Sticky floors or glass ceilings? The role of human capital, working time flexibility and discrimination in the gender wage gap

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).


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.

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

The human capital paradox: A measurement issue?

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


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.