BETTER HEALTH FROM A BETTER ENVIRONMENT: A CROSS-COUNTRY EMPIRICAL ANALYSIS. By Hsiang-Chih Hwang. July Abstract

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1 BETTER HEALTH FROM A BETTER ENVIRONMENT: A CROSS-COUNTRY EMPIRICAL ANALYSIS By Hsiang-Chih Hwang July 2007 Abstract In recent decades, affluent countries have implemented many new regulations intended to reduce air pollution. Although numerous studies have attempted to measure the economic cost of these regulations, few researchers have evaluated their benefits, especially for countries other than the United States. In this paper, I estimate the contemporaneous and longer-term adverse health consequences of four types of air pollution in 23 European countries from 1987 to Health effects are estimated separately by age group, controlling for cross-country heterogeneity in economic and social conditions through panel data fixed-effects regressions. The results indicate substantial health benefits from reductions in specific air pollutants. For example, a one percent reduction in lagged average concentration of ozone would have saved 112 infant lives in these countries over the study period. International Business School, Brandeis University, MS 032, Waltham, MA hchwang@brandeis.edu. 1

2 1. Introduction In 2001, the European Commission promulgated a series of important air pollution regulations and initiated its first integrated environmental policy, Clean Air for Europe (CAFÉ). The aim was to combat the negative effects of air pollution on the environment and human health. 1 In September 2005, the Commission presented a Thematic Strategy on Pollution targeting five important air pollutants. Although the policy was expected to cost the 25 member states approximately 7.1 billion (or US$8.7 billion) a year, estimated benefits to health alone were significant enough to outweigh the costs by a factor of at least 6 to 1, so the estimated net benefit to the 25 nations was about 42 billion (or US$51.3 billion) per year (Dimas, 2005). It is widely recognized that air pollution can have a significant impact on health (e.g., Brunekreff and Holgate, 2002; Dockery, Pope, Xu, Spengler, Ware, Fay, Ferris, and Speizer, 1993; Kunzli, Kaiser, Medina, Studnicka, Chanel, and Fillinger, 2000; Pope, Bates, and Raizenne, 2002; Schwartz and Marcus, 1990). For instance, a high concentration of carbon monoxide (CO) is associated with adverse health effects for infants and elevated risk of death due to cardiovascular disease; exposure to nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) can impair functioning or exacerbate existing conditions of the respiratory system. Since health directly affects labor productivity and medical expenditure, the economic benefits from reducing air pollution may exceed the cost. However, health benefits that occur over time may be difficult to measure because economic determinants of health status are also changing. 1 Earlier environmental policies in Europe included European emission limits for passenger cars (1970), the Auto Oil Programme (AOP) for vehicle emission standards (1992); and the Auto Oil Program II (AOP- II) regarding future vehicle emission limit values and fuel quality standards (1997). 2

3 Previous economic studies have investigated health impacts from air pollution for the United States using both cross-sectional and time-series analysis (Lave and Seskin, 1977; Chay, Dobkin, and Greenstone, 2003; Chay and Greenstone, 2001 and 2003; Neidell, 2003; Currie and Neidell, 2004; and Neidell, 2004). Chay and Greenstone (2001 and 2003) study the effects of reduced total suspended particulates (TSPs) on infant mortality across counties in the United States. Reductions in TSPs in were due to the 1970 Clean Air Act Amendments (CAAA), while declines in 1982 were attributable to the recession. Chay and Greenstone s results suggest that a 1% decline in TSPs due to the CAAA resulted in a 0.5% decline in the infant mortality rate, which implies that approximately 1,300 infant lives were saved in 1972 due to the improved air quality resulting from the CAAA. A 1% reduction in TSPs due to the recessions caused a 0.35% decline in the infant mortality rate, which implies that about 2,500 infant lives were saved. Currie and Neidell (2004) conducted a zip-code level study using California Birth Cohort files for They examined the impact of CO, particulates (PM10), NO2, and O3 on infant death using panel data fixed-effects estimation. Their results suggest that the reduction in CO and PM10 due to the 1990 CAAA saved over 1,000 infant lives in California over the decade of the 1990s. Neidell (2004) is also a zip-code level study with panel data fixed-effects estimation. The study uses the California Hospital Discharge Data for to investigate the impact of air pollution on child hospitalizations for asthma. Neidell s results show that CO has a significant impact on asthma for children ages 1-18; his results imply that asthma-related admissions in 1998 would been a 5-14% higher if CO concentrations had remained at 1992 levels. 3

