Do neighbors' actions affect a household's decision to seek safe water?
Malgosia Madajewicz, Alexander Pfaff, Juan Robalino
Columbia University
September 14, 2005
Very preliminary and incomplete. Please do not cite or distribute without authors'
permission.
Authors' contact information:
Madajewicz: [email protected]
Pfaff: [email protected]
Robalino: [email protected]
1
1 Introduction
The effects of social interaction on behavior and socio-economic attainment have
been the subject of considerable interest in sociology and more recently in economics.
Social interaction affects people's decisions when their utilities depend on the actions or
utilities of other people. For example, an action such as using contraceptives or admitting
to the doctor that one has AIDS may become less costly the more people take this action.
Interaction can also affect behavior and outcomes when other people have resources
whose availability is subject to market imperfections, for example information about jobs
or about one's own quality as a job candidate. We are interested in the role which social
interaction may play in influencing people's response to information about a risk to
health, specifically arsenic in drinking water in Bangladesh. We examine whether people
are more likely to seek safe water when more of their neighbors do so.
The effect of social interaction on the response to an environmental risk has a
potentially important policy implication. Information about such risks is expensive to
disseminate and much time often passes before an information campaign is organized.
For example, most people in our study area in Bangladesh learned that their drinking
water may be contaminated with arsenic in 2001, through our information campaign,
even though the first news about arsenic emerged in 1994. If the behavior of those who
obtain the information can affect the behavior of others sufficiently strongly, then it may
be possible to target information campaigns toward dispersed individuals or small groups
and rely on interactions between social groups to diffuse both information and behavior,
thereby lowering the cost and possibly the logistical complexity of information
campaigns. However, interaction effects have to be sufficiently strong in order to be
useful for policy.
Bangladesh is facing a drinking water crisis caused by the discovery of arsenic in the
groundwater. Since the early 1990s, the great majority of the country's population has
been obtaining drinking water from wells which draw water from underground aquifers.
The displacement of surface water from rivers and ponds by groundwater as the primary
source of drinking water reduced the incidence of microbial, water-borne diseases.
However, the water in about one-third of the wells contains concentrations of arsenic
above the official Bangladeshi safety standard of 50 micrograms per liter.1 About onethird of the population is exposed to unsafe levels of the contaminant.
Exposure to unsafe levels of arsenic causes a variety of deadly diseases whose
gestation periods vary from 10 to 20 years or more. Skin diseases, such as keratosis, have
the earliest onset, and they can lead to disfiguration and loss of limbs. High blood
pressure, strokes, heart disease, internal organ failure and various cancers take longer to
develop.
Efforts to provide access to safe water to those affected by the arsenic problem have
been few and localized. Widely available surface water is not a viable alternative,
because of microbial contaminants. One fortunate aspect of the arsenic contamination is
1
The WHO lowered its standard for safe concentrations from 50 to 10 micrograms a few years ago.
2
that it is unevenly distributed. Wells which are nearby each other can have very different
concentrations.2 Therefore, the most accessible sources of safe water are currently safe
wells belonging to one's neighbors, nearby safe community-owned wells or drilling one's
own new well in the hope that it will tap safe water.
In our study area, everyone knows about the possible presence of arsenic in the water
and 90% of people know whether their own well is safe or unsafe. 60% of those whose
well is unsafe changed to another well. This large response is surprising since the
consequences of exposure to arsenic are not yet visible; people in our study area are not
yet falling ill from arsenic. One potential reason for the strong response may be
neighbors' influence, if only a portion of people need to be convinced of the risk, while
others follow their example.
Neighbors' example may influence the decision whether or not to seek safe water for
a number of reasons. People may learn information from neighbors. A person may ask
her neighbors why they did or did not change and thereby learn something new about the
threat which arsenic poses to health. She may also receive information from neighbors
which contradicts her own, in which case the effect of the information depends on her
beliefs about the validity of her own information relative to that obtained from the
neighbors. In section 6, we provide some evidence that information does diffuse through
neighbors.
Several papers provide evidence of social learning in other contexts, primarily
learning about a new agricultural technology.3 The learning taking place in these papers
is somewhat different in that people respond to their neighbors' outcomes, e.g. profits. In
the case of arsenic, one could learn about the benefit of changing to another source by
observing the change in the health of a neighbor who changed. However, in our study
area almost no one is sick from arsenic yet. Therefore, observation of one's neighbors
cannot yet be a source of learning about the effects of arsenic on health.
Another reason for neighbors' influence may be herd behavior.4 People may believe
that with some probability their neighbors have more information than they do. The fact
that their neighbors switch to another well may lead them to revise upward their estimates
of the benefit of switching.
A third reason may be that the neighbors' behavior may lower the costs of one's own
change to another well. 34% of our sample own their own well and another 47% use the
well of a relative who lives around the same courtyard, i.e. in the same bari.5 People who
use wells owned by a neighbor who does not live in the same bari and, even worse, is not
a relative, are poorer and such use carries some social stigma. However, the more people
of various socio-economic backgrounds in one's neighborhood use neighbors' wells, the
smaller may be the stigma. Furthermore, anecdotal evidence suggests that those who
2
Van Geen et al (2002) find that most people in our study area live within 100 meters of a safe well.
Besley, Case (1994), Foster, Rosenzweig (1995), Conley, Udry (2002), Munshi (2004)
4
Banerjee (1992)
5
A bari is a cluster of houses set around a common courtyard, most often inhabited by relatives.
3
3
switch to a neighbor's well sometimes encounter unpleasant treatment as the owner of the
well attempts to discourage them from continuing to use the well. As more people who
know each other use neighbors' wells, they may be better able to coordinate sanctions
against unpleasant behavior by owners of safe wells.
The above reason is similar to the model in Munshi and Myaux (2001) who examine
how the use of contraception diffuses in a population. Their point is that behavior which
may encounter social sanctions is likely to diffuse slowly and different equilibria are
possible in otherwise similar populations. We observe a much more rapid change in
behavior, but we do not observe several different populations, therefore we do not know
whether multiple equilibria arise here.
