AbstractAir pollution makes us feel bad when we think about it – but do bad air days really affect our subjective well-being (SWB) when we are not thinking about them? And if so, do they affect the range of possible measures of SWB in similar ways? Using data from over 165,000 individuals in the UK, we model evaluative, experiential and eudemonic SWB as a function of demographic and local area characteristics including the background concentration of particulate matter. Our results indicate that air pollution adversely affects all of the positive measure of SWB included in our analysis; how satisfied people report being with their lives overall, how happy they report feeling on the previous day and how worthwhile they rate their activities as being, and that it does so over and above its effects on self-reported health. These effects can be monetized and may imply greater priority being afforded to pollution abatement programs than is currently warranted based on existing estimates of the health effects alone.
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Our preferred specification in Model 4 finds evidence of significant
relationships between pollution and all three positive measure of
well-being: life satisfaction; worthwhileness and happiness yesterday.
The decrease in satisfaction associated with a increase in fine particulate matter in the main analysis is 0.0171 on an 11-point scale. This result is remarkably similar to that found by Orru et al. (2016) which documents a negative association of 0.0171, on a 10-point scale, with increase in coarse particulate matter, using data from the European social survey. Interestingly, the associations between PM2.5 and reports of happiness yesterday and worthwhileness of activities are almost as large in magnitude: an increase of in PM2.5 concentrations is associated with an average reduction of 0.0138 and 0.0150 points, respectively. No such associations were found between PM2.5and reports of anxiety yesterday.
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Air pollution may act to reduce an individual’s SWB indirectly through its impact on their health and also directly. By running regression models that incorporate self-reported health status, we are estimating the effect of air pollution on SWB over and above its effect through health. Here we present the regression estimates for the association between air pollution and SWB with and without controlling for self-reported health status and note that self-reported health status appears to partially mediate the relationship between air pollution and SWB. The association between PM2.5 and satisfaction, for example, is reduced by approximately 6% when health is controlled for in the model, compared to the same coefficient when health controls are removed. Similarly, the coefficients are reduced by nearly 7% and 4% in the happiness yesterday and the worthwhile model, when health is controlled for. These results suggest that health is one mechanism through which air pollution influences life satisfaction but that it is not the exclusive pathway. In contrast to this, we find no evidence of an association between anxiety yesterday and PM2.5 concentrations, either when health controls are present or absent from the model.
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Air pollution may act to reduce an individual’s SWB indirectly through its impact on their health and also directly. By running regression models that incorporate self-reported health status, we are estimating the effect of air pollution on SWB over and above its effect through health. Here we present the regression estimates for the association between air pollution and SWB with and without controlling for self-reported health status and note that self-reported health status appears to partially mediate the relationship between air pollution and SWB. The association between PM2.5 and satisfaction, for example, is reduced by approximately 6% when health is controlled for in the model, compared to the same coefficient when health controls are removed. Similarly, the coefficients are reduced by nearly 7% and 4% in the happiness yesterday and the worthwhile model, when health is controlled for. These results suggest that health is one mechanism through which air pollution influences life satisfaction but that it is not the exclusive pathway. In contrast to this, we find no evidence of an association between anxiety yesterday and PM2.5 concentrations, either when health controls are present or absent from the model.
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Income is found to have a significant relationship with all measures of
SWB. Qualitatively equivalent results were found between both PM2.5
and all measures of satisfaction, worthwhileness and happiness in the
regressions that control for income as were found in the main analysis.
However, once income was controlled for the magnitudes of the
associations do increase, for example, a increase in PM2.5 is associated with a 0.0209 point drop in life satisfaction when income is controlled for as compared to a 0.0171 drop in the main analysis.
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Based on the estimates of the effects of income and PM2.5 on
SWB, it is possible to calculate the utility-constant trade‐off ratio
between income and air pollution for the subsample for whom income data
are available. The units of analysis are based on pounds sterling of
total gross weekly income (£) and micrograms per meter cubed of annual
background concentrations of fine particulate matter (). Total gross weekly income has a mean value of £462 and a standard deviation of £575. Annual background concentrations of PM2.5 have a mean value of and a standard deviation of .
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Once income is controlled for in the sample of individuals for whom we have income data (i.e. those in employment, n 64,000) based on the satisfaction coefficient on an extra unit () of PM2.5 of 0.0209 and on an extra unit (£) of total gross income of 0.0000800. The MRS between a one‐unit reduction in PM2.5
and income is calculated as £261.25, if we take life satisfaction as
the relevant measure of well-being. In comparison to this, we calculate
an MRS of £305.86 if we base it on reports of happiness yesterday and
£379.38 if we base our calculations on the worthwhile measure of
well-being.
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by Paul Dolan 1 and Kate Laffan 2
1. London School of Economics, Social Policy, Houghton Street, London, WC2A 2AE, UK
2. London School of Economics, Social Policy, Houghton Street, London, WC2A 2AE, UK, e-mail: k.m.laffan@lse.ac.uk
Journal of Benefit-Cost Analysis http://journals.cambridge.org/action/displayJournal?jid=BCA via Society for Benefit-Cost Analysis https://benefitcostanalysis.org/ and Cambridge University Press
Volume 7, Issue 1; Spring, 2016, pages 147-195
2. London School of Economics, Social Policy, Houghton Street, London, WC2A 2AE, UK, e-mail: k.m.laffan@lse.ac.uk
Journal of Benefit-Cost Analysis http://journals.cambridge.org/action/displayJournal?jid=BCA via Society for Benefit-Cost Analysis https://benefitcostanalysis.org/ and Cambridge University Press
Volume 7, Issue 1; Spring, 2016, pages 147-195
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