4 However, these studies focus only on a single state in a country or a single country; there has not been a study that controls for heterogeneous economic and social factors across countries in quantifying health consequences from various air pollutants. This paper aims to fill this gap by examining the impact of air quality on health of different age groups in 23 European countries for the period The air quality indicators used in this paper are annual maximum and median concentration of CO, NO2, O3, and SO2. 2 The data are from the AirBase project of the European Topic Center for Air and Climate Change (ETC/ACC). The health indicators used are infant mortality rates, and mortality rates due to disease of the cardiovascular, pulmonary, and respiratory systems. These health statistics are from the European Health for All Database (HFA-DB) and its supplementary database, European Mortality Database (HFA-MDB). Since each of these health statistics is an aggregate over ages within a 15 year range, I am able to estimate health impact from air pollution for each type of disease and for each age group. This analytical strategy is important for several reasons. First, medical studies suggest that air pollution is especially harmful to infants and young children because of their incompletely developed biological systems. On the other hand, middle-aged people are more likely to be in the labor force and thus might be more exposed to pollution at work. Second, people accumulate toxic substances in their bodies over time, and their biological systems become more vulnerable as they grow older. The health impact from pollution is therefore expected to be cumulative and to be greater in the elderly. Third, the cost of impaired health is different for people of different ages. For instance, poor health in children and teenagers increases the possibility of school absenteeism, which 2 The AirBase project also reports measurements of nitrogen oxides and particulates. Unfortunately, there are too few observations to use these to implement a panel analysis. 4

5 might jeopardize human capital formation; an unhealthy labor force results in lower productivity; and health expenditures might increase if pollution exposure aggravates existing health problems and causes complications in those who are already sick. I also control for the institutional factors that differ between the Western European countries and the post-communist ones. Western European countries are more industrialized and have better-developed health care systems. In contrast, Central and Eastern European countries were Communist states with relatively poor quality of health care. Average life expectancy at birth reflects overall health conditions; in 2003, life expectancy at birth was about 8 years higher in Western than Central and Eastern European countries. While emissions of NO2 and O3 have been falling in Western Europe, they are still a serious problem in Central and Eastern Europe. One possible reason is that the Western European countries have had environmental regulations since the 1970s, while the Central and Eastern European countries have given less priority to environmental quality. Thus, the two groups differ both in terms of air quality and in their residents health. I control for institutional differences by introducing a country group dummy. This dummy variable is equal to 1 for countries that became EU members before May 2004 or have special agreements with the Union (G1): Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden, and United Kingdom. Iceland and Norway are not EU members but have special agreements with the Union. The dummy variable is equal to zero for countries that became EU members in May 2004 (G2): Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, and Slovenia. 5

6 Since there are both contemporaneous and longer-term adverse health effects of pollution exposure, I use a current pollution measure as the air quality indicator for the contemporaneous effect and a 3-year moving average of the pollution measure as the air quality indicator for the cumulative effect. The econometric methods are pooled OLS with year fixed-effects and panel data fixed-effects. Pooled OLS is the prevailing technique used in the previous literature; however, panel data fixed-effects estimation can alleviate omitted variable problems such as differences in diet and exercise habits across countries. Some representative results from my analysis include these: (1) Lagged O3 exposure is associated with a statistically significant increase in infant mortality. Based on the 112,212,100 live births from 1980 to 2002 in these countries, approximately 112 infant lives would have been saved by a 1% reduction in O3 concentrations. (2) Both current and previous pollution exposure increase the incidence of respiratory disease. My estimation results show that a 1% reduction in average annual maximum CO concentration would have saved 0.38 life per 10 million people, a 1% reduction in average annual maximum O3 concentration would have saved 29 lives per 10 million people, and a 1% decline in average median SO2 concentration would have saved 14 lives per 10 million people in an average year in my data, through resulting declines in acute respiratory disease. Likewise, my estimation results suggest that a 1% reduction in average annual maximum O3 concentration would have saved 129 lives per 10 million people, and a 1% reduction in average annual median O3 concentration would 6

7 have saved 5 lives per 10 million people in an average year in my data, through resulting declines in chronic respiratory disease. 3 (3) Cumulative effects from pollution exposure on disease of the pulmonary and cardiovascular system are more pronounced than contemporaneous effects, i.e., the magnitude of the estimate is larger with 3-year moving average of pollution concentration, and the estimated effect increases with age. In the case of pulmonary disease, a 1% reduction in average annual median NO2 concentration would have saved 98 lives per 10 million people, and a 1% reduction in average annual maximum SO2 concentration would have saved 79 lives per 10 million people in an average year in my data. Likewise, a 1% reduction in average annual median CO concentration would have saved 6 live per 10 million people, and a 1% reduction in average annual maximum O3 concentration would have saved 599 lives per 10 million people in an average year in my data, in the case of ischemic heart disease. The rest of the paper will proceed as follows: Section II discusses air pollution and its adverse health consequences, and the conceptual framework. Section III describes the data. Section IV discusses the estimation method. Section V presents the estimation results. Section VI is the conclusion. 3 The effects of pollution reduction have been translated into lives saved where this refers to the reduction in the number of deaths in a given period from a particular cause and for a particular age cohort. However, the economic significance of a reduction in a given year s mortality differs according to the age cohort. A better measure would be life-years saved, an indicator that takes into account both the reduction in mortality in a given year and the life expectancy of individuals in that age cohort. Using life-years saved would magnify the benefits from saving an infant life relative to those from saving the life of someone 75 or older. One problem with computing life-years saved in the current context is that the individuals whose lives are saved by reduced exposure to pollution are not typical of their age cohort. Rather, they are likely to be the individuals who are most vulnerable to the effects of pollution and to have lower life expectancy than the average for their age cohort. 7

