Linear regression models usually utilized so far were based on the implicit assumption that all individuals are affected by an intervention in the same way. This assumption is often implausible, though. Rather, one would expect that some people are affected more than others by a policy change or a particular program. Some people may benefit a lot while others to a much lesser extent. The effects might differ by quantiles and be context dependent. It is thus important to ascertain who and how many people lose or gain from a certain reform alternative. This means that not only average gains or losses need to be assessed but also the distribution of the effects should be analyzed, especially in fields where inequality of opportunities or outcomes particularly matter, e.g. education, health, incomes and poverty. New econometric methods based on nonparametric models admit analysis of heterogeneity and distributional effects, thus allowing a much more differentiated approach to impact evaluation.
Impact evaluations with heterogeneity analysis specifically account for the inter-individual and inter-group diversity and for differences in the impacts that certain interventions may have. Such heterogeneity in effects needs to be analyzed in order to learn which intervention or program works best for whom. Impact heterogeneity analysis, if embedded in the analysis of controlled trials as well as quasi-experimental designs, presents interesting potential to find and develop tailored optimal solutions.
To provide a concrete example of effect heterogeneity, one can imagine that supplying new, pedagogically adapted textbooks would improve especially the learning outcomes of students who used to perform lower previously. It may be that the program has an average treatment effect of zero, but does have a positive impact on lower-achieving students. In the context of granting chance equality through education, adoption and scaling-up of the program may make sense, even if average impacts are small. Such effects on inequality would be missed by conventional regression models. The ability of quantile treatment effects to characterize the heterogeneous impact on different points of an outcome distribution makes them appealing in many applications.