ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E16-6

Arsenic Contaminated Groundwater and theSocioeconomic-Gradient in Child Health:
Evidence from the Arsenic Mitigation Campaign
in Bangladeshby
Ava CasEiji Mangyo

August 2016

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Arsenic Contaminated Groundwater and the Socioeconomic-Gradient in Child
Health: Evidence from the Arsenic Mitigation Campaign in Bangladesh
Ava Cas* and Eiji Mangyo+

Abstract
We examine the impact of arsenic exposure on child health in Bangladesh. The
geographic variation in groundwater arsenic level as well as the massive welltesting and awareness campaign in the late 1990s offer a natural experiment
inducing variation in child’s exposure to arsenic. Given the government’s efforts
to encourage households to switch away from “unsafe” wells, areas with “unsafe”
groundwater arsenic levels prior to the campaign had greater improvement inchild height-for-age relative to areas with “safe” arsenic levels. Results are
statistically significant for children from educated households but not for children
from uneducated households.
JEL classification: I14, O15
Keywords: child health; socioeconomic status; arsenic; Bangladesh; early life*

School of Business and Economics, The Catholic University of America, 620 Michigan Avenue
NE, Washington, DC 20064. We are grateful to Gianfranco Piras, James Monaghan and theparticipants of The Catholic University of America Institute of Policy Studies as well as the
Nagoya University-Chulalongkorn University Health Seminar for helpful comments. We
especially thank the Economics Research Center of Nagoya University for research funding.
Please direct correspondence to email: cas@cua.edu.
+
Graduate School of Economics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601
Japan, email: eijim@soec.nagoya-u.ac.jp.

1 Introduction
Little is known about whether environmental and natural toxic releases atthe level that generally occurs in the population have effects on human health. An
obvious challenge for empirical studies is the difficulty in identifying the
population’s exposure to toxicities as toxic matters are mostly unobservable or
unknown. In cases where it is known or observable, there is a concern that
individuals exposed to such matters are likely to differ from the unexposed
individuals in unobservable ways. A few studies that have carefully explored thisquestion examined the effect of air pollution on infant health (Chay andGreenstone, 2003; Currie and Neidell, 2005; Neidell, 2004). But majority ofstudies that attempted to examine the health impact of other globally occurring
toxic substances such as lead and arsenic are either based on small sample
epidemiological studies or are subject to methodological weaknesses.
Moreover, the question of whether parental socioeconomic status plays arole in cushioning children from the deleterious impact of health shocks is ofinterest to many economists. Studies show that there is a strong and consistent
relationship between parental socioeconomic status (SES) and child health (Caseet. al., 2002; Currie and Hyson, 1999; Currie et. el., 2004, Currie and Lin, 2007).
In particular, this relationship tends to be more pronounced as child gets older(Case et. al., 2002; Currie and Stabile, 2003) due to accumulated exposure to
health shocks. However, the existing evidence is mainly based on correlations andit is difficult to ascertain that this relationship is causal.
In this paper, we examine the impact of arsenic exposure on child health in
Bangladesh. With over 90 percent of the Bangladeshi population (and 95 percent
in rural areas) relying on groundwater as their main source of drinking water, thediscovery of the arsenic contamination of groundwater prompted the government
to conduct massive well-testing and awareness campaign in the late 1990s to
encourage households to switch to arsenic-free drinking water sources. The paper1uses the geographic variation of naturally occurring arsenic in groundwater aswell as the massive well-testing and awareness campaign upon its discovery as anatural experiment inducing variation in child’s exposure to arsenic.
Our paper offers several contributions to the existing literature. First, we
provide new evidence on the economic status-gradient in child health. In theirpaper, Case et. al. (2002) suggest that the well-known association between income
and health in adulthood may start from childhood, and they show consistentassociation between household income and child’s subjective health status. Usinga natural experiment that leads to variation in child’s exposure to arsenic, we
examine the impact on child height-for-age depending on parental socioeconomic
status. Second, to our knowledge, our study is the first to examine the impact ofarsenic exposure at the population level on child’s long-run measure of nutritional
status (height-for-age). Asadullah and Chaudury (2011) examines the correlation
between arsenic exposure and test scores while Field et. al. (2011) investigates theunintended consequences of the arsenic mitigation efforts on infant and child (upto age 2) mortality. Third, we use a rich dataset to investigate the effects on otherchild and maternal health measures as well as on child health parental
investments. Although our main outcome of interest is height-for-age, as heightreflects environmental influences particularly during the first few years of life, we
also look at the occurrence of diarrheal disease. The latter allows us to also
corroborate the results of studies suggesting that switching to fecal contaminatedsurface waters or remote tube wells which requires water storage led to incidenceof diarrhea (Field et. al., 2011; Escamilla et. al., 2011; Wu et. al., 2011). In
addition, we examine the pathways to child health by investigating the impact of
the health shock and the subsequent public health efforts on maternal health andlabor supply as well as prenatal and postnatal parental investments related to childhealth.

2

Given the government’s efforts to encourage households to switch awayfrom “unsafe” wells, our results show that districts with average groundwater
arsenic level of 51-100 µg/L prior to the massive public health effort had greater
improvement in child height-for-age after it had begun relative to those districts
considered to have “safe” level of average groundwater arsenic (0-50 µg/L). In
particular, among children from households whose heads have no education, we
find no significant differences in improvement in height-for-age among children
from various areas with varying arsenic level after the massive public health effort
had begun. On the other hand, among children from households whose heads haveany formal schooling, we find positive and statistically significant difference inheight-for-age among children in areas with average arsenic level of 51-100 µg/L
relative to those areas with average arsenic level of 0-50 µg/L. Furthermore, wefind that such pattern persists once we stratify the sample by younger (0-24months) and older (25-48 months) age groups, although the estimates are larger
and statistically significant for the older age group. These results are similar to thepatterns observed by earlier studies that look at the correlation between householdincome and child health (Case et. al., 2002; Currie and Stabile, 2003).
Consistent with the patterns we observe among children, we find thatmothers in districts with average groundwater arsenic level of 51-100 µg/L havebetter nutritional status (as measured by body mass index) relative to mothers
living in districts that have, on average, “safe” level of groundwater arsenic. Thisis particularly the case for mothers from educated households. Since women are
the ones who draw water from the wells in Bangladesh, we also examined theirawareness of the government’s well-safety campaign and whether they adhere toit. Our results suggest that educated women were more aware of the well-safety
campaign and were more likely to adhere to it. These results thus also add to thegrowing body of literature that highlight the importance of mother’s education on
child’s health.

3The remainder of the paper is laid out as follows. Section 2 provides theessential background. Section 3 lays out the theoretical and empirical framework.
Section 4 provides the data and descriptive statistics. Section 5 presents theestimates and robustness checks. Section 6 concludes.

2 Arsenic Contamination of Groundwater and the Subsequent Mitigation
Efforts
2.1 Groundwater Arsenic Contamination: A Major Public Health Issue
Prior to the early 1970s, people from Bangladesh have been drinking
mainly from bacterial contaminated surface water which has led to high incidenceof water-borne diseases and parasitic infections. In order to address this publichealth issue, the government of Bangladesh and the international organizations(United Nations and World Bank) started promoting shallow tube wells as a safe
alternative source of drinking water. This led to massive construction of tube
wells in the 1980s (around 11 million), the vast majority of which were privately
owned (van Green et. al., 2005). At the time of this mass installation, the aquifers
were not tested for arsenic as the tests for metal impurities were not mandatory
until years later.
By 1990s, groundwater was the main drinking water source for over 90
percent of the population, and for 95 percent of the population in the rural areas(World Bank, 2007). Unfortunately, this initiative driven by the government’s
good intention to help control water borne diseases had the unintended
consequence of exposing the population to another staggering health problem
caused by toxic arsenic in groundwater. Several natural geological andanthropogenic processes are deemed responsible for the arsenic contamination.
One explanation is that the arsenic’s source in sediments is mainly the parent rockor minerals from which it was deposited. The arsenic is absorbed onto particles of4

iron oxyhydroxides and sulphides, which are easily oxidized and become watersoluble, releasing arsenic that is transported along during erosion andprecipitation (Shanker et. al., 2014; Nickson et. al., 2000; McArthur et. al., 2001).
It was in 1994 when arsenic contamination of water in tube wells wereconfirmed and documented. But by the time it was discovered, it was found thatapproximately 28 to 35 million Bangladeshis have been drinking arsenic
contaminated groundwater for over 2 decades (BGS/DPHE, 2001). After several
testing and ruling out of other sources, in 1997, the World Health Organization(WHO) publicly declared arsenic contamination of groundwater as a major publichealth issue and in a later report considered it as “the largest mass poisoning of a
population in a history” (Smith, 2000).
2.2 Testimonials on the impact of arsenic poisoning
Anecdotal evidence suggests that the arsenic contamination of groundwater had
debilitating impact on its victims, not only in terms of physical health but in theirability to carry out day-to-day activities. In New York Times article, Rohde andManik (2005) describes Salma Begum, aged 30 and the mother of three, who lives
in the village of Abirpara in central Bangladesh and is one of the victims ofarsenic poisoning.
All over Ms. Begum's tiny, slowly withering body, signs of arsenic poisoning haveemerged. Ugly and painful boils cover her hands and feet. Her veins protrude from her
skin. Dark spots cover her arms, legs and parts of her body she declines to show. Her
skin itches endlessly when exposed to sunlight.
She said her largest problem was a creeping weakness and constant ache in her arms
and wrists. Her arms have grown so feeble that she struggles to hold her 2-year-old son
and care for her husband and two daughters. As she speaks, she constantly kneads themuscles in her forearms and wrists, as if trying to wring the pain from her flesh.
Ms. Begum said she could not afford to buy medicine to counter some of thesymptoms. [She] grows weaker and more frustrated by the day.

