How is Covid-19 really affecting the job market?

 

The impact of Coronavirus on the UK job market isn’t so much about where the most cases have been recorded. Geographically we have seen regions that are more dependent on manufacturing, rental or holiday accommodation, travel & tourism being hit much harder than those regions with large numbers of cases, or where there has been more ‘Stay-at-Home working’ capability.

 

Detailed and comprehensive research and data analysis by leading AI software and labour analytics company Burning Glass has been conducted across the United States. The regions suffering the greatest jobs impact from the coronavirus pandemic aren’t those with the highest number of cases too. Those most dependent on the vulnerable industries, such as those previously mentioned, have been the worst affected.

 

Nationwide, new UK job postings dropped to more than 70% below the annual average in April. CV applications initially dropped to 21% below, but recovered to just 12% under the annual average, according to job board data provided by WaveTrachR.

 

 

Regions with the highest number of reported COVID-19 cases and early social distancing restrictions saw declines in job postings. Yet other regions with lower per capita reported cases saw much larger declines in job postings.

 

“The economic shock of the pandemic is a nationwide phenomenon, but just as some places are suffering more infections and deaths than others, some regions and sectors are suffering more economically – and the two aren’t always related,” said Matt Sigelman, CEO of Burning Glass Technologies. “The jobs impact is driven more by the underlying economy of a region or sector than how many COVID-19 infections it has or what kind of social distancing rules are in place.”

 

WavetrackR data and CV-Library data, confirmed that application numbers in the UK were down in 9 out of the 10 largest industry sectors in Q1 of 2020.

 

 

Other key trends in Burning Glass job posting data include:

• Job postings are up around 10% for many health care roles such as Pharmacists, Respiratory Therapists, and Registered Nurses. There’s an even larger demand for cleaning workers, vital in keeping clinical areas sterile. Demand for housekeeping staff is up over 10% in the health care field. Even as overall demand dropped by over 30%. Demand for health care Laundry and Dry-Cleaning Workers is up around 30%, even as it dropped overall by 40%.
• Yet health care postings dropped by over 15% overall. Driven by sharp declines in back-office postings and roles that require non-essential but personal contact.

 

Predicting the future

One thing is apparent from the lack of clarity coming out of both professional and non-professional channels. There is a lot of uncertainty when it comes to what the post Covid-19 business world will look like. History tells us the job market and the recruitment of staff provides real-time insights into economic recovery. It is likely that this crisis will be no different.

 

Common sense says changes will come at some point in the next few weeks. As the Government implements the first phase or wave or return to work directives. Especially for the most critical jobs and sectors, with a caveat that work-from-home jobs should remain as such, for as long as possible.

 

The sectors affected most by the crisis are also likely to be those effected longest. Travel, Events and anything that involves large unnecessary gatherings of people will require long term plans to help re-establish themselves.

 

Here is another data set generated by David Whitfield at The HR Datahub this week, outlining some of the key decisions being taken by businesses during the crisis:

 

 

Obviously there are some slightly shocking statistics in here. Most notably that nearly 50% of all companies surveyed anticipate making redundancies or enforcing reduced pay within 3-6 months.

 

Over the coming week, Job posting and application numbers will no doubt provide us with more meaningful real-time insights.

 

We wish to acknowledge and thank The HR Datahub, Burning Glass, CV-Library and WaverTrackR for providing data for this article.

 

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