Journal of Economic Literature 2013, 51 ( 3 ) , 689–730 http://dx.doi.org/10.1257/jel.51.3.689

689 1. Introduction 

# T he recognition that environmental fac

tors can affect human health can be traced at least as far back as the thirteenth century when the King of England banned the burning of sea-coal in London because it was “prejudicial to health” (Brimblecombe 1999). In the eight-hundred years that have followed, our understanding of biology, chemistry, and medicine have evolved con

siderably. Alongside large-scale increases in pollution due to industrialization, modern environmental concerns were born. Today, nearly every country in the world regulates the environment to some degree, and pol

lution is a canonical example of both exter

nalities and public goods in microeconomic textbooks. 1 The principal motivation for environmental regulation is the protection

1 See Vahlsing and Smith (2012) for a list of nations that regulate two important pollutants—particulate matter and sulfur dioxide. 

# Environment, Health,

and Human Capital †



### J oshua G raff Z ivin and M atthew N eidell *

In this review, we discuss three major contributions economists have made to our understanding of the relationship between the environment and individual well-being. First, in explicitly recognizing how optimizing behavior, particularly in the form of residential sorting, can lead to nonrandom assignment of pollution, economists have employed a wide range of quasi-experimental techniques to develop causal estimates of the effect of pollution. Second, economic research has placed a considerable focus on the role of avoidance behavior, which is an important component for understanding the difference between biological and behavioral effects of pollution and for proper welfare calculations. Lastly, economic research has expanded the focus of analysis beyond traditional health outcomes to include measures of human capital, including labor supply, productivity, and cognition. Our review of the quasi-experimental evidence on this topic suggests that pollution does indeed have a wide range of effects on individual well-being, even at levels well below current regulatory standards. Given the importance of health and human capital as an engine for economic growth, these ! ndings underscore the role of environmental conditions as an important factor of production. ( JEL I12, I31, J24, Q51, Q53)

* Graff Zivin: University of California, San Diego, and National Bureau of Economic Research. Neidell: Columbia University and National Bureau of Economic Research. We thank Janet Currie, Prashant Bharadwaj, Nick Sanders, and Reed Walker for many helpful sugges

tions and Jamie Mullins for excellent research assistance. We are grateful for funding from the National Institute of Environmental Health Sciences (1R21ES019670-01). The usual disclaimer applies.
† Go to http://dx.doi.org/10.1257/jel.51.3.689 to visit the article page and view author disclosure statement(s).Journal of Economic Literature, Vol. LI (September 2013) 690

of human health, with signi 0 cant impacts on the welfare of both producers and consum

ers around the globe. 2 Historically, much of our understanding about this relationship between the envi

ronment and health comes from the health sciences literature. The 0 eld of toxicology uses controlled settings akin to randomized experiments to study the adverse effects of environmental stressors. While the con

trolled setting allows researchers to isolate biological impacts, ethical concerns over providing humans with known poisons gen

erally leads to the use of studies based on the dosing of animal subjects, which provides limited external validity for the policy mak

ing context at hand. When the contaminant can be delivered in a suf 0 ciently non-harmful way to allow human experimentation, studies typically focus on surrogate outcomes, such as spirometry measures of lung function, that are straightforward to measure but do not clearly map into realized health impair

ment, particularly for sensitive populations who are often omitted from such studies (Hazucha, Folinsbee, and Bromberg 2003). Epidemiology, on the other hand, exploits real-world contamination to examine the relationship between environment and health in situ in an effort to better inform environmental policy. In this uncontrolled setting, however, humans can respond to environmental conditions, thus complicating statistical inference. Given that there are literally thou

sands of investigations on the relationship between pollution and health in the health sciences—a search on PubMed for “pol

lution” and “health” revealed 25,754 pub

lications—what can economists add to an already crowded 0 eld? In this essay, we

2 The U.S. Environmental Protection Agency, for exam

ple, describes its mission as ensuring that “all Americans are protected from signi 0 cant risks to human health and the environment where they live, learn and work. . . .” highlight three important contributions from the burgeoning economics literature on this topic over the past decade. First, economists explicitly recognized how opti

mizing behavior, particularly in the form of residential sorting, can lead to nonrandom assignment of pollution. For example, since air quality is capitalized into housing prices (Chay and Greenstone 2005), individuals with higher incomes are likely to sort into locations with better air quality. 3 Conversely, cities attract high skilled workers because of greater employment opportunities, but are also a major source of pollution. These same individuals may also make additional investments in their health, and failing to account for these investments will bias esti

mates of the effects of pollution. In light of concerns regarding endogenous exposure to pollution, economists have employed a wide range of quasi-experimental techniques to develop causal estimates of the effect of in vivo pollution levels on health and human capital. Such causal inference provides esti

mates more relevant for policy making. Second, economic research has placed a considerable focus on avoidance behavior. Since the consequences of toxic exposures are costly, individuals may engage in activi

ties to avert them. This can muddy pure biologic signals in epidemiologic research. Ignoring avoidance behavior can also lead to gross mischaracterizations of social welfare since a narrow focus on the costs of mor

bidity and mortality will exclude avoidance activities that can be quite costly (Courant and Porter 1981; Harrington and Portney 1987; Bartik 1988). Encouraging avoidance behavior has also become an increasingly important policy lever through the use of informational approaches that empower

3 Moreover, since air quality is bundled with other neighborhood attributes, locational sorting based on those attributes alone can also lead to the nonrandom assign

ment of pollution.691 Graff Zivin and Neidell: Environment, Health, and Human Capital

citizens to make individual-level decisions regarding these tradeoffs (Magat and Viscusi 1992; Shimshack, Ward, and Beatty 2007; Neidell 2009; Graff Zivin, Neidell, and Schlenker 2011).

Lastly, economic research on the impacts of environmental pollution has expanded the focus of analysis beyond traditional health outcomes. Many health shocks can affect human capital and productivity, both in the short run (Strauss and Thomas 1998; Currie and Stabile 2006) and the long run (Cunha and Heckman 2007; Currie and Hyson 1999). A blossoming literature has begun to make these links more explicit by exam

ining outcomes ranging from labor supply and productivity to cognitive formation and performance (Graff Zivin and Neidell 2012; Hanna and Oliva 2011; Lavy, Ebenstein, and Roth 2012; Almond, Edlund, and Palme 2009). Many of these impacts, particularly those on the intensive margin, are quite subtle with little known about their perva

siveness throughout the economy. Given the importance of human capital as an engine for economic growth (Schultz 1961; Nelson and Phelps 1966; Romer 1986), these impacts may also be large and quite long lasting rela

tive to those associated with acute morbidity. In some sense, these human capital impacts invoke the early economic models of Smith and Ricardo which viewed the environment, albeit mostly land and natural resources, as an essential factor of production. Together, these papers underscore the role of envi

ronmental protection as a national invest

ment, in addition to a consumption good, and thus should not be treated purely as a tax on producers and consumers that retards economic growth. 4

4 Since exposure to poor environmental quality within and across countries tends to correlate with low income, these results also suggest a new sort of poverty trap. This logic runs counter to much of the literature on the Environmental Kuznets Curve, which sees causality The remainder of this paper is organized as follows. We begin with a brief scienti 0 c background section followed by a conceptual model of the environmental health produc

tion function and its implications for esti

mation and optimal policy design. Section 4 highlights the primary challenges to empiri

cal economic research in this area. In sec

tion 5, we summarize key quasi-experimental evidence on both health and human capital

outcomes. 5 Section 6 offers some concluding remarks and suggestions for future research.

2. Scienti ! c Background

The scienti 0 c literature on the environ

mental health risk generation process typi

cally describes the process through which the environment impacts human health as comprised of three principal components. Contamination describes the amount of “toxic” materials in a particular site and media. Exposure is a measure of human con

tact with the contaminant. The dose-response function translates a given human expo

sure to pollution into a physiological health response. Since each element is treated as independent and quasi-exogenous within this framework, its direct application in eco

nomics has generally been limited to theoret

ical examinations of optimal regulation when individual behaviors are assumed 0 xed in the face of the pollutant (e.g., Lichtenberg and Zilberman 1988; Graff Zivin and Zilberman 2002). While the subsequent section will present a conceptual model that departs from this framework in order to better serve economic lines of inquiry, we will use this trichotomy as an organizing theme to brie 2 y review some salient scienti 0 c features of

exclusively running in the other direction (see Dasgupta

et al. 2002 for a succinct summary of this literature).

5 Note that we do not focus on temperature in this review, as a recent review of the health effects from tem

perature extremes can be found in Deschenes (2012).Journal of Economic Literature, Vol. LI (September 2013) 692

environmental problems in the remainder of this section.

2.1 Contamination

Contamination of the environment comes in many forms, with thousands of compounds suspected of damaging human and animal health. The U.S. Environmental Protection Agency alone regulates nearly 200 toxic air pollutants along with six criteria air pollut

ants that are commonly found all over the United States, which include carbon mon

oxide, sulfur dioxide, nitrogen oxide, ozone, lead, and particulate matter. Drinking water regulations also set standards for approxi

mately 100 contaminants. The list of regu

lated hazardous wastes that despoil land is even longer. 6 While pollutants can be attrib

uted to many different sources, a consider

able amount of pollution can be traced to industrial processes, electricity generation, and the transportation sector.

Two features of contamination are particu

larly important for economic analyses. First, many pollutants form as the result of interac

tion with other environmental variables. For example, ozone pollution is not directly emit

ted, but rather forms as the result of complex interactions between two other emitted pol

lutants—nitrogen oxides (NOx) and volatile organic chemicals (VOCs)—in the presence of heat and sunlight (see Auffhammer and Kellogg 2011 for a discussion). Furthermore, many pollutants are coemitted from the same source and wind up in multiple medium, such as air, water, and soil. As such, the scope for copollutant confounding is potentially large since each of these elements may also impact human health either directly or through its
in 2 uence on activity choice. Second, pollutants can vary widely in their deposition patterns. Many pollutants fall

6 Although climate forcing pollutants, such as carbon dioxide, are another concern, as previously mentioned we do not include them in this review. relatively close to their source while others can travel great distances. For example, a siz

able fraction of the mercury contamination in the Western United States originates at coal- 0 red power plants in China and other parts of Asia (Seigneur et al. 2004). Apart from the obvious importance of deposition in designing policy—cap-and-trade only works for pollutants with nonlinear dose response functions when pollution does not accumulate in hot spots—pollution transport also matters for the estimation of the health effects from pollution. Local pollutants are generally correlated with economic activity within the region that also impact health, making causal inference more challenging. This problem is lessened for distant pollut

ants. In either case, meteorological condi

tions can affect deposition: rain can “clean” the air and 2 ush toxins from the soil, wind can move pollution around, and temperature can affect the formation of pollutants. Since meteorology can also have a direct impact on one’s health (Deschenes and Greenstone 2011), it is also an important variable to con

trol for as a confounder.

2.2 Exposure

The existence of pollution is only a prob

lem from a human health perspective if people are exposed to the pollutant. The relevant measure of exposure, and thus the appropriate identi 0 cation strategy for empirical research, will depend on the con

taminant of interest. For some pollutants, acute exposure is suf 0 cient to cause illness while for others illness only occurs after a prolonged exposure to pollution over days, weeks, or even years for some carcinogens. The other important aspect of exposure is the role of avoidance behavior. While laboratory studies force exposure in order to estimate a pure biologic effect, outside of this experimental setting individuals can respond to ambient pollution levels by tak

ing actions to limit their exposure to it. This693 Graff Zivin and Neidell: Environment, Health, and Human Capital

avoidance behavior drives a wedge between “potential” exposure—ambient levels of pol

lution in one’s community—and “realized” exposure—the amount of ambient pollution inhaled or ingested. As such, reduced form estimates of the impacts of potential expo

sure on health, which is often all that can be measured in observational analyses, may differ considerably from laboratory-based estimates of the impact of realized exposure. Welfare calculations and thus the design of optimal policy will depend critically on these avoidance behaviors.

While this wedge between potential and realized exposure can arise due to inciden

tal avoidance, e.g., well-insulated homes may limit exposure to pollution although they were not adopted with such attributes in mind, deliberate avoidance is of particular interest as its costs are a direct result of pol

lution and thus part of the economic impacts of pollution. Clearly, deliberate avoidance can only exist for observable pollutants. Some pollutants are detectable by smell or sight, while others are colorless, odorless, and tasteless. Public warnings, such as air quality alerts and water quality violations, can inform people of dangerous pollution levels, which is particularly useful for the less detectable pollutants. For those pollutants with rapid health effects, effective avoidance behavior may also be instigated by experi

enced changes in health. 7

2.3 Dose-Response Conditional on realized human exposure to a given contaminant, the dose-response function can be viewed as a damage function in an economic model, although perhaps one that only paints a partial picture of aggregate

7 Note that even when pollution is neither ex ante nor ex post observable, avoidance behavior will remain a con

cern if pollution is correlated with other conditions that affect activity choice, e.g., individuals may spend less time outside when it is very hot and thus avoid ozone due to temperature effects. economic damages. Several features of the biological effects of pollutant exposure are important for thinking about the estimation of health effects and the design of policy. First, dose-response functions come in a variety of shapes. While some are (quasi-) linear, others can be nonlinear and even contain thresholds. For example, chamber studies of ozone pollution suggest a thresh

old of approximately forty parts-per-billion, below which respiratory function appears unaffected (Dimeo et al. 1981). Moreover, pollutants also vary in the types of health responses they elicit and the temporal sig

nature associated with them. Some will impair respiratory function and manifest quite quickly while the effects of exposure to carcinogens appear with a considerable time delay (e.g., Folinsbee, McDonnell, and Horstman 1988; Huff and Hasemen 1991; Kampa and Castanas 2008). Cardiovascular impacts can appear in both the short and long run (Le Tertre et al. 2002; and Kaufman

et al. 2012). Thus, choice of functional form and lag structure are essential for estimation in this context and, when appropriate, should be attentive to the underlying science. 8 Second, there is considerable inter-indi

vidual heterogeneity in responses to a given dose of pollution. For example, children tend to be more vulnerable because limita

tions in their immune system and partial lung development make it dif 0 cult for them to cope with environmental assaults (Schwartz 2004). Comorbid conditions can also impact dose response functions. HIV patients will be more susceptible to nearly all pollutants due to their compromised immune system. Asthmatics may only be more sensitive to those pollutants that act upon the respira

tory system. While this heterogeneity will

8 That said, some study designs that are correlational in nature are more limited in their usefulness for economic studies of environmental health effects tied to particular policies or programs.Journal of Economic Literature, Vol. LI (September 2013) 694

not limit the estimation of average treatment effects, it could mask potentially important outcomes. Perhaps more interestingly, since much of this heterogeneity is known ex ante, it broadens the sets of hypotheses that can be tested and parameters that can be estimated in many empirical settings. Lastly, while the public health and medical literatures have generally de 0 ned responses in terms of physical health out

comes, it is plausible that many pollutants generate nonhealth sequelae of interest to economists. Poor health as a result of pol

lution exposure can contemporaneously impact earnings by increasing worker absen

teeism and diminishing worker productivity for adults and school absenteeism and per

formance for children. It can also reduce earnings in the long run by limiting human capital formation, through both direct chan

nels (via neurological insults) and indirect ones (via subsequent investments and skill formation). Estimation of these effects rep

resents an exciting frontier of economic research in this area.

3. Conceptual Framework The natural departure point for economic models of environmental health is the explicit recognition that individuals can play a direct and deliberate role in the production of their own health, principally through defensive and ameliorative actions. Here we build upon the seminal model of Grossman (1972) that characterized health as an investment good to examine the particular case of environmental health, extending the model

to re 2 ect the fact that health can in 2 uence labor productivity, with one signi 0 cant departure from the existing literature. Attention to the impacts of health on labor has generally been limited to the extensive margin whereby illness reduces labor supply (Smith 2005; McClellan 1998). In the spirit of Currie and Madrian (1999), we extend our model to include the intensive margin as well, where labor productivity is impacted holding labor supply 0 xed. This adjustment allows the model to capture more subtle health effects. For simplicity, we model the health production function of a representative individual. 9 In its simplest form individual health can be expressed as a function of ambient pollution levels P , exposure to that pollution, which is mitigated by avoidance behavior A , and medical care
M that ameliorates the negative health consequences from pollution exposure (Harrington and Portney 1987; Cropper and Freeman 1991):

(1) H = H ( P, M, A ).

While avoidance behavior and the con

sumption of medical care both reduce the health burden from pollution, they are quite distinct in terms of their timing and their costs. Avoidance behavior is a preventive measure that takes place before pollution exposure is realized. Its costs include any expenditure on defensive measures, e.g., air 0 ltration, as well as the disutility associated with reallocations of time across activities that constitute part of the avoidance behav

ior. 10 In contrast, medical care consump

tion takes place after exposure is realized in response to an illness episode. Medical treat

ment costs include direct healthcare costs (such as doctor visits and the use of medi

cations) as well as any disutility that results from those medical encounters.

9 As noted in the previous section, individuals may differ in their susceptibility to pollution for a variety of reasons. This heterogeneity will affect the relative returns to avoid

ance and ameliorative behaviors, but the basic insights from the model remain the same.

10 For expositional simplicity, we focus our attention on short-run avoidance behavior but the same logic applies to long-run avoidance behaviors, such as sorting. This distinc

tion is discussed in greater detail in section 4.3.695 Graff Zivin and Neidell: Environment, Health, and Human Capital

While we eschew the complexity of a formal dynamic model, we reexpress the health production function in a nonconven

tional form to better re 2 ect these features as well as to draw connections between sev

eral strands of empirical literature in envi

ronment, health, and labor economics. In particular, we make a distinction between individual health H and an illness episode φ and allow the health production function to take the following form:

(2) H = H ( M ( φ )

, φ ( P, A )).

