How commuting affects subjective wellbeing

Commuting and satisfaction with life overall

A review of the economic literature on SWB found that higher life satisfaction is associated with being in good physical health, being employed, being married or cohabiting and not being in middle age (Dolan et al. 2008). Income has a positive association with life satisfaction, but with diminishing returns. Women report higher life satisfaction scores than men (ibid). Turning to the effect of commuting on life satisfaction, cross-sectional studies have consistently found a negative relationship between commute time and life satisfaction, including studies in Sweden (Olsson et al. 2013), the United States (Choi et al. 2013), Great Britain (ONS 2014) and China (Nie and Sousa-Poza 2018).

With concern that cross-sectional associations between commute time and life satisfaction may be spurious, researchers have conducted longitudinal analysis by obtaining panel data and estimating fixed effects regression models (which consider within-individual differences in SWB and account for unobserved differences between individuals which persist over time such as personality factors). Stutzer and Frey (2008) used data from the German Socio-economic Panel Study (GSOEP), which included observations for the same individuals between 1985 and 2003. Fixed effects models indicated that longer commute times were associated with lower life satisfaction. Stutzer and Frey (2008) noted that economic theory implies rational individuals will only take on burdensome commutes if compensated by better jobs or housing. Hence, they would not have expected the negative association between longer commute times and life satisfaction.² The authors offered the explanation that this ‘commuting paradox’ arises from people underestimating the negative effects of commuting and their ability to adapt to it.

By contrast to Stutzer and Frey (2008), Lorenz (2018) found no association between commute distance and life satisfaction using the same data source and method but data for years 2007–2013. In the UK context, no association was found between commute time and life satisfaction in a fixed effects regression analysis of data from 1996 to 2008 for participants of the British Household Panel Survey (BHPS) (Dickerson et al. 2014).

With respect to commute mode, Stutzer and Frey (2008) found no significant differences in the negative association between commute time and life satisfaction for those using different modes. Similarly, the non-significant association between commute time and life satisfaction applied to all commute modes for the UK analysis by Dickerson et al. (2014).

Commuting and satisfaction with sub-domains of life

Some researchers have considered how commuting affects different domains of life, including satisfaction with family and social networks, housing, employment, leisure time availability and health. One reason for exploring sub-domains is that self-reported life satisfaction responses are relatively stable over time (which has been attributed to genetics and personality) and only differ substantially after extreme events (e.g. bereavement, unemployment) (Lucas 2007). Furthermore, it is plausible that commuting might affect one sub-domain differently from another.

A number of studies have found longer commute times to be associated with reduced satisfaction with leisure time (Dickerson et al. 2014; Stutzer and Frey 2008). Lorenz’s (2018) assessment of multiple life domains (based on GSOEP data for 2007–2013) found that longer commute distances have a negative association with satisfaction with leisure and family life. But no association was identified between commute distance and job satisfaction. This differs with the result of Stutzer and Frey (2008) which showed a negative association between commute time and job satisfaction (using the same panel data, but for years 1985–2003). It could be (1) that there are differing effects of commute time and distance on job satisfaction and/or (2) that the relationship between commuting and job satisfaction in Germany has changed over time.

Other studies have shown a relationship between commuting and subjective assessments of physical health. Künn-Nelen (2016) found (from an analysis of BHPS data for 1991–2008, using fixed effects models) that longer commute times are associated with lower health satisfaction, but not with objective health measures such as the presence of health problems and sickness absences from work.

The studies that have analysed the relationship between commuting time and satisfaction with sub-domains of life have not reported differences by commute mode but their main focus has been on the effects of commute time and this might reflect absence of analysis rather than lack of association.

Commuting could be considered a sub-domain of life given it occupies a considerable amount of time in many workers’ lives. Recent research has investigated satisfaction with the commute and the factors that influence this. These generally show that longer commute durations decrease commute satisfaction and that commuters using active modes of transport (walking and cycling) report the highest levels of commute satisfaction and public transport users the lowest levels (e.g. Olsson et al. 2013, St-Louis et al. 2014).

