to improve development outcomes using impact evaluations. Impact evaluations assess changes in the well-being of individuals, households, communities or firms that can be attributed to a particular project, program or policy (Baker, 2000). In practice, it is often extremely challenging to reliably estimate the outcomes attributable to an intervention because this necessitates an answer to the question of what would have happened to those receiving the intervention if they had not in fact received the program. Over the last decade, innovative analytic techniques that fall under the broad category of experimental and quasi-experimental designs have made it possible to make a lot of progress on this challenge. The resulting impact evaluations have generated highly rigorous evidence of whether innovations really make the differences they intend to make.

Randomized control trial (RCT) studies represent the highest standard of impact evaluation evidence in development. This economics research methodology derives from techniques used in the physical sciences. Overall, the approach is to isolate a specific innovation, working to ensure that the innovation is the only systematic difference between the general population and the “treatment” group that participates in the experiment. The central design feature of the RCT approach is that the treatment group is randomly selected, so that, for the purposes of statistical analysis, treatment and control groups are indistinguishable, save for the fact that the treatment group is subjected to the innovation whose impact the study is evaluating. By comparing development outcomes in the treatment group versus the control group, research that uses experimental methods is able to rigorously isolate whether the tested innovations make an attributable difference.

Quasi-experimental designs are likewise highly rigorous approaches to gathering evidence that isolates the impact of development innovations. While these methods do not randomly assign participation in treatment and control groups, they use econometric techniques to exploit features of the process of how and where innovations were implemented that introduced random variation. Taking advantage of this variation, researchers using quasi-experimental methods are able to rigorously isolate whether the tested innovations make an attributable difference. Quasi-experiment designs are sometimes seen as slightly less rigorous than randomized control designs. At the same time, they offer a number of advantages. In particular, quasi-experiment designs are often cheaper to implement than RCTs and offer the possibility to evaluate programs after their introduction using existing data. Besides, with quasi-experiments, there are sometimes fewer concerns about the external validity of results as this type of design is frequently used to analyze interventions introduced on either the national level or at least in a large geographical area.

Experimental and quasi-experimental evidence has been applied with increasing prevalence in countries around the world, considering innovations in different sectors. Within that growing set of experimental and quasi-experimental design impact evaluations, there are a relatively large number of experimental and quasi-experimental impact evaluations that have tested and gathered high quality evidence on education in South Asia. As presented below in Section 3, there are 29 distinct studies that document the results of impact evaluations of education interventions in South Asia that reached our standard for rigor. This provides a rich body of high-quality analytic literature which we seek to bring together in this paper.

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