Labour demand and skill shortages have historically been difficult to assess
given the high costs of conducting representative surveys and the inherent
delays of these indicators. This is particularly consequential for fast
developing skills and occupations, such as those relating to Data Science and
Analytics (DSA). This paper develops a data-driven solution to detecting skill
shortages from online job advertisements (ads) data. We first propose a method
to generate sets of highly similar skills based on a set of seed skills from
job ads. This provides researchers with a novel method to adaptively select
occupations based on granular skills data. Next, we apply this adaptive skills
similarity technique to a dataset of over 6.7 million Australian job ads in
order to identify occupations with the highest proportions of DSA skills. This
uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019.
Finally, we propose five variables for detecting skill shortages from online
job ads: (1) posting frequency; (2) salary levels; (3) education requirements;
(4) experience demands; and (5) job ad posting predictability. This contributes
further evidence to the goal of detecting skills shortages in real-time. In
conducting this analysis, we also find strong evidence of skills shortages in
Australia for highly technical DSA skills and occupations. These results
provide insights to Data Science researchers, educators, and policy-makers from
other advanced economies about the types of skills that should be cultivated to
meet growing DSA labour demands in the future