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Basic sanitation facilities are still lacking in large parts of the developing world, engendering serious environmental health risks. Interventions commonly deliver in-kind or cash subsidies to promote private toilet ownership. In this paper, we assess an intervention that provides information and behavioral incentives to encourage villagers in rural Mali to build and use basic latrines. Using an experimental research design and carefully measured indicators of use, we find a sizeable impact from this intervention: latrine ownership and use almost doubled in intervention villages, and open defecation (OD) was reduced by half. Our results partially attribute these effects to increased knowledge about cheap and locally available sanitation solutions. They are also associated with shifts in social norms governing sanitation. Taken together, our findings, unlike previous evidence from other contexts, suggest that a progressive approach that starts with ending OD and targets whole communities at a time can help meet the United Nations’ 2015 Sustainable Development Goal of ending OD.
We survey the recent, fast-growing literature on peer effects in networks. An important recurring theme is that the causal identification of peer effects depends on the structure of the network itself. In the absence of correlated effects, the reflection problem is generally solved by network interactions even in nonlinear, heterogeneous models. By contrast, microfoundations are generally not identified. We discuss and assess the various approaches developed by economists to account for correlated effects and network endogeneity in particular. We classify these approaches in four broad categories: random peers, random shocks, structural endogeneity, and panel data. We review an emerging literature relaxing the assumption that the network is perfectly known. Throughout, we provide a critical reading of the existing literature and identify important gaps and directions for future research.
Community-led total sanitation (CLTS) uses participatory approaches to mobilise communities to build their own toilets and stop open defecation. Our aim was to undertake the first randomised trial of CLTS to assess its effect on child health in Koulikoro, Mali.
We did a cluster-randomised trial to assess a CLTS programme implemented by the Government of Mali. The study population included households in rural villages (clusters) from the Koulikoro district of Mali; every household had to have at least one child aged younger than 10 years. Villages were randomly assigned (1:1) with a computer-generated sequence by a study investigator to receive CLTS or no programme. Health outcomes included diarrhoea (primary outcome), height for age, weight for age, stunting, and underweight. Outcomes were measured 1·5 years after intervention delivery (2 years after enrolment) among children younger than 5 years. Participants were not masked to intervention assignment. The trial is registered with ClinicalTrials.gov, number NCT01900912.
We recruited participants between April 12, and June 23, 2011. We assigned 60 villages (2365 households) to receive the CLTS intervention and 61 villages (2167 households) to the control group. No differences were observed in terms of diarrhoeal prevalence among children in CLTS and control villages (706 [22%] of 3140 CLTS children vs 693 [24%] of 2872 control children; prevalence ratio [PR] 0·93, 95% CI 0·76–1·14). Access to private latrines was almost twice as high in intervention villages (1373 [65%] of 2120 vs 661 [35%] of 1911 households) and reported open defecation was reduced in female (198 [9%] of 2086 vs 608 [33%] of 1869 households) and in male (195 [10%] of 2004 vs 602 [33%] of 1813 households) adults. Children in CLTS villages were taller (0·18 increase in height-for-age Z score, 95% CI 0·03–0·32; 2415 children) and less likely to be stunted (35% vs 41%, PR 0·86, 95% CI 0·74–1·0) than children in control villages. 22% of children were underweight in CLTS compared with 26% in control villages (PR 0·88, 95% CI 0·71–1·08), and the difference in mean weight-for-age Z score was 0·09 (95% CI –0·04 to 0·22) between groups. In CLTS villages, younger children at enrolment (<2 years) showed greater improvements in height and weight than older children.
In villages that received a behavioural sanitation intervention with no monetary subsidies, diarrhoeal prevalence remained similar to control villages. However, access to toilets substantially increased and child growth improved, particularly in children <2 years. CLTS might have prevented growth faltering through pathways other than reducing diarrhoea.
We provide the first empirical application of a new approach proposed by Lee (Journal of Econometrics 2007; 140(2), 333–374) to estimate peer effects in a linear-in-means model when individuals interact in groups. Assumingsufficient group size variation, this approach allows to control for correlated effects at the group level and to solve the simultaneity (reflection) problem. We clarify the intuition behind identification of peer effects in the model. We investigate peer effects in student achievement in French, Science, Mathematics and History in secondary schools in the Province of Québec (Canada). We estimate the model using conditional maximum likelihood and instrumental variables methods. We find some evidence of peer effects. The endogenous peer effect is large and significant in Mathematics but imprecisely estimated in the other subjects. Some contextual peer effects are also significant. In particular, for most subjects, the average age of peers has a negative effect on own test score. Using calibrated Monte Carlo simulations, we find that high dispersion in group sizes helps with potential issues of weak identification. Copyright © 2012 John Wiley & Sons, Ltd.
We provide new results regarding the identification of peer effects. We consider an extended version of the linear-in-means model where interactions are structured through a social network. We assume that correlated unobservables are either absent, or treated as network fixed effects. We provide easy-to-check necessary and sufficient conditions for identification. We show that endogenous and exogenous effects are generally identified under network interaction, although identification may fail for some particular structures. We use data from the Add Health survey to provide an empirical application of our results on the consumption of recreational services (e.g., participation in artistic, sports and social activities) by secondary school students. Monte Carlo simulations calibrated on this application provide an analysis of the effects of some crucial characteristics of a network (i.e., density, intransitivity) on the estimates of peer effects. Our approach generalizes a number of previous results due to Manski [Manski, C., 1993. Identification of endogenous social effects: The reflection problem. Review of Economic Studies 60 (3), 531-542], Moffitt [Moffitt, R., 2001. Policy interventions low-level equilibria, and social interactions. In: Durlauf, Steven, Young, Peyton (Eds.), Social Dynamics. MIT Press] and Lee [Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics 140 (2), 333-374]. © 2009 Elsevier B.V. All rights reserved.
The “common effect” model in program evaluation assumes that all treated individuals have the same impact from a program. Our paper contributes to the recent literature that tests and goes beyond the common effect model by investigating impact heterogeneity using data from the experimental evaluation of the Mexican conditional cash transfer program PROGRESA. Our analysis builds upon and extends that in Heckman, Smith and Clements (1997) and more recent studies of quantile treatment effects and random coefficient models. We find strong evidence of systematic (i.e. subgroup) variation in impacts in PROGRESA and modest evidence of heterogeneous impacts conditional on the systematic impacts. We find evidence against the perfect positive dependence assumption that underlies the interpretation of quantile treatment effects as impacts at quantiles of the untreated outcome distribution. Our paper concludes with a discussion of the policy relevance of our findings and of heterogeneous impacts more generally.