Effect of early life exposure to a clean water intervention on health ...

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<strong>Effect</strong> <strong>of</strong> <strong>early</strong> <strong>life</strong> <strong>exposure</strong> <strong>to</strong> a <strong>clean</strong> <strong>water</strong> <strong>intervention</strong> on health
and socioeconomic status in adulthood: Evidence from a quasiexperimental
cohort study in Mexico
Venkataramani and Bhalotra
July 2013
Working Paper No. 13/310
Centre for Market and Public Organisation
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CMPO Working Paper Series No. 13/310
<strong>Effect</strong> <strong>of</strong> <strong>early</strong> <strong>life</strong> <strong>exposure</strong> <strong>to</strong> a <strong>clean</strong> <strong>water</strong> <strong>intervention</strong> on health
and socioeconomic status in adulthood: Evidence from a quasiexperimental
cohort study in Mexico
Venkataramani 1 and Bhalotra 2
1 Harvard Medical School
2 University <strong>of</strong> Bris<strong>to</strong>l
Abstract
July 2013
Background: Early childhood diarrheal disease jeopardizes child development by diverting nutrition
away from physical and mental growth <strong>to</strong>wards fighting illness. Consequently, <strong>early</strong> <strong>exposure</strong> <strong>to</strong>
<strong>clean</strong> <strong>water</strong> <strong>intervention</strong>s, which reduce diarrheal risk, may confer positive effects on downstream
adult health and well being, though such long-run benefits are typically not accounted for in
resource allocation decisions. We examined the effects <strong>of</strong> <strong>early</strong> <strong>exposure</strong> <strong>to</strong> a nationwide <strong>clean</strong>
<strong>water</strong> <strong>intervention</strong> in Mexico in 1991 on adult health and socioeconomic status.
Methods: We used a quasi-experimental cohort design. Data on height, body-mass index (BMI), and
schooling for over 20,000 individuals born between 1987-1993 were taken from a nationally
representative 2012 survey. Diarrheal mortality data were taken from vital statistics. We compared
outcomes for cohorts exposed <strong>to</strong> the <strong>clean</strong> <strong>water</strong> program in infancy versus those exposed
afterwards, across states that benefitted most from the <strong>intervention</strong> (those with high pre-program
diarrheal disease mortality rates) versus those benefitting less. We controlled for birth state and
year fixed effects, household characteristics, and measures <strong>of</strong> the <strong>early</strong> <strong>life</strong> macroeconomic and
disease environment.
Findings: A one standard deviation decrease in diarrheal mortality rates induced by the <strong>clean</strong> <strong>water</strong>
<strong>intervention</strong> during infancy was associated with a 1 cm increase in height for men (95% CI 0.47-1.6)
and a 0.2 year increase in women’s schooling (95% CI 0.05-0.37). Put differently, reducing the
diarrheal disease burden from the highest <strong>to</strong> the lowest quintile pre-<strong>intervention</strong> would have
reduced gaps in men’s height and women’s schooling across these states by 30% and 43%,
respectively. Results were robust <strong>to</strong> an extensive set <strong>of</strong> controls and checks.
Interpretation: Failure <strong>to</strong> account for long-run program benefits may lead <strong>to</strong> underinvestment in
<strong>water</strong> <strong>intervention</strong>s. The differential gender findings suggest that investments made later in
childhood help individuals achieve the full long run potential <strong>of</strong> healthier infancies.
Funding: Grand Challenges Canada, University <strong>of</strong> Bris<strong>to</strong>l Water Strategic Fund, and Bris<strong>to</strong>l Centre for
Market and Public Organisation.

CMPO Working Paper Series No. 13/310
Keywords: <strong>early</strong> childhood health, developmental origins, <strong>clean</strong> <strong>water</strong>, diarrhea, quasi-experiment,
public health, global burden <strong>of</strong> disease, health policy, differences-in-differences
Electronic version: www.bris<strong>to</strong>l.ac.uk/cmpo/publications/papers/2013/wp310.pdf
Address for correspondence
CMPO
University <strong>of</strong> Bris<strong>to</strong>l
2 Priory Road
Bris<strong>to</strong>l BS8 1TX
Cmpo-admin@bris<strong>to</strong>l.ac.uk
www.bris<strong>to</strong>l.ac.uk/cmpo/

<strong>Effect</strong> <strong>of</strong> <strong>early</strong> <strong>life</strong> <strong>exposure</strong> <strong>to</strong> a <strong>clean</strong> <strong>water</strong> <strong>intervention</strong> on health and
socioeconomic status in adulthood: Evidence from a quasi-experimental
cohort study in Mexico
Atheendar S. Venkataramani
Department <strong>of</strong> Medicine
Massachusetts General Hospital
Harvard Medical School, USA
Sonia R. Bhalotra
Department <strong>of</strong> Economics
University <strong>of</strong> Bris<strong>to</strong>l, UK
Abstract:
Background: Early childhood diarrheal disease jeopardizes child development by diverting
nutrition away from physical and mental growth <strong>to</strong>wards fighting illness. Consequently, <strong>early</strong>
<strong>exposure</strong> <strong>to</strong> <strong>clean</strong> <strong>water</strong> <strong>intervention</strong>s, which reduce diarrheal risk, may confer positive
effects on downstream adult health and well being, though such long-run benefits are
typically not accounted for in resource allocation decisions. We examined the effects <strong>of</strong> <strong>early</strong>
<strong>exposure</strong> <strong>to</strong> a nationwide <strong>clean</strong> <strong>water</strong> <strong>intervention</strong> in Mexico in 1991 on adult health and
socioeconomic status.
Methods: We used a quasi-experimental cohort design. Data on height, body-mass index
(BMI), and schooling for over 20,000 individuals born between 1987-1993 were taken from
a nationally representative 2012 survey. Diarrheal mortality data were taken from vital
statistics. We compared outcomes for cohorts exposed <strong>to</strong> the <strong>clean</strong> <strong>water</strong> program in infancy
versus those exposed afterwards, across states that benefitted most from the <strong>intervention</strong>
(those with high pre-program diarrheal disease mortality rates) versus those benefitting less.
We controlled for birth state and year fixed effects, household characteristics, and measures
<strong>of</strong> the <strong>early</strong> <strong>life</strong> macroeconomic and disease environment.
Findings: A one standard deviation decrease in diarrheal mortality rates induced by the
<strong>clean</strong> <strong>water</strong> <strong>intervention</strong> during infancy was associated with a 1 cm increase in height for
men (95% CI 0.47-1.6) and a 0.2 year increase in women’s schooling (95% CI 0.05-0.37). Put
differently, reducing the diarrheal disease burden from the highest <strong>to</strong> the lowest quintile pre<strong>intervention</strong>
would have reduced gaps in men’s height and women’s schooling across these
states by 30% and 43%, respectively. Results were robust <strong>to</strong> an extensive set <strong>of</strong> controls and
checks.
Interpretation: Failure <strong>to</strong> account for long-run program benefits may lead <strong>to</strong>
underinvestment in <strong>water</strong> <strong>intervention</strong>s. The differential gender findings suggest that
investments made later in childhood help individuals achieve the full long run potential <strong>of</strong>
healthier infancies.
Funding: Grand Challenges Canada, University <strong>of</strong> Bris<strong>to</strong>l Water Strategic Fund, and Bris<strong>to</strong>l
Centre for Market and Public Organisation.
Keywords: <strong>early</strong> childhood health, developmental origins, <strong>clean</strong> <strong>water</strong>, diarrhea, quasiexperiment,
public health, global burden <strong>of</strong> disease, health policy, differences-in-differences
1

