2009). 3 However, contemporaneous measures ofpollution might underestimate the total welfare
effectsof environmental toxicants if these toxicants negatively affect the developing brain, and consequently, long-run outcomes.
This paper provides some ofthe first evidence that prenatal exposure to industrial pollutioncancause negative long-run human capital outcomes with important distributional consequences. This paper is also the first to investigate how pollution might affect wages in adulthood through both healthandeducational channels. I use very detailed data from surveys ofthe children ofthe National Longitudinal Survey of Youth 1979 (NLSY79) and their parents that allows the matching of siblings and geographic information on families to examine how TRI pollution affects children’s long run outcomes. By leveraging TRI plant openings and closings, I compare siblings within the same family in which one sibling was exposed to TRI pollution during gestation andthe other was not exposed because the plant had not opened yet or because it closed before a later child was conceived. I consider two different approaches – comparing siblings who do not move away from close proximity to a TRI site and estimating an intent-to- treat (ITT) model that assigns initial TRI proximity and open/close dates to all siblings in the same family regardless of whether or not the family moved. By exploiting the short distance over which TRI toxicants can travel through air (i.e., one mile) and using within-family comparisons,
human capital, andeconomicoutcomes throughout the life-cycle. 7 At the same time, our estimates represent lower bounds for the overall effect onthe population as there might be additional negative impacts onhealthand productivity ofpollution exposure at older ages. 8
Our analysis provides important insights for policy makers and society at large. First, the approximately linear healtheffectsofpollution in the observed range calls into question the notion that there is a “safe level” of car pollution. We show that moderate variation in car pollution harms health even in counties with pollution levels below the EPA’s safety threshold. This insight is particularly informative for the current policy debate that regulators should only account for environmental harm from pollution that occurs at levels above the EPA threshold (Friedman, 2019). Second, our study is the first to show that cheating diesel cars had measurable impacts on ambient air pollutionand population health. This is important information for a prominent industry scandal that already has been one ofthe costliest in recent history. To date, Volkswagen has paid about $25 billon in fines to the U.S. government and in compensation to owners of cheating diesel cars. Our results demonstrate that the group of individuals harmed by the emissions cheating widely expands beyond Volkswagen’s customer base.
pop 15-64 102 57.30 6.71 46.33 76.81
intensive industrial units. Urbanization also provides a unique opportunity for people to access politicians and policy makers, which may be not the case in a country with a higher share ofthe rural population [Torras and Boyce (1998), Rivera-Batiz (2002) and Farzin and Bond (2006)]. It is, however, unlikely that the benefits of urbanization outweigh its negative consequences. The second demographic variable is population density. It is often emphasized that a high population density leads to an unsustainable exploitation ofthe environment [Hilton and Levinson (1998)]. The age structure of population, in particular the working age population (15-64 years old), can have an effect onthe environmental degradation. Some scholars refer to the positive role of this part ofthe population in reducing pollution, while some others oppose this view. Farzin and Bond (2006) have explained these different views ontheeffectsof age composition on environmental quality. They point out, for example, that young people can bear more risks of environmental pollution compared to the older part of population. The youngers have a higher option value of waiting for future improvements in environmental quality. Older people feel thehealth problems ofpollution more directly and are willing to put higher pressures onthe government for stricter environmental regulation. Older people also may have more spare time to participate in local NGOs, supporting environmentally-friendly policies by the government. Thus, onthe basis of Farzin and Bond argument, we expect that a higher share ofthe working age population on total population (pop 15-64 ) increases the environmental degradation.
ozone) as 150 equivalent gasoline cars. 3 Hereafter, we refer to cars with “clean diesel” technology as cheating diesel cars.
