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Estimating causal effects is an important aim in the field of program evaluation, but many programs and policies are implemented in geographically defined jurisdictions, such as school districts or states, and not by randomly assigning participants to a treatment or control group. How might evaluators estimate causal effects in the case of treatment assignment based on geographic borders? Regression discontinuity is a quasi-experimental design and statistical modeling approach that can yield causal estimates that are comparable to those derived from randomized controlled trials. Spatial regression discontinuity is a special case that recognizes geographic borders as sharp cutoff points where local effects can be estimated. This presentation details how evaluators can implement spatial regression discontinuity, as well as some strengths and weaknesses of the approach. A hierarchical spatial regression discontinuity analysis is demonstrated in the context of a well-known study of minimum wage effects by Card and Krueger (1994).
Session Title: Hot Topics in Quantitative Methods
Multipaper Session 385 held in Sebastian Section I2 on Thursday, Nov 12, 4:30 PM to 6:00 PM
Sponsored by the Quantitative Methods: Theory and Design TIG
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