As can be seen in (2), ambient pollu

tion levels and avoidance behavior jointly determine environmentally driven illness episodes. Medical expenditure, in turn, depends on these illness episodes. Since medical expenditure is meant to decrease the severity, i.e., disutility, of illness, individ

ual health depends jointly on illness episodes and medical expenditure. Of course, the marginal productivity of medical treatment will differ by condition, and thus the relative importance of avoidance behavior will also vary by illness type and thus pollutants. We assume that the usual concavity assumptions apply to the health function and its subparts described in (2).

While individual utility depends on health, it also depends on consumption ( X ) and leisure ( L ): U ( X

, L

, H ). Labor produc

tivity is presumed to increase in health at a decreasing rate. Importantly, since indi

viduals are allocating scarce time between work and leisure, this labor productivity effect and its resulting impact on wages may lead to changes in hours worked. 11 Letting I

11 This basic framework could be simpli 0 ed if one assumes that sickness does not directly enter the utility function but only indirectly through its impacts on labor productivity, i.e., if health is a pure investment good in the Grossman sense. In that case, an individual would invest in avoidance/ameliorative behavior such that the costs of denote nonwage income, w denote the wage,

c j denote the price of good j

, 12 and T the total time endowment, the individual’s util

ity maximization problem can be expressed

as

(3) max X, L, A, M  = U ( X, L, H )

+ λ [ I + w ( H )[ T

− L ]

− c X X

− c A A

− c M M ].

The 0 rst order conditions are

(4) ∂

_ ∂ X = ∂ U

_ ∂ X − λ c X = 0

(5) ∂

_ ∂

L = ∂ U

_ ∂ L − λ w = 0

(6) ∂

_ ∂ A = ∂ U

_ ∂

H 

# ( ∂ H

_ ∂

M ∂ M

_ ∂φ ∂φ

_ ∂ A + ∂ H

_ ∂φ ∂φ

_ ∂ A 

# )

− λ 

# ( c A + ∂ w

_ ∂

H 

# ( ∂ H

_ ∂

M ∂ M

_ ∂φ ∂φ

_ ∂ A + ∂ H

_ ∂φ ∂φ

_ ∂ A 

# )

× [ T

− L ] 

# )

= 0

(7) ∂

_ ∂

M = ∂ U

_ ∂

H ∂ U

_ ∂

M − λ 

# ( c M + ∂ U

_ ∂

H ∂ H

_ ∂

M [ T

− L ] 

# )

= 0.

Equations (4) and (5) highlight the stan

dard trade-offs between labor and leisure.

those behaviors are equal to the marginal utility gain asso

ciated with the extra earnings due to avoidance and medi

cal treatment.

12 For simplicity, we assume that the costs of avoidance are all market-based, e.g., the purchase of air 0 lters. As discussed in section 4.3, activity reallocations are another form of avoidance behavior. The costs of nonmarket behav

ioral responses should be captured by the utility foregone due to this reallocation.Journal of Economic Literature, Vol. LI (September 2013) 696

Equations (6) and (7) can be combined to yield the following intuitive expression:

(8) 

# ( dH

_ dA 

# )

_ 

# ( dH

_ dM 

# ) = c A

_ c M .

Avoidance behavior and medical treat

ment will be consumed such that the ratio of the marginal productivities of each in increasing health is equal to their ratio of prices. Perhaps more importantly, equa

tions (4)–(7) implicitly de 0 ne avoidance behavior and medical treatment as a func

tion of all exogenous variables: A ( P

, φ , c j ) and
M ( P

, φ , c j ) for all j . Optimal avoidance behav

ior and medical treatment will depend on pollution levels, the function that translates pollution into illness episodes, and the costs of avoidance, medical care, and all other con

sumption goods. Since avoidance behavior and medical care consumption depend on ambient pollu

tion levels, the relationship between health and pollution levels can be expressed as the following total derivative of equation (2):

(9) dH

_ dP = 

# ( ∂ H

_ ∂

M ∂ M

_ ∂φ + ∂ H

_ ∂φ 

# )

· 

# ( ∂φ

_ ∂

P + ∂φ

_ ∂ A ∂ A

_ ∂

P 

# )

.

(++)++* (++)++* dH

_ d φ d φ

_ dP

The reduced form effect of pollution on population health depends on two dis

tinct components: the relationship between pollution and illness (as captured by the second parenthetical expression) and the degree to which illness is translated into poor health status (as captured by the 0 rst parenthetical expression). We begin with a breakdown of the second expression. The 0 rst term ( δφ / δ P ) describes the pure bio

logical effect of pollution, while the second term ( δφ / δ A * δ A / δ P ) describes the role of avoidance behavior in averting illness episodes by limiting contact with pollut

ants. Thus, the entire second parenthetical expression ( d φ / dP ) describes the net, or reduced form, effect of pollution on illness episodes based on individual exposure lev

els. Importantly, it is possible to observe no change in illness despite the existence of a biological effect if avoidance behavior is suf

0 ciently productive. On the other hand, if avoidance behavior is not possible or ineffec

tive, the biological and reduced form effects will be identical. 13 The 0 rst expression also has two com

ponents. The 0 rst term ( δ H / δ M * δ M / δφ ) describes the degree to which medical treat

ment, a postexposure intervention, ame

liorates the negative effects of pollution on health. The second term ( δ H / δφ ) describes how health responds to illness, which re 2 ects the degree to which pollution-induced ill

ness episodes are not treated, either because the condition is untreatable or because indi

viduals do not seek treatment for it. Clearly this 0 nal term will vary by medical condition, but it can also be viewed as capturing some of the transient suffering that accrues before medical treatments take effect.

The principal value of equation (9) is conceptual. Data limitations imply that all empirical investigations in this area will paint a partial picture of this total derivative. Nonetheless, it connects a wide range of empirical research, within not only the envi

ronmental 0 eld but also in labor and health economics, in a uni 0 ed framework grounded in basic economic theory. We will refer back to it as we review the contributions and limi

tations of the relevant empirical literature throughout the remainder of this paper.

13 Avoidance behavior may be unproductive if a pollut

ant can’t be avoided despite defensive actions. For exam

ple, while going indoors greatly reduces ozone exposure because it rapidly breaks down indoors, the penetration of 0 ne particular matter indoors can be as high as 80 percent (Jones, Mark, and Harrison 2000).697 Graff Zivin and Neidell: Environment, Health, and Human Capital

This basic model also yields results that can serve as a guide for policy. Optimal regu

lation requires policy choices that balance the costs and bene 0 ts of regulation designed to reduce pollution levels in order to maxi

mize social welfare. 14 Policy design will necessarily attend to economies of scale in pollution abatement as well as the costs and consequences of private actions to reduce the impacts of pollution. Denoting the costs of regulation as c R , optimal regulation will occur at the point where the marginal costs of regulations R designed to reduce pollution levels are equal to the averted health, avoid

ance, and medical costs associated with that marginal reduction in pollution:

(10) ∂ P

_ ∂

R c R = ∂ w

_ ∂

H dH

_ dP + ∂ U

_ ∂

H dH

_ dP 1

_ λ

+ ∂ A

_ ∂

P c A + ∂ M

_ ∂

P c M .
The costs of abatement technologies and their impacts on pollution levels are fre

quently derived from engineering as well as economic studies. Estimates of the ben

e 0 ts from regulation are more exclusively the domain of economists. The right-hand side of equation (10) can be usefully viewed as a measure of willingness-to-pay (WTP) to reduce pollution. 15 The 0 rst term re 2 ects the impacts of pollution on earnings, the second term is the direct disutility associated with pollution driven morbidity, the third captures the avoidance costs, and the fourth represents

14 As mentioned in the introduction, informational approaches to regulation that attempt to engage avoidance behavior directly have also been used in a limited number of policy settings over the past decade. In this framework, such an intervention can be viewed as a change in the price of avoidance behavior. While policies designed to alter medical care prices are not generally viewed as part of the environmental regulator’s toolkit, they would operate in a similar manner, though with potentially important general equilibrium effects.
15 For alternative expressions for WTP, see Cropper and Freeman (1991). pollution-driven medical expenditures. Given that researchers estimate different variants of (9), each has a slightly different relationship to (10) that must be considered when esti

mating WTP. Moreover, even if avoidance/ amelioration fully insulates one from negative health effects, abatement may still be optimal if its marginal costs are suf 0 ciently lower than those associated with those individual actions.

4. Empirical Issues

In this section, we highlight the frequent empirical challenges faced by researchers in this 0 eld. The approaches that have been used to overcome many of these challenges are detailed in the section that follows.

4.1 Data Empirical analyses examining the impact of pollution on health and human capital are data intensive, even by empirical economic stan

dards. The 0 rst signi 0 cant hurdle is obtaining environmental data on a suf 0 cient spatial and temporal scale. Data on water pollution and toxins typically come from either proprietary projects using small samples, or are generally not measured in units conducive to estimat

ing health effects. 16 Air pollution data, on the other hand, are much more widely available, and in many cases were expressly designed for the purposes of health impact assessment. As a result, the vast majority of empirical research on the relationship between envi

ronmental quality and health/human capital focuses on air pollution.

16 For example, water quality is continuously monitored at all public water systems, but the only publicly available data is for reported violations (see Graff Zivin, Neidell, and Schlenker 2011). Likewise, the toxic release inven

tory (TRI) contains self-reported data on the release of hundreds of toxins at their source, but does not include measures of ambient concentrations. See, however, Currie and Schmieder (2009), Agarwal, Banternghansa, and Bui (2010), and Currie, Greenstone, and Walker (2013) for a health impact analysis using the TRI.Journal of Economic Literature, Vol. LI (September 2013) 698

Ambient air pollution monitors typically measure pollution concentrations at very high frequencies, such as hourly, at a ! xed location. While this frequency of measure

ment generates data at a ! ne temporal scale, the limited number of monitor locations relative to the size of a country and the geo

graphic distribution of the population gen

erates data that is rather coarse on a spatial scale, even in the most highly monitored areas. Furthermore, since these data are typi

cally collected by government agencies, most research has focused on developed countries where such data are more widely abundant, although many pollution problems are more extreme in developing countries. Remote sensing (i.e., satellite) data offers promise for developing countries, where institutions are often limited in their ability to directly moni

tor environmental quality. Several limitationsmake it an imperfect substitute for groundbased data collection, although the science and technology is rapidly evolving in this area (Martin 2008). 17 Figure 1 shows air pollution levels over time for China, Mexico, and one city in the United States, Pittsburgh, focusing on particulate matter less than ten microns in diameter (PM10). 18 Several features of this ! gure are noteworthy. First, since devel

oped countries began monitoring environ

mental quality earlier than their developing

17 In addition to the limited scope of pollutants for which remote sensing is feasible, other problems include poor spatial resolution, the inability to distinguish surface from upper atmospheric pollution, and the interference cloud cover causes in obtaining reliable estimates.

18 We focus on PM10, rather than PM2.5, because of data availability. We also focus on Pittsburgh because of the availability of a particularly long time series (Davidson

et al. 2000; Rawski 2009); values for Pittsburgh are, how

ever, quite close to the average across all major cities. Since PM10 has only been measured more recently, older values of PM10 are obtained by multiplying measures of Total Suspended Particles (TSP) by 0.55. A complete time series for TSP was imputed using data on dust fall. We thank Thomas Rawski for generously sharing this data, originally obtained from Cliff Davidson. country counterparts and are more likely to place that data in the public domain, we can construct a longer time series for the United States than for Mexico or China. 19 Second, air pollution has improved tremendously over time in all three countries, regardless of development status. Levels in Pittsburgh dropped by over 80 percent since 1950 and 40 percent since 1990, and levels in both China and Mexico have fallen by roughly 50 percent since 1990. Third, although pollu

tion levels in China and Mexico are always higher than levels in the United States at the same point in time, the levels experienced in those countries today are not unlike histori

cal levels in the U.S. Contemporary pollu

tion levels in China and Mexico are similar to those found in Pittsburgh in the mid-1970s and mid-1990s, respectively. As such, stud

ies based on historical pollution levels in the United States may also be informative about current health and human capital impacts in developing countries.

Acquiring time-stamped health data with geographic identi ! ers that permit the merging of environmental data is an additional challenge, regardless of country development status. Health surveys often contain limited geographic identi ! ers in order to protect subject con ! dentiality, although increased access to nonpublic ver

sions via various Research Data Centers has eased this constraint. 20 Various health censuses, such as birth and death records stored in Vital Records and Hospital Discharge Data, often provide easier access

19 While reported pollution levels in China may be sub

ject to manipulation, evidence also indicates that reported pollution levels are highly correlated with data from inde

pendent sources (Chen et al. 2013).

20 Although access to nonpublic versions of these health surveys offer promise, it is important to keep in mind that such data were not designed to be used for spatial analyses. For example, the National Health and Nutrition Examination Survey (NHANES) only samples from a small number of counties in order to keep survey costs down, which greatly limits spatial variability.699 Graff Zivin and Neidell: Environment, Health, and Human Capital

to geographic identi ! ers as well as the exact date of the birth, death, or hospital admission. As we describe in the follow

ing section, several studies have acquired administrative data sets with detailed geo

graphic identi ! ers to more precisely assign pollution exposure. Once environmental and the relevant out

come data sets are identi ! ed, merging them is a nontrivial exercise as well. It requires important assumptions about individual mobility and the spatial distribution of pol

lution, which is often nonuniform even over relatively small spatial scales. For example, the New York City Community Air Survey (NYCCAS), a unique project launched by both the city and academic institutions within the city, found that differences in building heating oils, proximity to traf ! c, and vegeta

tive cover lead to considerable variation in particulate matter contamination in the air across closely located city blocks (Clougherty

et al. 2009).

4.2 Measuring Pollution

4.2.1 Assigning Pollution to Individuals

Given the geographic information con

tained in large-scale data sets, studies often 0 20 40 60 80 100 120 140 16019511954195719601963196619691972197519781981198419871990199319961999200220052008PM10(ug/m3)

China

Mexico Pittsburgh

Year

Figure 1. Trends in Air Pollution for United States (Pittsburgh, PA), China, and Mexico

Notes: All data are annual average of daily measures of particulate matter less than 10 microns in diameter (PM10). Data for Mexico and China are averages across all major cities, obtained from the World Bank’s database of World Development Indicators. Pittsburgh data from 1990–2009 are from the Environmental Protection Agency Air Quality System Data Mart. Data from before 1990 are courtesy of Cliff Davidson via Thomas Rawski; these data are total suspended particles multiplied by 0.55, where missing values for TSP are imputed using dustfall.Journal of Economic Literature, Vol. LI (September 2013) 700

approximate contemporaneous pollution levels based on an individual’s general loca

tion and the location of the monitor. This crude approach leads to measurement error that increases with an individual’s distance from the monitor and the degree to which pollutants disperse nonuniformly. This mea

surement error will typically bias estimates downward, but with a large enough data

set researchers can use data from multiple monitors, various weighting techniques, and factors that affect the dispersion of pol

lution to obtain more precise assignments of localized pollution. 21 A 0 ner level of geo

graphic disaggregation for individuals, such as a residential address, also allows for bet

ter assignment of relevant pollution levels and hence is more likely to provide precise
estimates.

The usual mobility of individuals throughout their life (i.e., not as a form of avoidance behavior in response to pollu

tion, which we discuss below in 4.3), both within a day and over time, can also pres

ent a challenge for assigning potential expo

sure. On a daily basis, individuals spend their time not only at home but at work, school, and other possible locations that are not typically recorded. Although the use of personal monitors attests to this mobility (Tonne et al. 2004), two issues remain: 1) the high costs of personal monitoring often result in the use of a small, unrepresenta

tive sample without a clearly de 0 ned con

trol group; and 2) the link to policy is less clear because indoor sources also contrib

ute to pollution, making it dif 0 cult to pin down the sources of pollution and the scope for regulation. Mobility over time also pres

ents a signi 0 cant measurement challenge in assigning cumulative exposure over lon

ger periods of time. Focusing on children,

21 Such methods include inverse distance weighted average (Currie and Neidell 2005), kriging (Lleras-Muney 2010), and land-use regression (Jerrett et al. 2005). and in particular infants, whose shorter life span has permitted less mobility, can greatly limit this concern (Joyce, Grossman, and Goldman 1989; Chay and Greenstone 2003b). Clearly, this comes at a cost since studies of children may not tell us much about impacts on alternative populations of interest, such as the elderly or those with respiratory problems. Instrumental vari

ables offers one approach for combating “classical” measurement error in pollution, and below we describe several instruments that have been used in the literature.

4.2.2 Functional Form of Pollution Early epidemiological investigations on the health effects of pollution predominantly focused on extreme pollution events, with one of the most famous being the “killer fog” in London in December 1952 (Logan and Glasg 1953). A temperature inversion com

bined with windless conditions led to a sud

den and dramatic increase in air pollution. Since residents were used to winter fogs, there were little, if any, changes in behavior, leading to a rather clean measure of pollu

tion impacts in this case. The dramatic rise in mortality that precisely coincided with the timing of the fog, along with results from studies with similar research designs (e.g., Townsend 1950; Firket 1936; Greenburg

et al. 1962), have produced compelling evi

dence that high levels of pollution pose a sig

ni 0 cant threat to human health.

