Employed adults spend the majority of their waking hours at work. But does the type of work they do play a role in their wellbeing -- even after you account for factors like how much money they make and other demographics that might shape their lives?
A study published in Preventive Medicine Reports -- based on data drawn from Gallup surveys of more than 50,000 employed adults in the U.S. in 2014 -- suggests the answer is "yes."
While socioeconomic factors, income in particular, are well recognized as determinants of wellbeing, little research to this point has looked at how wellbeing varies across people's job categories.
So, with the goal of investigating this potential relationship, the authors of this study looked at the distribution of worker wellbeing across 11 broad job categories. They studied wellbeing both at the overall level and through the lens of five interconnected dimensions: career (formerly known as purpose), social, financial, physical and community.
Key Findings
The authors' analysis revealed that overall wellbeing, as well as its five distinct dimensions, does vary by job category among U.S. workers. But more than that, they found these associations persisted even after they adjusted for household income and other demographic factors known to affect wellbeing, including age, household size, marital status, education, gender, race and part-time or full-time work status.
Across all 11 job categories, overall wellbeing (the composite of the five individual elements) was higher among business owners, professionals, managers, and farming/fishing workers and lower among clerical/office, service, manufacturing/production and transportation workers.
However, the differences the authors found within the domains reiterate the importance of digging deeper and assessing multiple domains of wellbeing:
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Career wellbeing (liking what you do and being motivated to achieve your goals) varied the most across job categories. There were small differences across income levels for business owners, professionals, managers, and farming/fishing workers, and statistically significant gaps between the high-income group and the two lower-income groups among clerical/office, service, manufacturing/production and transportation workers.
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Manufacturing and transportation workers scored lowest in community (liking where you live, feeling safe, and having pride in your community) and social (having supportive relationships and love in your life) wellbeing, while farming/fishing workers had one of the highest scores for community wellbeing and one of the lowest for social wellbeing.
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No one job category was dominantly high or low in every dimension, but business owners reported the highest wellbeing on all but the financial dimension (managing your economic life to reduce stress and increase security).
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Physical wellbeing (having good health and enough energy to get things done) showed the smallest gaps across income groups within job categories.
Conclusions
The inequality that the authors observed in wellbeing across job categories suggests that job category is an important component of worker wellbeing. As such, they conclude that the relationship warrants further research, and that continued efforts to track multiple dimensions of wellbeing across different jobs in the U.S. -- and elsewhere around the world -- are critical to identifying new interventions to promote worker and workplace wellbeing.
Read the full paper, Worker well-being in the United States: Finding variation across job categories.
The authors of the study are Emily Stiehl, School of Public Health at the University of Illinois at Chicago; Nkenge H. Jones-Jack, Carter Consulting, Inc. and the National Center for Chronic Disease Control and Public Health Promotion, Centers for Disease Control and Prevention; Sherry Baron, Queens College, City University of New York; and Naoko Muramatsu, School of Public Health and Institute for Health Research and Policy, University of Illinois at Chicago.
The National Institute for Occupational Safety and Health provided access to the Gallup database that made this analysis possible.
