The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession Kenneth Y. Chay and Michael Greenstone

Background  The impact of air pollution on infant health is a topic of considerable interest to a wide range of researchers and policy analysts. Previous research has documented a statistical association between differential pollution levels across sites and variation in adult health outcomes.  However, a heated debate has arisen about whether these documented correlations are causal. At issue is the possibility that since air pollution is not randomly assigned across localities, previous studies may not adequately control for a number of confounding determinants of mortality.

Previous Studies on TSPs pollution  cross-sectional analyses of the correlation between adult mortality rates and pollution levels across U.S. cities  time-series analyses of the correlation between daily adult mortality rates and pollution levels within a given site  cohort-based longitudinal studies of adults that suggest that particulates pollution results in excess mortality

Research Question  Does air pollution increase the infant mortality rate?  This study uses sharp, differential air quality changes across sites attributable to geographic variation in the effects of the recession to estimate the relationship between infant mortality and particulates air pollution.

Main findings  The recession induced substantial variation across sites in air pollution reductions.  1% reduction in TSPs results in a 0.35 % decline in the infant mortality rate at the county level  The analysis also reveals nonlinear effects of TSPs pollution and greater sensitivity of black infant mortality at the county level.

Data

Empirical Methodology (I)  Cross-sectional model of infant mortality Yjt = f(Xjt, Zjt, Wjt) + ejt where Yjt is the infant mortality rate in county j in year t Xjt jt is the average TSPs reading across all monitors in the county Zjt is per-capita income Wjt jt is a vector of the other (observed or unobserved) determinants of county-level infant mortality rates

Empirical Methodology (II)  Yjt = Xjt β + Zjt θ + Wjt΄П + ɛ jt β is the true effect of TSPs on infant mortality ɛ jt = α j + ujt  ΔYjt = Yj82 – Yj80 = Xj82 β - Xj80 β + ɛ j82 - ɛ j80 = Δ Xjt β + Δ ɛ jt.  Δ Yjt = Δ Xjt β + Δ Zjt θ + Δ Wjt ΄П + Δ ɛ jt where λ st are state fixed effects in infant mortality changes Δ ɛ jt = λ st + Δ ujt

Quasi-Experimental Results  The association between changes in TSPs and infant mortality rates is strong during the recession and non-existent in the periods before and after the recession.  Further, when compared to the non-recession years, the period has much greater variation in TSPs changes that is less correlated with the relatively small changes in the observed covariates.  This suggests that the omitted variables problem may be reduced by narrowly focusing on the period.

 Examine infant deaths within 24 hours, 28 days (neonatal), and one year of birth  Examine infant deaths due to internal health reasons and deaths due to external “non-health” reasons  Examine the effects of TSPs changes on infant birth weight

Evidence on the Pathophysiologic Mechanism  It appears that TSPs pollution has a much larger impact on infant mortality than on birth weight. Further, the estimated effect of TSPs on infant mortality falls very little when flexible controls for birth weight are included in the instrumental variables specifications.  It should be noted that our estimates of the effects of TSPs on birth weight may be biased down due to censoring.  Specifically, if declines in TSPs also reduce the likelihood of a miscarriage or stillbirth, then conditioning on fetuses that survive to birth will lead to selection bias.

Robustness of Findings

Reducing bias: Matching on Income Shocks  Refine the treatment and control groups by matching counties with similar income changes in the period.  Limit the sample to counties with low employment levels in manufacturing that do and do not neighbor counties with high manufacturing employment levels.

Reducing bias: “Neighboring” Counties  Use the contiguous county data file which provides information on the locational relationship between counties

Alternative Explanations  First, there are several medical interventions that may affect an infant’s health.  The literature on the “production” of infant health emphasizes the importance of a mother’s ability to support a fetus for nine months. This ability, also known as the maternal health endowment, is believed to affect an infant’s survival probabilities.  Finally, a criticism of previous research on air pollution and adult mortality is that pollution may have caused those who were already very ill to die slightly earlier than they would have otherwise, implying minimal loss in life expectancy

Policy Implications  On the whole, the regulation of TSPs appears to pass a cost- benefit test if the estimated benefits from the hedonic study are used.  However, the results of a cost-benefit analysis are less clear if the benefits are limited to those associated with reductions in infant mortality.

Conclusion  We find that a 1-µg/m3 reduction in TSPs is associated with 4-7 fewer infant deaths per 100,000 live births at the county level, which is an elasticity of  we find significant effects of TSPs reductions on deaths within 24 hours of birth and on infant birth weight.  The analysis also reveals nonlinear effects of TSPs and large infant mortality effects at TSPs concentrations below the EPA- mandated air quality standard.  Overall, the estimates imply that about 2,500 fewer infants died from than would have in the absence of the reductions in air pollution