Air Pollution and Premature Mortality in Rapidly Growing Economies: Evidence from Santiago, Chile

Transcription

1 Air Pollution and Premature Mortality in Rapidly Growing Economies: Evidence from Santiago, Chile Abstract This paper looks at the effects of air pollution on premature mortality by using a municipality-week panel data of mortality and particulate matter of diameter of 10 micrometers or less (PM10) for Santiago, Chile. We use exogenous variation induced by thermal inversions and a spatial mapping method (Kriging) to reduce the attenuation bias from the measurement error of imputed municipalitylevel air pollution data. We find significant effects of PM10 on infant mortality and large effects for premature mortality (both neonatal and infant mortality) for respiratory and cardiovascular causes. 1. Introduction Estimating the adverse effects of air pollution on human health has important implications for assisting policymakers in weighing the costs and benefits of public policies that aim to curb air pollution. Air pollution is known to have damaging effects on human health particularly to the very young and the elderly. Exposure to air pollution can cause a constriction of the bronchial system that impairs lung functioning and this in turn can cause respiratory and cardiovascular problems that may have long lasting effects. In this paper we estimate the effects of air pollution on a particularly vulnerable population, neonates and infants. We look at how air pollution, specifically particulate matter of diameter of 10 micrometers or less (PM10), determines premature mortality of these groups. There is a large body of literature that looks at the health effects of ambient air pollution on human health in the developed world (Chay and Greenstone 2003) and a growing literature that looks at the health effects of indoor air pollution in developing countries (Ezzati and Kammen 2001). However, the literature that looks at the effects of outdoor air pollution for rapidly growing economies is rather scarce (Arceo, Hanna, and Oliva 2015). Studying air pollution for rapidly growing countries provides some important lessons on the challenges that both rapidly growing and developing countries will face on their path to development. In this paper we look at the case of Chile, a rapidly growing economy that has suffered the adverse consequences of air pollution during the last 40 years (Instituto de Asuntos 1

2 Públicos, (2003). 1 Using data on air pollution and human health we construct a panel dataset for 31 municipalities in Santiago s Metropolitan Area spanning the period This paper provides new evidence of the effects of PM10 on premature mortality for the high levels of pollution seen in rapidly growing economies. Furthermore, we introduce two methodological contributions to the existing literature. First, we employ the Kriging method for mapping pollution data from monitoring stations and imputing air pollution spatially at the municipality level. This greatly improves the precision of the imputed pollution variable in its spatial dimension. Second, we employ detailed meteorological data that explains city-wide thermal inversions thus providing exogenous variation of the imputed air pollution variable in its time dimension. Together, this methodology greatly reduces the measurement error of the imputed pollution data (both in its spatial and time dimension), reducing the attenuation bias of the estimates of the effects of air pollution on mortality. We find significant effects of air pollution (PM10) on premature mortality, both neonatal and infant mortality. Although we do not find statistically significant effects for all-cause neonatal mortality, our results show that PM10 increases neonatal mortality caused by respiratory and cardiovascular problems. Also, we find that high concentrations of PM10 increase infant mortality, having a much more severe effect on mortality for respiratory and cardiovascular causes. Furthermore, we find a considerable larger effect of cumulative (four-week) exposure to PM10 and a significant non-linear effect of cumulative (four-week) PM10 on infant mortality. The rest of the paper is organized as follows. The next section reviews the related literature and then section 3 presents and overview of premature mortality and air pollution in Santiago, Chile. Section 4 discusses the data and the methodology employed. The main results are presented in section 5 and additional results are discussed in Section 6. Finally, section 7 concludes. 2. Literature Review There is a vast epidemiological literature that examines the association between air pollution and its effects on mortality outcomes (Ostro 1993; Dockery et al. 1993; Dockery and Pope 1994; Schwartz 1994). Ostro et al (2003) review these studies and remarks that the effects of PM10 on mortality are 1 In the early years it was largely a problem only in Santiago (the capital), but more recently it has become a latent problem in several smaller southern cities (such as Temuco, Valdivia, Chillan, etc). 2