While high pollution levels may be rele

vant in developing countries, these extremes are dramatically higher than those faced by nearly all people in developed countries today (refer to 0 gure 1). This is important because the processing of pollutants by thehuman body is subject to a number of ratelimiting steps that imply nonlinear health effects that have been widely supported by laboratory studies in the toxicology lit

erature (e.g., Lefohn et al. 2010; Smith and Peel 2010). Indeed, thresholds below which701 Graff Zivin and Neidell: Environment, Health, and Human Capital

no harmful effects are observed have been found for some pollutants (Stoeger et al. 2006; Pottenger et al. 2009), raising serious concerns about extrapolating health effects from high pollution levels to low ones.

As such, interest has largely shifted to understanding the health effects from more modest pollution levels, with an emphasis on identifying “safe” levels below which pollut

ants have no meaningful health effects. This shift in emphasis to the lower end of the pollution distribution, however, makes the choice of study outcomes particularly impor

tant. It may be that mortality or hospital admissions are only induced when pollution exceeds a certain threshold, while more sub

tle forms of morbidity and impairment arise at lower levels. Our limited understanding of the human capital and productivity effects of pollution at any level, however, under

score the importance of studies throughout the pollution level spectrum in order to bet

ter explore the full range of impacts in this emerging area of importance.

To explore possible nonlinear effects, the most widely used approach is to discretize pollution levels through the use of dummy variables, which can be speci 0 ed in several ways. One approach is to specify thresh

olds based on government standards, which helps to relate estimates directly to policy. For example, Currie et al. (2009) include a series of dummy variables for pollutants as they relate to National Ambient Air Quality Standards. Another approach is to use labo

ratory evidence on thresholds, though mea

surement error in assigning pollution may limit its effectiveness. The most 2 exible but also data demanding approach is to de 0 ne pollution as a series of dummy variables, with somewhat arbitrarily chosen knots. This approach is akin to a nonparametric regression with a uniform kernel and no overlap; unlike nonparametric regression it can be estimated in an ordinary least squares framework, and is therefore amenable to a wide range of econometric tools for causal inference.

4.2.3 Duration of Exposure

Specifying the appropriate duration of exposure is also important. Some pollutants have a nearly immediate effect—exposure to
ozone can in 2 ict symptoms in as quickly as 1–2 hours—while some have a longer incu

bation period. Even more complicated, some have both immediate and delayed effects. Since we may not know which period of expo

sure is most important a priori, this is largely an empirical question. A distributed lag speci 0 cation, which allows for both contem

poraneous and lagged exposure, allows for a 2 exible duration. Correlations in pollution values over short periods of time, however, can lead to multicollinearity, hampering our ability to precisely identify the coef 0 cients for speci 0 c time periods. A joint F -test of all time periods enables one to obtain an overall understanding of the relationship between multi-day exposure and outcomes without distinguishing between individual days. The precise temporal pattern of impacts is gen

erally unimportant for policy, which typi

cally uses rather blunt instruments to limit contamination and exposure at a broad level rather than on speci 0 c days. For examining long-run effects, analyses become increasingly complicated, particu

larly for understanding the impacts from cumulative exposure over a lifetime. In addi

tion to individual mobility over time hamper

ing the assignment of cumulative exposure, specifying the proper functional form for this relationship is a major obstacle. Accounting for the other behaviors over one’s lifetime that affect health and thus potentially con

found this relationship is equally challenging.
In section 5, we discuss quasi-experimental evidence on long-run effects that arise due to a latent response to an acute exposure in the distant past, but we are unaware of anyJournal of Economic Literature, Vol. LI (September 2013) 702

quasi-experimental evidence on the cumula

tive effects of pollution exposure. 22

When focusing on birth outcomes, an area of intellectual inquiry that has grown tremen

dously in recent years, the relevant period of exposure is also important, albeit more from a developmental perspective than an envi

ronmental policy one (Salam et al. 2005). For instance, the 0 rst trimester is the period during which the neural tube is transformed into the brain and spinal cord and many other organs experience rapid development (de Graaf-Peters and Hadders-Algra 2006; Cunningham et al. 2010), making this a par

ticularly vulnerable stage in terms of envi

ronmental insults. One complication when parsing exposure by trimester of pregnancy is that length of gestation can be affected by pollution, making the de 0 nition of each tri

mester, and thus the total in utero exposure, endogenous (Currie et al. 2013). More chal

lenging, however, is that including multiple trimesters of pollution simultaneously can lead to severe multicollinearity, sometimes resulting in seemingly bene 0 cial effects from pollution in certain exposure periods.

4.3 Endogeneity of Pollution

Early research on the health impacts of pollution took a rather fatalistic approach— people (and thus markets) are unaware of ambient pollution levels such that once it is in the air nothing can be done about it. As knowledge about pollution has grown, both in terms of our ability to detect it and to understand its health effects, the fallacy of this original assessment has become clear. Pollution exposure can be altered in a variety of ways, making it an endogenous variable

22 For recent evidence on the association between cumulative exposure to pollution over several years and health, see Janes, Dominici, and Zeger (2007), Pope et al. (2002), Jerrett et al. (2009), Rojas-Martinez et al. (2007), and Miller et al. (2007). with all of the usual concerns that come with it. Recognizing these sources of endogene

ity has led to the use of quasi-experimental research designs that effectively eliminate (or signi 0 cantly reduce) this problem.

4.3.1 Residential Sorting The major driver of endogeneity is resi

dential sorting: individuals choose residen

tial locations based on the attributes of that area, which leads to a nonrandom assignment of pollution. Preferences over residential neighborhoods depend on the employment opportunities, commuting costs, and local amenities in the area (Tiebout 1956; Roback 1982), where local amenities include ele

ments such as school quality, parks, housing stock, crime, hospitals, and environmental quality. 23 Importantly, these amenities are bundled such that environmental quality is packaged with other attributes in a loca

tion, although the speci 0 c contents of a par

ticular bundle vary by location. For example, urban areas may have worse air quality but better schools than rural areas, while subur

ban areas may have both better air quality and schools than inner cities. The key pointis that optimizing individuals make tradeoffs along multiple dimensions based on the intensity of their preferences for each local attribute, which implies that the characteris

tics of the neighborhood in which individuals live, including pollution levels, are endoge

nously determined. 24

23 For simplicity, we assume preferences over environ

mental quality solely because of health bene 0 ts, but the same basic intuition holds if we extend this to include pref

erences over environmental quality because of visibility or odor.

24 Of course, an individual’s ability to trade off attributes will be a function of prices, which depend on aggregate preferences over attributes and thus market demand for and supply of housing in a given location (see, for example, Bayer, Ellickson, and Ellickson 2010). For an explicit “test” of the Tiebout mechanism in an environmental context, see Banzhaf and Walsh 2008).703 Graff Zivin and Neidell: Environment, Health, and Human Capital

Different levels of pollution exposure due to sorting can be driven by three factors. The 0 rst is heterogeneity in preferences over local amenities other than pollution. Since these local amenities are often correlates of air quality, we can view this as indirect preferences over air quality. The second is income. If local amenities are normal goods, then wealthier people will live in areas with better local amenities, which can affect air quality to the extent that it is correlated with other amenities. The third is heterogeneity in susceptibility to pollution. In this case, we view sorting as a direct result of preferences over air quality.

The importance of highlighting these three factors is that they have different implications for cross-sectional estimates of the relationship between pollution and health/human capital. The former two fac

tors lead to omitted-variable bias. Wealthier individuals might live in neighborhoods with better air quality (driven by preferences for local amenities correlated with air qual

ity), and they also are likely to make other investments in their health that are dif 0 cult to observe; this would bias estimates down. On the other hand, people who live in or near cities face worse levels of air quality but could have access to better quality health care and jobs that improve health; this would bias estimates up. Clearly, the overall direc

tion of bias introduced by this sort of endo

geneity is theoretically ambiguous. In contrast, residential sorting due to direct preferences for cleaner air can lead to a simultaneity bias. If susceptible people relocate to less polluted areas to reduce the onset of symptoms, then health is affecting one’s pollution exposure. To 0 x ideas, imag

ine an extreme case where there are two types of individuals, nonasthmatics and asth

matics. Pollution causes asthmatics to have hospital admissions, but not nonasthmatics. Initially, individuals are evenly distributed across a “dirty” area and a “clean” area. If the asthmatics relocate to the clean area to reduce clinical symptoms induced by pol

lution, the average health stock in the clean area will decrease while the health stock in the dirty area will improve. If this sorting were not recognized, it would look as if pol

lution actually improved health. Although the stylized nature of this extreme case is unrealistic, it underscores one important mechanism through which sorting may hin

der inference.

Table 1 illustrates the sorting problem. Data for this Table are from the 2001–06 waves of Behavioral Risk Factor Surveillance System (BRFSS). 25 We focus on these six years because it already includes merged air pollution data at the MSA level; a rather crude measure but one that is still suf 0 cient for illustrating our point. In each row, we present the mean and the coef 0 cient of each variable, where the coef 0 cient is obtained by regressing each variable on the Air Quality Index (AQI)—a summary measure of air quality across several pollutants—and dum

mies for each survey wave. For example, the 0 rst row shows that roughly 77 percent of respondents have participated in some form of exercise in the past month. The esti

mate of –0.118 implies that, for each tenunit increase in the AQI, there is an 11.8 percentage point drop in the rate of exer

cise. Consistent with sorting, we see that respondents of higher socioeconomic status and those with higher levels of health invest

ments generally live in neighborhoods with better air quality, though not necessarily in a monotonic pattern. This underscores the nonrandom assignment of pollution levels. While it is possible to control for these fac

tors, it is unclear whether one can adequately control for all relevant factors, highlighting the potential for bias under cross-sectional approaches.

25 For more details on the BRFSS, see http://www.cdc. gov/Brfss/.Journal of Economic Literature, Vol. LI (September 2013) 704

Although a complicated and seemingly insurmountable empirical challenge, the main approach for tackling sorting is to 0 nd “shocks” to air quality that push the mar

ket temporarily out of equilibrium, often accompanied by 0 xed effects that hold other characteristics of the area constant. These shocks can be driven by air quality regulations, abrupt changes in industrial pro

duction (such as strikes and plant closings), or catastrophic events (such as temperature inversions or wild 0 res). Finding such shocks presents a major obstacle, and it is not sur

prising that many of the same shocks are used across studies. Controlling for other major changes that may accompany a shock T

ABLE 1 E vidence of R esidential S orting , BRFSS 2001–06

Mean Difference SE
Any exercise 0.771 –0.118 ** [0.041] Ever smoked 100 cigarettes 0.467 –0.016 [0.038]
Received 2 u shot 0.352 –0.140 ** [0.040]
Health insurance 0.891 –0.031 [0.031] High school drop out 0.0817 0.092 ** [0.023] High school graduate 0.269 0.013 [0.052] Attended any college 0.649 –0.105 [0.064]

Income < $10k 0.039 0.032 * [0.013] Income $10–15k 0.043 0.026 [0.013] Income $15–20k 0.060 0.022 [0.014] Income $20–25k 0.077 –0.02 [0.017] Income $25–35k 0.113 –0.036 [0.020] Income $35–50k 0.149 –0.049 ** [0.014] Income $50–75k 0.160 –0.034 * [0.015]

Income > $75k 0.240 0.075 [0.066]
Number of children in HH 0.693 0.117 [0.071]
Married 0.537 –0.100 * [0.042]
Divorced 0.141 –0.017 [0.020]
Number of adults in HH 1.854 –0.03 [0.121] Age 49.29 –1.877 [1.405] White, non-Hispanic 0.755 –0.163 [0.231] Black, non-Hispanic 0.113 0.326 ** [0.092] Hispanic 0.0693 0.079 [0.084]

Notes: Column 1 shows the mean for each variable. Column 2 shows the coef 0 cient from the regression of each variable on the Air Quality Index (/10) and year dummies. Column 3 shows standard errors clustered on MSA in brackets.

** Signi 0 cant at the 1 percent level.

* Signi 0 cant at the 5 percent level.705 Graff Zivin and Neidell: Environment, Health, and Human Capital

is also a challenge. For example, a plant clos

ing that lowers pollution might lead to dis

ruptions in income and health insurance that could also impact health and human capital. Since many pollutants exhibit a high level of variability from one day to the next, high frequency variation in pollution can also be exploited to address sorting. 26 Figure 2 pro

vides a glimpse of this for ozone. The 0 rst panel plots demeaned, daily ozone levels for a downtown Los Angeles monitor for

26 Of course individuals can modify their activities (and the location of activities) in response to these daily 2 uctua

tions, a point we return to in the next subsection. June–September, 2010. Immediately evi

dent, ozone swings from one day to the next are substantial, often nearly as large as the overall mean level of ozone of 0.043 parts per million (ppm). Focusing on such short-run variation, however, requires careful consid

eration of what causes the higher frequency changes in pollution levels to ensure they are not driven by local activities that might also affect health and human capital. In the case of ozone, this variation is due to weather, regional transport of ozone and its precur

sors, and the highly nonlinear ozone forma

tion process. Since weather is an important confounder, the second panel of 0 gure 2 Figure 2. Daily Variation in Ozone

Notes: “Adjusted” plots the residual from a regression of ozone on mean daily temperature, solar radiation, dew point, barometric pressure, wind speed and direction, and day of week dummies. Data are taken from central Los Angeles monitor (North Main Street, AIRS# 060371103) or closest monitor when unavailable. –0.02 –0.01 0 0.01 0.02 0.03 0.04

01jun2010 31aug2010 01jun2010 31aug2010 Unadjusted (demeaned) AdjustedOzone(ppm)Journal of Economic Literature, Vol. LI (September 2013) 706

plots the residuals from a regression of ozone against several weather variables (along with day of week dummies), and the variation is only minimally dampened, providing addi

tional support to the notion that daily varia

tion can be viewed as plausibly exogenous. One concern with using such high fre

quency variation, however, is that daily changes in pollution may be less informative about possible impacts from new regula

tions, which lead to more permanent shifts in pollution. A second concern, and one that only arises when examining mortality impacts, is short-term mortality displace

ment, commonly referred to as “harvesting.” Mortality for an otherwise healthy individual represents a signi 0 cant loss to society, but mortality for an already ill person, whereby pollution serves to hasten the death by a few days or weeks, presumably imposes less social cost. While an offered solution is to assess the degree to which estimates change when aggregating to a lower frequency, this is an imperfect solution because it shifts away from the underlying premise of this approach.

To illustrate the value of exploiting plausibly exogenous changes in pollution, we present some basic estimation results in table 2 using the same BRFSS sample. We approximate a “shock” by including MSA 0 xed effects—an admittedly imperfect quasi-experiment that exploits the natu

ral year-to-year 2 uctuations in pollution, but one that again illustrates our principal point. The 0 rst panel focuses on tooth loss as a dependent variable. Tooth loss should not be affected by air pollution, so evidence to the contrary suggests model misspeci 0

- cation. 27 The 0 rst three columns pres

ent cross-sectional estimates and the last

27 While pollution could indirectly affect tooth loss through interactions with comorbidities over long time periods, the gradual nature of tooth loss implies it will be insensitive to a short-term change in pollution. three 0 xed effects estimates, with a gradual increase in controls within each model as we move rightward. The estimate in column 1 implies that a ten-unit increase in the AQI leads to a 6.3 percentage point increase in having lost any teeth, which represents a 13 percent increase from the mean. The result becomes somewhat smaller as we add more controls, but remains statistically signi 0 cant at conventional levels, supporting the surpris

ing conclusion that air pollution makes one’s teeth fall out. A more likely explanation is that oral health is the result of an accumulation of unobserved investments in health, and people living in more polluted areas have lower lev

els of investment. In support of this, when we include 0 xed effects to capture time invariant characteristics of MSAs, this odd 0 nding dis

appears, shown in columns 4–6. These results illustrate the importance of 0 xed effects even when a rich set of controls are available. Since 0 xed effects over such a short time frame may have the unintended conse

quence of removing too much of the varia

tion in pollution, we continue this example focusing on two self-reported outcomes plausibly affected by pollution. The 0 rst is the number of days in the past month with bad physical health (panel 2), which can be viewed as a measure of illness ( φ ). The sec

ond focuses on self-reported general health status (panel 3), a measure of health capi

tal ( H ). Repeating the same set of regres

sions, cross-sectional estimates for days of bad health are statistically insigni 0 cant and quite unstable; the addition of a handful of behavioral factors (exercise, smoking, and whether one had the 2 u shot) halves the estimate. The 0 xed effect estimates, how

ever, are much more stable, considerably larger in magnitude and statistically sig

ni 0 cant, suggesting ample variation across years for detecting changes in health 2 ows. The estimate of 1.225 from the last column implies a ten-unit increase in AQI increases bad health days by over a third. Since health707 Graff Zivin and Neidell: Environment, Health, and Human Capital

status has less variation than illness, we do

not 0 nd a statistically signi 0 cant relation

ship between AQI and being in poor, fair, or good health in any speci 0 cation. While this table highlights the potential strength from using 0 xed effects, it also demonstrates the caution needed in interpreting results across dependent variables that arise from different processes, a point we discuss in more detail below in 4.4.

