The author of this dissertation, in consultation with coauthors, presents essays on the application and impact of using deferred acceptance algorithms (DAA) in matching individuals with specific jobs or activities in diverse settings.
The first essay reports on a quasi-experimental test of the application of DAA in the Teach for America (RFA) project. TFA is an effort to match high-school teachers to schools in Chicago, while keeping the mechanism for matching elementary school teachers unchanged. The study concluded that adopting a variant of the DAA reduced teacher attrition through the start and end of teachers' first school year by seven and eight percentage points, respectively. The second essay used medium-term results from two randomized controlled trials in Chicago to better understand why summer jobs reduce violence and for whom such programs work best. The project found that criminally involved males who are still enrolled in school but have poor attendance and minimal criminally involved females early in their high school career benefited most from summer jobs. More disconnected males who were older, more criminally involved, and did not have recent formal work experience were most adversely affected by the program. The third essay develops theory to demonstrate that matching problems within an organization are distinct from traditional applications in public markets. The essay shows there are no guarantees that assignment mechanisms that respect preferences will perform well from an organization's perspective. In some cases, an organization can better achieve its objectives by ignoring preferences and randomly choosing assignments. 21 tables, 11 figures, and 82 references
Report (Grant Sponsored)
Date Published: June 1, 2016
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