3 fairly consistent. 2 Furthermore, in an earlier study, Ostro et. al. (1995) examine the effects of air pollution on all-age mortality for Santiago, Chile, finding that a change of 10 micrograms per cubic meter (µg/m 3 ) in daily air pollution was associated with a 0.75 percent increase in daily all-cause mortality. In addition, Cifuentes et al (2000) examined the impact of PM2.5 on daily mortality in Santiago from 1988 to Using a four-day moving average, the authors find that a 10 μg/m 3 change in PM2.5 was associated with a 0.8 per cent change in all-cause mortality. 3 However, most of this literature fails to successfully control for possible confounders such as differences in socio-economic status, access to health care or exposure to other sources of pollution that may affect mortality and that are likely to be correlated with both air pollution and human health. 4 In a study that successfully accounts for possible confounders, Chay and Greenstone (2003) exploit exogenous variation on air pollution to estimate its effects on infant mortality. To estimate the impact of air pollution (Total Suspended Particles or TSPs) on infant mortality the authors examine reductions in TSPs in the U.S. induced by the economic recession. 5 By exploiting geographic variation in TSPs, and comparing it to changes in infant mortality rates, Chay and Greenstone (2003) find that a 1 µg/m3 reduction in TSPs results in about 4-7 fewer infant deaths per 100,000 live births at the county level. 6 Currie and Neidell (2005) account for possible confounders by developing an identification strategy based on within zip-code variation in pollution levels that controls for time-invariant unobserved characteristics of high pollution areas. The authors estimate infant mortality hazard-functions at the week-level using data from selected zip-codes in California for years 1989 to They find that air 2 The mean estimated change in daily mortality associated with a one-day 10μg/m 3 change in PM10 implied by these studies is approximately 0.8 per cent, with a range of 0.5 per cent to 1.6 per cent Ostro et al (2003) 3 The authors claim that these results are surprisingly consistent with results from industrial countries Ostro et. al. (1995). For mortality associated with specific particle sizes, the findings in Santiago appear to be slightly lower than, but generally consistent with, findings from other studies Ostro (2003). 4 Chay and Greenstone (2003) stress this potential problem of lack of controls for confounders and, thereby, failure to establish a causal relationship between air pollution and mortality. Chay and Greenstone (2003) remark that since air pollution is not randomly assigned across locations, previous studies may not be adequately controlling for a number of potential confounding determinants of adult mortality [Pope and Dockery 1996; Fumento 1997]. Furthermore, lifetime exposure of adults to air pollution is unknown. The epidemiological analysis implicitly assumes that the current pollution concentration observed at a site accurately measures each resident's lifetime exposure. Moreover, it is possible that the excess adult deaths that are attributed to changes in air pollution occur among the already sick and represent little loss in life expectancy. 5 TSPs = all particles with diameters less than or equal to 40 micrometers (µm). 6 This corresponds to a 0.35 elasticity. 3

4 pollution has a significant effect on infant mortality. Currie and Neidell (2005) s estimates imply that 1 µg/m3 reduction of PM10 led to a decline of infant deaths per 100,000 births. In a very recent study, Arceo, Hanna, and Oliva (2015) [henceforth, AHO] examine whether estimates on the effects of air pollution on infant mortality from developed countries can be transfered to rapidly growing economies. The authors use a similar strategy as Currie and Neidell (2005) to estimate municipality-level fixed effects regressions using weekly-level data for Mexico City for years AHO find significant effects of PM10 on infant mortality, although of much smaller magnitude than previous studies in developed countries. Using a similar methodology as Currie and Neidell (2005), AHO impute air pollution data at the municipality level by weighing municipality-level air pollution data by the inverse of the distance to the closest air quality monitoring stations (IDW). However, this geospatial imputation suffers from severe measurement error, and this biases down their estimates (creating attenuation bias). 7 This attenuation bias would be further exacerbated by using fixed effects estimation in panel data (Cameron and Trivedi 2005). To address this problem, AHO exploit exogenous variation in air pollution given by a weekly count of (city-wide) thermal inversions, as an instrument for imputed air pollution data for each municipalityweek. By estimating two-stage least squares (2SLS), AHO partially reduce the attenuation bias and, thereby, obtain considerable larger effects of air pollution on infant mortality. AHO find that these 2SLS estimates are then comparable to those results from studies in developed countries. In this paper we follow the methodology employed by Currie and Neidell (2005) and AHO. We estimate the effect of air pollution on neonatal and infant mortality using week-level data for Santiago, Chile for years 1997 to However, instead of a using week count of thermal inversions, as in AHO, we use disaggregated meteorological data about the climatological phenomena that explains both existence and intensity of thermal inversions at the city-wide level for Santiago s Metropolitan Area. We use this meteorological data to instrument for pollution data and obtain 2SLS estimates of the effects of air pollution on premature mortality 7 This measurement error in air pollution imputation is more likely to arise in the case of developing countries where, oftentimes, there are few and sparse air pollution monitoring stations, or these monitoring stations fail to report pollution data for certain periods. 4

5 Furthermore, to impute air pollution for each municipality-week, we employ the Kriging method. 8 By using the Kriging method we obtain a more accurate air pollution imputation for each municipality than the IDW method (employed by both Currie and Neidell (2005) and AHO). We contrast the estimates from the Kriging imputed data to those using the IDW method and find significant gains in accuracy by using the Kriging method. These gains in accuracy translate into a smaller attenuation bias of both OLS as well as FE estimates. Therefore, by using disaggregated meteorological data that explains thermal inversions as well as an accurate methodology for imputing air pollution data we minimize the measurement error of the air pollution data in both of its dimensions: in its time dimension (using disaggregated meteorological data that explains occurrence and intensity of thermal inversions) and its spatial dimension (employing the Kriging method for imputing air pollution). As a consequence, by reducing measurement error in the air pollution variable we considerable reduce the attenuation bias when estimating the effects of air pollution on mortality. 3. Background of Premature Mortality and Air Pollution in Santiago, Chile Ambient air pollution can cause severe damage to human health. The evidence of the consequences of particulate matter suggests that air pollutants can cause bronchia pulmonary and cardiovascular diseases such as lung inflammation and blood coagulation that can obstruct blood vessels, leading to angina or even to myocardial infraction (Kampa and Castanas 2008). The most sensitive population to air pollution are the elderly and the very young. Unlike the elderly, the very young present short histories of past diseases that, while having little to do with air pollution, could result in lagged adverse health consequences similar to those caused by air pollution. Furthermore, also due to their young ages, this group is likely to be exposed to air pollutants for the entire span of their short lives. 9 This means that, for this particular population, when examining the adverse effects of air pollution on human health, there are fewer potential confounders so that we can better identify a more direct effect of air pollution on human health. As a consequence, in this paper we 8 Unlike using the inverse distance to nearby monitoring stations as weights for imputing air pollution data, by exploiting the geographical dispersion of the air quality monitoring stations, Kriging performs a full spatial mapping of air pollution for a large number of geographical points for each municipality. In the next section we explain in detail the Kriging method. 9 If we assume that pregnant women are not likely to move to across cities, those of young age are exposed to the adverse consequences of air pollutants even starting from the womb. 5

