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Who Responds to Air Quality Alerts?

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Abstract

This paper investigates behavioral response to air quality alert programs using detailed time diary data. Specifically, we investigate whether individuals targeted by mandatory air quality warnings respond by reducing time spent in proscribed activities—the most important of which are outdoor activities that raise breathing and heart rates—thereby mitigating the health effects of pollutants on high-pollution days. We find that individuals engage in averting behavior on alert days by reducing the time they spend in vigorous outdoor activities by 18 % or 21 min on average. We find differential responses to alerts, with the largest responses amongst the elderly.

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Notes

  1. Sensitive populations are defined as the elderly, children under 14, pregnant women, and individuals with heart or lung disease.

  2. For example, Mansfield et al. (2006), Beatty and Shimshack (2011) and Beatty and Shimshack (2013) and (2009), Neidell and Moretti (2011).

  3. Part 58.50 of Title 40 of the U.S. Code of Federal Regulations—AMBIENT AIR QUALITY SURVEILLANCE, Air Quality Index Reporting states that Metropolitan Statistical Areas (MSAs) with a population of more than 350,000 are required to report the AQI daily to the general public.

  4. Specifically, these include playing baseball, playing basketball, biking, boating, climbing, spelunking, caving, participating in equestrian sports, fencing, fishing, playing football, golfing, hiking, playing hockey, hunting, playing racquet sports, participating in rodeo competitions, rollerblading, playing rugby, running, skiing, ice skating, snowboarding, playing soccer, playing softball, playing volleyball, walking, participating in water sports, wrestling, attending sports or recreational events, exercise, and recreation, work, sports and exercise as part of job, home exterior maintenance, home exterior repair and decoration, lawn and gardening, and playing with animals. We tested the sensitivity of our results to the inclusion/exclusion of various activities including attendance at sporting events and recreational activities, outdoor work activities, and gardening and we found the results extremely robust.

  5. Roughly 20 % of responses were dropped because of missing CBSA information, which was reported in the ATUS as “not identified or non-metro.” CBSAs do not cover all rural areas which also don’t experience high pollution days. Thus we don’t believe dropping these responses biases our results.

  6. Dots mark the location of the ozone monitor closest to the center of the CBSA where the alert was issued.

  7. The data summary statistics are consistent with the US population according to both the 2000 and 2010 Census. For example, in 2000 12.3 % of the ATUS population was reported to be black, while 3.6 % were reported Asian. These statistics were similar in the 2010 census. Additionally, the male-female breakdown is similar, with 6 % more females in the final data than the census.

  8. The link to a Poisson regression model is clear. However, the Poisson distributional assumption imposes strong restrictions on the conditional moments of \(y_{it}\), namely that \(E(y_{it})=Var(y_{it})\), that are violated in many applications. Gourieroux et al. (1984) show that the estimated parameters \((\beta )\) are consistent provided the conditional mean is correctly specified, and the Poisson assumption is needed only for efficiency. In other words, the estimated coefficients are not affected by the validity of the Poisson assumption, but the standard errors are. This realization gave rise to the Poisson quasi-maximum likelihood estimator (QMLE) or the GLM-LL, which we implement by using a Poisson model and estimating the variance-covariance matrix of the estimates (the standard errors are the square root of the diagonal of this matrix) using the Huber/White/Sandwich linearized estimator. This estimator does not assume \(E(y_{it})=Var(y_{it})\), and in fact, it does not even require that \(Var(y_{it})\) be constant.

  9. Recall that our measure of VOAs encompasses a broad range of activities, so a response of this magnitude would be consistent with individuals scaling back as many VOAs as was possible on an alert day, but still performing some activities regarded as essential, such as watering landscape plants on hot days.

  10. These are omitted in the interests of space but are available from the authors on request.

  11. When the bins are further coarsened, these significant results disappear.

  12. The coefficient on the 200 plus AQI indicator is insignificant and positive, which is likely due to the limited number of observations with AQI levels above 200.

  13. 185 % of the federal poverty line is a common threshold used for government subsidies such as the Women, Infant, and Children (WIC) program.

  14. Again, results are similar for the discrete specification, which in the interest of space we omit.

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Authors and Affiliations

  1. Precision Health Economics, 201 Mission Street, 12th Floor, San Francisco, CA, 94105, USA

    Alison L. Sexton Ward

  2. Department of Agricultural and Resource Economics, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA

    Timothy K. M. Beatty

Authors
  1. Alison L. Sexton Ward
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  2. Timothy K. M. Beatty
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Correspondence to Alison L. Sexton Ward.

Appendix

Appendix

See Table 9.

Table 9 Results—total population
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Ward, A.L.S., Beatty, T.K.M. Who Responds to Air Quality Alerts?. Environ Resource Econ 65, 487–511 (2016). https://doi.org/10.1007/s10640-015-9915-z

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