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Men and Women Pay Different Health Costs for Unstable Work Hours, New Research Suggests
In A Nutshell
- A large U.S. study found that workers whose weekly hours swing widely from month to month are more likely to see their health decline within a year, compared with workers on steady schedules.
- For men, bigger swings were linked to worse self-reported health while still employed, with the odds climbing from about 28% to about 38% at the highest volatility measured.
- For women, the effect showed up differently: instead of worsening health scores, volatile hours were linked to leaving work, cutting hours, or missing work for health reasons.
- The study is observational, so it cannot prove volatile hours caused these health changes, and it cannot say whether the swings came from employers, employees, or shifting demand.
Millions of American workers never know how many hours they’ll log week to week, some weeks are light, while others are far more packed. For employees whose weekly hours swing widely from one month to the next, that instability appears tied to something more serious than scheduling hassle: declining health, according to a new study.
Research published in the journal SSM: Population Health finds that workers whose weekly hours bounce around the most are more likely to see their health worsen over the following year, compared with workers on steady schedules. But that connection shows up in sharply different ways depending on whether the worker is a man or a woman, a distinction the researchers say has been largely overlooked.
For men, bigger swings in hours were linked to a higher chance of reporting worse overall health while still holding a job. For women, the pattern was harder to see at first, not because their health wasn’t affected, but because the effect showed up elsewhere: cutting back on work, missing work, or leaving the workforce for health reasons.
A Steady 40-Hour Week Scored Zero. A Wild Swing Didn’t
Researchers at the University of Illinois Urbana-Champaign used data from the Current Population Survey, a large government survey that asks about health once a year and tracks work hours more often. Linking individuals across survey years let researchers connect a later health change to how much a person’s weekly hours had varied earlier on.
To measure that variation, called work-hour volatility, researchers calculated how much a worker’s usual weekly hours changed month to month. Someone who logged 40 hours every week scored a zero; someone who swung between 20 and 60-hour weeks scored much higher. The measure captures how much hours moved, not why, or whether the worker had advance notice.
More than 55,000 working-age adults, ages 18 to 64, made up the main sample, with data drawn from 2016 through 2024, covering periods before, during, and after the COVID-19 pandemic.
Men’s Health Declined While Still Employed, Women’s Didn’t Show Up the Same Way
Among men, the results were fairly direct. As hour volatility increased, so did the odds of reporting worse health the next year, climbing from about 28% at the steadiest schedules to about 38% at the most volatile ones. Reports of stable health declined over that same stretch, while improvement rates barely budged. Volatile hours appeared to push men’s health in one direction: downward.
Among women, something more complicated emerged. Looking only at women continuously employed through both years, researchers found no clear link between hour volatility and worsening health. That might suggest women were unaffected. But prior research shows women are more likely than men to cut hours, take leave, or stop working altogether when their health worsens. If a volatile schedule hurts a woman badly enough that she cuts back or quits, she vanishes from a standard study before her health outcome ever gets counted.
To catch that, researchers added a fourth category: workers who, by the second year, were working part-time for health reasons, absent from work due to illness or injury, or out of the labor force due to illness or disability. They called this health-related work limitation.
Once that category was added, the picture for women changed. Higher hour volatility was clearly linked to women moving into health-related work limitation. Comparing a volatile schedule, like alternating between part-time weeks and 60-hour weeks, against four steady 40-hour weeks, the odds of this kind of disruption more than doubled, rising from about 2.5% to 5.2%. The researchers note that’s a jump comparable to the employment disruption seen after serious medical events like heart attacks or breast cancer diagnoses.
Long Hours Made the Damage From Volatile Schedules Worse
Hour volatility’s health cost wasn’t uniform. For both men’s health decline and women’s health-related work limitations, the risks were larger among those also working more hours overall. Volatility layered on long hours seems to be a particularly damaging combination, a reminder that work time isn’t just about how many hours someone puts in, but how much those hours shift.
Employed-Only Studies Missed Women’s Health Toll
One of the study’s central contributions is methodological. Most occupational health research looks only at people who remain steadily employed, ignoring anyone whose health got bad enough to push them out of work. That creates a distortion: the workers most affected, particularly women who tend to exit under health strain, end up excluded from studies meant to measure workplace risks. By tracking outcomes after workers reduced or left employment for health reasons, researchers captured part of the story conventional approaches would miss.
Fair workweek laws, adopted in a handful of U.S. cities and states, generally require advance schedule notice and compensation when shifts change at short notice. A federal proposal, the Schedules That Work Act, would extend similar protections nationally. The study’s authors cite findings like these as support for policies promoting stable hours, while stressing their data cannot identify who initiates the swings measured.
Erratic work hours have long been treated as an inconvenience or an economic problem. This research suggests they’re also connected to health, showing up differently in men and women, but showing up nonetheless.
Disclaimer: This article is based on a single peer-reviewed observational study and describes associations, not proven cause and effect. It is not medical advice. Anyone concerned about their own health or work situation should consult a qualified professional.
Paper Notes
Limitations
The study has several important limitations to keep in mind. Health was measured at only two points in time, one year apart, so researchers could not track longer-term patterns or observe whether health recovered after periods of volatility. Self-reported health, while a well-validated measure, is subjective and may be influenced by social and cultural factors, including gender norms around how people describe their own health. The work-limitation category captures a meaningful disruption but cannot distinguish between temporary absences and permanent exits from the workforce, nor assess how medically serious those situations were. The study is observational, meaning it cannot prove that hour volatility caused the health changes observed; workers are not randomly assigned to volatile or stable schedules, and unmeasured factors, such as employer practices or early health problems not captured at baseline, could be influencing results. Finally, because the analysis spans pre-pandemic, pandemic, and post-pandemic periods, the reasons behind hour fluctuations may have differed across those contexts in ways the study does not separately address.
Funding and Disclosures
Based on the authorship contribution statement in the paper, Tim F. Liao is listed as responsible for funding acquisition. No specific grant numbers or funding agency names are identified in the provided content. Authors declare they have no known competing financial interests or personal relationships that could have influenced the work. During manuscript preparation, the authors used Grammarly and ChatGPT to assist with clarity and language, after which they reviewed and edited the content themselves and take full responsibility for the published article. The study uses publicly available, de-identified government data and was determined to be exempt from institutional review board oversight.
Publication Details
Authors: Sinyee Qianyi Lu and Tim F. Liao, University of Illinois Urbana-Champaign, Urbana, IL | Paper Title: ‘Unstable hours, unequal health: Gendered occupational health costs of work-hour volatility in the U.S.’ | Journal: SSM: Population Health, Volume 35, 2026, Article 101949 | DOI: https://doi.org/10.1016/j.ssmph.2026.101949 | Published: Available online July 13, 2026 | Data Source: IPUMS CPS ASEC 2016–2024, provided by the Institute for Social Research and Data Innovation at the University of Minnesota