4.3.2 Environmental Confounding In addition to optimizing behavior caus

ing endogenous pollution exposure, omitted variable bias may also arise from concurrent changes in the environment. Of particular T

ABLE 2 C ross -S ectional and F ixed E ffect R egression E stimates of the R elationship

between P ollution and H ealth , BRFSS 2001–06

Cross-sectional model MSA 0 xed effects model

1 2 3 4 5 6 1. Any teeth missing (mean = 0.480, SD = 0.500)
AQI * 10 0.063 * 0.058 * 0.059 * –0.012 –0.017 –0.018 [0.033] [0.031] [0.031] [0.014] [0.014] [0.014]
Observations 312,963 312,963 312,963 312,963 312,963 312,963

R 2 0.247 0.262 0.265 0.252 0.267 0.27

2. Number of days in past month with bad physical health (mean = 3.39, SD = 7.82)
AQI * 10 0.407 0.204 0.22 1.299 ** 1.240 * 1.225 ** [0.336] [0.361] [0.357] [0.633] [0.629] [0.613]
Observations 592,134 592,134 592,134 592,134 592,134 592,134

R 2 0.077 0.106 0.112 0.079 0.108 0.114

3. Self-reported health status poor, fair or good (mean = 0.437, SD = 0.496)
AQI * 10 0.024 0.015 0.017 0.014 0.011 0.009 [0.020] [0.019] [0.017] [0.023] [0.023] [0.021]
Observations 592,134 592,134 592,134 592,134 592,134 592,134

R 2 0.137 0.161 0.181 0.139 0.162 0.182
Behavior N Y Y N Y Y
Other health N N Y N N Y

Notes: Standard errors that cluster on MSA in brackets. AQI is median of daily AQI for MSA throughout year. All regressions include number of children in household, number of adults in household, respondent’s age, separate dummy variables for whether respondent is married, divorced, black non-Hispanic, and Hispanic, income dummies, education dummies, a dummy for insured, and year dummies. “Behavior” controls include dummy variables for any exercise, smoked at least 100 cigarettes in lifetime, and received 2 u shot. “Other health” includes BMI based on selfreported height and weight.

** Signi 0 cant at the 1 percent level.

* Signi 0 cant at the 5 percent level.Journal of Economic Literature, Vol. LI (September 2013) 708

concern is weather. As previously mentioned, weather interacts with some types of emis

sions to form pollution. Weather may also have a direct impact on health (Deschenes and Greenstone 2011), making it a potentially important confounder. Since weather is typi

cally observable at the same or 0 ner scale than pollution data, this challenge can be obviated through the careful control of relevant vari

ables. As with pollution, the functional form of weather must be carefully considered. Environmental confounding can also occur because the emissions of many pollutants are highly correlated. Many air pollutants, especially in urban areas, are coemitted. For instance, automobiles emit particulate matter, carbon monoxide, and contribute to ozone pollution. Similarly, industrial mix and geog

raphy can create pollution hot spots, with high levels of toxics in air, water, and soil. As with meteorology, the careful selection of controls is essential and thus requires an understand

ing of both the pollution generation process and its likely impacts. For example, nitrogen dioxide leads to the formation of ozone, but may also have direct health effects; controlling for it may unnecessarily dampen estimates of the impact of ozone pollution on health and human capital, but not controlling for it may overstate the impact.

The fact that many pollutants can be traced back to the same emission source introduces a complication for instrumental variable (IV) approaches. A single shock to an emission source, such as a plant closure or unexpected changes in boat or vehicle traf 0 c, can affect multiple pollutants simultaneously, mak

ing the model unidenti 0 ed without further assumptions. Since meteorological conditions, such as wind speed and direction, interact with emissions to impact pollution forma

tion and deposition, knowledge of this pro

cess can be incorporated to aid in identifying the effects of multiple pollutants. However, weather is also an important confounder in its own right, necessitating additional assumptions regarding the functional form of the relationship between health, pollu

tion, and meteorological conditions in order for this to improve identi 0 cation. See, for example, Schlenker and Walker (2011) and Knittel, Miller, and Sanders (2011), for applications along these lines. Reduced form relationships focusing on the source of emis

sions, rather than pollutants per se, may be estimated to circumvent this issue.

4.3.3 Avoidance Behavior

Another source of endogeneity stems from avoidance behavior—transient actions indi

viduals deliberately take to reduce their real

ized exposure to pollution. 28 For example, on a high ozone day, spending less time outside or shifting outdoor activities toward twilight hours is a highly effective means for reducing exposure. Such short-run responses require knowledge about daily and even hourly pol

lution levels. Certain pollutants are observ

able at high levels of concentration, thereby facilitating avoidance. Others are correlated with observable phenomenon and thus can be inferred, e.g., the proliferation of face masks in many Asian cities is in response to an observed haze that is indicative of ozone and 0 ne particulate matter pollution. When pollution levels are more modest and thus less easily discernible by the citizenry, direct observation has largely been replaced by air quality alerts and other public information campaigns. The most susceptible individuals can also independently monitor their lung functioning to approximate their sensitivity on any given day, indicating a role for private

28 Although residential sorting with respect to prefer

ences for clean air can be viewed as a form of avoidance behavior, we distinguish it from this more temporary avoid

ance behavior because failing to account for the two differ

ent types of avoidance behavior have different implications for estimates, as elaborated below. A similar logic applies to more permanent actions designed to limit exposure to pol

lution, such as home air or water 0 lters that run constantly once purchased.709 Graff Zivin and Neidell: Environment, Health, and Human Capital

information in avoidance behavior as well.In the end, the degree to which such shortrun behavioral responses will be important depends upon the “visibility” of pollution, either literally, through information dis

semination, or through health feedbacks that allow individuals to infer it based on physi

ological responses. Unlike sorting, which affects the ambi

ent levels of pollution where an individual resides, avoidance behavior is a response to ambient levels. That is, avoidance behavior occurs after an individual learns the ambient pollution level (a “posttreatment” variable). As such, including or excluding avoidance behavior does not introduce a bias per se, but affects the interpretation of estimates. For example, if focusing on hospital admis

sions, directly controlling for avoidance behavior yields estimates of the biological effect ( δφ / δ P ), while omitting it yields esti

mates of the reduced-form effect ( d φ / dP ). Moreover, the scope for short-run avoid

ance behavior complicates the use of shocks to identify the effect of pollution. When a shock leads to an abrupt change in pollution that is unobservable by the populace, behav

ioral responses are not feasible and the shock can be used to derive a measure of the bio

logical impacts of pollution. However, when a shock is more gradual (such that informa

tion about pollution can be publicly dissemi

nated), or individuals can directly observe the change (possibly because a pollutant or its correlates are visible), the shock does not obviate the need to account for avoidance behavior. Clearly the degree to which shocks allow potential time-varying behavioral responses to changes in pollution levels will vary across settings depending on the avail

ability of this public and private information.

The question then becomes, should one control for avoidance behavior? The reduced form is generally more convenient for valu

ation, described in more detail below in 4.5, because the econometrician does not need to specify the functional form of the envi

ronmental health production function with respect to P and A . This is particularly helpful since data limitations often necessitate the use of proxy or incomplete measures for avoidance behavior. The pure biological effect may be of interest to economists for its generalizabil

ity, at least across settings that are relatively homogenous in terms of age composition and underlying health. 29 Avoidance behavior is clearly very context speci 0 c, even within the same population over time (Graff Zivin and Neidell 2009), so reduced form estimates are likely to vary across settings. Furthermore, it is important to know the biological effect in order to design policies to encourage avoid

ance behavior. Ideally, one would estimate both the biological and reduced form effects, with the difference re 2 ecting the bene 0 ts from avoidance behavior— δφ / δ A * δ A / δ P (or δ H / δ A * δ A / δ P ). When avoidance behavior is precipitated by the provision of information, this difference then reflects the value of the information provided.

4.4 Outcome Measurement Pollution can have myriad health effects and a simultaneous accounting of all of them is essential for welfare calculations and the design of optimal policy. For example, a high pollution concentration may cause an individual to use more medication, visit the ER, and then, ultimately, to die prematurely. Many additional impacts may occur that are not captured by health encounters. Data limitations require all studies to paint a par

tial picture, which can often be considered a

29 While toxicologists should have a comparative advan

tage in measuring the pure biological effect through the use of chamber studies, as previously mentioned the end

points used in these laboratory settings are often of limited value for policy design since they are frequently designed solely to understand the mechanisms of action. This leaves ample room for learning about the biological effects in a nonexperimental setting by controlling for avoidance behavior.Journal of Economic Literature, Vol. LI (September 2013) 710

lower bound of the full effects. Yet, taken as a whole, economists have examined a wide range of outcomes that result from an equally varied set of quasi-experiments. These results have deepened our understanding of which impacts are economically signi 0 cant. Moreover, by bounding impacts they may help us determine threshold rules for policy whereby regulatory action is taken when a subset of the bene 0 ts exceed the costs. In the discussion that follows, we group health outcomes as they relate to equa

tion (9). The 0 rst distinction we make is between health capital ( H ) and illness ( φ ), where health capital can be thought of as a stock measure and illness as a 2 ow that draws down that stock, at least until medi

cal treatment is completed or the disease has run its self-limited course. The second and perhaps more novel distinction is to separate between classes of illnesses. Some illnesses lead to health encounters, such as hospital admissions, doctor visits, and medication use. These highly visible encounters end up in standard health data sets, and as such are readily observable by the econometrician.

The other class of illnesses is more subtle and, while it may not result in any formal health encounters ( δM / δφ = 0), it none

theless leaves a “signature” of impacts. For example, pollution may cause an individual to feel minor discomfort, irritation, or labored breathing, not unlike that from a common cold or seasonal allergies. This does not pre

vent them from participating in usual activi

ties, but affects performance conditional on participating—a distinction between the extensive and intensive margin we will return to later. Alternatively, a fetus exposed to pollution may experience physiological changes that result in lifelong impacts, but such changes may be latent and not readily detectable and treatable at birth. Although these effects are more subtle, they may be more pervasive, suggesting potentially large welfare effects. We maintain this distinction here since the absence of a health encounter that can be directly associated with pollution exposure makes them particularly dif 0 cult for the econometrician to observe. 30

4.4.1 Health Capital Since health is a complicated construct often in 2 uenced by subjective interpretations, there is unfortunately a rather limited set of reliable measures of health capital available. 31 One of the most commonly used measures in environmental and health research is mortal

ity. As quite possibly the most objective mea

sure of health, it serves as a useful benchmark for making comparisons across large spatial and temporal scales. Furthermore, since it typically comes from vital records maintained by governmental agencies, it often captures a census of deaths, permitting large samples for analysis. Reasonably detailed geographic identi 0 ers, such as the county of residence, are also routinely available, facilitating the assignment of pollution to individuals. In the context of our conceptual model, it is use

ful to de 0 ne mortality as health stock falling below a certain threshold ( H < h * ). In that case, researchers typically estimate dH / dP as de 0 ned in equation (9). 32

30 It is worth noting that the lack of a health encoun

ter may arise for at least three distinct reasons: (1) effec

tive treatments are unavailable; (2) symptoms are minor enough that they do not necessitate the use of formal care; or (3) symptoms are suf 0 ciently subtle that they are not “observed” by the individual experiencing them.

31 Despite our use of self-reported health status in table 2, reliability concerns with self-reported data of this nature have limited their use in the environmental economics literature.

32 Note that these studies seldom employ controls for medical treatment ( M ) that may have preceded death. For example, if particulate matter induces a death from a heart attack, that individual likely received hospital treatment for their cardiovascular complications before passing. These expenditures are often not considered because mortality and hospitalization data come from distinct sources that are not linked. Since the value of a statistical life (VSL), which is often used to monetize these impacts, does notinclude end of life spending, estimates of willingness-topay to reduce pollution based solely on VSL will miss this component, though they may be small relative to the VSL.711 Graff Zivin and Neidell: Environment, Health, and Human Capital

Birth outcomes, such as birth weight, gestation, and APGAR scores, are another desirable measure of health capital, albeit for a select population. Since a fetus goes through rapid development in a short period of time, understanding the effects of pollu

tion on this group are particularly impor

tant. Birth outcomes have been linked with both higher healthcare costs at birth and later in life (Almond, Chay, and Lee 2005; Currie and Hyson 1999). Since these data generally come from vital records, they share many of the desirable properties of mortality data (large samples, date of event, and detailed geographic information). Since pollution may affect both conception (Buck
Louis et al. 2009) and fetal deaths (Sanders and Stoecker 2011), focusing on birth out

comes also introduces a potential concern regarding the endogeneity of births.

While these outcomes measure speci 0 c aspects of health, it is also important to recall that health is affected by how illness episodes are treated, as discussed in the conceptual framework. This link between health and illness suggests that health capital will show less variation than illness, which can greatly in 2 uence statistical infer

ence. For example, pollution may induce a large increase in hospital admissions for myocardial infarctions (i.e., heart attacks), but a considerably smaller change in mor

tality because major medical advances have signi 0 cantly improved survival rates (Cutler, Deaton, and Lleras-Muney 2006). A study only focusing on mortality may fail to uncover this relationship; this is pre

cisely the pattern found in table 2, which focused on self-reported health status and daily episodes of compromised physi

cal health. Since this link between health and illness also varies with technology and access to high-quality health care, esti

mates of the relationship between pollu

tion and health may vary considerably over time and across space. 4.4.2 Illness

In addition to measuring impacts on health capital, research often focuses on the impact of pollution on illness ( φ ). Throughout, we use the term illness to broadly refer to any underlying physiological impact, some of which may be readily treatable through medical interventions while others may be less conducive to medical management.

4.4.2.1 Observable Illnesses The vast majority of studies that examine illness endpoints rely on health encounters as their data source. Illnesses that result in hospital and emergency department (ED) visits have been the bread and butter of epi

demiological studies, and a common focus in economics as well. The appeal of such outcomes lies in the quality and quantity of data: these visits come from administrative hospital 0 les, form a census of patients, rely on the standard “International Classi 0 cation of Diseases” to identify speci 0 c health con

ditions, contain the date of discharge, and provide detailed geographic residence, such as the zip code of the patient, that allows this information to be merged with pollu

tion data. 33 Despite the signi 0 cant appeal, using these visits may introduce sample selection bias. Those who have a relationship with a pri

mary care physician (PCP) may receive rou

tine or preventative care so that they never experience a hospitalization; access to a PCP is clearly endogenous. To illustrate, imagine two areas where pollution has biologically equivalent effects on people and increases identically over time, but the areas differ in terms of access to PCPs. The high access area may experience little change in hospi

talizations, while the low access area may

33 The exact date of discharge and patient zip code is typically only available in nonpublic versions of the data, which often requires some form of human subjects review.Journal of Economic Literature, Vol. LI (September 2013) 712

witness signi 0 cant increases. Since access to non-hospital-based healthcare is often highly correlated with other determinants of health (Smedley, Stith, and Nelson 2002), this introduces a nonrandom selection of data for analysis. 34 Administrative data from coun

tries with more universal access to health

care can help overcome this challenge when all forms of care are reported. Hospital data can also be used to calculate medical expenditures. Medical expenditure data has two advantages: it is already mone

tized and it can re 2 ect the severity of disease. For example, some hospital admissions for respiratory dif 0 culties are relatively minorevents that are remedied by the use of quickrelief inhalers and steroids, while others can be life threatening and require the use of infusions and ventilators. Hospital charges will differ across the two types of admissions and thus serve as an indicator of disease severity. 35 The validity of this construct will depend, in part, on the marginal productivity of medical care ( δ H / δM ) for each of the two disease types since health at discharge is an important component of impacts. Since this approach also involves use of the admissions data, the same strengths and limitations described above apply. Increases in the avail

ability of emergency room data in the United States can lessen selection bias concerns by capturing the universe of individuals seek

ing hospital-based care, including individu

als who may use the emergency room as a source of primary care. Medical care expenditures can also be assessed based on sales of medications.

34 In principle researchers could focus directly on phy

sician visits as another measure of illness, but obtaining this data with suitable geographic identi 0 ers is a signi 0 cant challenge. Moreover, sample selection bias remains a con

cern, unless used in conjunction with data on substitute encounters, e.g., emergency room and pharmacy visits, for the same sample.
35 Note, however, that many studies that use hospital admissions value the pollution effects using average hospi

tal charges, which does not capture severity. Asthmatics, for example, often take both controller medications on a routine basis and quick-relief medications to relieve symptoms as they arise. Hence, the use of quick-relief medications provides a unique opportunity to examine more subtle health effects that may not be captured in the use of health care services. Since recovery using these inhalers is generally quite rapid, medi

cation expenditures likely capture most of the pollution-induced costs associated with that particular exposure episode. Despite this potential advantage, storability of these medications—quick-relief asthma inhalers can deliver hundreds of “puffs”—may lead to little variation in purchases over short time periods. Obtaining such data is also complicated by the fact that it is often the proprietary information of retailers. Through individual agreements, researchers can sometimes obtain data on store-level sales. Some agencies, such as IMS Health, obtain and aggregate data from multiple retailers, though such data is often only available at a signi 0 cant price. Even with store-level sales data, assigning pollution to those sales can be tricky as the transaction location may differ from the location where exposure took place, e.g., exposure may be at work while one may use a pharmacy near their home. Given these limitations, medication data has seldom been used in the environmental economics litera

ture, though Deschenes, Greenstone, and Shapiro (2012) are an important exception.

It is worth noting that illness encounters are typically viewed as indicative of illness, but they also represent ameliorative actions. Therefore, how these illnesses translate into health impacts vis-à-vis our conceptual model is governed by the demand for health care ( δ M / δφ ) and its effectiveness ( δ H / δ M ). Both are areas of major preoccupation within the 0 eld of health economics (e.g., Manning et al. 1987; Finkelstein et al. 2011; Card, Dobkin, and Maestas 2009; Cutler 2007; Lichtenberg 2007). When combined713 Graff Zivin and Neidell: Environment, Health, and Human Capital

they reveal the transient and perhaps resid

ual, untreatable health effects due to pollu

tion (as implied by δ H / δφ ). To be concrete, imagine a patient that is hospitalized with an acute cardiac condition. The relevant health measure would not describe the patient’s health condition at admission but rather the one at discharge. In practice, this complete accounting is rarely done.