Introduction
Diarrheal diseases are a leading cause <strong>of</strong> under-5 morbidity and mortality in the developing
world, resulting in n<strong>early</strong> 1.7 billion infections and one million deaths annually.(1) While
<strong>clean</strong> <strong>water</strong> <strong>intervention</strong>s greatly reduce diarrheal incidence,(2, 3) over a third <strong>of</strong> the world’s
population continues <strong>to</strong> lack access <strong>to</strong> quality <strong>water</strong>.(4) Thus, policies <strong>to</strong> improve <strong>water</strong>
quality may be critical for the success <strong>of</strong> recently initiated WHO and UNICEF efforts <strong>to</strong>
reduce child deaths from diarrhea (and pneumonia) by 80% by 2025.(5) However, the choice
<strong>to</strong> invest in <strong>clean</strong> <strong>water</strong> programs may rest on their effectiveness relative <strong>to</strong> other public
health <strong>intervention</strong>s. Recent research places poor quality <strong>water</strong> well below other risk fac<strong>to</strong>rs
(such as high blood pressure, high salt, and low micronutrient diets) in terms <strong>of</strong> projected
impact on global burden <strong>of</strong> disease, even among children in many less-developed regions.(6,
7)
The use <strong>of</strong> comparative effectiveness methods in informing health policy decisions requires
a complete assessment <strong>of</strong> all costs and benefits accruing from a given <strong>intervention</strong>. Clean
<strong>water</strong> programs will be undervalued if their potential economic benefits(8) and long-run
impacts are ignored. It is plausible that children exposed <strong>to</strong> <strong>clean</strong> <strong>water</strong> <strong>early</strong> in <strong>life</strong> benefit in
terms <strong>of</strong> improved health and socioeconomic status in adulthood. Inflamma<strong>to</strong>ry responses
<strong>to</strong> recurrent diarrheal infections in <strong>early</strong> childhood pilfer scarce nutrients during a critical
time <strong>of</strong> physical and mental development. While adaptive responses <strong>to</strong> such stresses increase
the odds <strong>of</strong> short-run survival, they may hamper longer-run health and human capital,(9-12)
as evinced by associations between <strong>early</strong> <strong>life</strong> infections and increased disability, and reduced
cognition, schooling, and wages through adulthood.(13-20) Indeed, <strong>exposure</strong> <strong>to</strong> frequent
bouts <strong>of</strong> diarrhea <strong>early</strong> in <strong>life</strong> has been linked <strong>to</strong> increased stunting and reduced cognitive
performance among primary school-aged children.(21-23) However, causality cannot be
definitively established in these studies and no research has examined the adulthood impacts
<strong>of</strong> <strong>early</strong> <strong>exposure</strong> <strong>to</strong> <strong>clean</strong> <strong>water</strong> programs.
We examined the relationship between <strong>early</strong> childhood <strong>exposure</strong> <strong>to</strong> <strong>clean</strong> <strong>water</strong> and health
and socioeconomic outcomes in adulthood using a large, nationally representative dataset <strong>of</strong>
Mexican adults. We exploited the introduction <strong>of</strong> a rapidly implemented, large-scale <strong>clean</strong>
<strong>water</strong> <strong>intervention</strong> initiated in mid-1991(24-26) as a quasi-experimental source <strong>of</strong> change in
access <strong>to</strong> <strong>clean</strong> <strong>water</strong> and, consequently, the risk <strong>of</strong> contracting diarrhea during infancy.
Methods
Setting – Mexico’s National Clean Water Program
Mexico’s National Clean Water Program was introduced nationwide in April 1991 due <strong>to</strong>
fear <strong>of</strong> cholera epidemics in nearby countries extending <strong>to</strong> domestic soil.(26) Of the
initiatives’ many facets, efforts <strong>to</strong> increase <strong>clean</strong> <strong>water</strong> access were most extensive and largest
in scope. Previously untreated <strong>water</strong> supplies were chlorinated and irrigation using sewage
<strong>water</strong> was banned.(24, 26)
The program was implemented rapidly. Within a single year, access <strong>to</strong> chlorinated <strong>water</strong>
almost doubled and land area irrigated with waste<strong>water</strong> declined by over 80% (Figures 1a and
1b).(27) This led <strong>to</strong> a sharp drop in diarrheal disease morbidity and mortality for children
under 5 declined <strong>of</strong> n<strong>early</strong> 50% within the year (Figure 2a).(24, 25) We established elsewhere
that <strong>water</strong> program activities and reductions in diarrheal disease were causally linked.(28)
2

