Research Design Meets Market Design

该奖项资助一个研究项目，该项目将开发新的统计方法，以评估参加特定公立学校对考试成绩，出勤率和毕业率等结果的影响。 新方法可用于评估公立学区的学校成果，这些学区使用集中分配方法将学生分配到学校。 PI计划使用该方法分析位于波士顿，丹佛和新奥尔良的学校的数据。 研究结果将为如何最好地比较不同类型的公立学校的结果提供新的见解，包括特许学校和传统公立学校之间的比较。 研究结果也将有助于其他学区正在考虑是否使用集中分配方法。一些大型公立学区采用集中匹配机制已有一段时间。 经验表明，学校的优先事项往往是粗糙的，许多学生有相同的偏好。 因此，所使用的分配机制必须包括打破平局的特征。 一些地区采用使用随机分配来打破联系的算法，而另一些地区则使用回归不连续风格的运行变量。 在这两种情况下，打破平局的方法使得准实验性的影响评估成为可能。 PI将开发和演示计量经济学方法，充分利用这些准实验设计的因果推理潜力。他们将开发理论结果，展示如何在日益复杂的市场设计中隔离随机性，并将这些方法应用于三个大型城市公立学区的数据。

This award funds a research project that will develop new statistical methods to evaluate the effect of attending a specific public school on outcomes such as test scores, attendance, and graduation rates. The new method can be used to evaluate school outcomes for public school districts that use centralized assignment methods to assign students to schools. The PIs plan to employ the method to analyze data on schools located in Boston, Denver, and New Orleans. The results will shed new insight into how to best compare the results of different kinds of public schools, including comparisons between charter schools and traditional public schools. The results will also be useful to other school districts that are considering whether or not to use a centralized assignment method. Centralized matching mechanisms have been used in a number of large public school districts for some time. Experience has shown that school priorities are often coarse and many students have identical preferences. Therefore the assignment mechanism used must include a tie-breaking feature. Some districts employ algorithms that use random assignment to break ties, while others use a regression discontinuity style running variable. In either case, the tie-breaking method makes quasi-experimental impact evaluation possible. The PIs will develop and demonstrate econometric methods that fully exploit the potential of these quasi-experimental designs for causal inference. They will develop theoretical results that show how to isolate randomness in market designs of increasing complexity, and will apply these methods to data from three large urban public school districts.