By Ashley Ward and Belén Zinni

Human capital, the stock of knowledge and skills embodied in people, is a key input in economic production. Changes in both the “quantity” and the “quality” of a country’s human capital stock influence economic growth and productivity performance (Égert et al., 2022). Traditional measures of labour input in economic growth and productivity analyses, such as total hours worked, focus solely on changes in the quantity of labour input, ignoring changes in the skill composition of the workforce. For example, these measures equate an hour worked by a highly experienced surgeon and an hour worked by a junior retail salesperson, disregarding their vastly different experience and skills.

Firms recognise that workers with different skills and experience are not perfect substitutes by paying them different wages. It is therefore possible to account for differences between workers by weighting their hours worked by their respective shares in total wages. Such measures are often referred to as Composition Adjusted Labour Input (CALI), Labour Services, or Quality Adjusted Labour Input (QALI). CALI measures provide an improved understanding of whether the average “quality” of labour is increasing or decreasing over time. In addition, they can play a crucial role in productivity analysis by more closely explaining the sources of economic growth. Economists often break down output growth into that explained by changes in labour input, capital input, and multifactor productivity (MFP) growth. In this framework, referred to as growth accounting, multifactor productivity (MFP) growth is estimated as a residual, capturing all growth left unexplained by growth in labour and capital inputs. When a traditional measure of labour input is replaced with a CALI measure, a larger share of output growth is attributed to labour, reducing the “unexplained” share attributed to MFP and improving the explanation of the sources of economic growth. Nonetheless, the estimation of CALI comes at the cost of timeliness and resources, as it necessitates access to microdata sources.

The practice of using wages to reflect varying skills among workers assumes that hourly wages equate to hourly productivity. However, numerous factors can lead to disparities between wages and actual productivity. These factors include wage-setting methods, seniority within the workforce, workplace discrimination, and gender pay gaps, among others. Given the current lack of more precise measures of productivity, the existing literature employs wages as a proxy for productivity in constructing CALI measures.

Labour input has grown more than thought

Ward and Zinni (2024) reviews the most relevant literature on CALI estimates and follows a generic approach to produce CALI estimates for 21 countries. The study tests the sensitivity of CALI estimates to the selection of workers’ characteristics, classifying workers according to their educational levels, age (a proxy for years of working experience), gender, industry of work, and occupation. It then analyses the evolution of CALI over time and establishes the impact of accounting for CALI on MFP growth.

Growth in the composition of labour contributed positively to CALI growth between 2004 and 2018 in all countries covered in the study (Figure 1). The greatest contributions from changes in labour composition, and hence the largest increases in the average growth rate of CALI, are observed in Portugal, Poland, and Spain, where the labour composition component grew by 1.9%, 1.2% and 1.1% per year between 2004 and 2018, respectively, well above the average annual growth rate of all countries included in the study (0.6%). In a few countries, including Estonia, Latvia, Portugal, and Romania, accounting for the composition of the workforce changes the sign of growth in labour input from negative to positive, as compared with that suggested by the volume of hours worked.

Figure 1: Changes in the composition of labour contributed positively to CALI growth

Average annual percentage change in age-education CALI and its components, 2004-2018

Note: Data for the United Kingdom corresponds to 2004-2014.
Source: Authors estimates based on EU-LFS, EU-SES, STATCAN LFS, CPS and OECD Productivity Statistics (database).

During a recession and often in the years that follow, the compositional effect of CALI tends to be higher, pointing to an increase in the average skill level of those in employment (i.e. an increase in labour quality). Indeed, firms tend to shed labour and/or reduce hours worked among lower-skilled workers during a recession, while hoarding higher-skilled individuals. During the 2008-2009 recession, most countries experienced an increase in the composition component, which counterbalanced the decline in total hours worked and cushioned the fall in CALI (Figure 2).

Figure 2: Most countries saw a decline in CALI during the 2008-2009 recession

Growth in age-education CALI and its components in selected countries, percentage change

Source: Authors estimates based on EU-LFS, EU-SES, CPS and OECD Productivity Statistics (database).

MFP growth is revised downwards when accounting for labour composition

MFP growth is revised downwards for all countries when using a CALI measure, suggesting that labour plays a larger role as a source of output growth than previously understood. While the revision to average annual MFP growth over 2004-2018 remains small in most countries, the impact on MFP growth may be relevant in countries that have experienced larger improvements in labour quality (Figure 3). A significant downward revision in average MFP growth ranging between 0.8% and 1.6% per year is found in Greece, Portugal and Spain, which is equivalent to a cumulative downward revision ranging between 10 and 20 percentage points in the MFP index over the whole period of analysis.