Critically for our research strategy, Figure 2b shows that states with the highest pre<strong>intervention</strong>
diarrheal mortality rates experienced the largest declines in the same as a result
<strong>of</strong> the program. Our models exploited this convergence pattern: we examined whether the
post-<strong>intervention</strong> convergence in diarrheal disease burdens across the states was mirrored by
a similar convergence in adult outcomes for those born after the National Clean Water
program commenced.
Data
Individual-level data came from the Enquesta Nacional de Salud y Nutricion (ENSANUT) 2012,
a large, nationally representative survey <strong>of</strong> over 50,000 households and 194,000 individuals
conducted by the Mexican School <strong>of</strong> Public Health in 2011-2012 (http://ensanut.insp.mx).
The data included information on socioeconomic and demographic characteristics,
indica<strong>to</strong>rs <strong>of</strong> health and health care utilization, and anthropometric measurements. We used
height, a marker <strong>of</strong> <strong>early</strong> <strong>life</strong> health and nutritional status that is predictive <strong>of</strong> long-run health
outcomes, (29, 30) and body mass index (BMI), a measure <strong>of</strong> medium-run health. We used
schooling years as a measure <strong>of</strong> socioeconomic status. The sample was restricted <strong>to</strong> the
1987-1993 birth cohorts, aged 18-24 years at the time <strong>of</strong> survey
To capture <strong>exposure</strong>, as explained below, we used state and gender-specific under-5 diarrhea
mortality rates obtained from the Mexican Secretary <strong>of</strong> Health database
(http://sinais.salud.gob.mx, ICD-9 codes A0-A9), averaged between 1988 and 1990, <strong>to</strong> help
proxy the effective intensity <strong>of</strong> the <strong>clean</strong> <strong>water</strong> program by state. In doing so, we followed a
well-established tradition <strong>of</strong> using mortality <strong>to</strong> proxy for morbidity.(29) In the
Supplementary Appendix we discuss how the use <strong>of</strong> mortality rates may be preferred <strong>to</strong>
detailed information on program activities (which were not available) from a causal inference
standpoint.
For individual and household level controls, we created binary indica<strong>to</strong>rs for urban
residence, indigenous language, piped <strong>water</strong>, and modern <strong>to</strong>ilet in the household, attained
schooling <strong>of</strong> the household head, and an asset index denoting the number owned from a set
<strong>of</strong> queried items (see Table 1 legend). For disease environment and macroeconomic controls,
we gleaned data on the rate <strong>of</strong> respira<strong>to</strong>ry (ICD-9 codes 460-466 and 480-487) and vaccine
preventable diseases (MMR, diphtheria, tetanus) from Ministry <strong>of</strong> Health data, and state
GDP per capita(31) and rainfall in the individual’s birth state and birth year (from
http://smn.cna.gob.mx).
The birth state was not asked in ENSANUT 2012, so the <strong>exposure</strong> and macroeconomic
variables were matched using the current state <strong>of</strong> residence. We anticipated the consequent
measurement error <strong>to</strong> be small given that, for the sample cohorts, state <strong>of</strong> birth and
residence were identical in over 80% <strong>of</strong> cases in contemporaneous census data.(32)
Statistical Analysis
To identify whether indica<strong>to</strong>rs <strong>of</strong> long-run health and socioeconomic outcomes varied
systematically by <strong>exposure</strong> <strong>to</strong> <strong>clean</strong> <strong>water</strong> in the first two years <strong>of</strong> <strong>life</strong>, we estimated the
following model using ordinary least squares:
Y ijt = β 0 + β 1 *Exposure_Birth t *BaseDiarrhea j + β 2 *Exposure_1-24Months t *BaseDiarrhea j + β*X jt +
η j + θ t + e ijt
3

Y ijt is the outcome for individual i born in birth state j in birth year t, and Exposure_Birth t and
Exposure_1-24Months t are binary indica<strong>to</strong>rs = 1 if the individual was born on or after April
1991 and between April 1989 and March 1991, respectively. The Exposure terms were
selected <strong>to</strong> capture age groups most affected by diarrhea and most likely <strong>to</strong> face its long-term
consequences.(1, 33) We separated <strong>exposure</strong> at the time <strong>of</strong> birth and <strong>exposure</strong> before 24
months given that the development potential and plasticity <strong>of</strong> younger infants may differ
from older infants and <strong>to</strong>ddlers. BaseDiarrhea j is the pre-<strong>intervention</strong> under-5 diarrheal
mortality rate in the birth state. This represents a state’s “treatability” by the Clean Water
program given that high BaseRate states witnessed larger absolute reductions in diarrheal
disease burdens after improved <strong>clean</strong> <strong>water</strong> access (Figure 2b).
The interactions between the Exposure variables and BaseDiarrhea test whether the sharp
post-<strong>intervention</strong> convergence in child diarrhea mortality rates driven by the <strong>clean</strong> <strong>water</strong>
program were mirrored in long run outcomes. Positive coefficients on β 1 and β 2 would be
consistent with treated cohorts in states with high pre-<strong>intervention</strong> diarrheal burdens
showing larger health and education gains. Our approach is thus akin <strong>to</strong> a differences-indifferences
model, where changes in outcomes across cohorts in “treated” (high
BaseDiarrhea) are compared against those in “control” (low BaseDiarrhea) states.(18, 20, 34,
35)
Individual and household level controls are represented by X jt , η j are birth state fixed effects,
and θ t are birth year fixed effects. The state fixed effects purge any time invariant fac<strong>to</strong>rs that
may jointly influence childhood diarrhea and adult outcomes, and allow comparison <strong>of</strong>
cross-cohort changes in outcome within states. The birth year fixed effects account flexibly
for potentially confounding national trends. The state and time fixed effects subsume the
main effects <strong>of</strong> BaseDiarrhea and Exposure, respectively.
A possible source <strong>of</strong> confounding in differences-in-differences type models are time-varying
characteristics correlated with the <strong>exposure</strong>s and outcomes.(35) We addressed this in several
ways. First, the sample was restricted <strong>to</strong> a narrow window around the date <strong>of</strong> the reform
(1987-1993), so as <strong>to</strong> limit the number <strong>of</strong> potential confounding events. Second, we directly
controlled for the one event in this period that we were aware <strong>of</strong>, the 1990 measles
pandemic,(36) by including the state specific child mortality rate from vaccine preventable
diseases. Third, <strong>to</strong> account for state-specific public health efforts and secular improvements
in the disease environment,(25, 37) we controlled for the under-5 respira<strong>to</strong>ry disease
mortality rate. Measures <strong>of</strong> state GDP per capita and rainfall were included <strong>to</strong> further
control for variation in the economic and epidemiological environment.
Finally, we included birth region-birth year fixed effects and birth state specific time trends.
The flexible region-year fixed effects controlled for any public health events missed by the
use <strong>of</strong> a narrow cohort window and the aforementioned controls; the state trends allowed
for the possibility that the outcomes <strong>of</strong> interest were converging prior <strong>to</strong> the reform. We
contended that any long-run impacts identified conditional on this aggressive suite <strong>of</strong>
controls were likely causal.
All the models were estimated separately for men and women given gender differences in
vulnerability <strong>to</strong> <strong>early</strong> <strong>life</strong> health shocks.(38, 39) All standard errors were clustered at the birth
4

