Why Don’t People Take their Medicine? Experimental Evidence from Kenya

Edward Miguel* University of California, Berkeley

Michael Kremer Harvard University

November 2002

Abstract: The standard public finance approach to health care in less developed countries involves identifying and heavily subsidizing priority health interventions that generate treatment externalities (e.g., vaccines). A popular recent approach instead argues that health education, community mobilization, and cost-recovery are also necessary for successful programs. We focus on the case of intestinal worms – which affect one in four people worldwide – and provide evidence from a series of randomized evaluations that subsidies are more effective than other factors in influencing deworming drug take-up among schoolchildren in Kenya. An effort to educate children on worm prevention was ineffective at changing behavior. Those who were randomly exposed to more information about the deworming drugs through their social networks were significantly less likely to take them. A verbal commitment “mobilization” intervention from social psychology led to significantly lower treatment rates. In contrast, drug take-up was highly sensitive to cost: the introduction of a small fee led to a massive eighty percent reduction in treatment rates (relative to free treatment). Cost-sharing appears particularly inappropriate for medical conditions characterized by large treatment externalities – like worms and other infectious diseases. The results suggest that, to promote new health technologies in less developed countries, scarce public resources should be focused on subsidies.

* Contact: Edward Miguel ([email protected]) or Michael Kremer ([email protected]). The authors thank ICS Africa and the Kenya Ministry of Health, Division of Vector Borne Diseases for their cooperation in all stages, and would especially like to acknowledge the contributions of Elizabeth Beasley, Laban Benaya, Simon Brooker, Pascaline Dupas, Alfred Luoba, Sylvie Moulin, Robert Namunyu, Polycarp Waswa, Peter Wafula, and the entire PSDP field staff and data group, without whom the project would not have been possible. Gratitude is extended to the teachers, school children, and households of Busia for participating in the study. Melissa Gonzalez-Brenes and Tina Green have provided excellent research assistance. We thank George Akerlof, Guido Imbens, Botond Koszegi, David Laibson, Kaivan Munshi, Mark Rosenzweig, and Chris Udry for helpful conversations. We are grateful for financial support from the World Bank, the NIH Fogarty International Center (R01 TW05612-02), and U.C. Berkeley Center for Health Research. All errors are our own. 1. Introduction

Under a standard public finance approach, central government public health policy makers in less developed countries identify and heavily subsidize cost-effective public health interventions that generate positive treatment externalities. The successful national childhood vaccination campaigns of past decades serve as a model for such efforts. An alternative approach argues that intensive health education and local community mobilization – as well as cost-recovery from beneficiaries to finance these activities – are also necessary for successful public health programs. This split on the design of public health programs runs through World Bank reports on health policy (World Bank 1993, 1994,

2002).1 Yet little rigorous work has examined the impact of such efforts on health technology adoption, and their cost-effectiveness remains unproven.

We report on a series of randomized evaluations designed to promote deworming drug adoption in an area of rural Kenya where over 90 percent of school children suffer from intestinal worm infections. Worm infections also affect one in four people worldwide, and school-based mass treatment of worms was identified among the most cost-effective health interventions in the 1993

World Development Report (World Bank 1993). In a previous study, within the context of a randomized evaluation, we found that deworming drugs are highly effective at improving health and certain educational outcomes (Miguel and Kremer 2001). Still a large minority of children opted not to receive free treatment through the program.2

The empirical results in this paper indicate that increasingly popular public health approaches relying on health education, community mobilization, and cost-recovery may fail, and may even backfire and lead to lower drug treatment rates in a rural African setting. Cost-sharing appears

1 To illustrate, a recent World Bank (2002) report on water provision advocates the second approach, advising ministry of health officials: “Don’t provide hardware (water pipes and latrines) without the software (hygiene promotion) and community training and organization to sustain/maintain services.” 2 Technology adoption is central to the study of economic growth and development, especially related to the adoption of health, family planning, and agricultural technologies. Many health-related anomalies have been studied, including slow adoption of the flu vaccine and beta-blockers in the United States (Schneider et al. 2001, Krumholz et al. 1998), water boiling in Peru, the use of well water rather than stream water in Egypt (Rogers 1995), and safer sexual behavior during the AIDS epidemic (Philipson and Posner 1995).

1 particularly inappropriate for medical conditions characterized by large treatment externalities – like worms and other infectious diseases. In the remainder of this section we describe our other main finding, on social learning about deworming drugs.

1.1 Health Education and Social Learning

Intestinal worm infections can be reduced in several different ways. One approach emphasizes medical treatment with low cost and safe deworming drugs, while others have argued that a more sustainable approach also addresses the root causes of worm infection – which lie in poor hygiene – rather than simply using the “silver bullet” solution of medication (especially since re-infection is rapid and people must be treated twice per year with drugs to remain largely worm-free). We find that the original deworming program’s intensive school health education efforts had no impact on worm prevention behaviors over a year after the start of the program (Miguel and Kremer 2001). Of course, examining only “direct” health education impacts maybe misleading if health education has its biggest impacts by influencing the behavior of the social contacts of those who receive health education, through a process of social learning, but given that there was no direct health education effect in this setting, prevention behaviors are unlikely to have diffused to others. However, we are able to explore a different aspect of social learning – learning about the deworming drugs themselves.

It has been hypothesized that inadequate information on new technologies is a primary cause of slow adoption (Rogers 1995). The estimation of information social effects in this paper relies on the experimental design of the original deworming treatment program. The “early treatment” schools and “late treatment” schools were randomly selected, producing exogenous local variation in the proportion of individuals eligible for the deworming program. We then collected survey data on social networks and explore how exogenous variation in exposure to the deworming drugs through social contacts affected individuals’ own adoption decisions. The experimental identification of social effects is the main methodological contribution of this study.

2 Children whose parents have randomly more social links to early treatment schools in rural

Kenya are themselves significantly less likely to take deworming drugs in 2001: for each additional social link that a parent has to an early treatment school, her child is 3.2 percentage points less likely to take deworming drugs in 2001. This suggests that individuals with more exposure to the project learn not to take deworming drugs despite their benefits – an apparent case of failed social learning.

However, a simple learning model with rational agents is consistent with these seemingly anomalous findings. If individuals have prior beliefs on drugs benefits that are too “optimistic” – in the sense of expecting very large health gains from deworming – then additional information lowers posterior beliefs and drug take-up rates. In previous work, we found that mass deworming treatment leads to substantial externality health benefits for individuals near treated schools, since mass treatment reduces disease transmission and subsequent re-infection. These large externalities provide a plausible explanation for relatively low drug take-up, since they dramatically reduce private benefits to deworming and thus may have led people to hold overly optimistic priors (for instance, if people expected private benefits to be as large as they would have been in the absence of externalities). More broadly, the results indicate that social learning may fail to generate high drug adoption rates for diseases characterized by large treatment externalities, and that large subsidies may be necessary to increase take-up. This is likely to be especially important for Africa, where half of the disease burden is associated with infectious and parasitic disease (WHO 1999).

1.2 The structure of this paper

The remainder of the paper is structured as follows. Section 2 discusses medical aspects of worm infections, and competing health paradigms in rural western Kenya, and Section 3 describes the deworming project in Kenyan schools. Section 4 discusses the related literatures on social effects, verbal commitment, and cost-sharing. Section 5 presents a simple learning model, Section 6 lays out our estimation strategy, and Section 7 presents the empirical results. The final section concludes.

3 2. Intestinal Helminth Infections

2.1 Background on Worms (Helminths)

Over 1.3 billion people worldwide are infected with hookworm (Necator americanus, Ancylostoma duodenale), 1.3 billion with roundworm (Ascaris lumbricoides), 900 million with whipworm

(Trichuris trichura), and 200 million with schistosomiasis (Bundy 1994). The distribution of worm burden is typically highly skewed: most infected people have light infections, while a minority are heavily infected. Though children with light helminth infections are often asymptomatic, more severe worm infections can lead to iron deficiency anemia, protein energy malnutrition, stunting (a measure of chronic undernutrition), wasting (a measure of acute undernutrition), listlessness, and abdominal pain. Schistosomiasis may have even more severe clinical consequences.3

Helminths do not reproduce within the human host, so high worm burdens are the result of frequent re-infection. The geohelminths (hookworm, roundworm, and whipworm) are transmitted through contact with, or ingestion of, infected fecal matter. This can occur, for example, if children do not use a latrine and instead defecate in the fields near their home or school, areas where they also play. Schistosomiasis is acquired through contact with infected freshwater. For example, people in the study area in Kenya often walk to nearby Lake Victoria to bathe and fish. Children often exhibit greater prevalence and higher infection intensity than adults due to a combination of high exposure and immunological factors (Bundy 1988, Muchiri et al. 1996).

The geohelminths and schistosomiasis can be treated using low-cost single-dose oral therapies of albendazole and praziquantel, respectively. Existing studies show reductions in worm burden of more than 99 percent against schistosomiasis, hookworm, and roundworm a few weeks after treatment. But reinfection is rapid, with worm burden often returning to eighty percent or more of its original level within a year (Anderson and May 1991), and hence albendazole is taken twice per year and praziquantel is taken once per year. The World Health Organization has endorsed mass school-

3 Refer to Adams et al. (1994), Corbett et al. (1992), Hotez and Pritchard (1995), and Pollitt (1990).

4 based deworming in areas with prevalence over a fifty percent threshold, since mass treatment eliminates the need for costly individual screening (Warren et al. 1993, WHO 1987). Medical treatment with albendazole and praziquantel delivered through a large-scale mass treatment program may cost as little as 49 cents per person per year in Africa (PCD 1999).

Side effects for both albendazole and praziquantel are minor and transient, rarely lasting more than one day, but do include stomach ache, diarrhea, dizziness, fever and even vomiting in some cases

(WHO 1992). Praziquantel side effects are reported to be more severe for heavier schistosomiasis infections.4 Due to concern about the possibility that albendazole could cause birth defects (WHO

1992, Cowden and Hotez 2000), standard practice in mass deworming programs has been to not treat girls of reproductive age (Bundy and Guyatt 1996).

