Identifying Network Effects in the Adoption of Sanitary Latrines

Transcription

1 Identifying Network Effects in the Adoption of Sanitary Latrines FARAH MALLAH Mentor: PROFESSOR DANIEL WESTBROOK Household decisions are highly influenced by social interactions. In this paper I identify the effect of social interactions (networks) on the likelihood of a rural household in Vietnam installing a sanitary latrine between 2012 and I do so using the baseline and midterm surveys collected to evaluate the CHOBA intervention. 2,139 households were surveyed in the provinces of Hai Duong and Tien Giang. The survey was not specifically designed to capture network effects. Therefore, I use indirect measures based on the information provided and previous literature. The reference group in this paper is uniquely defined by proximity and economic stratum. Higher weighting is given to closer households to the household in question, or near neighbors, and only those within the same economic stratum are included. I capture network effects by examining the correlation between the prevalence of near households who adopted the sanitary latrine before the baseline and the likelihood of the household in question adopting the sanitary latrine after the baseline. I find that a higher prevalence of sanitary latrines among near neighbors has a positive and significant effect on the likelihood of installing the sanitary latrine in the second phase. This network effect is stronger among households with lower access to formal sources of information and among poor households. In addition, I look at the frequency of circulation of information through word of mouth and the influence of receiving the information on the benefits of sanitary latrines from family and/or acquaintance. Both of these network-effect measures come out to be insignificant among poor households. The results presented in this paper point to the necessity of controlling for network effects especially among households highly dependent on informal services. The results also encourage the use of public events to recognize households installations of the sanitary latrine. 1

2 I. Introduction Household decisions are highly influenced by social interactions. Our understanding of humans as social beings makes it difficult to predict a household s decision in isolation of others decisions. To draw a complete model of economic behavior, we need to understand the influence of social interactions on the final decision made by the household in question. I have two main aims in this paper. One, to explore the theory of network effects in the specific context of rural Vietnam and the Community Hygiene Output-Based Aid (CHOBA) intervention; two, to evaluate the CHOBA intervention while controlling for network effects (to avoid potential omitted variable bias) as well as to inform the design of future interventions. If the network effect is strong, future interventions can be designed to exploit that strength. 1 I achieve both of these aims by using a rich dataset from the baseline and midterm CHOBA surveys conducted in 2012 and The baseline survey was administered to a sample of 2,139 households in 131 communes in Hai Duong and Tien Giang provinces. My first aim was motivated by the weak predictive power of past empirical results on network effects in the context of rural Vietnam. However, that weakness could be due to the ambiguity of the net effect of two theoretical predictions. High dependence on informal institutions in rural Vietnam would strengthen the network effect, while high access to formal sources of information would weaken the network effect. Manksi (1993) emphasizes the link between the propensity of a household adopting a certain behavior and the prevalence of this behavior in the reference group containing the household. Empirical results in the literature support this theory. The findings suggest a strong and statistically significant relationship between program participation and network effects, increasing program participation by a range of approximately 15% to 50% (Bobonis and Finan 2009; Oster and Thornton 2012; and Dahl; Loken and Mogstad 2014). Moreover, according to Munshi (2014), households in areas with no well-established institutions highly depend on social networks. This theory leads us to predict strong network effects among rural households in Vietnam, where there is relatively high dependence on informal arrangements. In the CHOBA context, 40% of the households surveyed noted borrowing the money to install a larine from relatives/friends/neighbors. 1 The CHOBA intervention is discussed in details in section II of the paper. 2

3 That said, according to Dahl, Loken and Mogstad (2013), the network effect is prominent if product information from formal institutions is scarce. Furthermore, Dahl, Loken and Mogstad (2014), and Oster and Thornton (2012) find the strongest channel of network effects is through the spread of information on benefits. With regard to the CHOBA intervention, households had high levels of access to formal sources of information on benefits, funding, and material sources associated with installing a sanitary latrine because of the Women s Union (WU) and other organizations. 2 This leads us to expect a weak network effect among rural households in Vietnam in the adoption of Sanitary Latrines. My second aim is motivated by the important issue of open defecation by members of rural households in Vietnam. Only 55% of rural households in Vietnam have access to a sanitary latrine (Water and Sanitation Sector Assessment Report Vietnam, 2011). Open defecation and use of unsanitary latrines are associated with serious public health issues. For example, exposure to fecal matter is associated with a number of diseases, including diarrhea. Briceno et al. (2015) report that diarrhea is the second largest killer of children under five in the world. To overcome the public health issues created by lack of sanitation, it is essential that all households make use of sanitary facilities. Since poor rural areas lack sewage disposal systems, it is necessary for each household to install a facility such as a flush toilet with septic tank. Septic tanks prevent fecal waste from entering the environment and their composting function ensures that pathogens are eliminated. As part of the movement towards preventative measures to counter diseases, greater emphasis was put on improving access to sanitary toilets and knowledge on sanitary practices in rural, low-income areas. One such initiative is the CHOBA initiative led by East Meets West Foundation and the local Women s Unions (WU) which I further discuss in Section II. I measure network effects in this paper through two routes that build on the defining features of a network presented by Jackson (2014). The first feature is defined by the prevalence of the behavior of interest in the reference group. In this case it is the proportion of households in the reference group of the household in question who have installed a sanitary latrine before the baseline survey (in 2012). The second feature would be defined by the frequency with which a target household received relevant information from the reference group. Unfortunately, I was not able to calculate the frequency with which a target household received information from someone in the reference group because the survey was not 2 Refer to figure 2 in Section V. 3

4 specifically designed to capture network effects. Instead, I measured it indirectly by looking at the proportion of individuals in the reference group who received information on the benefits of sanitary latrines from family and/or acquaintance (presumably, these are members of their reference groups). I find a statistically significant positive effect for higher initial proportion of sanitary latrines among reference households. However, I find no clear indication that a higher proportion of reference households hearing of the benefits of sanitary latrines through word of mouth exerts any effect on the households in question. In this paper I define each household s reference group as the set of near neighbors in the same economic stratum (poor or non-poor). Nearer neighbors are more likely to be influential, so the closer a neighbor is to the household in question, the more heavily that household is weighted when calculating summary statistics for the reference groups. This is based on the idea that proximity increases the likelihood of interaction among households and as a consequence exerts a stronger influence on the household s decision. Refining the reference goups by economic stratum is based on theories of relative deprivation (Stark and Bloom 1985) and group formation based on similar characteristics (in this case by income) (Akerlof 1997; Schaffner and Torgler 2008). Other routes of network effects that I touch upon in this paper include the impact of receiving information from friends and/or acquaintances, spill-over effects from treatment households to control households in the reference group, and the influence of lower access to formal information sources on the network effect. The results indicate that less access to formal sources of information may increase the strength of the network effect. This paper is divided into seven sections. In Section II I define the context within which this paper measures network effects: the CHOBA intervention. I then present how network effects would ideally be captured as well as how they are captured specifically in this paper in Section III, followed by the empirical specification in Section IV. The summary statistics in Section V are followed by the results in Section VI and the conclusion in Section VII. 4

