Journal of Economic Literature 2013, 51(3), 689–730

http://dx.doi.org/10.1257/jel.51.3.689

689

1. Introduction

The recognition that environmental factors 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 considerably. 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 pollution is a canonical example of both externalities 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†

Joshua Graff Zivin and Matthew Neidell*

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 suggestions 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).
690 Journal of Economic Literature, Vol. LI (September 2013)

of human health, with signi0cant impacts on 

the welfare of both producers and consumers around the globe.2

Historically, much of our understanding 

about this relationship between the environment and health comes from the health 

sciences literature. The 0eld of toxicology 

uses controlled settings akin to randomized 

experiments to study the adverse effects of 

environmental stressors. While the controlled setting allows researchers to isolate 

biological impacts, ethical concerns over 

providing humans with known poisons generally leads to the use of studies based on the 

dosing of animal subjects, which provides 

limited external validity for the policy making context at hand. When the contaminant 

can be delivered in a suf0ciently 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 impairment, 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 thousands of investigations on the relationship 

between pollution and health in the health 

sciences—a search on PubMed for “pollution” and “health” revealed 25,754 publications—what can economists add to an 

already crowded 0eld? In this essay, we 

2 The U.S. Environmental Protection Agency, for example, describes its mission as ensuring that “all Americans 

are protected from signi0cant 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 optimizing 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 estimates 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 estimates 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 activities 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 morbidity 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 assignment of pollution. 
Graff Zivin and Neidell: Environment, Health, and Human Capital 691

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 examining 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 pervasiveness 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 relative 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 environmental protection as a national investment, 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 scienti0c 

background section followed by a conceptual 

model of the environmental health production function and its implications for estimation and optimal policy design. Section 4 

highlights the primary challenges to empirical economic research in this area. In section 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 scienti0c literature on the environmental health risk generation process typically 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 contact with the contaminant. The dose-response

function translates a given human exposure to pollution into a physiological health 

response. Since each element is treated as 

independent and quasi-exogenous within 

this framework, its direct application in economics has generally been limited to theoretical examinations of optimal regulation when 

individual behaviors are assumed 0xed 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 brie2y 

review some salient scienti0c 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 temperature extremes can be found in Deschenes (2012). 
692 Journal of Economic Literature, Vol. LI (September 2013)

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 pollutants that are commonly found all over the 

United States, which include carbon monoxide, sulfur dioxide, nitrogen oxide, ozone, 

lead, and particulate matter. Drinking water 

regulations also set standards for approximately 100 contaminants. The list of regulated hazardous wastes that despoil land is 

even longer.6

 While pollutants can be attributed to many different sources, a considerable amount of pollution can be traced to 

industrial processes, electricity generation, 

and the transportation sector. 

Two features of contamination are particularly important for economic analyses. First, 

many pollutants form as the result of interaction with other environmental variables. For 

example, ozone pollution is not directly emitted, but rather forms as the result of complex 

interactions between two other emitted pollutants—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 

in2uence 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 sizable fraction of the mercury contamination 

in the Western United States originates at 

coal-0red 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 pollutants. In either case, meteorological conditions can affect deposition: rain can “clean” 

the air and 2ush 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 control for as a confounder.

2.2 Exposure

The existence of pollution is only a problem from a human health perspective if 

people are exposed to the pollutant. The 

relevant measure of exposure, and thus 

the appropriate identi0cation strategy for 

empirical research, will depend on the contaminant of interest. For some pollutants, 

acute exposure is suf0cient 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 taking actions to limit their exposure to it. This 
Graff Zivin and Neidell: Environment, Health, and Human Capital 693

avoidance behavior drives a wedge between 

“potential” exposure—ambient levels of pollution 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 exposure 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 incidental 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 pollution 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 experienced 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 concern 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 threshold 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 signature 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-individual heterogeneity in responses to a given 

dose of pollution. For example, children 

tend to be more vulnerable because limitations in their immune system and partial lung 

development make it dif0cult 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 respiratory 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. 
694 Journal of Economic Literature, Vol. LI (September 2013)

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 de0ned 

responses in terms of physical health outcomes, it is plausible that many pollutants 

generate nonhealth sequelae of interest to 

economists. Poor health as a result of pollution exposure can contemporaneously 

impact earnings by increasing worker absenteeism and diminishing worker productivity 

for adults and school absenteeism and performance for children. It can also reduce 

earnings in the long run by limiting human 

capital formation, through both direct channels (via neurological insults) and indirect 

ones (via subsequent investments and skill 

formation). Estimation of these effects represents 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 re2ect the fact that health can in2uence 

labor productivity, with one signi0cant 

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 0xed. 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 consumption 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 

0ltration, as well as the disutility associated 

with reallocations of time across activities 

that constitute part of the avoidance behavior.10 In contrast, medical care consumption takes place after exposure is realized in 

response to an illness episode. Medical treatment costs include direct healthcare costs 

(such as doctor visits and the use of medications) 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 avoidance 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 distinction is discussed in greater detail in section 4.3.
Graff Zivin and Neidell: Environment, Health, and Human Capital 695

While we eschew the complexity of a 

formal dynamic model, we reexpress the 

health production function in a nonconventional form to better re2ect these features 

as well as to draw connections between several strands of empirical literature in environment, 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 pollution 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, individual 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 productivity is presumed to increase in health at 

a decreasing rate. Importantly, since individuals 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 simpli0ed 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,

cj

 denote the price of good j,

12 and T the 

total time endowment, the individual’s utility maximization problem can be expressed 

as

(3) max

X,L,A,M

 = U(X, L, H)

+ λ [I + w(H)[T − L]

− cX X − cAA − cM M].

