One challenge in assembling micro-LAYS estimates is how to handle a study that reports impacts on both participation and learning. If we sum the estimates, we will double-count in cases where gains in learning resulted directly from gains in participation or where gains in learning led to gains in participation (e.g., because students had a greater incentive to attend schools that delivered more learning). As an alternative to adding the two estimates, we could choose to use only estimates from either participation or learning. However, under this approach we would be assuming that one is the central output, and that the other outcome dimension is largely captured within that central output. Instead, for the purposes of this paper, we use the LAYS impact that is greater—whether that was obtained through schooling or learning increases—for each evaluation. This approach places _a priori_ equal weight on schooling and learning, introduces no new assumptions, and avoids double-counting.

# **3 Data and Analysis Framework**

We compare impact estimates from over 150 evaluations of education interventions in 46 countries using a unified measure. In our comparison, we highlight findings from a subset of studies that have comparable cost data<sup>8</sup> and that therefore allow us to compare cost-effectiveness of interventions. We examine how many LAYS each policy or intervention delivers; how cost-effective those gains are; and how much of the gap between quality-adjusted years of schooling and actual years of schooling that intervention would close if it were scaled up, assuming that the effectiveness of the intervention remained constant. This assumption of scalability is not trivial, given that effectiveness at system scale is often substantially lower than effectiveness in even a large pilot study; we therefore carry out this calculation as a calibration exercise rather than a simulation exercise.

We start with over 300 studies compiled from a database produced by Evans and Yuan (2019), which draws studies from a wide range of reviews (Evans & Yuan, 2019; Ganimian & Murnane, 2016; Glewwe et al., 2011; Kremer et al., 2013; Krishnaratne et al., 2013). We then add 13 studies from the World Bank Strategic Impact Evaluation Fund (SIEF), as well as four additional recent studies that have rigorous evaluation methodologies and high-quality impact and cost data (Eble et al., 2021; Piper, Destefano, Kinyanjui, & Ong’ele, 2018; Romero, Sandefur, & Sandholtz, 2020; Sabates, Rose, Alcott, & Delprato, 2021).<sup>9</sup> Our inclusion criteria are that the study should be based on a credible causal inference strategy, using either randomized controlled trials or quasi-experimental methods, such as differences-in-differences, instrumental variables, regression discontinuity, fixed effects, or propensity score matching. To aggregate across outcomes, we code outcomes such that positive impacts always represent an improvement; for example, a reduction in absenteeism is coded as an 8Given that there are substantial difficulties when comparing cost data across contexts and interventions, we believe the field will benefit from efforts to standardize how these costs are reported, for example by consistently using $PPP.

> 9We also check for robustness of our results with the new data from the 2023 GEEAP report (Akyeampong et al., 2023). These results are analysed in Appendix C.

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