Association for Public Policy Analysis & Management

Teaching is the modal profession of college graduates in the United States. Policies designed to improve teacher quality by "deselecting" low quality teachers and replacing them with higher quality teachers may be prohibitively expensive due to the size of the teacher labor force. Recent work suggests that teacher to school match quality may explain up to a third of observed teacher quality (Jackson 2014) . Therefore, better matching teachers to schools may be a more cost effective approach to improving teacher quality. This paper presents the most rigorous quasi-experimental evidence to date of the impact of better matching teachers to schools on match quality using a new application of market design. I worked with Teach For America (TFA) to match high school teachers to schools in Chicago using the Deferred Acceptance Algorithm at a series of "interview days", while keeping the mechanism for matching elementary school teachers unchanged. I show that TFA's original interview day mechanism promotes strategic early hiring with incomplete information in theory and in practice. I estimate the effect of adopting the DAA by comparing the change in teacher retention rates over time for TFA high school teachers to the change among TFA elementary school teachers. Adopting a variant of the DAA reduces attrition through the start and end of teachers' first school year by 7 and 5 percentage points, respectively. These effects are arguably a lower bound for other markets because substantial heterogeneity in schools' preferences over teachers reduced inefficiency prior to the intervention. Back-of-the-envelope estimates suggest this increased retention is worth $54,000 in additional expected lifetime earnings for students at each of TFA's partner schools.