5 II. The CHOBA Intervention and Network Effect 3 The CHOBA intervention provides incentives to encourage mass installation of sanitary latrines in rural communes in Vietnam. It has two components: distribution of information and output-based aid. The distribution of information was led by the local committees of the Vietnam Women s Union (WU). The WU promoted hygiene awareness and awareness of the CHOBA intervention. They also helped households get access to funding, materials, and specialized labor for installing sanitary latrines. The output-based aid approach incentivizes the installation of sanitary latrines through conditional cash transfers (CCT) to the WU and rebates to the households. The CCT, about 100,000 VND ($5), was given to the local WU for each newly-installed sanitary latrine to incentivize their promotion activities. The rebate, about 500,000 VND ($25), was given to each household that installed an approved latrine. The rebate was approximately 3% of the total cost of installing a sanitary latrine in Hai Duong and 6% of the cost in Tien Giang. 4 Though the rebate amount was small, being able to offer it was a door opener, which allowed WU field staff to gain access to households to try to convince them to install sanitary latrines and to adopt good hygiene practices. In addition, the rebates were distributed as awards in ceremonies to honor sanitation-adopting households for good citizenship. To evaluate the effects of these interventions, the sample communes were randomly allocated into a control group and three treatment groups. All households in each commune were exposed to the same intervention. Treatment 1 (CCT & Rebates): local Women s Unions were eligible for CCTs and households were eligible for rebates. Treatment 2 (Rebates Only): households in these communes were eligible for rebates, but the local Women s Unions were ineligible to receive CCTs. Treatment 3 (CCT): local Women s Unions were eligible for CCTs but households were not eligible to receive rebates. The control households were in communes that were not supposed to receive the CHOBA interventions. However, in most cases, the Women s Unions did distribute information about the benefits of sanitary latrines even though no financial incentives were available. Since the rebates and CCTs were administered by the Commune People s Committees, and were limited to the treatment communes, there is no possibility of spillover 3 The information here is based on the Phase I and II CHOBA comprehensive report. 4 The average cost for installing a septic tank latrine in Hai Duong is 17,185, 000 VND and in Tien Giang it is 7,953,000 VND. The difference in costs goes back to the differing housing standards and geological conditions. 5

6 in terms of rebates and CCT. That said, there is a possibility of information spillovers from treatment communes to control communes as communes are very close to one another. This can be seen in Diagrams 3 and 6 in the Appendix: households in different treatment groups are close to one another. The surveys I use in this paper were developed to evaluate the CHOBA initiative. The baseline and midterm surveys are used in this paper. There was an 18 months gap between the baseline and midterm surveys. The baseline survey was administered in June 2012, while the midterm survey was administered in December The baseline survey was administered to a sample of 2,139 households in 131 communes in both provinces of Hai Duong and Tien Giang. Of these households, 1,602 belonged to treatment groups and 537 households made up the control group. In Hai Duong 58 communes were sampled. From each commune 18 households were randomly sampled, 6 from each economic stratum: 6 poor, 6 near-poor and 6 non-poor. In Tien Giang, 73 communes were sampled, 15 households in each commune were sampled, of which 5 were randomly drawn from each economic stratum: 5 poor, 5 near-poor and 5 nonpoor. The commune populations differ, as do the numbers of households classified into each economic stratum in each commune. In order to adjust the sample so that it is representative of the population, I used weights defined as the inverse of the sampling probabilities for all estimations. The results in the midterm CHOBA survey point to the possibility that network effects strongly influence the decision to install a septic tank latrine (sanitary latrines). 5 Of the surveyed households that have installed latrines approximately 14% mention other people in the hamlet also built it as a reason for choosing to install the latrine. 6 In addition, approximately 17% of those who have heard of the advantages of sanitary latrines, have heard it from friend and/or acquaintance. 7 Moreover, the majority of the households surveyed were aware of the benefits of septic tank latrines. This, along with the Women s Union efforts to reward households who have installed sanitary latrines in public events, associates 5 This paper focuses on the adoption of septic tank latrines (or sanitary latrines), as septic tank latrines are the most likely to meet hygienic standards of construction and maintenance at 97% and 79% respectively (Tung et al. 2014). 6 There are 8 main categories of latrines: septic-tank latrine (most hygienic), pour-flush latrine, biogas, doublepit dry latrine, ventilation pit dry latrine, single-pit dry latrine, fish-pond latrine, and ashes-bridge/bucket latrine. 7 Listed sources of information on advantages of sanitary latrines in the survey: staff from Women s Union, staff from other organizations, Family/acquaintance, TV, broadcasting stations, communal broadcasting station, books or newspapers, flip-pictures/leaflets/posters, and internet. Survey takers could list more than one source. 6

7 being a good citizen with installing a sanitary latrines. This could create social pressure on those who have not installed sanitary latrines to do so, especially in neighborhoods with higher average installations of sanitary latrines. In this paper I depend more on the mid-term survey data. The midterm survey has information on the year and month when the sanitary latrine was installed, therefore I can depend on it for information on sanitary latrine installation dates as well as demographic data. III. Network Effects: Definition and Application This section is divided into three sub-sections. In the first sub-section I define the reference group. In the second sub-section I discuss how I measure the network effects in terms of Jackson s (2014) description of network characteristics. In the last sub-section I describe certain empirical complications associated with measuring network effects and explain how I overcome them in this paper. A. Network and Reference Group: definition and application To define the network effect, it is important to clearly define the network, and by extension, the reference group I refer to in this paper. A network is defined by individual members (nodes) and the links among them through which information, money, goods and services flow (Maertens and Barrette 2012). In this paper I assume that there are relevant links among households in close proximity to each other. A reference group includes all member of the network under consideration, except for the individual or household in question (Maertens and Barrette 2012). The reference group can be defined by using observable characteristics and/or self-reported data (Manski 1993). The reference group I use in this paper is defined as near neighbors. The households around the household in question are weighted according to their proximity. Using inverse exponential weighting, closer households are weighted more heavily. This definition of reference group relies on the likelihood that proximity makes it easier for households to exchange information, goods, and services. So, the closer the households are, the more frequent their interactions will be. The network effect is then measured by looking at the correlation between the prevelance of a certain behavior in the reference group and the likelihood of the household in question adopting the same behavior. Proximity was also used by Miguel and Kremer (2004) to identify externalities of a school-based de-worming treatment. In their paper they look at nearby schools for possible treatment externalities. 7