6 analyze the effects of air pollution on two subgroups of the very young population: neonates (those 28 days old or less) and infants (those 1 year old or less). In particular, we focus on mortality as an extreme case of adverse health outcomes. Therefore, we examine the adverse effects of particulate matter on both neonatal and infant mortality. a. Air pollution Even though PM10 concentrations have been steadily declining in the last years for Santiago s Metropolitan Area, average PM10 concentrations has consistently exceeded the Chilean national standards for the period under study ( ), both average annual concentrations as well as daily 24-hour concentrations. 10 According to official environmental reports, emissions of PM10 in Santiago s Metropolitan Area are largely due to motors vehicles (accounting for 40.5% of total anthropogenic sources) and the combustion of petroleum derivates for industrial processes (24.6% of total anthropogenic sources) (De la Maza and Serrano 2013). i. Vertical Ventilation of Air pollutants in Santiago s Metropolitan Area Santiago is located in Chile s central valley in a basing surrounded by the Chile s coastal mountain range, to the west, and the much taller Andes Mountains, to the east and north east. 11 Mild winds blow from the west-south-west (WSW) at the elevation of the city (around 550 meters above sea level). Conversely, at elevations above Santiago s western coastal mountain range, strong winds blow from the west-north-west (WNW) at elevations starting at around 1,000 to 1,200 meters above sea level. The winds at the city s elevation bring in a constant fresh breeze of clean air to the WSW end of the city (see light blue arrows in the map of Figure 1 below). This breeze compresses the airshed against the Andes Mountains. That is, at the only gap of the mountains that surround Santiago (at the city s WSW bound), mild winds blow towards the basin, and thus further trapping air pollutants. However, once pollutants reach an altitude above Santiago northern and eastern mountain range, strong winds 10 The Chilean national standard for annual average concentrations of PM10 is 50 μg m 3 whereas the standard for daily 24-hour concentrations of PM10 is 150 μg m 3. This contrasts with guidelines by the World Health Organization of 20 μg m 3 (for annual concentrations) and 50 μg m 3 (for 24-hour concentrations) (WHO, 2005). 11 Whereas the coastal mountain range reaches elevations of 800 to 1,500 meters above sea level, at this latitude the Andes Mountains presents elevations between 4,500 to 6,500 meters above sea level. 6

7 from the WNW blow air pollutants away (see purple arrows in the map below) (Garreaud and Rutllant 2006). Figure 1: Geographical terrain and direction of winds of Santiago s airshed. The escape of air pollutants of Santiago s airshed is technically known as vertical ventilation. However, during the late autumn and winter an inversion layer prevails at about 400 to 600 meters above the city s elevation. These thermal inversions keep the air pollutants at an elevation below Santiago s northern and western coastal mountain range, thus obstructing air ventilation of the city s airshed. Conversely, during hot (summer) days air pollutants rise above the elevation of this mountain range and, in this way, strong winds from the WNW clean the pollutants away from the airshed. This means that thermal inversions highly determine the elevation at which air pollutants rest above the city, and this in turn drives the ventilation of the entire airshed of Santiago s Metropolitan Area. 7

8 4. Data & Methodology for Imputation of Air Pollution Data at the Municipality Level For the present study we construct a weekly panel of 31 municipalities for a period We explain the data in detail below. a. Neonatal and Infant Mortality data We use data from the Department of Health Statistics of Chile s Ministry of Public Health to generate municipality-level neonatal and infant mortality. Neonatal mortality is defined as number of those dead during the first 28 days of life divided by the total number of births, per 100,000. Similarly, infant mortality is defined as number of deaths of those individuals less than a year old divided by the total number of individuals less than a year old, per 100,000 inhabitants. We compute weekly municipality-level neonatal and infant mortality rates for the period for 31 municipalities within Santiago s Metropolitan Area. In addition, in order to analyze the more direct effect of pollution on infant health, we compute neonatal and infant mortality for those causes that are more directly linked to air pollution, namely, respiratory and cardiovascular diseases, by considering only those deaths with codes I and J according to the International Classification of Diseases (ICD-10). b. Air pollution data and Kriging imputation We obtain air pollution data from Chile s Air Quality National Information System (AQNIS). AQNIS has 11 air quality monitoring stations throughout Santiago s Metropolitan Area. The monitoring stations provide daily records of particulate matter less than 10 microns per cubic meter (PM10) for the period We use the Kriging method for imputing PM10 to each municipality. We impute air pollution for each municipality using Kriging in two steps. First we impute air pollution data for each school within a municipality and then we averaged across schools to obtain pollution data for each municipality. The reason that we impute for each school is that we are interested on the air pollution of those geographical areas were people actually live. That is, we assume that most people live in a proximity to public and private schools, so that we conduct the Kriging imputation for 8