state level <strong>to</strong> account for serial correlation.(40) Estimates from our models represent “intent<strong>to</strong>-treat”
effects, or average effects at the state and cohort level.(35) Impacts on the subset <strong>of</strong>
individuals who would actually have contracted diarrhea will be larger by a proportion
corresponding <strong>to</strong> pre-<strong>intervention</strong> infection rates. The Supplemental Appendix discusses our
empirical strategy and some further issues in greater detail.
Role <strong>of</strong> the Funding Source
The sponsors <strong>of</strong> the study had no role in study design, data collection, analysis, and
interpretation, or writing <strong>of</strong> the report. The corresponding author had full access <strong>to</strong> all data
and had final responsibility for the decision <strong>to</strong> submit for publication.
Results
Table 1 presents descriptive statistics <strong>of</strong> the study sample, which consisted <strong>of</strong> 11,591 women
and 11,179 men. Respondents on average were 21 years old and had attained 10.2 and 9.9
years <strong>of</strong> schooling, respectively. Seventy percent lived in urban areas, less than 70% had
modern sanitation systems in the household, and there was wide variation in household
assets. The mean under-5 diarrhea mortality rate was 5.2 and 5.6 deaths per 1,000 live births,
with a range <strong>of</strong> 1.4-13.9 and 1.6-15.4, for girls and boys respectively.
Table 2 presents the core findings. We found statistically significant impacts <strong>of</strong> <strong>early</strong> <strong>life</strong>
<strong>exposure</strong> <strong>to</strong> <strong>clean</strong> <strong>water</strong> on men’s height and women’s schooling, with no impacts on BMI.
The point estimates were robust <strong>to</strong> controlling for the birth state disease environment and
macroeconomic conditions (column 2), as well as birth region-birth year fixed effects and
birth state specific linear time trends (column 3). For men exposed <strong>to</strong> the <strong>intervention</strong> from
birth, a one standard deviation decrease in diarrheal mortality (3.3 deaths per 1,000 live
births) was associated with 1 cm increase in height (calculated from column 2, 0.318*3.3 =
1.05; 95% CI 0.47, 1.6 cm). Positive and slightly smaller magnitude estimates were found for
<strong>exposure</strong>s in the first two years <strong>of</strong> <strong>life</strong>, though these impacts were not statistically significant.
The coefficients for women’s height were negative and not statistically significant. Figure 3a
plots estimated impacts on men’s height resulting from a 40% decline in diarrheal mortality
(the typical decline experienced after the commencement <strong>of</strong> <strong>clean</strong> <strong>water</strong> program in each
birth state – see Figure 2b) for those born in states in the highest and lowest quartiles <strong>of</strong> the
BaseDiarrhea distribution. The figure cl<strong>early</strong> shows that, just as with short-run impacts on
diarrheal disease (Figure 2b), program impacts were largest for those born in high BaseDiarrhea
states (1.6 versus 0.4 cm gain).
For schooling, a one standard deviation decrease in BaseDiarrhea was associated with a 0.2
(column 1) <strong>to</strong> 0.4 year (column 3) increase for women exposed <strong>to</strong> <strong>clean</strong> <strong>water</strong> efforts at birth
(column 2, 95% CI 0.05, 0.37 years). The corresponding impacts for men were half as large
and statistically significant only in column 3. We found some evidence <strong>of</strong> impacts from
program <strong>exposure</strong> after birth and before the age <strong>of</strong> 2 (column 3 for both genders), though
these pale in comparison <strong>to</strong> the effects <strong>of</strong> birth <strong>exposure</strong>. Figure 3b plots the impact <strong>of</strong> a 40%
decline in diarrheal mortality driven by improved <strong>water</strong> quality on women’s schooling for
high and low BaseDiarrhea states. Again, the larger absolute impacts for those born in high
BaseDiarrhea states (0.4 versus 0.08 years) mirrors the impacts for short-run diarrheal
mortality.
5

Given the null results for BMI, we examined binary indica<strong>to</strong>rs for underweight (BMI25), and obese (BMI>30). We found no evidence <strong>of</strong> impacts at any <strong>of</strong>
these margins (available upon request). In terms <strong>of</strong> controls, indica<strong>to</strong>rs <strong>of</strong> higher
socioeconomic status (asset index, modern sewage systems in the household, nonindigenous
ethnic group) were associated with increased height and schooling (estimates
shown in the Web Appendix).
Finally, we conducted a number <strong>of</strong> additional checks, including quantile regression analysis,
tests <strong>of</strong> sample composition changes, and alternate standard error calculations. The results <strong>of</strong>
these checks (see Supplemental Appendix) support the robustness <strong>of</strong> the core findings.
Additionally, the results were robust <strong>to</strong> excluding the “outlying” state with BaseDiarrhea > 15
(see Figure 2b).
Discussion
Reduced <strong>early</strong> childhood <strong>exposure</strong> <strong>to</strong> diarrheal disease due <strong>to</strong> a large scale <strong>clean</strong> <strong>water</strong>
<strong>intervention</strong> was associated with increases in cohort averaged height and schooling in a large
sample <strong>of</strong> 18-23 year old Mexican men and women, respectively. The estimates were large in
magnitude, statistically significant, and robust <strong>to</strong> specification. They imply that reducing the
diarrheal disease burden faced by infants born in states in the highest quintile <strong>of</strong> the pre<strong>intervention</strong>
diarrheal mortality distribution <strong>to</strong> the lowest quintile would reduce gaps in
men’s height and women’s schooling across these two sets <strong>of</strong> states by at least 30% and
43%, respectively. Despite diarrheal morbidity being highest for those under the age <strong>of</strong> 2,
our impact estimates were largest for those exposed <strong>to</strong> <strong>clean</strong> <strong>water</strong> from birth, perhaps
because developmental plasticity is highest at this time.
Our results demonstrate that <strong>clean</strong> <strong>water</strong> <strong>intervention</strong>s have important long-run
socioeconomic impacts beyond any immediate impacts on child health. Failure <strong>to</strong> account
for these benefits may lead <strong>to</strong> underinvestment in <strong>water</strong> <strong>intervention</strong>s, and possibly other
policies that reduce disease risks faced by young children. Indeed, recent, influential work on
the global burden <strong>of</strong> disease does not account for such dynamic socioeconomic benefits and
perhaps as a result implicitly prioritizes <strong>water</strong> policies well below other health
<strong>intervention</strong>s.(6)
Our findings shed light on the mechanisms linking <strong>early</strong> childhood health <strong>to</strong> adult outcomes.
While a growing literature in biology and economics documents these associations, the
causal processes behind them are not well unders<strong>to</strong>od. The differential results by gender
suggest that behavioral fac<strong>to</strong>rs contribute <strong>to</strong> driving the link between <strong>early</strong> <strong>life</strong> health and
later <strong>life</strong> outcomes. Our findings cannot be explained by male frailty and there is no
biological reason <strong>to</strong> suspect that <strong>early</strong> <strong>life</strong> health would impact cognitive and physical
domains differently by gender. In a companion study, we showed larger impacts <strong>of</strong> <strong>clean</strong>
<strong>water</strong> <strong>exposure</strong> on girls’ than on boys’ test scores in adolescence (congruent with our
findings for schooling). We argued there that these findings can be explained by the
comparative advantage women hold in “brainy” tasks and men in “brawny” tasks combined
with the tendency for infant health <strong>to</strong> raise the productivity <strong>of</strong> subsequent investments in
children.(28) On this premise, theories <strong>of</strong> human capital investment predict that parents will
invest more in the educational attainment <strong>of</strong> girls and the physical attainment <strong>of</strong> boys after
an <strong>intervention</strong> that improves infant health.(41) The results here neatly accord with this
hypothesis. Consequently, they have the implication that <strong>intervention</strong>s like <strong>water</strong> reform that
6