4.4.2.2 Unobservable Illnesses

While the aforementioned measures of ill

ness are relatively straightforward to observe, health can be impacted in more subtle ways that do not generate health encounters of any sort. These “unobservable” effects from exposure can begin with symptoms such as eye, ear, nose, and throat irritation, and may manifest themselves in labored breathing, increased fatigue, and lack of focus, all of which create disutility and can affect perfor

mance at work or in school. They also include changes in human development, which are particularly important for understanding the impact of early life exposures. Given the dif 0 culty in observing these impacts, the emerging economic research in this area focuses on proximate outcomes, such as worker productivity, student test scores, and birth outcomes. Of course, such outcomes are also of direct interest to economists as human capital has long been viewed as an engine for economic growth (Schultz 1961; Nelson and Phelps 1966; Romer 1986).

In medical and epidemiological 0 elds, there is a long history of using surrogate health outcomes that are predictive of an endpoint of interest, such as forced expira

tory volume, microvascular function, and blood pressure (e.g., Hazucha, Folinsbee, and Bromberg 2003; Pope et al. 2011). 36

36 The primary reason for the focus on surrogate mea

sures is that it is often the only ethical way to conduct a chamber study (i.e., randomized experiment) without inducing disease. These surrogate measures capture subtle changes in physiologic function that do not result in medical treatment, and may even be unobserved by the individual. Nonetheless, these subclinical changes may have impacts on mental and physical functioning that impair learning and job performance, pro

viding a strong rationale for human capital and productivity impacts. 37

The biggest challenge to research in this
area is 0 nding suitable sources of data. An emerging body of economic research focuses on the manifestation of these subtle effects as captured by outcomes at school or the work

place, principally absenteeism and perfor

mance. The use of such data does not involve sample selection since outcomes are not only measured for the sick but for all. They are also generally straightforward to monetize, as test scores and performance can be readily linked with wages—a rich area of study in the labor and education 0 elds (Black, Devereux, and Salvanes 2007; Currie and Thomas 2001; Oreopoulos et al. 2008; Royer 2009)—and thus directly inform our understanding of δ

w / δ H * δ H / δ P

. 38 Limited data availability, especially for representative samples, is, how

ever, a formidable obstacle to the conduct of credible empirical work in this area.

4.5 Valuation

A common goal of environmental eco

nomics research is to take estimates of the environmental–health relationships to develop measures of willingness-to-pay for reductions in pollution, as speci 0 ed in

37 Such impacts are also consistent with the large eco

nomic literature that has found linkages between health, education, and labor market outcomes. See, for example, Strauss and Thomas (1998).

38 Since these effects are more subtle, there are often limited opportunities for avoidance behavior ( δ

A / δ P = 0) and medical consumption ( δ M / δ P = 0). As such, the biological and reduced-form effects on health will be identical ( δ H / δ P = dH / dP ).Journal of Economic Literature, Vol. LI (September 2013) 714

equation (10), in order to inform the design of optimal environmental policy. 39 To obtain the welfare impacts from a change in environ

mental quality, one would want to measure all of the aforementioned health impacts. For example, particulate matter pollution may affect mortality, hospitalizations, medication use, and productivity, so one would want to sum up all components, being sure not to double count. 40 In addition to measuring all of these com

ponents, one must also place a monetary value on each, and all are not without con

troversies. With the exception of mortality, which is monetized by using the value of a sta

tistical life (VSL), all valuations likely under

state the true economic costs. 41 Charges are typically the only measure available for hos

pital admissions, and they do not capture the costs associated with the pain and suffering experienced by sickened individuals or their family members. 42 The use of medications as an outcome provides a clean, direct measure in dollars, but it does not account for pos

sible side effects from medication use (e.g., routine use of inhalers may cause arrhyth

mias (Singh et al. 2011)). Changes in birth outcomes can be linked with future earnings

39 Several alternative approaches for valuing environ

mental quality exist, including the hedonic pricing method, contingent valuation, and the travel cost method. For a detailed review of these valuation approaches, see Braden and Kolstad (1991).

40 Nearly every study focuses on one component at a time. For a noteworthy exception, see Deschenes, Greenstone, and Shapiro (2012).

41 The potential direction of bias from using VSL is ambiguous. The use of VSL is also not without limita

tions, requiring strong theoretical and empirical assump

tions (see DeLeire, Khan, and Timmins 2013 for a list of common concerns). Furthermore, using VSL to monetize these impacts may be misleading if the loss only represents short-term mortality displacement.

42 Market imperfections also imply that hospital charges will not re 2 ect social costs. Cross-subsidization across clini

cal activities means some will be overestimates while oth

ers will be too low. Furthermore, insurance-induced moral hazard may induce individuals to seek more care than they otherwise would if they faced the full costs of healthcare at the point of consumption (Black, Devereux, and Salvanes 2007; Royer 2009), but this does not include the disutility or medical expenditures due to health effects later in life. In addition to these direct health impacts, avoidance behavior should also be included when valuing demand for environmental quality. 43 This is where the differing empiri

cal approaches for attending to avoidance behavior become especially important. If the reduced form approach is taken, a separate analysis is needed to estimate the demand for avoidance behavior as a function of pollution ( δ

A / δ P ), and then these changes in demand need to be valued. If the biologic approach is taken, avoidance will have already been measured, but the health impacts need to be adjusted to re 2 ect the effectiveness of avoidance behavior and thus value net health effects. Again, the costs of avoidance behavior must be tabulated. 44 Some types of avoidance behavior are market based, such as the purchase of air 0 lters, and can be valued using market prices. Other types of avoidance behavior, however, are nonmarket based, like spending time indoors, making valuation more complex. In either case, it is essential to measure and monetize all types of avoidance behavior to conduct a proper valuation, regardless of whether the reduced form or biological approach is taken.

5. Quasi-Experimental Evidence

While the vast literature in the health sciences pays particular attention to envi

ronmental confounding, the overwhelming

43 This distinction is a signi 0 cant difference between economic and epidemiological, or cost of illness, approaches. See Harrington and Portney (1987) for a full discussion of this distinction.

44 For formal derivations of willingness-to-pay expres

sions that include avoidance behavior, see Cropper and Freeman (1991), Deschenes and Greenstone (2011), and Harrington and Portney (1987).715 Graff Zivin and Neidell: Environment, Health, and Human Capital

majority do little, if anything, to address behavioral confounding, and thus are likely to provide signi 0 cantly biased estimates. 45 In light of this, we focus our review on quasi-experimental studies within econom

ics, nearly all of which have emerged in the past decade. This literature is focused on a set of interlinked questions of inter

est. Does pollution affect health or human capital? How do these effects vary across pollution levels and population subgroups? Do individuals engage in behaviors to limit these effects? What are the long-term con

sequences from these exposures? Since they differ widely in the identi 0 cation strat

egies and outcome variables employed, each provide different insights into these underlying relationships and their impor

tance for societal welfare. 46

5.1 Health Capital and Illness

5.1.1 Impacts of PollutionOne of the earliest examples of a quasiexperimental approach to estimate an envi

ronmental health relationship is found in a series of studies by Pope (1989) and Ransom and Pope (1992, 1995). The authors used changes in pollution that resulted from alabor strike that forced the closing and reopening of a steel mill, which was a major source of particulate matter in the central valley of Utah. Since the steel mill closed due to a labor strike, the temporary changes in pollution are credibly exogenous and unlikely to lead to any immediate residential sorting.

45 For a description of state-of-the-art epidemiological models, see Dominici, Samet, and Zeger (2000), Dominici

et al. (2002), Dominici, Crainiceanu, and Parmigiani (2008), Sacks et al. (2012), Peng, Dominici, and Louis (2006), and Bell, Samet, and Dominici (2004).

46 As previously discussed, all of the papers discussed in this review are focused on health effects due to acute exposures to pollutants. While correlational analysis pro

vides suggestive evidence of cumulative effects across mul

tiple years, there is no quasi-experimental evidence on this topic. Furthermore, the authors select a neigh

boring, unaffected community as a control group to account for time trends by estimat

ing difference-in-differences models. When the steel mill was closed, the authors found signi 0 cant declines in school absences, respi

ratory-related hospital admissions, and mor

tality. As an “event study,” one concern with this design is that, despite the availability of individual level health outcomes as depen

dent variables, the pollution variable is com

mon to all members in a group for a given time period (Moulton 1986). As a result their standard errors are likely to be nontrivially understated, making the appropriate statisti

cal inference in this setting particularly chal

lenging (Donald and Lang 2007). 47 One important study by Chay and Greenstone (2003b) overcame this prob

lem by focusing on the recession of the early 1980s in the United States. Manufacturing is a key input into the emissions process, soa slowing of the economy can produce farreaching changes in pollution. Furthermore, manufacturing is not evenly spread through

out the United States, so the shocks to manufacturing from this recession induced considerable spatial variation in pollution. In fact, the authors found that total sus

pended particulates (TSPs) declined by as much as 35 percent in three years in the most heavily manufacturing areas, with some areas experiencing relatively small changes. Since these changes in TSPs are driven by a global phenomenon, it is unlikely to be related to other factors affecting health. Chay and Greenstone use this exogenousvariation in levels of pollution at the countyyear level to identify environmental health effects. Although a potential concern with

47 Another potential concern with this study is that the steel mill closure also led to a temporary change in income, which may affect one’s use of time and consumption of health services. Such concerns are unlikely to impact causal inference with respect to school absences.Journal of Economic Literature, Vol. LI (September 2013) 716

this strategy is that income changes due to unemployment losses are correlated with the pollution changes, the authors carefully document that this is not the case. They 0 nd that a one-unit decline in TSPs associated with the recession prevented between four and seven infant deaths per 100,000 births.

The Chay and Greenstone results apply to a time period when pollution levels in the United States were considerably higher than today. Currie and Neidell (2005) turn their attention to infant mortality in California during the 1990s, a period that is much more

re 2 ective of contemporary pollution levels across much of the developed world. Absent a large-scale shock to pollution levels, they use zip code 0 xed effects to estimate mod

els at the weekly level. Switching to a higher time frequency exploits the strong temporal 2 uctuations in pollution (as described in 0 g

ure 2) that may act as a shock without allow

ing sorting to occur, while the 0 xed effects control for time invariant characteristics of the area. They 0 nd that reductions in carbon monoxide over the 1990s saved approxi

mately 1,000 infant lives in California, which translates into bene 0 ts of roughly $4.8 bil

lion. Currie and Neidell 0 nd quite similar effects when aggregating to a higher fre

quency (from weekly to monthly), suggesting that harvesting may not be a signi 0 cant issue in the case of pollution and infant mortality. In a more direct approach for accounting for sorting, Lleras-Muney (2010) uses the relocation of military personnel from 1989 to 1995 to estimate the effect of various pollut

ants on children’s health. Military personnel are assigned to military bases entirely based on “the needs of the army,” and any reloca

tion decisions follow this dictum as well. To the extent that military families are required to live on or very near the base, they do not choose neighborhoods based on their ameni

ties, thereby offering a plausibly exogenous source of variation in pollution. Furthermore, all military personnel are covered by identical health insurance plans, so the price of care is not a factor in determining usage. Using this design, Lleras-Muney 0 nds that a one standard deviation decrease in ground-level ozone exposure decreases the probability of a respiratory hospitalization for children by about 8–23 percent.

Currie, Neidell, and Schmieder (2009), like Currie and Neidell (2005), focuses on outcomes in a more recent time period, but they focus on birth outcomes (in addition to infant mortality), use the exact address of the mother to improve pollution assignment and estimate maternal 0 xed effect models to control for differences in family background and genetics. They 0 nd that a one unit change in mean CO during the last trimester of pregnancy increases the risk of low birth weight by 8 percent, and a one unit change in mean CO during the 0 rst two weeks after birth also increases the risk of infant mortal

ity by 2.5 percent relative to baseline levels. The authors calculate that the 0 fteen-year decline in CO from 1989 to 2003 translates into $720 million in lifetime earnings from improvements in birth weight and $2.2 bil

lion from the reduction in infant mortality for the 2003 U.S. birth cohort. The use of maternal 0 xed effects increases estimates, suggesting the importance of accounting for maternal characteristics within neighbor

hoods. The better assignment of pollution by using the mother’s exact address rather than zip code also increases point estimates, con

sistent with measurement error inducing a downward bias.

While the previous mentioned studies rely on data from well-maintained health and pollution records, such data are often not available in developing countries. Jayachandran (2009) creatively overcomes this limitation in her study of the health effects of the Indonesian wild 0 res, which led to an exogenous and sizable increase in particulate matter. First, she infers mor

tality by looking at “missing children” a la717 Graff Zivin and Neidell: Environment, Health, and Human Capital

Sen (1992) from surviving cohorts (available from a population census). Second, absent reliable ground-level pollution monitoring data, she uses daily satellite data of airborne smoke that reliably captures the movement of the 0 re given its size. She 0 nds that the 0 res caused a roughly 17 percent increase in under age two mortality.

Another context where data are dif 0 cult to observe is with respect to fetal deaths, as these are rarely reported and can even go unnoticed by the mother herself. Sanders and Stoecker (2011) cleverly overcome this problem by estimating the effect of pollution on sex ratios—the Trivers–Willard hypoth

esis suggests that males are more suscep

tible to fetal shocks than females (Trivers and Willard 1973). Their analysis closely fol

lows the identi 0 cation strategy of Chay and Greenstone (2003a) and Chay, Dobkin, and Greenstone (2003), which focused on the dif

ferential reductions in pollution levels under the 1970 Clean Air Act Amendments for counties that were in and out of attainment for air quality standards at the time. They 0 nd that a one standard deviation increase in the annual average TSPs (approximately 35 micrograms per cubic meter) decreases the probability a live birth is male by 3.1 per

centage points. Most studies on this topic focus on the impacts of air pollution, in large part due to data availability. A notable exception is Ebenstein (2012), who examines water pol

lution in China. The rapid industrialization in China has led to a tremendous increase in pollution, though the increase varied consid

erably across space—perhaps best describedas the Chay and Greenstone recessioninduced pollution changes in reverse. Mobility was also greatly limited for inhab

itants because of government restrictions, making sorting less likely to induce bias. Water pollution data come from a national water monitoring system (a rare availability in a developing or developed country), and mortality from a data set akin to vital records, although focused only on select cities. He 0 nds that a one-grade deterioration in water quality (on a six-grade scale) increases the incidence of digestive cancers by nearly 10 percent. Efforts to clean wastewater would avert one additional death due to digestive cancer at a cost of $30,000, well below even the most modest estimates of the VSL.

5.1.2 Impacts of Emissions

While most studies focus on the direct health effect of a particular pollutant, oth

ers focus on the impacts of emission policies. Recall from section 2 that emissions interact with meteorological conditions to form pol

lution, such that P = f ( E

, W ). Focusing on a pollutant provides a more generalizable esti

mate, whereas focusing on emissions may not because the effects may be speci 0 c to the meteorological conditions at the time of shock or the source of emissions. Focusing on emissions offers several advantages, how

ever. One, emissions, rather than pollution, are often all that can be directly regulated. Two, this provides an estimate of an exter

nality from a speci 0 c factor of production, which has a clearer economic interpretation. Three, as previously discussed, identifying the reduced form effect of emissions is often easier than identifying the effect of particu

lar pollutants. This focus on emissions is an area where economists can make particularly important contributions through the use of natural experiments. One notable paper taking this approach is Currie and Walker (2011). The authors examine the effects on birth outcomes from the introduction of an electronic toll collec

tion system (E-ZPass), which signi 0 cantly reduced traf 0 c congestion near highway toll plazas and thus local vehicle emissions. Their study provides estimates of an externality from highway congestion, which can con

tribute to the optimal design of congestion policies. To control for sorting, they exploitJournal of Economic Literature, Vol. LI (September 2013) 718

the introduction of E-ZPass to estimate difference-in-differences models, compar

ing areas close to toll plazas before and after E-ZPass to areas far from plazas. They 0 nd that the introduction of E-ZPass reduced the incidence of prematurity and low birth weight by roughly 11–12 percent in the areas directly adjacent to toll plazas. Nearly every paper on pollution and health—and certainly all thus far reviewed in this paper—focus on only one health con

struct at a time. 48 Deschenes, Greenstone, and Shapiro (2012) is an exception, pro

viding a more comprehensive assessment of the effect of NO

x emissions through an examination of impacts on mortality, hospi

talizations, and medication expenditures. To address sorting, the authors exploit the NO

x Budget Trading Program (NBP), which, beginning in 2003, created a cap-and-trade system for major contributors to NO

x emis

sions in Eastern and Midwestern States. Because NO

x emissions are a precursor to ozone formation, the NBP only oper

ated during the warmer times of the year, which is when ozone spikes. The authors

use a triple-difference model by comparing Eastern and Midwestern States to Western States, before and after the advent of NBP, and across seasons within each year. The authors 0 nd that the reduction in NO

x emis

sions led to a signi 0 cant decrease in ozone pollution. Furthermore, the NBP led to con

siderable decreases in mortality and medi

cation purchases, but surprisingly had no impact on hospitalizations. This is also the 0 rst analysis to directly evaluate the health impacts of an emissions trading program, with the authors 0 nding that the savings in medication expenditures alone exceeds the costs of the program.

48 Studies looking at birth outcomes often include sev

eral dependent variables, such as birth weight, gestation, and APGAR scores, but these are all designed to capture the same construct: health capital at birth. 5.1.3 The Role of Avoidance Behavior

The previously mentioned studies exploit “natural” experiments that generate exog

enous changes in ambient pollution or emissions in order to minimize concerns about residential sorting and other long-run behavioral responses to poor environmen

tal quality. They generally ignore short-run avoidance behavior that could also impact the environment-health relationship, and hence provide estimates of a reduced-form relationship between pollution and health. The degree to which this reduced-form rela

tionship differs from the biological effect depends on the existence and magnitude of avoidance behavior.

