13. Women at work in OECD countries
This chapter reviews women’s employment across the OECD. It starts with a discussion of trends in gender employment gaps over the past two decades. It then analyses the characteristics of the jobs that women are employed in, focussing on the incidence of part-time employment, and sectoral and occupational patterns of employment. The chapter concludes with a discussion on the challenges and opportunities of the digital transformation of labour markets in OECD countries.
In all OECD countries, working-aged women continue to have lower employment rates than men of the same age, but the overall time spent on paid and unpaid work on average exceeds that of men. Women and men are also to some degree segregated across industries and occupations, with women being more often employed in low-paying industries, such as the service sector, they are more likely to be in part-time employment and women are also underrepresented in management roles.
Women continue to face barriers to enter (full-time) employment. Persistent traditional gender roles, the burden of dual work-family responsibilities and a lack of affordable childcare options prevent many women to work (full-time), especially once they become mothers.
Across the OECD, labour markets are undergoing a digital transformation that poses challenges to gender equality in employment, especially as women on average have fewer digital skills than men, while they are also less likely to work in jobs that frequently require the use of digital tools. However, the emergence of new technologies and platforms offer opportunities to reduce gender employment gaps as the barriers to labour market entry can be reduced, upskilling may become easier and employment more flexible.
Despite long-term trends of convergence in male and female employment rates, working-aged (15-64 years) men continue to be more likely to be employed than working-aged women in every OECD country, even though women of working-age tend to have similar or higher levels of education than men in most countries. On average across OECD countries, men were 10.4 percentage points more likely to be employed than women in 2021. Gender employment gaps remain particularly large – above 20 percentage points – in Brazil, Colombia, Costa Rica, India, Indonesia, Mexico and Türkiye. The gaps are smallest – below 5 percentage points – in Estonia, Finland, Israel, Latvia, Lithuania, Norway and Sweden (Figure 13.1).
Gender gaps in employment rates have continued to narrow over recent years, albeit more slowly than over previous decades. The gender gap in employment rates fell by 5.4 percentage points between 2000 and 2010 and by another 2.2 percentage points between 2010 and 2021 (Figure 13.1). This is mainly because of increases in female employment: on average, 64.6% of women were employed in 2021, up from 58.7% in 2010 and 55.7% in 2000. Between 2000 and 2010, male employment rates fell from 73.8% to 71.4% (mainly due to the impact of the 2007-08 financial crisis; see below) but recovered to 75.1% by 2021. The reduction of the gender employment gap is associated with dual-earner households becoming the norm in many OECD countries (OECD Family Database).
Overall, between 2000 and 2021, Spain had the strongest overall declines in the gender employment gap among the working-age population (-20.9 percentage points), but Luxembourg (-18.4 percentage points), Chile (-16.6 percentage points) and Costa Rica (-15.2 percentage points) made substantial progress as well. Among OECD countries, only Poland saw a slight increase in the gender employment gap of men and women of working age between 2000 and 2021 (+0.6 percentage points) – so that it is now slightly above the OECD-average.
Note: Data for 2021 refer to 2020 for India and to 2019 for Indonesia. Data for 2010 refer to 2011 for Brazil. Data for 2000 refers to 2001 for Brazil, Colombia and South Africa as well as 2002 for Croatia.
Source: OECD Employment Database, https://www.oecd.org/employment/emp/onlineoecdemploymentdatabase.htm.
The trends in gender employment gaps were heavily influenced by the impact of the financial crisis of 2007-08 and the subsequent slow recovery in employment in many countries. In Estonia, Latvia and Lithuania, for example, the crisis led to substantial decreases in male employment, while female employment was much less affected. As a result, the gender employment gap declined noticeable between 2000 and 2010 – so much that at the time Latvia and Lithuania had higher employment rates for women than for men, though strong outmigration following their accession to the European Union (in 2004) will have played a role as well. However, strong recovery of male employment, in particular through rebounding activity in construction and trade industries, led to increases in the gender employment gap in the 2010s (Zasova, 2016[1]). Nevertheless, all three Baltic countries rank among the OECD countries with the smallest gender employment gaps (Figure 13.1).
The COVID-19 pandemic also had profound effects on labour markets in OECD countries. At the onset of the pandemic women’s employment was affected more strongly in many countries – mainly because many women worked in the most affected industries (see below). Yet, women’s employment rates rebounded faster than men’s and overall, the COVID-19 pandemic had only a small effect on gender differences in employment rates (Queisser, 2021[2]) (Chapter 1).