improve infant health stimulate investments in health and education later in childhood and,
in this way, the pay<strong>of</strong>f <strong>to</strong> the <strong>intervention</strong> is multiplied at no additional cost.(42)
Additionally, contrary <strong>to</strong> the extant literature,(21) the results for women’s schooling show
that cognitive impacts may occur even without concomitant changes in stature. Thus,
examining height alone <strong>to</strong> infer long-run impacts from health shocks may miss important
“subclinical” impacts. Additionally, the results for boys run counter <strong>to</strong> recent work in the
economics literature suggesting higher wages earned by taller men results solely from a
correlation between height and cognitive skills.(30)
A possible limitation <strong>to</strong> our study is that, despite our quasi-experimental design, we cannot
rule out the possibility that other events at the birth state and birth year level were driving
our results. However, robustness <strong>of</strong> our estimates <strong>to</strong> myriad controls and checks supports a
causal interpretation. While the use <strong>of</strong> pre-program disease <strong>exposure</strong> <strong>to</strong> proxy for short-run
program impacts has been successfully used elsewhere,(18-20, 34) the estimates we
recovered were cohort-averaged intent-<strong>to</strong>-treat effects. While we know that the effects on
those who were prone <strong>to</strong> diarrhea and actually treated by the <strong>water</strong> <strong>intervention</strong> were larger
by a fac<strong>to</strong>r corresponding <strong>to</strong> the infection rate, we cannot quantify this in our data. Thus, an
important area for future research is <strong>to</strong> directly identify these effects using longitudinal
individual data and studying the distribution <strong>of</strong> impact across individuals and by components
<strong>of</strong> the policy reform.
Finally, the cohorts in our study were relatively young when surveyed. Data on older cohorts
would allow exploration <strong>of</strong> impacts on chronic health conditions such as diabetes and
hypertension. The lack <strong>of</strong> variation in these outcomes among young adults (less than 2% <strong>of</strong>
the sample reported having diabetes or were hypertensive by cuff measurements) precludes
such analysis in these data. Data on older individuals would also be useful <strong>to</strong> examine
impacts on occupational attainment and wages, which we did not pursue here given young
adults are actively deciding between more schooling and labor force entry.
To conclude, we demonstrated that reduced <strong>early</strong> <strong>life</strong> <strong>exposure</strong> <strong>to</strong> diarrhea resulting from
access <strong>to</strong> <strong>clean</strong>er <strong>water</strong> leads <strong>to</strong> sizeable improvements in health and educational outcomes
later in <strong>life</strong>. Thus, improving access <strong>to</strong> higher quality <strong>water</strong> can have sizable population-level
economic returns. These longer-term benefits need <strong>to</strong> be taken in<strong>to</strong> account by policymakers
and researchers choosing between different health-sec<strong>to</strong>r investments, as they may
significantly alter cost-effectiveness calculations.
Contribu<strong>to</strong>rs
ASV compiled the data. ASV and SRB both conducted the literature review, designed and
carried out the empirical analysis, interpreted the findings, and drafted and edited the report.
All authors have seen and approved the final version <strong>of</strong> the report.
Conflicts <strong>of</strong> Interest
We declare that we have no conflicts <strong>of</strong> interest.
Acknowledgements
We thank the Grand Challenges Canada “Saving Brains” initiative, the University <strong>of</strong> Bris<strong>to</strong>l
Water Strategic Fund, and Centre for Market and Public Organisation at the University <strong>of</strong>
7

Bris<strong>to</strong>l for funding support. Jason Fletcher, Paul Schultz, and Jody Sindelar provided
insightful comments on previous versions with this work. Salomo Hirvonen provided
excellent research assistance. All errors are our own.
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10

Figure 1 – Timeline and Scope <strong>of</strong> Mexico’s National Clean Water Program
A. Population Access <strong>to</strong> Chlorinated Water
B. Land Area Irrigated with Waste Water
Notes: Source: Government <strong>of</strong> Mexico, National Water Commission
11

Figure 2 – Trends and Convergence in Under-5 Diarrheal Mortality Rates
A. Trends in Child Mortality from Diarrheal and Respira<strong>to</strong>ry Infections
B. Post-Intervention Convergence in Child Diarrheal Mortality
Notes: Source: Mexican Vital Statistics. In 2B, the average under-5 diarrhea mortality rate at the state level
prior <strong>to</strong> the <strong>water</strong> reform, averaged over 1988-1990, is plotted on the horizontal axis, and the vertical axis
plots the absolute reduction in under-5 diarrheal mortality between 1993 and the 1998-1990.. The positive
slope on the scatter shows that states with larger pre-<strong>intervention</strong> diarrheal mortality rates saw larger
absolute declines in the post-<strong>intervention</strong> period.
12

Figure 3 – Impact Estimates by Baseline Birth State Child Diarrheal Mortality
Rates
A. Men’s Height
B. Women’s Schooling
Notes: These graphs illustrate estimated impacts <strong>of</strong> the 1991 <strong>water</strong> reform on men’s height (3A) and
women’s schooling (3B) using the coefficients estimates from Table 2, column 2. The red boxes reflect the
estimated impacts for individuals born in birth states in the 75 th percentile or above <strong>of</strong> the BaseDiarrhea
distribution and the blue circles are the corresponding estimates for individuals born in states in the 25 th
percentile or below. Impacts were computed by multiplying the post-1991 decline in under-5 diarrheal
mortality for high and low BaseDiarrhea states (derived from Figure 2b) with the coefficient estimate. The
dashed lines reflect lower and upper bound impacts implied by the 95% confidence intervals around the
point estimates in Table 2. The graphs demonstrate a larger impact for those exposed <strong>to</strong> <strong>clean</strong> <strong>water</strong> (and
consequently reduced diarrhea) from birth than for those first exposed between 1 and 24 months. These
gains are larger for children born in states with higher pre-<strong>intervention</strong> diarrhea burdens. This pattern
mirrors that in Figure 2b.
13

