A845: The Economics of Education in Low- and Middle-Income Countries

*Lottery Enrollment Course* This course examines how key concepts and frameworks in economics may be leveraged to understand the frontier challenges in education in low- and middle-income countries (LMICs) and the circumstances under which policy changes may effectively address them. It seeks to provide you with an approach to help you: (a) diagnose the underlying reasons for existing challenges in education in LMICs; (b) assess the promise and potential pitfalls of proposed solutions; (c) design policies and programs with greater chances of success; and (d) monitor and/or evaluate the consequences of new or existing efforts. The course is intended for master’s and doctoral students seeking to apply insights from economics to policy design, analysis, and monitoring/evaluation. It draws on theory and evidence from labor, development, and behavioral economics. It focuses on pre-primary to secondary education—the levels in which enrollments have expanded most rapidly in LMICs.

This course is structured around four main parts. The first one aims to make you view education in LMICs through the eyes of economists (Why do they study education? How do they view education? What do they think are the main challenges in education in LMICs?). This part is essential for you to understand why economists study certain questions and not others and the perspective from which they approach such questions. The second and third parts synthesize the evidence produced by economists in recent decades to improve both the “quantity” (e.g., enrollment) and “quality” (e.g., learning) of education (How can we increase the share of the population that attends school? How can we improve the quality and relevance of instruction that students receive at school?). These parts constitute the core of the course, integrating economic theory and existing evidence. The fourth and final part identifies the main challenges in translating evidence into policy (How can we make sense of “bundled” interventions? How can we assess the relevance of evidence across contexts?) and offers an overview of the frontier in evidence generation.

Prerequisites: You are expected to have taken EVI-101 (“Evidence”) and S-040 (“Introduction to Applied Data Analysis”) or equivalent courses that introduce students to regression analysis. You should be comfortable interpreting regression coefficients, standard errors, p-values, and confidence intervals. (You will not be required to perform statistical analysis in R or Stata). If you have taken more advanced statistics courses, such as S-052 (“Intermediate and Advanced Statistical Methods for Applied Educational Research”) or S-290 (“Quantitative Methods for Improving Causal Inference in Educational Research”), you will be able to go beyond what is taught in class. Yet, this level of statistical proficiency is not necessary to participate in class and complete the course assignments.