2004), and early childhood education studies often report a social benefit-cost ratio (Heckman, Moon, Pinto, Savelyev, & Yavitz, 2010). Hendren and Sprung-Keyser (2020) propose a unified analysis using a new measure of Marginal Value of Public Funds (MVPF) and compare benefit and cost information (expressed in monetary terms) to prioritize among 133 social policies in the United States. Their analysis reveals that direct investment by governments in low-income children’s health and education in the United States has historically had the highest return on investment, with many such policies paying for themselves. Our study similarly demonstrates that there are investments in education interventions in low- and middle-income countries that can deliver large gains at relatively low cost, even when compared against a benchmark of education gains made by children in high-income countries. Of note, many of the studies we review examine individual policies; however, some of the most effective policies we review combine interventions, consistent with recent evidence suggesting coupling interventions has complementarities (Mbiti et al., 2019).

This work, like other syntheses and summary measures, has limitations. First, outputs of this type are only as good as the inputs, and in this case data are still limited—–especially on costs. Many education interventions have yet to be evaluated rigorously. As data inputs improve and the range of evaluated interventions expands over time, the outputs of comparative analysis will also improve. Second, in many cases, studies report learning outcomes only in standard deviations. We therefore have to use assumptions about the distribution of learning levels in the study area to translate the study findings into LAYS. However, if future studies use common, standardized tests, future comparisons will allow relaxation of that assumption. Third, because both impacts and costs are measured with imprecision (Evans & Popova, 2016a), it would be unwise to focus on small differences in cost-effectiveness. Rather, this analysis aims to inform broad trade-offs at the aggregate level in cases where there are large, consistent differences. For example, we consistently see that as a cost-effective tool for improving LAYS, cash transfers rank lower than investments in early childhood development. This pattern is robust to method, data inputs, and study or country contexts. Fourth, while access to school and learning proxied by test score performance capture important components of education, they do not capture all aspects of education (like socioemotional learning). However, the combination of these measures represents an improvement over the status quo where typically only one measure is used. Fifth, context matters. Even for the most cost-effective interventions, policymakers will have to consider whether contextual conditions support local adaptation of an intervention (Bates & Glennerster, 2017).

The rest of paper proceeds as follows. Section 2 provides a framework for the learning-adjusted years of schooling. Section 3 describes the set of studies and data included in the analysis of education policies and interventions. Section 4 presents the results in terms of both effectiveness and cost-effectiveness. Section 5 provides a series of robustness tests, and Section 6 concludes.

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