Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments

Abstract

Background: Many place-based randomized trials and quasi-experiments use a pair of cross-section surveys, rather than panel surveys, to estimate the average treatment effect of an intervention. In these studies, a random sample of individuals in each geographic cluster is selected for a baseline (preintervention) survey, and an independent random sample is selected for an endline (postintervention) survey. Objective: This design raises the question, given a fixed budget, how should a researcher allocate resources between the baseline and endline surveys to maximize the precision of the estimated average treatment effect? Results: We formalize this allocation problem and show that although the optimal share of interviews allocated to the baseline survey is always less than one-half, it is an increasing function of the total number of interviews per cluster, the cluster-level correlation between the baseline measure and the endline outcome, and the intracluster correlation coefficient. An example using multicountry survey data from Africa illustrates how the optimal allocation formulas can be combined with data to inform decisions at the planning stage. Another example uses data from a digital political advertising experiment in Texas to explore how precision would have varied with alternative allocations.