American Economic Review 2012, 102(7): 3652–3673

http://dx.doi.org/10.1257/aer.102.7.3652

3652

The Impact of Pollution on Worker Productivity†

By Joshua Graff Zivin and Matthew Neidell*

As one of the primary factors of production, labor is an essential element in every 

nation’s economy. Investing in human capital is widely viewed as a key to sustaining 

increases in labor productivity and economic growth. While health is increasingly 

seen as an important part of human capital, environmental protection, which typically promotes health, has not been viewed through this lens. Indeed, such interventions are typically cast as a tax on producers and consumers, and thus a drag on the 

labor market and the economy in general. Given the large body of evidence that 

causally links pollution with poor health outcomes (e.g., Bell et al. 2004; Chay and 

Greenstone 2003; Currie and Neidell 2005; Dockery et al. 1993; Pope et al. 2002), 

it seems plausible that efforts to reduce pollution could in fact also be viewed as an 

investment in human capital, and thus a tool for promoting, rather than retarding, 

economic growth.

The key to this assertion lies in the impacts of pollution on labor market outcomes. 

While a handful of studies have documented impacts of pollution on labor supply 

(Carson, Koundouri, and Nauges 2011; Graff Zivin and Neidell forthcoming; Hanna 

and Oliva 2011; Hausman, Ostro, and Wise 1984; Ostro 1983),

1

 their focus on the 

extensive margin, where behavioral responses are nonmarginal, only captures highvisibility labor market impacts. Pollution is also likely to have productivity impacts 

on the intensive margin, even in cases where labor supply remains unaffected. Since 

worker productivity is more difficult to monitor than labor supply, these more subtle 

impacts may be pervasive throughout the workplace, so that even small individual 

effects may translate into large welfare losses when aggregated across the economy. 

There is, however, no systematic evidence to date on the direct impact of pollution 

on worker productivity.2

 This paper is the first to rigorously assess this environmental productivity effect.

Estimation of this relationship is complicated for two reasons. One, although 

datasets frequently measure output per worker, these measures do not isolate worker 

1Numerous cost-of-illness studies that focus on hospital outcomes such as length of hospital stay also implicitly 

focus on labor supply impacts. 2 In a notable case study, Crocker and Horst (1981) examined the impacts of environmental conditions on 17 citrus harvesters. They found a small negative impact on productivity from rather substantial levels of pollution in 

Southern California in the early 1970s.

*Graff Zivin: University of California-San Diego, School of International Relations and Pacific Studies and 

Department of Economics, 9500 Gilman Dr. 0519, La Jolla, CA 92093, and NBER (e-mail: jgraffzivin@ucsd.

edu); Neidell: Columbia University, Department of Health Policy and Management, Mailman School of Public 

Health, 600 W. 168th Street, 6th Floor, New York, NY 10032, and NBER (e-mail: mn2191@columbia.edu). We 

thank numerous individuals and seminar participants at RAND, UC-Irvine, Maryland, Cornell, Tufts, Michigan, 

University of Washington, University of British Columbia, CUNY Graduate Center, Yale University, Columbia, 

UC-San Diego, and the NBER Health Economics meeting for helpful suggestions. We are also particularly indebted 

to Udi Sosnik for helping to make this project possible, and Shlomo Pleban for assistance in collecting the data, both 

of Orange Enterprises. We are grateful for funding from the National Institute of Environmental Health Sciences 

(1R21ES019670-01), the Property and Environment Research Center, and seed grants from the Institute for Social 

and Economic Research and Policy and the Northern Manhattan NIEHS. † To view additional materials, visit the article page at http://dx.doi.org/10.1257/aer.102.7.3652.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3653

productivity from other inputs (i.e., capital and technology), so that obtaining clean 

measures of worker productivity is a perennial challenge. Two, exposure to pollution levels is typically endogenous. Since pollution is capitalized into housing 

prices (Chay and Greenstone 2005), individuals may sort into areas with better air 

quality depending, in part, on their income, which is a function of their productivity 

(Banzhaf and Walsh 2008). Furthermore, even if ambient pollution is exogenous, 

individuals may respond to ambient levels by reducing time spent outside, so that 

their exposure to pollution is endogenous (Neidell 2009).

In this paper, we use a unique panel dataset on the productivity of agricultural 

workers to overcome these challenges in analyzing the impact of ozone pollution 

on productivity. Our data on daily worker productivity is derived from an electronic 

payroll system used by a large farm in the Central Valley of California that pays its 

employees through piece rate contracts. A growing body of evidence suggests that 

piece rates reduce shirking and increase productivity over hourly wages and relative incentive schemes, particularly in agricultural settings (Bandiera, Barankay, and 

Rasul 2005, 2010; Lazear 2000; Paarsch and Shearar 1999, 2000; Shi 2010). Given 

the incentives under these contracts, our measures of productivity can be viewed as 

a reasonable proxy for productive capacity under typical work conditions.

We conduct our analysis at a daily level to exploit the plausibly exogenous daily 

fluctuations in ambient ozone concentrations. Although aggregate variation in environmental conditions is largely driven by economic activity, daily variation in ozone 

is likely to be exogenous. Ozone is not directly emitted but forms from complex 

interactions between nitrogen oxides (NOx) and volatile organic chemicals (VOCs), 

both of which are directly emitted, in the presence of heat and sunlight. Thus, ozone 

levels vary in part because of variations in temperature, but also because of the highly 

nonlinear relationship with NOx and VOCs. For example, the ratio of NOx to VOCs 

is almost as important as the level of each in affecting ozone levels (Auffhammer 

and Kellogg 2011), so that small decreases in NOx can even lead to increases in 

ozone concentrations, which has become the leading explanation behind the “ozone 

weekend effect” (Blanchard and Tanenbaum 2003). Moreover, regional transport 

of NOx from distant urban locations, such as Los Angeles and San Francisco, has 

a tremendous impact on ozone levels in the Central Valley (Sillman 1999). Given 

the limited local sources of ozone precursors, this suggests that the ozone formation 

process coupled with emissions from distant urban activities are the driving forces 

behind the daily variation in environmental conditions observed near this farm.

Furthermore, the labor supply of agricultural workers is highly inelastic in the 

short run. Workers arrive at the field in crews and return as crews, thus spending 

the majority of their day outside regardless of environmental conditions. Moreover, 

since we have measures of both the decision to work and the number of hours 

worked, we can test whether workers respond to ozone, and in fact we are able to 

rule out even small changes in avoidance behavior. Thus, focusing on agricultural 

workers greatly limits the scope for avoidance behavior, further ensuring that exposure to pollution is exogenous in this setting, and that we are detecting productivity 

impacts on the intensive margin.

Although these workers are paid through piece-rate contracts, worker compensation is subject to minimum wage rules, which can alter the incentive for workers 

to supply costly effort. Since the minimum wage decouples daily job performance 
3654 THE AMERICAN ECONOMIC REVIEW december 2012

from compensation, workers may have an incentive to shirk. If pollution leads to 

more workers earning the minimum wage, and this in turn induces shirking, linear 

regression estimates will be upward biased. On the other hand, the threat of termination may provide a sufficient incentive to provide effort, particularly in our setting where output is easily verified and labor contracts are extremely short-lived, in 

which case linear regression models should be unbiased.

