Approaches to assessing the impact of poor sanitation on child nutrition

In this chapter, we discuss empirical methods for understanding poor sanitation and child growth in developing countries. We focus on applying different techniques and findings of association, implementation, cost-benefit analyses of interventions, and counterfactuals. We document multi-country experiences using different datasets and econometric methods. We review not only existing empirical techniques but also suggest new research issues. The empirical methods discussed are OLS, panel data techniques, logistic regression, decomposition techniques, propensity-score matching, randomised control trial, synthetic control methods, spatial analysis, small area estimation, causal analysis, difference-in-difference, instrumental variables, regression discontinuity design, machine learning, Bayesian techniques, dashboard analysis, directed acyclic graphs, and cost-benefit analysis. We also explore construction of visual maps, and systematic reviews using GRADE and LQAT. The research topics identified will be useful for replication exercises, usage of new datasets, improvised activities, and COVID-related work subject to quality data availability.