Shooting a moving target: Evaluating targeting tools for social programs when income fluctuates

A key challenge for policymakers in low- and middle-income countries is to design a method to select beneficiaries of social programs when income is unobservable and volatile. We use a unique panel dataset of a random sample of households in Colombia’s social registry that contains information before, during, and after the 2020 economic crisis to evaluate a traditional static proxy-means test (PMT) and three policy-relevant alternatives. We consider targeting metrics and social welfare under different curvatures of governments’ social welfare function, aggregate economic environments, and budgetary and political constraints. Updating the PMT data does not improve social welfare relative to the static PMT. Relaxing the eligibility threshold reduces the exclusion error, increases the inclusion error, and increases social welfare. A dynamic method that uses data on shocks to estimate a variable component of income reduces exclusion errors and limits the expansion in coverage, increasing social welfare during the economic crisis. • Using data from Colombia, we evaluate three policy-relevant alternatives to select beneficiaries for social protection with respect to a traditional static proxy-means test (PMT) approach. • None of the four targeting methods considered is optimal for all economic environments, social welfare functions, and political and fiscal constraints. • Dynamic targeting methods that account for income fluctuations can improve welfare, at a lower cost, during systemic crises. • Social protection programs that select beneficiaries using targeting methods that combine proxies for structural poverty with information on labor market shocks resemble a combination of an anti-poverty program and an unemployment insurance program for low-income households.