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The connection between working hours and body mass index in the U.S.: a time use analysis

Review of Economics of the Household volume 14, pages 131–154 (2016)Cite this article

Abstract

Over recent decades, Americans have transitioned from working in active jobs to working in sedentary jobs, and there have been dramatic increases in hours worked for certain demographic groups. While a body of research documents that time spent working is associated with increased body mass index (BMI), this paper explores possible mechanisms for that relationship using time use data. This paper finds that, for workers in non-strenuous jobs, 10 additional hours spent working are associated with an increase in BMI of 0.424 for women and 0.197 for men, representing an increase of 2.5 and 1.4 pounds, respectively. The paper does not find a relationship between working time and BMI for workers in strenuous jobs. For workers in non-strenuous jobs, the effect of time spent working on BMI becomes smaller after accounting for time spent sleeping for both men and women and time spent in exercise and food preparation for women only; the effect becomes larger after accounting for screen time for both men and women and time spent in secondary eating and commuting for women only. Screen time is the single time use channel associated with the largest differences in the estimated effect of time spent working on BMI for both women and men employed in non-strenuous jobs. After controlling for all time use channels, the effect of hours worked on BMI decreases for women, but increases for men. These findings suggest plausible mechanisms for the association between time spent working and obesity.

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Notes

  1. This analysis focuses on the intensive margin (hours of work) and not the extensive margin (labor force participation).

  2. Following Zick et al. (2011), a MET value ≥3.3 is used as a cut-off as this captures occupations such as building and grounds cleaning and maintenance, farming, and construction and extraction.

  3. This follows the methodology in Zick et al. (2011).

  4. Individuals with missing data are excluded from the analysis.

  5. Specifications with overweight status and obesity status as the dependent variables, each measured as dummy variables equal to 1 if the individual is classified as being overweight (BMI ≥25) or obese (BMI ≥30), respectively, and equal to 0 otherwise were also estimated, and the results were qualitatively similar.

  6. While data on actual hours worked on the diary day is available, these are not representative for many individuals in the sample since half of diary days are weekend days. Limiting the sample to only individuals with positive hours worked on the diary day could still not be representative of an individual’s usual work hours and reduces the sample greatly. Therefore, usual weekly work hours was used as the measure of time spent working.

  7. BMI is a stock measure, which is a function of cumulative excess caloric intake. The most appropriate measure of work time is then also a stock measure such as cumulative hours worked on the job or job tenure. However, since this information is not necessarily accurate or available in a consistent manner, current usual weekly work hours are used.

  8. Using log usual hours worked yielded qualitatively similar results.

  9. The majority of the sample consists of workers in non-strenuous jobs: 4 % of women (238) and 12 % of men (837) worked in strenuous jobs. Results for the effect of non-strenuous work hours were nearly identical to the estimated effect of hours worked from specifications including only workers in non-strenuous jobs.

  10. Marital status controls for whether the individual was living with a spouse or partner at the time of the CPS interview.

  11. Specifications including categorical controls for household annual income rather than own weekly income and specifications excluding the income control yielded qualitatively similar results.

  12. An alternative analysis could use time spent in the activity multiplied by the MET value associated with the activity to use a measure of activity duration and intensity as in Saffer et al. (2013) and Colman and Dave (2013a). This is discussed further in Sect. 7.

  13. Satia et al. (2004), Jefferey et al. (2006), and Chou et al. (2004) link a higher frequency of eating fast food to greater consumption of calories, fat, and saturated fat and also to obesity.

  14. Colman and Dave (2013b) show that increased physical activity is associated with a lower BMI.

  15. Gangwisch et al. (2005) and Taheri et al. (2004) suggest that sleep deprivation is associated with weight gain.

  16. For example, individuals with high levels of unobserved human capital may work longer hours and may also allocate their non-working time differently. In addition, based on unobserved characteristics, individuals may have selected into initial occupations which may influence their current BMI outcomes and health behaviors: Kelly et al. (2014) find that blue-collar work early in life is associated with an increased probability of obesity and decreased physical activity later in life.

  17. Weight in pounds is calculated as BMI multiplied by height in inches squared and then divided by 703. The magnitudes of the effects are calculated as the difference in weight in pounds before and after adding the coefficient estimates to average BMI, multiplying by average height in inches squared, and then dividing by 703. Average height and BMI are calculated from the analysis sample using sample weights. Average BMI is calculated to be 27.0 for women and 28.2 for men. Average height is calculated to be 64.5 inch. for women and 69.9 inch. for men. Calculation of changes in weight associated with effects estimated throughout the paper will use these values for average BMI and average height.

  18. Colman and Dave (2013b) find that physical activity reduces BMI.

  19. The exceptions are food preparation and commuting, which were found to only have a marginally significant change in the effect of hours worked on BMI for women.

  20. The specifications include all of the individual demographic controls used in the BMI regressions with the exception of poor health status.

  21. Including a control for overweight status yielded qualitatively similar results.

  22. Results available from the author upon request.

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Acknowledgments

I am grateful to Shoshana Grossbard, Anirban Basu, Shelly Lundberg, Seik Kim, Judith Thornton, Robert Plotnick, and Elaina Rose for their invaluable feedback. I would also like to thank seminar participants at the Federal Trade Commission and poster session attendees at the 2013 Population Association of America annual meetings for their helpful comments.

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Authors and Affiliations

  1. U.S. Census Bureau, 4600 Silver Hill Road, Washington, DC, 20233, USA

    Joelle Abramowitz

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  1. Joelle Abramowitz
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Correspondence to Joelle Abramowitz.

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The contents of this paper are of the author’s sole responsibility. They do not represent the views of the U.S. Census Bureau.

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Abramowitz, J. The connection between working hours and body mass index in the U.S.: a time use analysis. Rev Econ Household 14, 131–154 (2016). https://doi.org/10.1007/s11150-014-9267-4

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Keywords

  • Obesity
  • Time use
  • Working hours
  • Body mass index
  • BMI
  • Health behaviors

JEL Classification

  • I12
  • J22