8 A drawback to this method is that the influence of a neighboring household s decision is solely based on the household s distance from the household in question. That said, there is high dependence on informal institutions (or social networks) among the surveyed households. Therefore, interactions among neighbors to get these goods and services seems more likely. 8 Getting a good or service from a closer neighbor would, ceteris paribus, be less costly than getting the same good or service from a more distant neighbor. This relative ease of getting services from closer neighbors increases the expected interactions and as a consequence the expected influence of closer neighbors. Moreover, a given household is more likely to directly observe their near neighbors than their distant ones. In addition using proximity, I refine the reference group by economic stratum: poor and non-poor. This is based on the idea that individuals with similar characteristics (in this case similar income) group together (Akerlof 1997; Schaffner and Torgler 2008). Other papers define reference groups using self-reported data. Individuals are asked to identify their friends, family members, and those they receive services from. Oster and Thornton (2012) and Cai, de Janvry, and Sadoulet (2013) define their reference groups in this way. The individuals in both papers were surveyed before the start of the experiment. In the survey individuals were asked to name those who are their friends within the group participating. This method helps determine the network to which each of the individuals belongs. Later, the network effect was measured by tracking the spread of information and adoption from the first adopter to their reference group. That said, this method limits the reference group to friends in the participating group and therefore might not represent the full network. It also does not take into account the variation in the frequency of interactions among the friends in the group (or the micro density as will be discussed later). In the CHOBA surveys, all households were asked about the source of information of the advantages of sanitary latrines. One of the sources listed was family and/or acquaintance. This can be used as a self-reported measure of network effect through the spread of information. That said, I cannot identify if the choice made (to adopt the sanitary latrine or not) made by the family and/or acquaintance that the household refers to. The reference group can also be defined as the group the individual or household in question belongs to. This method was used by Dahl, Loken and Mogstand (2014) to identify 8 40% of the households which borrowed money to install sanitary latrines borrowed from relatives/friends/neighbors. 8

9 the network effect in small firms on the likelihood of fathers in the firm taking a paternity leave. In the paper the reference group was defined as work colleagues. This method faces the issue of endogenous group membership (further discussed below) as individuals in the same organization are more likely to be similar, but it includes a larger network in which it is not only friends and family who influence the individual s decision. We do observe commune membership in the data collected from the two provinces. Households in the same commune depend on the same local commune committee. They also attend the same monthly commune meetings. These setting may increase the influence of households in the same commune on one another. That said, as shown in diagrams 1 to 6, in the appendix, the communes are very close to one another and attending a commune meeting is not necessary for households to observe each other s decisions to install sanitary latrines. B. Measuring Network Effects: Theory and Application: Previous literature has focused on two channels contributing to the strength of the network effect. One, preferring to behave like the rest of their peers, increases the desire to get the product or participate. Two, peers help identify the benefits and use of the product, i.e., they increase access to information (Dahl, Loken and Mogstad 2014, and Oster and Thornton 2012). That said, the strength of the network effects varies depending on the context. The context is a function of the characteristics of the network (Jackson 2014) as well as the level of access to other sources of information (Dahl, Loken and Mogstad 2013). The characteristics of the network are defined by the density of the network as well as the opinions of those who first adopt the product (sanitary latrine). The likelihood of influencing the household in question depends on the network the household refers to (macro density) and the frequency of interactions within that network (micro density) (Jackson 2014). This is represented in the diagram below. 9

10 The circles or nodes represent the households in the social network The number of connections to other nodes determines the macro network density. The frequency of the interaction between the nodes determines the micro network density. If the opinion of the household adopting the sanitary latrine is positive (represented by the green nodes), he/she is more likely to spread the information to other households than if their opinion of the product is negative (represented by the red nodes) To track the contribution of a network to the likelihood that a household adopts a certain practice/product, the researcher first identifies the network before the information is distributed or the intervention implemented. This is done by identifying friends, family, relatives, co-workers and acquaintances of individuals/households in question. This determines the macro density of the network, while additional information on the frequency of the interactions determines the micro density of the network. After identifying this network of social interactions, the information/intervention is targeted to specific individuals in the network. Following this, the researchers track the spread of information/practice to untargeted individuals in the network. This method of determining social network effects was used by Banerjee, Duflo and Jackson (2013), Dahl, Loken and Mogstad (2014), and Emily and Thornton (2012). Though optimal, this method cannot be used in this paper because the survey was not specifically designed to capture network effects. Instead, I use near neighbors as a proxy for the macro density of the network. Specifically, I look at the proportion of near neighbors around the household in question who have installed sanitary latrines before the baseline survey. I take into account the varying micro density across reference groups through an indirect measure of the frequency of the circulation of information on the advantages of 10

11 sanitary latrines by word of mouth (which is a consequence of a higher frequency of interactions). To do so, I used the survey information on the sources from which households reported receiving information on the advantages of sanitary latrines and calculated the proportion of households in the reference groups who received their information on benefits of sanitary latrines from family and/or acquaintance. Other routes of network effects that I touch upon in this paper include: the impact of receiving information from friends and/or acquaintances, information spill-over effects from treatment households to control households in the reference group, and the influence on the network effect of less access to formal information sources. Control households and lowereducation level households both have less access to formal information sources. 9 C. Empirical Problems in Measuring Network Effects: Three main empirical problems are associated with measuring the network effect: the reflection problem (Manski 1993), the endogenous group membership problem (Bobonis and Finan 2009; Manski 1993), and the correlated unobservables problem (Jackson 2014; Argy and Rees 2008). The reflection problem can be thought of as bi-directional causality. It occurs when the target household s behavior is affected by the behavior of the reference group, and viceversa. Let be the dichotomous variable representing the decision of household ; the probability that is a function of the average behavior in the reference group, excluding the household in question. ( ) ( ) (1) where, shown in equation (1), is a function of the behavioral choice of the other households as shown in equation (2) below. The subscript signifies the absence of household from the calculation. (2) 9 Refer to tables 7 and 8 in the appendix. 11

12 At the same time, the outcome influences the behavioral choice of households in the reference group. For example, if household k s behavioral decision is defined as: ( ) ( ) (3) then is a function of the sum of where v is all households except k and v includes household i. This reflection problem is particularly hard to avoid. In theory, I could use the panel data to avoid the reflection problem. could be defined as a function of household i s reference group in period 1. If we then focus on household s decision in period 2 we could write: ( ) ( ) (4) Since household s period 2 decision cannot affect the reference group s period 1 decisions, there is no reflection problem. However, the irreversibility of the decision to install a sanitary latrine may undermine this strategy. In particular, if household i installed the sanitary latrine in period 1, in period 2 household i will still have the sanitary latrine i.e. = 1. Since the decision to install a sanitary latrine in period 1 is irreversible, the reflection problem remains, because household s decision to install the latrine in period 1 did, according to the model, affect the reference group in period 1, and this effect persists into period 2. Below is the decision tree we observe: Period 1 Period 2 y i y i 0 y i 0 y i 0 y i y i 12