After merging this worker data with environmental conditions based on readings from air quality and meteorology stations in the California air monitoring network, we first estimate linear models that relate mean ozone concentrations during 

the typical workday to productivity. We find that ozone levels well below federal 

air quality standards have a significant impact on productivity: a 10 parts per billion (ppb) decrease in ozone concentrations increases worker productivity by 5.5 

percent. To account for potential concerns about shirking, we artificially induce 

“bottom-coding” on productivity measures for observations where the minimum 

wage binds, and estimate censored regression models. Under this specification, the 

actual measures of productivity when the minimum wage binds no longer influence 

estimates of the impact of ozone on productivity. Thus, if the marginal effects of 

productivity on this latent variable differ from the marginal effects from our baseline 

linear model, this would indicate shirking is occurring. Our results, however, remain 

unchanged, suggesting that the threat of termination provides sufficient incentives 

for workers to supply effort even when compensation is not directly tied to output.

These impacts are particularly noteworthy as the US Environmental Protection 

Agency is currently contemplating a reduction in the federal ground-level ozone 

standard of approximately 10 ppb (Environmental Protection Agency 2010). The 

environmental productivity effect estimated in this paper offers a novel measure of 

morbidity impacts that are both more subtle and more pervasive than the standard 

health impact measures based on hospitalizations and physician visits. Moreover, 

they have the advantage of already being monetized for use in the regulatory costbenefit calculations required by Executive Order 12866 (The White House, 1994). 

In developing countries, where environmental regulations are typically less stringent and agriculture plays a more prominent role in the economy, this environmental 

productivity effect may have particularly detrimental impacts on national prosperity.

The paper is organized as follows. Section I briefly summarizes the relationship between ozone and health, and highlights potentially important confounders. 

Section II describes the piece-rate and environmental data. Section III provides a 

conceptual framework that largely serves to guide our econometric model, which is 

described in Section IV. Section V describes the results, with a conclusion provided 

in Section VI.

I. Background on Ozone and Health

Ozone affects respiratory morbidity by irritating lung airways, decreasing lung 

function, and increasing respiratory symptoms (Environmental Protection Agency 

2006). Studies have consistently linked higher ozone concentrations with increased 

health care visits for respiratory diseases (see, e.g., Neidell 2009), but ozone can 

also lead to minor insults that may not necessitate the use of formal health care. For 

example, research finds decreases in forced-expiratory volume in mail carriers in 
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3655

Taiwan (Chan and Wu 2005) and agricultural workers in British Columbia, Canada 

(Brauer, Blair, and Vedal 1996) even at levels below prevailing air quality standards. 

Symptoms from ozone exposure can arise in as little as one hour, with effects exacerbated by exercise and with continued duration of exposure (see, e.g., Gong et al. 

1986; Kulle et al. 1985; McDonnell et al. 1983), both of which are particularly relevant for our study population given the physical demands of the task and prolonged 

exposure. How these respiratory changes affect productivity is not well understood, 

though it is plausible to think that diminished lung functioning would negatively 

impact productivity for physically demanding work such as that found in agriculture.

Recovery from ozone, once removed from exposure, is also quite rapid. Nearly 

all lung functioning returns to baseline levels in healthy adults within 24 hours 

of exposure, although recovery can take longer for hyper-responsive adults with 

underlying health conditions (Folinsbee and Hazucha 2000; Folinsbee and Horvath 

1986).

3

 Since ozone levels fall considerably overnight as heat and sunlight decline, 

we expect lagged ozone to have minimal impacts on the productivity of our healthy 

worker population. As a result, we focus our analyses primarily on the contemporaneous relationship between ozone and productivity. The impact of lagged ozone concentrations is also explored in order to confirm that our workers are indeed healthy.

As noted in the introduction, ozone formation depends, in part, on ambient temperatures. Human exposure to high temperature can lead to severe negative health 

effects, including heat cramps, exhaustion, and stroke, as well as more subtle 

impacts on endurance, fatigue, and cognitive performance (e.g., González-Alonso 

et al. 1999; Hancock, Ross, and Szalma 2007), all of which may diminish the productivity of workers. The impacts can arise in less than an hour (Hancock, Ross, and 

Szalma 2007) and are likely nonlinear, as it is mostly temperature extremes outside 

the “comfort zone” that appreciably affect health (Hancock and Warm 1989). As 

such, our empirical models will include flexible controls for temperature.

II. Data

Our data comes from a unique arrangement with an international software provider, Orange Enterprises (OE). OE customizes paperless payroll collection for clients, called the Payroll Employee Tracking (PET) Tiger software system. It tracks the 

progress of employees by collecting real-time data on attendance and harvest levels 

of individual farm workers in order to facilitate employee and payroll management. 

The PET Tiger software operates as follows. The software is installed on handheld 

computers used by field supervisors. At the beginning of the day, supervisors enter 

the date, starting time, and the crop being harvested. Each employee clocks in by 

scanning the unique barcode on his or her badge. Each time the employee brings a 

bushel, bucket, lug, or bin, his or her badge is swiped, recording the unit and time. 

Data collected in the field is transmitted to a host computer by synchronizing the 

handheld with the host computer, which facilitates the calculation of worker wages.

We have purchased the rights to daily productivity data from a farm in the Central 

Valley of California that uses this system. To protect the identity of the farm, we can 

3Although lung functioning recovers after exposure, long-term damage to lung cells may still occur (Tepper et 

al. 1989).
3656 THE AMERICAN ECONOMIC REVIEW december 2012

only reveal limited information about their operations. The farm, with a total size of 

roughly 500 acres, produces blueberries and two types of grapes during the warmer 

months of the year. The farm offers two distinct piece-rate contracts depending on 

the crop being harvested: time plus pieces (TPP) for the grapes and time plus all 

pieces (TPAP) for blueberries. Total daily wages (w) from each contract can be 

described by the following equations:

(1) TPP: w = 8h + p · (q-minpcs · h) · I(q > minpcs · h)

TPAP: w = 8h + p · q · I(q > minpcs · h),

where the minimum wage is $8 per hour, h is hours worked, p is the piece rate, q is 

daily output, minpcs is the minimum number of hourly pieces to reach the piece rate 

regime, and I is an indicator function equal to 1 if the worker exceeds the minimum 

daily harvest threshold to qualify for piece-rate wages and 0 otherwise. In both 

settings, if the worker’s average hourly output does not exceed minpcs, the worker 

earns minimum wage. The marginal incentive for a worker whose output places 

them in the minimum wage portion of the compensation schedule is job security. In 

TPP, the marginal incentive in the piece rate regime is the piece rate. TPAP slightly 

differs from TPP in that it pays piece rate for all pieces when a worker exceeds the 

minimum hourly rate (as opposed to paying piece rate only for the pieces above 

the minimum). Hence, the payoff at minpcs is nonlinear and provides a stronger 

incentive to reach the threshold under this contract. The incentive beyond this kink 

remains linear as under TPP.