8 2. Air Pollution, Adverse Health Effects, and the Conceptual Framework Epidemiological studies have already shown that air pollution has a negative impact on health. Since toxic substances contained in the air enter our bodies through inhalation, many of the adverse health impacts from air pollution occur in the respiratory system. The most common symptoms are headache, eye irritation, coughing, and sneezing; more serious effects include increased incidence of circulatory and respiratory problems, such as breathing difficulties, heart and respiratory diseases, and complications of these diseases. Primary source and major health effects of each air pollutant used in this study are listed in Appendix A. Because of the nature of air pollution and the properties of pollution concentration data, there are several issues to note before carrying out statistical analysis. While it is impossible to know each individual s level of pollution exposure, the ambient level of pollution may be an appropriate proxy for the true pollution exposure for each individual, given data available. 4 I thus assume that pollution levels are uncorrelated with behavior. Second, pollution is not randomly assigned to each region. There are several important confounding factors that are correlated with both air quality and health. For instance, a highly polluted area is often poor and densely populated, with high crime rates. Failing to control for these confounding factors would yield a negatively biased result in estimating the effect of air pollution on health. 4 Bruce, Perez-Padilla, and Albalak (2002) suggest that indoor air quality is associated with adverse health impact on infants, children, and people with acute respiratory and pulmonary diseases. Although indoor air quality is an important factor affecting health, it is beyond the scope of this study. I do not control for it in this analysis. 8

9 Third, infants and young children are especially vulnerable to the effects of air pollution because they have incompletely developed biological systems. 5 Adverse health impacts on children and teenagers might result in school absenteeism, and consequently retard future human capital formation and reduce future labor productivity. Responsibility for care of unhealthy infants and children might reduce their parents productivity, or even prevent one or both parents from working. Likewise, the elderly are more vulnerable because their systems are often already impaired by age and disease. Fourth, pollution exposure has both contemporaneous and cumulative adverse health effects that vary by pollutant. A famous example of the acute impact from air pollution is the 1952 London smog crisis, which caused 3,500 to 4,000 more deaths than were normally expected. 6 Morbidity and mortality resulting from pollution exposure might have a latency period. For instance, morbidity and mortality that occur in January may have been caused by pollution exposure in the previous year, but the health outcomes are recorded in current statistics. Hence, it is important to examine the longerterm adverse health effects from pollution exposure. 5 Children s immune systems and lungs are not fully developed at birth. For instance, full functionality of lungs comes at about 6 years of age (Schwartz, 2004). 6 The London smog crisis occurred in greater London during December 5 th -8 th, According to a British government report, this crisis caused 3,500 to 4,000 more deaths than were normally expected during the first three weeks of December. Studies during this period showed that the sudden rise in mortality and morbidity was not due to infectious disease epidemics or a new type of new disease. This incidence affected mainly infants, the elderly, and those with pre-existing respiratory or cardiac disorder. Between 80 and 90 percent of the increase in death rates was due to respiratory and cardiovascular diseases. Infant mortality was approximately doubled. 9

10 3. Data Description 3.1 Dependent Variables In this paper, I use health statistics from the European Health for All Database (HFA-DB) and its supplementary database, European Mortality Database (HFA-MDB). Definition and types of diseases contained in each health statistic are summarized in the Appendix B. The dependent variables for adverse health outcome are annual infant mortality rate, and annual mortality rate due to disease of the cardiovascular, pulmonary, and respiratory systems. Infant mortality rate is a leading health indicator for infants, while the respiratory, pulmonary, and cardiovascular systems are the first biological systems to be affected when toxic substances in the air enter our bodies. These health statistics are broken down by five age groups, each of which is an aggregate over a 15 year range. Since children and the elderly have more vulnerable biological systems, and toxic substances may accumulate in our bodies over time, I expect the impact from pollution exposure to differ by age group. Table 2 displays the summary statistics for the health variables, which also indicates the countries where the highest and the lowest mortality rates occur Exogenous Variables Air Pollution Indicators This study uses air quality data from the AirBase project of the European Topic Center for Air and Climate Change (ETC/ACC). The AirBase project includes urban 7 It is unfortunate that the asthma death rate data provided by HFA-MDB include fewer than a hundred observations. Since it is not appropriate to implement a fixed-effects estimation with a small sample, I exclude asthma death rate from this study. 10