5

Even years later, Kirby (2013) describes the atrocious condition of a person who
suffered from arsenic poisoning during his visit in Alumpur, a tiny village inWestern Bangladesh.
__ is one of many villagers in Alumpur suffering from Arsenicosis, as particularly evident
from his cancerous hand. He is the only remaining survivor of his siblings, all of whomdied from arsenic poisoning. __ lives alone with his wife and two daughters, all of whom
are unemployed and financially dependent on __ for their survival. Since being forcedout of work 3 years ago, he has been unable to provide for his family, who are now
struggling to survive on basic subsistence agriculture. A few local villagers put theirsavings together and brought __ some painkillers in a desperate attempt to ease his
suffering, however he is still unable to afford a biopsy and amputation.

2.3 Bangladesh’s Arsenic Mitigation Efforts
The crisis led the government to create the Bangladesh Arsenic Mitigationand Water Supply Project (BAMWSP) in 1998. With a US$30 million loan fromthe World Bank and Swedish government, the Bangladesh government
implemented a large-scale well-testing campaign analyzing over 5 million tube
wells across the country and found that roughly 30 percent of the population(approximately 35 million people) have been drinking water from tube wells thathave arsenic level above the national standard of 50 micrograms/liter (µg/L) whileroughly 45 percent of the population (57 million people) were drinking water
above the WHO recommended limit of 10 µg/L (Bennear et. al., 2012).
The massive well testing followed by the comprehensive awareness andeducation campaign took place during 1999-2003. Tube wells with arsenic levels
above the Bangladesh standard of 50 µg/L were painted red and labeled “unsafe”
while those below 50 µg/L were painted green and labeled “safe”(Balasubramanya et. al., 2013). Households were strongly encouraged to stop
drinking from wells that were painted red and urged to find alternative drinking
water source such as deep tube wells, dug wells, surface water, piped water,
treatment of arsenic contaminated water, sharing of safe shallow tube wells andrainwater harvesting. Around the same time, the government also constructed6

over 9,000 deep tube wells across the country in order to tap into deep aquifers
that were less likely to be contaminated with arsenic (Field et. al., 2011).
Previous research show that the well-testing and labeling campaign
encourages significant switching of households to alternative water sources
especially among those with “unsafe” wells. In the 25 km2 area of Araihazar
region, Madajewicz et. al. (2007) find that over 50 percent of the households withunsafe wells have switched, despite the distance of alternative wells. One year
later, Opar et. al. (2007) finds that over two-thirds of the households with unsafe
wells have switched to alternative water sources.1 In contrast, only 15 percent of
the households with wells labeled as “safe” have switched. They also find that ingeneral, higher education increases the probability of switching away from unsafe
wells, and that more educated households tend to switch to private wells
compared to community wells. According to the authors, it may be that better
educated households are more likely to be aware of the arsenic status of theirneighbors’ private wells and have better negotiating power when convincing theirneighbors to share their private wells with them (Opar et. al. (2007)).

3 Theoretical and Empirical Framework
To motivate the empirical analysis, in equation (1) we model health Hijt ofchild i in each district j and time period t as a function of the quality of drinking
water Wijt consumed as well as all nutrition inputs which are unobservable to theeconometrician, ηijt. The quality of drinking water depends on whether there are“unsafe” level of toxic contaminants such as arsenic, Aijt and whether there are
diarrheal pathogens, Dijt present. For simplicity, it is assumed that exposure to1

The alternative water sources include a different existing private well (55%), new constructed
well (21%), community well (16%) and undetermined source (8%).

7certain level of arsenic has a larger negative impact on health than exposure todiarrheal pathogens.
(1) Hijt = W(Aijt,Dijt) + ηijt
Obtaining drinking water from tube wells exposes the child to arsenic but not
to diarrheal pathogens while sourcing water from alternative drinking water
sources such as surface water or marked “safe” tube wells that are farther away
protects the child from arsenic but increases the likelihood that the child gets
exposed to diarrhea-causing pathogens. Thus the child’s exposure to arsenic, Aijt,(or diarrheal pathogens, Dijt) in drinking water, depends on the household’s
probability of switching, Sijt, from arsenic-contaminated groundwater to arsenicsafe drinking water sources upon the government’s massive testing of tube wells.
In practice, effect of Sijt would vary by the safety of groundwater arsenic level, Lj,
which is determined by the government’s arsenic test results. The government
labeled wells as either “safe” to drink (less than 50 µg/L) or “unsafe” to drink(more than 50 µg/L) upon testing and prioritized its mitigation efforts onhouseholds with “unsafe” wells to encourage them to switch to alternative sourcesof drinking water.
(2) Aijt = g(Lj) * Sijt + υijt
Note Sijt is unobserved to the econometrician. We assume based on empiricaland anecdotal evidence, that two important factors have led parents to switch toalternative sources of drinking water. The most important one is the government’s
massive well testing and aggressive labeling and awareness campaign that started
in the late 1990s. As mentioned earlier, the aggressive well testing and labelingcampaign encouraged households to switch to alternative drinking water sources,
particularly those with unsafe wells.
Thus from (2), the first factor for identifying the effect of arsenic exposureon child health is the geographic variation in the pre-existing levels of naturally
occurring arsenic while the second factor is the exposure to the massive well8

testing and awareness campaign of the government. Combining these two factors
forms the main variable of interest in a difference-in-difference framework. In
particular, we compare cohorts born between 2000 and 2004 (aged 0-48 months
in 2004) after the massive testing and awareness campaign already began vis-àvis cohorts born between 1992 and 1996 (aged 0-48 months in 1996) in locations
with varying average district arsenic levels: 0-50 µg/L, 51-100 µg/L, above 100µg/L.
Estimating equation (3) yields the reduced form differences by preexisting arsenic level for some outcome Yijt for person i in district j at time t.
(3) Yijt = α + β1 As_51-100ij*Yr2004t + β2 As_100upij*Yr2004t + δj + γt + β3 Xijt +β4 Zjt + Ɛijtin which Yijt refers to the outcome of interest, As_51-100ij is a dummy variableequal to 1 if the mean district arsenic level is 51-100 µg/L and 0 otherwise,
As_100upij is a dummy variable equal to 1 if the mean district arsenic level isgreater than 100 µg/L and 0 otherwise, Yr2004t is a dummy variable equal to 1 if
the cohorts aged 0-48 months were born after the massive well testing andawareness campaign began and 0 otherwise, δj are geographic fixed effects, γt are
year of birth (time) fixed effects and Xijt is some vector of individual level control
variables such as gender and age (in months), indicator for mother’s education,mother’s height and indicator of household head’s education. Zjt refers to timevarying percentages of communities with secondary schools and health facilities
in the district.
If the government’s mitigation efforts have successfully led households toswitch from arsenic-contaminated wells to arsenic-safe drinking water sources in
districts with average arsenic levels of more than 50 µg/L, our frameworksuggests that β1 and β2 would be positive for the outcome height-for-age. At thesame time, our framework also suggests that β1 and β2 would be positive (althoughprobably small) for the likelihood of incurring recent diarrhea.

9Now, another factor that can lead parents to switch to alternative drinking
water sources is their education level. In Grossman’s (1972) concept of healthcapital, the stock of human capital, as measured by education, is known to lead toshifts in productivity. In particular, the effect of education on child health may beconsidered via health knowledge and the parent’s ability to make use of
information (McCrary and Royer, 2011). It is assumed that schooling increases
the ability of an individual to process information and thus enable one toefficiently improve health capital. By estimating (3) separately for educatedhouseholds and non-educated households, we can examine whether β1 and β2 arelarger and significant for educated households compared to non-educatedhouseholds. Likewise, following Case et. al. (2002), we assume that the protectiverole of education becomes more pronounced with the length of time the child has
been exposed to the health shock. Thus, we further estimate the model separately
for younger and older age groups so we can examine whether β1 and β2 are larger
for older children.
The estimates are unbiased under the identifying assumption thatoutcomes in each district would have changed to the same extent, apart from any
change due to the massive efforts of the government to encourage households toswitch away from “unsafe” groundwater. It is impossible to test this assumption
directly but the availability of data for the time period prior to 1996 helps to form
indirect tests to examine whether different categories of average district arseniclevels had been trending differently. In addition, the model also assumes nospillover effects or change in composition of households across the districts withvarying average arsenic levels. To investigate this assumption, we account forpossible migration and restrict the analysis to those households who havecontinuously resided in the same location since the time of massive testing andawareness campaign have begun.

