Variable Impacts of New Credentials for the Older Worker

By Workcred – Published March 2021 in WorkcredSubscribe to the WFMonitor eNewsletter

Workcred and the George Washington Institute of Public Policy (GWIPP) conducted an analysis of Participant Individual Record Layout (PIRL) data “to examine the impact of new credentials on reemployment for older workers.” PIRL data and definitions are the result of a joint project of the U.S. Departments of Labor and Education in conjunction with the Workforce Innovation and Opportunity Act (WIOA).

Older adults are categorized for this analysis as age 50+, average age of 57.7. Other averages are 46.1% women, 86.9% with a high school diploma or equivalent, 20.3 % with a bachelors, 11.2% veterans, and  $8,340 income over two quarters. 

Seventy-three percent of older adults who were enrolled in WIOA-funded training (69,311) use their funding to earn an industry-recognized certificate or nondegree certification, while 12.7% pursue an occupational license. The top six fields of study are motor vehicle operators (15.9%), computer occupations (11.4%), health technologists and technicians (5.7%), operations specialties managers (4.8%), secretaries and administrative assistants (4.3%), and home health aids and nursing assistants (4.1%). 18.6% comprise the “other” category.

Reemployment Outcomes
Previously employed workers in the fields of installation, maintenance and repair, health care support, and operations specialties management see their odds improve before reemployment after training. Veterans who receive training are 15.4% more likely to be reemployed than veterans who do not receive training. 

Most older adults who complete training earn less than what they earned before being displaced. However, health care diagnosticians, treating practitioners (e.g., registered nurses), home health aides, along with motor vehicle operators, earn more than before being displaced. 

In general, those who complete training in fields in which they were previously employed earn more than those who complete training in a different field than previously employed. 

Men who complete training generally earn more than women, except for women with bachelor’s degrees who average about $720 more than men per quarter. 

Interstate Variability and Completion Rates
“In most states, the percentage of older workers participating in WIOA programs who applied for and received training grants was less than five percent, and for nearly half the country – 24 states – the percentage was less than two percent.”

Regarding completion rates, older adults are more likely to complete training than younger workers (under 49 years of age). Overall, older adult health technologists, information and record clerks, health care diagnosticians, and treating practitioners (e.g., registered nurses) are less likely to complete training. 

Recommendations Derived from Analysis
More research is needed – More research on older adults earning credentials will need to address several factors, including credential value, labor market data, longevity issues, career shifting, and the breadth of interstate differences of older workers who participate in WIOA-funded training.

Stakeholders need to share data and support federal data efforts – Currently the National Student Clearinghouse is “working to link outcomes data on academic and industry credentials – such as credentials issued by the American Welding Society, NIMS [National Incident Management System], and the Manufacturing Skills Standards Council – for the first time.” Lumina is funding this work and will provide insights related to the education-workforce continuum. The overall world of many different credentialing bodies can contribute to a more comprehensive view about older adults earning credentials, resulting in a better understanding of credential-earning outcomes as they relate to older adults. Moreover, in addition to PIRL, other federal data collection processes and programs can “provide researchers, policy makers and the broader education and workforce community with rich variables for analysis.”   

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