9 the geographical location of every school in each municipality 12. Using a Kriging interpolation avoids the assumption that pollution was the same for all schools close to the same monitoring station. Furthermore, we restrict our sample of schools to those schools located (i) at a distance of no more than 5 kilometers from the nearest air quality monitoring station, and, (ii) between 5 to 10 kilometers to the nearest monitoring station but no more than 20 kilometers to the second-nearest monitoring station. Once we restrict the sample of schools, we averaged the imputed air pollution data across all selected schools for each municipality. The appendix presents technical details of the Kriging method used for mapping PM10 data for each week-municipality. c. Additional weather controls We obtain weather indicators for Santiago s basin from the Air Quality Meteorological Network for Metropolitan Region of Chile s Meteorological Office. Furthermore, we obtain municipality-level weather indicators (temperature and precipitation) from the Water Office of Chile s Public Works Ministry. 12 Imputing pollution data using the Kriging methodology is much more reliable for interpolation than for extrapolation. Pollution data from Kriging interpolation for a geographical area that has more than one monitoring stations is quite reliable, particularly, when the monitoring stations are located at a short distance from each other and surrounding this particular geographical area in its vicinity. Conversely, pollution data from Kriging extrapolation is much less reliable. That is, when there is only one monitoring station near the particular geographical area or, if there is more than one monitoring station, these are at a far distance from this particular geographical area and are not surrounding it in its vicinity. Because of this, in order to guarantee high quality of the air pollution data imputation, we selected only those schools that are at a distance no more than 5Km from the nearest monitoring station and at a distance no more than 20Km from the second nearest monitoring station. [See figure in APPENDIX] 9

10 5. Descriptive Analysis a. Descriptive statistics for, Neonatal and Infant Mortality Table 1 below presents descriptive statistics for aggregate neonatal and infant mortality as well as neonatal and infant mortality for respiratory and cardiovascular causes (henceforth, R&C) for the period Table 1: Mortality Rates for all-causes Neonatal and Infant, R&C Neonatal and Infant Mortality, and PM10 for Santiago s Metropolitan Area. Mean Std. Dev. Obs. Mean of Deaths in a year (per 100,000) Neonatal Mortality , Neo. Mort. (R&C) , Infant Mortality , Inf. Mort. (R&C) , PM ,068 It should be noted that those that die as neonates are also counted as infants. As a consequence, the table above shows that 53% (420.1/720.2) of those infants that died in the period actually died as neonates. However, of those that died for R&C causes, only 14% (7.5/51.8) died as neonates. That is, a large majority of those infants that die for R&C reasons actually die after their first 28 days of life. This suggests that R&C mortality is more severe after the first 28 days of life. As newborns grow older, passed their first 28 days of life, they are likely to be more exposed to air pollution (PM10) and subject to suffer more severely from its adverse health consequences. 13 The column on the right was obtained as follows. For the case of neonatal mortality, mean mortality was multiplied by the number of weeks in a year and then divided by the number of 28-day periods in a year. Thus, the values in the second column are multiplied by 52/13 to obtain the values in the last column. Similarly, for infant mortality, mean mortality was multiplied by

11 Infant Infant Mortality Inf. Mort. (Resp&Cardio) Neonatal Neo. Mort. (Resp&Cardio) b. Yearly trends for PM10 and neonatal and infant mortality Table 1 also shows that the mean PM10 concentration is 75 μ/m 3 for the period for Santiago s Metropolitan Area. The graphs below show the yearly time trend of PM10 concentration during the period as well as yearly time trends for neonatal and infant mortality (all-causes and those for R&C causes). Figure 1: Year time trends of Air pollution (PM10), All-causes Neonatal/Infant Mortality, and R&C Neonatal/Infant Mortality. Santiago s Metropolitan Region, period ). All-causes Mortality Mortality for R&C Causes PM10 & Neonatal Mortality Period PM10 & Neo. Mort. (R&C) Period year year Neonatal Mortality Neo. Mort. (Resp&Cardio) PM10 & Infant Mortality Period PM10 & Inf. Mort. (R&C) Period year Infant Mortality year Inf. Mort. (Resp&Cardio) The figure shows the steady decline in PM10 for the period Although the yearly time trend for all-causes neonatal mortality is not clearly observable from the graph above, the graph shows a steady decline in all-causes infant mortality. Furthermore, both R&C neonatal and infant mortality also present a steady decline during the period

12 Infant Infant Mortality Inf. Mort. (Resp&Cardio) Neonatal mort_neonatal_rc_regmon c. Seasonal variation of PM10 and neonatal and infant mortality As the figure above shows, there has been a considerable decline of air pollution by particulate matter during the period considered for this study. However, a great deal of the variability of air pollution (PM10) in Santiago s Metropolitan Area occurs within a year, across its different seasons. Concentrations of PM10 increase sharply during the autumn peaking during the late autumn and early winter (May and June). Then they decline sharply with the begging of the spring, remaining low throughout the rest of the summer. Figure 2: Seasonal trends of Air pollution (PM10), All-causes Neonatal/Infant Mortality and Respiratory & Cardiovascular Neonatal/Infant Mortality. Santiago s Metropolitan Region, period ). All-causes Mortality Mortality for R&C causes PM10 & Neonatal Mortality by month PM10 & Neo. Mort. (R&C) by month month month mort_neonatal_regmon mort_neonatal_rc_regmon PM10 & Infant Mortality by month PM10 & Inf. Mort. (R&C) by month month Infant Mortality month Inf. Mort. (Resp&Cardio) 12