Medical treatment for helminth infections creates externality benefits by reducing worm deposition in the community and thus reducing re-infection among other community members

(Anderson and May 1991). School-aged children are likely to account for the bulk of helminth disease transmission for two reasons. First, children typically have the highest rates of geohelminth and schistosomiasis infections: Muchiri et al. (1996) find that school children account for 85-90 percent of all heavy schistosomiasis infections in eastern Kenya. Second, children are most likely to spread worm infections because they are less likely to use latrines and have worse hygiene practices (Ouma

1987): Butterworth et al. (1991) conclude that children “are likely to contribute most to transmission

[of schistosomiasis], by indiscriminate defecation around the water bodies.”

2.2 Views on Worm Infections in Western Kenya

The existing anthropological and sociological literatures on health technology adoption in western

Kenya reveals a society in transition, where many traditional health views are strongly held while the bio-medical paradigm makes inroads, especially among the younger and better educated. Geissler

5 (1998a, 1998b, 2000) studies deworming take-up and health attitudes in a western Kenyan district that borders our study area, and has a nearly identical worm infection profile, so his findings should be relevant for our setting.5 He finds that individuals in western Kenya are typically willing to participate in mass deworming treatment programs, but do not place much value on these programs because worms are not seen as a pressing health problem, especially compared to malaria or HIV/AIDS. As a result, there was essentially no treatment with deworming drugs outside of the school health program in his area, and most children instead relied on local herbal remedies to alleviate the abdominal discomfort caused by helminth infections.

Survey evidence indicates that this was also the case in our study area, where only five percent of children had received deworming drugs outside the program in the previous year (Miguel and

Kremer 2001). Health expenditure data from 2001 indicates that in only 0.4 percent of cases was household health spending on worms, while over 50 percent of the nearly 3 U.S. dollars in health- related expenses during the two weeks preceding the survey went for malaria or fever.6

Geissler argues that individuals in western Kenya have a well-developed set of traditional beliefs regarding the causes and consequences of worm infections, and these co-exist with bio-medical knowledge. In the traditional view, worms are an integral part of the human body and are necessary for digestion, and many symptoms of helminth infections – including malnutrition and abdominal pain

– are instead attributed to malevolent forces (“witchcraft”) or to the breaking of taboos (Government

4 The manufacturer of praziquantel, Bayer, states that “Side effects are usually mild and temporary and include abdominal pain, nausea, vomiting, headache, fever, pruritus, drowsiness. Side effects may be more severe in heavy infestations.” (home.intekom.com/pharm/bayer/) 5 Geissler studies a Luo area, while the majority of our sample are from the Luhya ethnic group (a Bantu- speaking group), though Luos are a sizeable minority in our sample. However, research from other countries suggests that traditional Luo views toward worm infections are closely related to views found among Bantu- speaking groups in other parts of Sub-Saharan Africa, including Mozambique (Green et al. 1994, Green 1997), South Africa (Zondi and Kvalsig 1987), and Central Kenya (Kloos et al. 1987). 6 We also conducted a survey in 1999 of all hospitals, health clinics, dispensaries, and pharmacies, as well as many local shops (dukas) in this area to assess the availability of deworming drugs. Although nearly all local shops have medicine for headaches (aspirin) and malaria (chloroquine), we found that none of the 64 local shops surveyed had either albendazole (or a close substitute, mebendazole) or praziquantel in stock, though a minority of shops carried less effective deworming drugs (levamisole hydrochloride and piperazine). This pattern again suggests a general lack of demand for deworming drugs in this area.

6 of Kenya 1986). Geissler (1998a: 68) illustrates the traditional view: “All people need worms to live.

… All food one eats is eaten by worms. … Worms can be appeased, but not killed by herbal remedies, because without worms, one dies.”

Some residents do believe that worms have negative health consequences, especially young and better educated adults, and most individuals in the area are in fact familiar with both traditional and bio-medical health paradigms and express different views depending on the context in which they find themselves. This implies that survey responses on worm attitudes in this area should be interpreted cautiously: some individuals may have thought of the surveys as a formal situation in which they were expected to demonstrate their knowledge of the Western bio-medical view, and so answers may be tilted in this direction, while in reality both health models affect decision-making.

3. The Primary School Deworming Project (PSDP) in Busia, Kenya

3.1 Project Design

We study the Primary School Deworming Project (PSDP), a school health program carried out by a

Dutch non-profit organization, Internationaal Christelijk Steunfonds Africa (ICS), in cooperation with the Busia District Ministry of Health office. The project took place in Budalangi and Funyula divisions of southern Busia district, a poor and densely-settled farming region in western Kenya adjacent to Lake Victoria. The 75 project schools consist of nearly all the rural primary schools in this area, with over 30,000 enrolled pupils between the ages of six and eighteen.

In January 1998, the PSDP schools were randomly divided into three groups (Group 1, Group

2, and Group 3) of twenty-five schools each: the schools were first stratified by administrative sub-unit

(geographic zone) and by their involvement in other non-governmental assistance programs, and were then listed alphabetically and every third school was then assigned to a given project group. Figure 1 shows the approximate locations of the sample schools, using global positioning system (GPS) data. 7

7 Appendix Table A1 present a detailed project timeline.

7 Due to administrative and financial constraints, the health intervention was phased in over several years, and included both deworming medicine and health education, specifically on worm prevention behaviors. The Group 1 schools participated in the program in 1998, 1999, 2000, and

2001, and Group 2 schools in 1999, 2000, and 2001, while Group 3 began participating in 2001. This design implies that in 1998, Group 1 schools were treatment schools, while Group 2 and Group 3 schools were the comparison schools; and in 1999 and 2000, Group 1 and Group 2 schools were the treatment schools and Group 3 schools were comparison schools. Starting in 1999, signed individual parental consent was required for deworming treatment, while in 1998 only community consent

(consisting of a series of community meetings and discussions) was required. The introduction of written consent coincided with a drop in treatment rates (Miguel and Kremer 2001). All schools met the 50 percent geohelminth threshold in all years of the study and were mass treated with albendazole, while only a subset were mass treated with praziquantel.

The project included intensive health education on worm prevention behaviors – hand washing, wearing shoes, and avoiding infected fresh water – through frequent classroom lectures and culturally appropriate health education materials (developed by the Partnership for Child Development in neighboring Tanzania). This effort was considerably more intensive than is typical in Kenyan primary schools, and thus the program should be more likely than most government programs to find significant impacts on worm prevention behaviors. The health education component was not cheap: our best estimate is that the primary school teacher training on worm prevention (one regular teacher and the headteacher were trained in each school), teacher lectures in school, the health education lectures by experienced NGO staff, and classroom wall-charts and other educational materials cost at least 0.44 U.S. dollars per pupil per year in the assisted schools8 – which is roughly comparable to the cost of deworming drug delivery per pupil per year, at 0.49 U.S. dollars (Miguel and Kremer 2001).

8 This costing is based on the estimates that the health education teacher taught just two hours per school year on worm prevention behaviors in each grade (given an annual teacher salary of approximately 2000 U.S. dollars), and that the NGO field workers also lectured to the school for two hours per year (given their salary of 4000 U.S. dollars); costs would be considerably higher under an even more ambitious program. We also assume that

8

3.2 Health and Education Impacts of Deworming

In previous work, we examined the impact of the deworming program on child health and primary school outcomes (Miguel and Kremer 2001). After two years, school absenteeism among the deworming treatment group fell by approximately one-quarter, or seven percentage points, on average

(Table 1, Panel B), and there were significant gains in several measures of health status, including worm load (Table 1, Panel A), growth stunting, anemia, and self-reported health (Table 2, Panel A), although there were no significant academic or cognitive test scores gains (results not shown).

The other main finding of the previous research is that deworming significantly reduced worm burdens and increased school participation among untreated children in the treatment schools, and among children in neighboring primary schools. The drop in moderate-heavy infection rates among untreated children in treatment schools was 80 percent as large as the drop among the treated children

(Table 1, Panel A), and they also showed large school participation gains (Table 1, Panel B).9 Cross- school externalities in other schools located within six kilometers of treatment schools were also large, at approximately 40 percent of the effect in treatment schools on average (refer to Miguel and Kremer

2001 for the detailed cross-school externality results).10 Thus observed differences in health and education outcomes across the treated and untreated children within a treatment school – in other words, the private benefit to individual treatment, beyond the externality benefit – are substantially

educational materials need to be replaced and teacher training repeated every four years, since materials fall apart and teachers are transferred between schools. 9 There is no statistically significant difference between the gains of treated and untreated Group 1 pupils. 10 Other existing work also suggests that deworming externalities are substantial. Five studies find reductions of up to fifty percent in infection intensity among untreated individuals in communities where school children received mass deworming treatment (Bundy et al. 1990, Butterworth et al. 1991; Holland et al. 1996; Muchiri et al. 1996; Thein-Hlaing, Than-Saw, and Myat-Lay-Kyin 1991). However, these studies rely on pre-post comparisons in the same villages to estimate externalities for untreated individuals. This leaves them without a plausible comparison group, which is problematic since helminth infection rates vary widely over time (Kloos et al. 1997). The randomized phase-in across schools of the deworming intervention that we examine allowed us to more credibly estimate deworming treatment spillovers (Miguel and Kremer 2001).

9 smaller than the overall average effect of the program (and the same is true for naïve differences across the treatment and comparison schools due to cross-school externalities).11

Given these smaller private benefits of deworming treatment, together with the non-trivial expected costs of treatment – parent need to walk to the primary school to sign the program consent book, need to make sure their children are present on the day of deworming treatment, and there is the possibility of drug side effects – the decision to free-ride and not obtain deworming treatment may be privately optimal for some people in areas receiving mass treatment, where externalities are large.

3.3 Effect of the Health Education Component

The school health education program on worm prevention had a minimal impact on behavior: there are no significant differences across treatment and comparison school pupils in early 1999 on three worm prevention behaviors: observed pupil cleanliness12, the proportion of pupils wearing shoes, or self- reported exposure to fresh water (Table 2, Panel B). The results do not vary substantially by pupil age, gender, or grade (results not shown). The possibility that children in treatment schools neglected to adopt better worm prevention practices precisely because they were also taking deworming drugs – and thus felt protected from infection – complicates the interpretation of these results. Still there is no evidence that the intensive health education program led to any measurable behavioral change.

