The Nature of Work and Economic Health Risks

During the COVID-19 Pandemic

COVID-19 Pandemic and the Nature of Work

According to Malkov, the nature of work is a critical factor behind the uneven distribution of health and economic risks created by the current pandemic. Take the nature of work of grocery store workers, for example. COVID-19 stay at home orders forced many workers to work from home. However, most grocery store workers cannot perform their tasks remotely. Therefore, these workers either risk higher chances of virus exposure or unemployment because of how their work gets done. Malkov’s research considers two factors of work, highlighted in the above example, that have been important during the pandemic: teleworkability and contact intensity.[1]

Teleworkability of an occupation refers to whether the tasks can or cannot be performed at home. Contact intensity refers to physical proximity to others at the workplace. Occupations are classified as low physical proximity to high physical proximity at the workplace. Malkov’s results imply that the nature of work, manifested through the type of occupation and the occupation of a person’s spouse, impacts the level of exposure to both health and economic risks leaving some households harder hit by the economic downturn in the short- and long-term.  Malkov also finds significant differences in the skill requirements between occupations based on teleworkability and contact intensity. This may decrease a worker’s chances of finding a new job for the newly unemployed.

COVID-19 and Implications for Households

Based on Malkov’s findings, the occupational composition of a household matters because the nature of a spouses’ work influences the household’s ability to weather economic instability and the potential for exposure to COVID-19. The former is associated with teleworkability, while the latter is associated with contact intensity of occupations.

When researching the teleworkability factor of dual-earner U.S. households, Malkov emphasized that a household is exposed to higher income and unemployment risks when both members of a couple cannot perform their job remotely. As an illustration of this context, consider a dual-earner household where one spouse is a bartender, and the other is a physical therapist. Neither position is teleworkable. If bars cannot open and physical therapy sessions are put on hold, neither spouse could work from home and support the other financially if the other becomes unemployed. According to Malkov’s estimates, in about one-fourth of U.S. dual-earner couples, both spouses have non-teleworkable jobs and are excessively exposed to labor income and unemployment risks.

These findings have policy applications in the short-term, in particular, how policymakers design financial assistance measures. When creating future financial assistance packages, it is important to consider each household’s employment structure – are both spouses in non-teleworkable jobs? Or high contact intensity jobs? Or both unemployed from non-teleworkable jobs? Households where both spouses are unemployed will have unique and dramatic financial needs and how their benefits are structured may be different from other households. In the long-term, in a household where both spouses have non-teleworkable jobs, hence exposed to greater unemployment risk, one or both spouses may consider changing to a job that is teleworkable. A significant fraction of workers shifting to a less risky and remote-friendly occupation could impact how non-teleworkable positions are staffed in the future.

Now consider the scenario that one spouse is a computer programmer, a low contact intensity occupation, and the other is a physical therapist, a high contact intensity occupation, who interacts closely with multiple patients a day. In this example, intra-household contagion increases the risk of spreading the COVID-19 within households where at least one spouse works in a high contact intensity job. In other words, the computer programmer and anyone in the household are at higher risk of exposure to COVID-19 from the working physical therapist spouse. Malkov found that about two-thirds of U.S. dual-earner couples are exposed to excessive health risks through contact within their households due to the nature of their spouse’s work. Identifying households where one individual works in a high contact intensity occupation could prove essential to targeted COVID-19 testing, vaccination, contact tracking, and protective equipment provisions to mitigate transmissions.

COVID-19 and Implications for Occupation Mobility

Industries that are friendly to remote work arrangements, such as information technology, have helped keep the U.S. economy afloat. In contrast, others, such as hospitality and retail, have been forced to lay off millions of workers. According to a Brookings report, “The task of jumpstarting the economy will require millions of Americans to either remain in their current jobs or, for those laid off, return to the workforce as soon as possible.”[2] However, many workers who were laid off due to the COVID-19 pandemic, may not have the option of returning to their previous occupations yet. Furthermore, based on a nationally representative survey by the Strada Education Network, one in three American workers say they will change career fields if they lose their job during the COVID-19 pandemic.” For many workers, a career transition would mean reskilling.[3]

The need for reskilling limits occupational mobility – the workers’ ability to switch career fields to meet labor needs.[4] Malkov’s research found significant differences in the skill requirements between occupations based on teleworkability and contact intensity. This may decrease a worker’s chances of finding a new job for the newly unemployed. He used job posting data from September 2014 to September 2018 and found discrepancies in job requirements based on these two factors. In particular, there is an increased likelihood of a skill mismatch for workers who lost their jobs following the COVID-19 outbreak and the currently available jobs.