The literature on social interaction is very large and we do not attempt to provide an
overview.6 In addition to papers mentioned above, our paper is related to Robalino's and
Pfaff's (2005) analysis of the influence which neighbors' bear on each others' decision
whether or not to cut down forest. We use a similar instrumental variable strategy to
identify the effect of neighbors' behavior.
In general, the effects of neighbors' actions on one's own behavior are difficult to
identify. Neighbors could behave in a similar way for a number of reasons. Since location
is likely to be endogenous, people may locate near each other because they have similar
unobserved preferences and therefore they choose the same actions. Furthermore,
neighbors influence each other. Therefore, it is difficult to distinguish the effect on one
person of their neighbors' behavior from her/his effect on her neighbors.
As in Robalino and Pfaff (2005), we use instrumental variables (IVs) to address both
problems. The IVs are the fraction of one's neighbors who have unsafe wells and the
average level of arsenic in neighbors' wells. The arsenic is naturally occurring, not from
industrial sources. The chemical process which leads to its dissolution from the soil into
water results in a distribution which appears to be uncorrelated with either people's
characteristics or the physical characteristics of neighborhoods. We provide evidence that
the distribution of arsenic is uncorrelated with observable characteristics.
The weakness of arsenic in neighbors' wells as an IV is that it may well fail the
exclusion restriction. The variables proxy for the availability of safe wells nearby and
therefore belong directly in the second stage. The instruments could nevertheless be
helpful since they are positively related to the proportion of one's neighbors who switch
but negatively related to the probability that the respondent switches, i.e. the higher is the
percentage of unsafe wells among neighbors the less likely is the individual to switch.
We would expect the effect of neighbors' behavior to be positive, i.e. one should be more
likely to switch to another source if more of her/his neighbors switch. Therefore, the
negative direct effect of the IV on own behavior should reduce the estimated coefficient
on the fraction of neighbors who switched relative to what it would be if we could control
6
A large and important literature, which is less directly related to our paper, analyzes the effects of one's
neighborhood on outcomes such as education and employment. Case and Katz (1991), Cutler and Glaeser
(1997), Conley and Topa (2002) and (2003), Bayer, Ross, Topa (2004)
4
for availability of safe wells in the second stage, leading to an underestimate of the
neighbors' influence. A positive effect should then be strong evidence of such influence.
We do not find a significant effect of neighbors' behavior when we instrument the
fraction of one's neighbors who switched as described above. However, the regression
provides some indirect evidence. If the IVs do not satisfy the exclusion restriction, we
may expect the IV estimates of the effect of neighbors' behavior to be negative, reflecting
the direct, negative effect of neighbors' arsenic in the coefficient on neighbors' behavior.
However, the estimated effect of neighbors' behavior is not significantly different from
zero, suggesting that there is a positive effect of interaction between neighbors which is
mitigating the negative direct effect of neighbors' arsenic. One caveat is that the evidence
is not robust to the exclusion of village fixed effects; IV estimates of the neighbor effect
are negative and significant without the fixed effects.
A second caveat to the above argument is that the IVs may fail the exclusion
restriction because we misspecified the functional form of the effect of neighbors'
arsenic on neighbors' behavior. The IVs may have no direct effect on the respondent's
own behavior if the functional form were correct. Also, a misspecification would affect
the effectiveness of the IVs. We suspect a misspecification for the following reason. The
fraction of neighbors who have unsafe wells and average arsenic in their wells combine
two effects. The neighbor's own arsenic has a negative effect on that neighbor's own
probability of switching, while the higher is arsenic in the wells of the neighbors around
that neighbor, included in the average, the lower is that neighbor's probability of
switching. Furthermore, whether or not the neighbor's own well is safe has a much larger
effect on her/his behavior than does the fraction of safe wells among the other neighbors,
while we combine the two linearly in the average. The problem motivates an alternative
specification.
In the alternative approach, we predict the probability that each individual switches to
another well with a dummy variable for whether the person's own well is safe and the
arsenic level in the person's own well. We then use the average of the predicted
probabilities for one's neighbors in the second stage together with a dummy for whether
the respondent's well is safe and the arsenic level in that well.7 One problem with this
specification is that it does not allow us to control for individual household characteristics
other than arsenic. We provide some evidence that this problem should not have a
significant effect on the estimates. A potentially more serious problem is that the
variables which control for one's own arsenic in the second stage are excluded from the
first stage. Therefore, the estimator may be inconsistent. We control partly for the effect
of omitted second-stage arsenic by including the percentage of unsafe wells among one's
neighbors and the average level of arsenic in the first stage.
The effect of neighbors' behavior is positive and significant in the second
specification. An increase of one standard deviation in the fraction of neighbors living
within 50 meters who switch results in an increase of approximately .12 in the probability
of changing to another well. The size of the effect is .09 when we consider the behavior
7
We obtain bootstrapped standard errors.
5
of neighbors who live within 75 meters.8 The estimate provides a lower bound on the size
of the neighbor effect, because of the negative influence on the coefficient exerted by
neighbors' arsenic omitted from the second stage regression.
The result is suggestive but not conclusive since it is not robust to the more standard
specification in which we predict average neighbor behavior in the first stage. We need to
analyze further the properties of the alternative estimator in order to determine how to
reconcile the conflicting results.
The remainder of the paper proceeds as follows. Section 2 discusses the context of the
study and who are the groups among whom we would expect to find significant effects of
social interaction. Section 3 explains the data. In section 4, we present the model and the
results obtained from the standard approach, in which we predict the fraction of
neighbors who switch in the first stage. The alternative specification is the subject of
section 5. Section 6 presents evidence that people obtain information from neighbors.
Section 7 concludes.
