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How work hours affect well-being: A target trial emulation

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  • Ballerina X S Chong
  • Chris G Sibley
  • Joseph A Bulbulia

Abstract

Studies link longer work hours to multiple dimensions of well-being, but correlations do not show what would happen if hours changed. Target-trial emulation addresses this problem by specifying the experiment we would like to run and then approximating it with observational data. Using three annual waves of the New Zealand Attitudes and Values Study (NZAVS, N = 24,579; 2020–2023), we estimate how 28 well-being outcomes would differ if the same cohort of pre-retirement adults worked 10 more or 10 fewer hours per week than observed. We compare what would happen if weekly hours shifted up by 10 or down by 10 with what actually occurred, after accounting for dropout, using machine-learning methods to adjust for baseline differences. Increasing work hours by 10 most clearly raises fatigue and reduces sleep; body mass index (BMI) and perceived physical health also shift adversely but are more sensitive to residual confounding, while perceived support increases slightly but remains confounding-sensitive. Decreasing work hours by 10 most clearly lowers fatigue; BMI and perceived physical health also shift favourably but are likewise more sensitive to residual confounding. Most outcomes show little movement under either policy, and the downward shift is better supported by the data. Naive baseline associations are broader, larger, and sometimes reversed in sign, whereas sensitivity analyses (E-values) indicate that the clearest fatigue effects are robust to moderately strong residual confounding. Under the stated assumptions, work-hour shifts affect recovery and perceived physical health more than broad well-being.

Suggested Citation

  • Ballerina X S Chong & Chris G Sibley & Joseph A Bulbulia, 2026. "How work hours affect well-being: A target trial emulation," PLOS ONE, Public Library of Science, vol. 21(6), pages 1-15, June.
  • Handle: RePEc:plo:pone00:0350816
    DOI: 10.1371/journal.pone.0350816
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
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