The 0rst order conditions are

(4) _∂

∂X = _∂U

∂X − λcX = 0

(5) _∂

∂L = _∂U

∂L − λw = 0

(6) _∂

∂A = 

_∂U

∂H (_∂H∂M 

_∂M

∂φ 

_∂φ

∂A + 

_∂H

∂φ 

_∂φ

∂A )

−λ( cA + 

_∂w

∂H ( 

_∂H

∂M 

_∂M

∂φ 

_∂φ

∂A + _∂H

∂φ 

_∂φ

∂A 

)

× [T − L]) = 0

(7) _∂

∂M = 

_∂U

∂H _∂U∂M − λ(cM + 

_∂U

∂H _∂H∂M [T − L])

= 0.

Equations (4) and (5) highlight the standard trade-offs between labor and leisure.

those behaviors are equal to the marginal utility gain associated with the extra earnings due to avoidance and medical treatment. 12 For simplicity, we assume that the costs of avoidance 

are all market-based, e.g., the purchase of air 0lters. As 

discussed in section 4.3, activity reallocations are another 

form of avoidance behavior. The costs of nonmarket behavioral responses should be captured by the utility foregone 

due to this reallocation.
696 Journal of Economic Literature, Vol. LI (September 2013)

Equations (6) and (7) can be combined to 

yield the following intuitive expression:

(8)

(_dH

dA 

_

)

(_dH

dM )

 = _

cA

cM

 .

Avoidance behavior and medical treatment 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, equations (4)–(7) implicitly de0ne avoidance 

behavior and medical treatment as a function of all exogenous variables: A(P,φ, cj

) and 

M(P,φ, cj

) for all j. Optimal avoidance behavior 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 consumption goods. 

Since avoidance behavior and medical 

care consumption depend on ambient pollution 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

dφ

_dφ

dP

The reduced form effect of pollution 

on population health depends on two distinct 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 0rst 

parenthetical expression). We begin with a 

breakdown of the second expression. The 

0rst term (δφ/δP) describes the pure biological effect of pollution, while the second 

term (δφ/δA*δA/δP) describes the role 

of avoidance behavior in averting illness 

episodes by limiting contact with pollutants. Thus, the entire second parenthetical 

expression (dφ/dP) describes the net, or 

reduced form, effect of pollution on illness 

episodes based on individual exposure levels. Importantly, it is possible to observe no 

change in illness despite the existence of a 

biological effect if avoidance behavior is suf0ciently productive. On the other hand, if 

avoidance behavior is not possible or ineffective, the biological and reduced form effects 

will be identical.13

The 0rst expression also has two components. The 0rst term (δH/δM*δM/δφ)

describes the degree to which medical treatment, a postexposure intervention, ameliorates the negative effects of pollution on 

health. The second term (δH/δφ) describes 

how health responds to illness, which re2ects 

the degree to which pollution-induced illness episodes are not treated, either because 

the condition is untreatable or because individuals do not seek treatment for it. Clearly 

this 0nal 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 environmental 0eld but also in labor and health 

economics, in a uni0ed framework grounded 

in basic economic theory. We will refer back 

to it as we review the contributions and limitations of the relevant empirical literature 

throughout the remainder of this paper.

13 Avoidance behavior may be unproductive if a pollutant can’t be avoided despite defensive actions. For example, while going indoors greatly reduces ozone exposure 

because it rapidly breaks down indoors, the penetration of 

0ne particular matter indoors can be as high as 80 percent 

(Jones, Mark, and Harrison 2000).
Graff Zivin and Neidell: Environment, Health, and Human Capital 697

This basic model also yields results that 

can serve as a guide for policy. Optimal regulation requires policy choices that balance 

the costs and bene0ts of regulation designed 

to reduce pollution levels in order to maximize 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 cR, 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, avoidance, and medical costs associated with that 

marginal reduction in pollution:

(10) _∂P

∂R

 cR = _∂w

∂H

 _dH

dP + _∂U

∂H 

_dH

dP 

_1

λ

 

+ _∂A

∂P

 cA + _∂M

∂P

 cM .

The costs of abatement technologies and 

their impacts on pollution levels are frequently derived from engineering as well 

as economic studies. Estimates of the bene0ts 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 0rst term re2ects 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 estimating WTP. Moreover, even if avoidance/

amelioration fully insulates one from negative 

health effects, abatement may still be optimal 

if its marginal costs are suf0ciently 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 0eld. 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 standards. The 0rst signi0cant hurdle is obtaining 

environmental data on a suf0cient 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 estimating 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 environmental 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 inventory (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.
698 Journal of Economic Literature, Vol. LI (September 2013)

Ambient air pollution monitors typically 

measure pollution concentrations at very 

high frequencies, such as hourly, at a !xed 

location. While this frequency of measurement generates data at a !ne temporal scale, 

the limited number of monitor locations 

relative to the size of a country and the geographic distribution of the population generates data that is rather coarse on a spatial 

scale, even in the most highly monitored 

areas. Furthermore, since these data are typically 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 monitor environmental quality. Several limitations 

make 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 developed countries began monitoring environmental 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, however, 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 pollution 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 historical levels in the U.S. Contemporary pollution levels in China and Mexico are similar to 

those found in Pittsburgh in the mid-1970s 

and mid-1990s, respectively. As such, studies 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 versions 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 subject to manipulation, evidence also indicates that reported 

pollution levels are highly correlated with data from independent 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.
Graff Zivin and Neidell: Environment, Health, and Human Capital 699

to geographic identi!ers as well as the 

exact date of the birth, death, or hospital 

admission. As we describe in the following section, several studies have acquired 

administrative data sets with detailed geographic identi!ers to more precisely assign 

pollution exposure.