The worker dataset we obtained consists of a longitudinal file that follows workers over time by assigning workers a unique identifier based on the barcode of their 

employee badge. It includes information on the total number of pieces harvested by 

each worker,4

 the location of the field, the type of crop, the terms of the piece rate 

contract,5

 time in and out, and the gender of the worker.6 Data quality is extremely 

high, as its primary purpose is to determine worker wages. The analyses in this 

paper are based on data from the farm for their 2009 and 2010 growing seasons.

Our measures of environmental conditions come from data on air quality and 

weather from the system of monitoring networks maintained by the California Air 

Resources Board (2012). These data offer hourly measures of various pollutants 

and meteorological elements at numerous monitoring sites throughout the state. The 

4For one of the three crops, harvests are done in crews of three and individual productivity is measured as the 

total output of the crew divided by the crew size. While crew work could introduce free-riding incentives, our measure of the environmental productivity effect will only be biased if these incentives change due to pollution. This 

will only occur if both of the following are true: workers are differentially affected by ozone and the complementarities in team production are very high (e.g., Leontief production). While each member of a crew has a specific 

task, they typically help each other throughout the day, suggesting that labor is indeed substitutable within the 

crew. Moreover, Hazucha et al. (2003) find little evidence of heterogeneous health impacts of ozone across healthy 

men and women. Thus, assigning average productivity measures to individuals within a crew should not bias our 

estimates. 5Piece-rate contracts, and thus minimum daily harvest thresholds, are fixed to the crop for the duration of the 

season. For simplicity, we label the two types of grapes as two crops given that they have different contracts. 6Although we have limited data on the demographic characteristics of our workers, demographics of piece-rate 

agricultural workers in California obtained from the National Agricultural Workers Survey, an employment-based 

random survey of agricultural workers, indicates these workers are poor, uneducated, and speak limited English, 

with the vast majority migrants from Mexico.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3657

farm is in close proximity to several monitors: three monitors that provide measurements of ozone and other environmental variables are within 20 miles of the farm, 

with the closest less than 10 miles away.7

 For all environmental variables, we compute an average hourly measure for the typical work day, which starts at 6 am and 

ends at 3 pm.

We assign environmental conditions to the farm using data from the closest monitoring station to the farm. While studies find that ozone measurements at fixed 

monitors are often higher than measurement from personal monitors attached to 

individuals in urban settings (O’Neill et al. 2003), this is less of a concern in the 

agricultural setting where ratios of personal to fixed monitors have been found 

to be as high as 0.96 (Brauer and Brook 1995). Furthermore, even when the difference exists, the within-person variation is highly correlated with the withinmonitor variation (O’Neill et al. 2003). As a crude test for spatial uniformity of 

ozone levels, we regress ozone levels from the closest monitor to the farm against 

the second closest monitor with data available for both years, which is roughly 

30 miles away, and obtain an R2

 of 0.85.8 Thus, despite its simplicity, we expect 

measurement error using our proposed technique for assigning ozone to the farm 

to be quite small.

Our data follows roughly 1,600 workers intermittently over 155 days. Table 1 

shows summary statistics for worker output and characteristics, environmental variables, and a breakdown of the sample size. There are three main crops harvested by 

this farm.9

 Under the TPAP contracts, which are used to harvest crop type 1, workers 

reach the piece-rate regime 24 percent of workdays. For the crops paid under TPP, 

workers reach the piece-rate regime 57 percent of workdays for crop 2 and 47 percent of workdays for crop 3. Under these contracts, the average hourly wages are 

$8.41, $8.16, and $8.41 for each of the three crops, respectively. We also see that 

variation in worker output is equally driven by variation within as well as across 

workers. Worker tenure with the farm is rather short, averaging 20 days, and both 

genders are well represented.10

In terms of environmental variables, the average ambient ozone level for the day 

is under 50 ppb, with a standard deviation of 13 ppb and a maximum of 86 ppb. 

Since this measure of ozone is taken over the average workday from 6 am to 3 pm, 

it corresponds closely with national ambient air quality standards (NAAQS), which 

are based on eight-hour ozone measures. Current NAAQS are set at 75 ppb, suggesting that, while ozone levels during work hours can lead to exceedances of air-quality 

standards, most workdays are not in violation of regulatory standards.11 Consistent 

with the area being prone to ozone formation, mean temperature and sunlight (as 

proxied by solar radiation) are high, and precipitation is low.

7To protect the identity of the farm, we cannot reveal the exact distance.

8Comparable R2

 for temperature is 0.94 and for particulate matter less than 2.5 μg/m3, another pollutant of much 

interest, is only 0.27; hence we do not focus on this important pollutant but include it as a covariate. 9The timing of the harvest is determined by when each crop is ready to be picked, so workers have little discretion over which crop to harvest on any given day. We explore the potential impact of worker selection into crops 

in Section VC. 10Gender is not reported for 19 percent of the sample. 11Violation of NAAQS is based on the daily maximum eight-hour ozone. Since our measure of ozone begins 

at 6 am, a time when ozone levels are quite low, the daily maximum eight-hour ozone is generally higher than our 

measure.
3658 THE AMERICAN ECONOMIC REVIEW december 2012

For a deeper look at productivity, Figure 1 plots the distribution of average pieces 

collected per hour by crop and overall, with a line drawn at the rate that corresponds 

with the level of productivity that separates the minimum wage from the piece-rate 

regime (the regime threshold). To combine productivity across crops, we standardize average hourly productivity by subtracting the minimum number of pieces per 

hour required to reach the piece-rate regime and dividing by the standard deviation of productivity for each crop, so the value that separates regimes is 0. For the 

crop paid TPAP, we see evidence of mass displaced just before the regime threshold, which is consistent with the strong incentives associated with just crossing the 

threshold under this payment scheme. For the two crops paid TPP, the distribution of 

productivity follows a symmetric normal distribution quite closely, with the exception of some displacement immediately surrounding the regime threshold for crop 2. 