We exploit the dispersion of these cheating diesel cars across the United States as a natural ex- periment to measure the effect of car pollutionon infant and child health. This natural experiment provides several unique features. First, it is typically difficult to infer causal effects from observed correlations ofhealthand car pollution, as wealthier individuals tend to sort into less-polluted ar- eas and drive newer, less-polluting cars. The fast roll-out of cheating diesel cars provides us with plausibly exogenous variation in car pollution exposure across the entire socio-economic spectrum ofthe United States. Second, it is well established that people avoid known pollution, which can mute estimated impacts of air pollutiononhealth (Neidell, 2009). Moderate pollution increases stemming from cheating diesel cars, a source unknown to the population, are less likely to induce avoidance behaviors, allowing us to cleanly estimate the full impact ofpollution. Third, air pol- lution comes from a multitude of sources, making it difficult to identify contributions from cars, and it is measured coarsely with pollution monitors stationed only in a minority of U.S. counties. This implies low statistical power and potential attenuation bias for correlational studies of pollu- tion (Lleras-Muney, 2010). We use the universe of car registrations to track how cheating diesel
11 There are several plausible biological mechanisms explaining the association between air pollutionand cardio-respiratory diseases (8, 39, 40). Inhaled air pollutants can induce inflammation and oxidative stress in the lungs. Studies have shown that air pollutioncan accelerate and exacerbate the progression of chronic pulmonary conditions such as chronic obstructive pulmonary disease or asthma. Moreover, pulmonary reflexes are assumed to be affected by air pollution (8, 39, 40). The release of pro-inflammatory markers or vasculoactive molecules from the lung cells may lead to inflammation and systemic oxidative stress beyond the lungs resulting in adverse cardiovascular outcomes such as endothelial dysfunction and promotion of atherosclerosis (41). Constituents of PM and particularly UFP can be translocated from the alveolar space to other organs with the blood stream. PM and UFP in the blood might cause vascular inflammation, impaired vascular function and increased platelet aggregation (8, 39, 40). Systemic oxidative stress and inflammation, imbalance ofthe autonomic nervous system and endothelial dysfunction can lead to insulin resistance and therefore promote the progression of T2D (42, 43). However, we observed UFP effects rather onthe respiratory than cardiovascular system, whereas other studies found an association between UFP and cardiovascular as well as respiratory healthoutcomes (44-47). The described associations between air temperature and cardiovascular events might be explained by an activation ofthe sympathetic nervous system in response to temperature decreases. Activation ofthe sympathetic nervous system further leads to an increase in blood pressure, increased blood flow and heart rate and to vasoconstriction ofthe blood vessels (48). In the case of low NO bioavailability or arterial stiffness, subsequent vasodilatation ofthe blood vessels might be impaired. A decrease in temperature was also associated with increases in inflammatory markers, which might promote atherosclerosis (49). Individuals with diabetes are assumed to be especially susceptible to the described mechanisms. Due to the microvascular and macrovascular abnormalities, adaptation to short-term changes in air pollutionand air temperature is impaired in diabetics leading to clinical conditions that might not be significant in healthy individuals (50, 51).
2 1. Introduction
There has been growing interest in effectsofthe local environment onthehealthand growth of children in a variety of different countries and periods. Most ofthe focus has been on mortality rates of infants and children andonthehealth status of those who survived, either during childhood or later in life. Here we examine theeffectsofthe local environment onthe heights of men who were born in England and Wales in the 1890s. These data come from the records of army servicemen who enlisted in the First World War. Previous studies have suggested that although the socioeconomic status and demographic structure ofthe household were significant influences, conditions in the locality seem to have mattered even more. Here we investigate one key element ofthe local environment: atmospheric pollution. In the age of dark satanic mills coal combustion was the major causeof atmospheric pollution. Emissions of black smoke were fifty times higher than present day levels. As studies of more recent times have identified negative healtheffectsof polluted air we should expect to find even greater effects in the past. Recently, Beach and Hanlon (2016) have found a strong link between local coal use and mortality rates across districts in the mid-nineteenth century. Here we adopt a similar approach to the measurement of atmospheric pollutionand we use it to explore theeffectson adult heights of exposure to pollution during childhood. Our approach has the advantage that we can identify the cumulative effects during childhood, as represented by final height of those that survived. By controlling for household-level characteristics we can separately identify individual and locality effects, avoiding the hazards of inferring individual effects from group-level analysis.
As mentioned, for Colombia, academic literature addressing these problems is almost non- existent. Llorente and Wilkinson (2009) and Uribe-Botero (2004) focus on studying exposure to air pollution by using risk assessment tools and analyzing air pollution data: they find that higher concentrations of air pollution in Bogot´ a and Medell´ın produce a great risk for human health. These methodologies seek to document and highlight how high levels of air pollution put health at risk for different population groups in Bogot´ a and Medell´ın. Franco et al. (2009) use four schools in Bogot´ a, close to heavy traffic streets, as a treatment group, and four schools in rural areas near Bogot´ a, where levels of air pollution are low. Their results focus on comparing levels ofpollution among schools (treatment and control), but besides this, they do not make conclusions about theeffectsof exposure to air pollutiononhealth or educationaloutcomes. Within Colombia, other studies have associated levels of air pollution with the incidence of respiratory infections in children under five years old for Bogot´ a (Hern´ andez et al., 2013a,b; ?), as well as levels of air pollutionand their effectson vulnerable people in high polluted areas of Downtown Medell´ın (Gaviria et al., 2012).