Once environmental and the relevant outcome 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 pollution, 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 vegetative 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 contained in large-scale data sets, studies often 

0

20

40

60

80

100

120

140

160

19511954195719601963196619691972197519781981198419871990199319961999200220052008

PM10(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.
700 Journal of Economic Literature, Vol. LI (September 2013)

approximate contemporaneous pollution 

levels based on an individual’s general location 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 measurement error will typically bias estimates 

downward, but with a large enough dataset researchers can use data from multiple 

monitors, various weighting techniques, 

and factors that affect the dispersion of pollution to obtain more precise assignments 

of localized pollution.21 A 0ner level of geographic disaggregation for individuals, such 

as a residential address, also allows for better 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 pollution, which we discuss below in 4.3), both 

within a day and over time, can also present a challenge for assigning potential exposure. 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, unrepresentative sample without a clearly de0ned control group; and 2) the link to policy is less 

clear because indoor sources also contribute to pollution, making it dif0cult to pin 

down the sources of pollution and the scope 

for regulation. Mobility over time also presents a signi0cant measurement challenge 

in assigning cumulative exposure over longer 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 variables 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 combined with windless conditions led to a sudden 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 pollution 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 evidence that high levels of pollution pose a signi0cant threat to human health. 

While high pollution levels may be relevant in developing countries, these extremes 

are dramatically higher than those faced 

by nearly all people in developed countries 

today (refer to 0gure 1). This is important 

because the processing of pollutants by the 

human 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 literature (e.g., Lefohn et al. 2010; Smith and 

Peel 2010). Indeed, thresholds below which 
Graff Zivin and Neidell: Environment, Health, and Human Capital 701

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 pollutants 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 important. It may be that mortality or hospital 

admissions are only induced when pollution 

exceeds a certain threshold, while more subtle 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, underscore the importance of studies throughout 

the pollution level spectrum in order to better 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 speci0ed in several 

ways. One approach is to specify thresholds 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 laboratory evidence on thresholds, though measurement error in assigning pollution may 

limit its effectiveness. The most 2exible but 

also data demanding approach is to de0ne 

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 in2ict symptoms in as quickly as 

1–2 hours—while some have a longer incubation period. Even more complicated, some 

have both immediate and delayed effects. 

Since we may not know which period of exposure is most important a priori, this is largely 

an empirical question. A distributed lag 

speci0cation, which allows for both contemporaneous and lagged exposure, allows for a 

2exible duration. Correlations in pollution 

values over short periods of time, however, 

can lead to multicollinearity, hampering our 

ability to precisely identify the coef0cients 

for speci0c 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 generally unimportant for policy, which typically uses rather blunt instruments to limit 

contamination and exposure at a broad level 

rather than on speci0c days. 

For examining long-run effects, analyses 

become increasingly complicated, particularly for understanding the impacts from 

cumulative exposure over a lifetime. In addition to individual mobility over time hampering 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 confound 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 any 
702 Journal of Economic Literature, Vol. LI (September 2013)

quasi-experimental evidence on the cumulative effects of pollution exposure.22

When focusing on birth outcomes, an area 

of intellectual inquiry that has grown tremendously in recent years, the relevant period of 

exposure is also important, albeit more from 

a developmental perspective than an environmental policy one (Salam et al. 2005). 

For instance, the 0rst 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 particularly vulnerable stage in terms of environmental insults. One complication when 

parsing exposure by trimester of pregnancy 

is that length of gestation can be affected by 

pollution, making the de0nition of each trimester, and thus the total in utero exposure, 

endogenous (Currie et al. 2013). More challenging, however, is that including multiple 

trimesters of pollution simultaneously can 

lead to severe multicollinearity, sometimes 

resulting in seemingly bene0cial 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 endogeneity has led to the use of quasi-experimental 

research designs that effectively eliminate 

(or signi0cantly reduce) this problem.

4.3.1 Residential Sorting

The major driver of endogeneity is residential sorting: individuals choose residential 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 elements 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 location, although the speci0c contents of a particular bundle vary by location. For example, 

urban areas may have worse air quality but 

better schools than rural areas, while suburban areas may have both better air quality 

and schools than inner cities. The key point 

is that optimizing individuals make tradeoffs along multiple dimensions based on the 

intensity of their preferences for each local 

attribute, which implies that the characteristics of the neighborhood in which individuals 

live, including pollution levels, are endogenously determined.24

23 For simplicity, we assume preferences over environmental quality solely because of health bene0ts, but the 

same basic intuition holds if we extend this to include preferences 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).
Graff Zivin and Neidell: Environment, Health, and Human Capital 703

Different levels of pollution exposure due 

to sorting can be driven by three factors. The 

0rst 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 factors lead to omitted-variable bias. Wealthier 

individuals might live in neighborhoods with 

better air quality (driven by preferences 

for local amenities correlated with air quality), and they also are likely to make other 

investments in their health that are dif0cult 

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 direction of bias introduced by this sort of endogeneity 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 0x ideas, imagine an extreme case where there are two 

types of individuals, nonasthmatics and asthmatics. 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 pollution, 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 pollution actually improved health. Although 

the stylized nature of this extreme case is 

unrealistic, it underscores one important 

mechanism through which sorting may hinder 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 suf0cient 

for illustrating our point. In each row, we 

present the mean and the coef0cient of each 

variable, where the coef0cient is obtained by 

regressing each variable on the Air Quality 

Index (AQI)—a summary measure of air 

quality across several pollutants—and dummies for each survey wave. For example, 

the 0rst row shows that roughly 77 percent 

of respondents have participated in some 

form of exercise in the past month. The estimate of –0.118 implies that, for each tenunit increase in the AQI, there is an 11.8 

percentage point drop in the rate of exercise. Consistent with sorting, we see that 

respondents of higher socioeconomic status 

and those with higher levels of health investments 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 factors, 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/.
704 Journal of Economic Literature, Vol. LI (September 2013)

Although a complicated and seemingly 

insurmountable empirical challenge, the 

main approach for tackling sorting is to 0nd 

“shocks” to air quality that push the market temporarily out of equilibrium, often 

accompanied by 0xed effects that hold 

other characteristics of the area constant. 