4 Experiments in Drug Take-up

4.1 Estimating Social Effects in Western Kenya

Most empirical studies of social learning in technology adoption in agriculture, health, and family planning are subject to well-known critiques regarding the difficulty of credibly identifying social

11 However, the short-run ratio of private to social benefits is undoubtedly larger than this, since it takes time for local environmental contamination with worm larvae – and thus re-infection – to fall following mass deworming. 12 This also holds controlling for initial 1998 cleanliness levels, or using a difference-in-differences specification.

10 effects using non-experimental methods.13 This paper instead uses an experimental methodology to estimate social effects.14 The experimental design of the PSDP created exogenous random variation in the proportion of individuals in a given area whose children attend “early treatment” schools (Groups

1 and 2) and “late treatment” schools (Group 3). We test the hypothesis that the children of individuals with more social links in early treatment schools were more likely to take deworming drugs in 2001, conditional on their total number of links in project schools.

Social networks data was collected in the 2001 Parent Questionnaire and Pupil Questionnaire, surveys administered by experienced non-profit organization enumerators under the close supervision of field managers. The Parent Questionnaires were collected among a random subsample of parents with children currently enrolled in Group 2 and Group 3 schools. A random sample of children actually in school on the day of survey administration were also administered the 2001 Pupil

Questionnaire. Survey refusal rates were very low, as is typical in this region.

Parent Questionnaire respondents were asked for information on their closest social links: the five friends they speak with the most, the five relatives they speak with the most, additional individuals whose children attend local primary schools, and individuals they speak with specifically about child health issues. These relatives, neighbors, and friends are referred to collectively as the respondent’s “social links”. The Parent Questionnaire also collected information on the deworming treatment status of their social links’ children, and the effects of this treatment on health; how often the respondent speaks with each social link; which primary schools links’ children attend; and the global positioning system (GPS) location of their home, as well as the respondent’s knowledge of and attitudes toward deworming.

The Parent Questionnaire was administered in two rounds, with households randomly allocated between the rounds. The main difference is that Round 2 collected more information on the

13 Manski (1993) is the classic reference. Conley and Udry (2000) and Munshi and Myaux (2001) are two excellent studies that study technology adoption in less developed countries using non-experimental methods. 14 Several other recent papers use experimental methods, including our previous study of health externalities, Katz et al (2001), Ludwig et al (2001), Kremer and Levy (2001), Sacerdote (2001), and Duflo and Saez (2002).

11 impact of deworming treatment on social links’ children. This leaves us with three distinct samples in the analysis. Sample I is the principal sample, and contains 1690 parents surveyed in Rounds 1 and 2 with complete social network and child treatment data.15 Sample II contains the 890 parents with complete data surveyed in Round 2, and we use this sample to analyze the impact of social links’ deworming experiences on the respondent’s choices. Sample III contains pupil level information for the 1244 children with both complete 2001 Parent Questionnaire and Pupil Questionnaire data, and is used in the evaluation of the verbal commitment intervention (described in Section 4.2).

4.2 Commitments and Drug Take-up

Social psychology studies suggest that being asked to predict one’s own behavior (“self-prophecy”), and making verbal or written commitments may have a profound impact on actions, presumably because individuals strive for consistency in their words and deeds. The study most closely related to our work is Greenwald et al. (1987), who asked U.S. university students whether they would vote in an upcoming election. All voters in the sample were reminded that Election Day was coming up, and a random half of these voters were also asked if they intended to vote (all answered that they did).

Using county election records, they found that 81 percent of the voters who made the verbal commitment to vote actually did vote in the election, compared to only 57 percent of those just reminded about Election Day.16

In an application of this approach, a random subsample of pupils in the PSDP schools were asked whether they would take deworming drugs in the coming treatment round, thus providing experimental evidence on the effect of this mobilization technique on subsequent drug take-up. The Verbal Commitment intervention was administered to a subsample of children

15 Twenty percent of the households were dropped due to child ineligibility for deworming (if the child was an older girl excluded from treatment), due to missing parent network information or child treatment information, or difficulty matching observations across datasets. 16 Another related study is Cioffi and Garner (1998), who examine blood donation on a U.S. university campus.

12 interviewed in the 2001 Pupil Questionnaire, and consisted of the following script delivered by an enumerator (in Swahili):

Doctors have found that worms and schistosomiasis lead to poor health and nutrition in children. They often make children weak and tired. There are some good drugs that can get rid of these worms. ICS will be coming to your school to give out the drug for intestinal worms. We will come on [date]. The Headteacher will remind you of the date just before so that you don’t forget. It will be good if you can be there on that day. (1) Are you planning to come on the treatment day?17 (2) We have to decide how many pills to bring on that day. Shall we bring a pill for you?

Ninety-eight percent of children answered “Yes” to both questions. The random subsample of pupils selected for the survey (including both those offered the opportunity for verbal commitment and those not offered this commitment) were informed that participation in the data collection and in deworming treatment was completely voluntary, as well as information on the effects of deworming and on the date of upcoming medical treatment.

4.3 Cost-sharing for Deworming Drugs

Cost-sharing through user fees has been advocated as necessary for the sustainability of public health services in many less developed countries, due to government budget cuts and the limited resources of non-governmental organizations (World Bank 1993). User fees should theoretically promote more efficient use of scarce public health resources, since free treatment leads to wasteful spending and those willing to pay for services should ideally be those in greatest need.18 Although a number of studies from Africa have found massive drops in health care utilization after the introduction of user fees (e.g., McPake 1993) – including in Kenya, where Mwabu et al (1995) find health care utilization fell by 52 percent following the introduction of user fees in 1989 – it remains unclear to what extent user fees have causally affected the utilization of health services, since cost-sharing is typically introduced during periods of fiscal crises, making it difficult to separate out the effect of crisis from the effect of cost-sharing.

17 Children could answer “Yes”, “No”, or “Do not know” to these two questions.

13 We employ a prospective design to investigate how the introduction of user fees affected parent participation in the Primary School Deworming Project, the first such study (to our knowledge) in sub-Saharan Africa.19 Of the fifty Group 1 and Group 2, schools twenty-five were randomly selected to pay user fees for medical treatment in early 2001, while the remaining twenty-five continued to receive free medical treatment. Comparing take-up rates across the cost-sharing and free treatment schools allows us to identify the effect of user fees on take-up. The deworming fee was set on a per family basis, like most Kenyan primary school fees, and this introduces further within-school variation in the cost of deworming that we also use to estimate the impact of price on take-up.

Of the thirteen Group 2 schools participating in cost-sharing, nine received albendazole at a cost of 30 Kenya shillings per family (approximately 0.40 U.S. dollars in 2001), and four schools received both albendazole and praziquantel at a cost of 100 Kenya shillings per family (approximately

1.30 U.S. dollars).20 Parents have 2.7 children in school on average, so the average cost of deworming per child in the cost-sharing schools was slightly more than 0.30 U.S. dollars – a partially subsidized price, since the average per child treatment cost of the program was approximately 1.49 dollars

(Miguel and Kremer 2001).21

4.4 2001 Survey Summary Statistics

Summary statistics are presented in Table 3. Children in approximately 75 percent of households in the free treatment schools received deworming drugs in 2001, while the rate was only 18 percent in cost-sharing schools, for an overall average of 0.61. On average, respondents have 10.2 social links with children in primary school, of whom 4.4 attend the respondent’s child’s own school, 2.8 attend other project schools (Groups 1, 2 or 3), and 1.9 attend nearby “early treatment schools” (Groups 1

18 Refer to Jack (1999) for a general discussion of health care finance issues in less developed countries. 19 In another experimental study, Gertler and Molyneaux (1996) found that increased user fees led to sharply reduced medical service utilization in Indonesia, especially on expenditures for children, although cost-sharing in that study was only randomized across a few districts. 20 Thirty-three of the fifty Group 2 and 3 schools received only albendazole (since schistosomiasis was not a problem in those schools), while the remaining 17 schools received both albendazole and praziquantel.

14 and 2). There is considerable variation across individuals in the number of early treatment links, the standard deviation is 2.0, and approximately one-third of respondents have no social links to Group 1 or Group 2 schools, one-third have one or two links, and one-third have three or more such links.

5. A Simple Social Learning Model

Imagine an individual i in school j deciding whether or not to adopt a new technology, Tij ∈ {0, 1}, in our case, a deworming drug for their child. With homogeneous individuals, we represent the expected

private benefit of adoption by φ = E[V (Tij =1) − V (Tij = 0)] , which may include gains in health, education, and other dimensions. This is the private deworming benefit conditional on the deworming treatment choices of other individuals. As we mention above, the existence of large epidemiological externalities to deworming treatment is likely to reduce this private benefit in mass treatment areas, relative to areas where few individuals had previously taken the drugs, and this may lead individuals to hold overly optimistic priors on the benefits of deworming.

Individuals share a common prior on the expected private benefits, denoted φ0, which may be greater or less than the actual expected benefit, φ. Individuals combine their prior belief with additional information they receive from social links to early treatment schools. Individuals are

E randomly assigned N ij early treatment links, and each such link yields an additional “signal” about the private benefits of adoption. We assume that each social link passes along accurate information about the expected deworming benefits based on their own experiences and their observations of others in their community (we modify this below). Under Bayesian updating, the posterior belief on

E expected benefits for an individual with N ij early treatment school links is:

E E E E[V (Tij =1) − V (Tij = 0) | N ij ] =α(N ij ) ⋅φ0 + (1 − α(N ij )) ⋅φ (1)

21 Annual income per worker in Kenya is 570 USD, but incomes are much lower in Busia (World Bank 1999).

15 E E −1 where α(N ij ) = (1 + N ij ) . As individuals accumulate additional information through their social network, their posterior beliefs approach the true expected benefit.22 Individuals are not learning about the complicated underlying epidemiological model of deworming treatment spillovers here, but rather simply compare observed outcomes across treated and untreated children to estimate the private treatment benefit – the variable of interest to them. There is also an individual-level idiosyncratic benefit to adoption (discussed more in Section 6 below) which we take to be normally distributed, and

E thus the probability of adoption increases in expected benefits, conditional on information, N ij :

E Pr(Tij = 1) = Φ(E[V (Tij = 1) −V (Tij = 0) | N ij ]) (2)

When the prior is greater than the actual expected benefit (φ0 > φ), by Equation 1 those with more early treatment social links have lower posterior beliefs about expected benefits, and thus the likelihood of adoption declines in the number of early treatment links. The prior on private benefits to adoption may be too “optimistic” if individuals neglect to consider treatment externalities in areas of mass deworming treatment, and instead expect private benefits to be as large as they would have been in the absence of externalities.