Since crop 2 is harvested at a rate roughly 50 percent higher than crop 3, as shown 

in Table 1, it may be easier for workers who are close to the threshold to push themselves just above it by collecting a little more. If shirking occurs when the minimum 

wage binds, then we would expect part of the distribution to be shifted away from 

the area just left of the regime threshold and into the left tail. These plots, however, 

Table 1—Summary Statistics

Observations Mean SD

SD 

within worker

SD between 

workers

Panel A. Productivity variables (N = 35,461)

Minimum wage regime

Time + all pieces, $0.5/Piece 11,752 2.03 0.57 0.44 0.47

Time + pieces, $0.3/Piece 3,761 3.07 0.78 0.65 0.70

Time + pieces, $1/piece 5,918 2.29 0.48 0.31 0.44

Hours worked 21,431 7.64 1.29 0.76 1.20

Piece-rate regime

Time + all pieces, $0.5/Piece 3,675 3.42 0.40 0.30 0.32

Time + pieces, $0.3/Piece 5,115 4.93 0.86 0.70 0.64

Time + pieces, $1/piece 5,240 3.88 0.82 0.50 0.66

Hours worked 14,030 7.34 1.53 0.96 1.36

Worker characteristics

Tenure (weeks) 35,461 2.78 2.49

Percent male 35,461 0.30 0.46

Percent female 35,461 0.51 0.50

Mean SD Min Max

Panel B. Environmental variables (N = 155)

Ozone (ppb) 47.77 13.24 10.50 86.00

Temperature (F) 78.15 8.52 56.30 96.98

Atmospheric pressure (mb) 1,001.55 6.48 988.86 1,012.59

Resultant wind speed (mph) 2.74 0.53 1.61 4.60

Solar radiation (W/m2

) 837.33 174.07 187.00 1,083.33

Precipitation (mm) 2.40 5.05 0.00 35.48

Dew point (F) 51.96 5.81 33.14 63.43

Particulate matter <2.5 (μg/m3) 11.69 5.74 1.00 24.44

Panel C. Sample

Number of dates 155

Number of employees 1,664

Notes: The sample size in panel A refers to worker-days, while the sample size in panel B refers to the number of 

harvest dates. SD: Standard deviation. Crop 1 is time plus all pieces, with a piece rate of $0.5/piece and minimum 

pieces per hour of three. Crop 2 is time plus pieces, with a piece rate of $0.3/piece and minimum pieces per hour of 

four. Crop 3 is time plus pieces, with a piece rate of $1/piece and minimum pieces per hour of three.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3659

do not exhibit such patterns, suggesting that shirking among those receiving a fixed 

wage is minimal.

The significant variation in pieces collected in Figure 1 is also noteworthy, as this 

is critical for obtaining precise estimates of the impact of ozone. Figures 2 and 3 

0

0.2

0.4

0.6

0

0.2

0.4

0.6

−5 0 5 −5 0 5

Crop 1 (time + all pieces) Crop 2 (time + pieces, $0.3/p)

Crop 3 (time + pieces, $1/p) Total

Density

Figure 1. Standardized Average Hourly Pieces Collected by Crop and for All Crops

Notes: This figure plots the standardized average hourly pieces for each of the three crops and all crops, along with 

a nonparametric kernel density estimate. We standardize average hourly productivity by subtracting the minimum 

number of pieces per hour required to reach the piece-rate regime and dividing by the standard deviation of productivity for each crop. The vertical line reflects the regime threshold for crossing from the minimum wage to the 

piece-rate regime, which is zero for all crops given the standardization.

0

0.2

0.4

0.6

Density

−4 −2 0 2

Figure 2. Variation in Productivity by Worker, All Crops

Notes: This figure plots the mean of the standardized average hourly pieces for all crops by worker. We standardize 

average hourly productivity by subtracting the minimum number of pieces per hour required to reach the piece-rate 

regime and dividing by the standard deviation of productivity for each crop.
3660 THE AMERICAN ECONOMIC REVIEW december 2012

further illustrate this variation both within and across workers. For Figure 2, we collapse the data to the worker level by computing each worker’s mean daily productivity over time. For Figure 3, we collapse the data to the daily level by computing 

the mean output of all workers on each day. This significant variation suggests that 

both worker ability and environmental conditions appear to be important drivers of 

worker productivity.

To illustrate the relationship between ozone and temperature, Figure 4 plots the 

demeaned average hourly ozone and temperature by day separately for the 2009 

and 2010 ozone seasons, with an indicator for days on which harvesting occurs for 

each crop. This Figure reveals considerable variation in both variables over time. 

Importantly, while ozone and temperature are often correlated—temperature is an 

input into the production of ozone—there is ample independent variation for conducting our proposed empirical tests.12 We also control for temperature flexibly to 

ensure that we are properly accounting for this relationship.

III. Conceptual Framework

In this section, we develop a simple conceptual model to illustrate worker incentives under a piece-rate regime with a minimum wage guarantee. We begin by assuming that the output q for any given worker is a function of effort e and pollution levels 

Ω. Workers are paid piece rate p per unit output, but only if their total daily wage 

12The R2

 from a regression of ozone on temperature alone is 0.61. When we more flexibly control for temperature and also include additional environmental variables as specified in the econometric model (described below), 

the R2 increases to 0.85.

0

0.2

0.4

0.6

Density

−3 −2 −1 0 1

Figure 3. Variation in Productivity by Day, All Crops

Notes: This figure plots the mean of the standardized average hourly pieces for all crops by day. We standardize 

average hourly productivity by subtracting the minimum number of pieces per hour required to reach the piece rate 

regime and dividing by the standard deviation of productivity for each crop.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3661

is at least as large as the daily minimum wage _y .

13 In anticipation of our empirical 

model, we let zero denote the threshold level of output at which workers graduate 

from the minimum wage regime. Since employment contracts are extremely shortlived, we assume that the probability of job retention τ is an increasing function of 

output levels q when q < 0.14 Denoting the costs of worker effort as c(e) and the 

value associated with job retention as k, we can characterize the workers’ maximization problem above and below the threshold output level.

For those workers whose output level qualifies them for the piece-rate wage 

(q ≥ 0), effort will be chosen in order to maximize the following:

(2) maxe p ⋅ q (e,Ω) − c(e).

13While minimum wage standards are typically fixed at an hourly rate, the fixed-length workday in our setting 

allows us to translate this into a daily rate. 14The assumption of perfect retention for those above the threshold is made for simplicity. As long as the probability of job retention is higher for those workers whose harvest levels exceed the threshold, the basic intuition 

behind the results that follow remain unchanged.

Panel A. Year 2009 

−40

−30

−20

−10

0

10

20

30

40

5/1/2009 6/1/2009 7/1/2009 8/1/2009 9/1/2009 10/1/2009

Demeaned value

ozone temperature crop 1 crop 2 crop 3

Panel B. Year 2010 

−40

−30

−20

−10

0

10

20

30

40

5/1/2010 6/1/2010 7/1/2010 8/1/2010 9/1/2010 10/1/2010

Demeaned value

ozone temperature crop 1 crop 2 crop 3

Figure 4. Average Demeaned Daily Ozone and Temperature, and Crop Harvest Days, by Year

Note: These figures plot demeaned ozone and temperature levels by day for 2009 and 2010, and indicate the days 

each of the three crops were harvested.
3662 THE AMERICAN ECONOMIC REVIEW december 2012

For those workers whose output level places them under the minimum wage regime 

(q < 0), effort will be chosen to maximize the following:

(3) maxe 

_

y − τ (q (e,Ω)) k − c (e).

The first-order conditions for each are

(2′) p ⋅ _

∂q

∂e

 − _∂c

∂e

 = 0;

(3′) − 

_∂τ

∂q

 _

∂q

∂e

 k − _∂c

∂e

 = 0.

