Testing the water: Drinking water quality, public notification, and child outcomes Michelle Marcus * February 3, 2020 Abstract Health-based drinking water violations affect about 1 in 12 Americans each year, yet we know relatively little about the health and human capital benefits of drinking water regulation. This is the first paper to study the impact of drinking water violations on children and adolescents in the US. I quantify the costs of poor drinking water quality using a comprehensive approach that considers avoidance behavior, human health, and human capital. Individuals successfully avoid negative health and human capital impacts of coliform bacteria only when informed immediately. Timely public notification is a cost-effective way to induce avoidance behavior and protect health. Disasters like the Flint water crisis have highlighted the need for careful monitoring of drinking water supplies in the United States. Estimates of the total number of waterborne illnesses associated with drinking water range from 11.69-19.5 million cases per year in the U.S. (Reynolds et al., 2008; Colford et al., 2006). In 2015, there were 12,137 health-based drinking water violations at 5,009 community water systems serving about 27 million people across the country, or nearly 1 in 12 Americans. Despite the large number of individuals potentially impacted by these violations, we know relatively little about the benefits of drinking water regulation or how behavioral responses to public notifications protect health. As disadvantaged children are more often exposed to water quality violations, the impacts from regulation may contribute to existing gaps in health and human capital accumulation by socio-economic status. To understand the benefits of drinking water regulation, it is critical to quantify the costs of poor drinking water quality, which may include costs of avoidance behavior, human health consequences and lost productivity, and even long-run implications for human capital formation. Regulations allowing delay in public notification * Vanderbilt University, Department of Economics, 415 Calhoun Hall, Nashville, TN 32740. E-mail: [email protected]. For their helpful comments, I would like to thank Anna Aizer, Brian Beach, Ryan Brown, Christopher Carpenter, Arnaud Chevalier, Janet Currie, Chloe East, Maria Fitzpatrick, Dennis Guignet, Kanghyock Koh, Angelica Meinhofer, Paulina Oliva, Sheila Olmstead, Emily Oster, Daniel Rees, Jay Shimshack, David Slusky, Will Wheeler, and participants at AERE, NBER Summer Institute, ASSA, SEA, and CEPR-EBRD- EoT-LSE Symposium. The conclusions drawn from the Nielsen data are those of the researchers and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. 1
Transcript
Testing the water:
Drinking water quality, public notification, and child outcomes
Michelle Marcus∗
February 3, 2020
Abstract
Health-based drinking water violations affect about 1 in 12 Americans each year, yet we knowrelatively little about the health and human capital benefits of drinking water regulation. Thisis the first paper to study the impact of drinking water violations on children and adolescents inthe US. I quantify the costs of poor drinking water quality using a comprehensive approach thatconsiders avoidance behavior, human health, and human capital. Individuals successfully avoidnegative health and human capital impacts of coliform bacteria only when informed immediately.Timely public notification is a cost-effective way to induce avoidance behavior and protect health.
Disasters like the Flint water crisis have highlighted the need for careful monitoring of drinking
water supplies in the United States. Estimates of the total number of waterborne illnesses associated
with drinking water range from 11.69-19.5 million cases per year in the U.S. (Reynolds et al., 2008;
Colford et al., 2006). In 2015, there were 12,137 health-based drinking water violations at 5,009
community water systems serving about 27 million people across the country, or nearly 1 in 12
Americans. Despite the large number of individuals potentially impacted by these violations, we
know relatively little about the benefits of drinking water regulation or how behavioral responses
to public notifications protect health. As disadvantaged children are more often exposed to water
quality violations, the impacts from regulation may contribute to existing gaps in health and
human capital accumulation by socio-economic status. To understand the benefits of drinking
water regulation, it is critical to quantify the costs of poor drinking water quality, which may
include costs of avoidance behavior, human health consequences and lost productivity, and even
long-run implications for human capital formation. Regulations allowing delay in public notification
∗Vanderbilt University, Department of Economics, 415 Calhoun Hall, Nashville, TN 32740. E-mail:[email protected]. For their helpful comments, I would like to thank Anna Aizer, Brian Beach, RyanBrown, Christopher Carpenter, Arnaud Chevalier, Janet Currie, Chloe East, Maria Fitzpatrick, Dennis Guignet,Kanghyock Koh, Angelica Meinhofer, Paulina Oliva, Sheila Olmstead, Emily Oster, Daniel Rees, Jay Shimshack,David Slusky, Will Wheeler, and participants at AERE, NBER Summer Institute, ASSA, SEA, and CEPR-EBRD-EoT-LSE Symposium. The conclusions drawn from the Nielsen data are those of the researchers and do not reflectthe views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparingthe results reported herein.
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may affect the ability of individuals to protect themselves from exposure. Public notification rules
are an important policy lever that can be used to encourage avoidance and reduce exposure. Yet,
it remains unknown whether information dissemination is socially efficient, as individuals may
over-respond to pollution information.
This paper takes a comprehensive approach to studying the consequences of drinking water
quality violations on children and adolescents in the US by considering the impacts on both avoid-
ance behavior and several different measures of health and human capital. This paper further
considers whether the health and human capital benefits from reduced exposure to poor drinking
water exceed the cost of avoidance behavior. First, I explore how the method and timing of public
notification impacts avoidance behavior through bottled water purchases. Next, I quantify the hu-
man health impacts of poor water quality by looking at both emergency room visits and purchases
of over-the-counter remedies for gastrointestinal illness. Because emergency room visits can only
capture extreme health effects, I also consider the impact of drinking water violations on school
absences, which may provide a more sensitive measure of health. Moreover, school absences are
directly of interest since they may impact school performance, graduation rates, grade retention,
later-life earnings, and the intergenerational transmission of human capital. Absences may also
reduce parental earnings if parents miss work to take care of a sick child, and may even reduce
school funding when funding is dependent on student attendance. Even if students are not sick
enough to miss school, there may be additional effects of poor water quality on concentration and
learning. Therefore, I also examine the impact of drinking water violations throughout the year
and during the student testing window on test scores.
Findings show that immediacy of public notification allows behavioral responses that protect
health and improve human capital accumulation. Violations requiring immediate public notification
increase bottled water purchases, but have no significant impacts on health, while violations requir-
ing notice only within 30 days have no impact on bottled water purchases, yet increase purchases of
common stomach remedies, ER visits for gastrointestinal illness, and school absences, and decrease
math test scores. Back-of-the-envelop calculations suggest that providing immediate information
for all coliform violations would have increased avoidance cost by about $365,000 during the study
period, which is small relative to the estimated $10.4 million due to the health and human capital
consequences of exposure.
Relative to the growing evidence on the harmful effects of air pollution, research on the impacts
of water pollution has been limited (Keiser and Shapiro, 2019). Existing research has focused
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only on the impact of water quality on mortality and infant health. Historically, the adoption of
clean water technologies during the early 20th Century sharply reduced typhoid mortality, which
also had important impacts on human capital formation (Cutler and Miller, 2005; Beach et al.,
2016; Anderson et al., 2018). At modern levels of water pollution, some evidence shows that
drinking contaminated water harms infant health at birth, especially among infants born to less
educated mothers (Currie et al., 2013; Hill and Ma, 2017). However, less is known about the effects
of exposure to modern levels of water pollution during childhood or adolescence. Exposure to
pollution during this critical period of growth and development may have detrimental impacts on
short- and long-term health, as well as human capital formation and subsequent earnings. This
paper focuses on children and adolescents, a new and sensitive population, and is the first to study
several new measures of health and human capital: ER visits, over-the-counter stomach remedy
purchases, school absences, and test scores. By studying these outcomes simultaneously, this paper
provides the first comprehensive view of the impacts of modern day water quality on child health
and human capital accumulation.
This paper also contributes to a literature on information and transparency (Weil et al., 2006;
Loewenstein et al., 2014). Research has shown that individuals respond to information about
pollution by exhibiting costly avoidance behaviors to protect themselves from health threats. Much
of this research has focused on air pollution (Neidell, 2004, 2009; Moretti and Neidell, 2011).
However, Zivin et al. (2011) show that bottled water purchases increase in response to drinking
water violations in northern California and Nevada from 2001 to 2005, especially for coliform
bacteria violations. I highlight similar avoidance behavior for a new population with a longer and
more recent time series. Importantly, I expand upon this work by separating violations by the
immediacy of public notification required: within 24 hours vs. within 30 days.
In general, it is difficult to estimate a causal effect of water pollution on child outcomes. Existing
evidence has established the presence of extensive residential sorting. Parental income, as well as
preferences for school quality, neighborhood amenities, and environmental quality can generate
bias in cross-sectional estimates, because many of these parental characteristics and preferences are
likely related to underlying health and human capital accumulation. To overcome this challenge,
I exploit several confidential administrative datasets that allow me to control for detailed location
fixed effects, such that variation comes from changing water quality within place over time. I define
exposure to poor water quality using detailed geographic data on community water supply system
service areas in North Carolina.
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First, I explore the role of public notification in inducing avoidance behavior. The Environ-
mental Protection Agency’s (EPA) Public Notification Rule determines when and how community
water systems must inform their customers of health-based violations. Some violations require
immediate public notice within 24 hours, while other violations only require notice within 30 days.
Not only does this paper provide new evidence of the impact of water pollution on child outcomes,
but it tests whether information may be important in mitigating poor outcomes. I focus on viola-
tions of the Total Coliform Rule (TCR), the most commonly violated drinking water standard, and
find a 78 percent increase in bottled water sales during TCR violations that require 24 hour public
notice (“acute” violations), but no statistically significant effect on bottled water sales during TCR
violations that require public notice only within 30 days (“monthly” violations). This suggests the
timing of public notification is important in inducing avoidance behavior.
Next, I explore the effect of water quality violations on direct measures of health, through
emergency room visits and purchases of common stomach remedies from the Nielsen household
consumer panel data. The results show no statistically significant effect on emergency room visits
or purchases of common stomach remedies during TCR violations that require 24 hour public
notification, yet during TCR violations that require public notice only within 30 days purchases
of common gastrointestinal illness remedies increase by 23 percent of a standard deviation and
emergency room visits for gastrointestinal illness increase by 14 percent overall and by 23 percent
among school-aged children. It appears that individuals successfully avoid negative health impacts
only when informed immediately.