From Equation 1, the decline in the probability of treatment with respect to early treatment links will be convex, as in Figure 2; the decline in the likelihood of treatment is decreasing in each additional early treatment link as the posterior asymptotically approaches the true expected benefit.23

5.1 Heterogeneous Prior Beliefs and Information

We extend the model by allowing heterogeneous prior beliefs and information. The prior is a function of an individual characteristic Xij, φ0,ij = φ0(Xij), and for concreteness we assume that φ0′ > 0. In the context of rural western Kenya, formal schooling is an important predictor of receptiveness to new

22 For simplicity, we do not explicitly model the time dimension. 23 In the case where the prior is below the actual benefit (φ0 < φ), the probability of treatment is, conversely, increasing and concave in the number of early treatment links.

16 Western health and contraceptive technologies (for example, Akwara 1996 and Kohler et al 2001), and such individuals are thus likely to have more “optimistic” priors. When φ0(Xij) > φ for all Xij, individuals with more education have higher adoption rates, but each additional early treatment link leads to a sharper drop in their adoption, as all individuals converge to the true beliefs. Formally,

2 E ∂ E[V (T ij= 1) −V (Tij = 0) | N ij ] − φ0 '(X ij ) E = E 2 < 0 (3) ∂N ij ∂X ij (1+ N ij )

Different early treatment links may also provide more or less favorable information regarding

H deworming. For instance, individuals may have N ij links who report that the technology has a high

L payoff (φH) and N ij links who report it has a low payoff (φL), where φH > φ >φL. High (low) payoff links might be those that experienced large (small) treatment benefits themselves, or those who observed large (small) benefits in their community. The number of high payoff links has a more positive impact on take-up than low payoff links in this framework, and people who receive more favorable information are more likely to adopt.

5.2 Heterogeneous Treatment Effects

E Treatment benefits may be a function of an individual characteristic, Wij = W( N ij ), such that φij =

E φ(W( N ij )). For this study, Wij may be thought of as the individual worm infection level, where those with higher infection levels should have a greater benefit from deworming treatment, φ′ > 0. Infection levels are a function of social links due to epidemiological externalities: children whose families have close social interactions with households in early treatment schools may experience lower helminth re- infection rates and substantial reductions in infection intensity. Thus in this setting W′ < 0. The impact of early treatment links on the likelihood of adoption is presented in Equation 4 (where we make the convenient assumption that φ0′ = φ′ and φ0′′ = φ′′ = 0 at all infection levels):

17 ∂E[V (T =1,W (N E )) −V (T = 0,W (N E )) | N E ] ij ij ij ij ij (4) E ∂N ij E E φ(W (N ij )) − φ0 (W (N ij )) E =  E 2  + {}W '(N ij ) ⋅φ'  (1+ N ij ) 

The first term on the right hand side is the information social effect, and is negative for the case where priors are too optimistic, as above. The second term is the infection social effect, and this term is also negative, because having more early treatment links leads to lower infection levels (due epidemiological externalities), and this in turn reduces the benefits to treatment and lowers take-up.

We argue below that the infection social effect is likely to be small – since child infection levels only weakly affect deworming take-up – and thus that the impact of early treatment links on drug take-up is largely driven by the information social effect.

6. Estimation Strategy

The main outcome measure is an indicator variable for deworming treatment in 2001, although we also employ the same framework to examine the effect of social links on deworming knowledge and attitudes. The analysis is conducted at the household level using probit estimation, and the main outcome measure takes on a value of one if any child in the household received treatment with deworming drugs in 2001, and zero otherwise (though results are similar if the analysis is conducted

24 using the child as the unit of observation, results not shown). Tij is the dependent variable, the 2001 treatment indicator, where j refers to the school and i to a household in that school. The idiosyncratic benefit term, εij, captures unobserved variation in parent beliefs about deworming, costs to obtaining treatment, or whether the pupil was sick on the day of deworming treatment (which increases the cost of attending school that day). From Equation 2 above, the individual treatment decision becomes:

24 Treatment choices across children in the same family are highly correlated due to the common parent decision that underlies them, and hence the focus is mainly on the household as the unit of observation.

18 E 1 if α + X ij ' β + λN ij + ε ij > 0 Tij =  (5) 0 else

E The term N ij is the number of parent social links in early treatment schools (not including the respondent’s own school), where “early treatment schools” in 2001 are Group 1 and Group 2 schools.25 We also examine the proportion of social links in treatment schools, rather than the number of links, in certain specifications as a robustness check.

Among the other explanatory variables, Xij, we include total links to all program schools other than the respondent’s own school, as well as the number of links to non-program schools (in the vector

N ij ). Given the experimental design of the program, the number of social links to early treatment

schools is randomly assigned conditional on total social links to other program schools. Vij takes on a value of one for children randomly chosen for the verbal commitment intervention. The cost-sharing indicator variable, Cj, takes on a value of one for schools participating in the cost-sharing project, though in some specifications we also include the deworming drug price per child and family structure controls. Zij are additional household socioeconomic characteristics (education level of parents and asset ownership), demographic characteristics (respondent fertility), and other controls (respondent membership in local community groups, a Group 2 school indicator, and child helminth infection status in some specifications) that may affect perceived deworming costs and benefits. Idiosyncratic disturbance terms may be correlated within each school as a result of common influences, such as headmaster effort. Equation 6 presents the main empirical specification:

E Pr(Tij =1) = Φ{α + λ1 N ij + N ij 'λ2 + δVij + γC j + Z ij 'θ + ε ij } (6)

The framework is extended to include different types of social links – to close friends (with whom the respondent speaks at least twice per week) versus distant friends, for example – as explanatory variables, shedding light on their relative importance in information transmission. We

19 also include interaction terms between household characteristics and social links in some specifications to explore the possibility of heterogeneous treatment effects (for example, for individuals with different levels of education).

Social links’ experiences with deworming may also affect the information individuals receive.

In particular, we test whether take-up is higher when social links had “good” experiences with deworming. This non-experimental analysis may suffer from omitted variable bias – people with favorable reactions to deworming may move in the same social circles, and individuals who report their links had good effects may themselves be more positive toward treatment – but it complements the experimental estimates. Social links’ own take-up choices may also provide information on the benefits of treatment, or they may affect individual adoption through pure imitation effects.26

Similarly, the deworming experiences and choices of people in social links’ communities may affect respondent take-up. For each study school, we compute the average difference in school attendance between treated and untreated pupils in 1999, and use this to classify schools into “large treated minus untreated difference” schools (those above the median difference) and “small treated minus untreated difference” schools. The treated-untreated difference is a measure of the average observed private benefit to deworming in that school. We also categorize links’ schools into “high deworming take-up” schools in 1999 (those above the median take-up rate) and “low deworming take-up” schools, and estimate whether social links to schools with a “good” reception to deworming have a different impact than links to schools with a “bad” reception.

6.1 Identifying Assumptions

The deworming project experiments succeeded in creating “treatment” and “comparison” groups that are similar along a range of characteristics. The number of social links to early treatment schools, the cost-sharing indicator, and a Group 2 indicator variable are generally not significantly associated with

25 Results are similar if we consider Group 1 and Group 2 social links separately. 26 Munshi (2002) discusses estimation of the impact of social links’ experiences versus their choices.

20 observable characteristics (Table 4), including parent years of education, community group membership, the total number of children in the household, asset ownership (iron roof at home), and the distance from home to the primary school (although the number of early treatment links is positively and statistically significantly associated with iron roof ownership in one specification), or with household ethnic group or religious affiliation (results not shown). The verbal commitment intervention indicator is also robustly not significantly associated with any of these household characteristics, in the 2001 Pupil Questionnaire sample (results not shown).27

Even though the randomizations were largely successful, social links to early treatment schools could potentially affect adoption through the infection social effect described above. We find that children with additional social links to early treatment schools do in fact have somewhat lower rates of moderate-heavy helminth infection as expected (Table 4, regression 6), but the effect is relatively small and not statistically significant (coefficient estimate –0.014 and standard error 0.018, relative to a mean infection rate of 0.28).

In terms of the second link – from infection status to treatment decisions – we find that prior infection status is not significantly associated with drug treatment for either Group 1 in 1998 or Group

2 in 1999 (Table 1), and the point estimates suggests that moderate to heavy worm infection is actually somewhat negatively related to treatment rates. Of course, the cross-sectional correlation between infection status and drug treatment rates cannot be interpreted as causal due to omitted variable bias: children from unobservably low socio-economic status households may both have high infection rates and low take-up, for example. However, the treated and untreated look remarkably similar along many observable baseline socioeconomic and health characteristics (Table 1), weakening the case for strong selection effects into deworming treatment. Additional evidence on the weak impact of changes in infection status on deworming drug take-up is provided by cross-school infection externality estimates from 1999, which are identified using the exogenous variation in the local

27 This is not surprising given that the unit of randomization for the verbal commitment intervention was the pupil (rather than the school) and there are a large number of pupils in Sample III (1244 in all).

21 density of early treatment schools (Miguel and Kremer 2001). Although we find large average reductions in moderate-heavy worm infection rates as a result of cross-school externalities (23 percentage points with a standard error of 7 percentage points), proximity to early treatment schools leads to an average reduction in drug take-up of only 2 percentage points (standard error 3 percentage points), which has the expected sign but is near zero and statistically insignificant (results not shown).

Taken together, the evidence indicates that infection levels only weakly affect take-up, if at all.

In any case, the relationship between infection status and treatment rates would have to be implausibly large and positive for changes in health status to explain anything more than a small fraction of the total estimated social effect . For example, if eliminating a moderate to heavy infection reduced the likelihood of take-up by a massive fifty percentage points on average, health externalities could only account for a (0.5)*(0.014)=0.007 reduction in deworming treatment rates, or approximately twenty percent of the total social effect estimated below. We conclude that infection social effects cannot account for the overall effect, and that the effect is instead driven by information.