2. Institutional context
2.1 Background
We collected the data, which we describe in section 3, during a multi-disciplinary
study of the arsenic problem in Bangladesh. The project, launched in 2000, focuses on a
small section of the Araihazar district 20 km south-east of Dhaka. The study region is a
21 square-kilometer, rural area containing 70,000 people and 6000 wells in 54 villages.
Everyone in the area knows about the arsenic problem. The three teams which have
been conducting the project, public health, geo-chemistry and economics, conducted an
information campaign which provided two pieces of information. First, the geo-chemistry
team tested the wells. The result of the test was communicated in person to each
individual in our survey. Also, the geo-chemistry team labeled each well with a tag
depicting in a picture whether the water is safe or not, with the level of arsenic written
below. Thus, the information whether each well is safe is publicly available to all, not
only the literate.
Second, a team of educators traveled to each neighborhood and called the residents to
a meeting. They then discussed the reasons why residents should not drink water from
unsafe wells and provided other information about arsenic. Only a subset of our sample
attended one of these meetings.
8
The estimate of the size of the effect is very preliminary. It is based on a saturated linear probability
model, which would yield the exact effect on the probability without an endogenous variable. However, the
predicted fraction of neighbors who switch is not a dummy variable, therefore its coefficient is not exactly
the effect on the probability. The predicted probabilities are within the 0 to 1 range for 98% of the sample.
6
As we mentioned in the introduction, the only sources of safe water currently
available to residents of Araihazar are existing wells which are safe or new wells, which
may be drilled subject to the risk that they may tap into unsafe water.9 The risk of drilling
new wells is smaller for those who can afford to drill deeper wells, since the deeper
aquifers tend to be free of arsenic. The choices made by the respondents to our survey
reflect these options. 63% of those who changed to a different source changed to a
neighbor's well, 24% installed their own new wells and 13% changed to a communityowned well.
2.2 The relevant social group
A number of types of groups in Bangladesh are known to have important social
interactions. People generally report that relatives and especially baris constitute the
social group with which they are most willing to interact and whom they most trust.
Other studies have found religious and gender divisions to be very important. We
consider the effect of neighbors' behavior. We do not argue that this is the most important
effect. Rather we have geographical information, while we do not know who one's
relatives are.10 We cannot explore the importance of religious affiliation, because we do
not have enough non-Muslims in our sample. The gender division does not apply to the
problem we consider, since the decision which source of water to use is only observable
to us at the level of a household.
The pre-conditions for neighbors' actions to influence one's behavior do exist in our
study area and in all of rural Bangladesh. Neighbors interact daily. The study area, and
most of Bangladesh, are very densely populated. People in rural areas live mainly in onestory houses or huts and much of life occurs outside in the courtyard. Within-village
transport is mainly on foot or by rickshaw, therefore people meet each other and often
talk as they pass by. Except in the wealthiest households, wells are located outside. Who
uses which well is easily visible. Wells also tend to be social centers.
3. Data
Our data comes from a survey of a random sample of 1089 couples, of whom we
interviewed both the husband and the wife, plus 502 individuals who had spouses but in
whose case we could not interview the spouse. We conducted the survey six to twelve
months after the information campaign, in order to allow time for people's behavior to
change. For every respondent, we know which well they were using before the
information campaign from a baseline survey conducted by the public health team. We
know the level of arsenic in these wells from the geo-chemistry database. In our survey,
we asked which wells people are using currently and why. We collected information
about various characteristics of the well including its GPS coordinates.
9
The geochemistry team is developing a technology which can determine whether the water will be safe
before the well is drilled.
10
Identifying the relatives and their effect is an important topic for future research.
7
We also asked a number of questions to determine what each respondent knows about
the arsenic problem. Everyone in our sample had heard of arsenic by the time of the
survey.11 90% of the respondents could correctly say whether their well is safe or not.
However, there was considerable variation in responses to other questions, such as
whether arsenic can be removed from the water by boiling it or whether arsenic-induced
diseases are contagious. Thus, essentially all respondents know whether they are
consuming unsafe levels of arsenic or not, but they differ in the extent of the information
they have which may help them assess the benefit of avoiding the risk..
We collected detailed information about socio-economic characteristics of each
respondent as well as GPS coordinates of the house. Table 1 presents descriptive statistics
of selected variables. We determined each respondent's neighbors by identifying those
respondents whose house is within a certain radius of hers/his, 25, 50, 75, 100 or 200
meters. If we interviewed both members of the couple, the spouse is not considered to be
a neighbor, but both members of a couple living in a neighboring house are considered to
be neighbors.
An important consideration in the analysis is whether arsenic is an exogenous
variable. Table 2 presents differences in the means of various characteristics of
respondents who have safe wells and those whose wells are unsafe. Table 3 reports a
regression of the dummy variable whether the well is safe or not on these characteristics.
The results suggest that the distribution of arsenic is not correlated with people's
observable characteristics. Whether or not the well is safe is not correlated with levels of
education, income or assets within villages, i.e. when we include village fixed effects.
Arsenic is negatively correlated with income across villages. This correlation would not
affect our within village results. Within villages whether or not the well is safe is only
correlated with respondent's age, weakly. One possible reason for this is if younger
couples tend to drill deeper wells, since arsenic concentrations tend to decline with depth.
Further evidence that arsenic is not correlated with characteristics of respondents
or of the neighborhoods they live in is that the chemical process which determines
whether the naturally-occurring arsenic dissolves from the soil into water does not
suggest a reason for such a correlation to the best of our knowledge. The arsenic is not a
pollutant released by human activity. The one known relationship may be based on the
fact that the amount of arsenic released is related to the presence of oxygen. More
permeable soils are somewhat associated with lower levels of arsenic.12 Soil permeability
is not generally desirable for cultivation of rice, which is the main crop in our study area.
