The age of automation, and on the near horizon, artificial intelligence (AI) technologies offer new job opportunities and avenues for economic advancement, but women face new challenges overlaid on long-established ones. Between 40 million and 160 million women globally may need to transition between occupations by 2030, often into higher-skilled roles. To weather this disruption, women (and men) need to be skilled, mobile, and tech-savvy, but women face pervasive barriers on each, and will need targeted support to move forward in the world of work.
A new Global Institute (MGI) report, The future of women at work: Transitions in the age of automation (PDF–2MB), finds that if women make these transitions, they could be on the path to more productive, better-paid work. If they cannot, they could face a growing wage gap or be left further behind when progress toward gender parity in work is already slow.
Women and men face a similar scale of potential job losses and gains, but in different areas. To adapt to the new world of work, they will need to be skilled, mobile, and tech savvy.
This new research explores potential patterns in “jobs lost” (jobs displaced by automation), “jobs gained” (job creation driven by economic growth, investment, demographic changes, and technological innovation), and “jobs changed” (jobs whose activities and skill requirements change from partial automation) for women by exploring several scenarios of how automation adoption and job creation trends could play out by 2030 for men and women given current gender patterns in the global workforce.
These scenarios are not meant to predict the future; rather, they serve as a tool to understand a range of possible outcomes and identify interventions needed. We use the term jobs as shorthand for full-time-equivalent workers.
The research examines six mature economies (Canada, France, Germany, Japan, the United Kingdom, and the United States) and four emerging economies (China, India, Mexico, and South Africa), which together account for around half of the world’s population and about 60 percent of global GDP.
Men and women tend to cluster in different occupations in both mature and emerging economies, and this shapes the jobs lost and gained due to automation for each. In the mature economies studied, women account for 15 percent on average of machine operators, but over 70 percent on average of clerical support workers. In the emerging economies in our sample, women make up less than 25 percent of machine operators on average, but over 40 percent of clerical support workers. Over 70 percent of workers in healthcare and social assistance in nine of the ten countries (the exception is India) are women. However, less than 15 percent of construction workers, and only around 30 percent of manufacturing workers, are female in many countries.
If a scenario of automation unfolds on the scale of past technological disruptions, women and men could face job losses and gains of a broadly similar magnitude. In this research, we explore various scenarios to 2030 developed using MGI’s past future of work research, and its analysis of jobs lost and gained. Our current research breaks new ground by adding a gender lens to that work, and by looking at a broad range of effects on women’s jobs including potential job displacement, opportunities for job creation, the changing nature of jobs, and a quantitative assessment of the transitions that women will need to make to capture these new opportunities, including implications for wages and average education levels. Our main scenario to 2030 is based on a “midpoint” scenario of automation adoption, which models automation at a similar scale to that of other major technological disruptions in the past.
In the case of jobs lost, women may be only slightly less at risk than men of their job being displaced by automation. In the ten countries, an average of 20 percent of women working today, or 107 million women, could find their jobs displaced by automation, compared with men at 21 percent (163 million) in the period to 2030 (Exhibit 1).
The composition of job displacements could be different for men and women, largely reflecting differences in the mix of occupations in which they tend to work, and the activities that make up those occupations. Some activities, and therefore occupations, are more automatable than others. For instance, both routine physical tasks and routine cognitive work are highly automatable, but those requiring more complex cognitive, and social and emotional skills are less so. Men predominate in physical roles such as machine operators and craftworkers; therefore, nearly 40 percent of jobs held by men that could be displaced by automation in our 2030 scenario are in these categories. Conversely, women predominate in many occupations with high automation potential due to routine cognitive work, such as clerical support or service worker roles; these occupations account for 52 percent of potential female job displacements.
The composition of job displacements could vary for men and women, largely reflecting differences in the occupations in which they tend to work.
There are differences among countries, too. In mature economies, men may tend to lose machine operator jobs while women could tend to lose clerical and service worker jobs. In emerging economies there is a visible trend of jobs being displaced in agriculture-related occupations in our scenario, even here, however, patterns vary among emerging economies. For instance, agricultural work is one of three top occupational groups driving job displacements for men (21 percent of losses) in Mexico but is not in the top three for women. However, in India where so many women work in subsistence agriculture, losses in this occupational category could account for 28 percent of jobs lost by women, compared with 16 percent of jobs lost by men.
There will be job gains, too. Even with automation, the demand for work and workers could increase as economies grow, partly fueled by productivity growth enabled by technological progress. Rising incomes and consumption especially in emerging economies, increasing healthcare for aging societies, investment in infrastructure and energy, and other trends will create demand for work that could offset the displacement of workers. Women could be somewhat better placed to capture these potential job gains than men because of the occupations and sectors in which they tend to work; however, this gain assumes that women maintain their share of employment in each sector and occupation from the present day to 2030.
