The Potential of Smart Matching Platforms in Teacher Assignment: The Case of Ecuador

Abstract

This paper studies the potential of personalized "smart" information interventions to improve teacher assignment results in the context of a centralized choice and assignment system (CCAS) in Ecuador. Specifically, we focus on the impact that a personalized non-assignment risk warning, coupled with a list of "achievable" teaching position recommendations, had on teacher applications in the “I Want to Become a Teacher” selection process. We study the causal effect of the intervention on teachers school choices, assessing its impact on the equilibrium probability of being assigned and on the overall results of the selection process, both in terms of the percentage of filled vacancies and the selection scores of as- signed teachers. We find that treated teachers, in equilibrium, are much more likely to modify their application and obtain an assignment. This result highlights the potential of similar information interventions in other contexts. We furthermore present evidence that the intervention led to increased overall assignment rates and selection scores.

Education in the Amazon Region

“…Low completion rates 2. Low learning outcomes feedback and recommendations not only showed a stronger inclination to expand their options and prioritize the suggested schools, but were also more likely to find employment (Elacqua et al, 2022).…”