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Time use and happiness: US evidence across three decades

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Abstract

We use diary data from representative samples from the USA to examine determinants and historical trends in time-weighted happiness. To do so, we combine fine-grained information on self-reported happiness at the activity level with data on individuals’ time use. We conceptually distinguish time-weighted happiness from evaluative measures of wellbeing and provide evidence of the validity and distinctiveness of this measure. Although time-weighted happiness is largely uncorrelated with economic variables like unemployment and income, it is predictive of several health outcomes and shares many other determinants with evaluative wellbeing. We illustrate the potential use of time-weighted happiness by assessing historical trends in the gender wellbeing gap. For the largest part of the period between 1985 and 2021, women’s time-weighted happiness improved significantly relative to men’s. This is in stark contrast to prominent findings from previous work. However, our recent data from 2021 indicates that about half of women’s gains since the 1980s were lost during the COVID-19 pandemic. Hence, as previously shown for several other outcomes, women appear to have been disproportionally affected by the pandemic. Our results are replicable in UK data and robust to alternative assumptions about respondents’ scale use.

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Data availability

All datasets used are publicly available.

Notes

  1. ‘Eudaimonic’ wellbeing is sometimes considered a third category (Deci and Ryan 2008; Clark and Senik 2011).

  2. Throughout this section, we will take the terms ‘wellbeing’ and ‘utility’, and the terms ‘experienced’ and ‘hedonic’ to be synonymous.

  3. This conclusion depends on assuming both separability (i.e. total utility does not depend on the order in which momentary utilities are experienced) and time-neutrality (i.e. the utility of each moment equally contributes to total experienced utility). Under these assumptions, time-weighed happiness satisfies a principle of temporal monotonicity: adding an extra period of enjoyment to an episode cannot reduce experienced utility of the episode. For a formal derivation see Kahneman et al. (1997).

  4. Hedonic wellbeing is often measured with global single-item measures, too. The question in the General Social Survey, ‘Taken all together, how would you say things are these days--would you say that you are very happy, pretty happy, or not too happy?’, is an example of this. Worries about memory bias would also apply in that case.

  5. Krueger (2007) also found that the percentage of time when the strongest emotion was negative (the so-called U-index) was greater for women than men. Using 2013 ATUS data, we calculate Krueger’s U-index. The index is based on data on four different feelings: happiness, sadness, tiredness, and pain. We estimate that the U-index in 2013 was 0.185 for men and 0.206 for women. This is consistent with Krueger (2007). Therefore, while women appear to experience greater happiness than men, women also experience intense negative emotions more often than men.

  6. Unlike Kaplan and Schulhofer–Wohl (2018) who found favourable occupational changes over time for women, we find no evidence that happiness during market work increased more for women than men between 1985 and 2013 (see Fig. 1).

  7. Together, all data relating to the USA, apart from the 1985 enjoyment ratings, are sourced from IPUMS. Because the IPUMS version does not contain the enjoyment information in the 1985 AUTP, we incorporate that information directly from the original 1985 data.

  8. As far as we know, the USA and UK are the only two countries where enjoyment or happiness information was collected along with time diaries in both the 1980s and 2010s. The time-use surveys in France and Italy collected enjoyment information in 2010 and 2008, respectively (Cornwell et al. 2019). Since the enjoyment data was collected only once in each country, however, these datasets cannot be used for studying long-term trends.

  9. Since respondents are not asked about a global concept of happiness, as asked in standard surveys, but instead about happiness during a particular activity, we think that this assumption is reasonable. Moreover, using DRM data, Kahneman and Krueger (2006) found that happiness and enjoyment are highly correlated (0.73) within episodes for the same individual.

  10. Sleeping and personal care are excluded when estimating TWH in Eq. (2) because happiness data are not available for these activities in the ATUS data.

  11. The cross-sectional nature of our data does not allow us to give an analysis in which we truly predict health outcomes at time t with time-weighted happiness at t − 1. Unfortunately, no such panel data was available to us. For analyses of this sort in the context of life satisfaction data, see, e.g. Steptoe et al. (2015).

  12. When regressing subjective variables on each other, picking up common-method variance is a concern. However, both objective and subjective variables are all answered on the same binary scale. Hence, any concerns about common-method variance should affect the regressions of subjective and objective outcomes similarly.

