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Mental health: who is more vulnerable to high work intensity? Evidence from Australian longitudinal data

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Abstract

Aim

The adverse impacts of exposure to work intensity on mental health have been widely studied. However, there is a lack of research examining who is most vulnerable in terms of position on the mental health distribution. The current study aims to: (a) initially estimate the average impacts of work intensity on workers’ mental health in Australia, and then (b) estimate the extent to which this effect varies across the mental health distribution.

Materials and methods

The current study uses data from waves 2005­–2017 of the Household Income and Labour Dynamics in Australia (HILDA) survey. It first employs Average Treatment Effect (ATE) to provide a baseline/average treatment effect for the whole population, and then applies Quantile Regression fixed effects models for various quantiles on the mental health distribution.

Discussion and conclusion

The baseline estimates show that there are significantly negative effects of work intensity on mental health for the whole population, but importantly the quantile fixed effect estimates show that these adverse effects are substantially stronger for those with the poorest mental health (i.e. at the bottom of the distribution). When ATE alone is estimated, the significant effect is averaged over the mental health distribution, missing important information regarding the heterogeneity of the effect. The findings have important implications for understanding and reducing mental health inequality, particularly inequality driven by workplace stress. First, they align with existing research demonstrating the importance of reducing psychosocial job stressors. Second, given workers with mental health problems were most susceptible to the adverse effects of work intensity, there is a need to offer additional support (and be sensitive of workloads) for this group in particular.

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Fig. 1
Fig. 2

Source: Estimates from HILDA waves 5–17

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Notes

  1. Our estimate from the Household Income, and Labour Dynamics in Australia Survey (HILDA) data shows a very high correlation between job demand and work intensity.

  2. HILDA SF-36 includes a set of questions that make up the Medical Outcomes Study Short-Form General Health Survey (SF-36), one of the most widely used measures of subjective health. The SF-36 is a self-reported multidimensional measure of general health status or quality of life.

  3. The Cronbach’s alpha for internal consistency for work intensity was acceptable (0.73), and the alpha for work flexibility was 0.82.

  4. We considered including industry variables to control for work environment, however, our FE models showed little effects of this inclusion because there is a little variation in industry over time within each worker. Models with an industry dummy variable control results in a little change in our estimated coefficient of work intensity.

  5. Mundlak model estimates random-effects regression models adding group-means of variables which vary within groups as further controlling variables. This technique was proposed by Mundlak (1978) as a way to relax the assumption in the random-effects estimator that the observed variables are uncorrelated with the unobserved variables.

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Authors and Affiliations

  1. Australian National University, Research School of Population Health, Canberra, Act, Australia

    Tinh Doan & Liana Leach

  2. Thuong Mai University, Hanoi, Vietnam

    Van Ha

  3. University of Canberra, Canberra, Australia

    Anh La

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  1. Tinh Doan
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  2. Van Ha
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Appendices

Appendix 1

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Table 4 Summary statistics for employed people, weighted estimates (pooled data 2005–2017)
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4.

Appendix 2

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Table 5 OLS and quantile regression with lags of potentially endogeneinous covariates including work intensity, 2005–2017
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5.

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Doan, T., Ha, V., Leach, L. et al. Mental health: who is more vulnerable to high work intensity? Evidence from Australian longitudinal data. Int Arch Occup Environ Health 94, 1591–1604 (2021). https://doi.org/10.1007/s00420-021-01732-9

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