Next, school absences provide both an indirect and more sensitive measure of health, as well
as an important input into the production of human capital. I use detailed administrative data
on school absences from two sources. The Principal’s Monthly Report (PMR) provides monthly
absences at the school-grade level. With these data, I show that the timing of absences within
the school year coincides with the timing of violations. I supplement this analysis with student-
level data from the North Carolina Education Research Data Center’s (NCERDC), which provides
latitude and longitude for each student residence and links students across time. These data provide
a precise measure of individual exposure based on the exact location of the home or school address.
Whereas previous research on the health effects of pollution has been limited to studying exposure
at one location, this study is able to explore the role of exposure at two locations where children
spend large portions of their time: home and school. I find that exposure to coliform bacteria at
both home and school matters. While TCR violations requiring immediate 24 hour notice have no
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significant impact on absences, TCR violations requiring notice within 30 days increase absences
by 0.2 percentage points, or 5 percent from the mean.
Finally, I explore the effect of water quality violations on human capital accumulation by looking
at test scores. School absences due to poor water quality may be one channel through which test
scores are affected, but poor water quality may also decrease comprehension and learning even
conditional on attendance. The results suggest that, during the school year, a TCR violation
requiring notice within 30 days decreases math scores by 3 percent of a standard deviation. This
does not appear to be driven by absences, but rather through a separate channel affecting learning
and comprehension.
These findings are especially timely given that recent revisions to the TCR rule, effective as of
2016, eliminated the “monthly” violations that previously required public notice within 30 days.
It was thought that these violations did not have any direct public health effects. Yet, this paper
shows evidence that these violations harm both health and human capital accumulation. “Monthly”
violations increased ER visits for gastrointestinal illness, purchases of common stomach remedies,
school absences, and decreased math test scores among exposed children and adolescents. Not only
is it important to test for “monthly” violations, but providing immediate public notification of
these violations is a low-cost policy tool that can allow individuals to protect themselves from the
harmful effects of exposure.
1 Background
1.1 Water quality and health impacts
Specific disease-producing organisms are difficult, expensive, and time consuming to detect in water.
Instead, coliform bacteria are used as “indicator organisms,” whose presence indicates that harmful
pathogenic organisms may be in the water (see Table 10 in the appendix for pathogens capable of
causing waterborne or water-based disease). Coliform bacteria are microorganisms that are often
associated with human and animal fecal matter. Contamination of drinking water supplies can occur
in several ways: a heavy rain may increase run-off entering the drinking water source, the water
treatment process may be ineffective, or a break in the distribution system may allow contaminants
to enter. Approximately 26% of the distribution pipes in the U.S. are in poor condition and there
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are approximately 237,000 main breaks per year (Reynolds et al., 2008).1 Samples collected in the
field often show higher concentrations of fecal coliforms in surface water, relative to ground water,
especially where run-off from manure is likely (Cox et al., 2005). Water systems with surface water
sources or with distribution systems in poor condition may be at higher risk of coliform bacteria
contamination.
Potential health effects from exposure to waterborne or water-based diseases associated with
coliform bacteria include gastroenteric infections and diseases. Common symptoms include nausea,
vomiting, diarrhea, and stomach cramps. Children, the elderly, and individuals with compromised
immune systems are especially vulnerable. Acute gastroenteritis can be deadly to vulnerable in-
dividuals. Microorganisms can be removed by boiling tap water for at least one minute before
drinking or purchasing bottled water. The costs associated with these avoidance behaviors include
convenience costs, time, and money.
Children drink approximately 7.7 L of tap water per month, with higher consumption among
older children, non-Hispanic whites, and children from higher income families (Drewnowski et al.,
2013). Hispanic children consume relatively larger amounts of bottled water, which is consistent
with existing literature documenting lower perceived tap water safety among Hispanics in the U.S.
(Pierce and Gonzalez, 2017; Hobson et al., 2007).
1.2 Water quality regulation and public notification
The Safe Drinking Water Act (SDWA) was passed in 1974 to protect public health and regulate
public drinking water supplies throughout the US. The SDWA applies to all public water systems
in the US.2 There are currently over 170,000 public water systems in the US that provide water to
almost all individuals in the country.
SDWA authorizes the US EPA to set national health-based standards to protect the public
against both naturally-occurring and man-made contaminants. Threats to drinking water in-
underground, and naturally-occurring substances, for example. Improperly treated or disinfected
drinking water may also harm health. Through the National Primary Drinking Water Regula-
tions the EPA has set enforceable maximum contaminant levels (MCL) for over 90 different con-
1See Ercumen et al. (2014) for a review of the epidemiological literature on the impact of distribution systemdeficiencies on gastrointestinal illness.
2Public water systems are defined as having at least 15 service connections or serve at least 25 people per day for60 days of the year.
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taminants, including microorganisms, disinfectants, disinfectant by-products, inorganic chemicals,
organic chemicals, and radionuclides.
Originally, SDWA focused on water treatment as the primary means of providing safe drinking
water, but amendments to the law in 1996 introduced source water protection, operator training,
funding for water system improvements, and the provision of public information. One component
of the SDWA Amendments in 1996 required suppliers to adhere to the Public Notification Rule,
which specifies when and how the public receives information about violations. This rule is required
across all states. In North Carolina, the revised Public Notification Rule became effective May 6,
2002, prior to the sample period studied here.
The EPA classifies violations into 3 tiers, which vary in their notification requirements. These
notification requirements are consistent across the US. Tier 1 violations require immediate notifi-
cation within 24 hours via radio, TV, hand delivery, posting or other methods. Tier 2 violations
require public notification as soon as possible within 30 days. Tier 3 violations require an annual
notice within one year of the violation date. If 30 percent or more of the public water supply
customers are non-English speaking, the system must provide the notification in the appropriate
language(s) or provide information on how to get assistance or a translated copy.
Each notification must include a description of the violation and contaminant levels, the date the
violation occurred, potential adverse health risks, a description of the population at risk, whether
alternative water supply should be used, what action consumers should take, what the system is
doing to correct the violation, and when the system expects to return to compliance.3
1.3 Total Coliforms Rule
The Total Coliforms Rule (TCR) sets federal standards for bacteria in drinking water and is the
most commonly violated regulation (Bennear and Olmstead, 2008). Systems are required to take
a certain number of routine samples for total coliforms each month. Compliance is determined
each month based on the presence or absence of total coliforms. The number of samples required
depends on the size of the population served, with larger systems required to sample more. Samples
must be collected at regular time intervals throughout the month at sites that are representative of
water quality throughout the distribution system.4 Samples are tested for the presence or absence
3The notification must also include contact information and a statement encouraging recipients to distributethe notice to other individuals served. Example notifications are available on the North Carolina Department ofEnvironmental Protection website: https://deq.nc.gov/public-notification-rule-tier-levels.
4Sampling locations are based on a written sample siting plan subject to state review and revision. Groundwatersystems serving 4,900 or fewer people may collect their samples on the same day.
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of total coliforms or fecal coliforms, rather than the amount or concentration of coliforms present.5
If a sample tests positive for total coliforms, the system is required to take three repeat samples
(or four if the system routinely takes 1 sample per month or fewer): at the same tap, within five
service connections upstream, and within five service connections downstream. If repeat samples
test positive for total coliforms, another set of samples must be taken, as before, unless a violation
has been triggered. In addition, each sample that tests positive for total coliforms must also be
tested for the presence of fecal coliforms or E. coli.
The TCR outlines two types of violations: acute and monthly. An acute violation of occurs
when any repeat sample is positive for fecal coliform or E. coli, or the system has a positive total
coliform repeat sample following a positive fecal coliform or E. coli routine sample (40 CFR 141.63).
Acute violations require the presence of fecal coliforms or E. coli, which found in greater quantities
than total coliform in animal fecal matter. If E. coli is detected along with total coliforms, there is
strong evidence that sewage is present and greater potential for a health threat. Therefore, acute
coliform violations are classified as Tier 1 under the Public Notification Rule and require public
notification within 24 hours. Acute coliform violations may result in a boil water notice.
Monthly coliform violations are classified as Tier 2 and require notification only within 30 days.
A monthly violation occurs when a specified number of samples test positive for total coliform in a
given month, even when tests for E. coli and fecal coliforms are negative. Systems taking fewer than
40 samples per month (that serve 33,000 or fewer people) incur a monthly violation if two or more
samples test positive for total coliforms. Systems taking at least 40 samples per month (that serve
more than 33,000 people) incur a monthly violation if more than 5 percent of samples test positive
for total coliforms. This structure provides some incentives for strategic avoidance of regulatory
action. Systems that would incur a violation if over 5 percent of samples test positive for total
coliforms can avoid triggering a monthly TCR violation by taking additional water quality samples.
Bennear et al. (2009) find evidence of strategic oversampling to avoid triggering a monthly TCR
violation among systems serving over 33,000 people. In the sample used for this study, however,
there are no violations of the TCR by systems serving over 33,000 people, so all monthly violations
that occur in the sample are triggered by the presence of two positive total coliform samples. To
the extent that strategic oversampling occurred in our sample, violations that would have been
recorded at large systems in the absence of oversampling are unrecorded, and estimated effects
5Because of the discrete nature of the tests and the fact that only 2 positive samples are necessary to trigger aviolation, it is not possible to utilize a regression discontinuity in this context.
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shown here may be understated.
The TCR was effective through March 2016, at which time the Revised Total Coliform Rule
(RTRC) became effective. The EPA is required to review each drinking water standard no less
than every six years. While the RTCR maintains the acute coliform violation (renamed as an
“E. coli” violation), the monthly coliform violation has been eliminated. As there is no longer
a monthly coliform violation, there are also no public notification requirements. The RTRC also
requires systems with an indication of coliform contamination to initiate assessments to find sanitary
defects and take corrective action.
2 Data
This paper combines data from a number of different sources, each of which is described in detail
below. Using detailed geographic information, I combine water quality data with several key out-
comes of interest: household purchases of bottled water and stomach remedies, emergency room
visits for gastrointestinal illness, school absences, and test scores. Data on weather, air pollution,
and employment are used as controls.
Water systems and water quality data
The EPA maintains detailed records on all water quality violations in the Safe Drinking Water
Information System (SDWIS). These data provide information on both procedural violations (e.g.
requirements for timely testing and reporting) and maximum contaminant level (MCL) violations.
I obtain all health-based MCL violations in North Carolina from 2004 to 2015. Monitoring and
reporting violations during this period are also collected and used as controls. The North Carolina
Department of Environmental Quality (NCDEQ) also provided public notification distribution
dates, but does not maintain information on the method of communication.6 The study focuses
on community water systems, which supply water to the same population year-round.7 Additional
information includes characteristics about the water supply system.
