# Conclusion

There is policy and academic demand for a measure of education quality that is comparable across countries and over time. The growth of international standardized achievement tests is a huge step in this direction. However, the countries that participate in these tests are often high and middle-income countries. This limits the ability to track, compare, or understand education patterns in developing countries – the countries that often have the most to gain from quality education and human capital formation.

One option is to wait to make comparisons for low-income countries to participate in international assessments. Although this is a worthy aspiration and we hope it occurs, it will take time and render a rich array of retrospective data null, limiting longitudinal data analysis. Alternatively, we can use a rigorous approach – albeit with caveats – to harmonize available learning data across different types of international and regional assessments. This is the approach we take in this paper, creating a Harmonized Learning Outcomes (HLO) database. Until there is a global proficiency assessment for numeracy and literacy, and to enable rich longitudinal panel data analysis, the harmonization of existing learning assessments provides the next best alternative to compare education quality on a global scale. Moreover, as more countries join international and regional assessments, and do so for longer, the accuracy and robustness of the harmonization exercise will improve.

We build a globally comparable database of 164 countries/territories from 2000-2017, approximately two-thirds of which are in developing economies, representing more than 98 percent of the global population. While our methodology has limitations, our robustness tests indicate that this dataset produces similar results to each underlying assessment used, as well as Item Response Theory (IRT)-linking methodologies where there is overlap. We include double the number of countries of any individual assessment or IRT-linking methodology while demonstrating consistent estimates where there is overlap.

We contribute to the literature in several ways. This is the most current and expansive cross-country dataset on education quality, including the most developing countries. We include, for the first time, the Early Grade Reading Assessment (EGRA) in addition to TIMSS, PISA, PIRLS, SACMEQ, LLECE, PASEC and MLA. This enables us to extend our database substantially, including 48 countries with recent data in the last ten years, all of which are developing economies. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries that are often excluded from large international assessments. We introduce a series of methodological updates, including measures of uncertainty, fixed conversion factors for greater comparability over time, and year-by-year data. All data are derived directly from test score data. In contrast to black-box and complex imputation, this enables methodological transparency. Moreover, it produces a clear policy lever: when countries perform better on RSATs and ISATs they can be certain the HLO will improve.

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