Figure 3: MFP growth is revised downwards for all countries when accounting for changes in labour composition

MFP growth using total hours worked (Standard MFP growth) and the age-education CALI measure (Adjusted MFP growth), average annual percentage change, 2004-2018

Key findings

The OECD study leads to a few key takeaways:

1. The integration of CALI into the growth accounting framework is essential in countries undergoing significant shifts in the composition of their workforce. In these countries, accounting for changes in both total hours worked (quantity) and the composition (quality) of labour is crucial for improving the understanding of the sources of economic growth.
2. Educational attainment and age (a proxy for years of working experience) emerge as the two essential workers’ characteristics to consider when building CALI estimates and investigating the contribution of labour input to output growth. Occupation holds some explanatory power of changes in labour quality, possibly accounting for skills mismatches when considered alongside educational levels. Industry of work is found to be largely irrelevant.
3. The inclusion of gender as a dimension to classify workers into different categories has little explanatory power and can be avoided. The use of wages in the estimation of CALI relies on the assumption that hourly wages equal hourly productivity. Gender pay gaps bring important limitations to this assumption, as they often reflect discrimination between women and men in the workplace, alongside differences in self-selection, propensity to compete, negotiation behaviour and risk aversion, rather than differences in workers’ actual productivity.
4. NSOs are typically best placed to produce CALI measures, as they benefit from access to a much wider range of data sources, sometimes confidential, and have the expertise to address representative biases to maximise the quality of their estimates. Decisions regarding its calculation will depend on the expected changes in the composition of the workforce overtime and/or the cost of producing CALI, including considerations of timeliness and resources.

References

Égert, B., C. de la Maisonneuve and D. Turner (2022), “A new macroeconomic measure of human capital exploiting PISA and PIAAC: Linking education policies to productivity”, OECD Economics Department Working Papers, No. 1709, OECD Publishing, Paris, https://www.oecd.org/en/publications/a-new-macroeconomic-measure-of-human-capital-exploiting-pisa-and-piaac-linking-education-policies-to-productivity_a1046e2e-en.html

Ward, A. and B. Zinni (2024), “The composition of labour input: Sensitivity testing and results for productivity analysis”, OECD Publishing, Paris, https://www.oecd.org/en/publications/the-composition-of-labour-input_5d9b866a-en.html

By Balázs Égert, Christine de la Maisonneuve and David Turner, OECD Economics Department.

While human capital is widely regarded as a key driver of economic growth, measuring it is challenging. The empirical literature aimed at constructing a measure of human capital at the country level has combined quantity (such as mean years of schooling) and quality (typically internationally standardised test scores) components either using arbitrary, most often equal, weights or by including them separately in estimations. Results tend to show, surprisingly, that one component is dominant and the other completely insignificant. A further weakness of many studies that try to incorporate notions of quality into measures of the human capital stock is that they are based on contemporaneous flow measures that most often relate to students tested at age 15, which are unlikely to be representative of the skills of the entire (and older) working age population.

Our recently published working paper: A new macroeconomic measure of human capital exploiting PISA and PIAAC: Linking education policies to productivity (Égert et al, 2022) addresses these two weaknesses by constructing a new stock measure of human capital that makes use of OECD data from the Programme for International Student Assessment (PISA) and the Programme for the International Assessment of Adult Competencies (PIAAC). Improving aggregate measures of human capital is important given the central role that education, training and skills play as a driver of economic performance.

A new aggregate stock measure of human capital

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). Contrary to the existing literature, the relative weights of the quality and quantity components are not imposed or calibrated but are estimated directly. Our calculations involve three stages.

• First, a matching is established between PIAAC adult test scores, mean years of schooling and PISA student test scores of the corresponding cohort who took the student tests as 15-year olds. Such matching is reasonable given that both PIAAC and PISA tests capture similar skills: PISA scores cover reading, maths and science; PIAAC adult test scores cover literacy, numeracy and problem solving.
• Second, PIAAC test scores are regressed on matched PISA test scores and mean years of schooling. The estimation results show 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.
• Third, these estimated elasticities are used to construct an aggregate time series stock measure of human capital by cumulating quantity and quality measures across all cohorts in the working age population.

This approach relies on combining data from both PISA and PIAAC in order to overcome inherent problems with using either in isolation. On the one hand, PIAAC provides a measure of skills for the entire adult working age population, but has no time series and limited country coverage, hence ill-suited for empirical regression analysis. On the other hand, PISA only applies to those aged 15 but, very importantly, especially when combined with similar international test scores, has a more extensive time series and country coverage.