These shocks can be driven by air quality 

regulations, abrupt changes in industrial production (such as strikes and plant closings), 

or catastrophic events (such as temperature 

inversions or wild0res). Finding such shocks 

presents a major obstacle, and it is not surprising that many of the same shocks are 

used across studies. Controlling for other 

major changes that may accompany a shock 

TABLE 1

Evidence of Residential Sorting, 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 2u 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 coef0cient 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. 

**Signi0cant at the 1 percent level.

*Signi0cant at the 5 percent level.
Graff Zivin and Neidell: Environment, Health, and Human Capital 705

is also a challenge. For example, a plant closing that lowers pollution might lead to disruptions 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 provides a glimpse of this for ozone. The 0rst 

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 2uctuations, a point we return to in the next subsection.

June–September, 2010. Immediately evident, 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 consideration 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 precursors, and the highly nonlinear ozone formation process. Since weather is an important 

confounder, the second panel of 0gure 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) Adjusted

Ozone (ppm)
706 Journal of Economic Literature, Vol. LI (September 2013)

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 additional support to the notion that daily variation can be viewed as plausibly exogenous.

One concern with using such high frequency variation, however, is that daily 

changes in pollution may be less informative 

about possible impacts from new regulations, which lead to more permanent shifts 

in pollution. A second concern, and one 

that only arises when examining mortality 

impacts, is short-term mortality displacement, commonly referred to as “harvesting.” 

Mortality for an otherwise healthy individual 

represents a signi0cant 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 

0xed effects—an admittedly imperfect 

quasi-experiment that exploits the natural year-to-year 2uctuations in pollution, 

but one that again illustrates our principal 

point. The 0rst 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 misspeci0-

cation.27 The 0rst three columns present 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 0xed 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 signi0cant 

at conventional levels, supporting the surprising 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 levels of investment. In support of this, when we 

include 0xed effects to capture time invariant 

characteristics of MSAs, this odd 0nding disappears, shown in columns 4–6. These results 

illustrate the importance of 0xed effects even 

when a rich set of controls are available. 

Since 0xed effects over such a short time 

frame may have the unintended consequence of removing too much of the variation in pollution, we continue this example 

focusing on two self-reported outcomes 

plausibly affected by pollution. The 0rst 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 second focuses on self-reported general health 

status (panel 3), a measure of health capital (H). Repeating the same set of regressions, cross-sectional estimates for days of 

bad health are statistically insigni0cant and 

quite unstable; the addition of a handful of 

behavioral factors (exercise, smoking, and 

whether one had the 2u shot) halves the 

estimate. The 0xed effect estimates, however, are much more stable, considerably 

larger in magnitude and statistically signi0cant, suggesting ample variation across 

years for detecting changes in health 2ows. 

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 health 
Graff Zivin and Neidell: Environment, Health, and Human Capital 707

status has less variation than illness, we do 

not 0nd a statistically signi0cant relationship between AQI and being in poor, fair, or 

good health in any speci0cation. While this 

table highlights the potential strength from 

using 0xed 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 causing endogenous pollution exposure, omitted 

variable bias may also arise from concurrent 

changes in the environment. Of particular 

TABLE 2

Cross-Sectional and Fixed Effect Regression Estimates of the Relationship

between Pollution and Health, BRFSS 2001–06

Cross-sectional model MSA 0xed 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

R2 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

R2 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

R2 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 2u shot. “Other health” includes BMI based on selfreported height and weight.

**Signi0cant at the 1 percent level.

*Signi0cant at the 5 percent level.
708 Journal of Economic Literature, Vol. LI (September 2013)

concern is weather. As previously mentioned, 

weather interacts with some types of emissions 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 typically observable at the same or 0ner scale than 

pollution data, this challenge can be obviated 

through the careful control of relevant variables. 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 geography 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 understanding 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 traf0c, can affect 

multiple pollutants simultaneously, making the model unidenti0ed without further 

assumptions. Since meteorological conditions, 

such as wind speed and direction, interact 

with emissions to impact pollution formation and deposition, knowledge of this process 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, pollution, and meteorological conditions in order 

for this to improve identi0cation. 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 emissions, 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 individuals deliberately take to reduce their realized 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 pollution levels. Certain pollutants are observable 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 0ne 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 preferences for clean air can be viewed as a form of avoidance 

behavior, we distinguish it from this more temporary avoidance behavior because failing to account for the two different 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 pollution, such as home air or water 0lters that run constantly 

once purchased.
Graff Zivin and Neidell: Environment, Health, and Human Capital 709

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 dissemination, or through health feedbacks that 

allow individuals to infer it based on physiological responses. 

Unlike sorting, which affects the ambient 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 admissions, directly controlling for avoidance 

behavior yields estimates of the biological 

effect (δφ/δP), while omitting it yields estimates of the reduced-form effect (dφ/dP). 

Moreover, the scope for short-run avoidance 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, behavioral responses are not feasible and the shock 

can be used to derive a measure of the biological impacts of pollution. However, when 

a shock is more gradual (such that information about pollution can be publicly disseminated), 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 availability of this public and private information. 

