is the “STEP” school gain in cognitive skill compared to that in other education systems? First, we can compare the absolute learning gains. Using PIACC data from Hanushek et al. (2015) and the same descriptive method (method 1), we estimate that students from Finland – one of the top performing education systems – improve reading proficiency by approximately 0.3 standard deviation with an additional year of schooling, or one standard deviation is an equivalent of 3.3 years of Finnish schooling. Similarly, the average improvement in reading from an additional year of schooling in grades 3‐8 in Texas is also about 0.3 standard deviation, or one standard deviation is an equivalent of 3.3 EYOS of Texas education. Hence, in absolute terms, students in these two countries learn about twice as much as students in the 5 STEP countries.

However, taking the quality of education systems into account, students in high‐performing systems learn even more. Although one additional year of schooling is associated with 0.3 standard deviation in both Texas and Finland, it does not mean that students in Texas and Finland make the same improvement within one school year, because the quality of the two education systems is not the same. This can be illustrated by the fact that within the PIAAC pooled sample, with a common test, an additional year of schooling in the US is associated with 0.2 standard deviation of reading proficiency improvement, while that number is 0.3 standard deviation in Finland.<sup>14</sup> In this paper, we propose the pooled STEP standard as a metric for low‐ and middle‐income countries for two reasons. First, we do not have data for all countries where the interventions were implemented; second, we try to evaluate the impacts of interventions using the same STEP EYOS to avoid biases from the variance in the quality of different education systems.

# 4.2 Impacts of Increasing Cognitive Skills

The data available to calculate the relationship between cognitive skills and earnings are available in only a limited number of countries. In fact, only two countries (Colombia and Kenya) have both data on cognitive skills and earnings as well as impact evaluations results from an expansive database (Snilstveit et al. 2015). As a result, we use the return to learning estimated using the pooled sample to demonstrate the potential impacts of interventions. Taking these numbers as predictive would require strong assumptions that interventions across countries would have the same effect, which is not the case. However, these estimates are intended to be demonstrative rather than predictive. Again, we acknowledge the limitations of this extrapolation, but we propose that this work still serves to demonstrate the potential returns to learning interventions. Over time, more data on the return to cognitive ability will improve the precision of this exercise.

Table 3 shows the effect sizes at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentiles of three categories of interventions: structured pedagogy, school‐based management and computer assisted learning. Table A5 shows the full list of interventions. These impacts correspond to EYOS estimated through three sets of methods. Across all pedagogical interventions that have been evaluated, the median effect size is 0.13 standard deviation.<sup>15</sup> If the effect were sustained over time, the increased learning from this program would have an impact equivalent to an additional 0.52 to 0.88 EYOS, depending on the estimation method. Likewise, a more promising intervention – a decentralized schooling system intervention in Brazil that provided new curriculum, teaching materials and teacher training – yielded an impact that corresponds to 1.16 EYOS under the most conservative approach and reaches 2 years of equivalent schooling using the descriptive

> 14 Calculations are available upon request.

> 15 That median effect is from an intervention in Kenya that provided teachers with teaching manuals, training and follow‐up workshops (Jukes and Dubeck 2015).

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