Vietnam : Identifying Reliable Predictors of Learning for Results-Based Financing in Education

Many education systems around the world have reached nearly universal access to schooling, but ensuring high quality learning for all students has proven to be more difficult to achieve. Results-based financing (RBF) has the potential to transform the way in which education systems improve by incentivizing students, parents, teachers, school administrators, and other stakeholders to achieve better results. RBF mechanisms work by linking financial incentives to measurable results such as school attendance, dropout rates, student test scores, or other indicators of education quality. Conditional cash transfers (CCTs), teacher performance pay systems, and disbursement- linked indicators (DLIs) are all examples of RBF that have been shown to be effective at improving learning outcomes at the student, parent, teacher, and school district levels. However, directly financing learning outcomes can be problematic for many reasons - because it can add such distortions to real learning as teaching to the test, because it is difficult to set targets for learning for all students with widely diverse abilities, and because teachers, students, and policymakers may not know how to improve learning. Therefore, as a precondition to establishing RBF systems, it is first necessary to identify the intermediate drivers of student learning to shed light on the mechanisms through which learning is achieved.