Pupil transfers among local primary schools during the course of the study are another potential source of bias. For example, parents with more health-conscious social links – whose children may have been more likely to transfer to early treatment schools to receive deworming – may themselves also be more health-conscious and eager to have their own children receive medical treatment, biasing the estimated effect of the number of early treatment social links upward. However, the rate of pupil transfers between treatment and comparison schools in 1998 and 1999 was low and nearly symmetric in both directions, suggesting that the transfer bias is likely to be small regardless of its direction (Miguel and Kremer 2001). Moreover, the hypothesized transfer bias is positive, so it cannot account for our negative social effect estimates.

Another identification issue is whether social networks measured in 2001 – three years after the program started – were themselves affected by the program. Social effect estimates could be biased to the extent that health-conscious individuals tended to become socially “closer” to individuals with children in early treatment schools, and are thus more likely to name them as social links in the

22 survey, but we think this is unlikely for several reasons. First, most social links are close relatives or neighbors with whom respondents appear to have stable social ties. Second, the number of social links to early treatment schools is nearly exactly two-thirds of the total number of links to project schools, indicating that respondents were no more likely statistically to name early treatment links than links to other local schools.28 Third, if pupils did not even disproportionately transfer into early treatment schools, it seems implausible that households would change their closest social links in response to the program. Finally, any endogenous network formation bias would again be positive, not negative, and thus cannot explain our estimates.

7. Empirical Results

7.1 Survey Evidence on Deworming Take-up

Table 5 describes parent attitudes toward deworming among Group 2 parents, schools that had already received two years of deworming by 2001. Sixty-six percent of parents whose children had been treated reported that deworming improved their health, nineteen percent said it had no effect, fifteen percent reported side effects, and fourteen percent did not know the effects (Panel A). Reported side effects take the form of transient gastrointestinal discomfort, and occasionally vomiting or diarrhea.

Lack of awareness about the deworming program, as well as chance – for example, illness on the day of drug administration – appear to have driven low take-up, rather than active opposition to deworming. The main stated causes of non-compliance are lack of parental consent for medical treatment (70 percent, Panel B) and pupil absence from school (15 percent). In some cases (8 percent of the total), illness was the cause of pupil absence. (This constitutes a partial explanation for the weak correlation between child infection levels and treatment rates, namely that children with serious worm infections are somewhat more likely to miss school on treatment days due to illness.) The most

28 The average number of links to early treatment schools is 1.92, while (Total number of links to project schools) * (Total # Group 1 and Group 2 pupils / Total # Group 1, Group 2, and Group 3 pupils) =1.91. Although this alone does not guarantee that individual social networks did not change in response to the program, it is strong suggestive evidence that the program did not lead to large shifts in network composition.

23 common stated causes of no parental consent include not knowing that signed consent was required

(58 percent, Panel C), being too busy to go to school to sign the consent book (16 percent), too forgetful (5 percent) or too ill (1 percent) to sign the consent book, or that the respondent’s spouse

(usually husband) was supposed to sign but did not (9 percent). A very small number of respondents stated that they did not sign because of their opposition to deworming (2 percent) or because they believe worms are not a serious health problem (1 percent), although it remains possible that more parents do in fact oppose deworming treatment for their children but were reluctant to state this potentially confrontational view in the presence of the NGO enumerator.

7.2 Social Effect Estimates

Each additional social link to an early treatment school is associated with 3.2 percentage point lower likelihood that the respondent’s children received medical treatment in 2001, and this effect is significantly different than zero at over 95 percent confidence (Table 6, regression 1). 29 This suggests that the respondent’s relatively small, self-defined reference group has a major impact on health choices. Figure 3 graphically presents the non-parametric social effect estimates (using a Fan local regression with an Epanichnikov kernel, conditional on the explanatory variables) and indicates that the relationship between the number of early treatment links and take-up is negative and convex, as predicted by the theory.

None of the demographic or socioeconomic controls is significantly associated with 2001 take-up, except for distance from home to school, which is negatively related to take-up (this makes sense, since going to the school to provide written consent is more costly for parents in distant households). Children in cost-sharing schools are also 62 percentage points less likely to receive deworming in 2001, and this effect is statistically significant at 99 percent confidence (we discuss this result in Section 6.5 below).

29 Marginal probit coefficient estimates evaluated at mean values are presented.

24 Social effects are larger for Group 3 schools (point estimate –0.041) than for Group 2 schools

(point estimate –0.024), although the difference is not statistically significant (Table 6, regression 2).

This result is consistent with the theoretical model: Group 2 parents have observed the impact of deworming treatment in their own community and household, and should thus be less influenced by the experiences of their early social links than Group 3 parents (by Equation 1). Nonetheless, it is remarkable how persistent the influence of early social links is on their behavior after two years of treatment. One plausible explanation is that early pieces of information carry disproportionate weight in subsequent individual decision-making (Rabin and Schrag 1999).

The results are robust to including the proportion of social links with children in early treatment schools rather than the number of such links (Table 6, regression 3), controlling for the total number of links to program schools and to other schools. Close social links – defined as individuals with whom the respondent speaks at least twice per week – largely drive the negative reduced form result: each additional close social link to an early treatment school is associated with 3.6 percentage points lower probability of deworming treatment in 2001, while the effect on distant social links is negative but statistically insignificant (regression 4), although we are unable to reject the hypothesis that social effects are the same for close and distant links (p-value=0.29). Social effects are more strongly negative for respondents with more years of education, and are essentially zero for respondents with no formal education (regression 5). This is consistent with the theoretical prediction that those with the most “optimistic” priors should experience the largest drops in take-up when they receive more information about deworming.

The social effect results are robust to a specification without socioeconomic control variables

(Appendix Table A2, Regression 1), as well as to the inclusion of a rich set of ethnic and religious controls and indicators for whether the individual is a member of the dominant local ethnic and religious group (Regression 2); none of the six ethnic group indicator variables is significantly related to take-up, suggesting that ethnic group-specific “mobilization” is unlikely to be driving take-up

25 patterns.30 The results are similar when the local density of early treatment school pupils (those located within three kilometers of the respondent’s home) and the density of all local primary school pupils are included as controls (Regression 3), but the point estimate on early treatment links becomes smaller by about one-third and statistically insignificant – possibly because the local density measures pick up the effect of interactions with other local individuals not mentioned in the roster of social links. More social links to Group 1 schools (the early treatment schools in 1999) was also associated with significantly lower 1999 take-up rates among Group 2 households: 2.0 percent points lower take- up for every additional social link in a Group 1 school, and this effect is statistically significant at 99 percent confidence (Table A2, Regression 4), thus similar social effects prevailed in the first year of the project as in later years.31

There is no evidence that the treatment choices and experiences of social links, and the experiences of others in links’ schools, affected take-up decisions in our setting. Early treatment links to schools with low deworming take-up rates do have a more negative effect on respondent treatment rates (Table 7, regression 2, point estimate –0.037) than links to schools with high take-up (point estimate -0.026), and this is consistent with the theoretical prediction in Section 5.2, but the difference between these coefficient estimates is not significantly different than zero. Similarly, there is no statistically significant difference between take-up as a function of links to schools where the difference in school participation between treated and untreated pupils was large, versus those where the difference was small (Regression 3).

The choices and experiences of the social links themselves could also affect respondent choices. One drawback of the dataset is that we only have information on social link deworming

30 Of the seven of religion indicator variables, only Anglican (positive) and Muslim (negative) are significantly related to take-up. The project field officers believe that part of the low take-up rate among Muslims could be due to the fact that the non-profit organization has a “Christian” affiliation, although it does not proselytize. 31 Although we focus on parent social links in this paper, we also collected pupil social network information for a subsample of pupils. As expected, the proportion of pupil links to early treatment schools is strongly correlated with the proportion of parent links (correlation coefficient 0.3). We found that pupil early treatment links are also negatively, though insignificantly, related to deworming drug take-up.

26 outcomes provided by the respondent herself (we did not collect the full names of social links for privacy reasons, and are thus unable to match them to the database). There is no evidence that social links’ treatment choices have an impact on individual adoption (Table 7, regression 4), and thus this non-experimental social effect estimate is markedly different than the experimental estimates, which are consistently negative. However, there is weak evidence that having more social links whose children had “good effects” from deworming is associated with somewhat higher take-up among respondents, while those who had more links with “side effects” are less likely to be treated

(regression 5) – the p-value on the hypothesis test that the two coefficient estimates are equal is 0.21.

Respondents with more social links to early treatment schools are significantly more likely to claim that deworming drugs are “not effective” (Table 8, row 1). The existence of large within-school health externalities from mass deworming could partially explain these results: people who observe children in their own school community – some of whom received deworming drugs and some of whom did not – will correctly infer that the private benefits to deworming are relatively small (in the presence of large treatment externalities), and will thus underestimate the real impact of mass deworming treatment. We find no significant impact of additional early treatment links on the belief that deworming drugs are “very effective” (although the point estimate is negative, row 2) or that the drugs have “side effects” (row 3).

Although early treatment links affect attitudes, they do not affect beliefs that “worms and schistosomiasis are very bad for child health” (Table 8, row 4). Of course, it is possible that parents simply say what they believe that enumerator wants to hear (or repeat the standard Western bio- medical view) regarding the health consequences of worms, as suggested by Geissler (1998a): 92 percent of the respondents claimed that helminth infects are “very bad” for child health. The number of social links to early treatment schools has no effect on parents’ (self-reported) claim to “know about the ICS deworming program” (row 5), “know about the effects of worms and schistosomiasis” (row

6), or their knowledge of any worm infection symptoms (rows 7-10), although parent years of education and community group membership are strongly positively correlated with all of these

27 outcomes (results not shown).32 On the other hand, the actual number of treated social links, and the number of social links with whom the respondent speaks about deworming, are positively and significantly related to nearly all deworming attitudes and measures of deworming knowledge, once again highlighting important differences between experimental and non-experimental social effect estimates. It appears that individuals with (unobservably) more interest in child health discuss worms more frequently with their social links, who are themselves more likely to have their children receive deworming treatment. Thus correlation within social networks is driven by omitted variables rather than social effects in this case.

