The Relationship between Ozone and Hospitalizations

168 The Journal of Human Resources Table 3 OLS and IV regression results for effect of ozone on respiratory illnesses 1 2 3 OLS IV IV A. First stage Boat traffic 100,000 – 4.608 4.409 – [0.029] [0.044] Boat traffic 100,000 distance – ⳮ 0.198 ⳮ 0.181 – [0.001] [0.003] Boat traffic 100,000 distance2 1000 – – ⳮ 0.293 – – [0.048] B. Second stage Eight-hour ozone 0.113 0.454 0.442 [0.023] [0.162] [0.162] Wu-Hausman F test 1,1927109 – 4.820 4.485 P-value – 0.028 0.034 Percent effect 1.16 4.66 4.54 Notes: significant at 5 percent, significant at 1 percent. N⳱1,927,187 in all regressions. Robust standard errors clustered by date in brackets. Dependent variable is the number of respiratory related hospital admissions per day, zip code, and age category. All regressions include controls for carbon mon- oxide, nitrogen dioxide, maximum temperature, minimum temperature, precipitation, wind speed, humidity, cloud cover, age dummies, year-month dummies, day of week dummies, cubic day trend, and zip code fixed effects. “Percent effect” is the estimated percentage change in the dependent variables from 0.01 ppm increase in ozone based on the estimated ozone coefficient. second-stage estimates will be consistent estimates of the biological effect of ozone on asthma hospitalizations.

