利用报名不足的学校抽签获得可信的学校增值估计

中文摘要

我们提出两种实证策略，利用学校分配机制中的随机性来测量学校增值。第一种估计量控制学校分配概率，并将实际入学视为可忽略的。我们利用分配中的随机性检验这一假设。第二种方法将分配作为工具变量（IV），用于低维增值模型，并基于这些IV估计构造经验贝叶斯后验。两种策略都解决了因学校报名不足而产生的不可识别难题。在丹佛和纽约市，控制分配风险与滞后成绩的模型产生了可靠的增值估计。对于成绩控制变量质量较低的模型，其估计结果通过IV得到改善。

Abstract

We introduce two empirical strategies harnessing the randomness in school assignment mechanisms to measure school value-added. The first estimator controls for the probability of school assignment, treating take-up as ignorable. We test this assumption using randomness in assignments. The second approach uses assignments as instrumental variables (IVs) for low-dimensional models of value-added and forms empirical Bayes posteriors from these IV estimates. Both strategies solve the underidentification challenge arising from school undersubscription. Models controlling for assignment risk and lagged achievement in Denver and New York City yield reliable value-added estimates. Estimates from models with lower-quality achievement controls are improved by IV.