10

4 Data and Descriptive Statistics
This study uses the children, household, and community level data of the1996 and 2004 Demographic Health Surveys, which we merge with the National
Hydrochemical Survey (NHS) of wells conducted in 1998 and 1999 by the
Department of Public Health Engineering of Bangladesh (DPHE) in collaboration
with the British Geological Survey (BGS). We calculate the average arsenic levelat the district level using the NHS data. Figure 1 shows the union-level variationin arsenic level across the country. The red dots show the areas that have averagearsenic level of greater than 100 µg/L, blue dots show the areas that have an
average arsenic level of 51-100 µg/L and the green dots show the areas with 0-50
µg/L.
The BGS/DPHE carried out the survey in two phases: the first phase(1998) covered what were thought to be the most affected southern and eastern
districts of Bangladesh, while the second phase (1999) completed the rest of the
districts of Bangladesh except for the three districts of the Chittagong Hill Tracts(for a total of 61 out of 64 districts surveyed). 2 Thus for the main analyses in thispaper, we will focus on children in the 61 districts where the data on arsenic tests
of wells are available, but as a robustness check we replicate the main analysis on
all 64 districts by calculating and imputing the average arsenic level for the otherthree districts based on the average arsenic levels of wells in the areas
surrounding these districts.
Our main outcome of interest in this paper is child height-for-age. Heightreflects the influences of both genetics and environmental influences with thelatter being particularly important from prenatal to first few years of life
(Martorell and Habicht, 1986). Using the WHO growth reference (WHO2

The NHS provides chemical test results for the 3534 boreholes from 61 out of 64 districts of
Bangladesh.

11

Multicentre Growth Reference Study Group 2006; de Onis et al. 2007), heightfor-age is calculated for children from ages 0 to 48 months. To support our main
findings, we also check whether the switching away from unsafe wells toalternative sources of drinking water such as surface water or safe wells that are
farther away have led to diarrheal diseases as suggested in some studies (Field et.
al., 2011; Escamilla et. al., 2011; Wu et. al., 2011). The advantage of using DHS
is that it allows us to also examine the mothers’ body mass index, employment
status as well as mothers’ prenatal and postnatal health behavior. In addition, we
take advantage of additional information (although available in 2004 DHS only)
on the mother’s and household head’s knowledge of arsenic, their understandingof the labeling of wells and information on which well they obtain their waterfrom. These variables help us to further pin down the possible pathways by which
the comprehensive mitigation efforts across the different areas with varying preexisting arsenic levels have affected child health.
Table 1 shows the means and standard deviations of the backgroundcharacteristics of children in 1996 prior to the massive well testing andinformation campaign, by average district arsenic level. In general, the averageage of children, percentage of male children and mothers’ average years ofeducation are not statistically different across the areas with varying intensity of
average district arsenic level. On the other hand, mothers in the districts withhighest average arsenic level tend to be slightly older than those in other areas.
The household head’s education is higher in districts with average arsenic level of
0-50 µg\L relative to districts with average arsenic level of 51 µg\L and above.
This may be partly reflecting the effects of the arsenic crisis on the socioeconomic
outcomes of the previous generation. In addition, there are also statisticallysignificant differences in the share of communities in the district with access tosecondary schools and health services.

12

Table 2 shows the simple difference-in-differences for the child outcomes:
height-for-age and diarrheal disease indicator. Examining the mean height-for-age
of cohorts aged 0-48 months in 1996 (Panel A), the statistics suggest that althoughchildren living in the districts with “safe” average arsenic levels are slightly taller
relative to those in districts with “unsafe” average arsenic levels, pairwise
comparisons yield statistically insignificant results. Meanwhile, across the threedifferent districts with varying average arsenic levels, mean height-for-age
increased for the cohorts aged 0-48 months in 2004. However, it increased more
for those children living in the districts with “unsafe” average arsenic levels,
particularly for those in districts with average arsenic level ranging 51-100 µg/L.
This reflects the government’s efforts to inform and convince households with“unsafe” tube wells to switch away to safer alternative drinking water sources.
Comparing the height-for-age of children over time and between “unsafe” and“safe” districts, the coefficient for the difference-in-difference is positive andstatistically significant for districts with average arsenic level of 51-100 µg/L. On
the other hand, the coefficient for difference-in-difference is positive but small
and not statistically significant for districts with average arsenic level of greaterthan 100 µg/L.
In Panel B we observe an increase in the probability of incurring recent
diarrhea among young children in these districts as well, over time and relative to
districts with arsenic level below 50 µg/L, reflecting the switching of households
to arsenic-safe drinking water sources in the districts with average arsenic level of
51-100 µg/L,. However, the mean changes are quite small and not statisticallysignificant.

5

Estimation Results

5.A Child Health13

We begin our analysis of the impact of arsenic exposure on child health byshowing the results of a locally weighted regression of height-for-age onhousehold head’s education by categories of district-level average groundwater
arsenic level. The graphs in Panel A of Figure 2 show the results for children aged
0 to 48 months in 1996 and in 2004 or before and after the massive well-testing
and mitigation campaign have started. The figure shows that, prior to the start of
the arsenic mitigation strategy (1996, left graph), there were no significant
differences in child height-for-age across the districts with varying averagearsenic level at any level of household head education. The districts with averagearsenic level of more than 100 µg/L has a slightly different trajectory which may
reflect the dangers of having been exposed to very high level of arsenic for a longtime, but as shown earlier in the Table 1, on average there are no statisticallysignificant differences in the height-for-age of children across the three categories
of district-average arsenic level. On the other hand, performing the same analysis
for children aged 0 to 48 months in 2004 (right graph) shows that although there
are no significant differences in child height-for-age across districts with varying
arsenic level when household head has no formal education (or zero years ofeducation), the gap in height-for-age between children in districts with averagearsenic level of 51-100 µg/L and in districts with average arsenic level of 0-50
µg/L is increasing with higher household head’s education. Meanwhile, thedistricts with average arsenic level of more than 100 µg/L follows a similar
pattern observed in 1996.
Turning to more formal analysis, Table 3 shows the regression analyses ofchanges in height-for-age and in recent diarrhea incidence between 1996 and2004 across different categories of district-average arsenic level. Columns (1) to(3) presents the results for the full sample. The coefficient in column (1) suggeststhat children’s height-for-age improved from 1996 to 2004, on average about 0.21
standard deviations higher in districts with average arsenic level of 51-100 µg/L

14

relative to districts with average arsenic level of 0-50 µg/L. This estimate is
reduced (0.13 to 0.14 standard deviations) when individual and parental controls(column 2) as well as community-level services (column 3) are added to themodel, although still statistically significant. Columns (4) to (9) estimate equation(3) for the sample whose household head is educated and for those whose
household head is not educated. In this paper, we use household head’s educationas our measure of socioeconomic status. The advantage of using education insteadof current income is that the former allows us to capture the effect of permanent
or long-run average income, where investment in children decisions are likely to
be based on. As presented in columns (4) to (6), when the sample is restricted tothose children whose household head is educated, the estimates become larger(about 0.22 to 0.28 standard deviations) and also statistically significant. On theother hand, when the sample is restricted to those children whose household headhas no formal education (columns (7) to (9)), the estimates become very close to 0
and not statistically significant.
In panel B of Table 3, I examine the same regressions for recent diarrhea
indicator. If the hypothesis that families in districts with arsenic level of above 50µg/L are more likely to be encouraged to switch over to safe alternative water
sources, then it is likely that some of these families are also likely to return tousing surface water as an alternative water source. Therefore, children in these
districts have higher probability of incurring diarrhea relative to children in the
districts with average arsenic level that are considered to be safe by thegovernment (0-50 µg/L). Columns (1) to (3) support such hypothesis, showing amarginally significant probability (0.03 to 0.04) that children would incur
diarrhea. This likelihood increases slightly to 0.05 and becomes even moresignificant when the sample is restricted to those children with educatedhousehold heads. On the other hand, consistent with the pattern of findings for15

height-for-age, the coefficients become very small and insignificant when thesample is restricted to children whose household head is not educated.
Table 4 presents the robustness checks and alternative specifications of thebaseline results for those with educated and non-educated household heads. Panel
A shows the results for children with educated household heads. Column (1) of
Table 4 addresses the concern on migration and restricts the analysis to thosehouseholds who have continuously resided in the same residence in the past six
years or since the time of the massive well-testing and awareness campaign havestarted.3 Compared to the baseline results with full specification in column (6) of
Table 3, accounting for migration makes the estimates larger and statisticallysignificant for the outcomes height-for-age and diarrhea incidence.
The baseline results may also be confounded by mean reversion across thedifferent districts. That is, if some districts have high arsenic level and poor healthoutcomes due to some temporary shock, then we might expect that the health ofthe children will get better in the next period independent of the average district
arsenic level. In column (2), we add the interaction of 1996 height-for-age withthe birth year dummies. This effectively accounts for the initial levels ofchildren’s health in the districts with different average arsenic level. The resulting
estimate for height-for-age is slightly smaller than the baseline estimate but it isstill statistically significant while the resulting estimate for diarrhea occurrence isbasically unchanged from the baseline estimate.
One particular source of confounding might be certain shocks and policychanges that may have affected children’s health. While we do not know of any
particular shock or policy change that have coincided with the average level ofarsenic at the district level, to the extent that these shifts were at the level ofdivision by year, we implement a way to purge our estimates of this confounding.
3

The Demographic Health Survey asks women about the number of years they have been residingin their place of residence.

