increase in attendance. In addition, for studies that report impact on learning, we start with impact estimates that can be expressed in terms of standard deviations. The list of studies included in our analysis is illustrative rather than exhaustive, and in the future, we aim to continue adding more studies and build as comprehensive a database of education interventions as possible. In total, after applying our inclusion criteria, we analyze data from over 150 impact estimates across 46 low- and middle-income countries.<sup>10</sup> In this analysis, we make several choices for parameters and data inputs. First, we assume that the intervention’s effects ( _t_ ) last only for the duration of the evaluation since this is a known quantity and requires no further assumptions. In Appendix Figure B1 we explore the alternative assumption that the impacts last just one year ( _t_ = 1). A second choice that we make is to set the high-quality benchmark learning rate ( _δh,n_ ) equal to 0.8 standard deviations per year. This is a conservative estimate for high rates of learning, drawn from year-on-year learning gains in high-performing education systems, policy-relevant differences across education systems, and standard benchmarks. We choose an artificial benchmark for this analysis, because unlike the actual learning rates of high performers, such as Finland or Singapore, it has the advantage of being stable over time and of being apolitical. This approach to defining high-quality learning rates is similar to the one used to define the high-performance benchmark learning level in the World Bank Human Capital Index, which sets the benchmark at 625 on the scale of TIMSS and PISA. In the robustness section, we explore four plausible approaches to validate this 0.8-standard-deviation high-performance benchmark.

We calculate how much to adjust improvements in access for the level of learning (i.e., the learning adjustment rate _L_<sup>_h_</sup> _i_<sup>)usingHarmonizedLearningOutcomes(HLO),whichareglobal</sup> measures of learning introduced by Angrist et al. (2021) and used in the World Bank Human Capital Index. The paper generates comparable learning measures across 164 countries by linking psychometrically-designed international assessments to their regional counterparts to construct globally comparable learning outcomes at a national level.

We choose HLO data over alternative test score data for various reasons. First, these data enable us to use the same learning scale for interventions from 164 countries across the world, a wide range of countries from which we also draw impact evaluation education estimates. Second, these data are used in the World Bank Human Capital Index (HCI), which enables us to produce micro-LAYS that map directly to the macro-LAYS in the HCI. The international tests of student learning that are included in the HLO data are often scaled to a mean of 500 and standard deviation of 100. For micro-LAYS, we also derive a learning scale whose lower limit plausibly represents zero learning. We use data from early grade reading assessments (EGRA), where underlying test items have a plausible zero: no reading comprehension. In Appendix Figure B2 we show that the HLO score that corresponds to a floor of zero reading comprehension is 300. In accordance with this, in our analysis we scale the HLO data with a linear transformation of 300. In Section 5, we further

> 10LAYS is a unit of measurement and therefore any result, including descriptive and observational results, can technically be expressed in terms of LAYS. Thus, this approach has uses beyond comparison of effects of impact evaluations.

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