The new measure of human capital for the population aged 16 to 65 years suggests that the countries with the greatest human capital include Australia, Japan and Finland, while Chile and Thailand have the lowest levels of human capital among this group of 16 countries (Figure 1, Panel A).¹ These rankings for the last available year are broadly confirmed for the stock covering the population aged 16-39 (Figure 1, Panel B), although there are some differences suggesting that younger generations have been increasingly better educated in some countries than in others. For instance, Finland lags behind Japan for the total stock, but the countries flip places for the stock covering only the younger generations, because the stock of human capital of younger generations has risen more strongly in Finland compared to Japan.

Figure 1. The new measure of the stock of human capital

Human capital effects on productivity are potentially large but come with long lags

The new measure of human capital shows a robust positive correlation with productivity for OECD countries in cross-country time-series panel regressions, suggesting that improvements in human capital are accompanied by macroeconomic productivity gains.

Simulations suggest that the potential for long-run productivity gains is much greater from improvements in the quality than the quantity component of human capital. An improvement in PISA test scores, equivalent to closing the gap between the median and the top three performing OECD countries, eventually generates a long-run increase in multi-factor productivity (MFP) of between 3.4% and 4.1%. Alternatively, a similar increase in mean years of schooling, also corresponding to reducing the gap between the median and the top three performing OECD countries, generates an increase in TFP of between 1.8% and 2.2%.

Another finding is that gains in productivity through the human capital channel can be of the same order of magnitude as from improving product market regulation, again using the same benchmark of closing the gap between the median and the top three performing OECD countries. The lags are, however, also typically much longer from the human capital channel, particularly because it takes almost five decades before a sustained improvement in student skills are fully reflected in improvements in the skills of the entire working age population (Figure 2). These long lags can be shortened by putting more emphasis on adult learning including life-long learning and upskilling of the existing workforce, improving human capital at older ages.

Figure 2. Comparing policy responses to improve skills and product market competition

Change in multi-factor productivity, per cent

Assessing the effect of education policy reforms on human capital and productivity: the illustrative example of pre-primary education

An attractive feature of the new stock measure of human capital is that it opens up new avenues for evaluating the effect of education policy reforms on productivity and per capita income. Any education policy, which can be measured quantitatively through an indicator and linked to changes in student test scores, can be related to the new measure of human capital and so to productivity.

We use pre-primary education as an illustrative example. The first step in the quantification of the effect of pre-primary education attendance may be provided by the existing empirical literature using microeconomic student-level data, which finds that students previously enrolled in pre-school for more than one year perform better in student skill tests, improving their test scores by between 8.2 and 9.6 points. This corresponds to an increase of 1.7% to 1.9% compared to the OECD median PISA score in 2018.

In order to assess the policy effects on the stock of human capital and multi-factor productivity from reforming pre-primary education, two scenarios are considered: i.) closing the gap between the lowest level of pre-primary attendance observed in the OECD (9% in Turkey) and the average of the top three performers (84%, Spain, France and New Zealand), and ii.) closing the gap between the median OECD country (72%, Austria) and the average of the top three performers. Results indicate that a sustained effort to increase attendance in pre-primary education boosts productivity in the long run between 0.9% and 2.2% for the first scenario, and gives rise to a more limited increase of 0.1% to 0.3% in the second scenario.

Future work will focus on a systematic evaluation of a wide range of education policies on the new measure of human capital and macroeconomic outcomes as well as considering how adult training policies impact on human capital.

Reference

Égert, B., C. de la Maisonneuve and D. Turner (2022), “A new macroeconomic measure of human capital exploiting PISA and PIAAC: Linking education policies to productivity”, OECD Economics Department Working Papers, No. 1709, OECD Publishing, Paris, https://doi.org/10.1787/a1046e2e-en.

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[1] There are a limited number of countries with a sufficiently long time series of student test scores to be able to construct the measure for the full working age population. This implies a trade-off. A comprehensive measure covering the entire working age population can only be computed for 15 OECD countries. A measure covering those aged 16 to 39 is available for a total of 54 OECD and non-OECD countries in the last available year, and 12 countries have data series for more than 20 years and an additional 27 countries are covered for about a decade.

by Gabriele Ciminelli and Cyrille Schwellnus, OECD Economics Department

Despite changes in social norms and policy reforms over recent decades, gaps in hourly wages between similarly-qualified women and men remain significant, ranging between 10-20% in most OECD countries (Figure 1). The ongoing COVID-19 crisis has made the issue of addressing such gender wage gaps even more pressing, as most of the increased household responsibilities resulting from administrative closures fall on women, which may further reduce their earnings in the long term (Queisser et al., 2020).