By 2030, women could gain 20 percent more jobs compared with present levels (171 million jobs gained) vs 19 percent for men (250 million jobs gained) (Exhibit 2). Across the ten countries in our sample, on average 58 percent of gross job gains by women could come from three sectors: healthcare and social assistance, manufacturing, and retail and wholesale trade. On average, 53 percent of men’s gross job gains could come from the manufacturing, retail and wholesale trade, and professional, scientific, and technical services sectors. Women are well represented in fast-growing healthcare, which could account for 25 percent of potential jobs gained for them.
In our scenario to 2030 in the ten countries analyzed, over 150 million net jobs (factoring in both jobs displacement and jobs creation) could be added within existing occupations and sectors, the vast majority of which will be in emerging economies. Mature economies could experience minimal net jobs growth or even a net decline as any gains in employment in existing sectors and occupations are counteracted by increasing automation. Across the ten economies, 42 percent of net jobs gained (64 million jobs) could go to women, and 58 percent (87 million) to men if current employment trends in occupations and sectors hold.
In our scenario to 2030 in the ten countries analyzed, over 150 million net jobs (factoring in both jobs displacement and jobs creation) could be added within existing occupations and sectors, the vast majority of which will be in emerging economies.
In mature economies, net job growth (taking into account jobs lost and jobs gained) could be concentrated in only two sectors: professional, scientific and technical services, and healthcare. Today, women are well represented in the second, but underrepresented in the first in many countries; in Canada, Japan, the United Kingdom, and the United States women have lower representation in the professional, scientific, and technical services sector compared with their average share in the economy.
In emerging economies, net job growth could occur in a broader range of sectors including manufacturing, accommodation and food services, retail and wholesale trade, and construction (57 percent of net jobs gained in India, China, and Mexico). We find that in China, Mexico, and South Africa women tend to be more present than men in accommodation and food services relative to their overall share of employment and underrepresented in manufacturing and construction. In India, women are slightly overrepresented relative to economy-wide participation in manufacturing and strongly underrepresented in construction and accommodation and food services.
Waves of technological innovation not only displace or change the nature of many occupations, but also create entirely new ones. Historical trends in the United States suggest that up to 9 percent of the population could be employed in entirely new and emerging occupations by 2030. Examples from the past decade range from recently created jobs in machine learning and AI to ride-hailing drivers and roles in sustainability and resource management. If this estimate is extrapolated across our ten-country sample, that could mean that more than 160 million jobs could be created in these entirely new occupations by 2030. In order to meet the demands of such entirely new occupations, women will need the right skills—and also to have the labor mobility and networks to go after these jobs.
Even if women remain in their current jobs, the ways in which they work are likely to change as workplaces increasingly adopt new technology, and some of the component activities within women’s occupations are automated, creating “partial automation” of their work. In such circumstances, automation technology does not replace a job, but instead changes it in meaningful ways as humans learn to work alongside machines. For instance, the job requirements of secretaries, teachers, and other professionals alike have changed significantly as computers have “automated” several manual tasks in the 21st century, such as basic data collection and processing.
Using the United States as an example, we find that approximately half of occupations that are mainly held by women are less than 50 percent technically automatable by 2030, compared with about 20 percent of occupations largely performed by men. If this pattern holds across countries, women could be at less risk than men of their jobs being replaced in their entirety by machines.
- As partial automation becomes more common and other technologies, including digital platforms that enable independent work, for instance, become more prominent, women’s working lives (and men’s) could change in three ways: As machines increasingly handle routine physical and cognitive tasks, women could spend more time managing people, applying expertise, and interacting with stakeholders. In an emergency room in 2030, for instance, health workers could spend less time doing clerical work (due to the adoption of preregistration by mobile phone, computerized checkout and billing, and AI-led diagnostic tools), and physical work, but more time interacting with patients.
- Certain skills could become more important. By 2030, jobs in Europe and the United States could require up to 55 percent more time using technical skills and 24 percent more hours using social and emotional skills. Time spent using physical and manual skills and basic cognitive skills could decrease as those activities are automated.
- More women could work flexibly. Co-location with colleagues is an important part of working lives today, but technology could reduce the need to co-locate as telecommuting becomes more widely adopted, for instance. The rise of these new, more flexible ways of working is particularly helpful to women because they disproportionately carry the “double burden” of working for pay and working unpaid in the home in both mature and emerging economies.
Worldwide, 40 million to 160 million women—7 to 24 percent of those currently employed—may need to transition across occupations to ensure that they are positioned for shifts in labor demand. For men, the range is comparable at 8 to 28 percent. If women take advantage of transition opportunities; they could maintain their current share of employment; if they cannot, gender inequality in work could worsen (Exhibit 3). These wide ranges are based on a midpoint automation adoption and early automation adoption scenario.