13 To address this issue, I could exclude households that installed the sanitary latrine in period 1 from the regression and estimate ( ) (5) only for households that had 0 10 Excluding households for which avoids the reflection problem, but this strategy might introduce a sample selection problem. It may be that households which installed sanitary latrines before the baseline survey were systematically different from households that installed the sanitary latrine after the baseline survey. If so, the regression may be subject to sample selection bias. On the other hand, it could be argued that the intervention targeted households that did not install the sanitary latrine before the baseline and so the sub-sample is representative of the population of households targeted. The main difference between the selected sample and that not selected in this model is in the amount of knowledge available as well as income (refer to Appendix Table 6), both of which I control for. That said, there may be unobservable characteristics that differentiate the two sets of households. In such a case the residual captures the unobservable characteristics among the sample selected (and we have omitted variable bias). While each strategy has a shortcoming, I will use them both. In the conclusion I will also propose a better method for future work. The second issue, endogenous group membership, arises when an individual s social network (friends and family) forms because they have similar characteristics. As a result, the likelihood an individual adopts a certain product and the average adoption of the product in the reference group may be correlated due to the shared characteristics of the individual and the reference group (Manski 1993; Bobonis and Finan 2009). To avoid this issue the variables defining the reference group should be uncorrelated with the outcome variable (Manski 1993). This issue does not arise in my paper because I define the reference group by geographic proximity. In Vietnam, land was administratively allocated some decades ago, and relocation is administratively controlled. It is unlikely that households would have had opportunities to co-locate in an area on the basis of their propensity to adopt sanitary latrines. 10 The proportion of households that installed the sanitary latrine before the baseline survey ( ) is calculated using the midterm survey rather than the baseline survey. This is done by using information on the date of the installation of the sanitary latrine which is available in the midterm survey. 13

14 The third challenge, due to correlated unobservables, happens when a household and its reference group share characteristics that affect their behavior, but the characteristics are unobserved. In this case, omitted variables might create the appearance of a network effect (Jackson 2014, Argy and Rees 2008). Manski (1993) describes the example of a group of students performing similarly because they all have the same high-quality teacher. Here, the similar performance might be mistakenly attributed to network effect instead of the teacher. I address this issue by introducing controls which include household income, education of the household head, sources of information, and whether the household was included in one of the treatment groups. IV. Empirical Specification: I use a logit regression to estimate the effect of a higher initial proportion of sanitary latrine installations and a higher frequency of relevant communications among the reference group on the probability that a household installed a sanitary latrine between the baseline and midterm surveys. I use a logit regression because the dependent variable is binary. The general regression specification is: ( ) (6) The dependent variable is the probability of household i installing a sanitary latrine between the baseline and midterm surveys. I run this regression using both panel and cross section logit. The key components of a panel data can be represented by equation (7) below: (7) Where are the unobserved household-specific effects. The appropriate model then depends on whether is correlated with the regressors or not. If the household-specific effects are correlated with the regressors, the conventional method is to use first-differencing or fixed-effects to sweep out the household-specific effects from the model. These methods also sweep out any time-invariant regressors. In this paper I am interested in a number of time-invariant regressors (defined below). As such I would not want to use a method which sweeps them out. Instead, if the household-level 14

15 effects are uncorrelated with the regressors the random-effects estimation can be used to yield unbiased and efficient estimates. One way to ensure the household-level effects are uncorrelated with the regressors is to include as many household-level control variables as possible. Wooldridge (2013) explains like this: suppose the effects and the regressor were related according to the following linear model: (8) and that were uncorrelated with and is correlated with, The inclusion of as a household-level control in the regression yields (9) or (10) where the composite error term is uncorrelated with the regressors. OLS provides unbiased estimators of the coefficients in this model, but efficiency can be gained by exploiting the serial correlation in the error terms caused by the common presence of in each of the for given. Wooldridge (2013) illustrates this approach as follows. ( ) ( ) (11) [ ( ) ] (12) ( ) ( ) ( ) (13) This correlation is used to calculate in equation (12) and, then OLS is applied to the quasidifferenced equation (13). The, and in equation (5) are the household-level timeaverages of, and respectively. The panel logit random effects estimator uses baseline and midterm data for all the sampled households. By default, STATA s algorithm clusters at the household level and it does not support clustering at higher levels such as the commune. In addition, it does not 15

16 support probability weighting of the observations. I don t think either of these issues are important in this context, for the following reasons. All of the communes in Hai Duong and Tien Giang are close to one another. In each province, the sampled communes were drawn from a relatively small area which is culturally, economically, and topographically homogenous. Thus, commune-specific shocks that would induce error correlations within the communes do not seem likely. In regards to the probability weights, the target population of the intervention is the poor and near-poor, therefore we should allow for their over-representation when evaluating the intervention, i.e., do not weight the results. In this case, the results pertain to the sample at hand and not to the population from which it was drawn. As explained previously, I also estimate the model for the subset of households which had not installed a sanitary latrine before the baseline survey. This estimation was crosssectional rather than panel. Also, as explained previously, this estimation could be subject to sample selection bias. Comparing the panel data version (which may exhibit the reflection problem) with the cross-section version (which may be susceptible to selection bias) can be used as a robustness check, though it would be impossible to identify the separate effects of the reflection problem and selection bias. I will now define the variables of interest which appear in equation (6). The two network measures I focus on are and. is the proportion of households in the reference group of household i who had installed a sanitary latrine before the baseline (capture the macro density of the network). I calculated it as the sum of households j in the reference group of household i who had installed a sanitary latrine, over the sum of households j in the reference group. Households j only include households in the same economic stratum. 11 I used probability weights so that is representative of the households 11 The reference groups for poor and near-poor households include the poor and near-poor households near the household in question. The reference group of non-poor households only includes non-poor households near the household in question. The average incomes of the poor and near-poor households are not much different, therefore it seems plausible to consolidate them into one poor stratum. A number households moved from poor status to near-poor between the baseline and midterm surveys: 75 of the near-poor households in the baseline survey are classified as poor in the midterm survey; 86 of the poor households in the baseline survey are classified as non-poor in the midterm survey. 16