13 Whereas the data presented in Figure 2 above shows no clear seasonal pattern for all-causes neonatal mortality and R&C neonatal mortality, both all-causes infant mortality and R&C infant mortality follow a seasonal pattern similar of that of PM10, but with a lag. Whereas all-causes infant mortality increases steadily during the autumn and then sharply to peak early in the winter (June) and remain high during the first months of spring,r&c infant mortality increases sharply during the late months of autumn (May) to decrease later by the end of the winter. Although the seasonal pattern presented above suggests some correlation (with a lag) of air pollution (PM10) and infant mortality (both all-cause and those for R&C causes), this does not necessarily imply that there is a causal relationship. There are important factors that present similar seasonal variability that may as well drive the mortality outcomes. In the next section we will further discuss these correlations and our identifying strategy to determining causal effects. 5. Regression Analysis Regression analysis allows us to estimate the effect air pollution on health outcomes. In this section we first present the framework for regression analysis (subsection a.) and next we present the regression results (subsection b.) a. Estimating Equations OLS, FE and 2SLS i. Pooled Ordinary Least Squares (pooled OLS) and Fixed Effects (FE) Estimation for Panel Data In this subsection we contrast three regression models. The starting model is Ordinary Least Squares (OLS). For the particular structure of our panel dataset, we conduct pooled-ols by estimating the following regression equation across the entire panel of weekly observations during the period M iw = β 0 + β 1 P iw + β 2 W iw + β 3 T iw + ε iw (1) Where M iw denotes mortality rate at municipality i for week w, P iw denotes air pollution at municipality i during week w, W iw denotes weather variables such as weekly-average precipitation, weekly-average daily minimum and maximum temperature, T iw denotes year time-trend and dummies for month of the year, and ε iw denotes the unobserved error term. The assumption that guarantees 13

14 unbiased estimates for the βs is the orthogonality of the explanatory variables (P iw, W iw and T iw ) with the error term ε iw. However, there are might as well be uncontrolled factors (captured by the error term ε iw in the equation above) that can determine the mortality outcome and also be correlated with the explanatory variables above. 14 Characteristics of the population living in a particular municipality (such as income, level of education, etc.), health care infrastructure of the municipality (such as primary care facilities, hospitals, etc), exposure to air pollution due to emissions (industrial facilities) and ventilation of air pollutants (due to geographical characteristics that determine ventilation, etc) are all likely to determine premature mortality and are likely to be correlated with the explanatory variables, particularly, with air pollution P iw. For example, if those municipalities whose population has low income, or has limited access to health care (or access to deficient health care) are also exposed to higher concentrations of air pollution either because of their proximity to the source of emissions and/or poor ventilation, then the air pollution parameter β 1, in equation (1) above, would not only capture the effect of air pollution on mortality, but would also capture the effect of these uncontrolled factors (herein, these would be confounders ). One way of solving this omitted variable problems is to assume that all relevant omitted variables are time-invariant, that is, these variables only vary across municipalities (or that if they vary across time, their time-change can be captured by a common trend). This allows us to use Fixed Effects estimation on panel data. M iw = β 0 + β 1 P iw + β 2 W iw + β 3 T iw + ε i + ε iw (2) Where ε i is a municipality-specific error term that captures all (time-invariant) permanent differences across municipalities (municipality-level socio-economic conditions, access to health-care or exposure to other pollutants). 14 If there are omitted variables (that is, confounders such as ) that were left out of the regression equation so that orthogonality assumption above is not satisfied, then the OLS estimates of the βs are biased and inconsistent (meaning that the bias does not disappear as the sample grow larger). 14

15 ii. Measurement Error in Imputed Air Pollution Data and Two Stage Least Square (2SLS) Estimation We have emphasized throughout this paper that the presence of error in the imputation of pollution data causes attenuation bias of the parameter estimate associated to air pollution, β 1. In this section we discuss this problem in more detail. Suppose that the measurement error is additive, so that, if the true pollution variable at municipality i and week w is P iw, but we actually input P iw and do not account for the measurement error term υ iw, such that P iw = P iw + υ iw, then equation (2) above can be expressed as M iw = β 0 + β 1 (P iw + υ iw ) + β 2 W iw + β 3 T iw + ε i + ε iw (2 ) It can be seen from equation (2 ) above that, because the measurement error υ iw interacts with the parameter β 1, the regression equation (2 ) does not satisfy the orthogonality condition for the explanatory variable P iw to be uncorrelated with the composed error term (ε i + ε iw + β 1 υ iw ). 15 This yields biased (and inconsistent) estimates of the air pollution parameter β 1, commonly known as attenuation bias. 16 In addition, FE estimation of equation (2 ) above further exacerbates the attenuation bias problem as long as the intertemporal correlation of the true air pollution variable P iw is different from zero. That is, if there is time-persistence in air pollution such that the true air pollution at municipality i in period w is correlated with the true air pollution in any of the m previous periods [corr(p iw P iw m ) 0, for m > 0], then the attenuation bias by estimating equation via FE (2 ) will be larger than the attenuation bias by estimating equation (2 ) via OLS. 17 Here it is important to stress that air pollution in Santiago s Metropolitan Area tends to accumulate over time during the autumn and winter (as Figure 2 above shows), thus showing strong persistence over consecutive weeks. 18 Therefore, due to the strong autocorrelation of air pollution at the week-level, it is quite likely that the FE estimation produces severe attenuation bias in the estimates of the air pollution parameter β 1. This problem poses further challenges for the unbiased (and consistent) estimation of regression equation (2 ). One way to partially circumvent this problem is to find a variable (or a set of variables) that create exogenous variation in the true air pollution variable, P iw, but that do not correlate with the 15 E[P iw (ε i + ε iw + β 1 υ iw )] = E[(P iw + υ iw )(ε i + ε iw + β 1 υ iw )] = +β 1 σ 2 υ 0 16 plim β OLS = (Σ x x + Σ εε) 1 Σ x x β β. Where Σ x x denotes the variance-covariance matrix of the true covariates, and Σ εε denotes the variance-covariance matrix of the error term in (2 ). 17 plim β FE = ((1 ρ)σ x x + Σ εε) 1 (1 ρ)σ x x β plim β OLS, where ρ = corr(p iw P iw m ), for m > We will show this in more detail in a latter section below. 15