Women will likely need higher educational attainment and different skills to make successful transitions. In mature economies, most women (and men) are likely to have to transition into occupations that will require higher educational requirements. In five of the six mature economies in our sample, net labor demand only grows for jobs with a college or advanced degree. Women in mature economies are generally graduating at rates on a par with, or even higher than, men. This should position them well for the jobs that will be most in demand, but it remains important that they match their skills as closely as possible to where the most job opportunities will be.
In five of the six mature economies in our sample, net labor demand only grows for jobs with a college or advanced degree.
The same applies to women in the workforce today that will need to reskill to enter the jobs of the future. In three of the four emerging economies in our sample—China, India, and Mexico—net labor demand could rise strongly for occupations requiring a secondary education for both men and women. This could pose a challenge to women in some emerging economies, where female education rates continue to lag behind men. In India, in particular, low-skill women in the agriculture sector could face a significant need to reskill as labor demand declines for jobs requiring less than a secondary education.
Additionally, the adoption of automation technologies and the areas where jobs are created could drive a stronger growth in demand for higher-paid jobs. The situation carries both opportunity and risk for women. If they manage to transition between occupations and retrain themselves to meet demand for jobs that are higher-paying and associated with different skills, they could be looking at a future of more productive and more lucrative employment.
However, if they cannot make the necessary transitions, many women could face an intensifying wage gap relative to men. Workers in middle-wage jobs in mature economies could be the most vulnerable to job displacement—male workers more so in many countries than women in the short term. A potential glut of workers in lower-wage jobs, including men displaced from manufacturing, could put downward pressure on wages. Over the longer term, some women could leave the labor market entirely as the economic costs associated with being in the labor force rise.
Gender wage disparity is a feature of both mature and emerging economies. Currently, more men tend to be employed in higher paying occupations compared with women. In mature economies for example, 5 percent of women are in the highest paying occupation category, legislator, senior official, and manager, compared with 8 percent of men. At the same time, a higher percentage of employed women work in the two lowest paying occupational categories—elementary occupations and clerical support work.
Looking ahead to 2030, our scenario suggests that gender wage disparity may lessen slightly in certain mature economies if women are able to gain the necessary skills and successfully navigate the occupational transitions discussed. Women could make inroads in the relatively high-paying professional and associate professional occupation category (for example, 38 percent of women in mature economies could be in this group by 2030, compared with 34 percent in 2017). The bulk of women’s job losses could occur in relatively low-paying occupational categories such as clerical services (for example, 14 percent of women in mature economies could be in this group by 2030, a decline from 17 percent in 2017). However, it is important to note that men could still outnumber women in the highest-paying occupation category: legislators, senior officials, and managers. In our scenario, 9 percent of men in mature economies could be employed in these high-paying leadership roles, compared with only 6 percent of employed women. Emerging economies see a similar story, with women (and men) facing an imperative to transition away from lower-wage occupations like agriculture into higher-wage occupations such as professional roles.
Women and men face a period of disruption and change. It will be vital for both to develop (1) the skills that will be in demand; (2) the flexibility and mobility needed to negotiate labor-market transitions successfully; and (3) the access to and knowledge of technology necessary to work with automated systems, including participating in its creation. Unfortunately, women often face long-established and pervasive structural and societal barriers that could hinder them in all three of these areas—and has made progress toward gender equality in work slow.
The good news is that the forces of technology and innovation that characterize the automation age can also pave the way for more gender equality in the workforce. There is a huge opportunity for private- and public-sector leaders to enable women to make the necessary transitions in three areas (Exhibit 4).
Women in mature economies are generally graduating at rates on a par with, or even higher than, men. According to the World Economic Forum (WEF), across developed economies, more women than men graduate with at least a secondary degree. However, they still need to match their skills as closely as possible to where the most job opportunities will be. There is some concern that women are not acquiring skills needed for high-growth fields such as professional, scientific, and technical services.
In emerging economies, education of girls and women has improved markedly in recent years, suggesting that women should be better positioned now than in the past to take advantage of shifts in labor demand. However, there are still large gender gaps in education, and even more so in the skills that women will need. In low- and lower-middle-income countries such as India, where more than 60 percent of employed women are in agriculture and tend to have a narrow set of skills that may be hard to adapt, transitioning into new occupations and sectors is likely to be highly challenging. More than ever, women need to embrace lifelong learning from school to employment, and throughout their working lives.
To address these needs, the private sector can invest more in training and reskilling their employees within their organizations or in partnership with academic and other institutions. Increasingly, mid-career workers will need to refresh or develop new skills. One study found that in 2018, 54 percent of employers were providing additional training and development opportunities to their existing workforce in order to fill skills gaps, compared with only 20 percent in 2014. Public and private investment in digital learning platforms would open up another avenue for women. Governments can weigh in by providing women with subsidies for undertaking training.