17 observed by household i and exponential weighting to represent the higher expected frequency of interactions with households closer to household i. 12 (14), on the other hand, captures the frequency of the circulation of information through word of mouth in the reference group (micro density). It is measured as the sum of households j in the reference group of household i who have heard of the benefits of sanitary latrines from family and/or acquaintance divided by the sum of households in the reference group who have heard of the benefits of sanitary latrines. Both are weighted by the distance and probability weighting. (15) Here, family and/or acquaintance refers to the source of information received by members of household s reference group. That said, as argued earlier, the high dependence and interaction with neighbors in the communes makes it likely that this proxy is capturing the circulation of information in the reference group itself. I also capture the impact of the household in question receiving the information on the advantages of sanitary latrines from family and/or acquaintance ( ). As a robustness check I also define near neighbors as households within a 2- kilometer radius of the household in question. In this case all households within the 2- kilometer radius are weighted only by their inverse sampling probabilities and not by any function of distance. 12 The exponential weighting strengthens the influence of households closer to the household in question. The closer distance (d) is to 0 km, the closer the household influence is to 1. The further away, the smaller the household influence. At 5 km, the household influence is multiplied by

18 Referring to the literature cited earlier, the network effect is influenced by a number of factors including the access of the household in question to other sources of information. Control households tend to have lower access to formal sources of information (like the WU) as shown in Table 3 in the appendix. This is in addition to getting no access to rebates, which are designed to incentivize the installation of sanitary latrines and act as a door opener for the WU, and CCT which would have encouraged the WU personnel to work harder. All these factors contribute to expecting the network effect to be higher among control households. Therefore, I expect the coefficients on the interaction terms between control households and network measures,, to be positive and significant. Furthermore, households with lower levels of education (no degree up to primary only) in both provinces of Hai Duong and Tien Giang had lower access to formal sources of information as shown in Table 4. This would strengthen the network effect. Therefore, I expect the the coefficients on the interaction terms,, between low-education level households and the network measures to be positive and significant. The network characteristics (captured by and ) are expected to strengthen each other when present at the same time in high proportion. Both and increase the pressure to conform to the norm in the reference group of household i. A high proportion of installations of sanitary latrines in the reference group ( ) makes it less excusable for household i to choose not to install the sanitary latrine. This pressure to conform is only strengthened when the circulation of information through word of mouth is high in the reference group ( ). This interaction between and is captured by the coefficient. I expect to be positive and significant. Moreover, household i s recipient of information on the benefits of sanitary latrines from family and/or acquaintance ( ) would play a more influential role on the household s decision to install the sanitary latrine, if household i observes a high proportion of installations of sanitary latrines in the reference group ( ). This is because the risk of investing on sanitary latrines is mitigated by two complementary factors: a family/acquaintance s recommendation as well as observing a high trend of installations in the neighborhood. This is captured by the coefficient. I expect to be positive and significant. In addition to the direct network measures, I test for the possibility of spillovers from the treatment to the control households by looking at the effect of neighboring a higher 18

19 proportion of households receiving treatment in the vector i The vector also includes the treatment group the household belongs to. Finally, I control for the observed characteristics of household i, represented by vector. The control variables include the household s current income, economic status, education level of the household head, occupation of the household head, and the survey respondent s knowledge of sanitary latrines. 13 I depend on the midterm survey instead of the baseline survey in the cross-section logit regression for information on household characteristics as well as year of installation. In the panel logit model, I depend more on the baseline survey, but I depend on the midterm survey for the year of the installation of sanitary latrine (, and are the same in both regressions). I. CHOBA Data Description 14 In this section I summarize the network variables of interest, discuss why the data are particularly well-suited to capturing network effects by examining the variation in average adoptions of sanitary latrines across the communes, and I discuss the variation in information sources and the role of the Women s Union. Later, I will present the network variables of interest and the distribution of weighted proportions among households that installed the sanitary latrine between the baseline and midterm surveys vs. those who did not. These summary statistics will set the context for the expected results. The variation across communes in the proportion of households that have installed sanitary latrines, as well as the scope for increasing the proportion, lends the data to the possibility of capturing network effects. This variation can be seen in Figures 1 and 2, below. 13 The survey respondents were not necessarily the household heads. 14 The Information on the data collection is from Phase I (published December 2012) and Phase II (published September 2014) Comprehensive Report. For more information on the data please refer to the two reports referenced. In addition to Daniel Westbrook s notes on sampling strategy (2014). 19

20 Figure 1: Proportion of Households that Installed a Sanitary Latrine before the Baseline Figure 2: Proportion of Households that Installed a Sanitary Latrine between the Baseline and Midterm Surveys: 15 The ordering of communes on the horizontal axis of Figure 2 correspond to the ordering in Figure 1. It is not clear that there is a relationship between the initial commune average installations of sanitary latrines in Figure 1 and the commune average installation among those who did not install the sanitary latrine in the first phase, in Figure 2. The network effect is not limited to observing the reference group s behavior, but includes the circulation of information through the reference group. The availability of data on the sources from which the reference-group households received information on the advantages of sanitary latrines makes it possible to calculate the effect of greater circulation of information through word of mouth, as well as the influence of receiving the information on the benefits of sanitary latrines from family and/or acquaintance. 15 The communes are the same ones presented in Figure 1. The average only includes households that have not installed the sanitary latrine before the baseline. 20

21 The data collected in the surveys demonstrates that the opinions of those who adopted sanitary latrines before the baseline are overwhelmingly positive; 96% of those in Hai Duong and 79% of those in Tien Giang prefer using sanitary latrines. This increases the likelihood that first adopters spread information on the advantages of sanitary latrines to neighboring households. In Figure 3 we see that between 10% and 15% of the households surveyed received the information on the benefits of sanitary latrines from friends and/or acquaintances. It is also important to note that the Women s Union was the most prominent source of information on the advantages of sanitary latrines. Relative to the activity of the WU, the amount of information on benefits received from friends/acquaintance is small, ranging between 10% and 15%. Figure 3: The Source of Information on the Benefits of Sanitary Latrine by Province 16 In the next subsections I discuss summary statistics of the main variables used in the regressions. First, I summarize the variation in the distance-weighted neighboring households around the households in question in each of Hai Duong and Tien Giang. Second, I summarize the variation in the proportions of households in the reference groups who had installed sanitary latrines before the baseline and the circulation of information in the reference group through word of mouth. 16 The ratio for each information source is calculated as a fraction of all households in the commune. A household can note more than one source of information on the advantages of sanitary latrines. 21

22 A. Weighted Neighboring Households: Figure 4: Distance Weighted Number of Neighbors by Province 17 The weighted average number of near neighbors within a 3 kilometer radius of the household in question is approximately 3,930 in Tien Giang and 3,570 in Hai Duong. Note that this is an estimate of the number of near neighbors in the population, as the weighting is the inverse of the sampling probability. This suggests the communes, on average, are highly dense, but the graphs show that there is considerable variation in the number of neighbors across the households. 17 Presents the number of neighbors for each household in question, weighted by the sampling probabilities. Households that are 3 km away from the household in question are multiplied by the exponential weight: 0 0 in the denominator of equation 6. 22