11 hourly, 8-hour average, daily, and monthly pollution measures for 30 European countries. 8 I generate each year s average maximum and average median pollution concentrations of CO, NO2, O3, and SO2 for each country from urban hourly pollution measures from all the reporting cities within a country s boundaries. Estimates using annual maximum pollution concentrations reveal the most serious adverse health impact from pollution exposure, while those from annual median measures yield the general adverse health effects from pollution exposure. Since one of the estimation techniques is a panel data fixed-effects model, to include a city I need at least 3 entries for each pollution measure in question. This gives a total of 224 observations for the period Table 1 displays the number of pollution measures by pollutant and by country. Table 3 gives data descriptions and summary statistics for air pollution variables. Comparing the summary statistics for air pollution in Table 3 and the ambient air quality standards in Europe and the United States in Table 4 shows that the average of annual maximum concentration of NO2, O3, and SO2 in my data far exceeds these standards. As such, their predicted impacts on adverse health may be more pronounced than other air pollution indicators in this analysis Other Exogenous Variables Both current and previous per-capita income levels and quality of health care systems are important determinants of current and future health status. In the regressions I include a quadratic form of a 3-year moving average of GDP per capita and a 3-year 8 The thirty countries are Austria, Bosnia and Herzegovina, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Macedonia, Malta, the Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, and United Kingdom. 11

12 moving average of health expenditure as a percentage of GDP. The square of GDP per capita is included because the relationship between health conditions and per capita income may be nonlinear. GDP per capita is associated with education, living and working conditions, perceptions regarding the environment, and precautions regarding health; higher national health expenditure may provide residents with superior medical treatment and facilities. Higher GDP per capita and national health expenditure are both expected to reduce adverse health outcomes. 9 Demographic variables included in the regressions are population density and percentage of urban population as a percentage of total population (urban population shares). Since a densely populated area often has higher crime rates and lower environmental quality, higher population density is expected to increase adverse health outcomes. However, the pollution measures in my dataset are based on urban air quality. It is thus important to control for different urban characteristics that might influence health outcomes across countries. Urban population share reflects level of urbanization in a country. Although urban residents have better access to health services, education, and safe drinking water, urban living is also associated with crowding, municipal waste, and more traffic (Feenger, 1999). As such, there is no a priori assumption regarding the relationship between adverse health outcomes and urban population share Previous county-level and city-level studies also include years of education or high school attainment rate as an explanatory variable (Chay and Greenstone, 2003; Currie and Neidell, 2004). I have tried to include either years of education or national educational expenditure. Both yield a very similar result. However, GDP per capita is highly correlated with educational indicators at the national level. Including either of the educational variables in the regression delivers statistically insignificant results or counter-intuitive (and insignificant) negative coefficients on education, while the outcomes of the rest of explanatory variables remain unchanged. So, I exclude the educational indicator in the analysis. 10 In addition to the above two variables, the Gini index, which describes the level of income inequality in a society, is also an important factor of general health conditions. Given per-capita income level, health conditions tend to be better when income inequality is low (when the Gini index is low). But the 12

13 Personal behavioral variables contained in the regressions are female smoking prevalence and the percentage of regular daily smokers for people 15 years old or older in the population (regular daily smoker). The former is pertinent to analysis of infant health, whereas the latter is pertinent to other health statistics. As smoking is harmful to fetuses, infants, and the respiratory and circulatory systems, a larger percentage of smokers is expected to increase adverse health outcomes. Meteorological variables included in the regressions are the mean of annual maximum and minimum temperature in 0.1 C and annual aggregate precipitation in millimeters. 11 Extreme weather conditions might have negative health effects, especially for infants, young children, and the elderly. Weather conditions also affect pollution concentrations. For example, CO, nitrogen, particulate, and sulphur concentrations are higher on cold days because of fuels used in home heating. O3 concentrations are higher in the summer because ozone is formed in the presence of sunlight. Rainfall washes out some of the pollutants that were trapped in the air. Extreme weather conditions are thus expected to increase adverse health outcomes. 12 However, the estimates of the effect of explanatory power of the Gini index may be very weak in this analysis because of the following reasons. First, the Gini index does not distinguish between one country having more inequality amongst the poor and another with more inequality among the rich. Second, Western and Northern European countries have low income inequality as a result of high income taxes and generous social insurance systems. By contrast, the post-communist countries have low income inequality resulting from political institutions. Although health care systems and medical facilities are much better in the former countries, the Gini index can not distinguish between the two country groups. 11 Since precipitation data for Czech Republic are not available, I use Slovakia s as a surrogate. 12 Wind direction and wind speed are also important factors affecting ambient pollution levels. Prevailing wind is associated with SO2 deposition in Western Europe, where the source country is the United Kingdom. Fenger (1999) found that a fairly strong wind (8 miles per hour) may halve the concentration of NO2 at rush hour. Another study by Vignati et al. (1996) shows that Milan has much higher pollution levels for comparable emissions than Copenhagen because the frequency of low wind speeds is higher in Milan than in Copenhagen. I do not include wind direction and wind speed in the analysis because these data are not available. 13