Figure 1. Differences in women’s hourly earnings relative to men, controlling for age, tenure and education (%)

Note: The chart shows percent differences in average hourly earnings of women relative to men with the same level of educational attainment, age and tenure, in 2014.
Source: Ciminelli et al. (2021).

In order to devise effective policies to narrow gender wage gaps, we first need to understand what their drivers are. In a new paper (Ciminelli et al., 2021), we source data on earnings, hours worked and many other labour market and demographic characteristics for over 33 million individuals in 25 European countries to decompose the gender wage gap into three deep drivers: (i) social norms, gender stereotyping and discrimination, (ii) compensating wage differentials (the take-up of jobs with lower wages in return for specific non-wage characteristics, such as more flexible work arrangements), and (iii) slowed human capital accumulation, due to mothers interrupting their careers and/or transitioning to part-time work after giving birth to the first child.

Figure 2. The deep drivers of the gender wage gap across countries (percentage points)

Note: The chart shows drivers of the gender wage gap for each country, as estimated from the life-cycle accounting approach.
Source: Ciminelli et al. (2021).

Discrimination and social norms (“sticky floors”) account for about 60% of the gender wage gap in Central and Eastern Europe

We find discrimination and social norms to account for about 60% of the gender wage gap in Central and Eastern European countries. Gender wage gaps arise even among teenagers (age 14-19), which is before most women give birth to their first child. The teenager gap is about 13% in Central and Eastern European countries, while it seems to be less important in Southern Europe (around 9%) and particularly Northern and Western European ones (around 6%). This could reflect discriminatory practices by Central and Eastern European employers. Discrimination may reflect conscious or unconscious biases (taste-based discrimination), or the perception that women are on average less productive than men, for instance because they are expected to take long and costly leaves around childbirth (statistical discrimination). The teenager gap could also be due to the existence of entrenched social norms that lead women to sort into low-paying occupations or industries.

Compensating wage differentials and slowed human capital accumulation (“glass ceilings”) account for 75% of the gap in Northern and Western Europe and around 70% in Southern Europe

We find a large contribution (about 75%) of compensating wage differentials and slowed human capital accumulation to the gender wage gap in Northern and Western European countries. Wage differences between men and women in these countries increase sharply at ages 20-29 and 30-39.[1] Since this coincides with women’s peak childbearing ages, this pattern suggests that women face an important “child wage penalty”. This is consistent with the fact that working-time status alone explains about 20% of the gender wage gap in these countries, as a large share of women transition from full-time to part-time work during peak childbearing ages. The weight of compensating wage differentials in these countries is about twice as large as that of slowed human capital accumulation. Southern European countries are an intermediate case between Central and Eastern Europe and Northern and Western Europe, with child wage penalty-related explanations accounting for about 70% of the gender wage gap and discrimination and social norms explaining the remaining 30%.

Bold and country-tailored policies are needed to close gender wage gaps in Europe

What can we conclude from our analysis? The policy mix to close the gaps needs to be tailored to each individual country’s circumstances. Where compensating wage differentials are important, one focus of public policy could be to make non-wage job characteristics that are particularly valued by women more widely accepted by employers. For instance, in Denmark, which in 2002 gave workers the right to request a reduction in working time (with a pro rata reduction in monthly earnings but without re-negotiating the existing employment contract), a large proportion of both women and men work part-time and there is no wage penalty for part-time work. Governments could also promote policies to make telework an important aspect of post-COVID-19 labour markets, which would allow working mothers (and fathers) to spend more time on childcare without the need to switch to part-time jobs.

Especially in countries where slowed skill accumulation is an important driver of the gender wage gap, promoting flexible working arrangements should be complemented with measures to reduce the need for women to take up these arrangements in the first place. Such measures include family policies, like the provision of universal childcare and the harmonisation of parental leave for men and women. Reducing effective marginal tax rates on second earners would also promote female labour market participation and human capital accumulation. Evidence from Canada, Ireland and Sweden suggests that the partial or complete individualisation of income taxation significantly raises the employment rate of married women (Doorley, 2018; Selin 2014; Crossley and Jeon, 2007).

Finally, many countries, particularly in Central and Eastern Europe, need to reinforce policies to address discrimination against women. These may include promoting pay transparency; strengthening competition in product markets to drive out discriminating employers; raising wage floors where they are currently low; and combating gender stereotyping to reduce gendered educational and occupational sorting (Bertrand, 2020).

REFERENCES

Bertrand, M. (2020), “Gender in the Twenty-First Century”, AEA Papers and Proceedings, Vol. 110, pp. 1-24.

