# **2 Learning-Adjusted Years of Schooling Framework**

Learning-adjusted years of schooling for a given country—what we call macro-LAYS—are the product of years of schooling and a measure of schooling quality (Filmer et al., 2020). Specifically, they are produced by scaling the country’s average schooling by its test-score performance relative to a global high-performance benchmark.<sup>5</sup> Figure 1 shows an example using data from the World Bank Human Capital Index. For example, Singapore’s average student test scores are closer to the high-performance benchmark than any other country’s scores. As a result, its 14 average years of schooling are discounted only slightly, to 13 LAYS. By contrast, South Africa has 10 years of schooling but only about 5 LAYS, because its test scores are only about half of Singapore’s. In other words, macro-LAYS are produced at the country level by adjusting average schooling in a given country by the amount of learning in that country (relative to a high-performance benchmark). Expressing national education levels in terms of macro-LAYS provides a unified and user-friendly measure for a variety of education outcomes.

In this section, we show how LAYS can also be used at the micro level to compare education interventions. The number of rigorous studies evaluating the effect of interventions on educational outcomes is growing, with nearly 300 impact evaluations focused on learning outcomes in low- and middle-income countries as of 2016 (World Bank, 2018). An improved and more comparable metric would enable better evidence synthesis and clearer policy recommendations. As described below, we aim to address many of the challenges that limit current comparisons—most notably, that access and learning impacts are often discussed separately, and that learning gains can be expressed only relative to local performance. We do this by expressing education outcomes from interventions and policies in terms of LAYS units that offer a single, internationally comparable, and policy-salient metric. Hereafter, we will refer to LAYS gained from an intervention or policy as micro-LAYS.

If impact evaluation studies tested students, and reported results, against internationally agreed test scores such as the Trends in International Mathematics and Science Study (TIMMS), the Programme for International Student Assessment (PISA) or the Early Grade Reading Assessment (EGRA), the translation into LAYS would be straightforward. This is what we would hope to see in future studies. However, this is currently not the norm, and therefore a number of assumptions are needed to translate existing studies into LAYS.<sup>6</sup> To ensure a coherent unifying approach, the micro-LAYS methodology invokes assumptions similar to those used in constructing country-level

> 5The high-performance benchmark used in the World Bank Human Capital Index is an artificial benchmark of high performance of 625 as defined by the international assessment Trends in Mathematics and Science Study (TIMSS), which was chosen because that benchmark is stable over time and is apolitical (Kraay, 2019). Other high-performance benchmarks can also be used to construct LAYS estimates. For example, we can use the top-performing country. If Singapore is the highest-performing country in a given year, we can express every country’s LAYS in Singapore-equivalent years. That is, we could say that a student in South Africa achieves 10 years of schooling, but 5 years of Singapore-quality schooling.

> 6Comparing education gains across age groups and learning levels is methodologically challenging. The learning jump from single-digit subtraction to long division is inherently different from the jump between recognizing letters to being able to read a sentence. But if we conclude these are fundamentally different concepts that cannot be compared, we forfeit the ability to make comparisons across impact evaluations or advise policymakers on the most cost-effective approaches to improving education.

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