Flexibility and mobility
Labor mobility and flexibility help women and men move across employers, occupations, sectors, and geographies as needed in order to respond to the needs of an evolving labor market. However, women tend to face more structural challenges here than men.
Women are less mobile and flexible because they spend so much more time than men on unpaid care work—more than 1.1 trillion hours a year, compared with less than 400 billion hours for men.
Women are less mobile and flexible because they spend so much more time than men on unpaid care work—more than 1.1 trillion hours a year, compared with less than 400 billion hours for men. Technological change, in itself, should help to make women’s working lives more flexible by enabling teleworking, for instance. Nevertheless, a range of flexible work options are even important for women because many more of them take on paid and unpaid work. Governments can help by subsidizing maternity and parental leave and childcare. More companies can provide flexible options, yet one 2018 survey of employers found that 23 percent of employers were offering flexible or remote working options. In some cases, women face legal barriers to working, at least in some sectors, which limits their mobility between them. In 155 out of 173 economies, at least one gender-based legal restriction exists on women’s employment and entrepreneurship.
Another factor limiting women’s mobility is that women—in both mature and emerging economies—face dangers to their physical security when travelling around, potentially limiting where they can find employment. India’s IT and business-process outsourcing firms are providing safe transport for women employees using vehicles with tracking devices. In emerging economies, limited access to—and poor safety on—transportation systems is regarded as the greatest obstacle to women’s participation in the labor market, especially in the formal economy.
Persistent gender concentration within occupations and sectors makes it more difficult for women (and men) to cross over into those where they currently are the minority of workers. One recent US study showed that women’s sectoral and occupational choices accounted for more than 50 percent of the gender gap. More work needs to be done to reduce stereotypes that entrench gender concentration in some occupations.
Women (and men) may need financial support as they transition into new occupations or sectors including unemployment benefits and insurance. Labor agencies can focus on providing benefits and assistance to the unemployed: serving as job counselors, offering career guidance, and enabling access to potential training and job opportunities for those temporarily out of the workforce.
Women don’t have access to the same extent as men to networks that help them to develop their skills, achieve career progression, and transition into new jobs. Some companies are moving ahead on this front, but more needs to be done to create opportunities for women.
Engagement in tech
Women need to be more engaged in technology—more access, more skills, and more participation in its creation—to thrive.
Technology can break down many of the barriers facing women, opening up new economic opportunities, helping them to participate in the workforce, and, in the automation age, navigate transitions. For example, women are now working independently in what is popularly known as the gig economy, taking advantage of technology that enables new and more flexible ways of working. Digital work platforms are growing fastest in service roles where women are well represented, including retail and accommodation and food service. Digital platforms, and the flexibility and low access costs they offer, also help to explain why so many women have become e-commerce entrepreneurs who may find it challenging to make inroads in more traditional supply chains.
It is vital that women participate in the creation of technology, not only because diverse teams have distinct benefits, but also because their contribution can help tackle concerns about inbuilt gender bias in AI algorithms.
However, women continue to lag behind men in their access to technology, the skills to use it, and in employment in tech sectors, and could risk missing out on the potential benefits of technological innovation. As we have discussed, the future of work will likely require people to work more closely with technology. It is also vital that women participate in the creation of technology, not only because diverse teams have distinct benefits, but also because their contribution can help tackle concerns about inbuilt gender bias in AI algorithms.
The gender digital divide persists. Globally, men are 33 percent more likely than women to have access to the internet; that gap worsens when focusing on women in poor, urban communities. Women also lag behind men in developing tech skills. Globally, women account for only 35 percent of STEM students in higher education, and they tend to study natural sciences more than applied sciences related to information and communication technology (ICT). Women are significantly underrepresented in tech jobs—fewer than 20 percent of tech workers are female in many mature economies. Only 1.4 percent of female workers have jobs developing, maintaining, or operating ICT systems, compared with 5.5 percent of male workers, according to the OECD.
A number of interventions are needed to address these challenges. First, there is a need to create pathways for women in STEM fields. Nonprofits from Afghanistan to the United States are focused on developing girls’ coding skills. Companies in STEM fields can invest in and partner with nonprofits and colleges to develop a broader pipeline of women going into tech fields, and offer internships.
Women’s access to basic enabling technology, notably internet and mobile technologies, needs to expand alongside a stepping up of development of their digital skills. Barriers to women working in the gig economy, including worries about lack of digital and internet skills and physical safety, need to be addressed, as does the lack of social protection for such workers that may expose women to income insecurity.
Finally, more can be done to address the funding gap faced by women entrepreneurs, as part of a broad effort to encourage women actively to create technology and work in new ways. Consider that, in 2018, all-male founding teams received 85 percent of total venture capital investment in the United States, while all-women teams received just 2 percent, and gender-neutral teams just 13 percent.