14 air pollution on health might be negatively biased if weather conditions affect only health or only pollution rather than both (Samet, Zeger, Kelsall, Xu, and Kelstein, 1997). There are two types of countries in the dataset. The first group consists of countries that became EU members before May 2004, and those that are not EU members but have special trade agreements with the EU (G1). The second group consists of countries that have become EU members since May 2004 (G2). The numbers of observations of G1 and G2 countries are 180 and 44 out of 224, respectively. G1 and G2 countries differ in terms of political institutions, economic development, environmental regulations, and health care utilization. While G1 countries have regulated industrial pollution and automobile exhaust since the 1970s, G2 countries, which were part of the former Soviet Union or were Communist states, have given lower priority to environmental quality than to economic development. Because many of the G2 countries have very serious industrial pollution, adverse health effects from pollution exposure are expected to be higher in G2 countries. This is consistent with the health statistics; in 2003, average life expectancy at birth was about 8 years higher in G1 than G2 countries. Because G1 and G2 countries differ in terms of economic development, air quality, and health conditions, I introduce a country group dummy variable and interact it with GDP per capita to control for the differences across country groups. 13 Table 5 displays summary statistics for the explanatory variables and also indicates where the maximum and the minimum value occur. 13 Running separate regressions for each of the country groups may be a good option, but because of too few observations in G2 countries I am unable to do that. 14

15 4. Estimation Method My first step in this analysis is to quantify the contemporaneous adverse health effect of current pollution exposure. Next I estimate the longer-term adverse health effect from previous pollution exposure. The estimation techniques are pooled OLS with yearfixed-effects and a panel data fixed-effects model. Fixed-effects estimation can capture the effects of country-specific omitted variables that may influence health, such as water pollution, average age of marriage, mean age of women at first birth, and diet and exercise habits. It will thus yield a more accurate predicted result than a pooled-ols specification. I will use the following regression equations in this analysis; equation (1) is for pooled OLS and equation (2) is for the panel data fixed-effects model. H H it it = a + Pitγ + SEitb1 + DGitb2 + PBitb3 + Witb4 + G1b 5 + Yitb6 + ηt + ε it (1) = a + Pitγ + SEitb1 + DGitb2 + PBitb3 + Witb4 + Yitb5 + ϕi + ηt + ε it (2) where the subscripts i and t indicate country and year, respectively. H it are health statistics. P it are time-varying pollution measures. SE, DG, it it PB it, and W it are vectors of socio-economic indicators, demographic variables, personal behavior, and meteorological indicators, respectively. G 1 is a country group dummy variable. Y it is a time trend to capture all the other global macroeconomic influences. ϕ i are countryspecific fixed effects. η t are year fixed effects. The coefficient of interest here is γ, the effect of pollution exposure on adverse health outcomes. 15

16 5. Estimation Results To estimate the contemporaneous and cumulative health effects from pollution exposure, I run separate regressions with and without air quality indicators by pooled OLS with year-fixed-effects and panel data fixed-effects estimations. The estimation results are displayed in Tables 6-1 to 6-6. Most estimates of the covariates for health statistics have the expected signs. Most of the estimates are the same with and without the inclusion of air quality indicators, which assures us that the relationship between the health statistics and air quality indicators is not spurious. 14 But the regression outcomes are not the same in pooled OLS and panel data fixed-effects models (see Tables 6-1 to 6-6). This suggests there might be important country-specific factors that influence people s health but are unobservable in the data. For example, dietary and exercise habits are of great importance to health, but data on these are unavailable. Failing to control for these variables will yield negatively biased estimates of the effect of air pollution on health. As for the air quality indicators, the majority of coefficients have the same signs, but their magnitudes are greater and their significance levels are higher when I exclude the outliers in the air quality indicators. This implies that there is no dramatic impact on my results from the outliers. Hence, my analysis below is based on the panel data fixedeffects model with the exclusion of outliers in the pollution data. First I discuss estimation results for infant health and then for other health statistics. 14 I also run random-effects (controlling for year fixed-effects as well) models and Hausman test shows that fixed-effects estimation is mostly more efficient. 16

17 5.1 Effects of Pollution on Infant Health Effects of Pollution on Infant Mortality Rate Tables 7-1 and 7-2 display the panel data fixed-effects regression results for effects of pollution exposure on infant mortality. I first compare the impacts from current and 1-year-lagged ambient air quality on infant health based on equation (2). Current pollution exposure indicates the concurrent effect on infants, while lagged pollution exposure indicates the impact from exposure during their fetal and neonatal periods that might also harm infant health. Overall regression results suggest that lagged O3 concentration is associated with increased infant mortality, whereas current air pollution concentration does not have a statistically significant impact on infant health. The results concur with the findings in medical studies: exposure to O3 can impair lung growth and its function. A 1% increase in median O3 concentrations leads to 0.001% increase in infant death rates per 1,000 live births. Given the 112,212,100 live births from 1980 to 2002 in these countries, about 112 infant lives would have been saved by a 1% reduction in O3 concentrations Other explanatory variables for infant mortality rate (see Table 6-1) Estimated signs of all the other covariates in the panel data fixed-effects model are consistent with a priori assumptions. Some coefficients do not have the expected sign in the pooled OLS model, probably resulting from the omitted variable problems mentioned before. Specifically, 3-year moving average of GDP per capita and health expenditure both have a negative relationship with infant death rate, and population density has a positive relationship with infant death rate. The coefficient of urban 17

