Using Synthetic Controls to Evaluate the Effect of Unique Interventions: The Case of Say Yes to Education

Background: “Place-based” scholarships seek to improve student outcomes in urban school districts and promote urban revitalization in economically challenged cities. Say Yes to Education is a unique district-wide school reform effort adopted in Syracuse, NY, in 2008. It includes full-tuition scholar...

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Veröffentlicht in:Evaluation review 2017-12, Vol.41 (6), p.593-619
Hauptverfasser: Bifulco, Robert, Rubenstein, Ross, Sohn, Hosung
Format: Artikel
Sprache:eng
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Zusammenfassung:Background: “Place-based” scholarships seek to improve student outcomes in urban school districts and promote urban revitalization in economically challenged cities. Say Yes to Education is a unique district-wide school reform effort adopted in Syracuse, NY, in 2008. It includes full-tuition scholarships for public and private universities, coupled with extensive wraparound support services in schools. Objectives: This study uses synthetic control methods to evaluate the effect of Say Yes on district enrollment and graduation rates. It also introduces the synthetic control method and provides guidance for its use in evaluating single-site interventions. Method: Combining school district-level data from the National Center for Education Statistics’ Common Core of Data and New York State School Report Cards, this article uses synthetic control methods to construct a synthetic comparison district to estimate counterfactual enrollment and graduation trends for Syracuse. Results: We find that Say Yes to Education was associated with enrollment increases in the Syracuse City School District, a district that had previously experienced decades of sustained enrollment declines. We do not find consistent evidence of changes in graduation rates following adoption of the program. Conclusions: Graduation rate analyses demonstrate that estimates of treatment effects can be sensitive to choices that the researcher has to make in applying synthetic control methods, particularly when pretreatment outcome measures appear to have considerable amounts of noise.
ISSN:0193-841X
1552-3926
DOI:10.1177/0193841X17742233