There are few studies that measure SWB in terms of commuting as well as other sub-domains and life overall. One exception is a cross-sectional study of Swedish commuters which found satisfaction with the commute contributed to explaining both experiential wellbeing and life satisfaction (Olsson et al. 2013). It would be valuable to examine whether this relationship also holds with longitudinal data. However, large-scale, longitudinal data sets which capture different aspects of SWB do not capture satisfaction with the commute. Hence our study explores the relationship between different commuting behaviours and SWB, measured in terms of life overall and other major sub-domains, without directly considering satisfaction with the commute itself.

Commuting and experiential wellbeing

Kahneman et al. (2004) analysed how people rated their mood (positive affect) during various daily activities. Commuting received the lowest positive affect scores and one of the highest negative affect scores, indicating that mood is worsened during the journey to/from work. Some studies have examined whether there is a relationship between commuting and people’s overall experiential wellbeing (their emotions across all activities). Cross-sectional studies have shown longer commute times associated with reduced happiness (Olsson et al. 2013; Choi et al. 2013; ONS 2014; Nie and Sousa-Poza 2018). However, Lorenz (2018) found no association between commute distance and frequency of feeling angry, worried, happy or sad (based on panel data and fixed effects models for workers in Germany). It is unclear whether these contrasting results might be explained by differences in exposure measures used (commute time versus distance) or differences in the data and type of analysis used (cross-sectional data/analysis versus longitudinal data/analysis).

Commuting and mental health

Other studies have examined the effect of commuting on mental health, as measured using the General Health Questionnaire (GHQ-12) scale (which involves 12 questions designed to detect symptoms of psychological distress). Mental health has been defined as a “state of well-being in which an individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and is able to make a contribution to his or her community” (WHO 2018).³ Mental health is distinct from SWB, since mental health refers to an objective state of the individual. However, there are some overlapping features—mental health is often measured through asking people about themselves, albeit with the aim to identify their level of cognitive-emotional functioning (Manwell et al. 2015).

Roberts et al. (2011) carried out fixed effects modelling analysis of BHPS data (1991–2004) and found that longer duration commutes were associated with worse mental health for women, but not men. Martin et al. (2014) analysed the same data set (for 1991–2008) and with fixed effects modelling found walking to work and using the bus to be associated with better mental health than commuting by car. Martin et al. (2014) also separately analysed associations between commute mode and responses for the 12 different GHQ-12 symptoms and found that car travel was associated with higher likelihood of reporting ‘being constantly under strain’ and ‘being unable to concentrate’ compared to walking or cycling to work, but no significant differences for other symptoms.

Model specification

The dependent variable is the SWB score (we tested six different measures of SWB) for individual i at time t. X_it is a vector of independent variables (including commuting time and mode and control variables) and \( \bar{X}_{i} \) is a vector of the means of the independent variables over the time interval 1,…6. β and γ are coefficients to be estimated by the model. The unknown components are α_i which is a time-invariant idiosyncratic (random) effect (assumed to be Normal distributed with mean 0 and standard deviation σ_α), uncorrelated with \( \bar{X}_{i} \) or with X_it, and ε_it which is a random error term that is assumed to be independently distributed over i with arbitrary serial correlation.