23 Density Density B. Network Measures and the Likelihood of Installing a Sanitary Latrine: Figure 5: Proportion of Neighboring Households Installed Sanitary Latrine before the Baseline, by Household Installation: 18 Proportion of Neighboring Households Installed Sanitary Latrine before the Baseline In Tien Giang Proportion of Neighbors Installed Sanitary Latrine before the Baseline Installed Sanitary Latrine after the Baseline Did not Install Sanitary Latrine Proportion of Neighboring Households Installed Sanitary Latrine before the Baseline In Hai Duong Proportion of Neighboring Households Installed Sanitary Latrine before the Baseline Installed Sanitary Latrine after the Baseline Did not Install Sanitary Latrine 18 The proportions are weighted by sampling probabilities and exponential distance weighting. The kernel density functions only includes households that did not install the sanitary latrine before the baseline. For the definition of the Proportion of neighboring households installed sanitary latrine before the baseline refer to equation (6) in Section IV. 23

24 Densit Densit Figure 6: Proportion of Circulation of Information through Word of Mouth by Installation of Sanitary Latrine: 19 Proportion of Circulation of Information through "Word of Mouth" in Tien Giang Proportion of Circulation of Information on Benefits of SL through F/A Installed Sanitary Latrine after Baseline Did not Install Sanitary Latrine Proportion of Circulation of Information through "Word of Mouth" in Hai Duong Proportion of Circulation of Information on Benefits of SL through F/A Installed Sanitary Latrine after Baseline Did not Install Sanitary Latrine In Figures 5 households who installed the sanitary latrine between the baseline and midterm surveys, on average, are surrounded by a higher proportion of households who have 19 The proportions are weighted by sampling probabilities and exponential distance weighting. The kernel density functions only includes households that did not install the sanitary latrine before the baseline. For the definition of the Proportion of circulation of information through word of mouth refer to equation (7) in Section IV. 24

25 installed the sanitary latrine before the baseline. The observations peak at a higher proportion among households who installed the sanitary latrine after the baseline. The results in Figure 6 further suggest that households who installed the sanitary latrine after the baseline are more likely to be surrounded by households who have heard of the benefits of sanitary latrines from family and/or acquaintance. Therefore, we expect a positive correlation between the likelihood to install and the network effects presented. VI. Results: The estimates in the three regressions in Table 7 tell a semi-consistent story of possible network effects. In regression (1) the network effect is captured in the first row, while in regressions (2) and (3) the network effect is captured in the interaction terms in rows (4) and (6). The variation in results may be attributed to the possible reflection problem in the regression (1) using the panel logit model, and the possible selection bias problem in regressions (2) and (3). The slight variation between regressions (2) and (3) are attributed to using probability weighting and clustering in regression (3). [Insert Table 7 here] The first three rows in Table 7 are variables used to measure the network effect. Rows (4) and (5) capture the effect of interacting the network variables with one another. Rows (6) (8) capture the effect of interacting the network variables with control households. Rows (9) (11) capture the effect of interacting the network variables with households where the household head has primary or lower levels of education. Rows (12) (14) capture the spillover effect due to neighboring treatment households and rows (15) (17) capture the treatment effect. In regression (1), the network marginal effects coefficient in row 1 suggests a positive and significant correlation between the proportion of initial (before baseline) installations of sanitary latrines in the reference group of the household in question, and the likelihood of the household installing a sanitary latrine in the second phase (between the baseline and midterm). In this model, an increase in the initial percentage of installations of sanitary latrines from 0% households to 50% of the neighboring households, increases the likelihood of installing a sanitary latrine by 3.3%. 25

26 In regressions (2) and (3) we still see a positive effect for a higher initial proportion of installations of sanitary latrines, but only among control households. In row (6), we observe that an increase in the initial percentage of installations of sanitary latrines from 0% households to 50%, increases the household s overall likelihood of installing a sanitary latrine in the second phase by 5.2% in regression (1), 34.6% in regression (2), and 40.75% in regression (3). Furthermore, the network effect in row (1) is strengthened when interacted with a higher proportion of circulation of information through word of mouth in the reference group (network measure in row (2)). The coefficients of the interaction terms are presented in row (4) in Table 7. I calculate the marginal effect including the interaction terms by referring to the first derivative of the variable of interest. In this case, I calculate the marginal effect of a higher proportion of installations of sanitary latrine in the reference group ( ) given a proportion of circulation of information through word of mouth ( ) as follows: 20 ( ) ( ( ))( ) (16) Referring to the above equation, 21 at an average circulation of information through word of mouth in the reference group of 17.5%, an increase in the initial percentage of installations of sanitary latrines from 0% to 50%, increases the household s likelihood of installing a sanitary latrine in the second phase by 9% in regression (2) and 11.8% in regression (3). There are also signs of possible network effects in row (3), if the household heard of the benefits of sanitary latrines from family and/or acquaintance. Hearing about the benefits of sanitary latrines from family and/or acquaintance, increases the likelihood of installing a sanitary latrine between 1% in regression (1) and 10% in regression (2). That said, the effect is significant only at a 15% level and 10% level respectively. The effect also disappears in regression (3). Furthermore, in regression (1) there are signs of an increase in network effect among households where the household head has a primary or lower educational degrees. This can be seen in row (9) where an increase in the percentage of circulation of information through word of mouth from 0% to 50% among neighbors, increases the likelihood of installing a sanitary latrine by 2.2%. 20 Refer to equation (6) in section IV for the full equation. 21 Note in tables 7 to 9 the logit regressions are evaluated at the mean. As such, the coefficient estimates in tables 7 to 9 are a product of ( ( )) where is the probability of installing a sanitary latrine if = 0.5, i= 1,2,3 n. 26

27 The treatment effects in rows (15) (17) are positive and significant in all three regressions. Suggesting there is a clear benefit to a higher circulation of information on the advantages of sanitary latrines by the Women s Union, in addition to getting access to output based aid: CCT and rebates. That said, there are no spill-over effects in rows (12) (14). It is important to note the results in Table 7 depend on the exponential definition of the reference group (higher weighting to households closer by). This definition of a reference group is based on theories that may not fit this context. It might be that households within a certain distance from the household in question exert the same level of influence. In addition, the results in the above table are limited to poor and near-poor households. The results may vary among non-poor households. I test the two possibilities in Tables 8 and 9. In Table 8, I compare the network coefficient results using two varying definitions of the reference group. In regression (1) the reference group is defined, as done previously, by exponential weighting such that the weight of the neighboring household diminishes exponential as the distance from the household in question increases. In regression (2) the reference group is defined as all households within a 2 km radius of the household in question. In this model all households within the 2 km radius receive the same weight of [Insert Table 8 here] The results in both regressions are consistent. In both models there is a positive and significant correlation between the network measure in row (1) and the likelihood of installing a sanitary latrine. In regression (2) the estimated coefficient of the network measure in row (1) is slightly higher, so that an increase in the initial percentage of installations of sanitary latrines from 0% to 50%, increases the household s likelihood of installing a sanitary latrine in the second phase by 4.7%. Furthermore, in both regressions the network effect is stronger among control households (refer to row (6)). In regression (2), the estimated network effect among control households is higher. An increase in the initial percentage of installations of sanitary latrines from 0% to 50%, increases the household s likelihood of installing a sanitary latrine in the second phase by 7.8%, if the household is in the control group. 22 In both models, the reference group is adjusted by sampling weights so that the proportion calculations are representative of the population. 27