Even though a larger share of women across the OECD are in employment today than ever before, women continue to be less likely to work full-time than men. While part-time employment can help women to remain in the labour force, part-time workers often have lower (hourly) earnings than full-time employees and frequently miss out on opportunities for career advancement.
On average across the OECD, 21.5% of working-aged women were working in part-time employment in 2021 – defined as usually working less than 30 hours per week on the main job – compared to only 7.7% of men (Figure 13.2), The incidence of part-time employment among men and women has changed little over the past decade: it declined by 2.3 percentage points for women, and increased by 0.2 percentage points for men (OECD Employment Database).
Note: Data for Australia refer to 2020 and for Indonesia to 2017. Data for the United States refer to dependent employment only.
Source: OECD Employment Database, Part-time employment rate, https://data.oecd.org/emp/part-time-employment-rate.htm.
There is considerable variation across countries, with the Netherlands having by far the largest share of women working in part-time employment – more than every second woman. The large share of Dutch women in part-time employment is a result of women selecting into occupations and sectors where part-time is the norm as well as reducing hours in paid work once they become mothers and typically assume a disproportionate responsibility for unpaid childcare (OECD, 2019[3]). Similar dynamics are at play in many other countries and have led to high female part-time employment rates in Australia, Austria, Germany, Japan and Switzerland (OECD, 2017[4]). Many Central and Eastern-European countries have particularly low rates of female part-time employment. Reasons for this include: many women are not in or temporarily drop out of the labour force when they become mothers – as can be facilitated through parental leaves that are generous in pay and duration; and part-time opportunities are not frequently available, and if they are they do not pay enough to be a viable option (OECD, 2022[5]; 2022[6]).
The differences in working time between men and women are well-reflected in the distribution of paid and unpaid work. The gender gaps in paid and unpaid work are largest where typically long working hours of full-time employees are incompatible with women’s disproportionate responsibility for unpaid work – for example in Korea, Germany and Mexico (OECD, 2019[7]; 2017[4]; 2017[8]). Even though working shorter hours than men, women on average spend more time on paid and unpaid work altogether.
In all OECD countries, men and women are noticeably segregated across industries and occupations, with women often being employed in relatively low-paying, female-dominated sectors, while facing a range of challenges to advancing in their careers (Chapters 16 to 18). Figure 13.3 presents the Duncan Segregation Index, which measures the degree of segregation between male and female employee across industries and occupations, ranging from 0 to 1 – from the lowest to the highest level of segregation (Duncan and Duncan, 1955[9]). A level of 0 for occupational segregation would mean that the proportion of women in every occupation exactly mirrors the proportion of women in the labour force. For example, if women were to make up 50% of the labour force, a segregation index of 0 means that they also make up 50% of the employees in every occupation. On the contrary, a level of 1 represents total gender segregation across occupations, meaning that each occupation is made up entirely of either men or women, but never of both men and women at the same time. Any level between 0 and 1 signifies some degree of imperfect segregation in which women are more likely to be employed in certain industries and occupations, while men are more likely to be employed in others.
Each OECD country records noticeable levels of segregation – with slightly higher levels across occupations (0.46) than for industries (0.40) on average across the OECD. This difference can, in part, be explained by the persistency of “glass ceilings” that prevent women from advancing in the highest levels of the occupational hierarchy, such as managers (Chapters 16 to 18).
High levels of segregation across industries and occupations often result from an increase in female employment along with a simultaneous expansion of the service and public sectors, both of which employ a significantly greater share of women than men in most countries (Ngai and Petrongolo, 2017[10]; Rendall, 2018[11]). For example, across OECD countries, women made up 55% of all service sector employees in 2020 (ILO, 2022[12]), but with great cross-country variation – from only 36% in Türkiye (Gedikli, 2020[13]), where female employment at 32% is also the lowest among OECD countries, to around 60% in Estonia, Latvia and Lithuania, where gender employment gaps are among the lowest (Figure 13.1).
Gendered labour market segregation underlies the differences in job losses and subsequent employment recovery in relation to the COVID-19 pandemic. Women were disproportionally employed in industries most strongly affected by the pandemic, for example through job losses in the service sector (Queisser, 2021[2]). However, the recovery of employment was especially strong among women and on aggregate in the European Union has coincided with a shift from low-paid in-person service-sector jobs to towards better paying knowledge-intensive jobs (Eurofound, 2022[14]).