Table 1 – Descriptive Statistics
Women Men
Mean SD N Mean SD N
Outcomes
Height (cm) 155.41 6.96 4025 168.31 7.58 3300
Schooling (years) 10.24 3.12 11591 9.88 3.18 11179
BMI (kg/m^2) 25.53 5.31 4009 25.13 5.00 3291
Individual and Household Controls
Age (Years) 20.78 2.04 11591 20.72 2.02 11179
Urban Residence (=1) 0.69 0.46 11591 0.71 0.45 11179
Indigenous Language (=1) 0.07 0.26 11591 0.06 0.25 11179
Asset Index 10.95 4.43 11591 11.09 4.39 11179
Piped Water (=1) 0.62 0.49 11591 0.63 0.48 11179
Modern Sewage (=1) 0.68 0.47 11591 0.69 0.46 11179
Household Head Schooling (1=Beyond Secondary) 0.23 0.42 11591 0.22 0.42 11179
State Level Variables
BaseDiarrhea (Under-5, per 1,000 births) 5.41 2.90 32 5.61 3.04 32
BaseRespira<strong>to</strong>ry (Under-5, per 1,000 births) 3.43 2.06 32 3.83 2.23 32
BaseVaccinePreventable (Under-5, per 1,000 births) 0.08 0.10 32 0.09 0.11 32
Rainfall in Birth Year (mm) 895.07 455.94 224 895.07 455.94 224
GDP in Birth Year (Pesos) 13830.92 7026.42 224 13830.92 7026.42 224
Notes:
Source: ENSANUT 2012. Statistics computed for estimation sample <strong>of</strong> men and women born between 1987 and 1993.,. The sample size for the anthropometric
variables was smaller than for the schooling variables because height and weight measurements were conducted for only one randomly selected household
member. Urban Residence is a binary indica<strong>to</strong>r for the household living in an urban area, Asset Index is a continuous indica<strong>to</strong>r (range 0-23) formed by summing
across reported ownership <strong>of</strong> assets such as car, bicycle, refrigera<strong>to</strong>r, microwave, television, cell phone, computer, etc., and Piped Water and Modern Sewage are
binary indica<strong>to</strong>rs <strong>of</strong> household access <strong>to</strong> a piped <strong>water</strong> source (within the household) or a modern <strong>to</strong>ilet with plumbing source, respectively. BaseDiarrhea,
BaseRespira<strong>to</strong>ry, and BaseVaccinePreventable refer, respectively, <strong>to</strong> the average pre-<strong>intervention</strong> under-5 state mortality rate from diarrhea, respira<strong>to</strong>ry
infections, and vaccine preventable diseases (measles, mumps, rubella, polio). These reflect averages taken over the period 1988-1990. . The Rainfall and GDP
variables vary by cohort and state (and hence the larger sample sizes). In the regressions, we used the logarithms <strong>of</strong> Rainfall and GDP.
14

Table 2 – Impacts <strong>of</strong> Exposure <strong>to</strong> Clean Water Early in Life on Adult Anthropometrics and Schooling:
Main Results
Women Men
(1) (2) (3) (1) (2) (3)
Height
Exposure_Birth*BaseDiarrhea -0.106 -0.0730 -0.146 0.260*** 0.318*** 0.226
(-0.27 ,0.06) (0.24,0.10) (-0.47, 0.18) (0.13, 0.39) (0.15, 0.48) (-0.36, 0.81)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea -0.137 -0.118 -0.172* 0.184 0.213* 0.279
(-0.32, 0.04) (0.29,0.06) (-0.37, 0.03) (-0.05, 0.42) (-0.02, 0.44) (-0.15, 0.70)
N 4025 3300
Schooling
Exposure_Birth*BaseDiarrhea 0.0608** 0.0630** 0.146*** 0.032 0.0354 0.0866**
(0.02, 0.11) (0.01, 0.11) (0.04, 0.25) (-0.02, 0.08) (-0.01, 0.08) (0.02, 0.16)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea 0.00402 0.0052 0.060* 0.042 0.042* 0.0575**
(-0.04, 0.04) (0.04, 0.05) (-0.003, 0.12) (-0.01, 0.09) (-0.003, 0.09) (0.01, 0.10)
N 11591 11179
BMI
Exposure_Birth*BaseDiarrhea 0.0367 0.0337 -0.0297 0.0444 0.0391 -0.00390
(-0.13, 0.20) (-0.15, 0.22) (-0.49, 0.43) (-0.06, 0.15) (-0.06, 0.14) (-034, 0.34)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea 0.0695 0.0672 -0.0257 -0.0190 -0.0225 -0.0221
(-0.16, 0.30) (-0.17, 0.30) (-0.33, 0.28) (-0.15, 0.11) (-0.15, 0.11) (-0.26, 0.22)
N 4009 3291
15

Controls
Birth State FE, Birth Year FE Yes Yes Yes Yes Yes Yes
Individual and Household
Yes Yes Yes Yes Yes Yes
Controls
State Level Controls No Yes Yes No Yes Yes
Birth Region*Birth Year FE No No Yes No No Yes
Birth State Trends No No Yes No No Yes
Notes:
Bold terms reflect coefficient estimates from the statistical model described in main text. Robust standard errors, corrected for clustering at the birth state level
are in parentheses. *** - p

Supplementary Web Appendix
To: Venkataramani A and Bhalotra S. “<strong>Effect</strong> <strong>of</strong> <strong>early</strong> <strong>life</strong> <strong>exposure</strong> <strong>to</strong> a <strong>clean</strong> <strong>water</strong>
<strong>intervention</strong> on health and socioeconomic status in adulthood: Evidence from a quasiexperimental
cohort study in Mexico”
Abstract
This Supplementary Appendix expounds on the empirical strategy used in the paper and
discusses specification checks that we conducted <strong>to</strong> test and support a causal
interpretation <strong>of</strong> our estimates.
I. Overview <strong>of</strong> Empirical Strategy
Our research strategy uses a differences-in-differences type model that aims <strong>to</strong> identity
causal effects by exploiting a sharp drop in diarrhea coincident with the timing <strong>of</strong> the
Mexican National Clean Water Program, along with the fact that states that were initially
more burdened by diarrhea gained more from the program. The use <strong>of</strong> the pre<strong>intervention</strong>
<strong>exposure</strong> <strong>to</strong> a disease as a proxy for how much a given area benefitted from
<strong>intervention</strong>s targeting that disease has been validated and used elsewhere in the
economics literature.(17, 18, 20, 34, 43, 44) There are no state level data on program
activities (such as the exact timing and extent <strong>of</strong> chlorination efforts and laws forbidding
waste<strong>water</strong> irrigation).
The disadvantage <strong>of</strong> our using a measure <strong>of</strong> state-level intensity <strong>of</strong> the program rather
than more local or component-specific program data is that there may be measurement
error in assigning <strong>exposure</strong> <strong>to</strong> individuals. The advantage <strong>of</strong> our strategy is that it is
robust <strong>to</strong> program endogeneity, in other words, the national implementation <strong>of</strong> the <strong>clean</strong>
<strong>water</strong> program was plausibly exogenous (i.e., uncorrelated with child attributes that may
influence adult health and well-being) given that it was spurred on by disease outbreak
outside <strong>of</strong> Mexico and our state level measure <strong>of</strong> program intensity is pre-determined. If
we had more local information on variation in the exact timing <strong>of</strong> implementation <strong>of</strong>
progam activities , we would have <strong>to</strong> be certain that such variation is uncorrelated with
the outcome measures. This is <strong>of</strong>ten hard since states or municipalities which
implemented the program earlier or more effectively may be states which also have taller,
better educated individuals. So we may ascribe <strong>to</strong> program activities the impact <strong>of</strong> local
time-varying characteristics, for example, bureaucratic efficiency, the socioeconomic
status <strong>of</strong> the local population or the local disease environment). Thus, from a causal
inference standpoint, our strategy may be preferred even in the fortui<strong>to</strong>us situation where
more detailed, program-level data was available.
As mentioned in the main text, our research strategy assumes that cross birth state
differences in pre-<strong>intervention</strong> diarrheal mortality proxy for differences in morbidity
since time series morbidity data are not available at the sub-regional level. This is a
commonly invoked assumption in the literature.(29) The validity <strong>of</strong> this assumption in
the Mexican context is borne out by national level data showing a drop in diarrheal
morbidity coincident with the drop in mortality between 1990-1992.(24)
17