This specification was suggested by Mundlak (1978) and is referred to as the correlated random effects model. It is a variant of the fixed effects models typically used to analyse panel data (by Stutzer and Frey (2008) and others in the previously reviewed commuting and wellbeing studies) and has some advantages. Fixed effects models provide coefficient estimates that only reflect within-individual variation of independent variables. The introduction of the mean values of time-varying independent variables removes cross-sectional variation from the coefficients of the time-varying variables which are then effectively the coefficients that would have been estimated from a fixed effects model. However, additionally, the coefficient estimates for the means of time-varying independent variables enable the association between SWB and time invariant variables (such as gender) to be estimated and also represent associations between SWB and between-individual variation in independent variables.⁴

In interpreting the results, we place greater weight on the coefficients for within-individual variation in independent variables. However, we do not dismiss associations based on between-individual variation in independent variables since we consider this still provides plausible evidence for effects on SWB (given that we control for other independent variables and given that six waves of data provides somewhat limited data on within-individual variation in commuting over time, a point argued by Dolan et al. (2008)).

The SWB indicators used in our analysis are measured on an ordinal scale but in our main models we treat them as cardinal variables and estimate linear, ordinary least squares (OLS) models. Substantive differences have not been found when comparing OLS and ordered models in previous work (Ferrer-i-Carbonell and Frijters 2004). This was checked and confirmed for our data by estimating fixed effect ordered logit models for each dependent variable, using the ‘Blow-up and Cluster’ (BUC) approach proposed by Dickerson et al. (2014).

It was observed that significant changes in commute time often correspond with a change in the origin or destination of the commute (see “Descriptive analyses” section). Binary variables were included to control for home and employment location changes, since each of these events (as choices made to improve personal/household circumstances) might be expected to improve SWB. As a robustness check we excluded the binary variables controlling for home and employment location changes. As expected, these variables were found to confound the commute time-SWB relationship i.e. the commute time coefficients were smaller, but remained significant when the move home/employment location controls were removed.

We examined the effects of working from home on SWB compared to commuting, by estimating a model variant in which working from home was included as a dummy ‘commute mode’ category. Commute time was coded as zero for observations where the individual worked from home. Compared to commuters with non-zero but low commute times, the working from home dummy variable represents the additive effects of working from home beyond the effect of a zero commute time. The dummy variable will also pick up unmeasured characteristics of respondents working from home relating to factors such as job type, but this effect is likely to be limited when considering associations based on within-individual variation (where the same person works from home on some occasions and elsewhere on other occasions).

Population sub-groups

We were also interested in identifying whether relationships between commuting and SWB differ for different sub-groups in the population (such as where Roberts et al. (2011) found that longer commute times had a more negative association with mental health of women than men). Sub-group analysis was conducted by estimating four separate sets of fixed effects models⁶ for each SWB indicator with interaction terms between commute time and categorical variables for: (1) commute mode, (2) gender, (3) age group, and (4) personal income quintile. This enabled an assessment to be made of whether the commute time—SWB relationship was stronger/weaker for particular sub-groups in the population.

Life satisfaction mediation

It is possible that the relationship between commuting and life satisfaction is mediated through the impact of commuting on SWB sub-domains such as satisfaction with leisure or job (as indicated in Fig. 1). Mediation of the life satisfaction relationship was examined by including selected SWB sub-domains as additional controls in the life satisfaction model. The development of this model variant and its interpretation is explained in further detail in the “Results” section.

Descriptive analyses

The mean (one-way) commute time across the pooled sample was 27 min, but there are large differences between modes. For example, rail commuters travelled for nearly an hour on average, compared to 25 min for drivers.

Bivariate associations

Figure 2 shows the bivariate (between-individual) associations between commute time and each measure of SWB (for the wave 1–6 sample). This indicates that the satisfaction with job, leisure time and life scores are lower amongst those with longer commute times. The trend is strongest for leisure time satisfaction and only modest for life satisfaction. Strain is higher amongst those with longer commute times, and this is a contributing factor to slightly lower mental health (GHQ12) for those with longer commutes. The higher job and life satisfaction and mental health for those with commutes of over 90 min only applies to a small group, constituting just 1% of the sample. A limitation in the data is that we do not know how many times in the week such long commutes are performed. Self-reported health is higher for those with longer commute times.