16

In column (3), we add a (division X after) fixed effects. Although this makes thestandard errors bigger and thus make estimates less precisely estimated, theresults are essentially similar to the baseline estimates.
In column (4), we replicate the analysis using data on all 64 districts by
calculating and imputing the average arsenic level for the other three districts
based on the average arsenic levels of wells in the areas surrounding these
districts (within 10 kilometers). Our resulting estimates for height-for-age isrelatively close to the baseline estimates and marginally significant but theestimate for recent diarrhea experience is zero and not statistically significant.
This may have partly to do with the size of the area considered for interpolation.4
Since the World Health Organization’s standard for safe arsenic level isless than 10 µg/L, which is way below the standard set by the Bangladesh
government, in column (5) we change our specification slightly to examine how
children living in districts with average arsenic level of 0-10 µg/L, between 51 to
100 µg/L and greater than 100 µg/L fared compared to children in districts withaverage arsenic level of 11-50 µg/L before and after the arsenic mitigation
campaign has begun. With this new comparison group, we find the same patternof results as the original estimates. Basically, children’s height-for-age in areas
with average arsenic level of 51-100 µg/L have improved over time by 0.21
standard deviations more compared to children in areas with average arsenic levelof 11-50 µg/L. Meanwhile, we do not find any statistically significant differencein child health when comparing children in areas with average arsenic level ofgreater than 100 µg/L and less than 10 µg/L, respectively, relative to areas with

4

The interpolation is similar to a weighted average where weight is inverse distance between thegeographic point of interpolation and the location of the measured tube well. We have tried
imputing the average arsenic level based on average arsenic level within 5 km and 2 km radius but
by doing so, there are many geographical areas which do not have any measured tube well,making interpolation impossible.

17average arsenic level of 11-50 µg/L. The pattern of results for diarrhea indicator isessentially the same.
Our results would be biased if the districts with varying arsenic level
would have evolved differently over time, even in the absence of the massive
arsenic mitigation efforts of the government. It is very hard to test this assumption
due to lack of past data especially for height-for-age. We attempt to takeadvantage of the 1993 Demographic Health Survey which has available data on
recent diarrhea indicator and use it to test for parallel trends, although this may
not be as reliable as using height-for-age. We re-run the above model comparing
children aged 0-24 months in 1996 vis-à-vis children aged 0-24 months in 1993.
We restrict our analysis to those ages 0-2 years old (0-24 months) to ensure that
there are no overlap in birth cohorts in 1993 and 1996. The result of this analysis
is somewhat surprising as we find negative and statistically significant change indiarrhea experience over time in districts with average arsenic level of 51-100µg/L relative to districts with average arsenic level of less than 50 µg/L. The
negative sign suggests that the families in the districts with arsenic level 51-100µg/L are possibly consuming water from contaminated wells which would makethem less likely to incur diarrhea. Given that this is opposite of what we observein the main results, we interpret this result as lack of evidence for pre-existing
positive trends in diarrhea incidence even before the arsenic mitigation campaign
was launched. However, this result may suggest that families in areas withaverage arsenic level of 51-100 µg/L may have better health behavior thanfamilies in other areas, which may partly explain the significant results observed
earlier. We will return to this discussion later once we examine the maternal
health investments towards their children.
Panel B of Table 4 shows the results for children with household heads
who are not educated. Consistent with the patterns observed in Panel A of Figure
2 and the results in Table 3, we find that the estimates are close to zero and not18

statistically significant. One exception is the sizable estimate observed in column(3) when we add the division X after fixed effects although this estimate is notstatistically significant.
Next, we estimate Equation (3) for those with educated and non-educatedhousehold head, stratified by child’s age. Before turning to the results of theformal analysis, Panel B of Figure 2 presents the locally weighted regression ofheight-for-age on child age in months by categories of district-averagegroundwater arsenic level. The left-hand side of Panel A show the results amongchildren aged 0 to 48 months in 1996 (prior to the arsenic mitigation campaign)
while the right-hand side show the results for 2004 or after the massive welltesting and mitigation campaign have started. As shown in the graphs, prior to thestart of the arsenic mitigation strategy (in 1996), there are no significant
differences in child height-for-age across districts with varying average arseniclevel over age in months. On the other hand, when we examine the same analysis
for same aged children in 2004, we find that although there are no apparent
differences in child height-for-age across districts with varying arsenic level whenchildren are very young (age 0 to 12 months), the differences across the districts
with varying average arsenic level tend to get larger as the child gets older.
Table 5 presents the formal analysis for the estimates of Equation (3) byhousehold head education indicator and by child’s age in months. The first twocolumns present the results for the case of educated household head. Panel A
shows the results for height-for-age. Column (1) examines the case of children
aged 0-24 months. The results show that from 1996 to 2004, average height-forage of children in districts with average arsenic level of 51-100 µg/L improved by
0.13 standard deviations more than in districts with average arsenic level of 0-50
µg/L but it is not statistically significant. However, when we examine the case ofolder children (aged 25-48 months) in column (2), the estimate is larger (0.48
standard deviations) and also statistically significant. On the other hand, when we19examine the same estimates for the children in non-educated household head incolumns (3) and (4), we find that estimates are small (close to zero) and notstatistically significant regardless of the age of the child. Meanwhile, theestimates for changes in height-for-age over time among children in districts withaverage arsenic level of greater than 100 µg/L are unusually large and have
opposite signs, although not precisely estimated.
Panel B of Table 5 presents the results for the recent diarrhea indicator. As
shown in column (1), for very young children (those in the first two years of life),
the probability of recent diarrhea occurrence increased more, over time, in
districts with average arsenic level of 51-100 µg/L relative to districts withaverage arsenic level of 0-50 µg/L. We do not observe similar pattern in the older
children (aged 25 to 48 months) which suggest that younger children are likely tobe affected more by the switching of households to alternative water sources thatare likely to be bacterial-contaminated. This is consistent with the findings of
Field et. al. (2011) which suggest that the switching away of households fromtubewells to alternative water sources like surface water may have had unintended
consequences on infant health.
5.B Maternal Health and Labor Force Participation
Since child health is likely to be affected by the health status of themother, we examine the changes in mothers’ health over the period 1996 to 2004
in districts with varying levels of arsenic contamination.5 Panel A of Table 6
estimates Equation (3) using mother’s body mass index as the outcome, excluding
the controls for child’s birth year dummies and household head’s education andincluding a time dummy instead (Yr2004). Body mass index (BMI) is commonlyused as measure for adult health and nutritional status. The Demographics Health5

Case et. al. (2002) finds that the health of the mother is more strongly correlated with the child’s
health than the health of the father. Thus, this is consistent with the notion that women with poorer
health may bear less healthy children.

20

Survey obtains the body mass index of women respondents and is defined asweight (in kilograms) divided by the square of height (in meters). Column (1)shows the results for all mothers of children in our sample. Consistent with thepatterns observed for children, the mother’s body mass index increased over timefrom 1996 to 2004 in districts with average arsenic level of 51-100 µg/L relative
to districts with average arsenic level of 0-50 µg/L. In contrast, in districts withaverage arsenic level of greater than 100 µg/L, there were essentially no change
observed. In columns (2) and (3), we stratify the sample by household headeducation indicator. Focusing on the sample of mothers with educated household
head, we find that estimate become larger (0.69) and statistically significant forthe observed changes in districts with average arsenic level of 51-100 µg/L
relative to districts with average arsenic level of 0-50 µg/L. While the estimate forthe change in mother’s BMI also becomes larger in the districts with averagearsenic level of greater than 100 µg/L, it is not statistically significant. On theother hand, when we examine the sample of mothers who have non-educatedhousehold head, the estimates are much smaller (0.26) and even negative (-0.34)
and not statistically significant.
Another possible explanation for the observed changes in child health overtime could be the changes in maternal labor supply. If healthier mothers in
districts with average arsenic level of 51-100 µg/L increase their labor supply
over time, this may affect child’s health through increase in household’s average
income. In Panel B of Table 6, we examine the effect on mother’s employment
status. The results for the overall sample in column (1) and the stratified samples
in columns (2) and (3) suggest that there is essentially no change in the labor
supply of mothers in districts with average arsenic level of 51-100 µg/L relative tothose in districts with average arsenic level of 0-50 µg/L.
The above results suggest that the observed changes in child health before
and after the massive arsenic mitigation campaign in districts with average arsenic

