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Labour Insight
Labour Insight™ - Data Caveats
Labour Insight™ - Data Caveats

Video Tutorial: Limitations associated to Labour Market data collected online

Updated over a week ago

Increasing data-set.

  • As we move into an increasingly digital age we are finding a larger share of the overall job market has come online. Resulting in our total counts annually increasing over time.

  • E.g. between 2012 to 2019 our total annual job listings within ANZ doubled. This does not necessarily mean the number of job openings have doubled, simply the number of listings collected.

  • Due to this we recommend bench-marking the results and using percentages rather than absolute numbers when running a point in time analysis.

Natural bias towards high paying jobs.

  • While more jobs are being advertised online, we have identified a natural bias towards higher paying jobs.

  • Some jobs such as IT and healthcare are almost comprehensively online. Other categories of jobs such as construction, the service sector, and other typically low-paying, rural, or roles in very small companies are not well represented in online job postings. Thus, we have little to say about these jobs.

Private sites

  • Our data is collected through autonomous spiders scraping data from job boards, therefore we are unable to access password protected sites.

  • To understand how this affects our data-set multiple studies have been run, both internally and externally, and we have found our figures have a strong correlation with other online vacancy measures.

Stock and Flow

  • Our data is a representation of the flow of job listings, rather than the stock of jobs in the market. Due to this, jobs with high turnover may be over-represented in our data. Turnover results in high numbers of job listings – this does not necessarily indicate growth.

Employer Demands.

  • As we only report on explicitly mentioned data points we are unable to report the skills or attributes of the successful candidate. Job listings are comprised of traits the employer would like the ideal candidate to possess, but may not necessarily be required in order to be successful in the roll.

Recall rates

  • Our recall rate is the percentage of listings that return from a query that also contain the data point you are reporting on. Different reports have differing recall rates as listings may include different data points.

  • E.g. In 2019 within ANZ we saw that 50% of all listings included an Employer name, whereas only 22% included a Salary. Due to this we recommend taking into consideration the effect the recall rate plays on the sample size.

Internet unreliability

  • Our data is sourced from online job listings. We do occasionally notice our source sites break which can have an effect on our total counts.

  • While we update our aggregation techniques accordingly to minimise the impact we have no control over the length of time a site may remain down.

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