38 M.B.A. Sghari, S. Hammami / Energy Reports 2 (2016) 35–39 bias and therefore include different explanatory variables ranging
from macroeconomic variables such as prices, population, income distribution and trade balances to education, technology, and human development indicators (Soytas et al. 2007). Including labor and gross fixed capital formation in their model, (Soytas et al. 2007) examined the effect of energy consumption and output on carbon emissions in the United States and explored the Granger 2 Antweiler et al. (2001) and Coxhead (2003) postulate that this non-linear relationship between environmental pollutionand income levels can be explained by three factors: scale, composition, and technique effects. The scale effect occurs as pollution increases with the size ofthe economy. The composition effect refers to the change in the production structure of an economy from agriculture-based to industry and service- based which results in the reallocation of resources. Finally, thepollution–income relationship also depends on techniques of production. An improvement in techniques of production, i.e., the technique effect, may reduce the amount of pollutant emissions per unit of production. 3. For a review ofthe Environmental Kuznets Curve research see for example the works of Stagl (1999), Yandle et al. (2002), Dinda (2004) and Stern (2004). 5 causality relationship between income, energy consumption, and carbon emissions. They found that income does not Granger- cause carbon emissions in the US in the long run, but energy use does. Hence, income growth by itself may not become a solution to environmental problems. The existing literature reveals that empirical finding studies differ substantially and are not conclusive enough to offer policy recommendations that can be applied across countries. In addition, few studies focus on testing the nexus of output–energy and output–environmental degradation under the same integrated framework. Ang ( 2007 and
Moreover, our analysis ofthe treatment eect heterogeneity shows that babies born to socially disadvantaged mothers are more vulnerable, implying that thehealth impacts of air pollution are unequally distributed. This knowledge gain is of direct policy relevance. In fact, if disadvantaged families are more likely to live in more polluted areas, exposure to air pollution may contribute to explaining the existing dierences in educational attainment and labor market outcomes across dierent socio-economic groups, or more generally, explaining social andeconomic inequality ( Isen et al. , 2017 ). This in turn implies that better air quality may help improve environmental conditions in low-income families and thus align endowments at birth, giving a fair chance in life to every child ( Germani et al. , 2014 ). If economicand environmental inequality reinforce each other, then actions directed to improve air quality may serve not only as environmental health policies but also as eective social policies to abate economic inequality. Furthermore, if air pollution is viewed as a factor of production which, similar to technology, is able to impact how other production factors such as labor, capital, and land can be combined to generate output, better air quality may also contribute to economic progress.
DISCUSSION OF PROS AND CONS
How air pollution may affect education
There are several potential mechanisms that can link air pollution to educationaloutcomes. First, scholastic achievement may be affected directly by deterioration in oxygen quality induced by air pollution, as the brain consumes a large fraction ofthe oxygen needed by the body. Studies also suggest that exposure to air pollution affects brain development during childhood and fetal development. However, as stated by one group of researchers, “the possible neurodegenerative effect of air pollution remains largely unexplored” . Another possible channel is via contemporaneous mild health conditions. More specifically, air pollution exposure may cause irritation ofthe eyes, nose, and throat, as well as asthma attacks, headaches, dizziness, and fatigue, which can all clearly affect student achievement. Finally, pollution may increase school absences, either due to pollution-related illness or parental decisions to keep children at home to avoid pollution exposure. This, in turn, may negatively affect educational achievement due to a reduction in formal contact time at school. It is important to note that the above mechanisms may also affect human capital formation (broadly defined as the skills, knowledge, and experience accumulated by an individual) and even labor market outcomes, since exams are often used as a primary method to assign students to further education and occupations.
Drug Regulatory Affairs, Pharmaceutical Institute, University Bonn, Deutschland
Abstract: Aim of this study was to assess the annual social cost of air pollution impact onhealthof Warsaw
population. The study consisted of three main parts, i.e. the determination of Warsaw citizens’ exposure to air pollution, the quantification ofthehealth effect as a result of this exposure andtheeconomic evaluation ofthe assessed health impact. Value of Statistical Life (VSL) derived from Willingness To Pay (WTP) for mortality risk reduction was used to assess the costs of premature mortality, whereas the Cost of Illness (COI) Approach was applied for the estimation ofthe costs of excessive cardiovascular and respiratory hospitalizations as well as restricted activity days. Thorough search was performed to find the best assessment of VSL for the Warsaw population and finally the value of 1.9 Mio PLN was chosen. Annually, approximately 2 264 premature death cases, 351 839 restricted activity days, 684 and 1 551 excessive hospital admissions due to (respectively) respiratory and cardiovascular problems can be attributed to air pollution. The total costs of air pollutionhealtheffects in Warsaw amount to about 4,4 Bn PLN. The cost of air pollution impact on human health is significant. Therefore, more attention should be paid for the integrated environmental health policy, with a focus on cities as a priority.
families lack access to food, clothing, and shelter, they do not have the resources to support even a minimum level ofhealth.