Note: The index of dissimilarity, or Duncan index, measures the sum of the absolute difference in the distribution of female and male employment across occupations or industries. It assumes that segregation implies a different distribution of women and men across occupations/industries: the less equal the distribution, the higher the level of segregation. It ranges from 0 to 1, from the lowest to the highest level of segregation. Here it was calculated using the ISCO-08 classification of occupations and the ISIC-4 classification of industries, both at 2-digit levels. For Australia, data refer to the previous ISCO-88 classification of occupations and the ISIC-3 classification of industries. For Israel and Italy, data refer to 2017. For the United Kingdom, data refer to 2019.
Source: OECD Secretariat calculations based on ILOSTAT Labour Force Statistics (https://www.ilo.org/shinyapps/bulkexplorer47/?lang=en&segment=indicator&id=EES_TEES_SEX_EC2_NB_A and https://www.ilo.org/shinyapps/bulkexplorer7/?lang=en&segment=indicator&id=EES_TEES_SEX_OCU_NB_A).
Men and women not only tend to work in different industries and occupations, but they also face differences in their exposure to digital technologies in the workplace. The ongoing digital transformation has substantial effects on labour markets – for example, an increasing share of employees are using information and communications technology (ICT) tools in their jobs (OECD, 2018[15]; 2019[16]) and Artificial Intelligence (AI) related skills are growing in demand in labour markets (UNESCO/OECD/IDB, 2022[17]; Green and Lamby, 2023[18]; Manca, 2023[19]). In this context, OECD countries risk facing a widening digital gender divide (OECD, 2018[15]).
While the digital transformation poses challenges for both men and women in the labour market, a gender gap in ICT skills and use of ICT processes and tools in the workplace already existed for years. For example, women aged 16-24 are less than half as likely to be able to programme as men of the same age (OECD, 2022[20]). Such gender gaps in digital skills emerge early and result from choices made well before entering the labour market. As girls and women are less likely to enrol in Science, Technology, Engineering and Mathematics (STEM), and particularly underrepresented among new entrants in ICT educational fields (Chapter 9), they risk to lag behind in terms of digital literacy and skills throughout their working lives (UNESCO/OECD/IDB, 2022[17]; UNESCO, 2019[21]). These gender gaps also imply that women are underrepresented in ICT task-intensive jobs (Figure 13.4) as well as in the part of the workforce that has the skills to develop and maintain AI systems (Green and Lamby, 2023[18]). These gaps have substantial consequences for labour market careers and pay, particularly as many ICT- and AI- related jobs are comparatively well-paid. There is a threat of increased inequality in employment and pay as the digital transformation unfolds (OECD, 2018[15]; Manca, 2023[19]).
Note: ICT task-intensive jobs are identified using factor analysis of the frequency of ICT tasks in different occupations based on data from the OECD Survey of Adult Skills. The ICT tasks considered cover the frequency of use of: Spreadsheets, the Internet, programming languages, real-time discussion tools, and word processing software, as well as the frequency of undertaking transactions via the Internet (online banking, e-commerce), reading and writing letters, e-mails, memos, etc., and the “level” of computer use required for the job. In addition, the frequency of performing manual labour over long periods is also accounted for as a factor that suggests that an occupation is less likely to be ICT-intensive. The occupations with the highest mean ICT task content are included in this indicator. Data for Türkiye refer to 2020, for the United Kingdom to 2019 and for the United States to 2017.
Source: OECD Going Digital Toolkit, https://goingdigital.oecd.org/theme/2, based on European Labour Force Surveys, national labour force surveys and other national sources.
While AI is already having an impact on workplaces in OECD countries – with somewhat different effects on the working lives of men and women (Box 13.1) – skills in AI are another factor that will shape the future of labour markets. The existing gender gaps in digital skills risk being perpetuated, as greater exposure to AI has been linked to higher employment in ICT task-intensive jobs, suggesting that strong digital skills may make it easier for employees to adopt and apply AI at work and benefit from these technologies (Georgieff and Hyee, 2021[22]). However, while the demand for AI skills will rise in the future, an increasing use of AI tools may increase the demand for human-only skills that are needed to complete activities for which AI systems are inadequate – for instance care-related roles, which women are more likely to choose as their career paths (UNESCO/OECD/IDB, 2022[17]; Roberts et al., 2019[23]). As it stands, women are significantly less likely to be employed in occupations at high-risk of automation through AI or robotics as occupations requiring interpersonal interaction abilities can typically not be performed by automation technologies. This mirrors the lower female exposure to past advances in automation (Lassébie and Quintini, 2022[24]; Webb, 2019[25]).
