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A continuous eligibility index: a continuous measure on which the population of interest is ranked (i.e.Two main conditions are needed in order to apply a regression discontinuity design: Causal inference is then made comparing individuals on both sides of the cutoff point. In RDD, assignment of treatment and control is not random, but rather based on some clear-cut threshold (or cutoff point) of an observed variable such as age, income, and score. Researchers have used it to evaluate electoral accountability SME policies social protection programs such as conditional cash transfers and educational programs such as school grants. The use of RDD has increased exponentially in the last few years. The method was first used in 1960 by Thistlethwaite and Campbell, who were interested in identifying the causal impacts of merit awards, assigned based on observed test scores, on future academic outcomes ( Lee and Lemieux, 2010). RDD is a key method in the toolkit of any applied researcher interested in unveiling the causal effects of policies. However, it does not answer whether a program should exist or not: in this case, the average treatment effect provides better evidence than the local average treatment effect. It provides useful evidence on whether a program should be cut or expanded at the margin. RDD estimates local average treatment effects around the cutoff point, where treatment and comparison units are most similar.The eligibility index must be continuous around the cutoff point and the population of interest around the cutoff point must be very similar in observable and unobservable characteristics.A RDD requires a continuous eligibility score on which the population of interest is ranked and a clearly defined cutoff point above or below which the population is determined eligible for a program.