16 composed error term (ε i + ε iw + β 1 υ iw ) in (2 ). The meteorological variables that drive thermal inversions described in section 3.a above meet this condition. Via driving the overall ventilation of Santiago s airshed, these meteorological variables generate city-wide variation in true air pollution concentrations without correlating with the composed error term in (2 ), and thereby, without directly determining mortality other than via their direct effect on true air pollution concentrations P iw. Henceforth, the meteorological variables that explain thermal inversions allow us to obtain unbiased (and consistent) estimates of the effect of air pollution on mortality (β 1 ). Therefore, in the next section we estimate equation (2) via Two Stage Least Squares (2SLS). In a first stage, we regress the imputed air pollution variable (P iw ) on the meteorological variables that drive thermal inversions, and then we use the predicted variable from this estimation (P iw ) to substitute for P iw in equation (2) and then we estimate equation (2 ) via FE. 19 M iw = β 0 + β 1 P iw + β 2 W iw + β 3 T iw + ε i + ε iw (2 ) b. Regression Results In this subsection we present estimates of the effect of PM10 on all-causes neonatal and infant mortality as well as respiratory and cardiovascular mortality (R&C mortality). Table 2 presents estimates for neonatal mortality and Table 3 presents estimates for infant mortality. Also, each table is divided into two panels, the upper panel presents estimates for all-causes mortality whereas the lower panel presents estimates for R&C mortality. Note that each cell in Tables 2 and 3 represents an independent regression. As Table 2 shows, all estimates of PM10 on all-causes neonatal mortality are not statistically significant. The first column presents estimates of pooled-ols regressions of weekly municipality-level neonatal mortality on week-average municipality-level PM10. Column 2 adds controls for weather (polynomials of precipitation, high and low temperature), monthly dummies and a year time trend. 19 In this first stage, we also add controls for weather and time variables as in (2 ) (that is, municipality-level precipitation, minimum and maximum temperature, monthly dummies and year trends). Therefore, the first stage regression equation is P iw = α 0 + α 1 TIV w + α 2 W iw + α 3 T iw + μ iw, where TIV w denotes the set of meteorological variables that drive thermal inversions, and μ iw is an unobserved error term. 16

17 Columns 3 and 4 present fixed effects estimates (FE) controlling for time-invariant unobserved determinants of mortality. Whereas column 3 presents estimates of PM10 only, column 4 adds controls for weather, monthly dummies and a year time trend. Furthermore, columns 5 and 6 present 2SLS estimates. 20 The results presented in the lower panel of Table 2 show that air pollution has a significant effect on R&C neonatal mortality. This estimate suggests that a marginal decrease in PM10 reduces R&C neonatal mortality by more than 0.02 deaths of neonates per births per week/municipality. To illustrate this figure, a 10 point decrease in average PM10 translates to around 1 less neonatal death per births in a year [10*0.0228*52/13=0.912]. This represents a 12.16% decrease with respect to mean R&C neonatal mortality presented in Table 1. Table 2: Estimates of the effects of PM10 (week avg.) on All-causes Neonatal Mortality and R&C Neonatal Mortality. Neonatal Mortality OLS FE 2SLS (FE) PM10 (week avg.) (0.0723) (0.0690) (0.0661) (0.0603) (0.0830) (0.0896) Controls (weather, month & year) No Yes No Yes No Yes Observations 16,865 16,865 16,865 16,865 16,865 16,865 Neo. Mort. (Respiratory & Cardiovascular) OLS FE 2SLS (FE) PM10 (week avg.) ** * ( ) ( ) ( ) ( ) ( ) (0.0123) Controls (weather, month & year) No Yes No Yes No Yes Observations 16,865 16,865 16,865 16,865 16,865 16,865 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Standard errors clustered by municipali On the other hand, we find more significant effects of PM10 on infant mortality. Columns 1 and 2 of Table 3 below present pooled-ols estimates for both all-causes infant mortality (upper panel) and R&C infant mortality (lower panel). Column 1 presents estimates for the effects of PM10 only whereas column 2 adds controls for weather, monthly dummies and a year time trend. It may seem that the 20 The first stage estimates are presented in Table 4 below yielding highly statistical significant estimates and passing Staiger and Stock (1997) test of weak instruments. 17