Several threats <strong>to</strong> causal inference were mentioned in the main text. In general, any event
or process varying at the birth state* birth year level and correlated with both <strong>exposure</strong> <strong>to</strong>
the <strong>clean</strong> <strong>water</strong> program and the outcome variables can confound the results. Such events
could include other public health <strong>intervention</strong>s or macroeconomic shocks. Even
<strong>intervention</strong>s initiated prior <strong>to</strong> the <strong>clean</strong> <strong>water</strong> program could serve as confounders, for
example, some <strong>of</strong> the convergence in diarrheal disease burdens across states between
1990 and 1992 may be driven by pre-existing state-level convergence created by
increased availability <strong>of</strong> oral rehydration salts nationwide. As explained in the main text,
we dealt with these threats by controlling for birth state-birth year macroeconomic
shocks, measures <strong>of</strong> other child diseases (respira<strong>to</strong>ry and vaccine preventable mortality,
in particular), and birth region-birth state fixed effects and birth state specific trends,
which account for any sharp regional changes in the disease environment or pre-existing
convergence that could confound main results. In practice, this is a very aggressive set <strong>of</strong>
control variables and finding stable impacts on <strong>to</strong>p <strong>of</strong> all this greatly increases one’s
confidence in a causal interpretation <strong>of</strong> the results.(18, 43) Table S1 provides coefficient
estimates on the control variables.
The remainder <strong>of</strong> this Appendix discusses some additional threats <strong>to</strong> inference and
illustrates how our results remain robust when addressing them.
II. Mortality Selection
Reduced mortality from diarrheal disease means more survivors. Marginal survivors are
likely biologically weaker than infra-marginal individuals who would have survived
regardless <strong>of</strong> the <strong>clean</strong> <strong>water</strong> <strong>intervention</strong>, and this biological weakness is likely
correlated with worse adult health and socioeconomic outcomes. Cohorts born after the
arrival <strong>of</strong> the National Clean Water Program will include more <strong>of</strong> these weaker survivors
than pre-<strong>intervention</strong> cohorts. This compositional effect will tend <strong>to</strong> bias our long-run
impact estimates downward.(13) Thus, we view our estimates as lower bounds <strong>of</strong> the true
impact. Note that our finding <strong>of</strong> large positive impacts despite mortality selection
validates our initial assumption that mortality rates proxy for morbidity rates. If they did
not, the mortality selection effects would dominate and we would recover null or negative
impacts.
III. Differential Fertility Selection
The prospect <strong>of</strong> healthier children and reduced child mortality may change fertility
decisions.(45) To the extent that program-induced changes in fertility vary by household
socioeconomic characteristics, which, in turn, may influence both child development and
adult well being, our results may reflect a cohort selection bias. To address this
possibility, we estimated our main model but this time used the household characteristics
<strong>of</strong> the sample cohorts as the dependent variables. If differences in fertility responses
across richer and poorer households with different underlying child survival chances
played a major role in driving our results, we would expect that children born after the
<strong>intervention</strong> in states benefitting most from the program would differ in terms <strong>of</strong>
household characteristics relative <strong>to</strong> children born before. Table S2 displays the results.
We find no statistically significant association between any <strong>of</strong> the household
18

characteristics and our measure <strong>of</strong> program treatment. and the estimated coefficients are
substantively small.
IV. Heterogeneous <strong>Effect</strong>s
As in most developing countries <strong>to</strong>day, diarrhea in Mexico was (and is) predominantly a
disease <strong>of</strong> poorer children. These same children are likely disadvantaged in other ways,
all <strong>of</strong> which may limit their growth and development and place them at increased risk for
poverty and ill-health later in <strong>life</strong>. Given the social epidemiology <strong>of</strong> diarrhea, it stands <strong>to</strong>
reason that poorer individuals benefited more from the National Clean Water program
both in the short and long run. Put differently, we would expect <strong>to</strong> see larger program
effects for those at the bot<strong>to</strong>m <strong>of</strong> the height, BMI, or schooling distributions than those at
the <strong>to</strong>p.
For height and BMI, we test this formally using a quantile regression approach, which
models the relationship between predic<strong>to</strong>r variables and outcomes at specific points <strong>of</strong> the
conditional distribution <strong>of</strong> the outcome.(46) The results are shown in Table S3. For boys,
we find the largest effects in the 10 th , 25 th , and 50 th percentiles <strong>of</strong> the height and BMI
distributions, though the BMI results are not statistically significant. For girls, we find
null results across the distribution. For schooling, we created binary indica<strong>to</strong>rs for
completing secondary schooling (9 th grade, which is technically the final year <strong>of</strong>
compulsory schooling, though this is poorly enforced) and high school (12 th grade or
higher). As seen in Table S4, we only find positive and significant impacts for girl’s
secondary schooling.
The results for height and schooling consistently show that the largest impacts <strong>of</strong> the
National Clean Water program occurred at the lower ends <strong>of</strong> the health and
socioeconomic status distribution. This supports the plausibility <strong>of</strong> our findings being in
line with the a priori expectation that impacts would be higher among less-healthy, less
able individuals.
V. Alternate Standard Error Calculations
Cluster-corrected standard errors are valid when there are a large number <strong>of</strong> clusters
(typically greater than 40 in Monte Carlo Simulations). However, in our sample, we have
32 Mexican states, which represent a relatively small number <strong>of</strong> clusters. As a small
number <strong>of</strong> clusters can lead <strong>to</strong> over-rejection <strong>of</strong> the null hypothesis with the usual White
cluster-correction methods, we also estimate p-values using the wild cluster-T bootstrap
method.(47) The statistical significance <strong>of</strong> our results (usual standard <strong>of</strong> p

