Labour markets are changing under the twin pressures of the COVID-19 pandemic and the adoption of new technologies. The World Economic Forum’s Future of Jobs Report 2020 revealed that 84% of employers are accelerating their digitalization agenda and 50% of employers intend to accelerate the automation of jobs. These changes are likely to speed the destruction of a set of roles which are increasingly redundant in the new world of work— and accelerate the creation of new roles which can drive prosperity in the new world of work. Monitoring gender parity in fast-growing “jobs of tomorrow” provides an early indicator of gender parity in the future of jobs. Through newly developed metrics, this chapter discusses whether this employment growth in the labour market today will help to equalize gender gaps in employment or whether additional strategies will be needed to ensure gender parity in the future of work.

Over the course of 2019 and 2020, the World Economic Forum worked with the LinkedIn Economic Graph Team to derive new insight into the jobs that are emerging in the labour market. Using real-time labour market data, the research identified 99 roles that are consistently growing in demand across 20 economies, grouped into eight distinctive job clusters on the basis of their unique skills profile.²⁷ The data suggests significant challenges for the future of gender parity. Only two of the eight emerging job clusters tracked are at gender parity and many show a severe under-representation of women. These emerging jobs clusters are presented in Figure 3.1. They include roles that underscore the continuing importance of human interaction in the new economy, such as Marketing, Sales, People and Culture, and Content Production, as well as roles which support the development of emerging technologies such as Cloud Computing, Engineering, Data and AI.

The data shows that gender gaps are more likely in fields that require disruptive technical skills—in particular, Cloud Computing, where women make up just 14% of the workforce; Engineering, where women make up 20% of the workforce; and Data and AI, where women make up 32% of the workforce. Representative examples of roles in those job clusters include Full Stack Developers, Data Engineers and Cloud Engineers.

While the eight fast-growing job clusters typically experience a high influx of new talent, at current rates those inflows do not re-balance occupational segregation. Figure 3.2 illustrates the representation of women by job cluster over time. The figures show that in the period since February 2018, gender gaps in emerging professions have seen little progress. This is specifically the case among those professions which currently demonstrate the largest gender gaps.

For example, the share of women in Cloud Computing is 14.2% and that figure has improved by a mere 0.2 percentage points, while the share of women in Data and AI roles is 32.4% and that figure has seen a mild decline of 0.1 percentage points since February 2018. The Product Development job cluster has seen the largest increase of female representation 1.7 percentage points, while Sales has seen a 1.4 percentage point increase and Marketing a 1.4 percentage point increase towards gender parity. Such striking data highlights the persistence of gender bias in emerging professions and calls for renewed efforts to examine their causes and put in place redressive action.

The Global Gender Gap Report 2020 examined the share of women in the talent pipeline and found that, far from being able to explain such gender gaps across professions exclusively through a shortage of adequately skilled talent, many of the professions outlined have untapped talent pools. In this year’s edition we continue to build on that analysis. The LinkedIn Economic Graph Team categorized the job transitions observed on their platform against a measure of skills similarity.

For jobs with high skills similarity, the similarity between the source and destination job of transitioning workers is measured as being above 70 out of 100. For a low skills similarity transition that figure is below 40 out of 100.²⁸ The observed job-switching behaviour is summarized in Figure 3.3. Findings indicate that some job clusters see a much higher share of low skills similarity transitions irrespective of gender. In particular, approximately 48% of men and women transition into the Data and AI cluster with low skills similarity, while for the Product Development cluster the shares are 18% of men and 15% of women.

This chapter presents a new measure which can be used to assess gender parity in job-switching behaviour and gender gaps for potential-based job transitions. The measure is calculated by looking at the difference between men and women’s low skills similarity transitions as a share of men’s low skills similarity transitions.

It captures the difference between men and women’s likelihood to make an ambitious job switch. The results from the calculation are illustrated in Figure 3.3. The data shows that women exhibit a larger job-switching gap in fields where they are currently under-represented, such as Cloud Computing, where the job switching gap is 58%; Engineering, where the gap is 42%; and Product Development, where the gap is 19%. That trend is partially reversed in the Data and AI job cluster, which offers similar opportunities to transition with low skills similarity for both male and female workers, and fully reversed in the Sales job cluster, where women are much more likely to make a large skills gap transition.

Labour markets continue to exhibit persistent trends towards the segregation of occupations along gender lines. Professions in technology and, in particular, computing have proven to be prime examples of how organizational and professional cultures may cement gender segregation.²⁹ The new analysis presented in this chapter has demonstrated that the challenge of the number of women who study in STEM fields—what has often been termed a ‘supply problem’—can in fact be seen as a symptom of wider biases which inform the job-switching behaviour of female workers. Gendered signals from the labour market—the social experience of learning in STEM classes and working in technology fields—go a long way toward shaping the potential employee base of the professions making them distinctively male.³⁰

Future gender gaps are likely to be driven by occupation segregation in emerging roles. Occupational differences are a key explanatory factor of wage inequality as the emerging roles with lower female representation see higher than average renumeration.³¹ Research has suggested that roles common among low- to middle-income women are likely to be disproportionately represented among jobs destroyed by automation.³² Without opportunities for re-employment and re-deployment into emerging roles, the share of women in the labour market could shrink further. We hope that the new measure presented in this chapter can serve as a key tool to monitor and close gender gaps in emerging professions.