Productivity, human capital and educational policies

The OECD regularly recommends to countries to reform their education and training systems. Economists often refer to this as improving ‘human capital’. Yet, at the macroeconomic level, quantifying the effects of human capital on growth and productivity has often proven frustratingly elusive, both in the academic literature and in OECD work.

What is human capital?

Human capital can be broadly defined as the stock of knowledge, skills and other personal characteristics embodied in people that helps them to be productive. Pursuing formal education (early childhood, formal school system, adult training programmes) but also informal and on-the-job learning and work experience all represent investment in human capital.

How do we measure human capital?

There is no comparable and consistent measure across countries reflecting all these elements available. Economists thus rely on inferior substitutes, such as years spent in the schooling system, rates of enrolment in education and literacy.

Previous work by the Economics Department suggested a measure of human capital, which, contrary to the existing literature, was based on realistic rates of return to education, allowed to vary substantially across countries and to some extent over time. This measure performs well in regression analysis explaining productivity across OECD countries and over time.
The measure was related to a number of education policies and a policy simulator (see below) to identify the policies that tend to boost human capital. However, the effect of policies could only be estimated
indirectly and considered as magnifying or shrinking the effects of education spending.
More details below under (Previous macroeconomic measures of human capital).

A weakness of the previous measure is that it ignores the quality of education. To address this issue, the Economics Department developed a new measure of human capital built using PISA and PIAAC surveys, and mean years of schooling.
The new measure is a cohort-weighted average of past PISA scores (representing the quality of education) of the working age population and the corresponding mean years of schooling (representing the quantity of education). In contrast to the existing literature, the relative weights of each component are not imposed or calibrated but directly estimated. 
The study finds that the elasticity of the stock of human capital with respect to the quality of education is three to four times larger than for the quantity of education.

Why does it matter?

Economists assume that countries with more educated population should have higher productivity. At the microeconomic level, those with more education and experience tend to earn higher salaries. It is more difficult to identify a robust positive relationship between economic outcomes and human capital at the macroeconomic level, where such link has been missing so far.

Our new measure has a strong link with productivity with the potential for productivity gains being much greater from improvements in the quality than quantity component of human capital. The magnitude of these potential gains in multi-factor productivity is comparable to a similarly standardised improvement in product market regulation, but the effects materialise with much longer lags.

For more details see: “A new macroeconomic measure of human capital exploiting PISA and PIAAC: Linking education policies to productivity” by Égert, de La Maisonneuve and Turner (2022) 

A tool to assess shocks such as COVID-19 on productivity through human capital channels

With the new human capital measure, it is possible to assess the impact of shocks such as the COVID-19 or the war in Ukraine on human capital and productivity.

A new paper, Quantifying the macroeconomic impact of COVID-19-related school closures through the human capital channel, estimates the long-term effect of the Covid-19 shock on aggregate productivity through the human capital channel.

  • Results suggest that school closure effects might be increasing over time, amounting to a loss of between 0.2% and 0.9% in human capital when all cohorts impacted will have entered the labour force between 2036 and 2067.
  • The effect will diminish until the last cohort affected retires from the labour force at the age of 65 in 2083, the year the effect of the pandemic vanishes.
  • Productivity losses are estimated to range between 0.4% and 2.1% after 45 years, broadly consistent with earlier findings in the literature.
  • Policies aimed at improving the quality of education and adult training will be needed to offset or, at least, alleviate the impact of the pandemic on human capital. 


Webinar: Quality aspects of human capital

Previous Measure of Human Capital

In a previous attempt to improve the measurement of human capital, the OECD provided a measure that builds on two components: years of schooling and rates of return to schooling. Its novelty comes from its assumptions on marginal rates of return to education. Previous studies used the same rates of return for all countries and these didn’t change in time. The paper (add link to WP 1575) applies different rates of return for countries (to be precise, for five groups of countries) and they change over time (over three time periods), to better reflect the recent micro-economic evidence.

 This measure shows that more human capital leads to more productivity.

Policy implications - What policies matter at the macroeconomic level for human capital?

Numerous studies examined educational policies at the student, school and country level (for literature overview see Smidova, 2019) and main OECD country experiences are summarised by the OECD’s Directorate for Education and Skills in Education GPS). 

As the new human capital measure is built on years of schooling and rates of return to education, policies influencing education matter. The empirical analysis shows that the following educational policies tend to boost human capital at the country level (Égert, Botev and Turner, 2019):

      • First, more children attending pre-primary education improve human capital, especially in countries with above-average share of disadvantaged children. This result based on macroeconomic data is in line with the microeconomic literature.
      • Another finding is that teaching resources matter. Fewer students per teacher boost human capital, which can be thought of as a crude measure of the teaching quality. We also found some evidence showing that higher share of qualified teachers is good for human capital.
      • Third, streaming children at a later age into different education tracks such as vocational and grammar schools based on their ability or achievement has a positive impact.
      • Schools having more autonomy and leeway on how to manage their resources is good for human capital. This positive effect is greater in countries with external central exams that captures external accountability imposed on schools.
      • Finally, countries, in which universities have more autonomy in how they can allocate their resources, have higher human capital and the ease of access to individual financing of university education helps to raise a country’s human capital. It should be noted, however, that these results are less robust.

Furthermore, the analysis shows that certain educational policies are ‘good value for money’, because they have a double dividend of boosting human capital as well as reducing spending pressures. These are: increasing attendance in pre-primary education, greater university autonomy and lower barriers to funding to students in tertiary education. Increasing school autonomy at primary and secondary level enhances educational outcomes, but does not reduce spending pressures. And, higher student-to-teacher ratio, higher age of first tracking and a reduction in the extent of tracking also boost human capital, but at a higher cost.

Illustrative impact on a median country: Reform that moves the policy to the average of the top 3 OECD performers.‌‌

 The full impact takes decades to show, but positive impact is visible already in medium term (5 years). The size of impact depends on each country’s scope for reform, i.e. where does it stand in terms of the policy today. To see potential reform impact in your country, go to the dataviz below.

Policy simulator: Potential reform impact in your country

Find out more – select a country and policy area below.

Further reading