Measuring human capital: learning matters more than schooling


For decades, human capital research has relied on measures of education (Mincer 1984, Mankiw et al. 1992). But replacing human capital with schooling presupposes that being in school translates into learning. Evidence suggests this is often not the case (Pritchett 2013). A recent analysis reveals that six in ten adolescents worldwide cannot reach basic proficiency levels in mathematics and reading (UNESCO 2017). This gap between schooling and learning is particularly acute in developing countries. In Kenya, Tanzania and Uganda, three-quarters of grade 3 students cannot read a basic sentence such as “The dog’s name is Puppy”. In rural India, half of grade 3 students cannot solve a two-digit subtraction problem like 46 minus 17 (World Bank 2018).

Measuring human capital around the world: the Harmonized Learning Outcomes (HLO) database

Several studies have advanced the argument that when human capital is measured by education, it does not produce the returns predicted by growth models. But when measured by learning, human capital is more strongly associated with growth (Pritchett 2006, Kreuger and Lindahl 2011, Hanushek and Woessmann 2012). Understanding growth (Akcigit et al. 2020) is essential, and while there are good country studies (Fazzio et al. 2020), we still need comparable global time series data.

To date, much of the effort to measure learning has focused on advanced economies. This has come at the expense of a significant portion of the global distribution, especially countries with the most potential to profit from human capital accumulation. This limitation is due to the lack of comparable measures of learning in developing countries.

In a recent article, we present a database of globally comparable learning outcomes for 164 countries, covering 98% of the world’s population from 2000 to 2017 (Angrist et al 2021). It is the largest and newest global learning database, and one of the first to disaggregate learning outcomes by gender or introduce methodological improvements such as inclusion of errors. types to quantify uncertainty. The the database is available for public use and updates are expected periodically as new training data becomes available. A large-scale effort to track human capital formation using this database is the World Bank’s Human Capital Index. We are contributing to a body of research in this area (e.g. Rindermann 2018).

Figure 1 Average apprenticeship from 2000 to 2017 in 164 countries

Source: Harmonized Learning Outcomes (HLO) Database.

The human capital deficit: schooling is not learning

We explore the contrast between changes in enrollment and changes in learning over time, from 2000 to 2015. We observe a clear trend of high and increasing enrollment, while learning progress appears to be limited in many cases. For example, in the Middle East and North Africa, enrollment rates peaked at 99% in 2010, up from 95% in 2000. In contrast, learning levels have remained low and have remained the same ( approximately an initial score of 380) from 2000 to 2015 in these regions. This model has been called in previous studies and by the international education community as “the learning crisis”. Using the HLO database, we demonstrate that this crisis continues globally.

Figure 2 Enrollment versus apprenticeship by region, subject to fixed effects by country.

Source: The data in this figure includes primary school enrollment rates from Lee and Lee (2016) and learning estimates from the Harmonized Learning Outcomes (HLO) database.

Learning contributes to substantial economic development

Several development accounting studies have explored the relative contribution of human capital to income differences between countries. The results were inconclusive, in part because of the difficulties in measuring human capital. Our results suggest that human capital accounts for between one-fifth and about one-half of the income differences between countries, falling somewhere in the middle compared to other work (Hall and Jones 1999, Jones 2014, Caselli and Ciccone 2019) , which ranges from zero to almost all (Table 1). This result is not intended to provide definitive estimates, but rather to motivate the use of a direct measure of learning in future analyzes of the development accounting literature.

Table 1 Basic development accounting results and comparison with the literature

We also find stronger links between our measure of human capital and growth than measures of human capital that rely heavily on education, such as the Penn World Tables (Feenstra et al. 2015) and the Human Development Index of United Nations (UNDP 2013).


This database comes at a time when a series of global efforts have been launched to measure and track learning globally. While recent analysis suggests the world is on track to meet the goal of universal primary education by 2030, if learning continues to stagnate, this achievement will make little sense.

Our results have important implications for the future of work. As developing economies become increasingly service-oriented and automation and digitization continue to expand, skilled labor will be essential for workers to secure the jobs of the future (Baldwin and Forslid 2020). Human capital is an essential catalyst for this transition. Our results reveal that schools – which for decades have been supposed to impart knowledge and skills – have often failed. But if they are effective in terms of learning, the development gains could be substantial.

The references

Akcigit, U, J Pearce and M Prato (2020), “Tapping into talent: Coupling education and innovation Policies for economic growth”,, October 10.

Angrist, N, S Djankov, PK Goldberg and HA Patrinos (2021), “Measuring human capital using global learning data”, Nature, March 10.

Baldwin, R and R Forslid (2020), “Covid 19, Globotics and Development,”, July 16.

Caselli, F and A Ciccone (2019), “The stock of human capital: a generalized approach. Comment”, American Economic Review 109 (3): 1155-1174.

Fazzio, I, A Eble, R Lumsdaine, P Boone, B Bouy, PJ Hsieh, C Jayanty, S Johnson and F Silva (2020)., December 16.

Feenstra, RC, R Inklaar and MP Timmer (2015), “The Next Generation of the Penn World Table”, American Economic Review 105: 3150-3182.

Hall, RE and CI Jones (1999), “Why do some countries produce so much more output per worker than others? “, Quarterly economic review 114 (1): 83-116.

Hanushek, EA and L Woessmann (2012), “Do the Best Schools Lead to More Growth? Cognitive skills, economic results and causality ”, Journal of Economic Growth 17 (4): 267-321.

Jones, BF (2014), “The stock of human capital: a generalized approach”, American Economic Review 104 (11): 3752-3777.

Krueger, AB and M Lindahl (2001), “Education for Growth: Why and for Whom? “, Economic literature review 39 (4): 1101-1136.

Lee, JW and H Lee (2016), “Long-term human capital”, Development economics journal 122: 147-169.

Mankiw, NG, D Romer and DN Weil (1992), “A Contribution to the Empirics of Economic Growth”, Quarterly economic review 107 (2): 407-437.

Mincer, J (1984), “Human capital and economic growth”, Journal of the economics of education 3 (3): 195-205.

Pritchett, L (2006), “Does Learning to Add Add Up? The returns to schooling in aggregate data ”, Textbook of economics of education 1: 635-695.

Pritchett, L (2013), The renaissance of education: school does not learn, Washington, DC: CGD Books.

Rindermann, H (2018), Cognitive capitalism: human capital and the well-being of nations. Cambridge, UK: Cambridge University Press.

UNESCO (2017), “More than half of the world’s children and adolescents are not learning”, UIS Factsheet 46

United Nations Development Program (2013), “Human Development Report Education Index”.

World Bank (2018), World Development Report 2018: Learning to Realize the Promise of Education, Washington DC.

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