evaluation studies in the areas of computer‐assisted learning and school‐based management yield an average impact that translates to 0.05 to 0.07 additional year of schooling. The impacts of the pedagogical improvements are sizeable, suggesting that interventions being explored to improve learning can make a significant difference. When one translates these learning gains into the wage gains associated with improved learning, the median wage gains across all pedagogical interventions with positive impacts are 8 percent, and the wage gains for the intervention with an effect size at the 75<sup>th</sup> percentile are 21 percent. In the latter case, that translates into a net present value of nearly US$24,369 in increased wages, with a number of interventions delivering even higher returns.

# 2. Empirical Strategy

## 2.1 Equivalent Years of Schooling (EYOS)

To report learning effects in terms of EYOS, ideally one would access a test that maps out the learning trajectory of how much students learn from each additional year of schooling. This requires administering a test with a vertical scale, which makes scores comparable across different grade levels. For example, the states of Utah and Texas in the United States assess student performance in grades 3‐8 for reading and math using a vertical scale (Utah Education Association 2013; State of Texas Assessments of Academix Readiness 2013). However, to the best of our knowledge, no existing cross‐country tests allow for that conversion. Some studies have attempted to estimate the learning gain from an additional year of schooling in other ways by comparing students of the same age in two different grades (e.g., with the Program for International Student Assessment, or PISA, which is applied to 15‐year‐olds across countries), either seeking to control for confounders in regressions (OECD 2014) or by using age‐cutoffs for grade enrollment to exogenously identify differences (Strom 2004; Frenette 2008; Benton 2014; Khaw and Wong 2012; Lau and Wong 2013; Marchionni and Vazquez 2015). Neither of these methods maps out a trajectory beyond two years, whereas skills may accumulate at different rates over the primary school cycle.<sup>7</sup> A few other tests assess reading and numeracy competencies of children in a wide age range, but the content of the tests is set at the level of early grades and provides limited information on the learning trajectory of later years in the education cycle. For example, the Annual State of Education Report (ASER) in India and Pakistan, Uwezo in East Africa, and the Independent Measurement of Learning (MIA) in Mexico test the basic reading and numeracy skills of children aged between 5 and 16 through household‐ based assessments. In addition, these tests are based on national curricula to guide domestic policy dialogue and are not necessarily comparable across countries (PAL Network 2017). Similar constraints apply to the Early Grade Reading Assessment (EGRA) and the Early Grade Math Assessment (EGMA), which have been widely used in Africa, the Middle East and South Asia (RTI International 2009).

In this paper, we use an adult skill assessment to estimate the relationship between learning gains throughout the primary cycle. The advantage of using adults is that the analysis avoids selection in terms of who has achieved a certain grade by a certain age. Selection in total years of schooling remains, to a degree that varies by the estimation method. We employ two alternative methods to estimate the EYOS associated with one standard deviation in learning. The first uses simple descriptive data to characterize 7 Another, related effort seeks to combine the quality and quantity of education into a single cross‐country measure – the Learning Adjusted Years of Schooling, or LAYS – to allow comparison of education systems across countries (Filmer et al. 2018). While the LAYS allows an aggregate measure of the quantity and quality of education in an education system, it is not naturally suited to characterizing the gains from a given intervention.

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