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Playing the system: address manipulation and access to schools
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Strategic incentives may lead to inefficient and unequal provision of public services. A prominent example is school admissions. Existing research shows that applicants "play the system" by submitting school rankings strategically. We investigate whether applicants also play the system by manipulating their eligibility at schools. We analyze this applicant deception in a theoretical model and provide testable predictions for commonly-used admission procedures. We confirm these model predictions empirically by analyzing the implementation of two reforms. First, we find that the introduction of a residence-based school-admission criterion in Denmark caused address changes to increase by more than 100% before the high-school application deadline. This increase occurred only in areas where the incentive to manipulate is high-powered. Second, to assess whether this behavior reflects actual address changes, we study a second reform that required applicants to provide additional proof of place of residence to approve an address change. The second reform significantly reduced address changes around the school application deadline, suggesting that the observed increase in address changes mainly reflects manipulation. The manipulation is driven by applicants from more affluent households and their behavior affects non-manipulating applicants. Counter-factual simulations show that among students not enrolling in their first listed school, more than 25% would have been offered a place in the absence of address manipulation and their peer GPA is 0.2SD lower due to the manipulative behavior of other applicants. Our findings show that popular school choice systems give applicants the incentive to play the system with real implications for non-strategic applicants.
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Trading off performance and human oversight in algorithmic policy: evidence from Danish college admissions
Simple machine learning models predict college completion better than GPA or human rankings in Danish admissions, and most of the benefit of AI admissions comes from models that remain interpretable.
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