6Thanks to Linda Raynor for sharing and assisting in the interpretation of these data.7Other types of public drinking water systems include transient non-community water systems (TNCWS) and
non-transient non-community water systems (NTNCWS). A public water system that regularly supplies water to atleast 25 of the same people at least six months per year is considered a NTNCWS (e.g. water systems that supplyschools, factories, office buildings, and hospitals). A public water system that provides water in a place such as agas station or campground where people do not remain for long periods of time is considered a TNCWS. About 14%of people in the U.S. supply their own water for domestic use, primarily through groundwater wells. There are nofederally required monitoring and treatment standards for private wells under the SDWA.
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Geographic information
Public Community Water Supply service areas comes from the North Carolina Center for Geo-
graphic Information and Analysis, available via NC OneMap.8 Figure 2 shows the community
water supply system service areas for North Carolina. Not all areas of the state are served by
community water systems. Unserved areas tend to be rural and usually supply their own water for
domestic use through fresh groundwater wells, which are not regulated under the SDWA.
To measure exposure, I intersect CWS service areas with point locations (e.g. geocoded res-
idences of students and school locations) or geographic boundaries (e.g. zip codes and school
attendance boundaries), depending on the outcome of interest. For each geographic boundary, I
calculate the percent of the total area that is served by each water supply system to measure the
probability of exposure. For point locations within the boundaries of a CWS area, I define exposure
based that CWS.9
School attendance boundaries come from the National Center for Education Statistics for the
2013-2014 school year. A school attendance boundary, or catchment area, is a geographic area from
which the students are eligible to attend a local school. Typically, a local school district determines
the school attendance boundaries for schools within its district. Magnet schools are dropped from
the sample since they draw students from across attendance boundaries.10 Figure 1 shows the
school attendance boundary areas for primary, middle, and high schools in North Carolina.
NCDPI also provides the Public School Location data. County and 5-digit zip code boundaries
come from the U.S. Census Bureau’s Cartographic Boundary Files. There are 100 counties and
1,080 5-digit zip codes in North Carolina.
Household purchases
Data on household purchases from 2004-2015 are from the Nielsen Company and are maintained by
the James M. Kilts Center for Marketing at Chicago Booth. The household-level consumer panel
data includes information about purchases made by a panel of consumer households across all retail
outlets. The data includes sales in dollars and quantity sold for 7,281 unique Universal Product
Codes (UPC) for bottled water (product module 1487). I define bottled water sales each month
8Geographic service areas were mapped during 2004-2006 to facilitate planning, siting, and impact analysis. Dataare available here: www.nconemap.com.
9Note that not all households within a water supply service area necessarily get their water from the CWS. It ispossible that some households supply their own water for domestic use by using a well. However, the vast majorityhouseholds (and schools) within the CWS area will get their water from the CWS.
10About 9% of schools in the data are magnet schools.
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as total dollar sales (before coupons).11 I also observe purchases of common remedies to treat
gastrointestinal illness: diarrhea remedies, antacids, pain remedies–children’s liquid, and pedialyte.
Using total dollar sales for each category, I create a standardized mean value index. The products
are normalized by the mean and standard deviation, as in Kling et al. (2007). I also use a number
of other purchase categories, including purchases of bread, eggs, butter, milk, juice, and soda. Poor
water quality may also increase purchases of milk, juice, or soda if these are substitutes for water.
Other categories, such as bread, eggs, and butter, act as placebo tests.
Demographic information includes household income range, household size, presence of children,
race, Hispanic origin, and education. Using the household’s 5-digit zip code of residence, I calculate
the percent of the zip code exposed to each water quality violation to measure the probability that
a household is exposed. The results are similar when I focus on non-movers or define exposure
based on a household’s first observed zip code of residence, rather than current zip code, to account
for possible endogenous moving behavior.
Emergency Department data
Emergency department visit data for 2007-2015 is from the Cecil G. Sheps Center for Health Services
Research at the University of North Carolina. Restricted-access patient-level data includes 5-digit
zip code of residence, patient characteristics and diagnosis and procedure codes. Similar to the
household purchase data, I calculate the percent of the zip code’s area exposed to each water
quality violation as a measure of the probability that an individual in the zip code is exposed.
I record patients as entering the ER with a gastrointestinal illness based on all 18 diagnosis codes
provided in the data. Visits to the ER for a gastrointestinal illness are restricted to those with the
following ICD-9 codes: 001-009 (Intestinal Infectious Diseases), 558.9 (Noninfectious Gastroenterit
NEC), 787.0 (Nausea and vomiting), and 787.9 (Diarrhea). I exclude newborn admissions and
visits with a diagnosis of Clostridium difficile (008.45), as it is primarily considered to be a hospital-
acquired infection. Using age-specific population for each zip code from the 2010 Census, I calculate
the number of admissions per 1000 population in each zip code and month. Separate measures are
calculated for the following age groups: 5-19, 20-39, 40-59, and 60 and over. School-age children,
age 5-19, tend to drink more tap water than very young children and are likely to be more vulnerable
to exposure than adults (Drewnowski et al., 2013).
As a control variable, I calculate the number of admissions to the ER for influenza and pneu-
11Results are similar when measuring bottled water sales in total ounces sold.
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monia (ICD-9 codes: 480-488). I also create a number of placebo outcomes using admissions for
fractures (ICD-9 codes: 800-869), burns (ICD-9 codes: 940-949), poisoning by drugs, medicinal and
and diseases of the nervous system and sense organs (ICD-9 codes: 320-389).
Absences, test scores, and school data
The two main sources of administrative school data come from the North Carolina Department
of Public Instruction (NCDPI): the Principal’s Monthly Report (PMR) and the North Carolina
Education Research Data Center’s (NCERDC) student-level data.
First, the PMR data contain grade-level enrollment, membership, and attendance information,
which is retrieved at the end of each of 9 PMR intervals from 2007-2015. I calculate the percent
of absent days as total absent days divided by total days in membership (Adays/Mdays). I drop
observations with percent of absences above the 99th percentile of 24.4 percent. Table 1 shows the
average rate of absences is 4.7 percent in the PMR data.
Each of the 9 PMR intervals cover approximately 20 school days, which is not necessarily a
calendar month.12 To combine these data with the water quality violations reported at the calendar
month, I limit the sample to schools with a traditional school calendar (i.e., I remove year-round
schools) and set the first PMR interval for each school year as September.13 The 9 PMR intervals
are therefore treated as representing months September through May.14 As there is no systematic
collection of water quality for private wells, which serve primarily rural households, the analysis
will focus on non-rural schools and violations at community water systems.
Second, the NCDPI also provides the NCERDC with information on absences and days in
membership for all students at the time of testing each year. Unlike the PMR data, it is not
possible to discern when absences occur within the school year. However, the student-level data
contain a unique code for each student that can be linked over time and the latitude and longitude
of residence. Detailed geocoded addresses from 2009-2015 allow for a more accurate definition of
exposure than exposure based on attendance boundaries. Individuals are linked to water quality
information based on the intersection of their precise home locations and community water supply
service areas. The results are similar when exposure is based on first observed home location, rather
12Policy directs that the school systems can only report 20 days each for months 1 and 2 for the purpose of allocatingfunds. The remaining intervals (3 - 9) can range from 16 days to 26 days, but are usually 20 days.
13Under North Carolina law the public school opening date was August 25th from 2005 to 2013 and was the Mondayclosest to August 26th from 2013 onward. About 6% of schools in the data have non-traditional calendars.
14In the appendix, I use school calendar information from 2012-2015 to test this assumption. On average, there is82% overlap between the PMR interval and the assumed month.
12
than actual location, to account for the possibility of endogenous moving behavior. In addition,
I can separately estimate the effect of water quality at the home and school location. Additional
demographic characteristics include birth month, gender, race, ethnicity, disability status, and
indicators for the economically disadvantaged and those with limited English ability. Table 1 shows
summary statistics for the NCERDC student-level data. The average absence rate is 4.4 percent
and the average days absent is about 7. The student body is 44 percent white, 33 percent black,
14 percent Hispanic, and 8 percent other races. About 53 percent of students are economically
disadvantaged and 10 percent have limited English ability.
The NCERDC data also contains end-of-year math and reading test scores for grades 3-8. These
tests aim to assess whether students have met grade-level expectations. I standardize each score
by year, grade, and local education agency.15
Additional school level characteristics come from Common Core of Data (CCD) Public Elemen-
tary/Secondary School Universe Survey Data. These data contain descriptive statistics on students
at all schools on a yearly basis, including Title I eligibility, free and reduced-price lunch eligibility,
and demographic composition of the student body. Other basic information includes type of school
calendar(traditional, year-round, modified, etc.), school program (magnet, charter, etc.), and loca-
tion type (rural, suburban, etc.).
Weather and air pollution
Daily weather data for each 2.5 by 2.5 mile square in North Carolina come from (Schlenker and
Roberts, 2009) and are based on the PRISM weather data set. To combine these grid squares with
school attendance, I calculate the average daily minimum temperature, maximum temperature, and
precipitation for each school using grid squares whose centroids fall within the school’s attendance
boundary. I calculate monthly and yearly measures of total precipitation, average precipitation,
and the percent of days with a maximum temperature that falls within 7 temperature bins in de-
grees Celsius: below 0, 0-5, 5-10, 10-15, 15-20, 20-25, and over 25. I also include controls for 7
precipitation bins in millimeters as a robustness check: 0-1, 1-5, 5-10, 10-15, 15-20, 20-25, and over
25.
School absences may also be driven by exposure to air pollution (Currie et al., 2009). As
a robustness check, I include controls for ozone and particulate matter measured at air quality
monitors throughout North Carolina. I include the percent of days within each of five categories
15All students in North Carolina take the same test conditional on school grade. Grading is done at the localeducation agency level by the test coordinator who is chosen by the superintendent of the district.
13
to allow for nonlinear effects: 0-25%, 25-50%, 50-75%, 75-100%, and over 100% of the relevant
EPA threshold for each criteria pollutant. For the school-level analysis, I measure air pollution at
the month-school attendance boundary level. For the individual analysis, I use inverse distance
weighted measures of air pollution over the past 12 months from monitors within 20 miles of a
student’s residence.
Employment
Employment rates at the county-month level come from the Bureau of Labor Statistics’ Local
Area Unemployment Statistics (LAUS) data. For the student-level analysis, employment rates are
collapsed to the county-school year level.