The question then becomes, should one 

control for avoidance behavior? The reduced 

form is generally more convenient for valuation, described in more detail below in 4.5, 

because the econometrician does not need 

to specify the functional form of the environmental 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 generalizability, at least across settings that are relatively 

homogenous in terms of age composition and 

underlying health.29 Avoidance behavior is 

clearly very context speci0c, 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 avoidance behavior. Ideally, one would estimate 

both the biological and reduced form effects, 

with the difference re2ecting the bene0ts 

from avoidance behavior—δφ/δA*δA/δP

(or δH/δA*δA/δP). 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 partial picture, which can often be considered a 

29 While toxicologists should have a comparative advantage in measuring the pure biological effect through the 

use of chamber studies, as previously mentioned the endpoints 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.
710 Journal of Economic Literature, Vol. LI (September 2013)

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 signi0cant. 

Moreover, by bounding impacts they may 

help us determine threshold rules for policy 

whereby regulatory action is taken when a 

subset of the bene0ts exceed the costs.

In the discussion that follows, we group 

health outcomes as they relate to equation (9). The 0rst 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 2ow that 

draws down that stock, at least until medical 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 nonetheless 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 prevent them from participating in usual activities, 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 dif0cult 

for the econometrician to observe.30

4.4.1 Health Capital

Since health is a complicated construct 

often in2uenced 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 mortality. As quite possibly the most objective measure 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 

identi0ers, 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 useful to de0ne mortality as health stock falling 

below a certain threshold (H < h*). In that 

case, researchers typically estimate dH/dP as 

de0ned in equation (9).32

30 It is worth noting that the lack of a health encounter may arise for at least three distinct reasons: (1) effective treatments are unavailable; (2) symptoms are minor 

enough that they do not necessitate the use of formal care; 

or (3) symptoms are suf0ciently 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 not 

include 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.
Graff Zivin and Neidell: Environment, Health, and Human Capital 711

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 pollution on this group are particularly important. 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 outcomes also introduces a potential concern 

regarding the endogeneity of births.

While these outcomes measure speci0c 

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 in2uence statistical inference. For example, pollution may induce 

a large increase in hospital admissions for 

myocardial infarctions (i.e., heart attacks), 

but a considerably smaller change in mortality because major medical advances 

have signi0cantly improved survival rates 

(Cutler, Deaton, and Lleras-Muney 2006). 

A study only focusing on mortality may fail 

to uncover this relationship; this is precisely the pattern found in table 2, which 

focused on self-reported health status 

and daily episodes of compromised physical health. Since this link between health 

and illness also varies with technology and 

access to high-quality health care, estimates of the relationship between pollution 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 epidemiological 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 0les, form a census of patients, rely 

on the standard “International Classi0cation 

of Diseases” to identify speci0c health conditions, 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 pollution data.33

Despite the signi0cant appeal, using these 

visits may introduce sample selection bias. 

Those who have a relationship with a primary care physician (PCP) may receive routine 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 hospitalizations, 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.
712 Journal of Economic Literature, Vol. LI (September 2013)

witness signi0cant 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 countries with more universal access to healthcare 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 monetized and it can re2ect the severity of disease. 

For example, some hospital admissions for 

respiratory dif0culties are relatively minor 

events 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 (δH/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 availability of emergency room data in the United 

States can lessen selection bias concerns by 

capturing the universe of individuals seeking hospital-based care, including individuals 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 physician visits as another measure of illness, but obtaining 

this data with suitable geographic identi0ers is a signi0cant 

challenge. Moreover, sample selection bias remains a concern, 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 hospital 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, medication 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 

signi0cant 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 literature, 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 (δM/δφ) and its effectiveness (δH/δM). 

Both are areas of major preoccupation 

within the 0eld of health economics (e.g., 

Manning et al. 1987; Finkelstein et al. 2011; 

Card, Dobkin, and Maestas 2009; Cutler 

2007; Lichtenberg 2007). When combined 
Graff Zivin and Neidell: Environment, Health, and Human Capital 713

they reveal the transient and perhaps residual, untreatable health effects due to pollution (as implied by δH/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 illness 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 performance 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 dif0culty 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 0elds, 

there is a long history of using surrogate 

health outcomes that are predictive of an 

endpoint of interest, such as forced expiratory 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 measures 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, providing a strong rationale for human capital 

and productivity impacts.37

The biggest challenge to research in this 

area is 0nding 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 workplace, principally absenteeism and performance. 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 0elds (Black, Devereux, 

and Salvanes 2007; Currie and Thomas 2001; 

Oreopoulos et al. 2008; Royer 2009)—and 

thus directly inform our understanding of 

δw/δH*δH/δP.

38 Limited data availability, 

especially for representative samples, is, however, a formidable obstacle to the conduct of 

credible empirical work in this area. 

4.5 Valuation

A common goal of environmental economics research is to take estimates of 

the environmental–health relationships 

to develop measures of willingness-to-pay 

for reductions in pollution, as speci0ed in 

37 Such impacts are also consistent with the large economic 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 

(δA/δP = 0) and medical consumption (δM/δP = 0). As 

such, the biological and reduced-form effects on health 

will be identical (δH/δP = dH/dP).
714 Journal of Economic Literature, Vol. LI (September 2013)

equation (10), in order to inform the design 

of optimal environmental policy.39 To obtain 

the welfare impacts from a change in environmental 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 components, one must also place a monetary 

value on each, and all are not without controversies. With the exception of mortality, 

which is monetized by using the value of a statistical life (VSL), all valuations likely understate the true economic costs.41 Charges are 

typically the only measure available for hospital 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 possible side effects from medication use (e.g., 

routine use of inhalers may cause arrhythmias (Singh et al. 2011)). Changes in birth 

outcomes can be linked with future earnings 

39 Several alternative approaches for valuing environmental 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 limitations, requiring strong theoretical and empirical assumptions (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 re2ect social costs. Cross-subsidization across clinical activities means some will be overestimates while others 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 empirical 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 