C. The Relationship between Ozone and Hospitalizations

Turning to estimates of the relationship between ozone and health, we present OLS and IV results in Panel B of Table 3. 32 OLS results, shown in Column 1, indicate ozone has a statistically significant relationship with respiratory related hospitaliza- tions. A five-day increase in ozone of 0.01 ppm is associated with a modest 1.2 percent increase in hospitalizations. To gauge the sensibility of this estimate, we can compare it to estimates from previous epidemiological studies. A meta-analysis by Thurston and Ito 1999, which also focuses on all respiratory hospital admissions for all ages, finds a 1.5 percent increase in hospitalizations from a 0.01 ppm increase in ozone, an estimate quite comparable to ours. 33 32. Our sample size of 1,927,187 comes from daily data from April-October from four age groups across roughly 350 zip codes in SCAQMD for eight years 1993-2000, less missing values. 33. Thurston and Ito 1999 compute a relative risk of 1.18 for a 0.1 ppm change in one-hour ozone. Translating this to percent change 1.18-11.18 reveals a 15.25 percent increase from a 0.1 ppm change, or 1.525 percent increase from a 0.01 ppm change. Moretti and Neidell 169 -0.010 -0.005 0.000 0.005 0.010 0.015 0.020 0.025 0.030 2 4 6 8 10 12 14 16 18 20 22 24 Distance from port miles O zo ne ppm linear -95 CI +95 CI quadatic Figure 3 Effect of average daily port activity on ozone levels Note: Results are based on regression coefficients from Columns 2 and 3 of Table 3. When we turn to our IV estimates we find estimates that are nearly four times larger than OLS estimates. The considerably larger estimates, shown in Column 2, imply a 0.01 ppm increase in the five-day average ozone is associated with a 4.7 percent increase in hospitalizations. This difference is statistically significant ac- cording to a Hausman test, which has a p-value of 0.028. In Column 3, when we use the quadratic in distance, we find a similar 4.5 percent increase in hospitaliza- tions. These estimates suggest that accounting for avoidance behavior, measurement error, and confounding increases estimates by a factor of four. Neidell 2009 finds estimates are roughly two times larger when controlling only for public air quality information. This difference is due to the fact that we also correct for measurement error, other unobserved sources of information for avoidance behavior, and potential confounding from other environmental factors, such as weather, suggesting the im- portance of accounting for these additional sources of bias. Since environmental factors may be an important potential source of confounding, in Table 4 we assess the sensitivity of our estimates to the weather variables and copollutants. If estimates are unaffected by excluding these variables, it lends support to the idea that our approach is accounting for confounders. Column 1 repeats our baseline estimates. Column 2 omits all weather and copollutant variables. Column 3 omits only the latter while Column 4 omits only the former. Lastly, in Column 5 we interact ozone with all of the weather variables and copollutants, and compute the marginal effect of ozone on health by evaluating ␦h␦ozone using the mean of 170 The Journal of Human Resources Table 4 Sensitivity of regression results for effect of ozone on respiratory illnesses to weather and copollutants 1 2 3 4 5 A. OLS Eight-hour ozone 0.113 0.130 0.118 0.097 0.105 [0.023] [0.020] [0.023] [0.022] [0.027] B. IV—First stage Boat traffic 100,000 4.608 4.733 4.959 4.037 24.463 [0.029] [0.035] [0.030] [0.031] [0.351] Boat traffic 100,000 distance ⳮ0.198 ⳮ 0.192 ⳮ 0.207 ⳮ 0.187 ⳮ 0.571 [0.001] [0.001] [0.001] [0.001] [0.003] C. IV—Second stage Eight-hour ozone 0.454 0.465 0.435 0.451 0.543 [0.162] [0.168] [0.155] [0.171] [0.210] Controls for weather Y N Y N Y Controls for copollutants Y N N Y Y Interactions with ozone N N N N Y Notes: significant at 5 percent, significant at 1 percent. N⳱1,927,187 in all regressions. Robust standard errors clustered by date in brackets. Dependent variable is the number of respiratory related hospital admissions per day, zip code, and age category. All regressions include age dummies, year-month dummies, day of week dummies, cubic day trend, and zip code fixed effects. “Weather controls” include maximum temperature, minimum temperature, precipitation, wind speed, humidity, and cloud cover. “Co- pollutant controls” includes carbon monoxide and nitrogen dioxide. For Column 5, ozone is interacted with weather and copollutant variables, and the coefficient shows the marginal effect of ozone evaluated at the mean of the weather and copollutant variables. All interactions are instrumented by boat arrivals and departures interacted with weather and copollutant variables, but only coefficients for boat arrivals and departures and distance from first stage are shown. each weather variable and copollutant. 34 Our estimates are clearly insensitive to these alternative specifications, suggesting the strength of our instrument in controlling for potential confounding from environmental factors. These cargo boats primarily emit NOx, but also emit particulate matter PM, which raises an issue of whether our instrument meets the necessary exclusion re- strictions since PM affects health Chay and Greenstone 2003a; Chay and Green- stone 2003b. Two correlation patterns across pollutants suggest this is not likely to be a major issue. One, the correlation between ozone and PM is very low. Ozone typically peaks in the summer because it forms in the presence of heat, whereas the other “criteria” pollutants, including PM, typically peak in the winter. For example, focusing on the Los Angeles region, Moolgavkar 2000 found a correlation with ozone of 0.20 for PM 10 and 0.04 for PM 2.5 . Furthermore, our analysis focuses on the “ozone season”—the months of April-October—where many of these other pol- 34. In this specification, we also instrument for each of the interaction terms by interacting boat traffic with the weather and copollutant variables. Moretti and Neidell 171 Table 5 Regression results for effect of ozone by type of respiratory illness 1 2 3 4 Any Respiratory Illness Pneumonia Bronchitis Asthma Other Respiratory Illnesses A. OLS Eight-hour ozone 0.113 0.032 0.038 0.043 [0.023] [0.014] [0.014] [0.011] Percent effect 1.16 0.86 1.15 1.59 B. IV Eight-hour ozone 0.454 0.277 0.145 0.032 [0.162] [0.101] [0.103] [0.080] Percent effect 4.66 7.41 4.39 1.19 See notes to Table 3. Dependent variable is the number of hospital admissions per day, zip code, and age category, by type of respiratory illness. lutants are at considerably lower levels they are unlikely to pose a health threat. Two, although we cannot directly control for PM, 35 it is highly correlated with both CO and NO 2 Currie and Neidell 2005 so that including the two is likely to serve as a sufficient statistic for PM. Because our results are insensitive to excluding CO and NO 2 , we do not suspect the omission of PM to present a problem. Becausee we have aggregated all respiratory illnesses and ozone may have a differential effect across the type of illnesses, in Table 5 we separately explore the effects of ozone on pneumonia ICD 480-486, bronchitis and asthma ICD 466, 490, 491, 493, 494, and other respiratory illnesses. Pneumonia, bronchitis, and asthma are conditions more likely to be exacerbated from current exposure, as op- posed to respiratory conditions like emphysema, where the effects from exposure are cumulative over time Environmental Protection Agency 2006. Therefore, we expect larger effects for pneumonia and bronchitis and asthma than for other res- piratory conditions. The OLS results indicate fairly comparable effects across the conditions with a slightly larger effect, if anything, for other respiratory conditions. The IV results, however, paint a different picture. Consistent with expectations, the effects are largest for pneumonia, followed by bronchitis and asthma, and then a small effect for other respiratory illnesses. We also perform a falsification test by specifying the dependent variable as ex- ternal injuries fractures, dislocations, and sprains, an outcome that should not be affected by pollution levels. In our IV model, we find a statistically insignificant estimate of -0.161 with a standard error of 0.109. However, we also find a statisti- 35. PM is only measured roughly every six days, so assigning a daily time series at the SRA level would involve nontrivial assumptions. 172 The Journal of Human Resources cally insignificant effect of -0.008 when we estimate this by OLS. Therefore, while this test is useful in that it supports our preferred model, it is not definitive because it does not rule out a model we believe to be incorrect.

VII. The Cost of Pollution and Avoidance Behavior