Analysis
SECONDARY
We assessed impacts on stunting, as well as on proximal matenal, newborn, and child health (MNCH) and sanitation‐related outcomes through which the project expected to reduce stunting, by comparing endline outcomes between the treatment and control groups using a multivariate ordinary least squares model to conduct an intention‐to‐treat analysis. The model controlled for district indicators (the level of implicit stratification) and individual socio‐demographic control variables related to MNCH outcomes (whether the respondent completed junior secondary school, wealth quintile, mother's age when the sampled child was born, birth order of the sampled child and the sampled child's gender). To assess differences in baseline characteristics and outcomes, we used a similar approach except without the socio‐demographic controls.In the case of binary outcomes, which include stunting, this approach amounted to a linear probability model. Although probit or logit models are often used for binary outcomes, we prefer a linear probability model because it is easier to interpret and relies on weaker parametric assumptions. In practice, probit or logit and linear probability models generally yield similar results for the types of marginal effects that we estimate here (Angrist & Pischke, Because the sample of interest consisted of children in a specific age range (0–35 months old) at the time of the survey, we measured key outcomes for different individuals at baseline and endline. Therefore, individual‐level baseline measures of the outcomes are not defined for the endline sample. Instead, in the endline analysis, we adjusted for the baseline mean of the outcome at the subdistrict level—the level of random assignment—by including this mean as an additional control variable. Conceptually, this is similar to a subdistrict‐level difference‐in‐differences approach but is a more flexible approach that allows the relationship between the endline and baseline means to be determined empirically. Using these control variables enabled us to adjust for any baseline differences between the treatment and control groups that arose by chance and improved the precision of the estimates.At both baseline and endline, we accounted for clustering at the subdistrict level, the level of random assignment, by estimating cluster‐robust standard errors. All analyses included the full set of 95 treatment and 95 control clusters, but individual‐level sample sizes vary across outcomes depending on relevance by child age and item nonresponse. In addition, the implicit stratification approach we used for random assignment led to slightly different random assignment probabilities across the subdistricts in the study. We therefore included weights at baseline and endline to adjust for the different random assignment probabilities, as well as different sampling probabilities at the PSU, SSU and household level. We conducted all analyses in Stata version 15.0.