28 In addition to exploring the effect of changing the reference group definition, it is worthwhile exploring the network measures among non-poor households and if it varies in relation to poor households. In Table 9 I compare the regression estimates among poor households to the estimates among non-poor households. [Insert Table 9 here] Among non-poor households, neighboring a higher proportion of households who have installed a sanitary latrine is only significant if the household has heard of the benefits of sanitary latrines from family and/or acquaintance. If the household in question heard of the benefits of sanitary latrines from family and/or acquaintance and observes 50% of the neighbors have installed a sanitary latrine, the household s likelihood of installing a sanitary latrine increases by 38.25%. That said, according to the estimated coefficients (refer to rows (1), (3) and (5) in regression (2)) hearing of the benefits of sanitary latrines from family and/or acquaintance has a positive effect only if the percentage of neighboring households who had installed the sanitary latrine is higher than 37.67%. Furthermore, if the degree of the household in question is added to the equation, the overall effect of receiving information on the benefits of sanitary latrines from family and/or acquaintance among households with low levels of education becomes almost non-existent (refer to rows (3), (5) and (11) in regression (2)). Though the estimated network effect defined in row (3) among non-poor households is ambiguous, the network effect defined in row (1) is clearly positive and significant among households in the control group (refer to row (6)). Interestingly, the network effect is even stronger among non-poor households in the control group, such that an increase in the initial percentage of installations of sanitary latrines from 0% to 50%, increases the household s likelihood of installing a sanitary latrine in the second phase by 78.25%. Moreover, the marginal treatment effects, in rows (15)-(17), are much higher among non-poor households. This might be because the cost of installing the sanitary latrine is not an issue among non-poor households, therefore, the small amounts of rebates and the increase in access to information are enough to incentivize the installation of sanitary latrines. 28

29 VII. Conclusion: The regression estimates suggest a positive correlation between the likelihood of a household installing a sanitary latrine between the baseline and midterm and the prevalence of sanitary latrines in the reference group of the household in question. This is particularly evident among households in the control communes. If a poor household is in a control commune, an increase in the percentage of initial installations of sanitary latrines in the reference group from 0% to 50%, increases the likelihood of a household installing a sanitary latrine by 5% to 40%. The percentage increase is lower in the panel logit model where the complete sample was utilized. The estimates also suggest the network effect is not stronger among households with lower levels of education. There is also no clear indication that a higher frequency of circulation of information on the advantages of sanitary latrines through word of mouth in the reference group increases the likelihood of installing a sanitary latrine. That said, a higher circulation of information through word of mouth increases the effect of a higher proportion of installations of sanitary latrines in the reference group. This effect is only evident in the cross-section logit model using the sub-sample of households that did not install a sanitary latrine before the baseline. The results vary when changing the economic stratum in focus. The variation in results between the poor and non-poor households can be best interpreted as due to the stronger treatment effect which diminishes the significance of network effects. This explanation is supported by the positive and significant network effect among control nonpoor households. If a non-poor household is in a control commune, if the percentage of installations of sanitary latrines increases in the reference group from 0% to 50%, the likelihood of the household installing a sanitary latrine in the second phase increases by 78.25%. Which is higher than any other estimated network effect coefficient. The results in this paper point to the importance of the role of network effects in estimating the effect of an intervention. That said, the results are not conclusive. Both the panel logit and cross-section logit models suffer from important shortcomings. For future work, I propose to utilize the extensive data in the midterm survey on the exact date when the sanitary latrine was installed. In the model, the household s reference group would include all households that installed the sanitary latrine a year (or 6 months) before the household in question. In this scenario the first adopters would have no reference group. This would present an interesting case in which we can observe the characteristics of the first adopters who resulted in the repel effect. We can also observe the changes in the network effect across time as the access to formal sources of information increases. 29

30 VIII. Appendix Table 1: Summary Data for the Province of Hai Duong 23 Unweighted summary Statistics for the Variables Province of Hai Duong Installed Sanitary Latrine Before the Baseline % Installed a Sanitary Latrine After the Baseline % Knows About Septic Tank Latrines % Knows Septic Tank Latrines are hygienic % Has installed a Septic Tank Latrine before the baseline and noted preferring Septic Tank Latrines after the baseline 96.05% Proportion of Neighboring Households within 2 km radius that have installed sanitary latrine before the baseline survey Around households that have installed the sanitary latrine before the baseline Around households that have installed the sanitary latrine after the baseline Around households that have not installed the sanitary latrine Has heard of benefits of sanitary latrines 1 4 From the Women's Union 56.98% From other organizations 34.37% Family/acquaintance 10.85% From the TV 35.08% From broadcasting stations 9.25% From a communal broadcasting station 38.59% From a book/newspaper 4.82% From a leaflet/poster/flip-picture 2.71% From the internet 0.40% Proportion of the information on benefits of sanitary latrines received from family/acquaintance Proportion of loans for building a sanitary latrine from relatives/friends/neighbors Main occupation of HH head 1 Farmer % Worker 5.630% Civil Servant 1.210% Business 3.920% Housewife 2.010% Laborer 4.820% Retired 7.840% Household's Economic Status 1 6 Poor % Near-Poor % Non-Poor % Economic Hardship 4.520% 1 As a percentage of all surveyed households. 2 As a percentage of surveyed households that have not installed the sanitary latrine before the baseline survey. 3 In reference to households that have not installed a sanitary latrine by the midterm survey conducted in December They might have installed it later on. 4 Survey takers were told to mark all the sources from which they heard about the benefit of sanitary latrines % survey of takers noted more than one source. 5 The fraction is calculated on the commune level. 6 A household was only marked as poor or near-poor if they have a certificate of poverty or a confirmation of near poverty from the Communal People s Committee. 23 Summary Statistics are based on the midterm survey and are not weighted. 30