(δA/δP), 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 re2ect 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 0lters, 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 environmental confounding, the overwhelming 

43 This distinction is a signi0cant 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 expressions that include avoidance behavior, see Cropper and 

Freeman (1991), Deschenes and Greenstone (2011), and 

Harrington and Portney (1987).
Graff Zivin and Neidell: Environment, Health, and Human Capital 715

majority do little, if anything, to address 

behavioral confounding, and thus are likely 

to provide signi0cantly biased estimates.45

In light of this, we focus our review on 

quasi-experimental studies within economics, nearly all of which have emerged in 

the past decade. This literature is focused 

on a set of interlinked questions of interest. 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 consequences from these exposures? Since 

they differ widely in the identi0cation strategies and outcome variables employed, 

each provide different insights into these 

underlying relationships and their importance for societal welfare.46

5.1 Health Capital and Illness

5.1.1 Impacts of Pollution

One of the earliest examples of a quasiexperimental approach to estimate an environmental 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 a 

labor 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 provides suggestive evidence of cumulative effects across multiple years, there is no quasi-experimental evidence on this 

topic.

Furthermore, the authors select a neighboring, unaffected community as a control 

group to account for time trends by estimating difference-in-differences models. When 

the steel mill was closed, the authors found 

signi0cant declines in school absences, respiratory-related hospital admissions, and mortality. As an “event study,” one concern with 

this design is that, despite the availability of 

individual level health outcomes as dependent variables, the pollution variable is common 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 statistical inference in this setting particularly challenging (Donald and Lang 2007).47

One important study by Chay and 

Greenstone (2003b) overcame this problem by focusing on the recession of the early 

1980s in the United States. Manufacturing 

is a key input into the emissions process, so 

a slowing of the economy can produce farreaching changes in pollution. Furthermore, 

manufacturing is not evenly spread throughout the United States, so the shocks to 

manufacturing from this recession induced 

considerable spatial variation in pollution. 

In fact, the authors found that total suspended 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 exogenous 

variation 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.
716 Journal of Economic Literature, Vol. LI (September 2013)

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 0nd 

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 

re2ective of contemporary pollution levels 

across much of the developed world. Absent 

a large-scale shock to pollution levels, they 

use zip code 0xed effects to estimate models at the weekly level. Switching to a higher 

time frequency exploits the strong temporal 

2uctuations in pollution (as described in 0gure 2) that may act as a shock without allowing sorting to occur, while the 0xed effects 

control for time invariant characteristics of 

the area. They 0nd that reductions in carbon 

monoxide over the 1990s saved approximately 1,000 infant lives in California, which 

translates into bene0ts of roughly $4.8 billion. Currie and Neidell 0nd quite similar 

effects when aggregating to a higher frequency (from weekly to monthly), suggesting 

that harvesting may not be a signi0cant 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 pollutants on children’s health. Military personnel 

are assigned to military bases entirely based 

on “the needs of the army,” and any relocation 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 amenities, 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 0nds 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 0xed effect models to 

control for differences in family background 

and genetics. They 0nd 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 0rst two weeks after 

birth also increases the risk of infant mortality by 2.5 percent relative to baseline levels. 

The authors calculate that the 0fteen-year 

decline in CO from 1989 to 2003 translates 

into $720 million in lifetime earnings from 

improvements in birth weight and $2.2 billion from the reduction in infant mortality 

for the 2003 U.S. birth cohort. The use of 

maternal 0xed effects increases estimates, 

suggesting the importance of accounting for 

maternal characteristics within neighborhoods. The better assignment of pollution by 

using the mother’s exact address rather than 

zip code also increases point estimates, consistent 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 wild0res, which 

led to an exogenous and sizable increase 

in particulate matter. First, she infers mortality by looking at “missing children” a la 
Graff Zivin and Neidell: Environment, Health, and Human Capital 717

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 0re given its size. She 0nds that the 

0res caused a roughly 17 percent increase in 

under age two mortality. 

Another context where data are dif0cult 

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 hypothesis suggests that males are more susceptible to fetal shocks than females (Trivers 

and Willard 1973). Their analysis closely follows the identi0cation strategy of Chay and 

Greenstone (2003a) and Chay, Dobkin, and 

Greenstone (2003), which focused on the differential 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 

0nd 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 percentage 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 pollution in China. The rapid industrialization 

in China has led to a tremendous increase in 

pollution, though the increase varied considerably across space—perhaps best described 

as the Chay and Greenstone recessioninduced pollution changes in reverse. 

Mobility was also greatly limited for inhabitants 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 

0nds 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, others focus on the impacts of emission policies. 

Recall from section 2 that emissions interact 

with meteorological conditions to form pollution, such that P = f(E, W). Focusing on a 

pollutant provides a more generalizable estimate, whereas focusing on emissions may 

not because the effects may be speci0c to 

the meteorological conditions at the time of 

shock or the source of emissions. Focusing 

on emissions offers several advantages, however. One, emissions, rather than pollution, 

are often all that can be directly regulated. 

Two, this provides an estimate of an externality from a speci0c 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 particular 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 collection system (E-ZPass), which signi0cantly 

reduced traf0c congestion near highway toll 

plazas and thus local vehicle emissions. Their 

study provides estimates of an externality 

from highway congestion, which can contribute to the optimal design of congestion 

policies. To control for sorting, they exploit 
718 Journal of Economic Literature, Vol. LI (September 2013)

the introduction of E-ZPass to estimate 

difference-in-differences models, comparing areas close to toll plazas before and after 

E-ZPass to areas far from plazas. They 0nd 

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 construct at a time.48 Deschenes, Greenstone, 

and Shapiro (2012) is an exception, providing a more comprehensive assessment 

of the effect of NOx emissions through an 

examination of impacts on mortality, hospitalizations, and medication expenditures. To 

address sorting, the authors exploit the NOx

Budget Trading Program (NBP), which, 

beginning in 2003, created a cap-and-trade 

system for major contributors to NOx emissions in Eastern and Midwestern States. 