  13. Using ATUS data, Kushlev et al. (2015) and Stone et al. (2018) found that income is not associated with activity-level happiness, but they did not explore its relationship with time-weighted happiness.

  14. Appendix Figures A3 and A4, respectively, replicate fig. 1 using a more detailed set of 46 activity categories, and using either OLS or an ordered probit model.

  15. Changes in happiness (time-use) between 1985 and 2013 are statistically insignificant for market work, education, and religious activities (shopping, education, and religious activities) for both genders.

  16. Nevertheless, many studies still find greater life satisfaction among women than men in current cross-sections (Graham and Chattopadhyay 2013; Tay et al. 2014; Meisenberg and Woodley 2015).

  17. One driving mechanism may have been increased loneliness during the pandemic (Lepinteur et al. 2022). Although in our data, men and women experienced a comparable increase in the share of time spent alone during the COVID-19 pandemic (see Appendix Figure A14); women’s TWH is more negatively affected by time spent alone (see the regression coefficients in Appendix Table A4).

  18. Since (0.014 − (−)0.021)/((0.045 − 0.033) + (0.014 − (−)0.021)) = 0.745.

  19. We also decomposed trends in the gender gap into contributions from each individual activity. Appendix Figure A6 shows that the increase in the gender gap between 1985 and 2013 was driven by women allocating more time towards market work, outdoor leisure, and travel, and away from domestic work and indoor leisure. The decline in the gender gap between 2013 and 2021 was mainly driven by reductions in happiness among women during domestic and market work, as well as watching TV and eating meals (see Appendix Figure A7).

  20. The larger increase in the gender gap in the time-weighted happiness in the UK relative to the USA may be explained by differences in trends in time use: We find a larger decline in the gender gap in domestic work time in the UK than the USA between the 1980s and 2010s. However, the USA and UK data differ in terms of sampling and survey design. Therefore, it is hard to be definitive on what drives the difference.

  21. We did so by first dividing the 1985 sample into 32 cells defined by four socio-demographic variables: age (18–29, 30–39, 40–49, 50+), education (H.S. degree or less, some college or above), gender, and marital status (single, married), and subsequently using the shares in each of the 32 cells as reweighting variables.

  22. To identify the model we also assume that τ1985, 8 = τt ≥ 2010, 5 = 0. Setting response thresholds to zero is arbitrary; any other value would have served equally well and would only have shifted the estimated intercept. The more positive overall trend in Fig. 5 is driven by our assumption that the 8th threshold on the 1985 scale has the same location as the 5th threshold on the 2010s scale. If we had made the (implausible) assumption that, say, the 2nd threshold on the 1985 scale was equivalent to the 8th threshold of the 2010s scale, we would have observed an overall negative trend in time-weighted happiness. Importantly, however, the differences in trends between men and women are not driven by this normalization.

  23. We also reproduced Fig. 3 using a 3-point and a 5-point collapse, again yielding the same qualitative results (see Appendix Figures A12 and A13).

  24. That said, mobile apps and online devices are increasingly used to collect time use data (Gershuny et al. 2021; Killingsworth 2021; MacKerron and Powdthavee 2022), and time-use data is of course not exclusively used for wellbeing research (see, e.g. Aguiar et al. 2013; Bauman et al. 2019).

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Acknowledgements

We thank editor Klaus F. Zimmermann and three anonymous referees for their suggestions and guidance. We also thank Jan-Emmanuel De Neve, Carol Graham, Daniel Hamermesh, Mark Fabian, Lucia Macchia, Joel McGuire, Alberto Prati, Wei Qian, and Julia Schmidtke, as well as seminar participants at Zhejiang University, the Chinese Economists Society 2021 conference, the ISQOLS 2021 annual conference, and the ASSA 2022 annual conference for their helpful comments. We also thank Jonathan Gershuny for providing the 1985 US and 1986 UK time use data.

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  1. Department of Economics, Baylor University, Waco, USA

    Jeehoon Han

  2. Warwick Business School, Coventry, UK

    Caspar Kaiser

  3. University of Oxford, Oxford, UK

    Caspar Kaiser

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Han, J., Kaiser, C. Time use and happiness: US evidence across three decades. J Popul Econ 37, 15 (2024). https://doi.org/10.1007/s00148-024-00982-4

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