5.1.3.1 Evidence of Avoidance Behavior Results from a nascent literature suggest avoidance behavior is unlikely to be trivial. Since avoidance behavior is typically a non

market behavior, researchers often must obtain unique sources of data to explore this topic. For example, Neidell (2009) uses attendance data from several outdoor facili

ties in Los Angeles to examine the relation

ship between air quality information and outdoor activities. Air quality information predominantly consists of “smog alerts,” which are based on ozone forecasts. Since smog alerts are only issued when ozone is forecasted to exceed a particular threshold, Neidell employs a regression discontinu

ity design to compare attendance on days just above the threshold to just below. He 0 nds signi 0 cant decreases in attendance of approximately 6 to 13 percent, though it remains unclear how these estimates gener

alize to other contexts. 49

49 In a closely related study, Graff Zivin and Neidell (2009) examine successive days of smog alerts, and 0 nd that responses to an alert issued on two consecutive days declines considerably, suggesting the costs of avoidance behavior are nontrivial as well.719 Graff Zivin and Neidell: Environment, Health, and Human Capital

Graff Zivin, Neidell, and Schlenker (2011) focus on water quality, examining the impact of drinking water violations on the consump

tion of bottled water using purchase data from a national grocery chain. Since viola

tions are issued at the local water district level, the authors use grocery store 0 xed effects to compare the changes in consump

tion over time in response to violations, with the change in violations unlikely to be related to changes in underlying consump

tion preferences. They 0 nd that violations increase bottled water consumption by 17 to 22 percent, depending on the contaminant responsible for the violation. Mercury, like other heavy metals, impairs neurological development in fetuses, infants, and children. The primary route of human exposure to mercury is through 0 sh con

sumption, either by children or pregnant mothers. In 2001 year, the Food and Drug Administration began issuing advisories encouraging at-risk populations to reduce consumption of certain types of 0 sh known to contain high mercury levels. Shimshack, Ward, and Beatty (2007) examine the impact of these advisories on the consump

tion of 0 sh as measured in the consumer expenditure survey. They estimate differ

ence-in-differences models by comparing consumption before and after the advisory for informed versus uninformed consum

ers, using newspaper readership and several other measures to de 0 ne informed. Among groups particularly sensitive to mercury, they found a roughly 19 percent reduction in 0 sh consumption.

The above studies on avoidance behavior all focus on the impact of publicly disclosed information; Madajewicz et al. (2007) focus on privately disclosed information. Arsenic pollution in drinking water is a signi 0 cant problem in Bangladesh, and this study exam

ines behavioral responses to information regarding contamination in tested wells. Since arsenic is naturally occurring and varies widely in its distribution, they conduct a simple cross-sectional comparison across households with tested wells. They 0 nd that households that are informed that their well contains high levels of arsenic are 37 percent more likely to use a neighbor’s well.

While many pollutants are not directly detectable, making the need for explicit information essential, people may also respond to risk independent of informa

tion campaigns when environmental qual

ity (or its correlates) is directly observable. Deschenes and Greenstone (2011) exam

ine the relationship between temperature and residential energy consumption, where variation in energy consumption is predomi

nantly driven by changes in the usage of air conditioning. While not a pollutant per se, we can view responses to temperature as a form of avoidance behavior because it repre

sents a response to changes in risk; air con

ditioning reduces the risks associated with heat. 50 The econometric models include county 0 xed effects to exploit the presum

ably random variation in temperature across years. The authors 0 nd that an additional day over 90 degrees F (relative to 50–60 degrees) increases annual energy consumption by 0.4 percent, suggesting people may avoid the health risk from higher temperatures.

5.1.3.2 Accounting for Avoidance Behavior

The above studies suggest that avoidance behavior may be nontrivial, such that con

trolling for avoidance behavior is likely to lead to considerable differences between the biological effect ( δφ / δ P ) and the reducedform effect ( d φ / dP ) of pollution. Since measuring avoidance behavior is challeng

ing, there are only a handful of studies that attempt to control for avoidance behavior

50 Since air conditioning may also create a more com

fortable indoor environment, it also provides bene 0 ts in the form of direct utility (Cropper and Oates 1992).Journal of Economic Literature, Vol. LI (September 2013) 720

to assess this gap. Building on the previous result, Neidell (2009) accounts for avoidance behavior indirectly by including smog alerts (and the ozone forecasts that determine them) when estimating the relationship between ozone and respiratory-related hos

pital admissions. Using zip code 0 xed effects and exploiting the strong daily temporal variation in ozone, he 0 nds that including these proxies signi 0 cantly increases the esti

mated impact of ozone on health. Estimates that ignore avoidance behavior 0 nd that a one ppb decrease in ozone decreases admis

sions for children by roughly 1 percent; this increases to nearly 3 percent when incorpo

rating avoidance behavior. Moretti and Neidell (2011) use daily boat arrivals and departures into the port of Los Angeles as an instrumental variable (IV) for ozone levels, which deals with both avoid

ance behavior and measurement error in pollution assignment. Boat traf 0 c repre

sents a major source of pollution for the Los Angeles region and, because of the extended length of travel and unpredictable condi

tions at sea, daily variation in boat traf 0 c is arguably uncorrelated with other short run determinants of health. Importantly, since boat traf 0 c is not included in ozone forecasts and is not directly observable, it is unlikely to encourage avoidance behavior. Therefore, to the extent that avoidance behavior is an omit

ted variable, using boat traf 0 c as an instru

ment uncovers the effect of ozone holding avoidance behavior 0 xed, i.e., the biologi

cal effect. While boat emissions may affect multiple pollutants, the authors attempt to limit concerns with identi 0 cation by focusing solely on the summer period, when ozone levels are at their highest and all other cri

teria pollutants are at their lowest. They 0 nd that holding avoidance behavior 0 xed leads to signi 0 cantly larger estimates for the impacts of pollution on health, with estimates nearly four times larger than estimates that do not control for avoidance behavior. 5.2 Environment and Human Capital

In addition to the health outcomes dis

cussed thus far, pollution may also affect human capital, either through direct or indirect routes. For example, pollution may lead to direct neurological insults that affect cognitive ability. Alternatively, decrements in lung functioning may affect one’s abil

ity to focus and thus perform a wide range of tasks. Data requirements are a particular obstacle to this line of research. As such, our understanding of the relationship between poor environmental quality and nonhealth elements of human capital is only just begin

ning to emerge.

5.2.1 Contemporaneous Effects

As with many of the respiratory-related health outcomes discussed above, we might expect pollution to have an immediate effect on school and job performance. It is not hard to imagine that a child experiencing an asthma attack might spend the night in the hospital, and as a result does not go to school the next day. Absences may also be a more sensitive measure of health capital than hospital admissions, picking up less severe impacts as when an individual does not feel well and takes a day off to recover. Impacts may occur on the intensive margin as well. For example, small changes in lung func

tioning may not result in a change in labor supply, but could make a worker less able to concentrate and thus underperform on cog

nitive and physical tasks. 51

5.2.1.1 Schooling Outcomes School is widely seen as the main vehicle for improving human capital, and miss

ing school because of illness may impede human capital development (Grossman

51 To draw a 0 ne distinction from absenteeism, this is sometimes referred to as “presenteeism” (Pauly et al. 2008).721 Graff Zivin and Neidell: Environment, Health, and Human Capital

and Kaestner 1997). In the aforementioned Ransom and Pope design, they also found a signi 0 cant decrease in school absences due to the steel mill strike. Currie et al. (2009) use administrative data from the thirty-nine largest school districts in Texas to estimate models of the impact of pollution on atten

dance in elementary and middle schools. By following schools over time, their model exploits variation in pollution over time at the same school, hence controlling for resi

dential factors that may be important deter

minants of absences. They 0 nd that when carbon monoxide (CO) levels rise, absences also rise, even when levels are well below current federal air quality standards.

While absences may ultimately impair knowledge acquisition, impacts may also occur even for those present. Pollution can affect a child’s ability to concentrate, and may also have a direct effect on brain devel

opment (Block and Calderón-Garcidueñas 2009). Zweig, Ham, and Avol (2009) merge data from the Children’s Health Study (CHS), a longitudinal respiratory health study of Southern California school chil

dren that contains detailed pollution data, with test score data from schools linked to the children. Using school 0 xed effects to account for sorting, they 0 nd that a 10 percent decrease in PM2.5 raises math test scores by 0.14 percent and reading scores by 0.21 percent. Lavy, Ebenstein, and Roth (2012) merge data on high stakes test scores from Israeli high school students with detailed environmental monitoring data. Employing student 0 xed effects, they 0 nd a ten-unit increase in PM2.5 decreases test scores by 1.9 percent of a standard deviation, and a ten-unit increase in CO decreases test scores by 2.4 percent of a standard deviation. While neither study can isolate which channel is driving these impacts, both underscore the important cognitive effects that can result from pollu

tion exposure. 5.2.1.2 Labor Market Outcomes

Just as schooling absences may be affected by illness, worker absences may as well. Hanna and Oliva (2011) focus on the labor supply of workers in Mexico City. To account for the endogeneity of pollution, they exploit the closing of a local oil re 0 nery that was a major emitter of sulfur dioxide, compar

ing residents near and far from the re 0 nery, before and after the re 0 nery closure. While similar in spirit to Ransom and Pope’s inves

tigations centered on a steel mill closure, the closure of the oil re 0 nery was in a densely populated, urban community, thus affecting hundreds of thousands of residents. A nota

ble limitation is that demand shocks from the

re 0 nery closure may have had an impact on hours worked as well, though they attempt to address this by incorporating data on imputed wind patterns. The authors 0 nd that a 1 percent increase in sulfur dioxide levels decreases hours worked by 0.72 percent. Carson, Koundouri, and Nauges (2011) also explore labor supply impacts, with a focus on arsenic pollution in drinking water wells in Bangladesh. They exploit the fact that the distribution of arsenic in tube wells is highly variable and that households were largely unaware of the problem and thus unlikely to have engaged in sorting or avoid

ance behavior. 52 It is notable that, while the distribution of arsenic is highly variable, regions near the Bay of Bengal tend to have much higher levels of contamination on average, thus some of the usual concerns regarding cross-sectional comparisons across regions apply in this case as well. In the end, the authors 0 nd that arsenic pollution leads to a large 8 percent reduction in labor supply.

The focus on the extensive margin, where behavioral responses are nonmarginal,

52 Unlike the sample from Madajewicz et al. (2007), these individuals were not targeted by informational campaigns.Journal of Economic Literature, Vol. LI (September 2013) 722

captures high-visibility labor market impacts. 53 Less visible impacts may also occur on the intensive margin whereby pro

ductivity is affected, even when labor supply does not change. These changes in perfor

mance are perhaps more dif 0 cult to detect than absenteeism, but may be pervasive so that even small individual effects may trans

late into large welfare losses when aggre

gated across the economy. Graff Zivin and Neidell (2012) address this issue by focusing on the impact of pollution on worker produc

tivity. They obtain daily measures of worker productivity using a unique panel data set on agricultural workers who are paid by piece rate. Furthermore, the labor supply of agri

cultural workers is highly inelastic in the short run, hence limiting the scope for avoid

ance behavior. Using models that exploit the plausibly exogenous daily variation in ozone levels, they 0 nd that a 10 ppb decrease in ozone concentrations increases worker pro

ductivity by 5.5 percent. 54 In a novel design, Clay, Troesken, and Haines (2010) examine the effect of lead exposure in the early twentieth century on several equilibrium measures of produc

tivity, thus capturing impacts on both the extensive and intensive margin. The authors identify the impact of differential lead expo

sure using differences in the use of lead (as opposed to other metal) service pipes as well as local acidity and hardness of water across cities, both of which affect lead lev

els in tap water. Importantly, lead pipes were generally believed to be safe at the time, so it is unlikely that residents sorted based on the type of metal used in the pipes servic

ing their homes. The authors 0 nd that cities

53 These nonmarginal behavioral responses may also be accompanied by health encounters that can be readily observed by the econometrician.

54 In a notable case study of seventeen citrus harvesters in heavily polluted Southern California in the early 1970s, Crocker and Horst (1981) document similar productivity effects. with lead-only service pipes and more acidic water, and thus higher levels of lead expo

sure, had substantially lower wages, value added per worker, and value of capital per worker.

5.2.2 Latent Effects In addition to contemporaneous effects, pollution may also have latent effects on human capital. In particular, the fetal origins hypothesis and its extensions into early child

hood suggests that negative shocks early in life may lead to a wide range of lasting effects, which may arise even without noticeable impacts at the time of exposure. 55 Moreover, self-productivity and dynamic complemen

tarities in skill formation may exacerbate the pure biologic effects of pollution (Cunha and Heckman 2007; Behrman, Rosenzweig, and Taubman 1994; Almond and Currie 2011). Empirical work in this setting is complicated, however, since the research requires not only information on current human capital lev

els but also information on residential loca

tion around the time of birth to assign early childhood exposure. Despite this challenge, understanding latent effects is particularly important from a policy perspective because short run changes in pollution can lead to lifelong changes in well-being.

Almond, Edlund, and Palme (2009) exam

ine the impact from prenatal exposure to radioactive fallout from the 1986 Chernobyl accident on both birth and schooling out

comes for children in Sweden. Although Sweden is more than 500 miles away from Chernobyl, weather conditions forced some of the plume to pass over Sweden, and local variation in rainfall levels led to stark geographic variation in the levels of fallout throughout the country. The authors also take advantage of the meticulous record

keeping that linked nearly all births (along

55 For more discussion of the fetal origins hypothesis, see Almond and Currie (2011).723 Graff Zivin and Neidell: Environment, Health, and Human Capital

with birth outcomes) of affected cohorts with schooling outcomes in high school, using the parish of birth to link to radiation exposure. Their study reveals that radiation exposure exhibits latent effects that shape human cap

ital development later in life. While they 0 nd little evidence of health effects as measured by birth outcomes and childhood hospitaliza

tions, they 0 nd signi 0 cant decreases in sev

eral schooling outcomes, including math test scores and graduation rates. They estimate that these impacts translate into a 3 percent reduction in future earnings for those most exposed to the fallout. 56 Sanders (2012) adds to this line of research by focusing directly on the effect of prenatal exposure to TSPs on student performance in high school. The author focuses on high schools in Texas, obtaining school perfor

mance records for the universe of students from the State. Absent information on the location of birth, Sanders assumes the county of residence in high school serves as the county of birth. While clearly not without limitations, migration records from an exter

nal data source at a later date provide little evidence that migration was related to pol

lution. Using a similar identi 0 cation strategy as that employed by Chay and Greenstone (2003b), only focused solely on Texas, he 0 nds that a one standard deviation decrease in ambient TSP levels during pregnancy led to 2 percent of a standard deviation increase in high school test performance.

A large body of epidemiological evidence shows that high levels of lead exposure cause neurological defects (see Skerfving and Bergdahl 2007 for a summary of evidence), but again the more probing question centers around the causal effects from lower levels of exposure to neurotoxins more commonly

56 It is notable that the radiation levels found in Sweden are quite comparable to those found in radon and medi

cal radiation, though policy levers for in 2 uencing both are quite different than typical environmental policy. found today. Understanding the effects from low-level exposure is particularly important during the early stages of human develop

ment. 57 Nilsson (2009) tackles this question by examining the effect of lead exposure in the 0 rst few years of life by following several cohorts of children born in Sweden from the early 1970s to the mid-1980s. He exploits the gradual phase-out of leaded gasoline from 1973–81, which induced sharp temporal and spatial decreases in lead levels, with the spa

tial decreases arising from differences in ini

tial lead levels. 58 He 0 nds that decreases in lead improve several aspects of human capi

tal, including years of schooling and cogni

tive ability, as well as labor market outcomes. Importantly, allowing for a nonlinear effect of lead through the use of dummy variables, Nilsson 0 nds effects from exposure at levels below current levels of concern (as dictated by, for example, the U.S. Center for Disease Control and Prevention), suggesting further reductions in lead levels are likely to yield signi 0 cant human capital bene 0 ts.

6. Conclusion

While the impacts of pollution on human health have long been recognized, research over the past few decades has revealed impacts at much more modest levels of pol

lution than previously imagined. Moreover, a blossoming literature has begun to link pollution-induced illnesses to a range of human capital outcomes. Since environ

mental contamination of varying degrees is part of the ether in which all human activity takes place, pollution should be viewed as an

57 For a compelling paper on the impact of lead on infant mortality, albeit at much higher levels, see Troesken (2008).

58 Note that the studies 0 nding a strong neurological effect of lead are what prompted the phase-out of leaded gasoline, so by de 0 nition they focus on higher levels of pol

lution that are less relevant for today.Journal of Economic Literature, Vol. LI (September 2013) 724

important factor of production—one that is conceptually similar to technology in that it “transforms” the ways in which labor, capital, and land can be combined to produce out

put. This relationship is made all the more interesting by the fact that most pollution is a direct byproduct of economic production.

All economies produce pollution and nearly all societies regulate it. Regulation is typically viewed as a tax on producers and consumers to be weighed against the con

sumption bene 0 ts associated with improved environmental quality. The evidence reviewed within this paper suggests that environmental regulation may also contrib

ute to economic productivity and thus fruit

fully be viewed as an investment in economic growth as well. 59 Moreover, since exposure to poor environmental quality within and across countries tends to correlate with low income, these results point toward a new form of pov

erty trap. Might the causality underpinning the Environmental Kuznets curve also run in the other direction (Dasgupta et al. 2002)? Could the poor, at least partially, regulate their way toward economic growth?