If places with high arsenic were worse for the cultivation of rice, one might expect to see
a relationship between arsenic and occupation and perhaps arsenic and income, at least
for land-owning households. Neither of these relationships exists in the data. One
possible reason is that houses are sufficiently far from fields that there is little
relationship between the soil in one's field and the soil around one's well. However,
whether the relationship between type of soil and arsenic concentration is a close one is
11
12
Only 50% had heard of it when the project began.
Personal communication with Alexander van Geen.
8
also questionable since there is considerable variation in arsenic levels between wells
which are located less than 100 feet from each other. . Finally, arsenic levels do not seem
to be affected by human activity.13
4. Standard instrumental variable empirical strategy
4.1 Empirical model
The model is a version of that presented by Moffitt (2001). Suppose that there are g =
1,…,G groups of neighbors, and there are i = 1,…,n individuals in each group. Assuming
the relationship has a linear form, we can represent each individual’s decision in the
following way.
yig = β 0 + β1 xig + β 2 y( − i ) g + β3 x( −i ) g + ηig
(1)
The dependent variable in this equation is individual i’s in group g’s decision whether or
not to switch to another source of water, xig is a vector of that individual’s own
characteristics, y(-i)g is the average over all individuals in group g who are not i of their
binary decisions whether or not to switch and x(-i)g is a vector of averages over all
individuals in group g who are not i of their characteristics. The main hypothesis we want
to test is that β 2 >0, i.e. that the larger is the fraction of the respondent's neighbors who
switch the more likely is the respondent to switch.
We assume that η ig is orthogonal to xig, however we cannot in general assume that it
is orthogonal to x(-i)g or y(-i)g. Correlation between the error term and group averages of
behavior and/or characteristics may arise from one or both of two different sources. One
source is simultaneity. The behavior of each member of the group is affected by the
behavior of every other member, and in turn affects the behavior of every other member.
In the above model, if we assume that each group consists of only two members, we can
write the model as
y1g = α 0 + α1 x1g + α 2 y2 g + α 3 x2 g + ε 1g
(2)
y2 g = α 0 + α1 x2 g + α 2 y1g + α 3 x1g + ε 2 g
(3)
In these equations, the number subscripts on the variables denote respectively individuals
one and two. As above, we assume that ε 1g is orthogonal to x1g and ε 2 g is orthogonal to
x2g, but, in general, each error may be correlated with the other individual’s
characteristics and the errors may be correlated with each other.
The second reason why the error in (1) may not be orthogonal to the neighbor
averages is the problem of correlated unobservables. Neighbors may have similar
preferences or other unobservable characteristics, which may influence their probability
of switching and may be correlated with their observable characteristics. The
unobservable characteristics may be correlated across neighbors because of endogenous
location decisions; people may locate next to others who are like them. However, such
13
In Araihazar, older wells tend to have higher levels of arsenic, suggesting that volume of water drawn
may affect arsenic levels. However, scientists have not found more direct evidence of this mechanism.
9
correlation may arise even without endogenous location decisions. It may be induced by
neighborhood characteristics. For example, people who live farther away from the road
may have poorer access to market therefore they may eat a worse diet and therefore they
may be more vulnerable to arsenic-related diseases. If they know this, they will all be
more likely to switch.
We address both types of problems with instrumental variables (IVs) based on the
distribution of arsenic. The variables are the fraction of neighbors who have unsafe wells
and the average of arsenic concentrations in neighbors’ wells. We treat all neighbors'
characteristics other than the arsenic concentrations in neighbors’ wells as
unobservable.14 Therefore, the equation we estimate is
(4)
yig = β 0 + β1 xig + β 2 y( − i ) g + η ig
and the first stage for the two-stage least squares (TSLS) estimation is
y( −i ) g = γ 0 + γ 1 xig + γ 3 z( −i ) g +ν ig
(5)
where z( −i ) g is the vector of IVs described above. The identifying assumptions are that
z( −i ) g and xig are orthogonal to both ηig and ν ig . The vector xig includes whether or not
the respondent’s own well is safe, the level of arsenic in the respondent’s own well as
well as her/his socioeconomic characteristics such as age, education, income and assets.
In the model in equations 2 and 3, we are assuming that only own characteristics help
to explain own behavior, therefore the neighbor's arsenic concentration serves as the
excluded variable needed to identify the effect of the neighbor's behavior.
We presented evidence that the distribution of arsenic is most likely uncorrelated with
individual characteristics in section 3. However, the arsenic in one’s neighbors' wells is a
proxy for the availability of safe wells nearby, therefore it is not likely to be excludable
from equation 4. The IV estimates can nevertheless be useful in identifying the effect of
neighbors' behavior. The direct effect of neighbors' arsenic on own behavior should be
negative, i.e. the more of the neighbors’ wells are unsafe the fewer safe wells are
available in the vicinity and the less likely is the individual to switch to another well. At
the same time, the effect of neighbors' arsenic on neighbors' behavior is positive, the
fewer of the neighbors’ wells are safe the more neighbors switch. Therefore, to the extent
that the coefficient on neighbors’ behavior captures the effect of neighbors’ arsenic on
own behavior, this effect will reduce the coefficient, leading to an underestimate of
neighbors’ influence on own behavior. Therefore, a positive and significant coefficient
should be strong evidence that neighbors’ actions influence one’s decision. It should
provide a lower bound on the size of the neighbors' influence, because of the negative,
direct effect of neighbors' arsenic.
The dependent variable in the second stage is binary, therefore by applying TSLS we
are estimating a linear probability model. We include village fixed effects. In most
household we have two respondents, the husband and the wife, who have made a joint
14
The coefficients on neighbors' average characteristics such as education and income would be
unidentified and they would bias the coefficient on neighbors' behavior.
10
decision. Therefore, we apply sampling weights in order that information from each
household receive the same weight in the regression and we allow errors to be clustered
by household.
4.2 Results
We estimate the effect of neighbors' behavior for several groups, neighbors who live
within 25 meters of each other, 50, 75, 100 and 200 meters.15 We present the results in
Table 4. The OLS estimates of the effect of neighbors' behavior with village fixed effects
are in the first column. These are positive and significant for all radii except 200 meters.