21

level of 51-100 µg/L may be partly attributed to changes in the health of themother in these areas. In particular, mothers in educated households seem to havebenefited from the government’s mitigation campaign more than the mothers in
non-educated households although the difference in the estimates is notstatistically significant (not shown). In the next section, we therefore examine
what possibly explains the differences we observe in the outcomes for mothers
and children in households with educated household head and non-educatedhousehold head.
5.C Knowledge/Awareness of the Government’s Well-Safety Campaign
In this section, we investigate whether there are any gaps in the knowledge
or awareness of the government’s well-safety campaign between the educated andnon-educated households in the areas with varying average arsenic levels. The
Demographic Health Survey added some questions in their 2004 survey to
examine the women’s (or mother’s) knowledge of tube wells' safety based on theinformation provided by the Bangladesh government.6 Table 7 examines thebinary outcomes that indicate whether the respondent knows what a red-painted
well means, what green-painted well means, whether the respondent obtains waterfrom green well vis-à-vis red or unmarked well and whether the respondentobtains water from unmark well vis-à-vis green or red painted well.
Panel A examines whether household head’s level of education, our proxyfor household’s socioeconomic status, affect the mother’s knowledge of wells’safety and whether they are more likely to obtain water from safe wells. We also
include an interaction of household head having any formal education with thevarying levels of arsenic in our specification: 51-100 µg/L and >100 µg/L, to
6

These are the same mothers whose health and labor supply were examined in Table 5. Although
the survey is conducted among all women aged 15-49, our sample for this analysis focuses on themothers of children in the sample for our main results.

22

examine whether the effect of household head’s education on the outcomes would
depend on the average level of arsenic level in the area. As shown in the results,
the more educated the household head is, the more likely is the child’s motherknowledgeable of what green-painted well means and the more likely is she able
to obtain water from the appropriate well (for instance, choosing a green-painted
over red painted or unmarked well). This last column (column 4) serves as avalidation to check whether the respondent (mother) avoids obtaining water fromunmark well as well. The results of the interaction terms suggest that the effect ofhousehold head’s education (socioeconomic status) on the mothers’ well-safetyknowledge and behavior do not seem to vary by the average arsenic level.
In Bangladesh’s culture, women are the ones who draw water from thewells. Thus in Panel B we examine how women’s education affect theirknowledge of the government’s well-safety campaign and their probability toobtain water from the safer wells. Based on the results given in columns (1) to(4), mothers who have any formal education have higher probability of knowing
what a red-painted and green-painted well means compared to mothers with noeducated. They are also less likely to draw water from the wells and more likely
to draw water from the green wells although the latter is not statisticallysignificant.
We also examine interaction of the indicator of mothers having any formal
education with the varying categories of arsenic levels in our specification: 51100 µg/L and >100 µg/L, to examine whether the effect of mother’s education on
the outcomes would depend on the average level of arsenic level in the area. In
contrast to the results in Panel A, we find that the effect of mother’s education on
her knowledge of what green-well means and on her ability to obtain water fromgreen wells seem to matter more in districts with average arsenic level of 51-100µg/L relative to districts with average arsenic level of 0-50 µg/L. If thegovernment’s mitigation campaign focused on encouraging households to switch

23

away from “unsafe” wells or those wells with arsenic level of more than 50 µg/L,
then it is possible that women who were more educated in areas with averagearsenic level of 51-100 µg/L would have been more likely internalize this new
information and had more ability to act upon it.
5.D Mother’s Prenatal and Postnatal Health Behavior
The robust estimates on child health (height-for-age) in the main results
may be due to differences in the parents’ prenatal and postnatal health behavior in
districts with varying average arsenic level. Choices made regarding how often
the child’s mother goes for antenatal visits and whether the mother gets tetanus
injection during pregnancy as well as whether the child gets the appropriate
vaccination (BCG, DPT, polio and measles) may have short-term and long-term
consequences on child health. In addition, these behaviors may be correlated withsocioeconomic status and so may potentially explain the differences in childhealth we observe in educated and non-educated households.
In Table 8, we estimate Equation (3) on various measures of prenatal andpostnatal investments in children. Panel A shows the results for the whole sample
while Panel B and C show the results for those with educated and non-educatedhousehold heads. The first two columns examine the prenatal behavior of thechild’s mother examining the number of antenatal visits (column 1) and whether
she gets tetanus vaccination (column 2). Interestingly, we find that mothers ofchildren in districts with average arsenic level of 51-100 µg/L have increased
their antenatal visits over time relative to mothers of children in districts withaverage arsenic level of 0-50 µg/L. This pattern persists even when we restrict the
sample to educated (panel B) and non-educated households (panel C). In contrast,
mothers in districts with average arsenic level of greater than 100 µg/L havedecreased their antenatal visits over time relative to mothers of children in
districts with arsenic level of 0-50 µg/L. If the health of the mothers in areas with24

dangerously high arsenic levels (greater than 100 µg/L) have been affected
negatively by arsenic poisoning, then that may explain the decline in the numberof antenatal visits among mothers in these areas. We find that this pattern persists
when we examine the sample for children with educated household heads but notwhen we examine the sample of children with non-educated household heads.
While these results provide an interesting picture of the resulting prenatal
behavior of the mothers in areas with varying arsenic level, they do not
sufficiently explain the main results presented earlier especially given the positive
antenatal behavior observed even among mothers from non-educated households
in the areas with average arsenic level of 51-100 µg/L. Meanwhile, we find nodifferential effects on the probability of mothers getting a tetanus injection (incolumn 2), regardless of the average arsenic level.
The rest of the columns (column 3 to column 6) examine the results forpostnatal investments in vaccination. Examining the results in Panel A to Panel C,
in general, we do not find statistically significant changes in vaccination behavior
over time in areas with varying arsenic level, except for the decline in theprobability of the child getting any DPT vaccination and any polio vaccination in
districts with average arsenic level of 51-100 µg/L relative to districts withaverage arsenic level of 0-50 µg/L in the sample of children with non-educatedhousehold heads.

6 Conclusion and Discussion
This study examines the impact of arsenic exposure on child health. We
used the geographic variation in groundwater arsenic level as well as the massive
well-testing and awareness campaign in the late 1990s as sources of variation inchild’s exposure to arsenic. Children in areas with average arsenic level of 51100 µg/L at the time of the massive well-testing campaign was launched25

experienced greater improvement in their height-for-age over time relative tochildren in areas with “safe” arsenic level based on Bangladesh’s standards (0-50µg/L). This result is robust to controlling for and addressing a variety ofalternative hypotheses including differential migration, differential trends acrossareas, confounding with certain shocks and policies and lack of arsenic data on
three areas in the country. We find the same patterns of results when we examine
mother’s health (body mass index) but not for maternal labor supply. We also findthat mothers who became pregnant after the massive arsenic campaign has begun
had better prenatal behavior in areas with average arsenic level of 51-100 µg/L,
although we observe this behavior for both educated and non-educatedhouseholds.
If exposure to the arsenic mitigation campaign did not vary systematicallywith any other unobserved factors not accounted for in the robustness checks andfurther tests conducted above, what might explain the lack of effect on the healthof children in areas with average arsenic level of greater than 100 µg/L? We
consider two possibilities: First, families in these areas have been exposed todangerously high levels of arsenic for over 20 years prior to its discovery. Thus
the debilitating effects of the arsenic poisoning and its health consequences on
individuals may have kept these households from taking the appropriate actions to
get access to safe water sources. As shown in Panel A and Panel B of Figure 2,even in 1996 (left-hand side graphs), children’s health in these dangerously higharsenic zone already follows a different trajectory relative to children in otherareas. Second, as shown in Figure 1, there is clustering of very high arsenic levels(red dots) in certain areas. This may have made it more difficult for families inthose areas to find alternative sources of safe water. That is, it would have been
more costly for them to find alternative sources of safe drinking water.
Our study is one of the few studies that examine the impact of arsenic (andin general, toxic substances) exposure on child human capital outcomes, and to26

our knowledge, the first to examine child height-for-age at the population level. In
addition, we provide new evidence on the socioeconomic status-gradient in childhealth and on the steepening of the relationship between parental SES and childhealth for older children. An important implication of our study suggests theimportance of educating women and ensuring that they have access to information
at the level that they can comprehend.
It remains an open question whether the arsenic mitigation campaign
which encouraged households to switch away from unsafe wells may have hadlong term effects on human capital outcomes. Investigating the long runconsequences of this early life exposure to toxicity would be an important avenueof future research.