While an analysis of regulatory approaches is beyond the scope of this paper (for a good review see Stavins 2003), it is important to underscore that optimal policy requires a careful enumeration of all costs and ben

e 0 ts. This tabulation is fraught with empiri

cal challenges and is rarely comprehensive in its scope. Indeed, only the most visible impacts described in sections 3 and 4 are typically included in policy design calcu

lations, with the more subtle but perhaps more pervasive impacts generally ignored. That said, cost–bene 0 t calculations even

59 In less developed countries, where individuals rou

tinely face higher levels of pollution and avoidance behav

ior is more costly since large sectors of the economy rely on industries where workers are routinely exposed to ambient conditions—such as agriculture, mining, and construc

tion—the human capital and productivity returns on this “investment” may be especially large. on this partial analysis, which understates the bene 0 ts from pollution reduction, often 0 nd that the bene 0 ts of regulation signi 0

- cantly outweigh the costs (see, for example, Deschenes, Greenstone, and Shapiro 2012; Ebenstein 2012). This almost certainly con

tributes to the contentiousness surround

ing nearly every environmental standard. Better tools and more evidence are needed to inform this debate. We utilize this oppor

tunity to highlight several areas that merit further investigation. First, the biomedical science literature
is 0 lled with pollution–health relations that have not been subjected to the causality tests typical of economic inquiry. A growing epi

demiology literature has found a relationship between pollution and mental health (see, e.g., Pedersen et al. 2004 for schizophrenia;
Perera et al. 2012 for behavioral problems; Volk et al. 2012 for autism). Our under

standing of epigenetics—how environmental stressors can alter gene expression—and what it implies for human development is a topic of immense interest in biology (see Goldberg, Allis, and Bernstein 2007; Baccarelli et al. 2009). Both have clear economic conse

quences and merit further scrutiny. Second, a better understanding of avoid

ance activities is also needed. This is an area—behavioral responses to incentives under incomplete information—that is especially ripe for economists to contribute. Avoidance requires individual actions that weigh costs and bene 0 ts. These, in turn, depend on a wide range of socioeconomic factors that in 2 uence, for example, mobil

ity, health insurance status, and the avail

ability of intellectual and 0 nancial resources required to avert. For those pollutants with subtle impacts, models of learning and inat

tention may be particularly important. The current stalemate on actions to avert climate change suggests that avoidance and adapta

tion are likely to play an increasingly large role in global welfare going forward.725 Graff Zivin and Neidell: Environment, Health, and Human Capital

Lastly, the evidence on human capital and especially labor market effects from pollu

tion exposure are just beginning to emerge. Despite its beginning stages, the limited evi

dence thus far suggests negative impacts on cognitive ability, school performance, and job performance. Much more should be done to replicate these results in other con

texts and to unpack the mechanisms driving them, which in turn will help to generalize them beyond the narrow settings that have thus far been necessary to pin down empiri

cal identi 0 cation.

The collection of environmental, health, and human capital data is being rapidly transformed. The past decade has witnessed a proliferation of cheaper and more sophisti

cated environmental sensors as well as signif

icant improvements in the science that allow us to transform satellite data into meaning

ful measures of environmental conditions on the ground. The proliferation of electronic surveillance and data capture in schools and the workplace, combined with increased digitization and linkage of historical data, has signi 0 cantly increased access to not only better human capital and productivity data but also to data that spans a lifetime of expo

sures, encounters, and outcomes. 60 These advances promise to improve the resolution and scope of our analytic purview. Ironically, these improvements are occurring at the same time that access to 0 nely geocoded health data, which have been the mainstay of research in this area, are being threat

ened by proposed NIH revisions regarding data con 0 dentiality. Nonetheless, we remain optimistic about the prospects for deepening our understanding of the role played by the

60 Examples of longitudinal data sets that cover multiple life stages include the National Children’s Study (under

way), birth data linked with health and schooling outcomes in Sweden and Chile (see, for example, Almond, Edlund, and Palme 2009; Nilsson 2009; and Bharadwaj, Løken, and Neilson forthcoming), and linked National Health Service data from England. environment as a largely ignored factor pro

duction in the coming decades.

R eferences Agarwal, Nikhil, Chanont Banternghansa, and Linda T.

M. Bui. 2010. “Toxic Exposure in America: Estimat

ing Fetal and Infant Health Outcomes from 14 Years of TRI Reporting.” Journal of Health Economics 29 (4): 557–74. Almond, Douglas, Kenneth Y. Chay, and David S. Lee.

2005. “The Costs of Low Birth Weight.” Quarterly Journal of Economics 120 (3): 1031–83. Almond, Douglas, and Janet Currie. 2011. “Killing Me

Softly: The Fetal Origins Hypothesis.” Journal of Economic Perspectives 25 (3): 153–72. Almond, Douglas, Lena Edlund, and Marten Palme.

2009. “Chernobyl’s Subclinical Legacy: Prenatal Exposure to Radioactive Fallout and School Out

comes in Sweden.” Quarterly Journal of Economics 124 (4): 1729–72. Auffhammer, Maximilian, and Ryan Kellogg. 2011.

“Clearing the Air? The Effects of Gasoline Content Regulation on Air Quality.” American Economic Review 101 (6): 2687–2722. Baccarelli, Andrea, et al. 2009. “Rapid DNA Meth

ylation Changes after Exposure to Traf 0 c Particles.” American Journal of Respiratory and Critical Care Medicine 179 (7): 572–78. Banzhaf, H. Spencer, and Randall P. Walsh. 2008. “Do

People Vote with Their Feet? An Empirical Test of Tiebout’s Mechanism.” American Economic Review 98 (3): 843–63. Bartik, Timothy J. 1988. “Evaluating the Bene 0 ts of

Non-marginal Reductions in Pollution Using Infor

mation on Defensive Expenditures.” Journal of Environmental Economics and Management 15 (1): 111–27. Bayer, Patrick, Bryan Ellickson, and Paul B. Ellickson.

2010. “Dynamic Asset Pricing in a System of Local Housing Markets.” American Economic Review 100 (2): 368–72. Behrman, Jere R., Mark R. Rosenzweig, and Paul

Taubman. 1994. “Endowments and the Allocation of Schooling in the Family and in the Marriage Market: The Twins Experiment.” Journal of Political Econ

omy 102 (6): 1131–74. Bell, Michelle L., Jonathan M. Samet, and Francesca

Dominici. 2004. “Time-Series Studies of Particulate Matter.” Annual Review of Public Health 25: 247–80. Bharadwaj, Prashant, Katrine Vellesen Løken, and

Christopher Neilson. Forthcoming. “Early Life Health Interventions and Academic Achievement.” American Economic Review

. Black, Sandra E., Paul J. Devereux, and Kjell G. Sal

vanes. 2007. “From the Cradle to the Labor Market? The Effect of Birth Weight on Adult Outcomes.” Quarterly Journal of Economics 122 (1): 409–39. Block, Michelle L., and Lilian Calderón-Garcidueñas.

2009. “Air Pollution: Mechanisms of Neuroin 2 am

mation and CNS Disease.” Trends in NeurosciencesJournal of Economic Literature, Vol. LI (September 2013) 726

32 (9): 506–16. Braden, John B., and Charles D. Kolstad, eds. 1991.

Measuring the Demand for Environmental Quality. Amsterdam; Oxford and Tokyo: North-Holland. Brimblecombe, Peter. 1999. The Big Smoke: A History

of Air Pollution in London since Medieval Times

. Cambridge and New York: Cambridge University Press. Buck Louis, G. M., J. Dmochowski, C. Lynch, P.

Kostyniak, B M. McGuinness, and J. E. Vena. 2009. “Polychlorinated Biphenyl Serum Concentrations, Lifestyle and Time-to-Pregnancy.” Human Repro

duction 24 (2): 451–58. Card, David, Carlos Dobkin, and Nicole Maestas. 2009.

“Does Medicare Save Lives?” Quarterly Journal of Economics 124 (2): 597–636. Carson, Richard T., Phoebe Koundouri, and C é line

Nauges. 2011. “Arsenic Mitigation in Bangladesh: A Household Labor Market Approach.” American Journal of Agricultural Economics 93 (2): 407–14. Chay, Kenneth Y., Carlos Dobkin, and Michael Green

stone. 2003. “The Clean Air Act of 1970 and Adult Mortality.” Journal of Risk and Uncertainty 27 (3): 279–300. Chay, Kenneth Y., and Michael Greenstone. 2003a.

“Air Quality, Infant Mortality, and the Clean Air Act of 1970.” National Bureau of Economic Research Working Paper 10053. Chay, Kenneth Y., and Michael Greenstone. 2003b.

“The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession.” Quarterly Journal of Economics 118 (3): 1121–67. Chay, Kenneth Y., and Michael Greenstone. 2005.

“Does Air Quality Matter? Evidence from the Hous

ing Market.” Journal of Political Economy 113 (2): 376–424. Chen, Yuyu, Ginger Zhe Jin, Naresh Kumar, and

Guang Shi. 2013. “Gaming in Air Pollution Data? Lessons from China.” National Bureau of Economic Research Working Paper 18729. Clay, Karen, Werner Troesken, and Michael R. Haines.

2010. “Lead, Mortality, and Productivity.” National Bureau of Economic Research Working Paper 16480. Clougherty, Jane E., et al. 2009. “The New York City

Community Air Survey: Results from Winter Moni

toring 2008–2009.” http://www.nyc.gov/html/doh/ downloads/pdf/eode/comm-air-survey-report-win

ter08-09.pdf. Courant, Paul N., and Richard C. Porter. 1981. “Avert

ing Expenditure and the Cost of Pollution.” Journal of Environmental Economics and Management 8 (4): 321–29. Crocker, Thomas D., and Robert L. Horst, Jr. 1981.

“Hours of Work, Labor Productivity, and Environ

mental Conditions: A Case Study.” Review of Eco

nomics and Statistics 63 (3): 361–68. Cropper, Maureen L., and A. Myrick Freeman, III.

1991. “Environmental Health Effects.” In Measur

ing the Demand for Environmental Quality , edited by John B. Braden and Charles D. Kolstad, 165–211. Amsterdam; Oxford and Tokyo: North-Holland. Cropper, Maureen L., and Wallace E. Oates. 1992.

“Environmental Economics: A Survey.” Journal of Economic Literature 30 (2): 675–740. Cunha, Flavio, and James Heckman. 2007. “The Tech

nology of Skill Formation.” American Economic Review 97 (2): 31–47. Cunningham, F. Gary, Kenneth J. Leveno, Steven L.

Bloom, John C. Hauth, Dwight J. Rouse, and Cath

erine Y. Spong. 2010. “Fetal Growth and Develop

ment.” In Williams Obstetrics , edited by F. Gary Cunningham et al., 78–106. New York and Chicago: McGraw Hill. Currie, Janet. 2011. “Inequality at Birth: Some Causes

and Consequences.” American Economic Review 101 (3): 1–22. Currie, Janet, Lucas Davis, Michael Greenstone, and

Reed Walker. 2013. “Do Housing Prices Re 2 ect Environmental Health Risks? Evidence from More than 1600 Toxic Plant Openings and Closings.” National Bureau of Economic Research Working Paper 18700. Currie, Janet, Joshua S. Graff Zivin, Katherine Meckel,

Matthew Neidell, and Wolfram Schlenker. 2013. “Something in the Water: Contaminated Drinking Water and Infant Health.” National Bureau of Eco

nomic Research Working Paper 18876. Currie, Janet, Eric A. Hanushek, E. Megan Kahn, Mat

thew Neidell, and Steven G. Rivkin. 2009. “Does Pollution Increase School Absences?” Review of Eco

nomics and Statistics 91 (4): 682–94. Currie, Janet, and Rosemary Hyson. 1999. “Is the

Impact of Health Shocks Cushioned by Socioeco

nomic Status? The Case of Low Birthweight.” Ameri

can Economic Review 89 (2): 245–50. Currie, Janet, and Brigitte C. Madrian. 1999. “Health,

Health Insurance and the Labor Market.” In Hand

book of Labor Economics: Volume 3C , edited by Orley Ashenfelter and David Card, 3309–3416. Amsterdam; New York and Oxford: Elsevier Science, North-Holland. Currie, Janet, and Matthew Neidell. 2005. “Air Pollu

tion and Infant Health: What Can We Learn from California’s Recent Experience?” Quarterly Journal of Economics 120 (3): 1003–30. Currie, Janet, Matthew Neidell, and Johannes F.

Schmieder. 2009. “Air Pollution and Infant Health: Lessons from New Jersey.” Journal of Health Eco

nomics 28 (3): 688–703. Currie, Janet, and Johannes F. Schmieder. 2009. “Fetal

Exposures to Toxic Releases and Infant Health.” American Economic Review 99 (2): 177–83. Currie, Janet, and Mark Stabile. 2006. “Child Mental

Health and Human Capital Accumulation: The Case of ADHD.” Journal of Health Economics 25 (6): 1094–1118. Currie, Janet, and Duncan Thomas. 2001. “Early Test

Scores, School Quality and SES: Longrun Effects on Wage and Employment Outcomes.” In Worker Well

being in a Changing Labor Market , edited by Solo

mon W. Polachek, 103–32. Amsterdam; London and727 Graff Zivin and Neidell: Environment, Health, and Human Capital

New York: Elsevier Science, JAI. Currie, Janet, and Reed Walker. 2011. “Traf 0 c Conges

tion and Infant Health: Evidence from E-ZPass.” American Economic Journal: Applied Economics 3 (1): 65–90. Cutler, David M. 2007. “The Lifetime Costs and Ben

e 0 ts of Medical Technology.” Journal of Health Eco

nomics 26 (6): 1081–1100.Cutler, David M., Angus Deaton, and Adriana LlerasMuney. 2006. “The Determinants of Mortality.” Journal of Economic Perspectives 20 (3): 97–120. Dasgupta, Susmita, Benoit Laplante, Hua Wang, and

David Wheeler. 2002. “Confronting the Environ

mental Kuznets Curve.” Journal of Economic Per

spectives 16 (1): 147–68. Davidson, Cliff I., Wei Tang, Susan Finger, Vicken

Etyemezian, Mary F. Striegel, and Susan I. Sher

wood. 2000. “Soiling Patterns on a Tall Limestone Building: Changes over 60 Years.” Environmental Science and Technology 34 (4): 560–65.deGraaf-Peters, Victorine B., and Mijna HaddersAlgra. 2006. “Ontogeny of the Human Central Ner

vous System: What Is Happening When?” Early Human Development 82 (4): 257–66. DeLeire, Thomas, Shakeeb Khan, and Christopher

Timmins. 2013. “Roy Model Sorting and Nonrandom Selection in the Valuation of a Statistical Life.” Inter

national Economic Review 54 (1): 279–306. Deschenes, Olivier. 2012. “Temperature, Human

Health, and Adaptation: A Review of the Empirical Literature.” National Bureau of Economic Research Working Paper 18345. Deschenes, Olivier, and Michael Greenstone. 2011.

“Climate Change, Mortality, and Adaptation: Evi

dence from Annual Fluctuations in Weather in the US.” American Economic Journal: Applied Econom

ics 3 (4): 152–85. Deschenes, Olivier, Michael Greenstone, and Joseph

S. Shapiro. 2012. “Defensive Investments and the Demand for Air Quality: Evidence from the NOx Budget Program and Ozone Reductions.” National Bureau of Economic Research Working Paper 18267. Dimeo, M. J., M. G. Glenn, M. J. Holtzman, J. R.

Sheller, J. A. Nadel, and H. A. Boushey. 1981. “Threshold Concentration of Ozone Causing an Increase in Bronchial Reactivity in Humans and Adaptation with Repeated Exposures.” American Review of Respiratory Disease 124 (3): 245–48. Dominici, Francesca, Michael Daniels, Scott L. Zeger,

and Jonathan M. Samet. 2002. “Air Pollution andMortality: Estimating Regional and National DoseResponse Relationships.” Journal of the American Statistical Association 97 (457): 100–111. Dominici, Francesca, Jonathan M. Samet, and Scott L.

Zeger. 2000. “Combining Evidence on Air Pollution and Daily Mortality from the 20 Largest US Cities: A Hierarchical Modelling Strategy.” Journal of the Royal Statistical Society: Series A (Statistics in Soci

ety) 163 (3): 263–302. Dominici, Francesca, C. Wang, C. Crainiceanu, and

G. Parmigiani. 2008. “Model Selection and Health Effect Estimation in Environmental Epidemiology.” Epedemiology 19 (4): 558–60. Donald, Stephen G., and Kevin Lang. 2007. “Inference

with Difference-in-Differences and Other Panel Data.” Review of Economics and Statistics 89 (2): 221–33. Ebenstein, Avraham. 2012. “The Consequences of

Industrialization: Evidence from Water Pollution and Digestive Cancers in China.” Review of Econom

ics and Statistics 94 (1): 186–201. Finkelstein, Amy, et al. 2011. “The Oregon Health

Insurance Experiment: Evidence from the First Year.” National Bureau of Economic Research Work

ing Paper 17190. Firket, J. 1936. “Fog along the Meuse Valley.” Transac

tions of the Faraday Society 32: 1192–97. Folinsbee, Lawrence J., William F. McDonnell, and

Donald H. Horstman. 1988. “Pulmonary Function and Symptom Responses after 6.6-Hour Exposure to 0.12 ppm Ozone with Moderate Exercise.” Journal of the Air and Waste Management Association 38 (1): 28–35. Goldberg, Aaron D., C. David Allis, and Emily Ber

nstein. 2007. “Epigenetics: A Landscape Takes Shape.” Cell 128 (4): 635–38. Graff Zivin, Joshua, and Matthew Neidell. 2009. “Days

of Haze: Environmental Information Disclosure and Intertemporal Avoidance Behavior.” Journal of Environmental Economics and Management 58 (2): 119–28. Graff Zivin, Joshua, and Matthew Neidell. 2012. “The

Impact of Pollution on Worker Productivity.” Ameri

can Economic Review 102 (7): 3652–73. Graff Zivin, Joshua, Matthew Neidell, and Wolfram

Schlenker. 2011. “Water Quality Violations and Avoidance Behavior: Evidence from Bottled Water Consumption.” American Economic Review 101 (3): 448–53. Graff Zivin, Joshua, and David Zilberman. 2002. “Opti

mal Environmental Health Regulations with Hetero

geneous Populations: Treatment versus ‘Tagging’.” Journal of Environmental Economics and Manage

ment 43 (3): 455–76. Greenburg, Leonard, Morris B. Jacobs, Bernadette

M. Drolette, Franklyn Field, and M. M. Braverman. 1962. “Report of an Air Pollution Incident in New York City, November 1953.” Public Health Reports 77 (1): 7–16. Grossman, Michael. 1972. “On the Concept of Health

Capital and the Demand for Health.” Journal of Political Economy 80 (2): 223–55. Grossman, Michael, and Robert Kaestner. 1997.