3 Methods
Given the existing evidence of extensive residential sorting, cross-sectional estimates of the impact
of water quality on child outcomes would likely be biased. Parental income, as well as preferences for
school quality, neighborhood amenities, and environmental quality are likely related to water quality
as well as underlying health and human capital. Table 2 shows summary statistics for students
exposed and unexposed to any coliform violation, based on their home and school location. Students
exposed to a coliform violation have higher absence rates, but are also 7-8 percentage points more
likely to be economically disadvantaged and about 8-9 percentage points more likely to be black.
This finding is broadly consistent with the environmental justice literature which consistently finds
higher pollution exposure among disadvantaged populations (Banzhaf et al., 2019). The empirical
strategies used here will control for time-invariant location characteristics by including zip code,
school, or individual fixed effects, thus accounting for potential bias from cross-sectional sorting.
I exploit plausibly exogenous variation in the timing of water quality violations to estimate the
within-location impacts of water quality violations on avoidance behavior and child outcomes.
3.1 Household purchases analysis
I explore the effect of violations on household purchases using data recording all purchases made by
a sample of households over time. Bottled water purchases act as a measure of avoidance behavior.
Purchases of common remedies for gastrointestinal illness are a proxy for health status.
The general specification for the household-level data is as follows,
where i indexes individuals and t indexes school year. Outcomes of interest include the absence
rate and end-of-year test scores for grades 3-8. Standard errors are clustered at the individual level.
The coefficient of interest, β1, captures the impact of a one month increase in exposure during the
past year on an individual’s absence rate or test score. The estimate would be biased if there are
time-varying unobserved factors correlated with absence rates or test scores that impact students
in specific areas with water quality violations during the year of violation.
4 Results
4.1 Avoidance behavior and health: Household purchases results
The two types of violations triggered by the Total Coliform Rule, acute and monthly, differ in their
public notification requirements. Whereas acute coliform violations require 24 hour public notice
and often include a boil water advisory, monthly coliform violations only require notice within 30
days. Using bottled water sales to measure avoidance behavior, we can see whether individuals
respond to either acute or monthly coliform violations.
Table 3 shows estimates from equation 1 for bottled water in column 1. These data include all
bottled water purchases for a panel of households over time. The specification includes household
fixed effects and controls for weather, employment, household size, year-month, zip code-year, and
zip code-month.17 Bottled water sales increase only during acute coliform violations when public
notice is required within 24 hours. Acute coliform violations increase bottled water purchases by
about 78 percent.18
Although there is no evidence of an increase in bottled water purchases during monthly coliform
violations, there is evidence of an increase in the purchase of common stomach remedies. Column
2 shows that monthly violations increase the purchase of stomach remedies by 0.23 standard de-
viations, with no significant increase during acute violations. If the purchase of common stomach
remedies is a proxy for health status, it seems that households are able to avoid harmful health
17Table 11 in the appendix builds up to this fully-specified model by sequentially adding in additional controls toshow how the point estimates change. Table 12 in the appendix shows the results for household purchases on theextensive and intensive margin.
18(e(.575) − 1) × 100
18
effects through bottled water purchases during acute, but not monthly, violations.
Figure 3 shows these effects visually for acute and monthly coliform violations. For each variable
of interest, I create a placebo violation in the months immediately before and after the true violation.
Violations in the data typically last one month before returning to compliance. If there are pre-
existing trends in purchases, these should show up in the months before the violation. If the
avoidance behavior or health effects are sustained beyond the violation month, this will show up
in the months after the violation. There is an increase in bottled water purchases during the acute
coliform violation, but no increase during monthly coliform violations. The increase in purchases
during acute violations is even more apparent for the intensive margin, while the intensive margin
shows no increase during monthly coliform violations. For either type of violation, there are no
significant effects for months before or after the violation month. Finally, Figure 4 shows the
effect of monthly violations on the purchase of common stomach remedies. There appears to be
an increase in purchases during the violation month with little evidence of any effects prior to the
violation. The confidence intervals for the months following the violation are wide, possibly due to
lingering health effects.
Using additional data on public notification timing, columns 3 and 4 in Table 3 explore whether
the delay in public notification for monthly violation matters. As monthly violations require no-
tification only within 30 days, there is variation in how quickly public information is delivered.
Figure 12 in the appendix shows the distribution of weeks between the determination date and
public notification date for monthly coliform violations. Here we can observe that many notifi-
cations occur within the required 30 day window from the determination date. In fact, about 60
percent of public notifications occur within the first week. However, this distribution has a long
right tail with about 30 percent of public notifications occurring more than 6 months after the
determination date. Using this variation in the timing of actual public notification, I compare
purchase behavior when the notification is provided within one day of the violation versus when
the notification is provided later. If the immediacy of public notification matters, we might expect
to find increases in bottled water purchases during monthly coliform violations when the public
notification occurred within 24 hours rather than later on. In fact, column 3 shows bottled wa-
ter purchases during monthly violations with quick notification are statistically indistinguishable
from bottled water purchases during acute violations, although not statistically different from zero.
When notifications are delivered within one day, the point estimates indicate a 78 percent and 40
percent increase in bottled water sales for acute and monthly violations, respectively. For monthly
19
violations with delayed notification, there is no increase in bottled water purchases and the point
estimate is actually negative. Column 4 also shows that stomach remedy purchases increase sig-
nificantly only during monthly violations with delayed notification. The point estimate suggests
stomach remedy purchases increase by about 0.32 standard deviations when notification is delayed
beyond one day. These results suggest that the timing of information is especially important to
protecting health.
An additional benefit of the household-level data is the ability to observe certain demographic
characteristics of each household. Previous research has shown that higher SES families may be
more likely to avoid exposure to pollution (Marcus, 2018; Currie, 2011). I explore demographic
heterogeneity in bottled water purchases during acute coliform violations in appendix Table 18 for
the intensive margin. All demographic groups increase bottled water purchases. Point estimates
are largest among white and Hispanic households, and households with high income, some children,
and high education. However, these differences are only statistically significantly larger for Hispanic
and white households, and those with high education. Figure 5 shows these estimates graphically.
These findings are generally consistent with existing evidence showing that avoidance behavior is
more common among high SES individuals.
4.2 Health: ER visit results
In addition to purchases of over-the-counter stomach remedies, emergency room visits for gastroin-
testinal illness provide a measure of health that is both direct and extreme. Columns 5-9 of Table 3
show the impact of water quality violations on ER visits from estimating equation 2 for all individ-
uals in column 5 and for different age groups in columns 6-9.19 Consistent with the previous results,
there is an increase in ER admissions for gastrointestinal illness during monthly coliform violations,
but there is no statistically significant impact on admissions during acute coliform violations. For
a zip code impacted by a monthly coliform violation, ER admissions for gastrointestinal illness
increase by about 0.6 per 1000 individuals, or about 14 percent from the mean.
The effect is especially strong for school-age individuals in column 6, which shows about a 23
percent increase in admissions during a monthly coliform violation. Figure 6 shows these results
visually for acute coliform in the left panel and monthly coliform in the right panel. As before, I
create a placebo violation in the months immediately before and after the true violation. During an
acute coliform violation, there is no evidence of an increase in visits to the ER for gastrointestinal
19Table 13 in the appendix builds up to this fully-specified model by sequentially adding in additional controls.
20
illness. However, there is a significant increase in ER visits during the violation month for monthly
coliform.
In Table 3, there is also a statistically significant impact on adults age 20 to 39, but no significant
impact on older adults in columns 8 and 9. As some waterborne pathogens, such as Giardia, are
highly contagious, it may be that increased illness among adults age 20 to 39 is related to parental
interactions with infectious children. Across all age groups, the effect of acute coliform violations
on ER admissions for gastrointestinal illness is not statistically different from zero.
4.3 School absences: School-level results
Column 1 of Table 4 shows the results from the school-level analysis using the monthly PMR data
to estimate equation 3. The outcome is the percent of days absent in the month for each grade in
each school. Exposure to a violation is measured as the percent of the attendance boundary area
affected by each type of violation. Column 1 includes grade dummies and weather controls, as well
as year-by-month, school-by-year, and school-by-month controls to account for abnormal months
statewide, school-specific yearly shocks, and school-specific seasonality, respectively.20
The effect of acute coliform exposure is quite small relative to the impact of monthly coliform
exposure. If all individuals in the school attendance boundary are exposed to an acute violation
or a monthly violation in a given month, the percent of absences is expected to increase by 0.9 or
18.2 percentage points, respectively. It is important to note, however, that 100 percent exposure is
outside of the observed range in the data for monthly coliform. An increase from zero exposure to
the maximum percent exposed to monthly coliform, 7.4 percent, leads absences to increase by 1.3
percentage points, or about 29 percent from the mean.
Figure 7 shows graphical evidence of the timing of absences relative to the violation. For each
variable of interest, I create a placebo violation in the months immediately before and after the true
violation. Any pre-existing trends in absences should show up in the months prior to a violation. If
the health effects are sustained beyond the period of the violation or if students increase attendance
following a violation to compensate for previous poor attendance, this would show up in the months
after the violation. For both types of violations, acute and monthly coliform, there is an increase
in absences at the time of the violation. The two months prior to a violation show no pre-existing
20Table 14 in the appendix shows how the point estimates change as each of these controls is added in sequentially.The coefficient for monthly coliform violations is consistently positive and significant across all specifications, sug-gesting an increase in school absences, while the coefficient for acute coliform is only positive and significant for thefully specified model.
21
trends. The confidence intervals in the month following a violation are fairly large, which may
suggest some heterogeneity in the longer term effects. Some students may compensate for previous
illness by increasing attendance in the following month, while other students may have lingering
health issues that decrease attendance beyond the initial month of the violation. However, none of
the post-violation months are statistically different from zero.
Next, I explore the results by age. There are a few reasons why we might expect the results
among younger students to be more precise. Younger students are more likely to require parental
permission to miss school. So their absences are more likely to be health-related. In addition,
school attendance boundaries for primary schools are much smaller and more accurately reflect the
residential location of students. Figure 8 shows the effect of monthly coliform violations for grades
1-6 and grades 7-12. There is a large increase in absences during the violation month for grades
1-6. There are no observable pre- or post-trends and the confidence intervals are fairly small. For
grades 7-12, the confidence intervals are very wide for the month during and just after the violation.
It appears there may still be a large impact on older students, but the estimates are much noisier,
as we might expect.
