Research Design Meets Market Design (Econometrica 2017)

Using Centralized Assignment for Impact Evaluation

ATILA ABDULKADIROGLU
Department of Economics, Duke University and NBER

JOSHUA D. ANGRIST
Department of Economics, MIT and NBER

YUSUKE NARITA
Department of Economics and Cowles Foundation, Yale University

PARAG A. PATHAK
Department of Economics, MIT and NBER

Econometrica, Vol. 85, No. 5 (September, 2017), 1373–1432

Key Outcome

This paper shows that getting your first choice increases your achievement by 0.4 standard deviations in Math and about 0.14 in Reading. Turns out this fact is only true if your first choice is a charter school though (and a CMO charter).

Abstract

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated randomization integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.