27

REFERENCES
Ahmad, S A, M.H. Salim Ullah Sayed, Shampa Barua, Manzurul Haque Khan,
M.H. Faruquee, Abdul Jalil, S. Abdul Hadi, and Humayun Kabir Talukder.
“Arsenic in Drinking Water and Pregnancy Outcomes,” Environmental Health
Perspective, 109, no. 6 (2001): 629-631.
Ahmed, M. F., S. Ahuja, M. Alauddin, S. J. Hug, J. R. Lloyd, A. Pfaff, T. Pichler,
C. Saltikov, M. Stute, and A. Van Geen. "Ensuring safe drinking water in
Bangladesh." Science 314, no. 5806 (2006): 1687.
Akresh, Richard, Emilie Bagby, Damien De Walque, and Harounan Kazianga.
"Child ability and household human capital investment decisions in Burkina
Faso." Economic Development and Cultural Change 61, no. 1 (2012): 157-186.
Asadullah, M. Niaz, and Nazmul Chaudhury. "Poisoning the mind: Arsenic
contamination of drinking water wells and children's educational achievement inrural Bangladesh." Economics of Education Review 30, no. 5 (2011): 873-888.
Aziz, Sonia and Khwaja M. S. Aziz and Kevin J. Boyle. “Arsenic in drinking
water in Bangladesh: factors affecting child health,” Frontiers in Public Health,(June 2014), doi: 10.3389/fpubh.2014.00057.
Balasubramanya, Soumya, Alexander Pfaff, Lori Bennear, Alessandro Tarozzi,
Kazi Matin Ahmed, Amy Schoenfeld, and Alexander van Geen. "Evolution ofhouseholds' responses to the groundwater arsenic crisis in Bangladesh:
information on environmental health risks can have increasing behavioral impact
over time." Environment and Development Economics 19, no. 05 (2014): 631647.
Behrman, Jere, and Mark R. Rosenzweig. "Does increasing women's schoolingraise the schooling of the next generation?" The American Economic Review, 92,
no. 1 (2002): 323-334.
Bennear, Lori, Alessandro Tarozzi, Alexander Pfaff, Soumya Balasubramanya,
Kazi Matin Ahmed, and Alexander Van Geen. "Impact of a randomized

28

controlled trial in arsenic risk communication on household water-source choices
in Bangladesh." Journal of Environmental Economics and Management, 65, no. 2(2013): 225-240.
British Geological Survey and Department of Public Health Engineering.
“Arsenic contamination of groundwater in Bangladesh.” Kinniburgh, D. G. and
P.L. Smedley (Editors), no. 2, Final Report, British Geological Survey Report
WC/00/19. British Geological Survey, Keyworth.
Case, Anne, Darren Lubotsky, and Christina Paxson. “Economic status and healthin childhood: The origins of the gradient.” American Economic Review, 92, no. 5(2002): 1308-1334.
Chay, Kenneth Y., and Michael Greenstone. “The impact of air pollution on
infant mortality: evidence from geographic variation in pollution shocks induced
by a recession.” Quarterly Journal of Economics, 118, no. 3 (2003): 1121-1167.
Chowdhury, Mushtaque R. “Arsenic Crisis in Bangladesh,” Scientific American,
August 2004.
Currie, Janet. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Healthin Childhood, and Human Capital Development.” Journal of Economic
Literature, 47, no. 1 (2009): 87-122.
Currie, Janet and Rosemary Hyson. “Is the Impact of Health Shocks Cushionedby Socioeconomic Status? The Case of Low Birthweight.” American Economic
Review 89, no. 2 (1989): 245–50.
Currie, Janet, and Wanchuan Lin. “Chipping Away at Health: More on the
Relationship between Income and Child Health.” Health Affairs 26, no. 2 (2007):
331–44.
Currie, Janet and Matthew Neidell. “Air Pollution and Infant Health: What Can
We Learn from California’s Recent Experience,” Quarterly Journal of Economics
120, no. 4 (2005): 1003-1030.

29

Currie, Janet and Mark Stabile. “Socioeconomic status and health: why is therelationship stronger for older children?” American Economic Review, 93, no. 5(2003): 1813-1823.
Das, Bhaskar, Mohammad Mahmudur Rahman, Bishwajit Nayak, Arup Pal,
Uttam Kumar Chowdhury, Subhas Chandra Mukherjee, Khitish Chandra Saha,
Shyamapada Pati, Quazi Quamruzzaman, and Dipankar Chakraborti.
"Groundwater arsenic contamination, its health effects and approach for
mitigation in West Bengal, India and Bangladesh." Water Quality, Exposure and
Health 1, no. 1 (2009): 5-21.
de Onis, Mercedes, Adelheid Onyango, Elaine Borghi, Amani Siyam, Chizuru
Nishida, and Jonathan Siekmann. Development of a WHO growth reference forschool-aged children and adolescents. Bulletin of World Health Organization 85,
no. 9 (2007), 660-667.
Escamilla, V., B. Wagner, M. Yunus, P. K. Streatfield, A. Van Geen, and M.
Emch. "Effect of deep tube well use on childhood diarrhoea in
Bangladesh."Bulletin of the World Health Organization 89, no. 7 (2011): 521527.
Field, Erica, Rachel Glennerster and Reshmaa Hussam, “Throwing the Baby out
with the Drinking Water: Unintended Consequences of Arsenic Mitigation Efforts
in Bangladesh,” 2011.
Grossman, Michael. “On the Concept of Health Capital and the Demand for
Health.” Journal of Political Economy, 80, no. 2 (1972): 223-255.
Hussain, A Z M Iftikhar. “Arsenic Contamination of Groundwater in Bangladesh
A Briefing Paper” Ministry of Health and Family Welfare.
Kirby, Patrick. “Arsenic poisoning in Bangladesh – an untold story of deadly
proportions” Green Magz.Info. http://www.greenmagz.info/arsenic/
MacArthur, J. M., P. Ravenscroft, S. Safiulla, and M. F. Thirlwall. “Arsenic ingroundwater: Testing pollution mechanisms for sedimentary aquifers in
Bangladesh.” Water Resources Research 37, no. 1 (2001): 109-117.
Madajewicz, Malgosia, Alexander Pfaff, Alexander Van Geen, Joseph Graziano,
Iftikhar Hussein, Hasina Momotaj, Roksana Sylvi, and Habibul Ahsan. "Can30

information alone change behavior? Response to arsenic contamination ofgroundwater in Bangladesh." Journal of Development Economics 84, no. 2(2007): 731-754.
McCrary, Justin and Heather Royer. “The Effect of Female Education on Fertility
and Infant Health: Evidence from School Entry Policies Using Exact Date ofBirth.” American Economic Review, 101 (2011): 158-195.
Murray, Michael P. and Raisa Sharmin. “Ground Water Arsenic and Education
Attainment in Bangladesh,” Social Science Research Network, (December 2013),
doi: 10.2139/2372684.
Naujokas, Marisa F., Beth Anderson, Habibul Ahsan, H. Vasken Aposhian,Joseph Graziano, Claudia Thompson, and William A. Suk. "The broad scope of
health effects from chronic arsenic exposure: update on a worldwide public healthproblem." Environmental Health Perspectives 121, no. 3 (2013): 295-302.
Neidell, Matthew, “Air Pollution, Health, and Socio-economic Status: The Effectof Outdoor Air Quality on Childhood Asthma,” Journal of Health Economics, 23(2004), 1209–1236.
Nickson, R. T., J. M. MacArthur, P. Ravenscroft, W. G. Burgess, K. M. Ahmed.
“Mechanism of arsenic release to groundwater, Bangladesh and West Bengal.”
Applied Geochemistry 15 (2000): 403-413.
Opar, Alisa, Alex Pfaff, A. A. Seddique, K. M. Ahmed, J. H. Graziano, and A.
Van Geen. "Responses of 6500 households to arsenic mitigation in Araihazar,
Bangladesh." Health & Place 13, no. 1 (2007): 164-172.
Rohde, David and Julfikar Ali Manik. “The Lethal Water Wells of Bangladesh.”
New York Times (2005), July 17. http://www.nytimes.com/2005/07/17/us/thelethal-water-wells-of-bangladesh.html?_r=0
Safiuddin, Md, and Md Masud Karim. "Groundwater arsenic contamination in
Bangladesh: causes, effects and remediation." In Proceedings of the 1st IEB
International Conference and 7th Annual Paper Meet, The Institution ofEngineers, Chittagong Center, Bangladesh. 2001.