The IV estimates with village fixed effects, in the second column, are negative and
significant for 25 meters and negative but not significant for all other radii. The results
suggest that the neighbors' effect in OLS results at least partly reflects the availability of
safe wells nearby. The IV estimates are negative and significant for all radii when we do
not include village fixed effects, as shown in the fourth column. This could imply that
neighbors influence each other only within villages, not across village boundaries, and
the positive effect of this influence reduces the negative effect of neighbors' arsenic
operating through neighbors' behavior in the fixed effect estimates. However, we will
find results in section 5.2 below which contradict this interpretation of the difference
between the fixed-effect and no-fixed-effect estimates.
We report the direct effect of neighbors' arsenic on whether or not people change to
another well in Table 5. The coefficient on the direct effect may also be picking up the
effect of interactions between neighbors. With village fixed effects, the direct effect is
negative, but it is not significant by itself, possibly suggesting that a positive effect of
interactions is raising the coefficient. However, the direct effect itself may simply not be
significant. The effect is negative and significant by itself without village fixed effects.16
If neighbors' arsenic does have a significant, direct effect on people's decision
whether or not to switch, the IV estimates with village fixed effects suggest the presence
of a positive influence of neighbors’ behavior, since without such an influence we would
expect the estimates to be negative and significant. It is somewhat puzzling that such
influence seems to be weakest for the closest neighbors, those within 25 meters.
15
We report results of regressions in which all respondents for whom we have valid information are the
sample, and the neighbor averages include all neighbors who have valid information. One may wonder
whether the behavior of people whose wells are safe is influenced by their neighbors and if so whether the
effect is different than it is for people whose wells are unsafe. Regressions in which the sample consists of
only people whose wells are unsafe, while all neighbors are included in neighbor averages, look very
similar to those reported. However, the reader may wonder whether people whose wells are unsafe are
influenced by the behavior of all neighbors or only by the behavior of those whose wells are also unsafe. If
we include in the neighbor average only those neighbors whose wells are unsafe, the neighbor effect is less
negative, sometimes positive, but not significant. However, these regressions are problematic, because we
can only use average neighbor arsenic, not percent of neighbors with safe wells, as IV. If we omit percent
of neighbors with safe wells in the regressions in which averages include all neighbors, results are
considerably different. The neighbor effect becomes generally positive and significant for some radii.
Therefore the omission of this IV is not a trivial change.
16
In a preliminary analysis, the results of an overidentification test based on the test in Sargan (1958) are
not significant, i.e. we cannot reject the null hypothesis that the IVs satisfy the exclusion restriction.
11
However, this may be an artifact of the small number of neighbors within the 25 meter
radius. The median number is 2, and it is 3 or less for 75% of the sample.
The indirect suggestion of the presence of neighbors’ influence is subject to two
caveats. First, as noted above, it is not robust to the inclusion of village fixed effects.
Second, any direct effect of neighbors' arsenic could be the result of a misspecified
regression rather than a true direct effect, if the functional form of the effect of neighbors'
arsenic on neighbors' behavior is not as we specified. The direct effect due to a
misspecified regression may not have the sign we expect, possibly nullifying the above
argument. Furthermore, a misspecification of the effect of neighbors' arsenic in the first
stage may affect how well the IVs predict neighbors' behavior.
We have reason to suspect that we have misspecified the functional form of the effect
of neighbors' arsenic on neighbors' behavior. Both variables, the fraction of neighbors
whose wells are unsafe and the average arsenic in neighbors’ wells combine two effects.
The first is a positive effect of each neighbor’s own arsenic on her/his behavior, i.e. if
her/his well is unsafe she/he is more likely to switch. The second is the negative effect of
the arsenic in the wells of that neighbor’s neighbors, who are included in the average.
The more of their wells are unsafe, the less likely is the neighbor to switch since the
fewer safe wells are available in the vicinity. The second source of misspecification is the
fact that we combine own arsenic and neighbors' arsenic linearly in the average, while
their effect is highly non-linear. Whether or not one’s own well is safe has a much
stronger effect on one’s own behavior than does the fraction of safe wells among
neighboring wells.17 These problems motivate the alternative approach which we
describe in the next section.
5. Alternative empirical strategy
5.1 Alternative empirical model
The identification strategy in this model is the same as above, and the model still
suffers from the problem that the instrumental variables may not satisfy the exclusion
restriction. However, here we use information about each person’s own well, whether or
not it is safe and the level of arsenic in it, to predict that person’s behavior. The first stage
is a regression of the binary variable whether the respondent changed or not only on
information about her/his well. We obtain the predicted probability of switching for each
17
The following is one piece of indirect evidence that this problem affects the results, in addition to the
evidence contained in the results of the alternative approach. The one specification in the standard IV
method which yields a positive and significant neighbor effect is one in which we estimate the influence of
the behavior of neighbors whose wells are unsafe on the behavior of individuals whose wells are unsafe,
using the fraction of all neighbors whose wells are safe and the average arsenic in wells of all neighbors as
IVs. Note that in this specification, we predict the behavior of those neighbors whose wells are unsafe with
the fraction of unsafe wells in the radius. This variable reflects only the effect of available safe wells, not
the effect of neighbors' own wells since all of those are unsafe. Therefore the mentioned misspecification
does not arise. It does arise for the average arsenic variable, but in all of our analyses the level of arsenic
has a weaker effect on the results than does the binary, safe/unsafe distinction.
12
person. We then form averages of the predicted probabilities for the neighbors in each
radius. We use these predicted probabilities in the second stage regression, which
includes whether or not the person’s well is unsafe and the level of arsenic in that well as
the only other right-hand-side variables.
