The individuals and communities most affected by the unprecedented changes brought about by COVID-19 are likely to be those which are already most disadvantaged—living in neighbourhoods with poor infrastructure, who have poor employment prospects and whose income does not equip them with a comfortable living standard, healthcare coverage or savings.²⁵ Furthermore, across several countries, the pandemic is set to broaden. An estimated 88 to 115 million people could fall back into extreme poverty in 2020 as a result of this recession.²⁶ The following wide array of characteristics typically pose a risk of social and economic exclusion among these populations: age and generation; gender and gender expression; sexual orientation; mental and physical abilities; level of health; race, ethnicity and religion; in-country geographic location, such as rural and urban. These characteristics are typically reflected in outcomes such as levels of education, employment type, income level and socio-economic status.²⁷

In some countries those affected have been disproportionately women, for whom the ILO reports higher unemployment rates. This is the case in the United States, Germany and Australia. In the United States between December and April 2020, women’s unemployment rose by 11% while the same figure for men was 9%. In Germany those figures were 1.6% and 0.8%, respectively. New sources of data can add more granularity to these trends. ADP Research Institute (ADPRI) has been able to track the impact of COVID-19 on the United States labour market in near real time.²⁸ The data shows that, within the observable shifts of workers’ employment over the period of February to May, 25% of workers left or were asked to leave their current role. Of those 25%, 82% of workers tracked by APDRI dropped out of employment and become displaced workers,²⁹ 14% of workers were initially displaced and then recalled by their companies, and just 5% made successful transitions elsewhere in the labour market (Figure 11). The data shows variations by gender, age and wage level. As revealed in Figure 12, women make up a smaller share of both those who were retained by companies and of those who are recalled. Displaced workers are in fact on average more female, younger and have a lower wage.

The metrics shared by ADPRI also reveal the effect of this disruption by industry and wage level. Figure 13 A details the industries which are most affected by the current disruption; in particular, workers in Arts, Entertainment, and Recreation, and Accommodation and Food Services. Significant numbers of workers have also been displaced from the Retail sector as well as from the Real Estate, Rental and Leasing sector. In addition to this measure of attrition, Figure 13 B presents an overview of the workers who transitioned in and out of jobs during the same period; in effect, the re-allocation of workers by industry sector. The data shows that, on average, workers who did transition moved towards sectors which provide essential services such as Retail and Health, as well as sectors which have been less disrupted, such as Financial Services and Construction. Across these transitions, workers were also able to increase their wages. By contrast, struggling sectors such as Arts, Entertainment and Recreation as well as Accommodation and Food Services gained fewer workers than they lost in the February to May period—and workers who transitioned to those sectors appear to have taken a pay cut, suggesting necessity rather than desirability dictated the change.

Figures 13 C and 13 D present the wage and age dynamics of workers in the United States who were retained, recalled, displaced or transitioned. The markers in brown denote displaced workers; in gold, those who transitioned to new opportunities; in light blue, those who were recalled; and in dark blue, those who were retained. Those recalled into the labour market have the highest average wage of the four cohorts, and those who are displaced have the lowest average wage. In Retail, those who were displaced earn on average a low $17.80 an hour while those recalled are earning $27.00 an hour. In Information and Media, those displaced earn $28.70 an hour while those recalled earn $61.20 an hour.

In addition, retained and recalled workers are, on average older, aged 40 and above, while displaced workers are more typically in their mid-to-late thirties or have just turned 40. For example, in Education Services, those displaced are on average aged 35, while those retained at nearing 43. In Retail and in Accommodation and Food Services these average ages are distorted by the relative youth of both sectors. In Retail, the average age for a displaced worker is 34, while those retained are nearing 40. Across the board, younger workers (those in their 30s) are more likely to have transitioned to new roles during these uncertain times.

Note: Short-cycle tertiary education provide professional knowledge, skills and competencies. Typically, programmes are practically based and occupationally-specific.

