, in Jobs data analysis and results , we use a similar set of indicators to analyse labour demand for journalists.…”

Section: Relevant Literature and Backgroundmentioning

confidence: 99%

Layoffs, inequity and COVID-19: A longitudinal study of the journalism jobs crisis in Australia from 2012 to 2020

et al. 2021

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In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include ‘social media’ and ‘generalist communications’ skills.

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Assessment of demand for personnel in textile industry on the basis of data analysis of online job portals

Vankevich¹,

Kalinouskaya²,

Зайцева³

2022

International Conference on Textile and Apparel Innovation (Ictai 2021)

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Predicting Skill Shortages in Labor Markets: A Machine Learning Approach

Cited by 3 publications

References 0 publications

Corporate Training and Skill Gaps: Did COVID-19 Stem EU Convergence in Training Investments?

Corporate Training and Skill Gaps: Did COVID-19 Stem EU Convergence in Training Investments?

Layoffs, inequity and COVID-19: A longitudinal study of the journalism jobs crisis in Australia from 2012 to 2020

Assessment of demand for personnel in textile industry on the basis of data analysis of online job portals

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