Since we use information about each individual’s own well to predict her/his
behavior, we no longer have the misspecification problems discussed at the end of the
last section. However, the approach may suffer from other problems. First, we can no
longer control for individual household characteristics, such as age, income, etc. This
should not affect results significantly. If we leave these characteristics out of the IV
estimator obtained in section 4, the estimates change very little. The only individual
characteristic which significantly affects the decision whether or not to change is the
respondent’s relation to the owner of the well. Treating this information as unobservable
does not seem to matter much.
A potentially more significant problem is that we are not controlling in the first stage
for the safety of the well and the level of arsenic included in the second stage. In other
words, the safety of the well and level of arsenic which determine the predicted
probabilities and therefore are in the average are those of one’s neighbors. In the second
stage, we control for arsenic in the respondent’s own well and this information did not
enter the average of predicted probabilities of that person’s neighbors. Including variables
in the second stage but not in the first stage in general yields an inconsistent estimator. In
order to mitigate this problem, and also to allow for the fact that people's behavior may
depend on neighbors' wells as well as on one's own well, we also estimate a first stage
which includes fraction of unsafe wells among neighbors and neighbors' average arsenic.
These averages should partly proxy for the individual well information left out of the first
stage.
As in section 4, we are using a linear probability model. The predicted probabilities
remain surprisingly close to the 0 to 1 limits. The largest is 1.01 and the smallest is -.04.
We report bootstrapped standard errors. As in section 4, we include village fixed effects
and respondents' weights.
5.2 Results
The specification yields a positive and significant influence of neighbors’ actions on
one’s own behavior for 50, 75 and 200 meter radii. The effect is not significant for the 25
and the 100 meter radii. The effect is even stronger and more significant without village
fixed effects, which contradicts the explanation we gave for the weaker evidence for
neighbors' influence without fixed effects in section 4.2, i.e. that neighbors influence each
other within villages but not across village boundaries. The coefficients change very little
when we include neighbors' arsenic in the first stage, regardless whether the neighbors
included in the first stage are those who live within 50, 75, 100 or 200 meters.18 Levels of
significance do not change.
18
We did not estimate a version with only those neighbors who live within 25 meters.
13
An increase of one standard deviation in the fraction of neighbors living within 50
meters who switch results in an increase of approximately .12 in the probability of
changing to another well. The size of the effect is .09 when we consider the behavior of
neighbors who live within 75 meters. The estimate of the neighbors' effect here should be
a lower bound, since the direct effect of neighbors' arsenic discussed above tends to
reduce the coefficient on neighbors' behavior. The coefficients in a linear probability
model are not good estimates of the size of the effect, except in the case in which the
model consists entirely of dummy variables. The estimates given here are based on such a
version, in which we controlled for the effect of arsenic with dummy variables for
various levels of arsenic. The endogenous neighbor behavior causes a problem since the
predicted fraction of neighbors who changed in the second stage is not a dummy variable.
However, predicted probabilities are between zero and one for 98% of the sample in this
model. In the next version, we plan to estimate a probit model.
The evidence of a significant and positive influence exerted by neighbors' actions in
this alternative approach is suggestive. However, the problems with this approach
together with fact that the effect does not appear in the standard IV approach demand
caution in drawing conclusions. We need to investigate further the properties of the
alternative estimator.
6. Effect of neighbors’ information
We noted that one reason why neighbors may influence each other is that they obtain
information from each other. We check directly for evidence of this mechanism. We
consider answers to the question whether arsenic can be removed from water by boiling
it. The correct answer is "no" and incorrect information can lead to ill health. We focus
on this question, because it is the only piece of information we asked about which
satisfies two criteria. People have more incentive to find out the correct answer if their
well is unsafe and fraction of unsafe wells does indeed significantly predict the fraction
of neighbors who answer correctly. Therefore we use fraction of neighbors whose wells
are unsafe and average arsenic in neighbors' wells as IVs.
The more neighbors the respondent has who answered correctly, the more likely is the
respondent to have this piece of information. The effect is significant for neighbors in a
50 meter and 100 meter radius. It is weaker and significant at 11% for the 200 meter
radius. It is not significant for the 25 meter radius and we could not yet check the 75
meter radius.
7. Conclusions and future work
Our analysis thus far provides some suggestive evidence that people who have more
neighbors who change to another well are more likely to change themselves. The
evidence is not conclusive because two different approaches yield contradictory results.
14
We need to analyze the properties of the second approach to reconcile the two sets of
results.
If neighbors do influence each others' actions, we have not yet determined the
mechanism through which this effect occurs. We provide some evidence that one of the
mechanisms is the diffusion of information.
We plan to examine the evidence for effects of social interaction on the decision to
switch which are different from those we have considered. If neighbors' arsenic does
indeed have a direct effect on people's decision whether or not to switch to another well,
by proxying for the availability of safe wells nearby, then the functional form of this
effect may be informative about the presence of such effects. As we noted in the
introduction, there may be a social stigma associated with using the wells of neighbors
who are not relatives and/or owners of such wells may not treat those coming to use them
well. If so, one may not be able to switch unless there is a safe well nearby which is
owned by a person with whom one has a sufficiently good relationship. One way to test
this idea is to examine whether the number of safe wells nearby have a non-linear effect
on the probability of switching, e.g. whether the effect of more than one well is much
smaller than the effect of one well, controlling for distance to the wells.
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17
Table 1: Descriptive statistics
Variable
Whether or not changed to
another well (= 1 if yes)
Unsafe
Arsenic
Fraction of neighbors who
changed, 25 meters
Fraction of neighbors who
changed, 50 meters
Fraction of neighbors who
changed, 75 meters
Fraction of neighbors who
changed, 100 meters
Fraction of neighbors who
changed, 200 meters
Fraction of unsafe wells,
25 meters
Fraction of unsafe wells,
50 meters
Fraction of unsafe wells,
75 meters
Fraction of unsafe wells,
100 meters
Fraction of unsafe wells,
200 meters
Average arsenic, 25 meters
Average arsenic, 50 meters
Average arsenic, 75 meters
Average arsenic, 100
meters
Average arsenic, 200
meters
Mean
.401
SD
-
# of observations
2675
.568
107.73
.414
118.68
.460
2680
2680
913
.401
.405
1726
.393
.363
2072
.393
.326
2181
.403
.242
2287
.416
.465
917
.432
.438
1732
.440
.410
2078
.429
.384
2187
.432
.337
2292
114.75
110.52
106.60
108.17
121.43
108.05
100.42
96.81
917
1732
2078
2187
107.42
84.68
2292
18
Table 2: Means of characteristics of households with safe and unsafe wells
19
Table 3: Determinants of whether a well is safe or not
Dependent variable: Is well safe or not?