Under the piece-rate regime, workers will supply effort such that the marginal cost 

of that effort is equal to additional compensation associated with that effort. For 

those workers being paid minimum wage, the incentive to supply effort is driven 

entirely by concerns about job security.15 Workers supply effort such that the marginal cost of that effort is equal to the increased probability of job retention associated with that effort times the value of job retention.

The threat of punishment for low levels of output is instrumental in inducing 

effort under the minimum wage regime. If workers are homogenous and firms set 

contracts optimally, the gains from job retention due to extra effort will be set equal 

to the piece-rate wage, i.e., − 

_∂τ

∂q

k = p, such that effort exertion will be identical 

across both segments of the wage contract. If firms are unable to design optimal 

contracts, effort will differ across regimes. Of particular concern is the situation in 

which termination incentives are low-powered; i.e., − 

_∂τ

∂q

k < p. In this case, workers 

essentially have a limited liability contract, and thus have incentives to shirk under 

the minimum wage regime. Moreover, since the productivity impacts of pollution 

increase the probability of workers falling under the minimum wage portion of the 

compensation scheme, pollution will also indirectly increase the incentive to shirk, 

which we must account for in our econometric model.

IV. Econometric Model

The worker maximization problem characterized in the previous section suggests 

the following econometric model:

(4) E[q|Ω,X] = P(q ≥ 0 |Ω,X) × E[q|Ω,X,q ≥ 0]

+ (1 − P(q ≥ 0 |Ω,X)) × E[q |Ω,X,q < 0],

where P is the probability a worker has output high enough to place them in the 

piece-rate regime, 1 − P is the probability a worker’s output places them in the 

15This is conceptually quite similar to the model of efficiency wages and unemployment advanced in Shapiro 

and Stiglitz (1984), where high wages and the threat of unemployment induce workers to supply costly effort.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3663

minimum wage regime, and X are other factors that affect productivity (described 

in more detail below). We are primarily interested in the direct effect of pollution 

on productivity (the environmental productivity effect), and use two approaches for 

estimating this relationship. First, we estimate the following linear model:

(5) q = βolsΩ + θolsX + εols,

where βols is the sum of the direct impact and, if it exists, the indirect impact of 

pollution on productivity via shirking. If the piece-rate contract is set optimally by 

imposing an appropriate termination threat as described in the previous section, 

there is no incentive to shirk, and βols will only capture the environmental productivity effect.16 To the extent that contracts are not set optimally and there is an incentive 

to shirk in the minimum wage regime, βols will instead reflect not only the environmental productivity effect, but also the indirect effect due to the interaction of this 

pollution effect with shirking incentives, and hence provide an upper bound of the 

estimate of the environmental productivity effect.

To account for potential shirking, as a second approach we estimate equation (4)

by artificially “bottom-coding” our data and estimating censored regression models. 

To do this, we leave all observations in the piece-rate regime as is, but assign a measure of productivity of 0 to all observations in the minimum wage regime.17 Thus, 

our estimation strategy can be viewed as a Type I Tobit model of the following form:

(6) q* = βcenΩ + θcenX + εcen

q = q* if q ≥ 0

q = 0 if q < 0,

where q* is the latent measure of productivity. Because we are interested in the 

impact of pollution on actual productivity, which can take on values less than zero, 

the environmental productivity effect is the marginal effect of pollution on the latent 

variable q*, which is simply βcen. Importantly, the actual values of productivity in the 

minimum wage regime will have no impact on the likelihood function, and hence on 

βcen. That is, if shirking occurs so that the distribution of productivity in the minimum wage regime is shifted to the left, this shift will no longer influence estimates 

of βcen because they have been censored. Therefore, even if workers are shirking 

when paid minimum wage, our estimates of βcen will only capture the environmental 

productivity effect.

We include data from all crops in one regression by using the standardized measures of productivity described in the data section. We specify ozone in units of 

10 ppb since this value is close to prior and recently proposed policy changes for 

ozone in the United States. Given our standardization of the dependent variable, the 

16Although environmental conditions may affect workers, they may also have a direct impact on crops. While 

there is considerable evidence to support the claim that chronic exposure to ozone affects crop yield (see, e.g., 

Manning, Flagler, and Frenkel 2003), there is no evidence to support an effect from acute exposure. 17Because of our standardization of productivity, a value of 0 represents the value when workers switch from the 

minimum wage to piece rate regime.
3664 THE AMERICAN ECONOMIC REVIEW december 2012

coefficients can be interpreted as a standard deviation change in productivity from 

a 10 ppb change in ozone. To control for other factors that may affect productivity, the vector X includes controls for gender, tenure with the farm (a quadratic), 

temperature, humidity, precipitation, wind speed, air pressure, solar radiation, and 

fine particulate matter (PM2.5), all measured as the mean over the typical workday. 

Since ozone is formed in part because of temperature and sunlight, it is essential 

that we properly control for these variables. To do this, we include a series of temperature indicator variables for every 2.5 degrees Fahrenheit, and also interact these 

indicators with solar radiation. To control for humidity, we use dew point temperature, a measure of absolute humidity that is not a function of temperature (Barreca 

2012), and also include indicator variables for every 2.5 degrees Fahrenheit. We 

also include a series of day-of-week indicators to capture possible changes in productivity throughout the week, indicator variables for the crop to account for the 

mean shift in productivity from different contracts, and year-month dummies to control for trends in pollution and productivity within and across growing seasons. All 

standard errors are two-way clustered on the date because the same environmental 

conditions are assigned to all workers on a given day and on the worker to account 

for serial correlation in worker productivity (Cameron, Gelbach, and Miller 2011).

In addition to the aforementioned concerns regarding shirking, several additional 

primary threats to identification remain. As previously discussed, potential confounding due to weather may bias results, so we control flexibly for temperature 

and sunlight—two important inputs into the ozone formation process. Furthermore, 

labor supply decisions may respond to ozone levels. Since we have measures of days 

and hours worked, we directly explore such responses. Lastly, if there is heterogeneity in the productivity effects of ozone and workers select into crops, this may hinder 

inference. To assess this, we explore both the heterogeneity of ozone effects and 

whether ozone or worker characteristics are related to crop assignment.

V. Results

A. Labor Supply Responses

We begin by assessing our earlier claim that the labor supply of agricultural workers is insensitive to ozone levels in this setting. We estimate linear regression models 

for the decision to work and the number of hours worked (conditional on working), 

both with and without worker fixed effects. Shown in Table 2, the results in the first 

two columns, which focus on the decision to work, provide no evidence of a labor 

supply response to ozone.18 The second two columns also reveal that the number of 

hours worked is not significantly related to ozone levels. Even at the lower 95 percent confidence interval, a 10 ppb increase in ozone is associated with a 0.28 drop in 

hours worked, which is a roughly 17-minute decrease in hours worked. The insensitivity of these results to including worker fixed effects strengthens our confidence in 

these findings. Thus, consistent with our contention that avoidance behavior is not 

18Marginal effects from logit and probit models for the decision to work are virtually identical to the results from 

the linear probability model.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3665

an issue in this setting, farm workers do not appear to adjust their work schedules in 

response to ozone levels.