18 population share is positive in the pooled OLS model but is negative in the panel data fixed-effects model. The coefficient of female smoking prevalence is negative in the pooled OLS model but becomes positive when I control for country-fixed-effects. Average annual maximum temperature has a negative relationship with infant death rate, while average annual minimum temperature and sum of annual precipitation have a positive relationship with infant death rate. The coefficients of year trend and year dummies are all negative, which means the infant death rate has been decreasing over time after controlling for other known determinants. 5.2 Other health statistics Definitions and types of diseases covered by the health statistics are summarized in Appendix B. For other health statistics, I compare their relationships with current ambient air quality and its 3-year moving average. This is because the majority of these health statistics are for people at least one year old, and both current and previous air pollution may affect their health. Overall results show that both current and previous air pollution increase mortality rates due to disease of the respiratory system. However, previous pollution exposure has a larger influence than current exposure on mortality rates due to disease of the pulmonary and cardiovascular systems. Air pollution exposure has different effects on the respiratory system versus other systems because when toxic substances in the air enter the body, they first affect the respiratory system through inhalation, and then affect other biological systems. The estimation results also suggest that the negative impact from pollution exposure increases with age, which may imply that there is a cumulative effect from pollution exposure or that biological systems 18

19 become more vulnerable with age. Again I discuss the estimation results for the health statistics first and then results for the other explanatory variables. Tables 8 and 9 show the regression results for equation (2) Effects of Pollution Exposure on Deaths from Pneumonia The results in this paper show that current NO2 and SO2 exposure has a significant effect on mortality from pneumonia, but none of the 3-year moving averages of air quality indicators has a significant impact on the pneumonia death rate. As mentioned earlier, this might reflect that the respiratory system is the first biological system to face air pollution exposure; hence, current pollution exposure tends to cause a more pronounced adverse health impact. Tables 8 and 9 display the estimation results for equation (2); the figures are the implied death rate from pneumonia per 10 million people due to a 1 unit increase in the pollutant in question. Combined with the mean values of annual pollution concentrations, these results imply that in an average year in my data, about 1 life per 100 million people in the age group of would have been saved if average annual maximum NO2 concentration had been 1% lower; and about 5 lives per 100 million people in the age group of would have been saved if average annual median SO2 concentration had been 1% lower. The impact of pollution exposure on the pneumonia mortality rate is prominent for people below 45 years old. Pollution exposure aggravates health problems in children and teenagers with pneumonia, probably because they spend more time outdoors. Tables 11 and 12 display the number of lives that would have been saved per year if there had been 19

20 a 1% decrease in the concentration of air pollution, based on the European age structure. A 1% reduction in average annual maximum NO2 concentration would have saved about 1 life per 100 million people in the age group of 15-29; likewise, a 1% reduction in average annual median SO2 concentration would have saved 5 lives per 10 million people in the age group of in an average year in my data Effects of Pollution Exposure on the Mortality Rate from Acute Respiratory Disease As suggested by medical studies showing that CO, NO2, O3 and SO2 all cause respiratory problems (Saldiva, 1994; Romieu et al., 1997; Lippmann, 1993; Katsouyanni et al., 1997; and U.S. EPA), my estimation results show that CO concentration and a 3- year moving average of CO, O3 and SO2 each have a significant impact on acute respiratory disease. Tables 8 and 9 display the regression results for equation (2); the coefficients represent the estimated impact on mortality from acute respiratory disease per 10 million people due to a 1 unit increase in pollution concentration. Combined with the mean values of pollution measures, these results imply that about 0.1 life per 100 million people in the age group and 0.2 life per 100 million people in the age group greater than 75 would have been saved if average annual maximum CO concentration had been 1% lower in an average year in my data. The estimated adverse health impact from pollution exposure is greater when a 3- year moving average of pollution concentration is the air quality indicator. This might imply that previous pollution exposure constantly aggravates existing health problems due to acute respiratory disease. My estimates suggest that, in an average year in my data, 20

21 about 2 lives per 100 million people in the age group would have been saved if average annual median SO2 concentration had been 1% lower; about life per 100 million people in the age group greater than 75 would have been saved if average annual maximum CO concentration had been 1% lower; and 0.09 life per 100 million people in the age group greater than 75 would have been saved if average annual maximum O3 concentration had been 1% lower. Pollution exposure strikes the elderly most, perhaps due to their already impaired respiratory systems. Based on the European age structure, in an average year in my data, a 1% reduction in average annual maximum CO concentration would have prevented 0.38 deaths per 10 million people; a 1% reduction in average annual maximum O3 concentration would have prevented 29 deaths per 10 million people; and a 1% reduction in average annual median SO2 concentration would have prevented 14 deaths per 10 million people from acute respiratory disease Effects of Pollution Exposure on Mortality from Chronic Lower Respiratory Disease Medical studies find that NO2 is associated with increased respiratory illness (Saldiva, 1994); long-term exposure in O3 can lead to chronic impairment of lung function (Romieu et al, 1997; Lippmann, 1993); and exposure to SO2 is associated with higher mortality due to respiratory disease (Katsouyanni et al., 1997). The estimation results show that impacts on chronic lower respiratory disease do not vary much between current air quality and its 3-year moving average; current NO2 and O3 concentrations and a 3-year moving average of NO2, O3, and SO2 concentrations both have statistically significant effects on chronic lower respiratory disease. Tables 8 and 9 show estimation 21