This Section ofthe Commission report presents data documenting that pollutionandpollution-related disease are concentrated among the poor and contribute to the intergenerational perpetuation ofpoverty. Pollution- related disease can result in lost income and increased health-care costs, thus imposing disproportionately great economic burdens on poor families and communities. 286 In children, early-life exposure to neurotoxic pollutants can impair cognitive function and diminish the ability to concentrate, further contributing to school failure and reducing lifetime earnings. In example, a long-term follow-up study 144 of children exposed to lead reported that an elevated blood lead concentration at age 11 years was associated with lower cognitive function and reduced socioeconomic status at age 38 years, with diminished IQ, and downward social mobility. Moreover, povertycan worsen health, for example, by forcing people to live in environments that make them ill, without decent shelter, clean water, or adequate sanitation. 287 When people live near polluting factories or downstream from hazardous waste sites, or when poor women have no alternative but to cook with traditional stoves in close quarters, or when children are forced to pick by hand through electronic waste to recover precious metals to sustain themselves and their families, 288 povertycan exacerbate poor health.
Ambient air pollution is a common causeof adverse health conditions, contributing to the occur- rence and severity of respiratory diseases and infections. Children, being one ofthe most sensitive subgroups ofthe population, can be highly vulnerable, and high air pollutioncan end up affect- ing children’s daily school performance. Several studies have identified theeffectsof ambient air pollutionon hospital admissions, mortality rates, absenteeism and cognitive deficits in children. Therefore, numerous mechanisms can explain the association between ambient air pollutionand school performance. For instance, absenteeism has been associated with negative effectson school attainment (Carroll, 2010). There is also a vast literature onthe general importance ofhealth for school achievement that views physical health as a necessary pre-condition for childrens daily school work. From early health problems to common illnesses, health deficiencies can limit chil- dren’s cognitive ability. Conversely, favorable environmental conditions can play an important role in childrens learning processes.
To analyze labor market outcomesand insurance coverage we use data from the American Com- munity Survey (2005-2016), the largest household survey that the U.S. Census Bureau administers ( Ruggles et al. , 2017 ). We start with 2005 since it’s the first year with a full one-percent sample ofthe U.S. population. 2016 is the last year for which the survey data is available. Designed as a replacement for the long form ofthe decennial census, ACS contains a detailed set of standard socio-demographic characteristics and labor market outcomes (e.g. employment, labor force par- ticipation, annual income). Furthermore, since 2008, the survey provides information onhealth insurance coverage andthe type of coverage. The ACS also contains information on US citizenship status, number of years spent in the US, quarter of birth, andeducational attainment, which can be used to determine respondents’ DACA eligibility status. However, the survey does not include information about individual criminal convictions, or whether the respondent has been honorably discharged from the military. As far as the sampling procedure is concerned, unauthorized im- migrants are no more or less likely to be selected into the sample than authorized immigrants or natives. This follows from the fact that U.S. Census Bureau uses a near universe of housing ad- dresses from its Master Address File as the sample frame from which it draws systematic sample of addresses each month. The ACS is then mailed to the selected addresses. Non-respondents are contacted one month later for a computer-assisted telephone interview. After that, one third of non-respondents who still remain are contacted in person to complete the ACS one month after the telephone survey attempt ( Pope , 2016 ). Between 2005 and 2016, The Master Address File covered 98.3-99.1% of all housing units and 76.2-99.8% of all group quarters in the U.S., encompassing 91.9-95.1% ofthe total U.S. population. The survey response rate in this period was 89.9-98.0% for the housing units and 95.1-98.0% for the group quarters. 6
Figure 4 Distribution of Percentage of Students Not Achieving Basic Standard Mathematics
As we have already discussed, peers’ ability is endogenous as people from the same peer group share common unobserved characteristics and because individuals might affect their peer group inasmuch as their peer group affects them. Therefore, we rely on a novel identification strate- gy based onthe peers of peers. In practice, for each LSYPE child, we look at her high school peers, and then we select her primary school peers who did not attend the same primary school as she did and do not currently attend the same high school. These individuals are likely to have affected the peers (through attendance ofthe same primary school) but have likely never met the student of in- terest. Therefore, these peers of peers cannot have had a direct effect onthe student’s achievements. Our analysis is limited to children who are in LSYPE, and, consequently, we do not have a complete overview of all students in a particular primary or high school and our estimates could potentially be affected by measurement error for this reason. However, the LSYPE sample was de- signed to be representative of various subgroups ofthe students’ population in England, and using students in LSYPE allows us to access to all the available information on their families and back- grounds (which are not included in NPD). Peers-of-peers ability is measured through achievements in KS2 tests taken at the end of primary school at Age 11.