To summarize this sub-section, the social effect results are consistent with a simple learning model in which individuals’ prior beliefs on private drug benefits are overly “optimistic” – in this case, because large deworming externalities in mass treatment areas reduce the observed private returns to adoption. First, non-parametric social effect estimates suggest that the relationship between additional early treatment social links and drug take-up is both negative and convex, as predicted by the learning model. Second, educated individuals – who are generally more receptive to novel medical technologies, and thus have the most “optimistic” priors – show the sharpest negative effect of additional early treatment social links on drug take-up. Third, parents with more links to the early treatment schools are significantly more likely to believe that deworming drugs are “not effective”, direct evidence on the impact of additional information on the evolution of beliefs. Fourth, there are somewhat larger estimated social effects on 2001 deworming drug take-up among parents whose children just began receiving deworming in 2001 (Group 3), compared with parents whose children had already experienced two years of deworming in their schools and thus have more independent information (Group 2). Overall, the results indicate that social learning may fail to generate high adoption rates for treatments characterized by large externalities.

32 The weak effects of early treatment social links on knowledge of worm infections may not be surprising, given there is no evidence the original project’s health education component had any impact on child worm prevention behaviors (Miguel and Kremer 2001).

28

7.3 The Impact of Verbal Commitments

The verbal commitment intervention appears to have backfired, reducing drug take-up by nearly seven percentage points in 2001 (Table 9, regression 1). This result is robust to controls for pupil age and gender (Regression 2), and the impact of the intervention did not vary significantly with age or sex

(Regression 3). The negative effect of the commitment intervention on treatment rates is somewhat larger for pupils in cost-sharing schools and those with moderate to heavy worm infections, although in neither case are the coefficient estimates significantly different than zero at traditional confidence levels (results not shown). The survey enumerators believe that the intervention led to confusion and increased suspicion of the NGO among some children, and we intend to explore the underlying causes of this result in greater depth in future research.33 More broadly, the results indicate that mobilization or marketing techniques found to be effective in the U.S. may fail in other contexts.

7.4 Cost-sharing results

The introduction of the small deworming fee dramatically reduced the treatment rate, by 62 percentage points (Table 10, regression 1), and the effect is similar across households with different socioeconomic characteristics (regression 2), providing further evidence on the low value most households in this area attach to deworming treatment. Cost-sharing had roughly the same effect on drug treatment rates regardless of the actual price that the household was required to pay per child

(Table 10, Regression 3).34 Variation in the deworming price per child was generated by the fact that cost-sharing came in the form of a per family fee, so parents with more children in primary school

33 The following quote from a field report illustrates: The children had mixed reactions to the encouragement [commitment] intervention. … They were wondering why we were pleading with them. … Arising from the suspicion, some children claimed to have been told by their parents not to take the ICS drugs.” 34 This result would not be surprising if the bulk of the overall cost of paying for deworming is the time, effort, and money needed to travel to the child’s primary school (which may be several kilometers away) in order to pay the fee. However, most parents already attend several school meetings per year, and may travel to a market – often located near their child’s primary school – once per week to trade, so we do not believe that the travel cost is likely to be prohibitively large in most cases.

29 faced a lower price per child; this specification also includes the inverse of the number of household children in treated primary schools as an explanatory variable in an attempt to control for the effect of household composition on demand. There is a moderate but statistically insignificant decrease in take- up in the albendazole and praziquantel schools (100 shillings per family) relative to the albendazole schools (with a deworming fee of 30 shillings per family) (regression 4, p-value=0.17), although the interpretation of this result is complicated by the fact that the actual drug treatment regime differs across these schools as well.35 The bottom line is that adequate drug subsidies – pushing the price down to zero – had massive effects on deworming take-up.

The cost-sharing results indicate that most parents in this area do not place much value on deworming drugs, despite the high worm infection rates and demonstrated effectiveness of the drugs, and there are a number of plausible explanations for this seeming anomaly. First, the traditional view in this area is that worms either have no effect on health, or may even be positive agents of digestion, and some parents in the area approached deworming with this prior. Once free deworming treatment was introduced most parents did elect to have their children treated: over 70 percent of parents in free treatment schools had their children treated in 2001, suggesting there was not widespread opposition to the drugs. Yet parents observed only moderate private adoption benefits due to large deworming treatment externalities (Table 1). As a result, those parents with exogenously more information about the program through their social network were significantly less likely to have their children participate in the program (Table 6), and they were more likely to believe that deworming drugs were

“not effective” (Table 8). Given the resulting beliefs, most parents opted not to pay 30-100 shillings for the drugs (though 17 percent still did).

Several other factors are likely to have played a role in leading people to place less value on deworming. Serious worm infection levels had fallen substantially in the cost-sharing schools by 2001

35 The reduction in treatment due to cost-sharing is not simply a result of the fact that only the sickest pupils choose to seek treatment in cost-sharing schools. In fact, sicker pupils were no more likely to pay for deworming drugs than healthier children: the coefficient estimate on the interaction between 2001 helminth infection status and the cost-sharing indicator is not significantly different than zero (results not shown).

30 – after several years of mass deworming in these communities – leaving fewer children who would gain the most from treatment (Miguel and Kremer 2001). Another plausible impediment is the fact that the people who provide written consent for treatment and need to pay for the drugs (parents) are not the same people who directly benefit from treatment (their children). This further reduces drug take-up – even in cases where there are no deworming treatment externalities – to the extent that there is imperfect altruism and inefficient bargaining within households.36

8. Conclusion

Our results indicate that a public health program relying on the combination of health education, an individual mobilization technique, and cost-recovery was not effective at boosting drug take-up in rural Kenya. The only approach that consistently improved deworming drug take-up in rural Kenya was providing the drugs for free, as introduction of a small fee led to an 80 percent drop in take-up

(relative to free treatment). Cost-sharing is particularly inappropriate when there are large treatment externalities – as for deworming, and the treatment of many other infectious diseases. The results also indicate that social learning may not lead to higher take-up of such technologies, where the observed private benefits to adoption are relatively small.

Of course, it remains possible that more effective health education and mobilization programs could be developed (although it is unlikely that most large-scale government programs would have field staff and management as capable as ICS). Yet in the absence of credible empirical evidence demonstrating that the education, mobilization, and cost-recovery “package” is effective at promoting the adoption of new health technologies, we feel that the burden of proof should rest with those advocating such an approach, and that public health resources in less developed countries should instead be focused on providing large subsidies – potentially including negative prices – for a limited set of highly-cost effective health technologies that generate positive health externalities.

36 Udry (1996) and Dercon and Krishnan (2000) present compelling empirical evidence on inefficient within- household resource allocation in other rural African settings.

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35 10. Tables and Figures

Table 1: PSDP Health and Education Treatment Effects and Externalities (1998-1999) Group 1, Group 1, Group 2, Group 2, (Group 1 (Group 1, Treated Untreated Treated in Untreated Treated Untreated in 1998 in 1998 1999 in 1999 1998) – 1998) – (Group 2, (Group 2, Treated Untreated 1999) 1999) Panel A: Health Outcomes Any moderate-heavy infection, 1998 0.39 0.44 - - - - Any moderate-heavy infection, 1999 0.24 0.34 0.51 0.55 -0.27*** -0.21** (0.06) (0.10) Panel B: School Participation School participation rate, 0.872 0.764 0.808 0.684 0.064** 0.080** May 1998 to March 1999†† (0.032) (0.039)

Panel C: Selection into Treatment Access to latrine at home, 1998 0.84 0.80 0.81 0.86 0.03 -0.06 (0.04) (0.05) Grade progression -2.0 -1.8 -1.8 -1.8 -0.2** -0.0 [=Grade – (Age – 6)], 1998 (0.1) (0.2) Weight-for-age (Z-score), 1998 -1.58 -1.52 -1.57 -1.46 -0.01 -0.06 (low scores denote undernutrition) (0.06) (0.11) Malaria/fever in past week (self- 0.37 0.41 0.40 0.39 -0.03 -0.01 reported), 1998 (0.04) (0.06) Clean (observed by field worker), 1998 0.53 0.59 0.60 0.66 -0.07 -0.07 (0.05) (0.10)

Notes for Table 1: These results use the data from Miguel and Kremer (2001). These are averages of individual- level data for grade 3-8 pupils in the parasitological survey subsample; disturbance terms are clustered within schools. Robust standard errors in parentheses. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Obs. for the 1999 parasitological survey: 670 Group 1 treated 1998, 77 Group 1 untreated 1998, 873 Group 2 treated 1999, 352 Group 2 untreated 1999. The data are for all boys, and for girls age 13 years and under (older girls are ineligible for deworming in mass treatment programs due to the potential embryotoxicity of the drugs).

†† School averages weighted by pupil population. The participation rate is computed among pupils enrolled in the school at the start of 1998. Pupils present in school during an unannounced NGO visit are considered participants. Pupils had 3.8 participation observations per year on average. Participation rates are for grades 1 to 7; grade 8 pupils are excluded since many graduated after the 1998 school year, in which case their 1999 treatment status is irrelevant. Preschool pupils are excluded since they typically have missing compliance data. Characteristics in Panel C are for grades 3 to 7, since younger pupils were not administered the Pupil Questionnaire.

36

Table 2: PSDP Health and Health Behavior Impacts (1999)

Group 1 Group 2 Group 1 – Group 2 Panel A: Nutritional and Health Outcomes Sick in past week (self-reported), 1999 0.41 0.45 -0.04** (0.02) Sick often (self-reported), 1999 0.12 0.15 -0.03** (0.01) Height-for-age Z-score, 1999 -1.13 -1.22 0.09* (low scores denote undernutrition) (0.05) Weight-for-age Z-score, 1999 -1.25 -1.25 -0.00 (low scores denote undernutrition) (0.04) Hemoglobin concentration (g/L), 1999 124.8 123.2 1.6 (1.4) Proportion anemic (Hb < 100g/L), 1999 0.02 0.04 -0.02** (0.01)

Panel B: Worm Prevention Behaviors Clean (observed by field worker), 1999 0.59 0.60 -0.01 (0.02) Wears shoes (observed by field worker), 1999 0.24 0.26 -0.02 (0.03) Days contact with fresh water in past week 2.4 2.2 0.2 (self-reported), 1999 (0.3)

Notes for Table 2: These are averages of individual-level data for grade 3-8 pupils; disturbance terms are clustered within schools. Robust standard errors in parentheses. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Obs. for hemoglobin results: 778 (292 Group 1, 486 Group 2). Obs. for 1999 Pupil Questionnaire health outcomes: 9,102 (3562 Group 1, 5540 Group 2 and Group 3). Hb data were collected by Kenya Ministry of Health officials and ICS field officers using the portable Hemocue machine. The self-reported health outcomes were collected for all three groups of schools as part of 1999 Pupil Questionnaire administration.

