Title: Using Regression Discontinuity with Implicit Partitions: The Impacts of Comunidades Solidarias Rurales on Schooling in El Salvador
Abstract: Regression discontinuity design (RDD) is a useful tool for evaluating programs when a single variable is used to determine program eligibility. RDD has also been used to evaluate programs when eligibility is based on multiple variables that have been aggregated into a single index using explicit, often arbitrary, weights. In this paper, we show that under specific conditions, regression discontinuity can be used in instances when more than one variable is used to determine eligibility, without assigning explicit weights to map those variables into a single measure. The RDD approach used here groups observations that are common across multiple criteria through the use of a distance metric and by creating an implicit partition between groups. We apply this model to the case of Comunidades Solidarias Rurales in El Salvador, which used partitioned cluster analysis to determine the order communities would enter the program as a function of the poverty rate and severe stunting rate. Using data collected for the evaluation as well as data from the 6th National Census of El Salvador, we demonstrate that the program increased both parvularia and primary school enrollment among children aged 6 to 12 years old. Among children of primary school age, we further show that enrollment gains were largest among younger children and older girls.
Publication Year: 2011
Publication Date: 2011-01-01
Language: en
Type: preprint
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Cited By Count: 15
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