Because NOx emissions are a precursor 

to ozone formation, the NBP only operated 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 0nd that the reduction in NOx emissions led to a signi0cant decrease in ozone 

pollution. Furthermore, the NBP led to considerable decreases in mortality and medication purchases, but surprisingly had no 

impact on hospitalizations. This is also the 

0rst analysis to directly evaluate the health 

impacts of an emissions trading program, 

with the authors 0nding that the savings in 

medication expenditures alone exceeds the 

costs of the program.

48 Studies looking at birth outcomes often include several 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 exogenous changes in ambient pollution or 

emissions in order to minimize concerns 

about residential sorting and other long-run 

behavioral responses to poor environmental 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 relationship 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 nonmarket behavior, researchers often must 

obtain unique sources of data to explore 

this topic. For example, Neidell (2009) uses 

attendance data from several outdoor facilities in Los Angeles to examine the relationship 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 discontinuity design to compare attendance on days 

just above the threshold to just below. He 

0nds signi0cant decreases in attendance of 

approximately 6 to 13 percent, though it 

remains unclear how these estimates generalize to other contexts.49

49 In a closely related study, Graff Zivin and Neidell 

(2009) examine successive days of smog alerts, and 0nd 

that responses to an alert issued on two consecutive days 

declines considerably, suggesting the costs of avoidance 

behavior are nontrivial as well.
Graff Zivin and Neidell: Environment, Health, and Human Capital 719

Graff Zivin, Neidell, and Schlenker (2011) 

focus on water quality, examining the impact 

of drinking water violations on the consumption of bottled water using purchase data 

from a national grocery chain. Since violations are issued at the local water district 

level, the authors use grocery store 0xed 

effects to compare the changes in consumption over time in response to violations, 

with the change in violations unlikely to be 

related to changes in underlying consumption preferences. They 0nd 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 0sh consumption, 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 0sh known 

to contain high mercury levels. Shimshack, 

Ward, and Beatty (2007) examine the 

impact of these advisories on the consumption of 0sh as measured in the consumer 

expenditure survey. They estimate difference-in-differences models by comparing 

consumption before and after the advisory 

for informed versus uninformed consumers, using newspaper readership and several 

other measures to de0ne informed. Among 

groups particularly sensitive to mercury, they 

found a roughly 19 percent reduction in 0sh 

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 signi0cant 

problem in Bangladesh, and this study examines 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 0nd 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 information campaigns when environmental quality (or its correlates) is directly observable. 

Deschenes and Greenstone (2011) examine the relationship between temperature 

and residential energy consumption, where 

variation in energy consumption is predominantly 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 represents a response to changes in risk; air conditioning reduces the risks associated with 

heat.50 The econometric models include 

county 0xed effects to exploit the presumably random variation in temperature across 

years. The authors 0nd 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 controlling for avoidance behavior is likely to 

lead to considerable differences between the 

biological effect (δφ/δP) and the reducedform effect (dφ/dP) of pollution. Since 

measuring avoidance behavior is challenging, there are only a handful of studies that 

attempt to control for avoidance behavior 

50 Since air conditioning may also create a more comfortable indoor environment, it also provides bene0ts in 

the form of direct utility (Cropper and Oates 1992). 
720 Journal of Economic Literature, Vol. LI (September 2013)

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 hospital admissions. Using zip code 0xed effects 

and exploiting the strong daily temporal 

variation in ozone, he 0nds that including 

these proxies signi0cantly increases the estimated impact of ozone on health. Estimates 

that ignore avoidance behavior 0nd that a 

one ppb decrease in ozone decreases admissions for children by roughly 1 percent; this 

increases to nearly 3 percent when incorporating 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 avoidance behavior and measurement error in 

pollution assignment. Boat traf0c represents a major source of pollution for the Los 

Angeles region and, because of the extended 

length of travel and unpredictable conditions at sea, daily variation in boat traf0c is 

arguably uncorrelated with other short run 

determinants of health. Importantly, since 

boat traf0c 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 omitted variable, using boat traf0c as an instrument uncovers the effect of ozone holding 

avoidance behavior 0xed, i.e., the biological effect. While boat emissions may affect 

multiple pollutants, the authors attempt to 

limit concerns with identi0cation by focusing 

solely on the summer period, when ozone 

levels are at their highest and all other criteria pollutants are at their lowest. They 0nd 

that holding avoidance behavior 0xed leads to 

signi0cantly 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 discussed 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 ability 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 beginning 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 functioning may not result in a change in labor 

supply, but could make a worker less able to 

concentrate and thus underperform on cognitive and physical tasks.51

5.2.1.1 Schooling Outcomes

School is widely seen as the main vehicle 

for improving human capital, and missing school because of illness may impede 

human capital development (Grossman 

51 To draw a 0ne distinction from absenteeism, this 

is sometimes referred to as “presenteeism” (Pauly et al. 

