Why US firms need to look for the 'hidden' workforce
US firms are reporting widespread labour shortages. Image: REUTERS/Keith Bedford
- US businesses have warned about challenges filling vacancies.
- A new report suggests that a 'hidden' workforce could help fill the gap.
- These are workers that have been overlooked because of hiring practices.
Since business has picked up with the COVID vaccine rollout, record numbers of employers have struggled to find workers. In August, half of U.S. small business owners had jobs they wanted to fill, a historic high, according to a trade group survey; 91 percent said there were few or no qualified applicants. The reasons for this labor-employment mismatch are complex and not fully understood, economists say.
A new report says there is a “hidden” workforce of 27 million people in the U.S. who would gladly, and capably, fill those jobs — if given the chance. But because of hiring practices, the applications of this diverse group usually go straight to the rejection pile.
Co-author Joseph B. Fuller ’79, M.B.A. ’81, co-chair of the Managing the Future of Work project at Harvard Business School, says corporate leaders could solve many of their labor problems if they gave these workers a closer look, and gain a real advantage over competitors unwilling to do so, and improve workplace diversity. Interview has been edited for clarity and length.
GAZETTE: What was the impetus for this report?
FULLER: The vast majority of academic research on labor markets is from the supply side. It doesn’t look at the employer as an animated object that makes decisions based on a rationale that may or may not be sound. Before I was a professor at HBS, I was in industry, and it always struck me that there were these anomalies. Communities with lots of people looking for work and employers bemoaning the lack of candidates, but employers essentially acting as if a [qualified] candidate is supposed to present her or himself [for] the job they have on offer for the terms they’re offering. And if that didn’t happen, there was something quote “wrong.” They weren’t very active in addressing it themselves. Why was that?
The second thing is, if you look at the government data, it’s not actionable. [It doesn’t delineate] “this is how many long-term unemployed there are; this is how many discouraged workers there are; this is how many underemployed workers there are.” Huge numbers of people, but very little nuance in explaining why. So, I wanted to understand what’s behind these numbers.
GAZETTE: Many screened out of the application process early are people with felony convictions and people without a college degree. Who else makes up this “hidden” workforce?
FULLER: Veterans tend to be hidden because their skills, and the way those skills are described, don’t match with the skill descriptions employers are seeking. If someone’s looking for a salesperson, they’re looking for sales experience. So, they’re looking for those kinds of keywords in your résumé description of yourself. If they’re not there, you don’t get considered.
People who’ve had gaps in their work history: Half the companies in the United States have a filter to exclude applicants who have not been employed in the last six months or if there’s a gap in their work history of more than six months.
The biggest category is called NEET: Not in Employment, Education or Training. That’s a person who doesn’t have a job, doesn’t have a degree, is not in school. [Automated screening systems don’t] know what to do with them.
A big part of this research effort is to take that number [of 27 million] and break it down into identifiable chunks and give both employers and policymakers some insight into what does it take to get this part of the population into the workforce.
GAZETTE: About 99 percent of Fortune 500 companies use artificial intelligence tracking systems to screen applicants and then winnow them down to a manageable number before starting the interview process. Those systems determine who makes the cut based on specific parameters or keywords. Why such an all or nothing approach?
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