Across established labour market indicators, unemployment figures for those with basic education are typically higher than for those who have completed a tertiary education degree. Current ILO figures list unemployment levels among those with an advanced degree as 6.5% and among those with basic education as 7.5%. The latest available figures by economy are listed in the Country Profiles in Part 2 of the report. It must be noted that such figures are still too rarely collected and that more timely unemployment figures remain unreliable. This trend can be further confirmed by focusing on country-level data with strong availability. Figure 14 presents unemployment levels among workers in the United States by education level over time. It shows that the unemployment rate among those with less than secondary education peaked at 21.2% in April, and stills stands at 12.6% as of the end of August. On the other hand, unemployment levels among workers who hold at least a tertiary degree spiked at 8.4% in April and stands at 5.3% as of the end of August. Comparing the impact of the Global Financial Crisis of 2008 on individuals with lower education levels to the impact of the COVID-19 crisis, it is clear that the impact today is far more significant and more likely to deepen existing inequalities.

Finally, such turbulent labour markets provide additional challenges to young professionals navigating their entry into working life. The FutureFit AI global data map combines job automation and growth forecasts, real-time labour market information, learner resumes and the professional profiles of individuals. As such, it can track the historic job trajectories of professionals through different roles and industries,³⁰ and in this instance the transition of young professionals who are in their first decade of working life in the United States observed between 2008 and 2019.³¹ The data in Figure 15 A reveals that, historically, the Retail, Restaurants, Hospitality, and the Food & Beverage sectors, as well some parts of Higher Education, have been among the top 20 starter-sectors for young people. However, as Figure 15 B indicates, these industries maintain a high attrition rate as workers tend to be transient. Thirty-seven percent of young professionals who work in Retail use the industry as a stepping-stone to another career and have historically moved onto another industry beyond the six affected sectors. The same figure is at 32% for those in the Restaurant sector. As roles in these sectors are temporarily or permanently displaced, those at the start of their careers will need to re-route and leapfrog into aspirational opportunities to work in high quality, well-remunerated jobs.

Figure 16 presents FutureFit AI data that documents past labour market transitions of young professionals over a decade. It shows the kinds of industries young professionals have targeted for their job transitions after entering the world of work in one of the six industries most affected by the COVID-19 pandemic. Figure 17 illustrates those next-step possible opportunities, which include new roles in the Healthcare, Financial Services, Not-for-Profit and Information, Technology and Services industries—roles such as Credit Analysts, Bank Tellers and Public Relations Coordinators in the Not-for-Profit sector, Certified Nursing Assistants in Healthcare, and Account Executives in the Information, Technology and Services sector.

This willingness to transition to new job opportunities, matched with new reskilling and upskilling capabilities, can help place young professionals back on track, helping them find routes from affected to new, growing opportunities. While the data shared above suggests that businesses and individuals have taken on significant initiative to adapt to the current labour market, economic scarring and persistent damage to the labour market have the potential to limit the scale of opportunities available to workers. However, governments have at their disposal a range of tools that can alleviate the impact on workers as economies recover.

Note: Figures refer to share of workers transitioning from source sub-industry to destination sub-industry

In previous recessions, the long-term impact on earnings among young people resulted in persistent earnings declines lasting up to 10 years, as young professionals started to work for lower-paying employers, then partly recover through a gradual process of mobility toward better firms. We have also seen young professionals start to work in occupations that do not match their education levels.³² As we consider the ways to revive the labour market, such insights can point to ways in which data-driven re-employment can support not only re-entry into one’s original industry or to an adjacent one, but also provide accelerated transitions to the ultimate career designation aspired to by young professionals.

The early indicators shared in this section signal that without adequate intervention, gains towards bridging societal inequalities might be reversed and wages further polarized. While data for the United States cannot be generalized to the world, the availability of such granular insights in this one economy serves as a stark reminder of the potential impact of these disruptions on equality within and across all economies.