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22

Table S1 – Estimates on Control Variables
Women
Men
Controls/Outcomes Height BMI Schooling Height BMI Schooling
Urban Residence (=1) -0.0338 0.592** 0.231** 0.0929 0.197 0.186*
(0.293) (0.248) (0.0872) (0.347) (0.238) (0.105)
Indigenous Language (=1) -3.77*** -0.829** -0.552** -3.93*** -0.0116 0.247
(0.384) (0.316) (0.231) (0.561) (0.257) (0.253)
Asset Index 0.315*** 0.00536 0.299*** 0.277*** 0.0834*** 0.333***
(0.0275) (0.0234) (0.0108) (0.0273) (0.0284) (0.00865)
Piped Water (=1) -0.671 0.523 -0.0919 -0.547 0.908 -0.0683
(1.327) (0.584) (0.338) (1.210) (0.775) (0.345)
Modern Sewage (=1) 0.458 0.471** 0.518*** 0.318 0.415 0.432***
(0.287) (0.218) (0.106) (0.361) (0.247) (0.0899)
Household Head Primary Schooling (=1) 0.644 -0.310 1.234** -0.267
(0.437) (0.311) (0.537) (0.323)
Household Head Secondary Sch (=1) 0.886** -0.614* 1.345** -0.200
(0.432) (0.345) (0.528) (0.350)
Household Head Beyond Secondary
Schooling (=1) 1.575*** -1.24*** 2.735*** -0.122
(0.517) (0.350) (0.625) (0.372)
Exposure_Birth*BaseRespira<strong>to</strong>ry 2.859 -7.616** 0.942 -1.867 -2.653** -0.276
(4.852) (3.405) (0.785) (1.595) (1.143) (0.370)
23

Exposure_Birth*BaseVaccinePreventable -40.61 66.65*** -2.269 0.988 -37.34** -7.651
(29.84) (21.26) (4.961) (23.98) (17.29) (5.470)
Logged GDP in Birth Year -0.701 -13.28 -0.670 -3.274 -12.48 1.762
(13.19) (9.222) (2.155) (12.70) (9.277) (2.856)
Logged Rainfall in Birth Year 0.401 -1.21*** 0.283** -1.022 0.224 -0.313***
(0.560) (0.308) (0.107) (0.610) (0.345) (0.107)
N 4025 4009 11591 3300 1191 11179
Notes: Estimates for control variables are from Column 3 <strong>of</strong> Table 2. Robust standard errors, corrected for clustering are in parenthesis. *** - p

Table S2 – Tests <strong>of</strong> Sample Composition Changes
Coefficient on
Exposure_Birth*BaseDiarrhea
Asset Index -0.0242
(0.0387)
Modern Sewage (=1) -0.00114
(0.00292)
Indigenous Language (=1) 0.000739
(0.00121)
Piped <strong>water</strong> (=1) -0.00166
(0.00122)
Urban Residence (=1) -0.00229
(0.00244)
N 23,510
Notes: Each set <strong>of</strong> estimates is from a separate regression, with robust standard errors, corrected for clustering at the birth state level, in parentheses. The dependent
variable for each regressions is listed in the first column, in bold. As described in section III above, we regressed each <strong>of</strong> the household level control variables against
Exposture_Birth*BaseDiarrhea, which is the coefficient shown in the second column, the birth state and birth year fixed effects, the state-level macroeconomic and
disease characteristics, birth region*birth year fixed effects, and the state-specific linear time trends. As in section III, the lack <strong>of</strong> any large or statistically significant
coefficients for any <strong>of</strong> these characteristics speaks against the possibility <strong>of</strong> reduced <strong>early</strong> <strong>life</strong> diarrhea leading <strong>to</strong> changes in fertility that generate cohort compositional
change effects favoring taller and more educated children.
25

Table S3 – Quantile Regression Estimates for Height and BMI
Women Men
Height
0.1 0.25 0.5 0.75 0.9 0.1 0.25 0.5 0.75 0.9
Exposure_Birth*BaseDiarrhea -0.193 -0.0857 -0.0700 -0.109 0.0928 0.373*** 0.399*** 0.344*** 0.104 0.174
(0.176) (0.0942) (0.102) (0.0988) (0.153) (0.135) (0.142) (0.121) (0.166) (0.213)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea -0.225 -0.112 -0.0658 -0.131 -0.139 0.0502 0.345** 0.292** -0.039 -0.0203
(0.199) (0.109) (0.118) (0.116) (0.185) (0.159) (0.166) (0.137) (0.187) (0.240)
N 4029 3300
BMI
Exposure_Birth*BaseDiarrhea -0.0197 0.0739 0.0467 0.0118 0.0791 0.0507 0.0682 0.0939 -0.026 0.00596
(0.0708) (0.0759) (0.0842) (0.114) (0.188) (0.0795) (0.0953) (0.0823) (0.129) (0.251)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea 0.0481 0.108 0.0600 -0.0678 0.202 0.0479 0.0890 0.0572 -0.099 0.0161
(0.0812) (0.0869) (0.0977) (0.130) (0.211) (0.0833) (0.105) (0.0928) (0.148) (0.294)
N 4009 3291
Notes: Estimates <strong>of</strong> the main model described in the main text and in section I <strong>of</strong> this Appendix using a quantile regression methodology. For each dependent variable
and gender, we estimated five separate models, one for each <strong>of</strong> the following quantiles: 0.1, 0.25, 0.5 (median), 0.75, and 0.9. Each model includes the control variables
listed in Column 2 <strong>of</strong> Table 2. Robust standard errors are in parentheses. *** - p

Table S4 – Exposure Impacts at Different Points <strong>of</strong> Schooling Distribution
Women
Men
(1) (2) (3) (1) (2) (3)
Completed Secondary School
Exposure_Birth*BaseDiarrhea 0.00508** 0.00575** 0.0163** -9.1e-05 0.000794 0.000598
(0.00220) (0.00219) (0.00623) (0.00295) (0.00305) (0.00641)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea 0.000941 0.00131 0.00715 0.000638 0.00105 -0.00164
(0.00230) (0.00232) (0.00486) (0.00422) (0.00407) (0.00584)
N 11591 11179
Completed High School
Exposure_Birth*BaseDiarrhea 0.00265 0.00221 0.0173** -0.00033 -0.00095 0.0131
(0.00399) (0.00461) (0.00830) (0.00435) (0.00453) (0.00846)
Exposure_1<strong>to</strong>24Months*BaseDiarrhea -0.00101 -0.00128 0.00710 0.00121 0.000645 0.00472
(0.00349) (0.00379) (0.00536) (0.00378) (0.00375) (0.00503)
N 11591 11179
Controls
Birth State FE, Birth Year FE Yes Yes Yes Yes Yes Yes
Household and School Controls Yes Yes Yes Yes Yes Yes
State*Year Controls No Yes Yes No Yes Yes
Birth Region X Birth Year FE No No Yes No No Yes
Birth State Linear Trends
No No Yes No No Yes
Notes: Model estimates is the same as in Table 2, but this time we examined binary indica<strong>to</strong>rs for having completed secondary and high school, respectively. Thus, the
regressions examine impacts <strong>of</strong> <strong>clean</strong> <strong>water</strong> <strong>exposure</strong> at different points <strong>of</strong> the schooling distribution. See Table 2 notes for further details.
27