4.4 School absences: Student-level results
Having shown that the timing of absences coincides with the timing of violations using the monthly
school-level data, we now turn to the student-level data, which provides residential location and
allows students to be observed over time. Columns 2-4 of Table 4 shows estimates of the main
results from equation 4.21
The magnitude of the estimates shown in column 2 suggest that exposure to an acute coliform
violation has no statistically significant impact on school absences. However, exposure to a monthly
coliform violation increases absences by about 0.2 percentage points, or 5 percent from the mean.
Using the average number of absent days per year, 7.16, this translates to an additional 0.4 days
absent for exposure to a monthly coliform violation at home.
One unique feature of these data is the ability to observe two locations in which individuals
are potentially exposed to water pollution: home and school locations. Children spend significant
21Table 15 in the appendix shows the results for a broad set of contaminant categories: total coliforms, radionu-clides, disinfectants and disinfection byproducts, synthetic organic compounds (SOCs), and volatile organic chemicals(VOCs). Exposure is measured as months of violation in the past year. The largest effects are found for total col-iforms (combining acute and monthly), but radionuclide violations at school and disinfectant violations at home arealso associated with small increases in absences, 0.07 percentage points (1.5%) and 0.009 percentage points (0.2%),respectively.
22
amounts of time at school and are likely to consume tap water at lunch, during recess, or at other
times throughout the school day. Most previous research quantifying the effects of pollution on
child health has focused on exposure either at the home or school location, but here we can consider
both. Understanding the effects of contaminated drinking water at school is especially important
since some drinking water regulations, such as the EPA’s Lead and Copper Rule, do not require
any testing at schools served by community water supply systems, and about half of Charlotte-
Mecklenburg schools recently tested had lead levels above the EPA action level of 15 ppb (Rumpler
and Dietz, 2019).
Column 3 of Table 4 shows the effect of coliform violations at school on student absences.
Monthly coliform violations at school increase absences, and the magnitude of the effect is very
similar to monthly coliform violations at home, about 0.2 percentage points. However, acute
coliform violations at school appear to decrease absences by about 0.2 percentage points. It’s
important to remember that acute violations require immediate notification and increase avoidance
of tap water. School administrators may be especially cautious in protecting their students. If
drinking water at schools is generally of poor quality, acute coliform violations may improve health
when schools restrict consumption of tap water. Column 4 looks at the interaction between home
and school violations. While there is a high correlation between violations at home and at school
since many children attend a school that is served by the same system as their home, there are
still many children who are served by different systems at home.22 Acute violations only at school
continue to reduce absences, but there are no statistically significant impacts for acute violations
only at home or when acute violations occur in both locations. On the other hand, monthly
violations statistically significantly increase school absences whether they occur only at home, only
at school, or at both locations. The point estimate is slightly larger when violations occur in both
locations. These finding are consistent with previous ER visit and stomach remedy purchase results
that showed evidence of worse health only during monthly coliform violations.
As the precise residential location allows me to link students to a specific community water
system, I can explore heterogeneous effects by system characteristics. Figure 9 shows estimates of
equation 4 where the coefficient of interest, monthly coliform violation, is interacted with system
characteristics.23 First, I estimate the effect of a monthly coliform violation on absences for systems
with groundwater and surface water sources, separately. Samples collected in the field often show
22The correlation between home and school violations is between 0.6 and 0.7.23Table 19 in the appendix shows regression estimates that correspond to Figure 9.
23
higher concentrations of fecal coliforms in surface water, relative to ground water, especially where
run-off from manure is likely (Cox et al., 2005). Estimates show a statistically significant impact
only for surface water systems, which is consistent with what we would expect based on field
samples. Next, I consider whether the response is different for water systems that are repeat and
non-repeat offenders. I define repeat offenders as water systems with more than one violation
of the TCR since 1990 and non-repeat offenders as systems that violate the TCR for the first
time. Individuals served by water systems with multiple violations over time may have a larger
incentive to switch to bottled water permanently, for example. If this is the case, there may be
little to no impact of violations on absences within these systems due to avoidance. However, we
the effect is statistically significant for both repeat and non-repeat offenders. Repeat offenders
have a slightly smaller point estimate, which is statistically different from first time offenders with
a p-value of 0.0207. When comparing by ownership type, the results are statistically significantly
larger for publicly owned water supply systems (p-value of 0.001), and there is no significant impact
of total coliform violations among privately owned systems. Finally, estimates by the size of the
system show point estimates that increase with system size, but the difference is only statistically
significantly when comparing the smallest and largest systems.24
Next, I explore heterogeneous effects by student demographic characteristics. Drewnowski et
al. (2013) explore tap water consumption among children in NHANES data and show that certain
demographic groups consume more tap water, including older children, non-Hispanic whites, and
children from higher income families.25 Figure 10 shows estimates of equation 4 where the co-
efficient of interest, monthly coliform violations, is interacted with demographic characteristics.26
In general, point estimates are larger among populations that tend to consumer more tap water
(non-disadvantaged, non-Hispanic, and older children), but not statistically significantly so.
4.5 Test scores: Student-level results
In addition to school absences, it is possible that student test scores may be negatively impacted by
exposure to water quality violations. Absences have been shown to decrease test scores (Aucejo and
Romano, 2016). Even if children are not sick enough to stay home from school, their concentration
24The SDWA classifies systems as very small (0-500), small (501-3,300), medium (3,301-10,000), large (10,001-100,000), and very large (100,000+). Nationally, only about 1% of CWS are very large. There are no very largesystems that violate the total coliform rule in North Carolina during the study period.
25Table 17 in the appendix reproduces their findings to show the level of tap and bottled water consumed bydemographic group.
26Table 20 in the appendix shows regression estimates that correspond to Figure 10.
24
and comprehension may be impacted. Exposure to poor water quality during different parts of the
year may impact test scores through different channels. For example, exposure during the testing
window could impact concentration during the test, while exposure during the school year could
impact absences or concentration and comprehension of material.
Table 5 shows estimates of equation 4 which includes controls for grade, year, and weather, and
student fixed effects. The outcome is math test scores.27 Columns 1-3 show the results for home
exposure to violations during during the school-year and during the testing window, columns 4-6
show the results for school exposure, and column 7 shows the results for both home and school
exposure in the same regression.
Exposure at home to a monthly coliform violation during the school year decreases math scores
by about 2.9 percent of a standard deviation in column 1. The estimate is significant at the 10
percent level. There is no statistically significant impact of exposure to an acute coliform violation
during the school year on math scores. Neither acute nor monthly violations significantly impact
math scores during the testing window. This may be due to limited power, as there are few
violations during the testing window, or due to health-related absences on the test day, which I
do not observe. Column 4 shows there is no statistically significant impact of exposure at school.
Column 7 shows a similar point estimate for home exposure to a monthly coliform violation when
both home and school exposure are included together.
To understand whether the impact of monthly coliform violations during the year on test scores
arises through increased absences or through decreased concentration and comprehension, columns
2 and 5 include the absence rate as a control. We can see that the point estimates do not change
much, suggesting that increased absences are not driving the effect of monthly coliform violations
during the school year on math scores. This is perhaps not surprising, as the magnitude of the
effect of water quality on absences is fairly small. Results indicate exposure to a monthly coliform
violation increases absences by about 0.4 days, while Aucejo and Romano (2016) find that it takes
about 10 additional absent days to decrease math scores by 5.5 percent of a standard deviation.
Instead, it is likely that these effects on test scores are driven through the impact of monthly
coliform violations on concentration and/or comprehension in the classroom.
Lower test scores have been linked to worse later-life outcomes, such as earnings. However, it’s
uncertain whether the test score impact of exposure to a monthly coliform violation during the
27There is no significant effect of exposure on reading test scores. Table 21 in the appendix builds up to the fullyspecified models for math and reading test scores. These results demonstrate the inherent bias in models that do notaccount for student ability by controlling for student fixed effects.
25
school year would persist into future years. In columns 3 and 6, I include lagged exposure to a
monthly coliform violation during the previous school year. The negative impact of home exposure
on math scores seems to persist. The magnitude of the effect is almost exactly the same magnitude
as exposure in the current year. These results suggest that the effect of monthly coliform violations
on test scores is not temporary and may impact later life outcomes.
5 Robustness
For the household purchase results and ER visit results, I conduct placebo tests using other out-
comes that should not be impacted by poor water quality. For household purchases, Table 6 shows
there is no impact of water quality violations on other common purchase categories that should not
be affected: bread, eggs, and butter. I also do not find any evidence of increased purchases of other
beverages, such as juice, soda, or milk, that could be substitutes for water.28 For emergency room
visits, Table 7 show there is no significant effect of either acute or monthly coliform violations on
ER visits for a variety of alternate diagnoses that should not be impacted by water quality: frac-
tures, burns, poisoning by drugs, medicinal and biological substances, acute respiratory infections,
and diseases of the nervous system and sense organs.
Table 8 tests the robustness of the school-level absence results. Column 1 replicates the baseline
results. All specifications include controls for grade, school, year-month, school-year, school-month,
and weather. Columns 2 and 3 add controls for the county-month employment rate and controls
for any monitoring and reporting violations. Column 4 includes controls for influenza and asthma
rates. These are measured as the weighted average of the number of ER admissions per person
in zip codes intersecting with the school attendance boundary, where the weights are the percent
of the school attendance area covered by each zip code. Columns 5 includes controls for ozone
and particulate matter, measured as the percent of days within five pollution-level bins to allow
for nonlinear effects. Column 6 excludes the month of September, since attendance-based funding
decisions may drive the unusually low absence rate in this month. In column 7, I exclude schools in
Mecklenburg county to test whether the results are driven by Charlotte. Column 8 drops the two
schools in North Carolina that ever experience an unplanned school closure in response to water
quality violations.29 Column 9 collapses the grade-level data to the school level. Next, as PMR
28In addition, there are no significant impacts on purchases of water filters. These results are available upon request.29Unplanned school closure data from 2011-2017 were generously shared from CDC/DGMQ/CI-ICU. Please consult
Wong et al. (2014) for data collection methods.
26
intervals do not perfectly overlap with calendar months, column 10 drops schools whose average
PMR coverage of calendar month days is less than 70% based on limited data from school years
2012-13 to 2014-15. Finally, column 11 includes flexible bins for precipitation: 0-1mm, 1-5mm,
5-10mm, 10-15mm, 15-20mm, 20-25mm, and over 25mm. Across all specifications the coefficients
and standard errors remain very similar.
Table 9 tests the robustness of the student-level results. Column 1 replicates the baseline re-
sults. All specifications include individual fixed effects and controls for grade, year, and weather.