18 estimates of the effects of PM10 on all-causes infant mortality are about twice as large as the estimated effects of PM10 on R&C infant mortality. However, as shown in Table 1, average all-causes infant mortality rate for the period is more than fifteen times larger than R&C infant mortality rate for the same period. This means that the proportional effect of PM10 on R&C neonatal mortality is much larger (about 7.4 times larger) than the proportional effect of PM10 on all-causes neonatal mortality. Columns 3 and 4 present fixed effects estimates (FE) that controls for time-invariant unobserved determinants of infant mortality. The FE estimates are just slightly smaller than those of pooled OLS. If there is measurement error on the Kriging imputation of PM10, FE regression should aggravate the attenuation bias due to measurement error. Table 3 shows that FE estimates are around 2% to 9% smaller than its OLS counterparts, suggesting a modest attenuation bias as a result of FE regression. However, instrumenting for PM10 using the meteorological data that drives thermal inversions yields much larger estimates in the 2SLS. The 2SLS estimates presented in columns 5 and 6 are much larger than both their pooled-ols and FE counterparts. The 2SLS estimates of the effect of PM10 on allcauses infant mortality (with controls) is 23% and 34% larger than their pooled-ols and FE counterpart, respectively. Similarly, the 2SLS estimates of the effect of PM10 on R&C infant mortality (with controls) are 29% and 34% larger than their pooled-ols and FE counterpart, respectively. To illustrate, these estimates suggest that a 10 point reduction in average PM10 yields around 18 fewer all-causes infant deaths per 100,000 infants a year [10*0.0345*52=17.94], which represents a 2.27 % decrease with respect to mean all-causes infant mortality presented in Table 1. Furthermore, a 10 point reduction in average PM10 translates in about 9 fewer infant deaths a year for R&C causes per 100,000 infants [10*0.0176*52=9.15], representing a decrease of % with respect to mean values for R&C infant mortality presented in Table 1. Since the all-causes infant mortality rate is much larger than R&C infant mortality rate, the proportional effect of PM10 on R&C infant mortality is indeed much larger than that of PM10 on all-causes infant mortality (17.66 % and 2.27 %, respectively). 18

19 Table 3: Estimates of the effects of PM10 (week avg.) on All-causes Infant Mortality and R&C Infant Mortality. Infant Mortality OLS FE 2SLS (FE) PM10 (week avg.) *** *** *** *** *** *** ( ) ( ) ( ) ( ) (0.0108) (0.0118) Controls (weather, month & year) No Yes No Yes No Yes Observations 16,865 16,865 16,865 16,865 16,865 16,865 Inf. Mort. (Respiratory & Cardiovascular) OLS FE 2SLS (FE) PM10 (week avg.) *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Controls (weather, month & year) No Yes No Yes No Yes Observations 16,865 16,865 16,865 16,865 16,865 16,865 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Standard errors clustered by municipali c) Geographical and Meteorological drivers of PM10 concentrations in Santiago s Metropolitan Area As we discussed in section 3 above, the ventilation of air pollutants in Santiago s Metropolitan Area is largely driven by the occurrence of thermal inversions in which air pollutants are trapped at low elevations preventing from vertical ventilation of the airshed. In this section we analyze how the meteorological conditions that drive thermal inversions and explain vertical ventilation of Santiago s airshed determine PM10 concentrations. We claim that these meteorological conditions affect neonatal and infant mortality only via changes in the concentrations of PM10, and are otherwise uncorrelated to any observable and unobservable factor affecting mortality. Thus, these meteorological conditions create a good instrument for PM10 imputed data, and 2SLS estimation allows us to circumvent the potential endogeneity problem created by measurement error in the imputation of PM10 at the municipality level. i) Descriptive analysis of determinants of PM10 concentrations Figure 3 below shows seasonal trends for PM10 and selected meteorological variables by week of the year for the period As discussed earlier, PM10 concentrations increase sharply during the autumn reaching peaks at around the 19 th week of the year (early May) to stay relatively high during 19

20 the winter time and then decline with the beginning of the spring. The black line in Figure 3 below illustrates this pattern. One of the main determinants of PM10 concentrations in Santiago s Metropolitan Area is the occurrence of precipitations, usually in the form of rain. The blue bars in Figure 3 below show the seasonal pattern of precipitations. Precipitations are extremely rare during the first weeks of the year (summer time) and increase slightly during the autumn. Precipitations start to become more prevalent during the last weeks of May and can turn very strong throughout the month of June. The presence of stronger precipitations in late May and throughout June coincides with a significant drop in PM10 concentrations (weeks 21 and 24 in Figure 3 below). On the other hand, as discussed earlier, another important determinant of PM10 concentrations is vertical ventilation of Santiago s airshed. The main meteorological variables that determine vertical ventilation are the difference of temperature at different elevations and the presence of strong winds at high elevations (elevation above the western coastal mountain range). The difference of temperature at different elevations drives thermal inversions and it is calculated as the difference of ambient air temperature at the city level (564 meters above sea level) with ambient air temperature at the peak of Santiago s western mountains (1068 meters above sea level). Also, we capture the presence of strong winds at high elevations by a measure of wind speed at the top of these mountains (at 1068 meters above sea level). 20

21 -20 0 PM10 (week avg) Figure 3: and Selected Meteorological variables for period (by week of the year). PM10 & Meteorological Variables by week of the year (period ) Week Precipitation (week total) Temp Diff (1068m vs 554m) Wind Speed at 1068m PM10 (week avg) ii) Regression analysis of determinants of PM10 concentrations (First Stage regression) Unlike AHO, in this paper we look at the geographical and meteorological determinants of ventilation of air pollutants in Santiago s Metropolitan Area and use the meteorological data that explains thermal inversions for Santiago s Metropolitan Area as source of exogenous variation of air pollution. More precisely, we look at the difference of ambient air temperature between two meteorological monitoring stations located at two different elevations: station Lo Prado, located at 1068 meters above sea level, on top of the Santiago s eastern mountain range; and, station La Platina, located at the city s elevation, at 556 meters above sea level. Furthermore, we use data on barometric air pressure and wind speed at station Lo Prado as well as data on precipitation, high and low temperature for each of Santiago s municipalities. We assume that neither temperature, air pressure nor wind speed at Lo Prado are determinants of mortality or determinants of respiratory and cardiovascular caused mortality. That is, we impose the exclusion restrictions that the parameters associated to these variables in the mortality regression are all equal to zero (equation 2 ). Hence, using this meteorological data we 21