Our main finding is that policy inattention substantially affects the transition dynamics. Present-centric policy thinking matters, it affects the transition dynamics, leading to quicker, but more expensive, transi- tions in both the case of growing emerging market economies, andthe case of advanced countries. Inde- pendently ofthe case considered, in fact, inattention always leads to an under-evaluation ofthe environ- mental costs. This means that inattention allows, in either of our two cases above, for a larger built up of a pollution stock that is likely to threaten the threshold - the carbon budget - below which the current Paris agreement onthe upper bound of temperature rise, namely 1.5 to 2 degrees Celsius, is not ensured.
to fewer crimes.
To assess the direction ofthe bias in estimating the impact of skyglow on preterm birth, we control for possible correlates of urbanicity and skyglow that could be related to maternal stress. In particular, we focus on crime and noise. From New Jersey’s annual Uniform Crime Reports (UCR) between 2011 and 2015, we obtained two crime variables measured at the city level: the yearly number of violent crimes (murder, rape, robbery, and aggravated assault) per 1,000 residents andthe yearly number of non-violent crimes (burglary, larceny-theft, and motor vehicle theft) per 1,000 residents in the eight New Jersey cities included in our study. We use these two annual city-level crime variables as proxy variables for maternal stress caused by higher levels of crime. In addition to crime, noise pollution is also likely to be positively correlated with skyglow due to urbanicity, and may lead to an over-estimate ofthe impact of skyglow onthe probability of having a preterm birth, since noise pollution is found to be associated with adverse birth outcomes (Gehring et al., 2014). To deal with this confounding factor, we obtain data on aviation and road noise from the U.S. Department of Transportation, where the noise data are available for year 2014 and vary by zip code. 35
The patterns between education attainment and unemployment suggest that several years of schooling is no guarantee of employment. Apart from the senior secondary completers the incidence of unemployment amongst tertiary completers andthe products of technical and vocational institutions are amongst the highest. This raises questions about the link between education policy andtheeconomicand development strategy. Do the outputs ofthe technical, vocational institutions andthe tertiary institutions have the requisite skills? A clear pattern seems to emerge between educational attainment and employment in some sectors. Persons with little or no formal education tend to be concentrated in the agriculture sector. Onthe other hand the expanding sectors ofthe economy, i.e. finance, insurance andthe ICT sectors do not tend to employ persons with low levels ofeducational attainment. The low level of skills and general education amongst the adult Ghanaian population will pose a constraint onthe evolution to a modern middle-income society.
an imitation behavior of countries can also reduce thepollution stock. In this case, we do not consider such a strong influence from an external administration. In the following, we distinguish between a basic imitation according to ( 5 ) and a more advanced imitation according to ( 6 ).
In both cases, we observe that the players agree on their investment into environmentally friendly policies but they converge to different pollution stocks x i ( T ) . This is due to the fact that in our model all countries ofthe EU are connected and imitate each other in terms of their investment u. This result is not very surprising as similar results by Ranjbar-Sahraei (e.g., in [ 46 , 47 ]) show. Comparing Figures 5 a and 6 a, we can see that this coinciding investment differs for different starting values for u. The results of Figure 5 are obtained when considering a short initial phase in which each country minimizes its own costs. Afterwards, they start with imitating the neighbors’ investment strategies according to ( 5 ) or ( 6 ). In contrast to this, the results displayed in Figure 6 are obtained after a long initial phase. Compared to the short initial phase, we consider here twice as much time in which the countries minimize their own costs. Of course, the investment into clean policies determines thepollution stock as well. For a consensus on a non-environmentally friendly behavior thepollution stock may even increase exponentially. The more advanced imitation behavior is more robust against the initial conditions. In both cases, see Figures 5 b and 6 b, we do not notice an exponential growth but rather a convergence ofthepollution stock towards different values x i ( T ) per country. Again, the countries differ in the amount ofpollution they produce. Furthermore, when the countries perform a more advanced imitation behavior, they converge to a lower amount ofpollution stock (see Figure 6 ).
estimation. In particular, following Lee (2007), the following model is estimated:
ܳ |, (߬|ܣ, ܺ) = ߙ + ܣҧ ߚ(߬) + ܆ ᇱ (ૌ) + ߳
andthe first step linear quantile regression is modelled as