Ciminelli, G., C. Schwellnus and B. Stadler (2021), “Sticky Floors or Glass Ceilings? The Role of Human Capital, Working Time Flexibility and Discrimination in the Gender Wage Gap”, Economics Department Working Paper No. 1668, OECD Publishing, Paris, https://doi.org/10.1787/02ef3235-en.

Crossley, T. and S. Jeon (2007), “Joint Taxation and the Labour Supply of Married Women: Evidence from the Canadian Tax Reform of 1988”, Fiscal Studies, Vol. 28/3, pp. 343-365.

Doorley, K. (2018), “Taxation, Work and Gender Equality in Ireland”, IZA DP No. 11495, Institute of Labor Economics.

Queisser, M., W. Adema, C. Clarke (2020), “COVID-19, Employment and Women in OECD Countries”, VoxEU.org, 22 April 2020.

Selin, H. (2014), “The rise in female employment and the role of tax incentives. An empirical analysis of the Swedish individual tax reform of 1971”, International Tax and Public Finance, Vol. 21/5, pp. 894-922.

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[1] Our data provide information on age only in terms of large brackets (14-19, 20-29, 30-39, 40-49, 50-59).

by Jarmila Botev, Balázs Égert, Zuzana Smidova, David Turner, OECD Economics Department

Human capital is widely regarded as a fundamental input in the theoretical growth literature. Recommendations to boost it feature prominently among reform priorities for a great number of countries (Figure 1). Yet, paradoxically, quantifying the macroeconomic effects of human capital has often proven frustratingly elusive.

As this blogpost explains, in part this is due to the challenge of measuring human capital. A newly released OECD measure of human capital works well in productivity regressions, providing the “missing” link between growth and human capital.

Human capital can be defined as the stock of knowledge, skills and other personal characteristics of people that helps them to be productive. Such knowledge is gained in formal education (e.g. early childhood care, compulsory schooling and adult training programmes) but also informally, via on-the-job learning and work experience. Health also influences one’s productivity. Nevertheless, there is no widely accepted empirical measure that captures all these dimensions across many countries and over time.

The early macroeconomic growth literature used various quantitative measures of education as a proxy for human capital, including literacy rates or enrolment rates at various levels of education. More recent studies use mean years of schooling (average number of completed years of education of a country’s entire population). However, the link of these proxies to macroeconomic outcomes has generally been poor. A meta-analysis of 60 studies published over the period of 1989-2011 found that around 20% of the reported coefficient estimates on human capital have the “wrong” (negative) sign (Benos and Zotou, 2014). In a dozen of papers by Robert J. Barro, based on similar specifications, techniques and datasets, only about a half of the coefficient estimates is positive and statistically significant. Recent OECD studies confirm the difficulty of finding a robust positive effect of human capital on income per capita or productivity levels when looking at the OECD countries (Botev et al., 2019; Guillemette et al, 2017, Fournier and Johanson 2016).

And, this is the paradox, the widely accepted importance of human capital, but the difficulty of finding an empirically relevant measure of it — which our recent work addresses. The OECD’s newly released human capital measure combines an up-to-date dataset of mean years of schooling (the 2018 update of Goujon et al, 2016) with rates of return based on recent evidence on wage premia compiled mostly by the World Bank (Psacharopoulos and Patrinos, 2004; Montenegro and Patrinos, 2014). Unlike earlier studies, it applies different returns for five groups of countries and three periods. Including such measure of human capital in various macroeconomic productivity regressions yields significant and positive relationships that economists have been looking for.

Find out more: http://www.oecd.org/economy/human-capital/

Botev, J. B. Égert, Z. Smidova and D, Turner (2019), “A new macroeconomic measure of human capital with strong empirical links to productivity“, OECD Economics Department Working Paper No. 1575

References

Benos, N. and S.
Zotou (2014), “Education and
Economic Growth: A Meta-Regression Analysis”, World Development, 64
(C), 669-689.

Fournier, J. and Å. Johansson (2016), “The Effect of the Size
and the Mix of Public Spending on Growth and Inequality“, OECD Economics Department Working Papers,
No. 1344, OECD Publishing, Paris.

Guillemette, Y., et al. (2017), “A
revised approach to productivity convergence in long-term scenarios“, OECD Economics Department Working Papers, No.
1385, OECD Publishing, Paris

OECD (2019), Economic Policy Reforms
2019: Going for Growth, OECD Publishing, Paris.

Psacharopoulos, G.
and H. Patrinos (2004), “Returns to
Investment in Education: A Further Update”, Education Economics,
12(2), 111-134.

Psacharopoulos, G.
and H. Patrinos (2018), “Returns to
Investment in Education A Decennial Review of the Global Literature”, World Bank
Policy Research Working Paper No. 8402.