37

Table 3: Summary Statistics

Mean Std dev. Obs. Panel A: Deworming Treatment Compliance Took deworming drugs in 2001 (Group 2 and 3) 0.61 0.49 1690

Panel B: Cost-Sharing and Verbal Commitment Interventions Cost-sharing school indicator 0.25 0.43 1690 Effective price of deworming per child (Kenyan shillings) 6.2 15.4 1690 Cost-sharing school indicator, albendazole treatment 0.17 0.38 1690 Cost-sharing school indicator, albendazole and praziquantel treatment 0.08 0.27 1690 Verbal commitment intervention indicator 0.43 0.50 1244

Panel C: Number of Social Links (Round 1 and Round 2 Data) Total 10.2 3.4 1690 With children in own school 4.4 2.8 1690 With children not in Group 1, 2, or 3 schools 3.0 2.4 1690 With children in Group 1, 2, 3 schools – not own school 2.8 2.4 1690

With children in Group 1, 2 schools – not own school 1.9 2.0 1690 (“early treatment schools”)

With children in Group 1 schools – not own school 0.9 1.4 1690 Proportion with children in early treatment schools 0.66 0.37 1370

With children in early treatment schools, with whom respondent speaks 1.2 1.6 1690 at least twice per week (“Close Links”)

With children in early treatment schools, with whom respondent speaks 0.7 1.1 1690 less than twice per week (“Distant Links”)

With children in early treatment schools, high deworming compliance 0.95 1.41 1690

With children in early treatment schools, low deworming compliance 0.96 1.47 1690

With children in early treatment schools, large difference in attendance 0.98 1.44 1690 between treated and untreated

With children in early treatment schools, small difference in attendance 0.91 1.38 1690 between treated and untreated

Panel D: Number of Social Links (Round 2 Data) With children in early treatment schools who received deworming 0.38 1.02 890

With children in early treatment schools who received deworming and 0.26 0.90 890 had “good effects” (according to respondent)

With children in early treatment schools who received deworming and 0.02 0.20 890 had “side effects” (according to respondent)

With children in early treatment schools who received deworming, 0.09 0.39 890 respondent does not know effects

With children in early treatment schools who did not receive deworming 0.16 0.56 890

With children in early treatment schools, respondent does not know 1.90 2.06 890 whether they received deworming

Notes for Table 3: From 2001 Parent and Pupil Surveys, 1999-2001 Parasitological Surveys, and administrative records.

38 Table 4: Validating the randomizations (Group 2 and Group 3 households)

Dependent variable: respondent community total iron roof at distance moderate- years of group number of home home to heavy education member children school (km) infection, 2001 OLS Probit OLS Probit OLS Probit (1) (2) (3) (4) (5) (6)

# Links with children in early treatment schools 0.06 -0.009 0.007 0.027** -0.17 -0.014 (Group 1, 2, not own school) (0.08) (0.012) (0.059) (0.012) (0.12) (0.018)

Cost-sharing school indicator 0.16 -0.01 0.04 0.02 1.3 0.08 (0.30) (0.04) (0.22) (0.06) (0.9) (0.12)

Group 2 school indicator -0.51* -0.03 0.15 0.01 -0.14 -0.23*** (0.30) (0.04) (0.18) (0.05) (0.28) (0.07)

# Links with children in Group 1, 2, or 3 schools, not 0.12* 0.013 -0.026 -0.009 0.13 -0.002 own school (0.07) (0.011) (0.054) (0.012) (0.11) (0.014)

# Links with children not in Group 1, 2, or 3 schools 0.07** 0.018*** -0.044 0.010* 0.02 -0.011 (0.03) (0.006) (0.030) (0.006) (0.03) (0.009)

# Links, total 0.13*** 0.007 0.057** -0.012** -0.01 0.006 (0.04) (0.006) (0.028) (0.006) (0.03) (0.008)

Socio-economic controls (excluding dependent variable) Yes Yes Yes Yes Yes Yes

R2 0.06 - 0.01 - 0.17 - Root MSE 3.8 - 2.3 - 1.9 - Number of observations 1690 1690 1690 1690 1690 575 Mean (s.d.) of dependent variable 4.6 (3.9) 0.58 (0.49) 5.5 (2.3) 0.61 (0.49) 1.7 (2.0) 0.28 (0.45)

Notes for Table 4: Uses Parent and Pupil Sample. Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. The socioeconomic controls include respondent years of education, community group member, total number of children, iron roof at home, and distance from home to school, but of course when any one of these is the dependent variable, it is not also included as an explanatory variable.

39

Table 5: Survey Responses on Deworming, Health, and Compliance (Group 2 only)

Proportion

Panel A: Effect of deworming on own child’s health Good effects 0.66 No effect 0.19 Side effects 0.15 Do not know effect 0.14

Panel B: Causes of no deworming treatment Parental consent not provided 0.70 Child absent from school, due to illness 0.08 Child absent from school, not due to illness 0.07 Do not know why 0.03 Parent opposed to deworming 0.02

Panel C: Causes of no parental consent Did not know needed to sign consent book 0.58 Parent too busy to sign 0.17 Spouse was supposed to sign 0.09 Parent forgot to sign 0.05 Parent opposed to deworming 0.02 Parent too ill to sign 0.01 Children do not have worms 0.01 Worms are not a serious problem 0.01

Notes for Table 5: Data are from the 2001 Parent Survey. Respondents could choose more than one response (for example, good effects on health but side effects), so the proportions need not sum to one.

40

Table 6: Experimental Social Effect Estimates (Groups 2 and 3)

Dependent variable: Child took deworming drugs in 2001 (1) (2) (3) (4) (5)

# Links with children in early treatment schools -0.032** -0.041** -0.004 (Groups 1 and 2, not own school) (0.014) (0.017) (0.017)

# Links with children in early treatment schools 0.017 * Group 2 school indicator (0.029)

Proportion links with children in early treatment -0.099** schools (0.044)

# Links with children in early treatment schools, with -0.036** whom respondent speaks at least twice/week (0.015)

# Links with children in early treatment schools, with -0.023 whom respondent speaks less than twice/week (0.016)

# Links with children in early treatment schools -0.006* * Respondent years of education (0.003)

# Links with children in Group 1, 2, or 3 schools, not 0.014 0.014 -0.006 0.014 -0.011 own school (0.011) (0.016) (0.008) (0.011) (0.014)

# Links with children not in Group 1, 2, or 3 schools -0.007 -0.008 -0.005 -0.007 -0.008 (0.007) (0.009) (0.007) (0.007) (0.010)

# Links, total 0.019*** 0.029*** 0.021*** 0.018*** 0.013* (0.005) (0.007) (0.007) (0.005) (0.008)

Respondent years of education 0.004 0.004 0.003 0.004 -0.014 (0.003) (0.003) (0.004) (0.003) (0.012)

Community group member 0.028 0.032 0.038 0.029 0.025 (0.025) (0.026) (0.029) (0.026) (0.025)

Total number of children 0.005 0.006 0.004 0.006 0.006 (0.006) (0.006) (0.007) (0.006) (0.006)

Iron roof at home 0.013 0.011 0.011 0.014 0.011 (0.027) (0.027) (0.032) (0.027) (0.028)

Distance home to school (km) -0.018** -0.18** -0.014 -0.017** -0.018** (0.008) (0.008) (0.010) (0.008) (0.008)

Group 2 school indicator 0.02 0.22** 0.01 0.02 0.02 (0.04) (0.09) (0.05) (0.04) (0.04)

Cost-sharing school indicator -0.62*** -0.62*** -0.62*** -0.62*** -0.63*** (0.08) (0.08) (0.09) (0.08) (0.08)

Number of observations (parents) 1690 1690 1370 1690 1690 Mean of dependent variable 0.61 0.61 0.61 0.61 0.61

Notes for Table 6: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Marginal probit coefficient estimates are presented. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Regression 2 also includes interaction terms (# Social links with children in Group 1, 2, or 3 schools, not own school)*(Group 2) and (# Social links with children not in Group 1, 2, or 3 schools)*(Group 2), and similarly in Regression 5 for educational attainment. Regression 3 excludes parents for which (# Social links with children in Group 1, 2, or 3 schools, not own school) = 0, since the proportion of links to treatment schools is undefined in that case. In Regression 4, we cannot reject the hypothesis that the coefficient estimates on (# Links with children in early treatment schools, with whom respondent speaks at least twice/week) and on (# Links with children in early treatment schools, with whom respondent speaks less than twice/week) are equal (p- value=0.29).

41 Table 7: Effects of Different Types of Social Links

Dependent variable: Child took deworming drugs in 2001 (1) (2) (3) (4) (5)

# Links with children in early treatment schools -0.032** (Groups 1 and 2, not own school) (0.014)

# Links with children in early treatment schools, high -0.026 deworming compliance (0.017)

# Links with children in early treatment schools, low -0.037*** deworming compliance (0.014)

# Links with children in early treatment schools, large -0.039** difference in attendance between treated, untreated (0.019)

# Links with children in early treatment schools, small -0.018 difference in attendance between treated, untreated (0.014)

# Links with children in early treatment schools whose -0.005 children received deworming (0.022)

# Links with children in early treatment schools whose -0.012 -0.006 children did not receive deworming (0.027) (0.028)

# Links in with children in early treatment schools, -0.016 -0.014 respondent does not know whether they received (0.017) (0.016) deworming

# Links with children in early treatment schools whose 0.002 children received deworming and had “good effects” (0.021)

# Links with children in early treatment schools whose -0.114 children received deworming and had “side effects” (0.082)

# Links with children in early treatment schools whose 0.006 children received deworming and respondent does (0.046) not know effects

Social links, other controls Yes Yes Yes Yes Yes

Number of observations 1690 1690 1690 890 890 Mean of dependent variable 0.61 0.61 0.61 0.56 0.56

Notes for Table 7: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Marginal probit coefficient estimates are presented. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Social links controls include total number of links, number of links to Group 1, 2, 3 schools (not own school), and number of links to non- program schools. Other controls include respondent years of education, community group member indicator variable, total number of children, iron roof at home indicator variable, and distance from home to school in km, as well as the Group 2 indicator and Cost-sharing school indicator. High (low) deworming compliance schools are those with compliance greater than (less than) median school compliance, and large (small) differences in attendance between treated and untreated schools are defined similarly.