2008).
Graff Zivin and Neidell: Environment, Health, and Human Capital 721

and Kaestner 1997). In the aforementioned 

Ransom and Pope design, they also found a 

signi0cant 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 attendance 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 residential factors that may be important determinants of absences. They 0nd 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 development (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 children that contains detailed pollution data, 

with test score data from schools linked 

to the children. Using school 0xed effects 

to account for sorting, they 0nd 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 0xed effects, they 

0nd 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 pollution 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 re0nery that was a 

major emitter of sulfur dioxide, comparing residents near and far from the re0nery, 

before and after the re0nery closure. While 

similar in spirit to Ransom and Pope’s investigations centered on a steel mill closure, the 

closure of the oil re0nery was in a densely 

populated, urban community, thus affecting 

hundreds of thousands of residents. A notable limitation is that demand shocks from the 

re0nery 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 0nd 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 avoidance 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 0nd 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.
722 Journal of Economic Literature, Vol. LI (September 2013)

captures high-visibility labor market 

impacts.53 Less visible impacts may also 

occur on the intensive margin whereby productivity is affected, even when labor supply 

does not change. These changes in performance are perhaps more dif0cult to detect 

than absenteeism, but may be pervasive so 

that even small individual effects may translate into large welfare losses when aggregated across the economy. Graff Zivin and 

Neidell (2012) address this issue by focusing 

on the impact of pollution on worker productivity. 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 agricultural workers is highly inelastic in the 

short run, hence limiting the scope for avoidance behavior. Using models that exploit the 

plausibly exogenous daily variation in ozone 

levels, they 0nd that a 10 ppb decrease in 

ozone concentrations increases worker productivity 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 productivity, thus capturing impacts on both the 

extensive and intensive margin. The authors 

identify the impact of differential lead exposure 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 levels 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 servicing their homes. The authors 0nd 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 exposure, 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 childhood 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 complementarities 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 levels but also information on residential location 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) examine the impact from prenatal exposure to 

radioactive fallout from the 1986 Chernobyl 

accident on both birth and schooling outcomes 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 recordkeeping that linked nearly all births (along 

55 For more discussion of the fetal origins hypothesis, 

see Almond and Currie (2011).
Graff Zivin and Neidell: Environment, Health, and Human Capital 723

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 capital development later in life. While they 0nd 

little evidence of health effects as measured 

by birth outcomes and childhood hospitalizations, they 0nd signi0cant decreases in several 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 performance 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 external data source at a later date provide little 

evidence that migration was related to pollution. Using a similar identi0cation strategy 

as that employed by Chay and Greenstone 

(2003b), only focused solely on Texas, he 

0nds 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 medical radiation, though policy levers for in2uencing 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 development.57 Nilsson (2009) tackles this question 

by examining the effect of lead exposure in 

the 0rst 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 spatial decreases arising from differences in initial lead levels.58 He 0nds that decreases in 

lead improve several aspects of human capital, including years of schooling and cognitive ability, as well as labor market outcomes. 

Importantly, allowing for a nonlinear effect 

of lead through the use of dummy variables, 

Nilsson 0nds 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 

signi0cant human capital bene0ts.

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 pollution than previously imagined. Moreover, 

a blossoming literature has begun to link 

pollution-induced illnesses to a range of 

human capital outcomes. Since environmental 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 0nding a strong neurological 

effect of lead are what prompted the phase-out of leaded 

gasoline, so by de0nition they focus on higher levels of pollution that are less relevant for today.
724 Journal of Economic Literature, Vol. LI (September 2013)

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 output. 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 consumption bene0ts associated with improved 

environmental quality. The evidence 

reviewed within this paper suggests that 

environmental regulation may also contribute to economic productivity and thus fruitfully 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 poverty 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 bene0ts. This tabulation is fraught with empirical 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 calculations, with the more subtle but perhaps 

more pervasive impacts generally ignored. 

That said, cost–bene0t calculations even 

59 In less developed countries, where individuals routinely face higher levels of pollution and avoidance behavior 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 construction—the human capital and productivity returns on this 

“investment” may be especially large.

on this partial analysis, which understates 

the bene0ts from pollution reduction, often 

0nd that the bene0ts of regulation signi0-

cantly outweigh the costs (see, for example, 

Deschenes, Greenstone, and Shapiro 2012; 

Ebenstein 2012). This almost certainly contributes to the contentiousness surrounding nearly every environmental standard. 

Better tools and more evidence are needed 

to inform this debate. We utilize this opportunity to highlight several areas that merit 

further investigation.

First, the biomedical science literature 

is 0lled with pollution–health relations that 

have not been subjected to the causality tests 

typical of economic inquiry. A growing epidemiology 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 understanding 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 consequences and merit further scrutiny.

Second, a better understanding of avoidance 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 bene0ts. These, in turn, 

depend on a wide range of socioeconomic 

factors that in2uence, for example, mobility, health insurance status, and the availability of intellectual and 0nancial resources 

required to avert. For those pollutants with 

subtle impacts, models of learning and inattention may be particularly important. The 

current stalemate on actions to avert climate 

change suggests that avoidance and adaptation are likely to play an increasingly large 

role in global welfare going forward.
Graff Zivin and Neidell: Environment, Health, and Human Capital 725

Lastly, the evidence on human capital and 

especially labor market effects from pollution exposure are just beginning to emerge. 

Despite its beginning stages, the limited evidence thus far suggests negative impacts on 

cognitive ability, school performance, and 

job performance. Much more should be 

done to replicate these results in other contexts 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 empirical identi0cation. 

The collection of environmental, health, 

and human capital data is being rapidly 

transformed. The past decade has witnessed 

a proliferation of cheaper and more sophisticated environmental sensors as well as significant improvements in the science that allow 

us to transform satellite data into meaningful 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 signi0cantly increased access to not only 

better human capital and productivity data 

but also to data that spans a lifetime of exposures, 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 0nely geocoded 

health data, which have been the mainstay 

of research in this area, are being threatened by proposed NIH revisions regarding 

data con0dentiality. 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 (underway), 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 production in the coming decades. 

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