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https://github.com/measuringhumancapital/MHC

This account includes code for the paper "Measuring Human Capital using Global Learning Data" published in Nature. The data is available on World Bank websites.
https://github.com/measuringhumancapital/MHC

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This account includes code for the paper "Measuring Human Capital using Global Learning Data" published in Nature. The data is available on World Bank websites.

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README 

        
# Measuring Human Capital Using Global Learning Data



This repository contains the data and code underlying the analysis presented in the paper "*Measuring Human Capital Using Global Learning Data*" published in *Nature*. Please cite all references to the data and analysis as: **Angrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, and Harry A. Patrinos. "Measuring Human Capital Using Global Learning Data." Nature (2021).**



# The Harmonized Learning Outcomes Database



Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction. However, such tests are administered primarily in high-income countries, limiting our ability to analyze learning patterns in low- and middle-income countries that may have the most to gain from the formation of human capital. The Harmonized Learning Outcomes (HLO) database bridges this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. The data is publicly available and will be updated regularly.



[The HLO database is publicly available and will be regularly updated on the World Bank Data Catalog here](https://datacatalog.worldbank.org/dataset/harmonized-learning-outcomes-hlo-database)



# Accompanying Paper, Analysis, and Technical Details



[Here is a link to the paper in Nature](https://doi.org/10.1038/s41586-021-03323-7), and below is the Abstract.



Human capital—that is, resources associated with the knowledge and skills of individuals—is a critical component of economic development. Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction. However, such tests are administered primarily in developed countries, limiting our ability to analyse learning patterns in developing countries that may have the most to gain from the formation of human capital. Here we bridge this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. Using this dataset, we show that global progress in learning—a priority Sustainable Development Goal—has been limited, despite increasing enrolment in primary and secondary education. Using an accounting exercise that includes a direct measure of schooling quality, we estimate that the role of human capital in explaining income differences across countries ranges from a fifth to half; this result has an intermediate position in the wide range of estimates provided in earlier papers in the literature. Moreover, we show that average estimates mask considerable heterogeneity associated with income grouping across countries and regions. This heterogeneity highlights the importance of including countries at various stages of economic development when analysing the role of human capital in economic development. Finally, we show that our database provides a measure of human capital that is more closely associated with economic growth than current measures that are included in the Penn world tables version 9.0 and the human development index of the United Nations.



# Applications



The HLO database has been used as a core ingredient in the [World Bank's Human Capital Index (HCI)](https://www.worldbank.org/en/publication/human-capital). Various aggregations of the metadata are possible. The technical notes accompanying the HCI describe the aggregations used for the index. This means the final aggregated HLO in the HCI could differ from alternative aggregations of the HLO metatdata, such as simple averages. Users of the database should use and interpret the data accordingly. A complete set of technical details is available in both this paper as well as the papers corresponding to the final HCI construction. 



Additional notable applications of the HLO database include: the [USAID Country Roadmaps to Self-Reliance](https://selfreliance.usaid.gov) and an effort to prioritze [Cost-Effective Approaches to Improve Global Learning](http://documents1.worldbank.org/curated/en/719211603835247448/pdf/Cost-Effective-Approaches-to-Improve-Global-Learning-What-Does-Recent-Evidence-Tell-Us-Are-Smart-Buys-for-Improving-Learning-in-Low-and-Middle-Income-Countries.pdf), convened by the UK Foreign, Commonwealth & Development Office (formerly DFID), the World Bank, and the Building Evidence in Education group.