(= 1 if safe)
Primary education
Secondary education
Higher education
2nd quartile of income
3rd quartile of income
4th quartile of income
2nd quartile of assets
3rd quartile of assets
4th quartile of assets
Own base well
Base well in bari
Distance to base well (minutes)
# of relatives
Frequency of religious services
# of days sick
Household size
Age
N
Pseudo R2
Probit, FE
.038
(.034)
.057
(.040)
-.002
(.078)
.022
(.040)
.047
(.045)
-.002
(.049)
.029
(.039)
.030
(.043)
.055
(.047)
.056
(.051)
-.036
(.045)
-.0004
(.030)
-.001
(.001)
-.006
(.011)
.000
(.0003)
.004
(.008)
-.003*
(.002)
1890
.22
Probit, no FE
-.046
(.030)
-.041
(.033)
-.097
(.072)
.076**
(.036)
.098**
(.039)
.134**
(.043)
.027
(.036)
.060
(.038)
.076*
(.042)
.052
(.043)
-.013
(.039)
.022
(.027)
.0004
(.0007)
.004
(.010)
.0002
(.0004)
-.005
(.007)
-.003*
(.001)
2097
.01
Table reports marginal effects.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level.
* denotes significance at 0.1 level.
20
Table 4: Standard approach IV estimates of the effect of neighbors' actions
Fraction of neighbors who
changed, 25 meters
Unsafe (=1 if well is unsafe)
Arsenic
N
R2
Fraction of neighbors who
changed, 50 meters
Unsafe (=1 if well is unsafe)
Arsenic
N
R2
Fraction of neighbors who
changed, 75 meters
Unsafe (=1 if well is unsafe)
Arsenic
N
R2
Fraction of neighbors who
changed, 100 meters
Unsafe (=1 if well is unsafe)
Arsenic
N
R2
Fraction of neighbors who
changed, 200 meters
Unsafe (=1 if well is unsafe)
Arsenic
N
R2
Dependent variable: Changed to a different well (= 1 if yes)
OLS, FE
2SLS, FE
OLS, no FE 2SLS, no FE
.101**
-.252*
.233**
-.234*
(.050)
(.142)
(.044)
(.124)
.345**
.336**
.330**
.364**
(.057)
(.060)
(.052)
(.059)
.0007**
.0008**
.0005**
.0009**
(.0002)
(.0003)
(.0002)
(.0003)
870
870
870
870
.39
.32
.30
.12
.150**
-.074
.233**
-.182**
(.039)
(.086)
(.036)
(.083)
.399**
.401**
.338**
.357**
(.040)
(.041)
(.038)
(.041)
.0006**
.0007**
.0004**
.0008**
(.0002)
(.0002)
(.0002)
(.0002)
1634
1634
1634
1634
.36
.33
.29
.18
.141**
-.087
.240**
-.205**
(.040)
(.098)
(.036)
(.090)
.386**
.389**
.330**
.357**
(.037)
(.037)
(.034)
(.037)
.0005**
.0006**
.0004**
.0007**
(.0002)
(.0002)
(.0001)
(.0002)
1964
1964
1964
1964
.33
.30
.26
.16
.098**
-.153
.232**
-.257**
(.046)
(.114)
(.040)
(.099)
.372**
.378**
.326**
.358**
(.036)
(.037)
(.034)
(.036)
.0006**
.0007**
.0004**
.0007**
(.0002)
(.0002)
(.0001)
(.0002)
2066
2066
2066
2066
.41
.30
.25
.15
.047
-.198
.288**
-.378**
(.070)
(.158)
(.052)
(.119)
.388**
.391**
.335**
.378**
(.035)
(.035)
(.033)
(.035)
.0006**
.0007**
.0004**
.0008**
(.0001)
(.0002)
(.0001)
(.0002)
2166
2166
2166
2166
.31
.31
.25
.16
Table reports five separate sets of regressions, one for fraction of neighbors who changed in each of five
different size neighborhoods. Double lines separate the results for each neighborhood.
Other controls in each regression are household size, age, education, income and assets.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level.
* denotes significance at 0.1 level.
21
Table 5: Analyzing the direct effect of arsenic in neighbors' wells
Fraction of unsafe wells,
25 meters
Average arsenic, 25 meters
N
R2
Fraction of unsafe wells,
50 meters
Average arsenic, 50 meters
N
R2
Fraction of unsafe wells,
75 meters
Average arsenic, 75 meters
N
R2
Fraction of unsafe wells,
100 meters
Average arsenic, 100
meters
N
R2
Fraction of unsafe wells,
200 meters
Average arsenic, 200
meters
N
R2
Dependent variable: Changed to a different well (= 1 if yes)
OLS, FE
OLS, no FE
-.047
-.057
(.060)
(.058)
-.0003
-.0003
(.0003)
(.0003)
872
872
.39
.26
-.051
-.106**
(.052)
(.049)
.00008
.00009
(.0002)
(.0002)
1636
1636
.35
.26
-.051
-.105**
(.052)
(.048)
.00005
.00007
(.0002)
(.0002)
1966
1966
.32
.24
-.075
-.123**
(.059)
(.052)
.00002
.00005
(.0002)
(.0002)
2068
2068
.31
.23
-.083
-.120*
(.096)
(.070)
-.00005
-.0001
(.0004)
(.0003)
2166
2166
.31
.24
Table reports five separate sets of regressions, one for fraction of unsafe wells and average arsenic in each
of five different size neighborhoods. Double lines separate the results for each neighborhood.