31

Shankar, Shiv and Uma Shanker, and Shikha. “Arsenic Contamination of
Groundwater: A Review of Sources, Prevalence, Health Risks, and Strategies for
Mitigation,” The Scientific World Journal, August (2014), doi:
10.1155/2014/304524.
Smith, Allan H., Elena O. Lingas, and Mahfuzar Rahman. "Contamination ofdrinking-water by arsenic in Bangladesh: a public health emergency." Bulletin of
the World Health Organization 78, no. 9 (2000): 1093-1103.
Thomas, Duncan and John Strauss and Maria-Helena Henriques. “How Does
Mother’s Education Affect Child Height?” The Journal of Human Resources, 26,
no. 2 (1991): 183-211.
Van Geen, A., M. Trevisani, J. Immel, Md Jakariya, N. Osman, Z. Cheng, A.
Gelman, and K. M. Ahmed. "Targeting low-arsenic groundwater with mobilephone technology in Araihazar, Bangladesh." Journal of Health, Population, andNutrition 24: 3 (2006): 282.
Wasserman, Gail A., Xinhua Liu, Faruque Parvez, Habibul Ahsan, Pam FactorLitvak, Alexander van Geen, Vesna Slavkovich, Nancy J. Lolacono, Zhongqi
Cheng, Iftikhar Hussain, Hassina Momotaj, and Joseph H. Graziano. “Water
Arsenic Exposure and Children’s Intellectual Function in Araihazar, Bangladesh,”
Environmental Health Persepctives, 112, 13 (2004): 1329-1333.
World Bank. “Implementation completion and results report on a credit in theamount of SDR 24.2 million to Bangladesh for arsenic mitigation water supply.”
Environment and Water Resources Unit, South Asia Region, 2007.
WHO Multicentre Growth Reference Study Group. WHO child growth standards:
Length/height-for-age, weight-for-age, weight-for-length, weight-for-height andbody mass index-for-age: Methods and development, (2006), WHO, Geneva.
Wu, Jianyong, Alexander van Geen, Kazi Matin Ahmed, Yasuyuki Akita JahangirAlam, Patricia J. Culligan, Veronica Escamilla, John Feighery et al. "Increase indiarrheal disease associated with arsenic mitigation in Bangladesh." PLoS ONE,6, no. 12 (2011): e29593.

32

Figure 1. Geographic Variation in Arsenic Contamination across Bangladesh

Legend:Green: 0-50 µg/L
Blue: 51-100 µg/L
Red: >100 µg/L

Source: GIS mapping by the authors based on data provided by the British Geological Survey (2000).

33

Figure 2. Height-for-Age of Children in Areas with Varying Average Arsenic Level
A. Pattern by Household (HH) Head’s Education
Age 0-48 months in 2004

-1.5

-1

Height-for-Age

-1.5

-2

-2.5

-2

Height-for-Age

-1

-.5

-.5

0

Age 0-48 months in 1996

0

5

10
HH Head's Education

0-50 ug/L

15

20

50-100 ug/L

05>100 ug/L

0-50 ug/L

10
HH Head's Education

15

20

50-100 ug/L>100 ug/L

B. Pattern by Age (in months)
Age 0-48 months in 2004

-1
-2

-1.5

Height-for-Age

-2

-1.5
-3

-2.5

-2.5

Height-for-Age

-1

-.5

-.5

Age 0-48 months in 19960100-50 ug/L

20

agemons

30

50-100 ug/L

40

50

0

10
0-50 ug/L>100 ug/L

20
30
Age (in months)
50-100 ug/L

40

50>100 ug/L

Notes. The graphs in Panel A runs a locally weighted regression of height-for-age on household head education
using 1996 (left) and 2004 (right) data. The graphs in Panel B runs a locally weighted regression of height-for-age
on child’s age (in months) using 1996 (left) and 2004 (right) data.

34

Table 1. Descriptive Statistics

Variable
Height-for-age
Recent diarrhea indicator
Age (months)
Male
Mother has formal education
Mother's Age
Mother's education (years)
HH head's education (years)
Mother's height% Communities with secondary school% Communities with health service
providers

Mean (Std. Dev) of Characteristics, DHS 1996
0 - 5051-100
>100
µg/L
µg/L
µg/L
P-value P-value
(1)
(2)
(3)
(2) - (1) (3) - (1)
-2.20
-2.30
-2.27
0.25
0.32
(1.66)(1.57)(1.66)
0.08
0.06
0.08
0.08*
0.90
(0.27)(0.23)(0.28)
23.63
23.99
23.85
0.63
0.72
(14.22)(14.23)(14.41)
0.50
0.50
0.52
0.81
0.36
(0.50)(0.50)(0.50)
0.44
0.46
0.43
0.32
0.65
(0.50)(0.50)(0.50)
25.38
25.65
26.11
0.41
0.01***(6.22)(6.34)(6.34)
2.41
2.22
2.20
0.27
0.13
(3.43)(3.04)(3.18)
3.21
2.84
2.91
0.02*
0.08*
(4.11)(3.98)(3.80)
149.65149.01149.400.23
0.57(9.94)(11.94)(10.76)
0.50
0.61
0.42
0.00*** 0.00***(0.26)(0.18)(0.27)
0.30
0.36
0.24
0.00*** 0.00***(0.22)(0.18)(0.24)
2627

435

699

Variable means displayed to the right of variable name. Standard deviations displayed in parentheses below themean. Based on a sample of children aged 0-48 months in 1996 Demographic Health Survey (DHS 1996).

35

Table 2. Simple Difference-in-Differences for Height-for-Age and Indicator of Recent Diarrhea
VariableAge 0-48 months, 1996
Age 0-48 months, 2004
Diff (2004 vs 1996)

Age 0-48 months, 1996
Age 0-48 months, 2004
Diff (2004 vs 1996)

District-Averaged As Level
0 - 50 µg/L
51-100 µg/L
>100 µg/L
(1)
(2)
(3)
A. Height-for-Age
-2.19
-2.30
-2.27
(0.03)(0.08)(0.06)
-1.91
-1.72
-1.94
(0.03)(0.06)(0.05)
0.29
0.58
0.33
(0.04)***(0.09)***(0.08)***
B. Indicator of Recent Diarrhea
0.08
0.06
0.08
(.01)
(.01)
(.01)
0.09
0.09
0.07
(.01)
(.01)
0.01
0.00
0.03
-0.01
(.01)
(0.02)*(.01)Diff(2) - (1)Diff(3) - (1)-0.10
(0.09)
0.19(0.07)***
0.29(0.11)***-0.07(0.07)-0.03(0.06)
0.04(0.09)

-0.02
(.01)
0.00
(.01)
0.03
(0.02)

0.00
(.01)
-0.02
0.01
-0.02
(0.02)

Means and standard errors for height-for-age and indicator of recent diarrhea, by categories of average arsenic levelin the district and by cohorts.

36

Table 3. Arsenic Level and the Socioeconomic Gradient in Child Health: Basic Results(1)
A. Height-for-Age
As 51-100*Yr2004
As 100up*Yr2004

As 100up*Yr2004(3)

HH Head Not Educated
(7)
(8)
(9)

0.13*
[0.063]0.02[0.120]-2.048156

0.14**[0.064]0[0.119]0.28**[0.125]0.09[0.127]0.22**[0.089]0.02[0.119]-1.834258

0.24***[0.088]0.01[0.101]

0.03[0.093]0.06[0.151]

0.02[0.107]0.04[0.149]
-2.28
3898

0.03
[0.098]0.02[0.172]0.03[0.019]-0.01[0.021]0.03*[0.018]-0.01[0.021]
0.08
8156

0.04*
[0.019]-0.02[0.019]0.05***[0.017]-0.01[0.032]0.05***[0.017]-0.01[0.032]0.08
4258

0.05***[0.018]-0.01[0.031]0.01[0.027]-0.02[0.020]0.01[0.026]-0.02[0.020]
0.09
3898

0.01
[0.027]-0.02[0.020]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No
No

Yes
No

Yes
Yes

No
No

Yes
No

Yes
Yes

No
No

Yes
No

Yes
Yes

Mean of dependent variable
Observations
Birth year FE and District FE
Individual and Parental
Controls
Time-varying Availability ofServices in the District

HH Head Educated
(4)
(5)
(6)

0.21***[0.076]0.06[0.128]Mean of dependent variable
Observations
B. Had Recent Diarrhea
Indicator
As 51-100*Yr2004

All
(2)

Notes. As 51-100 is a dummy variable indicating whether or not the district has an average arsenic level of 51-100 µg/L while As 100up is a dummy variableindicating whether or not the district has an average arsenic level of greater than 100 µg/L. The districts with average arsenic level of 0-50µg/L comprise thecomparison group. Yr2004 is a dummy variable which is equal to 1 if the child is born after the massive testing campaign has begun and equal to 0 otherwise.
Individual and parental controls refer to child’s age and gender, mother’s age, height and education and father education. Time varying availability of services inthe district refer to the time-varying percentage of secondary schools and health facilities available in the district. Heteroskedasticity-robust standard errors adjust
for clustering at the district level.

37

Table 4. Robustness Checks and Alternative Specifications: Children with Educated and Non-Educated HH HeadA.

HH Head EducatedRestricted tothose living inplace ofresidence forat least 6 years(1)

Add 1996
height-forage*birth year
dummies

Add Divisionby year FE

Interpolation(w/in 10 km)
for 3 missing
districts

Use fourcategories ofAs levelCompare 0-2
age cohorts in1996 vs 1993
(trend check)

(2)

(3)

(4)

(5)

(6)

0.33***[0.116]-0.06[0.125]0.18**[0.086]-0.02[0.082]0.20*[0.116]-0.09[0.137]0.21*[0.111]0.04[0.130]

-

2794

4258

4258

4271

0.21**[0.091]-0.02[0.111]-0.08[0.169]4258

II. Had Recent Diarrhea Indicator
As 51-100*Yr2004
0.07***[0.021]
As 100up*Yr2004
0[0.028]
As 10below*Yr2004

0.05***[0.019]-0.01[0.031]0.05[0.031]0.01[0.032]0.0[0.023]-0.01[0.033]-0.08**[0.031]0.02[0.035]Observations

4258

4258

4271

0.05***[0.018]-0.01[0.032]0.01[0.025]4258

I. Height-for-Age
As 51-100*Yr2004
As 100up*Yr2004
As 10below*Yr2004
Observations

2794

38

-

2977

Table 4. (Continued)
B.