Columns 2 and 3 add controls for the county-month employment rate and controls for any mon-
itoring and reporting violations. Columns 4 and 5 add controls for air pollution. Columns 6 and
7 drop Mecklenburg county schools and schools that experience an unplanned school closure in
response to water quality violations. Columns 8-10 limit the sample to regular schools (excludes
alternative education, exceptional children, and vocational education), regular programs (excludes
coop. innovative high schools, early college schools, magnet schools, etc.), and schools with tradi-
includes seven flexible bins for precipitation and column 12 includes temperature and precipitation
bins separately for each month. Across all specifications, the coefficients and standard errors remain
very similar.
Finally, I explore alternative specifications for the student-level results. Here, the outcome
variable is number of days absent, which is a count variable. Figure 13 in the appendix shows the
distribution of both percent absent and days absent. Table 16 in the appendix shows results for
Poisson and Negative Binomial models. Regardless of the specification, the coefficients of interest
indicate that exposure to monthly violations increase absences by about 5 percent.30 This is
consistent with the magnitude of previous findings from Table 4.
6 Discussion & Conclusions
This paper shows that drinking water violations can have important effects on health and human
capital. Monthly coliform bacteria violations increase ER visits for gastrointestinal illness, increase
purchases of common stomach remedies, increase school absences, and decrease math test scores.
Evidence has shown that the timing of these poor outcomes coincides with the timing of violations,
and that exposure at both home and school can affect child health.
30100 × [expβ −1]
27
Importantly, the results show that when information about coliform violations is provided imme-
diately, households avoid exposure by purchasing bottled water. When information is not provided
immediately, households cannot respond by avoiding exposure and there are important impacts on
health and human capital. However, whether immediate information dissemination is socially effi-
cient in this context is still unclear, as households may be over-responding to the information. With
some back-of-the-envelope calculations, we can compare the excess cost of bottled water purchases
to the health benefits from avoided exposure.
Acute coliform violations, which require immediate 24 hour notification, increase bottled water
purchases by 78 percent. This translates into about $61,000 in excess purchases of bottled water
from 2007-2015 in North Carolina.31 If requiring immediate notification for monthly coliform
violations yielded a similar behavioral response, bottled water purchases would have been about
$365,000 higher over this period.
In comparison, the health costs incurred during monthly coliform violations are large. Stomach
remedy purchases increase by 23 percent of a standard deviation, which amounts to about $441,000
in additional purchases over this time period. ER visits for gastrointestinal illness increased by 14
percent overall and by 23 percent among school-aged children. Using the average charges for
gastrointestinal illness visits, monthly coliform violations increased ER charges by about $422,000,
with about $80,000 due to school-aged children.32 These direct health costs alone exceed the cost
of avoiding exposure through bottled water purchases, yet there are additional effects on school
absences and test scores to consider.
Although it is difficult to put a dollar value to the health consequences associated with a missed
school day, absences also have a direct impact on parental earnings to the extent that parents stay
home to take care of sick children. Using the average hourly earnings in 2015 from the Bureau of
Labor Statistics, $25, and assuming each student absence equates to one lost 8 hour work day, the
estimates presented above suggest the total cost in lost earnings from monthly coliform violations
over this period was about $6.86 million. In addition to the effect on parental earnings, absences
have a direct effect on schools’ and districts’ budgets. For example, school absences in California
cost public schools $3.5 billion in state funding based on daily attendance between 2010-11 and
31Based on the percent of each zip code exposed to a violation over this period and population estimates from the2010 census, there were approximately 72,000 and 432,000 person-months exposed to acute and monthly coliformviolations, respectively. Of these, about 14,000 and 88,000 individuals were of school-age. Note that the estimatedeffects for bottled water and stomach remedy sales are at the household level, so using population level exposure willoverstate the excess costs.
32Average charges for gastrointestinal illness ER visits in North Carolina in 2007 were $1,648 and $1,258 for allindividuals and school-aged children, respectively.
28
2012-13 (Harris, 2014). School absences can also have important impacts on student outcomes,
including test scores, grade retention, and later life earnings (Aucejo and Romano, 2016).
Moreover, the findings showed monthly coliform violations during the school year decrease math
test scores by 3 percent of a standard deviation and the effect is persistent. Currie and Thomas
(2001) find a one standard deviation decrease in test scores is associated with 11-14 percent lower
wages at age 33. Based on their estimates, exposure to monthly coliform violations over this period
reduced yearly wages at age 33 by about $2.68 million.
All together, monthly coliform exposure over the study period cost about $10.4 million, which
is substantially larger than the estimated avoidance cost of providing immediate information about
these violations, $365,000. Importantly, the EPA revised the Total Coliform Rule in 2016 and elim-
inated monthly coliform violations. While the overall health effects of the Revised Total Coliform
Rule are unknown, the findings of this paper indicate that monthly coliform violations did in fact
negatively impact health and human capital accumulation. Not only does this suggest that testing
for monthly coliform violations is important, but providing immediate public information about
monthly coliform violations can allow individuals to avoid the health and human capital conse-
quences of exposure. In this way, timely information dissemination can be used as a low-cost policy
nudge to mitigate these negative health and human capital effects by encouraging individuals to
avoid exposure through boiling their tap water or finding a new source of drinking water.
29
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on test score performance,” Economics of Education Review, 2016, 55, 70–87.
Banzhaf, Spencer, Lala Ma, and Christopher Timmins, “Environmental Justice: The Eco-
nomics of Race, Place, and Pollution,” Journal of Economic Perspectives, 2019, 33 (1), 185–208.
Beach, Brian, Joseph Ferrie, Martin Saavedra, and Werner Troesken, “Typhoid fever,
water quality, and human capital formation,” The Journal of Economic History, 2016, 76 (1),
41–75.
Bennear, Lori S and Sheila M Olmstead, “The impacts of the “right to know”: Information
disclosure and the violation of drinking water standards,” Journal of Environmental Economics
and Management, 2008, 56 (2), 117–130.
, Katrina K Jessoe, and Sheila M Olmstead, “Sampling out: regulatory avoidance and the
total coliform rule,” 2009.
Colford, John M, Sharon Roy, Michael J Beach, Allen Hightower, Susan E Shaw, and
Timothy J Wade, “A review of household drinking water intervention trials and an approach
to the estimation of endemic waterborne gastroenteritis in the United States,” Journal of water
and health, 2006, 4 (S2), 71–88.
Cox, Stephen E., William Simonds, Llyn Doremus, Raegan L. Huffman, and Rose M.
Defawe, “Ground water/surface water interactions and quality of discharging ground water in
streams of the lower Nooksack River Basin, Whatcom County, Washington,” Technical Report,
U.S. Geological Survey 2005.
Currie, Janet, “Inequality at birth: Some causes and consequences,” American Economic Review,
2011, 101 (3), 1–22.
and Duncan Thomas, “Early test scores, school quality and SES: Longrun effects on wage
and employment outcomes,” in “Worker wellbeing in a changing labor market,” Emerald Group
Publishing Limited, 2001, pp. 103–132.
30
, Eric A Hanushek, E Megan Kahn, Matthew Neidell, and Steven G Rivkin, “Does
pollution increase school absences?,” The Review of Economics and Statistics, 2009, 91 (4),
682–694.
, Joshua Graff Zivin, Katherine Meckel, Matthew Neidell, and Wolfram Schlenker,
“Something in the water: Contaminated drinking water and infant health,” Canadian Journal
of Economics/Revue canadienne d’economique, 2013, 46 (3), 791–810.
Cutler, David and Grant Miller, “The role of public health improvements in health advances:
the twentieth-century United States,” Demography, 2005, 42 (1), 1–22.
Drewnowski, Adam, Colin D Rehm, and Florence Constant, “Water and beverage con-
sumption among children age 4-13y in the United States: analyses of 2005–2010 NHANES data,”
Nutrition journal, 2013, 12 (1), 85.
Ercumen, Ayse, Joshua S Gruber, and John M Colford Jr, “Water distribution system
deficiencies and gastrointestinal illness: a systematic review and meta-analysis,” Environmental
Health Perspectives, 2014, 122 (7), 651.
Gilliland, Frank D, Kiros Berhane, Edward B Rappaport, Duncan C Thomas, Ed-
ward Avol, W James Gauderman, Stephanie J London, Helene G Margolis, Rob
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Harris, Kamala D., “In school + on track: Attorney general’s 2013 report on California’s ele-
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Hobson, Wendy L, Miguel L Knochel, Carrie L Byington, Paul C Young, Charles J
Hoff, and Karen F Buchi, “Bottled, filtered, and tap water use in Latino and non-Latino
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Zivin, Joshua Graff, Matthew Neidell, and Wolfram Schlenker, “Water quality violations
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33
7 Figures
Figure 1: NC School Attendance Boundary Areas
Esri, HERE, DeLorme, MapmyIndia, ' OpenStreetMap contributors, and the GISuser community
Esri, HERE, DeLorme, MapmyIndia, ' OpenStreetMap contributors, and the GISuser community
Esri, HERE, DeLorme, MapmyIndia, ' OpenStreetMap contributors, and the GISuser community
Legend
Attendance Boundaries: Middle Schools
Legend
Attendance Boundaries: High Schools
Legend
Attendance Boundaries: Primary Schools
34
Fig
ure
2:N
CC
omm
un
ity
Wat
erS
up
ply
Are
as
Esri
, H
ER
E, D
eLo
rme
, M
apm
yIn
dia
, '
Ope
nS
tree
tMap c
ontr
ibuto
rs, an
d t
he
GIS
user
co
mm
unity
Leg
en
d
Com
mu
nity W
ate
r S
up
ply
Se
rvic
e A
reas
35
Figure 3: Purchases of Bottled Water Relative to Violation Timing(a) Water
−.5
0.5
11.
5
−2−2 −1−1 00 +1+1 +2+2
Acute Coliform Monthly Coliform
(b) Water: Intensive
−2
−1
01
2
−2−2 −1−1 00 +1+1 +2+2
Acute Coliform Monthly Coliform
Notes: Water is measured as the inverse hyperbolic sine of total dollar sales of bottled water.
Figure 4: Purchases of Stomach Remedies Relative to Monthly Coliform Violation Timing(a) Stomach Remedies
−.2
0.2
.4.6
−2 −1 0 +1 +2
(b) Stomach Remedies: Intensive
−4
−2
02
4
−2 −1 0 +1 +2
Notes: Stomach remedies is a standardized mean value index of the dollar sales for each of the following categories:diarrhea remedies, antacids, pain remedies–children’s liquid, and pedialyte.