Other controls in each regression are unsafe, arsenic, household size, age, education, income and assets.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
22
Table 6: Alternative approach IV estimates of the effect of neighbors’ actions
Fraction of neighbors
who changed, 25 meters
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Fraction of neighbors
who changed, 50 meters
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Fraction of neighbors
who changed, 75 meters
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Fraction of neighbors
who changed, 100 meters
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Fraction of neighbors
who changed, 200 meters
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Dependent variable:
Changed to a different well (= 1 if yes)
Alternative IV, FE
Alternative IV, No FE
.150
.825**
(.315)
(.103)
.488**
.444**
(.076)
(.061)
.0005**
.0003*
(.0002)
(.0002)
483
483
.29
.16
.540**
.917**
(.252)
(.075)
.415**
.435**
(.040)
(.039)
.0005**
.0004**
(.0002)
(.0001)
1021
1021
.23
.22
.510**
.978**
(.180)
(.062)
.388**
.402**
(.031)
(.031)
.0004**
.0003**
(.0001)
(.0001)
1363
1363
.26
.24
.127
.845**
(.154)
(.065)
.341**
.359**
(.031)
(.029)
.0006**
.0004**
(.0001)
(.0001)
1623
1623
.24
.23
.246*
.862**
(.153)
(.062)
.396**
.400*
(.028)
(.026)
.0005**
.0004**
(.0001)
(.0001)
2003
2003
.27
.25
Table reports five separate sets of regressions, one for fraction of neighbors who changed in each of five
different size neighborhoods. Double lines separate the results for each neighborhood.
There are no other controls in these regressions.
Bootstrapped standard errors are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
23
Table 7: Effect of neighbors’ information on own information
Fraction of neighbors who
think yes, 25 meters
N
R2
Fraction of neighbors who
think yes, 50 meters
N
R2
Fraction of neighbors who
think yes, 100 meters
N
R2
Fraction of neighbors who
think yes, 200 meters
N
R2
Dependent variable: Does boiling remove arsenic from water
(= 1 if think yes)
OLS, FE
IV, FE
-.043
-.133
(.041)
(.212)
868
864
.15
.15
-.016
.605*
(.034)
(.346)
1631
1625
.08
-.010
.822**
(.043)
(.354)
2052
2048
.08
.050
.471*
(.065)
(.291)
2148
2148
.08
.06
Table reports five separate sets of regressions, one for neighbors’ information in each of five different size
neighborhoods. Double lines separate the results for each neighborhood.
Other controls in each regression are unsafe, arsenic, household size, age, education, income and assets.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
24
Table 8: First stage regressions for standard IV estimation of effect of neighbors’ actions
Fraction of unsafe wells,
25 meters
Average arsenic,
25 meters
N
R2
Fraction of unsafe wells,
200 meters
Average arsenic,
50 meters
N
R2
Fraction of unsafe wells,
75 meters
Average arsenic,
75 meters
N
R2
Fraction of unsafe wells,
100 meters
Average arsenic,
100 meters
N
R2
Fraction of unsafe wells,
200 meters
Average arsenic,
200 meters
N
R2
Dependent variable: Fraction of neighbors who
changed in the given radius
OLS, FE
OLS, No FE
.305**
.308**
(.059)
(.059)
.0006**
.0008**
(.0003)
(.0003)
870
870
.38
.21
.383**
.333**
(.045)
(.044)
.0007**
.0008**
(.0002)
(.0002)
1634
1634
.40
.27
.339**
.290**
(.045)
(.041)
.0007**
.0008**
(.0002)
(.0002)
1964
1964
.38
.24
.302**
.243**
(.045)
(.040)
.0008**
.0008**
(.0002)
(.0002)
2066
2066
.41
.24
.283**
.234**
(.050)
(.041)
.0009**
.0007**
(.0002)
(.0001)
2166
2166
.59
.31
Table reports five separate sets of regressions, one for fraction of neighbors who changed in each of five
different size neighborhoods. Double lines separate the results for each neighborhood.
Other controls in each regression are unsafe, arsenic, household size, age, education, income and assets.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
25
Table 9: First stage regressions for alternative IV estimation of effect of neighbors’ actions
Unsafe (=1 if well is
unsafe)
Arsenic
N
R2
Dependent variable: Changed to a different well
(= 1 if yes)
OLS, FE
OLS, No FE
.395**
.361**
(.035)
(.033)
.0006**
.0006**
(.0001)
(.0001)
2293
2293
.30
.22
There are no other controls in these regressions.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
26
Table 10: First stage regressions for IV estimation of effect of neighbors’ information
Fraction of unsafe wells,
25 meters
Average arsenic,
25 meters
N
R2
Fraction of unsafe wells,
50 meters
Average arsenic,
50 meters
N
R2
Fraction of unsafe wells,
100 meters
Average arsenic,
100 meters
N
R2
Fraction of unsafe wells,
200 meters
Average arsenic,
200 meters
N
R2
Dependent variable: Fraction of neighbors in the
given radius who think boiling can remove arsenic
OLS, FE
-.149**
(.052)
-.0003
(.0003)
869
.23
-.114**
(.044)
-.00003
(.0002)
1634
.19
-.101**
(.038)
-.0002
(.0002)
2060
.27
-.201**
(.044)
.0002
(.0002)
2160
.42
Table reports five separate sets of regressions, one for neighbors’ information in each of five different size
neighborhoods. Double lines separate the results for each neighborhood.
Other controls in each regression are unsafe, arsenic, household size, age, education, income and assets.
Robust standard errors, clustered by household, are in parentheses.
** denotes significance at 0.05 level. * denotes significance at 0.1 level.
27