HH Head Not EducatedRestricted tothose living inplace ofresidence for atleast 6 years(1)

Add 1996
height-forage*birth year
dummies

Add Division
by Year FE

Interpolation(w/in 10 km)
for 3 missing
districts

Use fourcategories ofAs levelCompare 0-2
age cohorts in1996 vs 1993
(trend check)

(2)

(3)

(4)

(5)

(6)

-0.03
[0.102]-0.03[0.182]0.00[0.119]-0.01[0.158]0.18[0.130]0.09[0.195]0.03[0.119]0.04[0.175]

-

2844

3898

3898

3906

0.02
[0.106]0[0.172]-0.07[0.130]3898

II. Had Recent Diarrhea Indicator
As 51-100*Yr2004
0.02[0.026]
As 100up*Yr2004-0.03[0.022]
As 10below*Yr2004

0.02[0.028]-0.02[0.020]0[0.035]-0.03[0.024]-0.02[0.031]-0.03[0.017]0.01[0.029]-0.02[0.024]
-0.01

-0.02
[0.045]0[0.024]

Observations

3898

3898

3906

3898

3186

I. Height-for-Age
As 51-100*Yr2004
As 100up*Yr2004
As 10below*Yr2004
Observations

2844

-

Notes. As 51-100 is a dummy variable indicating whether or not the district has an average arsenic level of 51-100 µg/L while As 100up is a dummy variableindicating whether or not the district has an average arsenic level of greater than 100 µg/L. The districts with average arsenic level of 0-50 µg/L comprise thecomparison group. Yr2004 is a dummy variable which is equal to 1 if the child is born after the massive testing campaign has begun and equal to 0 otherwise. All
regressions control for child’s age and gender, mother’s age, height and education and father education as well as for time-varying percentage of secondary
schools and health facilities available in the district. Heteroskedasticity-robust standard errors adjust for clustering at the district level.

39

Table 5. Arsenic Level and the Socioeconomic Gradient in Child Health: Results by Child’s Age(in Months)
HH Head Educated

A. Height-for-Age
As 51-100*Yr2004
As 100up*Yr2004

Observations
B. Had Recent Diarrhea
Indicator
As 51-100*Yr2004
As 100up*Yr2004

Observations

HH Head Not EducatedAge 0-24months(1)

Age 25-48
months(2)

Age 0-24months(3)

Age 25-48
months(4)

0.13[0.123]-0.04[0.112]0.48***[0.146]0.08[0.141]0.02[0.113]0.31[0.210]0.07[0.157]-0.37*[0.198]

2165

2093

1972

1926

0.09**[0.041]-0.02[0.047]0[0.033]0[0.027]-0.03[0.033]-0.01[0.031]0.06[0.042]-0.02[0.023]

2302

2282

2164

2182

Notes. As 51-100 is a dummy variable indicating whether or not the district has an average arsenic level of 51-100µg/L while As 100up is a dummy variable indicating whether or not the district has an average arsenic level ofgreater than 100 µg/L. The districts with average arsenic level of 0-50 µg/L comprise the comparison group. Yr2004
is a dummy variable which is equal to 1 if the child is born after the massive testing campaign has begun and equal
to 0 otherwise. All regressions control for child’s age and gender, mother’s age, height and education and father
education as well as for time-varying percentage of secondary schools and health facilities available in the district.
Heteroskedasticity-robust standard errors adjust for clustering at the district level.

40

Table 6. Arsenic Level and Mother’s Health and Employment Status

A. Mother's BMI
As 51-100*Yr2004
As 100up*Yr2004

Observations
B. Mother currently working
As 51-100*Yr2004
As 100up*Yr2004

Observations

All
(1)

HH Head
Educated(2)

HH HeadNot Educated(3)0.53***[0.165]-0.01[0.212]0.69***[0.189]0.23[0.268]0.26[0.177]-0.34[0.212]

7345

3996

3349

0.02
[0.065]-0.07[0.043]-0.04[0.056]-0.08[0.049]0.07[0.078]-0.05[0.060]

7427

4034

3393

Notes. As 51-100 is a dummy variable indicating whether or not the district has an average arsenic level of 51-100µg/L while As 100up is a dummy variable indicating whether or not the district has an average arsenic level ofgreater than 100 µg/L. The districts with average arsenic level of 0-50 µg/L comprise the comparison group. Yr2004
is a dummy variable which is equal to 1 if the child is born after the massive testing campaign has begun and equal
to 0 otherwise. All regressions control for mother’s age, height and education as well as for time-varying percentage
of secondary schools and health facilities available in the district. Heteroskedasticity-robust standard errors adjust
for clustering at the district level.

41

Table 7. Knowledge/Awareness of the Government’s Well-Safety Campaign
Binary Outcomes

Panel A. Using HH Head Education
HH Head primary education level(ref: no schooling)
HH Head at least secondary education level(ref: no schooling)
As 51-100*HH head any education
As 100up*HH head any education

Panel B. Using Mother’s Education
Mother has any schooling(ref: no schooling)
As 51-100*Mother had any education
As 100up*Mother had any education

Know what redpainted well means(1)

Know what greenpainted well means(2)

Obtain water fromgreen well(3)

Obtain water fromunmark well
(4)0[0.010]0.01[0.009]-0.04[0.033]-0.02[0.029]0.04**[0.016]0.08***[0.021]-0.01[0.040]-0.03[0.030]0.04**[0.018]0.08***[0.023]0[0.037]-0.02[0.030]-0.05**[0.023]-0.08***[0.025]0.05[0.053]0.04[0.042]0.01*[0.005]-0.02[0.017]0[0.025]0.03*[0.014]0.09**[0.042]0.01[0.029]0.02[0.016]
0.10**[0.045]0.02[0.032]-0.03*[0.015]-0.09[0.058]-0.02[0.039]Notes. Regressions are based on 2004 Demographic Health Survey (DHS 2004) cross-section data. As 51-100 is a dummy variable indicating whether or not the
district has an average arsenic level of 51-100 µg/L while As 100up is a dummy variable indicating whether or not the district has an average arsenic level ofgreater than 100 µg/L. The districts with average arsenic level of 0-50 µg/L comprise the comparison group. HH head any education is a dummy variable whichis equal to 1 if the HH head has any formal education and equal to 0 otherwise. Mother had any education is a dummy variable which is equal to 1 if the motherhad any formal education and equal to 0 otherwise. All regressions control for household economic variables such as whether roof is made of wood, whether thewall is made of wood and whether the household has access to sealed-tank.

42

Table 8. Mother’s Investments: Prenatal and Postnatal Health Behavior
Dependent Variables

Panel A. Whole Sample
As 51-100*yr2004
As 100up*yr2004
Observations
Panel B. HH Head EducatedAs 51-100*yr2004
As 100up*yr2004
Observations
Panel C. HH Head Not EducatedAs 51-100*yr2004
As 100up*yr2004
Observations

No. ofAntenatal
Visits(1)

Received TT
Injections

BCG
Vaccination

Any DPT
Vaccination

Any Polio
Vaccination

Measles
Vaccination

(2)

(3)

(4)

(5)

(6)

0.54**[0.242]-0.40**[0.168]7555

0[0.051]0.02[0.040]7557

0[0.029]0.01[0.036]8154-0.02[0.033]0.01[0.038]8148-0.02[0.033]0.03[0.031]8149-0.01[0.035]0.01[0.052]8138

0.43*[0.227]-0.66***[0.198]3960

0.03[0.059]0.01[0.039]3965

0.05[0.039]0.02[0.030]4257

0.05[0.040]0.03[0.033]4254

0.04[0.046]0.03[0.021]4255-0.01[0.048]0[0.041]4249

0.64**[0.270]-0.06[0.192]3595-0.02[0.051]0.04[0.060]3592-0.04[0.026]
0[0.051]3897-0.07**[0.031]-0.01[0.050]3894-0.06**[0.028]0.02[0.047]3894

0[0.038]0.01[0.068]3889

Notes. As 51-100 is a dummy variable indicating whether or not the district has an average arsenic level of 51-100 µg/L while As 100up is a dummy variableindicating whether or not the district has an average arsenic level of greater than 100 µg/L. The districts with average arsenic level of 0-50 µg/L comprise thecomparison group. Yr2004 is a dummy variable which is equal to 1 if the child is born after the massive testing campaign has begun and equal to 0 otherwise. All
regressions control for mother’s age, height and education as well as for time-varying percentage of secondary schools and health facilities available in the
district. Heteroskedasticity-robust standard errors adjust for clustering at the district level.

43