22 instrument for (Kriging imputed) air pollution data to obtain estimates of the effects of PM10 on neonatal and infant mortality. Table 4 below presents results of a FE regression of PM10 on meteorological variables. Explanatory variables are as follows: (i) a fourth degree polynomial of temperature difference (550m vs. 1068m); (ii) a fourth degree polynomial of wind speed at 1068 meters above sea level; (iii) a two degree polynomial of barometric air pressure at 1068 mts.; (iv) a fourth degree polynomial of municipality-level weekly average of daily high temperature; (v) a fourth degree polynomial of municipality-level weekly average of daily low temperature; (vi) a fourth degree polynomial of municipality-level weekly average of daily precipitations; (vii) monthly dummies; and, (viii) a yearly time trend. As shown in Table 4, all coefficients are highly significant. Furthermore, regressors (i) through (iv) pass Staiger and Stock (1997) test of weak instruments at the 95% level. As explained above, we use these estimates to generate predicted values for PM10. In the estimation results presented in tables 2 and 3 we use these predicted values to substitute for Krigingimputed PM10 values and then conduct 2SLS estimation. 22

23 Table 4: Meteorological determinants of PM10 (first stage estimates) Meteorological Determinants of PM10 Ventilation VARIABLES Coef. SE Temperature Difference. Elevation 550m vs 1068m *** (0.095) TempDif_squared *** (0.024) TempDif_cubic *** (0.003) TempDif_tetra *** (0.000) WindSpeed at 1068m *** (0.974) WindSpeed_squared *** (0.485) WindSpeed_cubic *** (0.099) WindSpeed_tetra *** (0.007) Barometric Air Pressure at 1068m *** (17.691) Barometric Air Pressure_squared *** (0.010) Weekly average of daily High temperature, by comuna *** (4.533) HighTemp_squared *** (0.282) HighTemp_cubic *** (0.008) HighTemp_tetra *** (0.000) Weekly average of daily Low temperature, by comuna *** (1.392) LowTemp_squared *** (0.270) LowTemp_cubic *** (0.022) LowTemp_tetra *** (0.001) Weekly average of daily precipitation, by comuna *** (0.428) Precip_squared *** (0.132) Precip_cubic *** (0.012) Precip_tetra *** (0.000) Monthly dummies D_month_ *** (0.175) D_month_ *** (0.551) D_month_ *** (1.085) D_month_ *** (1.725) D_month_ *** (1.540) D_month_ *** (1.380) D_month_ *** (1.166) D_month_ (1.084) D_month_ *** (0.776) D_month_ *** (0.486) D_month_ *** (0.166) year *** (0.067) Observations 16,865 R-squared Number of comuna Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Standard errors clustered by municipality 23

24 6. Further results: Nonlinear effects of cumulative exposure to PM10 Thus far we have focus on the estimation of the effects on mortality of contemporaneous exposure of air pollutants, in particular, week-average PM10 at the municipality level. However, current epidemiological literature suggests that long-term exposure to high levels of air pollutants may actually be more harmful to human health than just contemporaneous exposure. In order to test the cumulative effects of exposure to high levels of air pollutants, in this section we estimate the effect of exposure to four-week-average PM10. We average week-level PM10 over the last four weeks and conduct regression analysis for both all-causes infant mortality and R&C infant mortality. Table 5 below presents estimation results for regressions with controls (polynomials on week average high temperature, low temperature and precipitations as well as monthly dummies and a year trend). Columns 1, 3 and 5 present linear estimates for the effects of four-week-average PM10 on both all-causes infant mortality (upper panel) and R&C infant mortality (lower panel). The estimates are of a magnitude considerable larger than those presented in Table 3 above. The estimate of the effect of four-week-average PM10 on all-causes infant mortality, presented in Table 5 below, are 38.7% and 34.2% (for OLS and FE, respectively) larger than those week-contemporaneous estimates presented in Table 3. Similarly, estimates for the effect of four-week-average PM10 on R&C infant mortality are 26.8% and 35.8% larger (for OLS and FE, respectively) than those presented in Table 3. Results for instrumented PM10 are of much higher magnitude. Estimates for 2SLS presented in Table 5 below are more than twice as large as those presented in Table 3 above. These results suggest that cumulative exposure to high levels of air pollutants (PM10) are considerable more harmful to infant health than merely short-term exposure. Furthermore, in order to gauge whether this cumulative exposure to air pollutants may have a more severe effect for high concentrations of pollutants, we added a quadratic term for four-week-average PM10 (columns 2, 4 and 6 in Table 5 below). The estimate of this quadratic term is statistically significant for the instrumented PM10 only. The 2SLS estimate of this quadratic term turns significant for both all-causes infant mortality as well as R&C infant mortality. These estimates suggest that there is a convex relationship between cumulative exposure to air pollutants and infant mortality. That is, the damaging effects of cumulative exposure to PM10 on infant health are more severe at higher 24