Simple and Credible Value-Added Estimation Using Centralized Assignment

Education Reform, School Assignment, December 2020

Many large urban school dis­tricts match stu­dents to schools using algo­rithms that incor­po­rate an ele­ment of ran­dom assign­ment. We intro­duce two sim­ple empir­i­cal strate­gies to har­ness this ran­dom­iza­tion for mea­sur­ing the causal effects of indi­vid­ual schools. In appli­ca­tions to data from Denver and New York City, we find that our mod­els yield high­ly reli­able school effec­tive­ness estimates. 

Joshua Angrist, Peter Hull, Parag Pathak, Christopher Walters