36
Figure 5: Heterogeneous effects of acute coliform violations on bottled water sales
Income < $50,000Income >= $50,000
No KidsSome Kids
< College Grad>= College Grad
Non−HispanicHispanic
Non−WhiteWhite
Income
Children
Education
Hispanic
Race
0 2 4 6 8Coefficient estimate: Acute Coliform
Outcome: ihs(sales) | sales > 0
Figure 6: ER Admissions for Gastrointestinal Illness: Age 5-19(a) Pct Acute Coliform
−2
−1
01
23
−2 −1 0 +1 +2
(b) Pct Monthly Coliform
−.5
0.5
11.
5
−2 −1 0 +1 +2
37
Figure 7: Absences Relative to Violation Timing(a) Pct Acute Coliform
Source: PMR school-grade-month level data (Column 1) and NCERDC student-year level data(Columns 2-4).
Notes: All specifications include grade and weather controls. Column 1 is weighted by cell size.Standard errors are clustered at the school level in column 1 and at the individual level in columns2-4. *** p<0.01, ** p<0.05, * p<0.1
42
Table 5: Student-level Math Scores
Home Exposure School Exposure
(1) (2) (3) (4) (5) (6) (7)
Acute Col: SY 0.0152 0.0259 0.0109 0.103(0.0883) (0.0867) (0.104) (0.270)
Notes: Student math scores for grades 3-8 are standardized by year, grade, and local education agency. All regressionsinclude controls for grade, year, and weather. Weather controls include separate measures for the summer, schoolyear, and testing window. Standard errors are clustered at the individual level. *** p<0.01, ** p<0.05, * p<0.1
Household FE yes yes yes yes yes yesYear-month FE yes yes yes yes yes yesZip-yr FE yes yes yes yes yes yesZip-month FE yes yes yes yes yes yes
Source: Nielsen household-month level data.
Notes: All regressions include controls for weather, employment rate, and household size. The categoriesinclude the following product groups: “Bread and Baked Goods,” “Eggs,” “Butter and Margarine,”“Milk,” “Juice, Drinks - Canned, Bottled,” and “Soft Drinks (Carbonated and Low Calorie),” and Theoutcomes in Panel A are measured as the inverse hyperbolic sine of the total purchase of each categoryin dollars. Panel B shows binary outcomes equal to one if the household purchases any dollar amount ofthat category in that month. Panel C limits the sample to non-zero purchase months for each category.Standard errors clustered at the zip code level are shown in parenthesis. *** p<0.01, ** p<0.05, * p<0.1
44
Table 7: ER admission placebos: Age 5-19
Acute NervousFractures Burns Poisoning Resp. System
Notes: All regressions are weighted by population and include controls for weather and in-fluenza and pneumonia ER visits. Outcomes are emergency room visits per 1000 population.Standard errors clustered at the zip code level are shown in parentheses. *** p<0.01, **p<0.05, * p<0.1
45
Tab
le8:
Sch
ool
-lev
elA
bse
nce
s:R
obust
nes
sE
mp
loym
ent
MR
Flu
and
Air
Dro
pD
rop
bad
PM
RR
ain
Bas
elin
era
tevio
lati
ons
Ast
hm
aP
ollu
tion
Dro
pS
ept.
Mec
kle
nb
urg
Dro
pU
SC
sS
chool
-lev
el-m
onth
matc
hb
ins
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Pct
Acu
teC
olif
orm
0.0
0857
***
0.00
902*
**0.
0079
8**
0.00
855*
**0.
0090
8***
0.00
854***
0.0
109**
*0.0
0857*
**
0.0
0867
***
0.0
072
4**
0.0
0850
***
(0.0
032
4)(0
.003
25)
(0.0
0370
)(0
.003
24)
(0.0
0323
)(0
.003
19)
(0.0
032
8)
(0.0
0324)
(0.0
0314)
(0.0
0338
)(0
.0032
2)
Pct
Mon
thly
Col
iform
0.18
2**
0.18
2**
0.18
3**
0.18
1**
0.17
9**
0.18
8**
0.1
83**
0.1
83*
*0.1
87**
0.2
05**
0.190
**
(0.0
881)
(0.0
879)
(0.0
889)
(0.0
894)
(0.0
886)
(0.0
867)
(0.0
879)
(0.0
881)
(0.0
924)
(0.0
891)
(0.0
891)
Ob
serv
atio
ns
176,
088
176,
088
176,
088
176,
088
176,
088
156,
212
154,
345
175,7
78
43,1
2214
9,0
53
176,
088
R-s
qu
ared
0.7
580.
758
0.75
90.
758
0.75
80.
742
0.7
44
0.7
58
0.8
55
0.7
56
0.7
58
Sou
rce:
PM
Rsc
hool-
gra
de-
month
level
data
.
Note
s:A
llsp
ecifi
cati
on
sare
wei
ghte
dby
cell
size
an
din
clu
de
contr
ols
for
gra
de,
sch
ool,
yea
r,m
onth
,yea
r-m
onth
,sc
hool-
yea
r,sc
hool-
month
,an
dw
eath
er.
Colu
mn
s2-4
ad
dco
ntr
ols
for
cou
nty
-month
emp
loym
ent
rate
,m
on
itori
ng
an
dre
port
ing
vio
lati
on
s,an
din
flu
enza
an
dast
hm
aE
Rad
mis
sion
s.C
olu
mn
s5
incl
ud
esco
ntr
ols
for
OZ
an
dP
M10
as
the
per
cent
of
day
sw
ith
inea
chof
five
cate
gori
esto
all
owfo
rn
on
lin
ear
effec
ts:
0-2
5%
,25-5
0%
,50-7
5%
,75-1
00%
,an
dov
er100%
of
the
rele
vant
EP
Ath
resh
old
.C
olu
mn
s6-8
dro
pSep
tem
ber
,sc
hools
inM
eckle
nb
urg
cou
nty
,an
dsc
hools
wit
hw
ate
r-re
late
du
np
lan
ned
sch
ool
closu
res,
resp
ecti
vel
y.C
olu
mn
9co
llap
ses
the
data
toth
esc
hool
level
.S
tan
dard
erro
rscl
ust
ered
at
the
sch
ool
level
are
show
nin
pare
nth
esis
.C
olu
mn
10
dro
ps
sch
ools
wit
hP
MR
inte
rvals
that
cover
less
than
70%
of
day
sin
the
cale
nd
ar
month
s.C
olu
mn
11
ad
ds
contr
ols
for
7p
reci
pit
ati
on
bin
s.***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
Tab
le9:
Stu
den
t-le
vel
Ab
sen
ces:
Rob
ust
nes
s
Em
plo
ym
ent
MR
Flu
and
Air
Dro
pD
rop
Reg
ula
rR
egu
lar
Tra
dit
ion
al
Rain
Month
lyB
asel
ine
rate
vio
lati
ons
Ast
hm
aP
ollu
tion
Mec
kle
nb
urg
US
Cs
Sch
ool
sP
rogr
am
sC
ale
nd
ars
Bin
sW
eath
er(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
(11)
(12)
Acu
teC
oli
form
-0.0
0013
0-0
.000
220
-0.0
0038
3-0
.000
163
0.00
0134
-0.0
0062
3-0
.000127
-0.0
00862
-0.0
00878
-0.0
00709
-0.0
0031
20.0
00195
(0.0
0068
4)(0
.000
684)
(0.0
006
84)
(0.0
0068
3)(0
.000
689)
(0.0
0068
5)(0
.000684)
(0.0
00674)
(0.0
00699)
(0.0
00716)
(0.0
00686)
(0.0
00739
)M
onth
lyC
oli
form
0.00
234*
**0.
00227
***
0.001
61**
*0.
0021
6***
0.00
189**
*0.
0021
3***
0.0
0234***
0.0
0197**
*0.0
022
3***
0.0
023
0***
0.0
0212
***
0.0
0192*
**
(0.0
0043
8)(0
.000
438)
(0.0
004
40)
(0.0
0043
8)(0
.000
439)
(0.0
0043
8)(0
.000438)
(0.0
00434)
(0.0
00455)
(0.0
00447)
(0.0
00439)
(0.0
00449
)
2,96
7,43
92,
967,
161
2,967
,439
2,96
5,46
42,
967,
439
2,47
4,634
2,9
65,8
23
2,8
93,4
42
2,4
62,3
18
2,7
53,8
93
2,9
67,4
39
2,9
67,
439
R-s
qu
ared
0.72
40.
724
0.72
40.
724
0.72
40.
732
0.7
24
0.7
29
0.7
46
0.73
30.7
24
0.72
4
Sourc
e:N
CE
RD
Cst
uden
t-yea
rle
vel
data
.
Note
s:A
llsp
ecifi
cati
ons
incl
ude
indiv
idual
fixed
effec
tsand
contr
ols
for
gra
de,
yea
r,and
wea
ther
.C
olu
mns
2-4
incl
ude
contr
ols
for
the
emplo
ym
ent
rate
,m
onit
ori
ng
and
rep
ort
ing
vio
lati
ons,
and
influen
zaand
ast
hm
aE
Radm
issi
ons.
Colu
mn
5in
cludes
contr
ols
for
OZ
and
PM
10
as
the
per
cent
of
day
sw
ithin
each
of
five
cate
gori
esto
allow
for
nonlinea
reff
ects
:0-2
5%
,25-5
0%
,50-7
5%
,75-1
00%
,and
over
100%
of
the
rele
vant
EP
Ath
resh
old
.C
olu
mns
6and
7dro
pM
eckle
nburg
county
schools
and
schools
wit
hw
ate
r-re
late
dunpla
nned
school
closu
res.
Colu
mns
8-1
0lim
itth
esa
mple
tore
gula
rsc
hools
,re
gula
rpro
gra
ms,
and
schools
wit
htr
adit
ional
cale
ndars
,re
spec
tivel
y.C
olu
mn
11
adds
contr
ols
for
7pre
cipit
ati
on
bin
sand
colu
mn
12
incl
udes
tem
per
atu
reand
pre
cipit
ati
on
bin
sse
para
tely
for
each
month
.Sta
ndard
erro
rscl
ust
ered
at
the
indiv
idual
level
are
show
nin
pare
nth
esis
.***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
46
For Online Publication
A Appendix Figures
Figure 9: Student-level absences by CWS characteristics
Notes: All regressions include controls for weather, employment rate, and household size. Stan-dard errors clustered at the zip code level are shown in parentheses. *** p<0.01, ** p<0.05, *p<0.1
52
Table 12: Household-level Purchases: Extensive and Intensive Margins
Extensive Intensive
Water Remedies Water Remedies Water Remedies(1) (2) (3) (4) (5) (6)
Observations 245,632 245,632 245,632 245,632 40,758 11,735R-squared 0.382 0.177 0.322 0.195 0.575 0.629Year FE yes yes yes yes yes yesMonth FE yes yes yes yes yes yesHousehold FE yes yes yes yes yes yesYear-month FE yes yes yes yes yes yesZip-yr FE yes yes yes yes yes yesZip-month FE yes yes yes yes yes yes
Source: Nielsen household-month level data.