B. Main Productivity Results

In Table 3, we present our main results. Column 1 presents results from our 

linear regression model. The estimated coefficient suggests that a 10 ppb increase 

in ozone leads to a statistically significant decrease in productivity of 0.143 of a 

standard deviation.19 Based on the distribution of ozone and productivity in our 

sample, this estimate implies that a 10 ppb decrease in ozone increases worker 

19Although we control for other local pollutants that might affect productivity, such as PM2.5, we do not control 

for NOx because it is a precursor to ozone formation. The transport of ozone, however, suggests that most of the 

Table 2—Regression Results of the Effect of Ozone on Avoidance Behavior

Extensive margin: 

probability(work)

Intensive margin: 

hours worked

(1) (2) (3) (4)

Ozone (10 ppb) 0.001 −0.001 0.015 0.026

[0.026] [0.027] [0.149] [0.154]

Worker fixed effect N Y N Y

Mean of dep. var. 0.905 0.905 7.52 7.52

Observations 39,223 39,223 35,461 35,461

R2 0.12 0.17 0.33 0.36

Notes: Standard errors clustered on date and worker in brackets. Hours worked is conditional 

upon working. All regressions include controls for gender, farm tenure (quadratic), temperature (2.5 degree F indicators), solar radiation, temperature (2.5 degree F indicators)×solar 

radiation, air pressure, wind speed, dew point (2.5 degree F indicators), precipitation, particulate matter < 2.5 μg, day of week dummies, month×year dummies, and piece rate contract 

type dummies. All environmental variables are the mean of hourly values from 6 am–3 pm.

Table 3—Main Regression Results of the Effect of Ozone on Productivity

(1) (2) (3) (4)

Ozone (10 ppb) −0.143** −0.174** −0.164 −0.155

[0.068] [0.074] [0.109] [0.100]

Model Linear Tobit Median Censored median

Mean of dep. var. −0.323 −0.323 −0.323 −0.323

Observations 35,461 35,461 35,461 25,955

(Psuedo) R2 0.34 0.12 0.22 0.28

Notes: Standard errors clustered on date and worker in brackets. The dependent variable is 

standardized hourly pieces collected, which is the average hourly productivity minus the minimum number of pieces per hour required to reach the piece rate regime, divided by the standard 

deviation of productivity for each crop. All regressions include controls for gender, farm tenure (quadratic), temperature (2.5 degree F indicators), solar radiation, temperature (2.5 degree 

F indicators)×solar radiation, air pressure, wind speed, dew point (2.5 degree F indicators), 

precipitation, particulate matter < 2.5 μg, day of week dummies, month×year dummies, and 

piece rate contract type dummies. All environmental variables are the mean of hourly values from 6 am–3 pm. Bootstrapped standard errors for both median regressions were obtained 

using 250 replications.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

 *Significant at the 10 percent level.
3666 THE AMERICAN ECONOMIC REVIEW december 2012

productivity by 5.5 percent. If wage contracts are set optimally, this is an unbiased 

estimate of the effect of ozone pollution. If contracts are not set optimally and 

workers shirk when the minimum wage binds, then this estimate will overstate the 

impact of ozone. In column 2 we show results from a Type I Tobit model, where 

we artificially censor observations when the minimum wage binds, and find a 

slightly larger estimate of 0.174 standard deviation effect from a 10 ppb change 

in ozone, with the difference not statistically different from those found under the 

linear model.20

Since this Tobit model assumes normality and homoskedasticity, we assess the 

sensitivity of our results to these assumptions by estimating a censored median 

regression model, also displaying results from an uncensored median regression 

model as a reference point.21 Shown in column 3, the median regression estimate 

of 0.164 is quite comparable to the linear regression estimate, which is not surprising given the distribution of productivity shown in Figure 1. The censored median 

regression estimate of 0.155, shown in column 4, is also quite similar to the estimates from the parametric censored models, lending support to the parametric 

assumptions of the Tobit model. The comparability of the four estimates in this table 

suggests that shirking due to the minimum wage is relatively minimal in this setting. 

Thus, the basic linear regression specification appears to yield unbiased estimates of 

the pollution productivity effect.22

In Table 4, we explore the sensitivity of the linear estimates to various additional 

assumptions. Column 1 repeats the baseline results. In column 2 we include worker 

fixed effects. Although this increases the explanatory power of our regressions considerably, the estimates for ozone fall somewhat to 0.101, though this change is 

not statistically significant. Thus, consistent with the notion that workers are not 

selecting into employment on any given day based on ozone concentrations, crosssectional and fixed effects estimates are quite similar.

Figure 1 provided some evidence that worker effort changes near the regime 

threshold, particularly for crop 1 where contracts are TPAP. If higher ozone levels 

reduce productivity and hence make it more likely for workers to fall into the minimum wage regime, this offsetting increase in effort may bias our results downward. 

In the next two columns of Table 4, we address this by excluding observations that 

are close to the regime threshold, varying our definition of “close.” Consistent with 

expectations, our results are slightly larger as we exclude more observations, but 

these differences are minimal.

While our data agreement entitles us to productivity data aggregated to the daily 

level, we have time-stamped measures for crop 1, thus allowing us to explore how 

the impacts of ozone vary throughout the day. There are two notable limitations in 

NOx that contributes to the production of ozone is emitted in urban centers far from the farm. Consistent with this, 

if we add a control for local NOx, the coefficient on ozone changes minimally. 20Consistent with these results, if we specify the dependent variable as the probability the worker reaches the 

piece-rate regime, we find that ozone reduces this probability by 5.9 percentage points and is statistically significant 

at the 10 percent level. 21We estimate a censored median model using the three-step procedure developed by Chernozhukov and Hong 

(2002). 22Consistent with the notion that shirking may be minimized through the threat of termination, we find that 

workers in the lower deciles of the productivity distribution are much more likely to separate from the farm than 

those in the upper deciles (unreported results available upon request from the authors).
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3667

this intraday analysis: (i) while pieces can be delivered at any time, environmental 

variables are measured by clock hour; and (ii) workers sometimes deliver several 

pieces at once. As a result, we construct hourly productivity measures using linear 

interpolation. We then use this linearly interpolated hourly data to examine intraday 

impacts by interacting ozone with the hour of the day, also controlling for hour of the 

day to account for changes in fatigue as the day progresses. Although the estimate 

for each hour is not statistically significant at conventional levels, which is not surprising given the measurement error induced by interpolation, the estimates suggest 

a pattern whereby ozone begins to impact productivity by 10 am and remains fairly 

steady from that point onward (results available upon request).

To address potential concerns about the cumulative effect of ozone exposure, we 

also present results that include one- and two-day lags of ozone. Since ozone levels 

may only reflect exposure on days when workers actually work, we limit our focus 

to days when workers have worked the previous day by excluding from our analysis 

the first one or two days of the workweek depending on how many lags we include 

in our specification. Shown in column 5 of Table 4 are results without any lags but 

excluding Monday, which are slightly higher than the baseline results. Including one 

lag of ozone, shown in column 6, we find that the coefficient on contemporaneous 

ozone remains the same, and lagged ozone is negative but statistically insignificant. 

