Crowdfunding the Front Lines: An Empirical Study of Teacher-Driven School Improvement
Jun Li, Ph.D., Associate Professor of Technology and Operations, and Michael R. and Mary Kay Hallman Fellow, Stephen M. Ross School of Business, University of Michigan
The US K–12 public education system has been notoriously hard to improve. Some argue new education technologies (EdTech) can help transform schools for the better. Yet, as large-scale policy reforms have had only limited success, it seems unlikely that small changes due to EdTech could have any measurable impact. In this paper, we study DonorsChoose, a nonprofit that operates a teacher crowdfunding platform. We ask whether DonorsChoose improves educational outcomes, specifically at low-income schools. Combining DonorsChoose data with data on student test scores in Pennsylvania from 2012-2013 to 2017-2018, we find an increase in the number of DonorsChoose projects funded at a school leads to higher student performance, after controlling for selection biases. For a school with zero funded projects, one funded project—of about $400 in value—translates to between 2 to 9 more students scoring basic and above in all subjects in high school and science and language arts in primary and middle school. We find this effect is driven mostly by low-income schools, indicating funded projects help close the gap in educational outcomes between students at low- versus high-income schools. Based on a textual analysis of 20,000 statements from all funded teachers describing how project resources are used, we find two channels of improvement most effective in the lowest income schools. We demonstrate that although DonorsChoose projects are small, they improve outcomes and reduce inequality because they come directly from frontline workers—teachers—who know most intimately the obstacles their students face and how to help.
Dr. Jun Li is an Associate Professor of Technology and Operations and the Michael R. and Mary Kay Hallman Fellow at Stephen M. Ross School of Business, University of Michigan. Her main research interests are empirical operations management and business analytics, with special emphases on revenue management, pricing, consumer behavior, economic and social networks. She has worked extensively with large-scale data, including transactions, pricing, inventory and capacity, consumer online search and click stream data, supply chain relationships and disruptions, clinical and healthcare claims. She has won several best paper awards and dissertation award. Her work has also enjoyed coverage by The Economist, New York Times and Forbes. Jun holds a Ph.D. in Operations Management from The Wharton School, University of Pennsylvania, and a Bachelor in Operations Research and Industrial Engineering from Tsinghua University, China.