22 results based on equation (2); the figures represent the impact of pollution exposure on the death rate due to chronic lower respiratory disease per 10 million people. Based on the European age structure, in an average year in my data, a 1% reduction in average annual maximum O3 concentration would have prevented 129 deaths per 10 million people; and a 1% reduction in average annual median O3 concentration would have prevented 5 deaths per 10 million people from chronic lower respiratory disease Other Explanatory Variables for Mortality Rate Due to Respiratory Disease Tables 6-2 to 6-4 display the estimation results for other explanatory variables in regressions for respiratory disease. The coefficient of a 3-year moving average of GDP per capita in the fixed-effects model is small and has a negative relationship with the death rates due to disease of the respiratory system. A 3-year moving average of health expenditure as a percentage of GDP does not always have the expected sign. It has a negative relationship with mortality due to pneumonia except for the age groups and 45-59, mortality due to acute respiratory disease for people above age 45, and mortality due to chronic lower respiratory disease except for the age group These finding may reflect that higher incidence of disease raises national health expenditure. The estimated coefficients of national health expenditure are positive, again suggesting possible reverse causation. The coefficient of smoking prevalence also has an unexpected sign in these cases in the fixed-effects model. This implies that national health expenditure intensity is negatively associated with smoking prevalence, but the inclusion of country-fixed-effects changes these signs. As for the demographic variables, estimation results show that population density has a positive relationship and urban 22

23 population share has a negative relationship with death from respiratory illness. This implies that problems due to crowding might exacerbate health problems of the respiratory system, possibly through contagion. In contrast, a more urbanized country tends to have lower mortality rates from respiratory disease, perhaps due to easier access to medical care. Estimation results also show that average annual maximum temperature, average annual minimum temperature, and average annual precipitation each have a negative relationship with mortality rates due to acute respiratory disease. Likewise, for death rates due to chronic lower respiratory disease, average annual maximum temperature has a negative relationship with death rates due to chronic lower respiratory disease; average annual minimum temperature has a negative relationship with death rates due to chronic lower respiratory disease; and average annual precipitation has a positive relationship with death rates due to chronic lower respiratory disease. Additionally, average annual maximum temperature, average annual minimum temperature, and average annual precipitation each have a positive relationship with pneumonia death rate for people above 30 years old. The two temperature variables have a negative relationship with pneumonia death rate for people below 30 years old. The coefficients of year trend and year-effects show that mortality due to acute respiratory disease is increasing over time, while mortality from pneumonia and chronic lower respiratory disease is decreasing over time. 23

24 5.2.5 Effects of Pollution Exposure on Pulmonary Heart Disease My estimation results show that there is a cumulative effect on deaths from pulmonary heart disease due to pollution exposure; the magnitude of the coefficient is significantly greater for a 3-year moving average of pollution concentrations than current pollution concentrations. Medical studies have found that exposure to O3 may cause lung congestion, and long-term exposure to SO2 may decrease pulmonary defense (U.S. EPA). I find that current CO, NO2, and SO2 exposure and 3-year moving average of NO2 and SO2 concentrations have statistically significant impacts on rates of death from pulmonary heart disease. This might reflect that CO can reduce oxygen delivered to organs and NO2 is an ozone precursor. I also find significant impacts on death due to pulmonary heart disease from exposure to CO and NO2. Tables 8 and 9 are regression results based on equation (2); the coefficients signify the impact on mortality due to pulmonary heart disease per 10 million people from a 1 unit increase in the pollutant in question. For current pollution exposure, maximum concentration of CO has a statistically significant but very small impact on the age group In terms of health effects from NO2, about life per 100 million people in the age group would have been saved; about 0.03 life per 100 million people in the age group 45-59, and about 0.6 life per 100 million people in the age group would have been saved in an average year in my data if average annual median NO2 concentration had declined by 1 unit. In terms of health effects from SO2, about life per 100 million people in the age group would have been saved if average annual median SO2 concentration had been reduced by 1 unit in an average year in my data. Based on the European age structure, in an average year in my data, a 1% 24