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Regression models

Commute time

The strongest relationship we identified is between commute time and leisure time satisfaction and there is a direct mechanism to explain it—the more time an individual spends commuting, the less time is available for other activities. The association is smaller in magnitude for those in the middle-income quintile, suggesting they are more willing to sacrifice leisure time to access employment.

The results also indicate that when commute time increases, job satisfaction reduces. The coefficient representing within-individual variation is significant at the 99% confidence level, indicating a robust relationship and the magnitude of effect is also quite large, at least when compared against the effect of income on job satisfaction—a 10-min increase in commute time (one-way) is equivalent to a 19% reduction in monthly income in terms of the effect on job satisfaction (on average). This finding implies that the journey to work has a bearing on how workers evaluate their jobs. Hence it is important for employers to recognise that the commute has a direct impact on employee satisfaction. This observation provides added justification for initiatives such as employer-based measures to improve commuting options and incentives for (new) employees to live within easy reach of the workplace. It also suggests potential to integrate employer-based initiatives for mobility management with wider ‘wellbeing at work’ policies.

The job satisfaction of women is more sensitive to longer commute times than men. This is likely to be a consequence of their greater household and family responsibilities, as suggested by Roberts et al. (2011). It is important for employers to recognise how the commute affects staff differently according to their life situation. It is notable that the commute time association with job satisfaction was not found to apply to young adults and those in the bottom two income quintiles. These groups appear willing to accept longer commutes for their job. For young adults, this could be because it is easier and more acceptable to perform longer commutes at this life stage, which may be seen as beneficial and necessary in terms of achieving future career goals. It may also be that expectations concerning commuting change as people get older. In later life, people may expect to achieve a more comfortable daily routine and may also have greater demands on their time such as those associated with looking after family. Alternatively, it could indicate that young adults and lower income workers have less choice over where they live and work.

The results indicate that when commute time increases, strain increases and mental health worsens. These relationships are not as strong as those for leisure time and job satisfaction (and we found no association between commute time and self-reported health). Nevertheless, they are based on within individual variation, providing robust evidence of a direct relationship, i.e. that longer commute times introduce greater stress into daily life. It is notable that the association with mental health was found to be stronger for women than men in a previous study covering the period 1991–2004 (Roberts et al. 2011), but this was not found to be the case in our study. It is possible that conditions for coping with long commutes have become more equitable. However, we have seen that job satisfaction of women is more sensitive to longer commutes than men and hence the evidence is inconclusive. This is an area that warrants further research.

The coefficient representing within-individual variation in commute time is not significant for life satisfaction. This is consistent with the findings of Dickerson et al. (2014), who used older panel data for Great Britain, indicating that there is no direct association between commute time and life satisfaction for workers in England. Longer commutes are taken on to accommodate family, housing, career and income preferences. On the other hand, longer commute journeys have been shown to be associated with negative impacts on the sub-domains of SWB described above. The mediation analysis indicated that longer commute times reduce satisfaction with leisure time which in turn reduces life satisfaction. Our overall interpretation for commuters in England is that they are generally successful in trading off the drawbacks of a longer commute journey against the benefits they bring in relation to overall life satisfaction (at least in the short-term as we found evidence that persisting with long commutes over the long-term was associated with lower life satisfaction).

Commute mode and working from home

Our results indicate that longer commute times by bus are associated with reductions in job satisfaction and mental health compared to driving and other modes. This indicates that commuting by bus is unsatisfactory for many and implies a policy need to improve the journey experience by bus between residential areas and employment centres in England.

The mode of transport used also plays a role beyond the time taken to get to work and active commuting is found to have some specific benefits. Walking to work is associated with improved SWB in terms of increased leisure time satisfaction and reduced strain. Cycling is associated with higher self-reported health, but we cannot be certain of the direction of the effect. It is possible that healthier people choose to cycle. Working from home is shown to be associated with increased job satisfaction and leisure time satisfaction, indicating benefits of working in this way for those for whom this is possible.