31 Table 2: Summary Data for the Province of Tien Giang 24 Summary Statistics for the Province of Variables Tien Giang Installed Sanitary Latrine Before the Baseline % Installed a Sanitary Latrine After the Baseline % Knows About Septic Tank Latrines % Knows Septic Tank Latrines are hygienic % Of those who have installed a Septic Tank Latrine before the baseline, percentage who noted preferring Septic Tank Latrines 79.01% Average number of Neighboring Households within 2 km that have installed sanitary latrine before the baseline survey Around households that have installed the sanitary latrine before the baseline Around households that have installed the sanitary latrine before the baseline Around households that have not installed the sanitary latrine Has heard of benefits of sanitary latrines 1 4 From the Women's Union 68.85% From other organizations 41.69% Family/acquaintance 15.57% From the TV 32.19% From broadcasting stations 16.62% From a communal broadcasting station 32.95% From a book/newspaper 5.70% From a leaflet/poster/flip-picture 12.82% From the internet 0.66% Fraction of the information on benefits of sanitary latrines received from family/acquaintance Fraction of loans for building a sanitary latrine from relatives/friends/neighbors Main occupation of HH head 1 Farmer 32.00% Worker 6.65% Civil Servant 2.85% Business 8.45% Housewife 3.80% Laborer 24.79% Retired 14.62% Household's Economic Status 1 6 Poor 29.63% Near-Poor 25.07% Non-Poor 39.13% Economic Hardship 6.17% 1 As a percentage of all surveyed households. 2 As a percentage of surveyed households that have not installed the sanitary latrine before the baseline survey. 3 In reference to households that have not installed a sanitary latrine by the midterm survey conducted in December They might have installed it later on. 4 Survey takers were told to mark all the sources from which they heard about the benefit of sanitary latrines % survey of takers noted more than one source. 5 The fraction is calculated on the commune level. 6 A household was only marked as poor or near-poor if they have a certificate of poverty or a confirmation of near poverty from the Communal People s Committee. 24 Summary Statistics are based on the midterm survey and are not weighted. 31

32 Table 3: Weighted Summary statistics for the Province of Hai Duong and Tien Giang by Treatment The weighting is based on the inverse sampling probability for each household. 32

33 Table 4: Weighted Summary statistics for the Province of Hai Duong and Tien Giang by Education Level 26 Variables Summary Statistics for the Province of Hai Duong Asymptotic z-ratio No Degree or Primary Secondary Vocational Training or Higher No Degree vs. Secondary No Degree vs. Vocational Knows About Septic Tank Latrines 93.36% 96.71% 98.65% Knows Septic Tank Latrines are hygienic 93.36% 96.71% 98.35% Has heard of benefits of sanitary latrines From the Women's Union 45.34% 52.07% 67.17% From other organizations 21.37% 32.95% 41.90% Family/acquaintance 3.65% 11.98% 12.05% From the TV 29.56% 36.22% 44.04% From broadcasting stations 7.53% 13.40% 12.47% From a communal broadcasting station 25.95% 41.50% 47.22% From a book/newspaper 0.00% 4.89% 8.83% From a leaflet/poster/flip-picture 0.00% 2.07% 5.82% From the internet 0.00% 0.00% 0.80% Variables Summary Statistics for the Province of Tien Giang Asymptotic z-ratio No Degree or Primary Secondary Vocational Training or Higher No Degree vs. Secondary No Degree vs. Vocational Knows About Septic Tank Latrines 73.93% 63.65% 86.54% Knows Septic Tank Latrines are hygienic 71.76% 62.27% 85.69% Has heard of benefits of sanitary latrines From the Women's Union 62.85% 70.56% 75.41% From other organizations 33.86% 40.63% 59.07% Family/acquaintance 20.21% 13.81% 21.58% From the TV 28.93% 33.47% 50.95% From broadcasting stations 7.89% 21.44% 24.51% From a communal broadcasting station 26.05% 38.08% 46.99% From a book/newspaper 5.36% 8.21% 16.60% From a leaflet/poster/flip-picture 7.74% 17.57% 22.01% From the internet 0.99% 1.77% 1.85% The weighting is based on the inverse sampling probability for each household. 33

34 Table 5: Summary Statistics for Neighboring Treatment Households: The neighboring households are defined as those within a 3 km radius of the household in question. The number of neighbors are weighted by the inverse of the sampling probabilities. 34

35 Table 6: 28 Probit Regression at the mean to detect if there is a systemic difference between the selected households (did not install the sanitary latrine before the baseline =0) and those who installed the sanitary latrine before the baseline, = 1: Characteristics Selected Households P>z Proportion of HHs Installed a SL before baseline Proportion of HHs Heard of Benefits of SL Knowledge of SL Knowledge that SL is Hygienic Education Level Primary Lower Secondary Education Upper Secondary Education Vocational Training or High Household Income Economic Condition Near Poor Non-Poor Has heard of benefits of sanitary latrines From the Women's Union From other organizations Family/acquaintance From the TV From a leaflet/poster/flip-picture Treatment 1 Standard Reb Only CCT Only The households income measure is inaccurate, but due to its importance in this regression, I included it. 35

36 Diagram 1: A Plot of Households in Hai Duong Province: Treatment and Control 30 Treatment Group Control Treatment 1 Treatment 2 Treatment 3 Missing 30 Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 36

37 Diagram 2: A Plot of Households Sanitary Latrine Installation in Hai Duong Province 31 Sanitary Latrine Did not install Installed before the baseline Installed after the baseline 31 Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 37

38 Diagram 3: A Closer Plot for Comparison Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 38

39 Diagram 4: A Plot of Households in Tien Giang Province; Treatment and Control Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 39

40 Diagram 5:A Plot of Households Sanitary Latrine Installation in Tien Giang 34 Sanitary Latrine Did not install Installed before the baseline Installed after the baseline 34 Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 40

41 Diagram 6: Closer Plots for Comparison Used Google Fusion Tables to plot the households, using longitude and latitude data from the CHOBA midterm survey. 41

42 Table 7: Panel and Cross-Section Logit Regressions for Poor and Near Poor Households in Hai Duong and Tien Giang; Marginal Effects Evaluated at the Mean Note: All regression coefficients are evaluated at the mean. All proportion calculations are weighted by the inverse sampling probabilities so that they are representative of the population. All the regressions look at poor and near-poor subgroup only. The regressions have education, income and knowledge level controls. Refer to appendix List 1 for a list of the control regressors. F/A stands for friend and/or acquaintance. HH stands for household. Regression (1) is using panel logit random effects model. It is not weighted or clustered. It includes both baseline and midterm regressors. All regressors are time-invariant except for knowledge variables and treatment. Regression (2) is cross-section logit model, not weighted or clustered. Regression (3) is cross-section logit model, weighted and clustered standard errors. Regressions (1) 1193 observations. Regressions (2) and (3): 986 observations. 42