25 reduction in average annual median NO2 concentration would have saved 98 lives per 10 million people, and a 1% reduction in average annual maximum SO2 concentration would have saved 79 lives per 10 million people with health problems of the pulmonary system. The number of lives that could be saved through pollution reduction increases with age (see Tables 11 and 12), which suggests that there is a cumulative health effect from pollution exposure or that the pulmonary system becomes more vulnerable with age Effects of Pollution Exposure on Ischemic Heart Disease Mortality due to ischemic heart disease is an indicator that measures the impact from pollution exposure on the cardiovascular system. As suggested by medical studies, the most serious threat from CO is to the cardiovascular system, and O3 affects the cardiac system. This study also finds statistically significant impacts from these pollutants on death rates from ischemic heart disease. My estimation results show that current NO2 and O3 exposure and 3-year moving average of CO, NO2, while O3 concentrations have statistically significant impacts on ischemic heart disease. Tables 8 and 9 display regression outcomes based on equation (2); the figures give the consequences of pollution exposure for the death rate from ischemic heart disease per 10 million people. Since the magnitude of the estimates increases with age, pollution exposure may have a cumulative effect on incidence of ischemic heart disease, or older people may be more vulnerable to the effects of pollution. Based on the European age structure, a 1% reduction in average annual maximum CO concentration would have prevented 6 deaths per 10 million people; likewise, a 1% reduction in average annual 25

26 maximum O3 concentration would have prevented 599 deaths per 10 million people from ischemic heart disease in an average year in my data Other Explanatory Variables for Mortality from Pulmonary Heart Diseases and Ischemic Heart Disease Based on the regression results in tables 6-5 and 6-6, the coefficient of a 3-year moving average of GDP per capita in the fixed-effect model becomes positive for people between in regressions of mortality due to pulmonary heart disease and for people below 30 years old in regressions of death due to ischemic heart disease. This might be because people in the age range experience more pollution exposure at work or experience more stress, while people below 30 might suffer from congenital heart problems. I find an unexpected sign for GDP per capita in these cases. The coefficients of a 3-year moving average of health expenditure in the fixed-effects model also have unexpected signs, especially in regressions of deaths from ischemic heart disease. The coefficients of population density and urban population share are mostly positive in the fixed-effects model. This suggests that crowding, high traffic flows, and a busier life in urban areas all increase mortality from pulmonary heart disease and ischemic heart disease. Estimation results show that average annual maximum temperature has a positive relationship with mortality due to pulmonary heart disease; average annual minimum temperature has a negative relationship with mortality due to pulmonary heart disease; and average annual precipitation has a negative relationship with mortality due to pulmonary heart disease. Likewise, average annual maximum temperature has a negative relationship with mortality due to ischemic heart disease; 26

27 average annual minimum temperature has a positive relationship with mortality due to ischemic heart disease; and average annual precipitation has a positive relationship with mortality due to ischemic heart disease. This implies that extreme heat or cold may increase mortality due to heart disease. As for the estimates of the year trend and year effects, the results reveal that the pulmonary death rate increased over time for people below 30 years old and decreased over time for people above 30 years old. This might be because there are congenital problems in the pulmonary system for people below 30 years old. By contrast, mortality due to ischemic heart disease decreased over time for all age groups over the study period, which might resulted from advances in medical technology. 6 Robustness Check Current health status depends on previous health condition, and their correlation rises with age, and the correlation coefficient ranges from below 1% in the age group of 0-14 to 80% in the age group of >75. I control for the correlation for a specific health statistic across years by assuming an AR(1) relationship for the health statistics. Regression results show that the signs of the estimates remain the same but the corresponding standard errors are smaller in most cases. Hence, my estimates continue to be robust after controlling for the serial correlation problem. 7. Conclusion While many previous studies have focused on the impact of ambient concentrations of CO on infant mortality or death rates from asthma in the United States, 27

28 this paper aims to quantify the adverse health consequences from current and previous pollution exposure by age groups on a cross-country basis. The data set covers the period from 1987 to 2002 for 23 European countries. The pollution indicators are annual maximum and median concentration of CO, NO2, O3, and SO2; the health statistics are infant mortality and mortality rates due to disease of the respiratory, pulmonary and cardiovascular systems. Since the health statistics are broken down by age groups, I am able to compare the health impact from pollution exposure on people in different age groups. Table 13 and 14 summarize the estimation results. Controlling for cross-country heterogeneity through a panel data fixed-effects model, I find that concentration of lagged O3 has an impact on infant mortality rate; and both current and previous pollution exposure increase deaths from disease of the respiratory, pulmonary, and cardiovascular systems. Results on mortality due to disease of the pulmonary and cardiovascular systems suggest that there may be a cumulative effect from pollution exposure, as the numbers of lives that could be saved by a reduction in pollution increases by age group. The estimation results also show that there are more instances when NO2 and O3 yield a statistically significant impact on adverse health outcomes than for CO or SO2. This might reflect that the average concentration of CO in these countries over the study period was far below the ambient air quality standards in Europe and the United States. As such, the estimated adverse health impact from CO exposure is either insignificant or very small in this study. For SO2, it could be that my dataset contains fewer observations, and hence I find fewer significant estimates of negative impacts from SO2 exposure. The AirBase project is expected to increase the number of countries and years included in the 28