Changes to the commute situation

It is important to recognise that significant changes in the commuting situation (time and/or mode) are most likely to occur in conjunction with a change in the home or employment location (as highlighted in the “Descriptive analyses” section). It is at these ‘moments of change’ that people have opportunities to reduce the burden of the commute and hence to achieve SWB benefits. Influencing the decisions of individuals is clearly challenging. The degree to which commute time can be reduced may be constrained by a number of factors including lack of (affordable/desirable) housing in closer proximity to work, and the spatial distribution of employment opportunities. This may have a disproportionate effect on lower socio-economic groups who may have less choice over housing and employment locations, be more likely to live in housing areas that are poorly connected to employment and hence be more willing to accept arduous commutes (as is indicated by our results). Nevertheless, the findings imply that: (1) it is useful to remind people of the wellbeing benefits of shorter commutes during employment and residential relocations; and (2) that macro-level land use, housing and employment policies should be designed over the longer term to enable people to live within easy reach of employment opportunities.

Limitations and opportunities for further research

This study draws on a large panel data set which supports examination of multiple aspects of commuting on individual-level SWB, including for selected population sub-groups. There are a number of limitations of the study.

Firstly, the study relies on basic measures of commuting. In common with other large-scale social surveys, no information was available on whether (1) people vary the mode used to get to work across the week/month/year; (2) whether people use more than one mode for their journey to work; (3) the extent to which people have any choice over when and how to travel to work; and (4) how many days a week the commute is undertaken. It was only possible to consider a very coarse measure of working from home in our models (whether workers usually work from home or not), and it was not possible to distinguish between those with fixed and flexible working schedules. People with the longest duration commutes may combine commuting with working from home and it is not known to what extent such lifestyle choices impact on SWB. It is also plausible that multi-modal commutes are more stressful than single mode commutes, as a consequence of interchange penalties or increased likelihood of unreliable journeys. Further research is warranted to investigate how variability/flexibility in commuting behaviour influences SWB.

Secondly, the study is based on up to six waves of data per individual and with more waves of data it will be possible to obtain more robust estimates of within-individual coefficients.

Thirdly, the effect sizes (implied by the coefficients) in most cases are small.⁸ The SWB benefits of commuting alone may not justify investment in the transport system to improve commuting, but there are likely to be many other justifications for such investment. It would be valuable to build up the evidence base by starting to evaluate the impact of transport interventions on SWB and other outcomes. Furthermore, whilst a strength of the dataset we have used is that it enabled us to examine multiple aspects of SWB and commuting, we have included a multitude of statistical analyses, with multiple comparisons. Thus statistical significance levels that are close to the standard thresholds used in statistical tests (e.g. p < 0.05) should be treated with due caution.

Fourthly, it is also necessary to recognise that there is a great deal of heterogeneity in both employment situations and the nature and experience of commuting. For example, shift work in a factory presents a very different employment context compared to working in an office—active commuting may have particular benefits for those with sedentary desk jobs which do not apply to those with more physical roles. This study was limited to evaluating differences in the relationship between commute time and SWB for four broad population groups (defined by commuting mode, gender, age and income quintile). Hence further research, including the application of qualitative methods, would be valuable to provide explanatory insights into (1) the level of choice people have over their commuting situation and (2) the mechanisms through which commuting influences different aspects of SWB for different groups in the population.

Concluding remark

A shorter commute time has benefits in terms of increased job satisfaction, increased leisure time satisfaction and reduced strain/improved mental health. Walking to work has benefits in terms of increased leisure time satisfaction and reduced strain. Hence shorter commute times and walkable commutes can contribute to improved SWB—particularly through the release of leisure time. However, life satisfaction overall will only be maintained if the benefits of undertaking the commute (earnings and satisfactory housing/employment) are not compromised. This implies a need for integrated policies across transport, land use, housing and employment to deliver affordable housing within easy reach of desirable employment.