In Regression 2, we cannot reject that the two coefficient estimates are equal (p-value=0.40). In regression 3, we cannot reject that the two coefficient estimates are equal (p-value=0.18). In regression 4, we cannot reject that the coefficient estimates on (# Links with children in early treatment schools whose children received deworming) and on (# Links with children in early treatment schools whose children did not receive deworming) are equal (p-value=0.83). In regression 5, we cannot reject that the coefficient estimates on (# Links with children in early treatment schools whose children received deworming and had good effects) and on (# Links with children in early treatment schools whose children received deworming and had side effects) are equal (p-value=0.21).

42

Table 8: Experimental and Non-Experimental Social Effect Estimates, on Deworming Attitudes and Knowledge

Estimate on Estimate on # Links Estimate on # Links # Links with with children in with children in children in early treatment early treatment Mean of early treatment schools whose schools with whom dependent schools children received respondent spoke variable deworming about deworming

Dependent variable [Experimental] [Non-experimental] [Non-experimental]

Panel A: Attitudes 1) Parent thinks deworming drugs “not 0.016*** 0.012 0.009** 0.12 effective” (0.007) (0.008) (0.004)

2) Parent thinks deworming drugs “very -0.007 0.029** 0.040*** 0.43 effective” (0.010) (0.013) (0.007)

3) Parent thinks deworming drugs have 0.000 -0.000 0.003 0.04 “side effects” (0.003) (0.003) (0.002)

4) Parent thinks worms, schistosomiasis -0.001 -0.004 -0.005* 0.92 “very bad” for child health (0.006) (0.006) (0.003)

Panel B: Knowledge 5) Parent “knows about ICS deworming 0.005 0.051*** 0.053*** 0.70 program” (0.011) (0.014) (0.010)

6) Parent “knows about the effects of -0.003 0.043*** 0.039*** 0.68 worms and schistosomiasis” (0.013) (0.013) (0.009)

7) Number of infection symptoms -0.006 0.017** 0.010** 1.8 parents able to name (range 0-10) (0.005) (0.008) (0.005)

8) Parent able to name “fatigue” as -0.005 0.027*** 0.022*** 0.20 symptom of infection (0.010) (0.009) (0.005)

9) Parent able to name “anemia” as 0.007 -0.004 0.010* 0.22 symptom of infection (0.009) (0.011) (0.005)

10) Parent able to name “weight loss” as 0.001 0.003 -0.001 0.06 symptom of infection (0.005) (0.005) (0.004)

Notes for Table 8: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Marginal probit coefficient estimates are presented. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. The ten possible infection symptoms include fatigue, anemia, weight loss, stunted growth, stomach ache, bloated stomach, blood in stool, worms in stool, diarrhea, and fever. Social links controls and other controls are included in all specifications. Social links controls include total number of links, number of links to Group 1, 2, 3 schools (not own school), and number of links to non-program schools. Other controls include respondent years of education, community group member indicator variable, total number of children, iron roof at home indicator variable, and distance from home to school in km, as well as the Group 2 indicator and Cost- sharing school indicator. The number of observations across regressions ranges from 1668 to 1690, depending on the extent of missing survey data.

43

Table 9: The Impact of a Verbal Commitment Intervention

Dependent variable: Child took deworming drugs in 2001 (1) (2) (3)

Verbal commitment intervention indicator -0.066** -0.067** -0.16 (0.027) (0.027) (0.25)

Pupil age -0.001 -0.003 (0.007) (0.011)

Pupil female -0.056 -0.077 (0.047) (0.068)

Commitment*Age 0.006 (0.019)

Commitment*Female 0.044 (0.084)

Social links, other controls Yes Yes Yes

Number of observations (pupils) 1244 1244 1244 Mean of dependent variable 0.65 0.65 0.65

Notes for Table 10: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Marginal probit coefficient estimates are presented. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Social links controls include total number of links, number of links to Group 1, 2, 3 schools (not own school), and number of links to non- program schools. Other controls include respondent years of education, community group member indicator variable, total number of children, iron roof at home indicator variable, and distance from home to school in km, as well as the Group 2 indicator and Cost-sharing school indicator. Summary statistics from the 2001 Pupil Questionnaire: pupil age mean (s.d) = 12.9 (2.2); pupil female=0.24 (older girls were dropped from the sample because they are not eligible for deworming in mass treatment programs, due to the potential embryotoxicity of the drugs).

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Table 10: The Impact of Cost-sharing

Dependent variable: Child took deworming drugs in 2001 (1) (2) (3) (4)

Cost-sharing school indicator -0.62*** -0.47*** -0.62*** (0.08) (0.14) (0.12)

Cost-sharing * Respondent years of education 0.005 (0.007)

Cost-sharing * Community group member 0.022 (0.069)

Cost-sharing * Total number of children -0.012 (0.015)

Cost-sharing * Iron roof at home -0.04 (0.07)

Effective price of deworming per child -0.001 [=Cost / # household children in the school] (0.002)

1 / (# household children in the school) -0.34*** (0.07)

Cost-sharing school indicator, albendazole -0.58*** treatment (30 shillings / parent) (0.10)

Cost-sharing school indicator, albendazole and -0.73*** praziquantel treatment (100 shillings / parent) (0.07)

Social links, other controls Yes Yes Yes Yes

Number of observations (parents) 1690 1690 1690 1690 Mean of dependent variable 0.61 0.61 0.61 0.61

Notes for Table 9: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Marginal probit coefficient estimates are presented. Robust standard errors in parentheses. Disturbance terms are clustered within schools. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. Social links controls include total number of links, number of links to Group 1, 2, 3 schools (not own school), and number of links to non- program schools. Other controls include respondent years of education, community group member indicator variable, total number of children, iron roof at home indicator variable, and distance from home to school in km, as well as the Group 2 indicator. We cannot reject that the two terms in regression 4 are equal (p-value=0.17).

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Figure 1: Map of Budalangi and Funyula Divisions, Busia District, Kenya

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E Figure 2: Probability of Adoption (Pr(Tij=1)) and Early Treatment Links ( N ij ) for φ0 > φ

Pr(Tij=1)

φ0

φ

E 0 N ij

Figure 3: Non-parametric Fan regression (Epanechnikov kernel): Effect of social links to early treatment schools (Group 1,2, not own school) on 2001 Deworming Drug Take-up, conditional on other covariates Fan regression Low er 95% confidence band Upper 95% confidence band .3

.2

.1

0

-.1 -3 -2 -1 0 1 2 Early Treatment Links

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11. Appendix

Appendix Table A1: Primary School Deworming Project (PSDP) timeline, 1998-2001

Dates Activity

1998 January 75 Primary schools first stratified by geographic zone, and then randomly divided into three groups of 25 schools (Group 1, Group 2, Group 3)

March-April First round of 1998 treatment (albendazole, praziquantel) in Group 1 schools

November Second round of 1998 treatment (albendazole) in Group 1 schools

1999 March-June First round of 1999 treatment (albendazole, praziquantel) in Group 1, Group 2 schools

October-November Second round of 1999 treatment (albendazole) in Group 1, Group 2 schools

2000 March-June First round of 2000 treatment (albendazole, praziquantel) in Group 1, Group 2 schools

October-November Second round of 2000 treatment (albendazole) in Group 1, Group 2 schools

2001 January-March 2001 Parent Survey (Wave 1) data collection in Group 2, Group 3 schools

2001 Pupil Survey (Wave 1) data collection in Group 2, Group 3 schools. Verbal Commitment intervention carried out during Pupil Survey, among a random subsample of pupils.

March-June First round of 2001 treatment (albendazole, praziquantel) in Group 1, Group 2, Group 3 schools. Cost-sharing in 25 (randomly selected) Group 1, Group 2 schools

May-September 2001 Parent Survey (Wave 2), and household GPS data collection in Group 2, Group 3 schools.

2001 Pupil Survey (Wave 2) data collection in Group 2, Group 3 schools. Verbal Commitment intervention carried out during Pupil Survey, among a random subsample of pupils.

October-November Second round of 2001 treatment (albendazole) in Group 1, Group 2, Group 3 schools. Cost-sharing continues in 25 (randomly selected) Group 1, Group 2 schools

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Appendix Table A2: Robustness of Social Effect Results

Dependent variable: Child took Child took deworming drugs in 2001 deworming drugs in 1999 (Group 2) Probit Probit Probit Probit (1) (2) (3) (4)

# Links with children in early treatment -0.028** -0.029** -0.018 schools (Group 1, 2, not own school) (0.014) (0.014) (0.014)

# Pupils in early treatment schools < 3 km from -0.19*** home (per 1000 pupils) (0.07)

# Pupils in all schools < 3 km from home 0.14** (per 1000 pupils) (0.07)

# Links with children in early treatment -0.020*** schools (Group 1 in 1999, not own school) (0.007)

Social links Yes Yes Yes Yes Other household controls No Yes Yes Yes Ethnic, religious controls No Yes No No

Number of observations (parents) 1690 1690 1690 761 Mean of dependent variable 0.61 0.61 0.61 0.83

Notes for Appendix Table A2: Data from 2001 Parent Survey, 2001 Pupil Survey, 1999 and 2001 Parasitological Surveys, and 1999 and 2001 administrative records. Social links controls include total number of links, number of links to Group 1, 2, 3 schools (not own school), and number of links to non- program schools. Other household controls include respondent years of education, community group member indicator variable, total number of children, iron roof at home indicator variable, and distance from home to school in km, as well as the Group 2 indicator and Cost-sharing school indicator. Ethnic controls include indicators for Samia, Nyala, Luo, Khayo, Marachi, and Teso groups, and an indicator for being a member of the largest ethnic group in the school (which is near zero and statistically insignificant). Religion controls include indicators for Catholic, Anglican, Pentacostal, Apostolic, Legio Mario, Roho, and Muslim faiths, and an indicator for being a member of the largest religious group in the school (which is negative and marginally statistically significant).

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