The results in column 7 show that excluding the first two workdays continues to 

increase the contemporaneous coefficient on ozone. Including two lags of ozone, 

column 8 shows that the coefficient on contemporaneous ozone remains statistically 

Table 4—Sensitivity of Regression Results of the Effect of Ozone on Productivity

(1) (2) (3) (4) (5) (6) (7) (8)

Ozone −0.143** −0.101* −0.148** −0.160** −0.197*** −0.197*** −0.248*** −0.229***

(10 ppb) [0.068] [0.059] [0.075] [0.080] [0.0683] [0.0686] [0.0788] [0.0842]

1 lag ozone 0.004 −0.066

(10 ppb) [0.045] [0.056]

2 lag ozone 0.114**

(10 ppb) [0.0493]

Sum of coefficients −0.193 −0.182

[0.076]** [0.100]*

Model Baseline Worker 

fixed 

effect

Exclude 

obs. 0.1 SD 

of regime 

threshold

Exclude 

obs. 0.2 SD 

of regime 

threshold

Exclude 

Monday

Exclude 

Monday

Exclude 

Monday 

and 

Tuesday

Exclude 

Monday 

and 

Tuesday

Mean of dep. var. −0.323 −0.323 −0.360 −0.389 −0.235 −0.235 −0.183 −0.183

Observations 35,461 35,461 31,706 29,376 25,456 25,456 17,498 17,498

R2 0.34 0.59 0.36 0.38 0.36 0.36 0.35 0.36

Notes: Standard errors clustered on date and worker in brackets. The dependent variable is standardized hourly 

pieces collected, which is the average hourly productivity minus the minimum number of pieces per hour required 

to reach the piece rate regime, divided by the standard deviation of productivity for each crop. All regressions are 

based on linear models that include controls for gender, farm tenure (quadratic), temperature (2.5 degree F indicators), solar radiation, temperature (2.5 degree F indicators)×solar radiation, air pressure, wind speed, dew point 

(2.5 degree F indicators), precipitation, particulate matter < 2.5 μg, day of week dummies, month×year dummies, 

and piece rate contract type dummies. All environmental variables are the mean of hourly values from 6 am–3 pm.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

 *Significant at the 10 percent level.
3668 THE AMERICAN ECONOMIC REVIEW december 2012

significant and again unchanged, while one lag of ozone is statistically insignificant 

and the second lag is significant but positive, with colinearity of ozone across days 

as one possible explanation for the seemingly perverse sign. Most notably, the sum 

of the ozone coefficients is quite close to the contemporaneous effect regardless 

of the lags included. Together, these estimates suggest that the predominant effect 

of ozone is from same-day exposure, with an overnight respite from ozone sufficient for lung functioning to return to baseline levels. Moreover, this rapid recovery 

implies that the environmental productivity effects measured in this paper are predominantly impacting a healthy population.23

Throughout our analysis, we have assumed ozone has a linear effect on productivity. In Figure 5, we present estimates that allow for a nonlinear effect by including 

indicator variables for every 4 ppb of ozone, omitting < 30 ppb as the reference 

category. As shown, the figure illustrates a relatively linear and steady increase in 

the productivity impacts of ozone over the entire range of ozone. Perhaps more 

importantly, the impacts appear to become statistically significant at 42–46 ppb, a 

23Recall from Section II that chamber studies suggest a rapid recovery from ozone exposure for healthy individuals. As further evidence consistent with these workers being generally healthy, we find that lagged ozone levels 

are not significantly related to the decision to work.

−2

−1.5

−1

−0.5

0

0.5

Effect of ozone

30–34 34–38 38–42 42–46 46–50 50–54 54–58 58–62 62+

Ozone (ppb)

Figure 5. Regression Results of the Effect of Ozone on Productivity 

Using More Flexible Controls for Ozone

Notes: This figure plots the coefficients for the ozone indicator variables (< 30 ppb reference category), with the 

95 percent confidence interval based on standard errors clustered on date and worker in gray. The dependent variable is standardized hourly pieces collected, which is the average hourly productivity minus the minimum number of pieces per hour required to reach the piece rate regime, divided by the standard deviation of productivity for 

each crop. The regression includes controls for gender, farm tenure (quadratic), temperature (2.5 degree F indicators), solar radiation, temperature (2.5 degree F indicators)×solar radiation, air pressure, wind speed, dew point 

(2.5 degree F indicators), precipitation, particulate matter < 2.5 μg, day of week dummies, month×year dummies, 

and piece rate contract type dummies. All environmental variables are the mean of hourly values from 6 am–3 pm.
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3669

concentration well below current air quality standards of 75 ppb or even proposed 

reforms of 60 ppb.

C. Heterogeneity of Productivity Results

To assess whether individuals are differentially affected by ozone, we explore potential heterogeneity by interacting ozone with the limited worker characteristics in our 

dataset (tenure with the farm and gender) and with the crop, shown in Table 5.24 While 

24We also estimated quantile regression models for each decile of worker productivity, and found that ozone has a 

similar effect on worker productivity throughout the entire productivity distribution (results available upon request).

Table 5—Heterogeneity of Regression Results of the Effect of Ozone on Productivity

(1) (2) (3) (4) (5)

Ozone (10 ppb) −0.143** −0.149** −0.169** −0.135* −0.006

[0.068] [0.075] [0.069] [0.076] [0.041]

Ozone (10 ppb)×tenure −0.007

[0.015]

Ozone (10 ppb)×tenure2 0.002

[0.001]

Ozone (10 ppb)×female 0.040**

[0.017]

Ozone (10 ppb)×unknown 0.029

[0.025]

Ozone (10 ppb)×crop1 −0.216***

[0.071]

Ozone (10 ppb)×crop2 0.149**

[0.060]

Tenure 0.038* 0.083 0.039* 0.054** 0.000

[0.023] [0.077] [0.023] [0.022] [0.015]

Tenure2 −0.002 −0.013* −0.002 −0.003* 0.002

[0.002] [0.007] [0.002] [0.002] [0.001]

Female −0.094*** −0.092*** −0.284*** −0.093*** 0.257***

[0.035] [0.035] [0.083] [0.035] [0.039]

Unknown 0.069 0.068 −0.07 0.062 0.093*

[0.050] [0.050] [0.125] [0.049] [0.053]

Model Baseline

Tenure

interaction

Gender

interaction

Crop

interaction y = pr(crop 2)

Mean of dep. var. −0.323 −0.323 −0.323 −0.323 0.443

Observations 35,461 35,461 35,461 35,461 20,034

R2 0.344 0.346 0.345 0.356 0.201

Notes: Standard errors clustered on date and worker in brackets. The dependent variable in columns 1–4 is standardized hourly pieces collected, which is the average hourly productivity minus the minimum number of pieces 

per hour required to reach the piece rate regime, divided by the standard deviation of productivity for each crop. 