“Effects of Education on Health.” In The Social Ben

e ! ts of Education , edited by Jere R. Behrman and Nevzer Stacey, 69–123. Ann Arbor: University of Michigan Press. Hanna, Rema, and Paulina Oliva. 2011. “The Effect of

Pollution on Labor Supply: Evidence from a Natu

ral Experiment in Mexico City.” National Bureau of Economic Research Working Paper 17302. Harrington, Winston, and Paul R. Portney. 1987.Journal of Economic Literature, Vol. LI (September 2013) 728

“Valuing the Bene 0 ts of Health and Safety Regula

tion.” Journal of Urban Economics 22 (1): 101–12. Hazucha, Milan J., Lawrence J. Folinsbee, and Philip

A. Bromberg. 2003. “Distribution and Reproducibil

ity of Spirometric Response to Ozone By Gender and Age.” Journal of Applied Physiology 95 (5): 1917–25. Huff, James, and Joseph Haseman. 1991. “Long-Term

Chemical Carcinogenesis Experiments for Iden

tifying Potential Human Cancer Hazards: Collec

tive Database of the National Cancer Institute and National Toxicology Program (1976–1991).” Envi

ronmental Health Perspectives 96: 23–31. International Labour Organization. 2011. Global

Employment Trends 2011: The Challenge of a Jobs Recovery . Geneva: International Labour Of 0 ce. Janes, Holly, Francesca Dominici, and Scott L. Zeger.

2007. “Trends in Air Pollution and Mortality: An Approach to the Assessment of Unmeasured Con

founding.” Epidemiology 18 (4): 416–23.Jayachandran, Seema. 2009. “Air Quality and EarlyLife Mortality: Evidence from Indonesia’s Wild 0 res.” Journal of Human Resources 44 (4): 916–54. Jerrett, Michael, et al. 2005. “A Review and Evaluation

of Intraurban Air Pollution Exposure Models.” Jour

nal of Exposure Science and Environmental Epide

miology 15: 185–204. Jerrett, Michael, et al. 2009. “Long-Term Ozone Expo

sure and Mortality.” New England Journal of Medi

cine 360: 1085–95. Jones, N. C., C. A. Thornton, D. Mark, and R. M.

Harrison. 2000. “Indoor/Outdoor Relationships of Particulate Matter in Domestic Homes with Road

side, Urban and Rural Locations.” Atmospheric Environment 34 (16): 2603–12. Joyce, Theodore J., Michael Grossman, and Fred

Goldman. 1989. “An Assessment of the Bene 0 ts of Air Pollution Control: The Case of Infant Health.” Journal of Urban Economics 25 (1): 32–51. Kampa, Marilena, and Elias Castanas. 2008. “Human

Health Effects of Air Pollution.” Environmental Pol

lution 151 (2): 362–67. Kaufman, Joel D., et al. 2012. “Prospective Study of

Particulate Air Pollution Exposures, Subclinical Ath

erosclerosis, and Clinical Cardiovascular Disease: The Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).” American Journal of Epide

miology 176 (9): 825–37. Knittel, Christopher R., Douglas L. Miller, and Nicho

las J. Sanders. 2011. “Caution, Drivers! Children Present: Traf 0 c, Pollution, and Infant Health.” National Bureau of Economic Research Working Paper 17222. Lavy, Victor, Avraham Ebenstein, and Se 0 Roth.

2012. “The Impact of Air Pollution on Cognitive Performance and Human Capital Formation.” Unpublished. Lefohn, Allen S., Milan J. Hazucha, Douglas Shadwick,

and William C. Adams. 2010. “An Alternative Form and Level of the Human Health Ozone Standard.” Inhalation Toxicology 22 (12): 999–1011. Le Tertre, A., et al. 2002. “Short-Term Effects of Particulate Air Pollution on Cardiovascular Diseases in Eight European Cities.” Journal of Epidemiol Community Health 56 (10): 773–79. Lichtenberg, Erik, and David Zilberman. 1988. “Ef 0

- cient Regulation of Environmental Health Risks.” Quarterly Journal of Economics 103 (1): 167–78. Lichtenberg, Frank R. 2007. “The Impact of New

Drugs on US Longevity and Medical Expenditure,1990–2003: Evidence from Longitudinal, DiseaseLevel Data.” American Economic Review 97 (2): 438–43. Lleras-Muney, Adriana. 2010. “The Needs of the Army:

Using Compulsory Relocation in the Military to Estimate the Effect of Air Pollutants on Children’s Health.” Journal of Human Resources 45 (3): 549–90. Logan, W. P. D., and M. D. Glasg. 1953. “Mortality in

the London Fog Incident, 1952.” Lancet 261 (6755): 336–38. Madajewicz, Malgosia, et al. 2007. “Can Information

Alone Change Behavior? Response to Arsenic Con

tamination of Groundwater in Bangladesh.” Journal of Development Economics 84 (2): 731–54. Magat, Wesley A., and W. Kip Viscusi. 1992. Informa

tional Approaches to Regulation

. Cambridge and London: MIT Press. Manning, Willard G., Joseph P. Newhouse, Naihua

Duan, Emmett B. Keeler, Arleen Leibowitz, and M. Susan Marquis. 1987. “Health Insurance and the Demand for Medical Care: Evidence from a Ran

domized Experiment.” American Economic Review 77 (3): 251–77. Martin, Randall V. 2008. “Satellite Remote Sensing of

Surface Air Quality.” Atmospheric Environment 42 (34): 7823–43. McClellan, Mark. 1998. “Health Events, Health Insur

ance, and Labor Supply: Evidence from the Health and Retirement Survey.” In Frontiers in the Econom

ics of Aging , edited by David A. Wise, 301–46. Chi

cago and London: University of Chicago Press. Miller, Kristin A., David S. Siscovick, Lianne Sheppard,

Kristen Shepherd, Jeffrey H. Sullivan, Garnet L. Anderson, and Joel D. Kaufman. 2007. “Long-Term Exposure to Air Pollution and Incidence of Cardio

vascular Events in Women.” New England Journal of Medicine 356: 447–58. Moretti, Enrico, and Matthew Neidell. 2011. “Pol

lution, Health, and Avoidance Behavior: Evidence from the Ports of Los Angeles.” Journal of Human Resources 46 (1): 154–75. Moulton, Brent R. 1986. “Random Group Effects and

the Precision of Regression Estimates.” Journal of Econometrics 32 (3): 385–97. Neidell, Matthew. 2009. “Information, Avoid

ance Behavior, and Health: The Effect of Ozone on Asthma Hospitalizations.” Journal of Human Resources 44 (2): 450–78. Nelson, R. R., and E. S. Phelps. 1966. “Investment in

Humans, Technological Diffusion, and Economic Growth.” American Economic Review 56 (1–2): 69–75. Nilsson, J. Peter. 2009. “The Long-Term Effects of729 Graff Zivin and Neidell: Environment, Health, and Human Capital

Early Childhood Lead Exposure: Evidence from the Phase-Out of Leaded Gasoline.” Unpublished. Oreopoulos, Philip, Mark Stabile, Randy Walld, and

Leslie L. Roos. 2008. “Short-, Medium-, and LongTerm Consequences of Poor Infant Health: An Anal

ysis Using Siblings and Twins.” Journal of Human Resources 43 (1): 88–138. Pauly, Mark V., Sean Nicholson, Daniel Polsky, Marc

L. Berger, and Claire Sharda. 2008. “Valuing Reduc

tions in On-the-Job Illness: ‘Presenteeism’ from Managerial and Economic Perspectives.” Health Economics 17 (4): 469–85. Pedersen, Carsten Bøcker, Ole Raaschou-Nielsen, Ole

Hertel, and Preben Bo Mortensen. 2004. “Air Pol

lution from Traf 0 c and Schizophrenia Risk.” Schizo

phrenia Research 66 (1): 83–85. Peng, Roger D., Francesca Dominici, and Thomas A.

Louis. 2006. “Model Choice in Time Series Studies of Air Pollution and Mortality.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 169 (2): 179–98. Perera, Frederica P., et al. 2012. “Prenatal Polycyclic

Aromatic Hydrocarbon (PAH) Exposure and Child Behavior at Age 6–7 Years.” Environmental Health Perspectives 120 (6): 921–26. Pope, C. Arden. 1989. “Respiratory Disease Associated

with Community Air Pollution and a Steel Mill, Utah Valley.” American Journal of Public Health 79 (5): 623–28. Pope, C. Arden, Jaron C. Hansen, Roman Kuprov,

Matthew D. Sanders, Michael N. Anderson, and Delbert J. Eatough. 2011. “Vascular Function and Short-Term Exposure to Fine Particulate Air Pol

lution.” Journal of the Air and Waste Management Association 61 (8): 858–63. Pope, C. Arden, Richard T. Burnett, Michael J. Thun,

Eugenia E. Calle, Daniel Krewski, Kazuhiko Ito, and George D. Thurston. 2002. “Lung Cancer, Cardio

pulmonary Mortality, and Long-Term Exposure to Fine Particulate Air Pollution.” Journal of the Ameri

can Medical Association 287 (9): 1132–41. Pottenger, L. H., M. R. Schisler, F. Zhang, M. J. Bartels,

D. D. Fontaine, L. G. McFadden, and Bhaskar Gollapudi B. 2009. “Dose-Response and Operational Thresholds/NOAELs for In Vitro Mutagenic Effects from DNA-Reactive Mutagens, MMS and MNU.” Mutation Research 678 (2): 138–47. Ransom, Michael R. and C. Arden Pope. 1992. “Ele

mentary School Absences and PM 10 Pollution in Utah Valley.” Environmental Research 58 (1–2): 204–19. Ransom, Michael L., and C. Arden Pope. 1995. “Exter

nal Health Costs of a Steel Mill.” Contemporary Eco

nomic Policy 13 (2): 86–97. Rawski, Thomas G. 2009. “Urban Air Quality in China:

Historical and Comparative Perspectives.” In Resur

gent China: Issues for the Future , edited by Nazrul Islam, 353–70. New York: St. Martin’s Press, Palgrave Macmillan. Reyes, Jessica Wolpaw. 2007. “Environmental Policy as

Social Policy? The Impact of Childhood Lead Expo

sure on Crime.” B.E. Journal of Economic Analysis and Policy: Contributions to Economic Analysis and Policy 7 (1). Roback, Jennifer. 1982. “Wages, Rents, and the Qual

ity of Life.” Journal of Political Economy 90 (6): 1257–78. Rojas-Martinez, Rosalba, et al. 2007. “Lung Function

Growth in Children with Long-Term Exposure to Air Pollutants in Mexico City.” American Journal of Respiratory and Critical Care Medicine 176 (4): 377–84.Romer, Paul M. 1986. “Increasing Returns and LongRun Growth.” Journal of Political Economy 94 (5): 1002–37. Royer, Heather. 2009. “Separated at Girth: US Twin

Estimates of the Effects of Birth Weight.” American Economic Journal: Applied Economics 1 (1): 49–85. Sacks, Jason D., Kazuhiko Ito, William E. Wilson, and

Lucas M. Neas. 2012. “Impact of Covariate Models on the Assessment of the Air Pollution–Mortality Association in a Single- and Multipollutant Context.” American Journal of Epidemiology 176 (7): 622–34. Salam, Muhammad T., Joshua Millstein, Yu-Fen Li,

Frederick W. Lurmann, Helene G. Margolis, and Frank D. Gilliland. 2005. “Birth Outcomes and Prenatal Exposure to Ozone, Carbon Monoxide, and Particulate Matter: Results from the Children’s Health Study.” Environmental Health Perspectives 113 (11): 1638–44. Sanders, Nicholas J. 2012. “What Doesn’t Kill You

Makes You Weaker: Prenatal Pollution Exposure and Educational Outcomes.” Journal of Human Resources 47 (3): 826–50. Sanders, Nicholas J., and Charles F. Stoecker. 2011.

“Where Have All the Young Men Gone? Using Gen

der Ratios to Measure Fetal Death Rates.” National Bureau of Economic Research Working Paper 17434. Schlenker, Wolfram, and W. Reed Walker. 2011. “Air

ports, Air Pollution, and Contemporaneous Health.” National Bureau of Economic Research Working Paper 17684. Schultz, T. W. 1961. “Investment in Human Capital.”

American Economic Review 51 (1): 1–17. Schwartz, Joel. 2004. “Air Pollution and Children’s

Health.” Pediatrics 113 (Supplement 4): 1037–43. Seigneur, Christian, Krish Vijayaraghavan, Kristen

Lohman, Prakash Karamchandani, and Courtney Scott. 2004. “Global Source Attribution for Mercury Deposition in the United States.” Environmental Sci

ence and Technology 38 (2): 555–69. Sen, Amartya. 1992. “Missing Women.” British Medical

Journal 304: 587–88. Shimshack, Jay P., Michael B. Ward, and Timothy K.

M. Beatty. 2007. “Mercury Advisories: Informa

tion, Education, and Fish Consumption.” Journal of Environmental Economics and Management 53 (2): 158–79. Singh, Sonal, Yoon K. Loke, Paul L Enright, and

Curt D. Furberg. 2011. “Mortality Associated with Tiotropium Mist Inhaler in Patients with Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-analysis of Randomised Controlled Trials.”Journal of Economic Literature, Vol. LI (September 2013) 730

British Medical Journal 342: D3215. Skerfving, Staffan, and Ingvar A. Bergdahl. 2007.

“Lead.” In Handbook on the Toxicology of Metals

, Third edition, edited by Gunnar F. Nordberg, Bruce A. Fowler, Monica Nordberg, and Lars Friberg, 599–643. San Diego and London: Elsevier, Academic Press. Smedley, Brian D., Adrienne Y. Stith, and Alan R.

Nelson, eds. 2002. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care . Wash

ington, D.C.: National Academies Press. Smith, James P. 2005. “Consequences and Predictors of

New Health Events.” In Analyses in the Economics of Aging , edited by David A. Wise, 213–37. Chicago: University of Chicago Press. Smith, Kirk R., and Jennifer L. Peel. 2010. “Mind the

Gap.” Environmental Health Perspectives 118 (12): 1643–45.Stavins, Robert N. 2003. “Experience with MarketBased Environmental Policy Instruments.” In Handbook of Environmental Economics: Volume 1: Environmental Degradation and Institutional Responses , edited by Karl-G ö ran Mäler and Jeffrey R. Vincent, 355–435. Amsterdam and Boston: Elsevier. Stoeger, Tobias, et al. 2006. “Instillation of Six Differ

ent Ultra 0 ne Carbon Particles Indicates a Surface Area Threshold Dose for Acute Lung In 2 ammation in Mice.” Environmental Health Perspectives 114 (3): 328–33. Strauss, John, and Duncan Thomas. 1998. “Health, Nutrition, and Economic Development.” Journal of Economic Literature 36 (2): 766–817. Tiebout, C. M. 1956. “A Pure Theory of Local Expen

ditures.” Journal of Political Economy 64 (5): 416–24. Tonne, Cathryn C., Robin M. Whyatt, David E.

Camann, Frederica P. Perera, and Patrick L. Kinney. 2004. “Predictors of Personal Polycyclic Aromatic Hydrocarbon Exposures among Pregnant Minority Women in New York City.” Environmental Health Perspectives 112 (6): 754–59. Townsend, James G. 1950. “Investigation of the Smog

Incident in Donora, Pa., and Vicinity.” American Journal of Public Health and Nation’s Health 40 (2): 183–89. Trivers, Robert L., and Dan E. Willard. 1973. “Natural

Selection of Parental Ability to Vary the Sex Ratio of Offspring.” Science 179 (4068): 90–92. Troesken, Werner. 2008. “Lead Water Pipes and Infant

Mortality at the Turn of the Twentieth Century.” Journal of Human Resources 43 (3): 553–75. Vahlsing, Candace, and Kirk R. Smith. 2012. “Global

Review of National Ambient Air Quality Standards for PM 10 and SO 2 .” Air Quality, Atmosphere and Health 5 (4): 393–99. Volk, Heather E., Fred Lurmann, Bryan Penfold,

Irva Hertz-Picciotto, Rob McConnell. 2012. “Traf

0 c-Related Air Pollution, Particulate Matter, and Autism.” JAMA Psychiatry 70 (1): 71–77. Zweig, Jacqueline S., John C. Ham, and Edward L. Avol.

2009. “Air Pollution and Academic Performance: Evidence from California Schools.” Unpublished.