To reap the full benefits of diversity, women must also lead and participate in AI research and development (R&D). AI research is still primarily dominated by men: in 2022, only one in four researchers publishing on AI worldwide was a woman. While the number of publications co-authored by at least one woman is increasing, women only contribute to about half of all AI publications compared to men, and the gap widens as the number of publications increases. Women contribute to roughly 45% of AI publications worldwide, compared to almost 90% that list at least one man as a co-author. More strikingly, only 11% of AI publications are authored solely by women, while 55% are penned by men alone (OECD.AI, 2023[26]). When it comes to female professionals with AI skills, they represent just a small proportion of workers. This is lower than 2% in most countries and in most cases, 50% or lower than the proportion of men with AI skills. However, promisingly, female AI talent is growing faster than male AI talent when it comes to the average annual growth rate of AI talent across countries (OECD.AI, n.d.[27]).
To assess how workers and employers view the current and future effects of AI on their workplaces, the OECD surveyed a total of 5 334 employees and 2 053 companies in the manufacturing and financial sectors in Austria, Canada, France, Germany, Ireland, the United Kingdom and the United States.
The results show that the effects of AI on productivity and working conditions are generally favourable. Almost 80% of AI users claimed that AI had enhanced their professional performance, while just 8% claimed that AI had worsened it. Users of AI were more than four times as likely to say that AI had improved working conditions as they were to say that AI had deteriorated them. This suggests that AI has the potential to improve productivity and job quality if deployed properly. However, men AI users were more likely than women AI users to say that AI had enhanced their performance and working conditions, especially in the financial sector. Such gender differences may partly stem from an overrepresentation of men in managerial and professional occupations, where attitudes about AI tend to be more favourable.
At the same time, 19% of workers in finance and 14% in manufacturing said that they were very or extremely worried about job loss in the next ten years and such worries were particularly common among AI users themselves. Importantly, compared to 19% of men, 22% of women reported feeling very or extremely worried. The fact that men are more likely to occupy managerial positions – where worries about job loss are less prevalent – helps explain their more muted concerns. Nonetheless, even after accounting for these and other factors, most of this gender gap still exists. At the same time, however, employers see female workers as one of the groups that are most likely to be “helped” by AI, as well as the group that is least likely to be harmed by AI.
Source: Lane, Williams and Broecke (2023[28]), The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers, https://doi.org/10.1787/ea0a0fe1-en.
While the digital transformation poses challenges for gender equality in the labour market, it also offers new opportunities for women’s economic empowerment (Chapter 33) if systems are designed to not perpetuate harmful biases that existed in the past. Digital advances have made it easier to benefit from upskilling opportunities and training as related content has become widely available and easily accessible. This can help to utilise digital technologies more effectively and get greater value from them (OECD, 2018[15]; Verhagen, 2021[29]). The spread of video-conferencing and teleworking software can ease the compatibility of family and work responsibilities, which can be particularly beneficial for women. Indeed, since the onset of the COVID-19 pandemic, the number of those usually working from home has increased at a higher rate for women than for men, and women are more often in jobs that could theoretically be done remotely (Chapter 26). Digital platforms ease the job search process, while the use of AI technologies in matching job seekers to vacancies could improve a better inclusion of women in the labour market (Urquidi and Ortega, 2020[30]; UNESCO/OECD/IDB, 2022[17]; Broecke, 2023[31]). The increasing use of platform work can reduce entry barriers and increase labour market flexibility, which can also help women to better integrate into the workforce. However, platform workers may face income instability, compromised access to social protection, limited career development, and inadequate rights to collective bargaining if these jobs remain insufficiently regulated (Lane, 2020[32]).
Governments policy should work towards reducing the large gender gap in part-time work and support men and women in the transition from part-time to full-time work. The availability of good quality and affordable early childhood education and care, out-of-school-hours services and smart workplace flexibility measures (Chapters 24 and 26) can help both fathers and mothers to engage in full-time employment. At the same time, Tax-Benefit systems must be designed to not discourage second earners from shifting to full-time jobs (Chapter 25).
To reduce occupational and sectoral segregation, countries must encourage both boys and girls, men and women to choose educational and employment pathways, without hindrance of prevailing attitudes or biases (Chapter 9).
Governments must ensure that the gender gap in digital skills narrows so that women do not lose out on the rapid digitalisation of economies and labour markets. Reskilling and upskilling programmes can help men and women to access and thrive in heavily digitalised occupations and sectors, with more career opportunities.
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