Having an effective teacher can dramatically improve students' educational and long-term outcomes (Araujo et al., 2016; Chetty et al., 2014; Hanushek and Rivkin, 2012). Recent experimental evidence in Ecuador shows that the impact of effective teachers is significantly larger for disadvantaged students (e.g. Cerrando Brechas, 2018). However, teacher allocation in Latin America and the Caribbean (LAC) is unequal. Empirical evidence shows that high-performing teachers tend to be assigned to more advantaged students (Bertoni et al., 2020).

In several LAC countries, the assignment of teachers to schools is also inefficient and not transparent. Teacher assignment systems often do not provide teachers with enough information on the available vacancies to allow them to make informed decisions. Teachers are more likely to be dissatisfied with their assigned school if they do not have enough information about their options, which can impact their effectiveness in the classroom (Jackson, 2012). Moreover, lack of information about vacancies also creates imbalances in supply and demand for teaching staff. For example, in Peru, more than one quarter of vacancies remain unfilled after the teacher selection process. Most of these vacancies are in disadvantaged schools.

To address these issues and improve equity, transparency, and efficiency in teacher allocation, some school systems around the world have adopted on-line centralized allocation systems (Elacqua et al., 2016). These centralized systems provide a unique opportunity to use new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to improve the allocation process and its outcomes (Agrawal et al., 2018). Moreover, AI can be paired with behavioral insights to improve the outcomes of the allocation systems. Recent experimental evidence in Peru and Ecuador also suggests that behavioral strategies can be effective at attracting teachers to hard-to-staff and vulnerable schools (Ajzenman et al., 2020).

The general objective of this project is to strengthen the centralized teacher allocation system in Peru. This TC will finance: (i) assessments and improvements in the mechanisms for teacher assignment, (ii) improvements in the front-end technology used to assign teachers including a) further exploration of behavioral strategies to motivate teachers to work in more disadvantaged schools, b) introduction of changes in the teacher assignment platform to enhance user experience to increase transparency and efficiency in teacher assignment, and c) introduction of new technologies, such as artificial intelligence and machine learning, to improve equity and efficiency in the allocation process and increase teacher satisfaction with their final allocation.

Technical Cooperation

ACCOUNTABILITY MECHANISM OF IADB

The Independent Consultation and Investigation Mechanism (MICI) is the independent complaint mechanism and fact-finding body for people who have been or are likely to be adversely affected by an Inter-American Development Bank (IDB) or Inter-American Investment Corporation (IIC)-funded project. If you submit a complaint to MICI, they may assist you in addressing the problems you raised through a dispute-resolution process with those implementing the project and/or through an investigation to assess whether the IDB or IIC is following its own policies for preventing or mitigating harm to people or the environment. You can submit a complaint by sending an email to MICI@iadb.org. You can learn more about the MICI and how to file a complaint at http://www.iadb.org/en/mici/mici,1752.html (in English) or http://www.iadb.org/es/mici/mici,1752.html (Spanish).