Notes: All regressions include controls for weather, employment rate, and household size. Columns 3 and 4are binary outcomes equal to one if the household purchases any dollar amount of bottled water or stomachremedies in that month, respectively. Columns 5-6 drop months without any bottled water or stomach remediespurchases. Standard errors clustered at the zip code level are shown in parenthesis. *** p<0.01, ** p<0.05, *p<0.1
Notes: All regressions include controls for weather and influenza and pneumonia visits.Regressions are weighted by population and standard errors clustered at the zip codelevel are shown in parentheses. *** p<0.01, ** p<0.05, * p<0.1
53
Tab
le14
:S
chool
-lev
elM
onth
lyA
bse
nce
san
dth
eIm
pac
tof
Wat
erQ
ual
ity
Vio
lati
on
s
(1)
(2)
(3)
(4)
(5)
(6)
Pct
Acu
teC
olif
orm
0.00
0265
-0.0
106
-0.0
0706
-0.0
0665
-0.0
0865
0.0
0857***
(0.0
0770
)(0
.008
09)
(0.0
0814
)(0
.008
19)
(0.0
0722
)(0
.00324)
Pct
Mon
thly
Col
ifor
m0.
346*
**0.
302*
**0.
289*
**0.
258*
**0.
303*
**0.1
82**
(0.1
05)
(0.0
843)
(0.1
03)
(0.0
907)
(0.0
838)
(0.0
881)
Pct
Ch
emic
al0.
0044
1***
0.00
275*
0.00
260*
*0.
0028
2**
0.00
426*
**0.0
0315***
(0.0
0160
)(0
.001
54)
(0.0
0126
)(0
.001
29)
(0.0
0095
2)(0
.00105)
Ob
serv
atio
ns
176,
088
176,
088
176,
088
176,
088
176,
088
176,0
88
R-s
qu
ared
0.28
70.
365
0.53
20.
554
0.64
70.7
58
Yea
rye
sye
sye
sye
sye
sye
sM
onth
yes
yes
yes
yes
yes
yes
Sch
ool
char
.yes
yes
yes
--
Sch
ool
yes
yes
yes
yes
Yea
r-m
onth
yes
yes
yes
Sch
ool
-yr
yes
yes
Sch
ool
-mon
thye
s
Sourc
e:P
MR
school-
gra
de-
month
level
data
.
Note
s:A
llsp
ecifi
cati
ons
are
wei
ghte
dby
cell
size
and
incl
ude
gra
de
dum
mie
sand
wea
ther
contr
ols
.Sch
ool
chara
cter
isti
csat
the
school-
yea
rle
vel
incl
ude
per
cent
by
raci
al
cate
gory
,ti
tle
Ist
atu
s,and
per
cent
rece
ivin
gfr
ee/re
duce
dpri
celu
nch
.Sta
ndard
erro
rscl
ust
ered
at
the
school
level
are
show
nin
pare
nth
esis
.***
p<
0.0
1,
**
p<
0.0
5,
*p<
0.1
54
Table 15: Student-level Absences and the Impact of Water Quality Violations
(1) (2) (3) (4) (5)
Total Coliform: Home 0.00258*** 0.00175*** 0.00138*** 0.00106**(0.000325) (0.000332) (0.000352) (0.000469)
Total Coliform: School 0.000976*** 0.000476(0.000350) (0.000456)
Radionuclides: Home -0.000534*** -0.000590*** 0.000109 -0.000456(0.000139) (0.000135) (0.000186) (0.000347)
Radionuclides: School 0.000671** 0.000976**(0.000271) (0.000397)
Disinfect: Home 0.000374*** 0.000224*** -9.16e-05*** -0.000163***(2.52e-05) (2.55e-05) (3.28e-05) (5.85e-05)
Disinfect: School -8.86e-05*** 3.72e-05(3.32e-05) (5.64e-05)
SOCs: Home 0.000525** 0.000523** 8.83e-05 7.99e-05(0.000263) (0.000264) (0.000234) (0.000419)
SOCs: School 0.000336 0.000276(0.000256) (0.000408)
VOCs: Home -0.000131 -6.70e-05 2.96e-05 -0.000407(0.000170) (0.000172) (0.000176) (0.000514)
VOCs: School 0.000104 0.000443(0.000199) (0.000493)
Notes: Demographics include sex, race/ethnicity, and birth month. Weather includes total and average precipitationand percent days in 7 temperature bins. Standard errors are clustered at the individual level in columns 3-5. ***p<0.01, ** p<0.05, * p<0.1
Observations 2,547,017 2,547,017Number of students 668,532 668,532Student FE yes yes
Source: NCERDC student-year level data.
Notes: All specifications include controls for grade, year,weather, and days in membership. The outcome of interestis days absent. Bootstrapped standard errors are shown inparentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 17: Consumption of Tap and Bottled Water, NHANES 2005-2010
Notes: All regressions include household fixed effects and controls for year, month, year-month, zip-year, zip-month, weather, employment rate, and household size. The sample is restricted to observations where the volumeof bottled water purchased is greater than zero. P-values are for tests of the equality of estimated coefficients.Standard errors clustered at the zip code are shown in parenthesis. *** p<0.01, ** p<0.05, * p<0.1
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Table 19: Student-level Absences by CWS Characteristics
(1) (2) (3) (4)
Groundwater × Monthly Coliform -0.00159(0.00171)
Surface water × Monthly Coliform 0.00245***(0.000460)
Notes: All regressions include fixed effects and controls for acute coliform, grade, year and weather.Standard errors are clustered at the individual level. *** p<0.01, ** p<0.05, * p<0.1
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Table 20: Student-level Absences by Demographics(1) (2) (3) (4) (5)
Notes: All regressions include fixed effects and controls for acute coliform, grade, year and weather. Standard errors are clustered atthe individual level. *** p<0.01, ** p<0.05, * p<0.1
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Table 21: Student-level Math and Reading and the Impact of Water Quality Violations
(1) (2) (3)
Panel A. Math
Acute Col: SY -0.514*** -0.210** 0.0152(0.0845) (0.0867) (0.0883)
Acute Col: Testing 0.0487 0.0592(0.0417) (0.0403)
Monthly Col: SY 0.0162 -0.00672 -0.0287*(0.0142) (0.0176) (0.0152)
Notes: Student scores for grades 3-8 are standardized by year, grade,and local education agency. Demographics include sex, race/ethnicity,birth month, disability, economically disadvantaged, and limited En-glish status. *** p<0.01, ** p<0.05, * p<0.1
C Data Appendix
C.1 PMR Intervals
The following two figures test the assumption that PMR Intervals 1-9 cover months September-May.Using school calendar information and the reported length of each PMR interval (ranging from 16to 26 days), I calculate the approximate start and end date of each PMR interval to compare with
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the assumed start and end dates (first and last day of months September-May).For each Local Education Agency (LEA) in the sample, I obtain information on the first day of
school, spring break, and the last day of school for school years 2012-13 to 2014-15 from the MyrtleBeach Area Chamber of Commerce. Unfortunately, these data are not available for earlier years.For the 4 LEAs where these dates were unavailable, I assumed their spring break fell on the mostcommon dates from the rest of the sample and that the first day of school was August 27th sinceNorth Carolina law requires public school opening dates to be the Monday closest to August 26thfrom 2013 onward. For all schools, I assume winter break begins December 21st and ends January1st (inclusive). Finally, I account for the fact that school is not in session each year during thefollowing holidays: Labor day, Veterans day, Thanksgiving, and Memorial day. Note that teacherwork days and weather related school closures are not taken into account here.
Overall, there is a high overlap between PMR intervals and calendar months. Figure 14 belowshows the percent of days in each month covered by the PMR interval associated with that month.On average, PMR intervals cover 82% of the days in their assigned month. Figure 14b showscoverage by each PMR interval. For example, the first PMR interval covers about 76% of the daysin September, on average. Coverage is better starting in December, with PMR interval 4 covering93% of the days in December. The school-grade-month level attendance results presented aboveare robust to excluding LEAs with lower than 70% average percent coverage.
Furthermore, the misalignment between PMR intervals and calendar months is very low. Fig-ure 15 shows the difference between PMR start/end dates and the first/last day of each month.Error is calculated as start (end) date of month minus start (end) date of PMR interval. Thereforepositive start (end) error indicates that the PMR interval started (ended) before the first (last)day of the month. On average, the PMR interval started 4 days before the first day of the monthand ended 4 days before the last day of the month. Figure 15b shows the start and end error byeach PMR interval. As before, we can see that there is greater error in the first 3 months. Overthe first 3 months, the average start error is 8 days and the average end error is 9 days, while theaverage start error and end error for the remaining months is 3 days and 1 day, respectively. Thesepatterns likely reflect the fact that the typical school year starts during the last week of August,rather than the first week of September.
Figure 14: Percent of assumed month covered by PMR Interval(a) Distribution
02
46
8D
ensi
ty
0 .2 .4 .6 .8 1% Days in month covered by PMR interval
(b) By PMR Interval
0.2
.4.6
.81
% D
ays
in m
onth
cov
ered
by
PM
R in
terv
al
1 2 3 4 5 6 7 8 9
Notes: Percent covered is the percent of the days in each month (Sept-May) covered by each PMR interval, wherePMR interval 1 is assigned to September and other months are assigned sequentially.
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Figure 15: Error in PMR interval start/end dates(a) Distribution
0.0
2.0
4.0
6.0
8
−20 −10 0 10 20 30Days
Start Error End Error
(b) By PMR interval
−20
−10
010
2030
Day
s
1 2 3 4 5 6 7 8 9
Start Error End Error
Notes: Error is calculated as start (end) date of month minus start (end) date of PMR interval. Therefore positivestart (end) error indicates that the PMR interval started (ended) before the first (last) day of the month.