The dependent variable in column 5 is whether the worker harvested crop 2, and the sample is restricted to days 

when only crop 2 or 3 are harvested. In addition to covariates shown, all regressions are based on linear models 

that include controls for temperature (2.5 degree F indicators), solar radiation, temperature (2.5 degree F indicators)×solar radiation, air pressure, wind speed, dew point (2.5 degree F indicators), precipitation, particulate 

matter < 2.5 μg, day of week dummies, month×year dummies, and piece rate contract type dummies. All environmental variables are the mean of hourly values from 6 am–3 pm. “Unknown” indicates that gender was not reported 

in our data.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

 *Significant at the 10 percent level.
3670 THE AMERICAN ECONOMIC REVIEW december 2012

workers with more experience may be more resilient to ozone by being better able 

to pace themselves throughout the day, column 2 finds no such evidence. Interacting 

ozone with a quadratic in tenure is statistically insignificant and the level effect of 

ozone is largely unchanged. Shown in column 3, we find that ozone has a smaller 

impact on productivity for women.25 While the magnitude of the difference between 

the effect for men and women is quite small, this result is contrary to laboratory studies that generally find no differential impact on lung functioning by gender (Hazucha, 

Folinsbee, and Bromberg 2003). Column 4 interacts ozone with crop dummy variables and reveals considerable heterogeneity in the productivity effects of ozone. The 

effect for crop 1 is significantly larger than crop 3 (the reference category), while the 

effect for crop 2 is significantly smaller. Since crops 2 and 3 are both paid time plus 

pieces, these differences are not driven by the different contract types.

To understand this source of heterogeneity, we first explore whether worker 

assignment to crop may explain these patterns. To assess this, we run a regression 

to predict working on crop 2, limiting our sample to days when only crop 2 or 

3 is harvested (since crop 1 is harvested in a different time period). As shown in 

column 5, gender is related to crop assignment: females are more likely to select 

into crop 2. Given that females are less affected by ozone, this suggests that gender 

selection into crops may explain some of this heterogeneity. Based on estimates 

from columns 3–5, however, gender selection can only explain 7 percent of the crop 

heterogeneity, suggesting that other factors must explain the differential effects by 

crop.26 Importantly, ozone is not related to crop assignment, confirming that our 

estimates represent a valid estimate of the average treatment effect across the crops.

One explanation for this heterogeneity may be the differing physical demands 

placed on workers across crops. While crops 2 and 3 (grapes) are trellised such that 

harvestable fruit is waist to shoulder height, crop 1 (blueberries) grows closer to the 

ground, which requires considerable bending for workers and thus requires more 

energy to harvest. Within grapes, the crop 2 varietal is a delicate one that requires 

a slower and more careful harvest to avoid fruit damage, thus placing less physical 

demands on workers. Therefore, our findings that crop 1, which places the greatest 

physical demands on workers, is most affected by ozone and crop 2, which places 

the least physical demands, is least affected is consistent with laboratory studies 

(discussed in Section II) that find lung functioning impairment due to ozone is exacerbated by exercise.

VI. Conclusion

In this paper, we merge a unique dataset on individual-level daily harvest rates 

for agricultural workers with data on environmental conditions to assess the impact 

of ozone pollution on worker productivity. We find that a 10 ppb change in average 

ozone exposure results in a significant and robust 5.5 percent change in agricultural 

worker productivity. Importantly, this environmental productivity effect suggests 

25Despite the smaller impact of ozone for females, the coefficient on gender reveals that female productivity is 

considerably lower than male productivity on average. As discussed in Table 1, gender is not reported for roughly 

19 percent of the sample. 26We obtain this estimate of 7 percent by multiplying the differential effect of ozone by gender (0.04) by the 

selection into crop 2 (0.257), and dividing it by the amount of heterogeneity (0.149).
VOL. 102 NO. 7 graff zivin and neidell: pollution and worker productivity 3671

that common characterizations of environmental protection as purely a tax on producers and consumers to be weighed against the consumption benefits associated 

with improved environmental quality may be misguided. Environmental protection can also be viewed as an investment in human capital, and its contribution to 

firm productivity and economic growth should be incorporated in the calculus of 

policymakers.

Our results also speak to the ongoing debates on ozone policy. Ozone pollution 

continues to be a pervasive environmental issue throughout much of the world. 

Debates over the optimal level of ozone have ensued for many years, and current 

efforts to strengthen these standards remain contentious. Defining regulatory standards depends, in part, on the benefits associated with avoided exposure, which has 

traditionally been estimated through a focus on high-visibility health effects such as 

hospitalizations. The labor productivity impacts measured in this paper help make 

these benefits calculations more complete. Our results indicate that ozone, even at 

levels below current air-quality standards in most of the world, has significant negative impacts on worker productivity, suggesting that the strengthening of regulations 

on ozone pollution would yield additional benefits.

These impacts of ozone on agricultural workers are also important in their own 

right. A back-of-the envelope calculation that applies the environmental productivity effect estimated in the Central Valley of California to the whole of the United 

States suggests that the 10 ppb reduction in the ozone standard currently being considered by EPA would translate into an annual cost savings of approximately $700 

million in labor expenditure.27 In the developing world, where national incomes 

depend more heavily on agriculture, these productivity effects are likely to have a 

much larger impact on the economy and the well-being of households. Nearly 1.1 

billion individuals—35 percent of the active labor force—work in the agricultural 

sector worldwide (International Labour Organization 2011). The impacts of ozone 

may be especially large in countries like India, China, and Mexico, where rapid 

industrial growth and automobile penetration contribute precursor chemicals that 

contribute to substantially higher levels of ozone pollution.

While the impacts of ozone on agricultural productivity are large, the generalizability of these findings to other pollutants and industries is unclear. Agricultural 

workers face considerably higher levels of exposure to pollution than individuals 

who work indoors. That said, roughly 11.8 percent of the US labor force works in 

an industry with regular exposure to outdoor conditions, and this figure is much 

higher for middle- and lower-income countries (Graff Zivin and Neidell forthcoming). Moreover, many forms of outdoor pollution diminish indoor air quality as well. 

For example, indoor penetration of fine particulate matter ranges from 38–94 percent for typical residential homes in the United States (Abt et al. 2000). Examining 

the generalizability of the environmental productivity effect estimated in this paper 

to other pollutants and industries represents a fruitful area for future research.

27Total labor expenditure in US agriculture was approximately $26.5 billion in 2007 (United States Department 

of Agriculture 2009). Ozone season in California runs from April through October. Using the conservative assumption that the seasonal distribution of agricultural labor expenditure is flat (it is likely lower in winter) yields a total 

annual expenditure of $13.25 billion that is exposed to ozone productivity risk. The calculation assumes that the new 

standard shifts the entire distribution of ozone down by 10ppb and not just values that exceed air quality standards.
3672 THE AMERICAN ECONOMIC REVIEW december 2012

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