Occupations that are more likely to allow remote work require higher levels of education and experience, in addition to greater cognitive, social character and computer skills, according to Malkov’s article. Using job posting data for the five-state area, Malkov found that 67% of teleworkable job ads with education preferences required a bachelor’s degree compared to 31% of non-teleworkable job ads with education preferences. In addition, using the same dataset, Malkov found that 39% of teleworkable job ads with experience requirements preferred 0-2 years of experience, and 24% preferred 8+ years of experience. This contrasts with the findings for non-teleworkable job ads with experience requirements, where 59% preferred 0-2 years of experience, and 10% preferred 8+ years of experience. (See Table 1 below)

In addition to greater education and experience requirements that limit occupational mobility, teleworkable occupations also require greater cognitive, social, character, and computer skills. Among teleworkable job posts, 42% required social skills in contrast to 21% of non-teleworkable job posts. (See Table 1 below) Consider the same Strada Education Network survey, the respondents who were interested in changing job fields said they would likely transition into the business field (18%), information technology field (14%), or finance field (9%). These fields require a high level of education, advanced computer skills, and advanced mathematical reasoning that may not have been developed in their previous positions.

Malkov’s research elaborates on the long-term effects of COVID-19 that result from potential skill mismatches of employment prospects and future earning potential for workers who had non-teleworkable or high contact intensity jobs at the onset of the pandemic. Among the low contact intensity job posts, 96% are for full-time positions, compared to 85% of high contact intensity job posts.

In Minnesota, labor mobility trends are directionally similar to the 5-state area in Malkov’s study. Malkov says, “We see the same patterns in terms of skills, education, and posted wages.” Malkov found similar gaps in educational preferences. In Minnesota, 71% of teleworkable job ads with education preferences required a bachelor’s degree compared to 32% of non-teleworkable job ads with education preferences. Trends in experience requirements in Minnesota job ads also followed the five-state region. Using the same dataset limited to Minnesota job postings, Malkov found that 35% of teleworkable job ads with experience requirements preferred 0-2 years of experience, and 27% preferred 8+ years of experience. In the same way, the Minnesota findings for non-teleworkable job ads with experience requirements, of which 58% preferred 0-2 years of experience and 11% preferred 8+ years of experience. Minnesota’s job ads show similar gaps in the availability of full-time work for high and low contact intensity work as the five-state region. Among the low contact intensity job posts, 96% of ads are for full-time positions, compared to 82% of high contact intensity job posts. (See Table 2 below)

These findings have important consequences for broader policy implications regionally, statewide, and in the Twin Cities Metro. “As the patterns are the same, speculations on policy implications will be meaningful in Minnesota and the Metro Area,” adds Malkov. Specifically, while enhanced unemployment benefits or stimulus payments for COVID-19 relief can insure workers against short-run losses, they fall short of insuring long-run losses. Furthermore, the existing differences in skill requirements based on teleworkable or high contact intensity jobs could inform future workforce training programs for the unemployed. While some hard skills, like basic computer skills, can be acquired through short trainings, social and character skills are often developed through experience, which takes time. Malkov’s insight regarding skill mismatches is just one of dozens of potential impacts of the COVID-19 pandemic on workforce concerns that could be used to plan for long-term impacts.

Divisions in our workforce that shape how our state’s workers experience the health and economic risk also underscore the crucial value of coordination across sectors and industries from public health to workforce development to policy. Malkov notes that this project is an excellent example of a successful collaboration between the academic world and the business and workforce worlds. He adds that “often our solutions are different, and we operate in silos that fail to influence each other.  Academics like beautiful solutions, but these solutions aren’t always practical.  Business and workforce solutions are grounded in reality but can struggle to be truly strategic and have a broad impact. This article is an attempt to collaborate across academia and policy.”

Conclusion

What happens over the next few months is difficult to predict. The decisions our region’s policymakers, businesses, workforce solutions organizations, academics, and workers make in the coming months will have a high impact on the regional economy in both the short- and long-term.

Malkov concludes that “if we look back to the Great Recession, firms inflated the skills for some occupations and certifications required.  Given Minnesota’s shortage of workers, how will businesses respond?  As we look to reopening our economy and restaffing, will employers seek talent similar to who was employed in the past, or demand more skills—or less?” Only time will tell.

 

Follow this link to read Malkov’s full article, “Nature of Work and Distribution of Risk: Evidence from Occupational Sorting, Skills, and Tasks.”

Follow this link to read Malkov’s Vox EU article, “The Viability of Working from Home: A Study of Couples in the US.”

Explore RealTime Talent’s insights into COVID-19 and the job market, here at our blog. Our team is also available if you have any questions about how we can support your work. Contact us here.

[1] Malkov bases the classification of occupations by teleworkability and contact intensity on previous studies using O*NET data. For skill requirements, he uses the online vacancy posting data from TalentNeuron with access provided by RealTime Talent. Finally, to study the occupational composition of the U.S. households he uses the American Community Survey data.

[2] https://www.brookings.edu/research/working-parents-are-key-to-covid-19-recovery/

[3] https://www.stradaeducation.org/publicviewpoint/#latest

[4] https://www.investopedia.com/terms/o/occupational-labor-mobility.asp#:~:text=Occupational%20labor%20mobility%20refers%20to,employment%20or%20meet%20labor%20needs.