This chapter analyses short- and long-term channels through which the pandemic can affect inclusiveness due to differences in its impact on employment opportunities, both between men and women, and among workers with varying levels and types of skills and education. The chapter first describes ways in which women have been impacted differently than men, thereby potentially exacerbating gender inequality. It outlines contributing factors such as job and industry types, childcare obligations, and digital skills. This chapter then focuses directly on skills, exploring how the skill and education levels of workers contribute to heterogeneous effects. Workers in the middle of the skill distribution, whose jobs were already more at risk from pre-existing trends such as automation, appear to also be more exposed to the employment effects of the COVID-19 crisis. Differentiating further between different types of skills, digital skills are highlighted as being particularly important for preventing job loss.
Strengthening Economic Resilience Following the COVID-19 Crisis
7. Inclusiveness across gender and skill groups
Abstract
Key findings
Essential industries rely disproportionately on women, shielding them from employment loss over the short term, and making their contribution to the workforce critical. Still, closures of school and childcare centres force women to drop out of the labour force to take on unpaid care work, jeopardising progress made towards gender equality in the labour market. Opening childcare facilities and fostering a more equal division of unpaid home and care work should therefore be a policy priority for the COVID-19 recovery period.
The digital acceleration poses a threat to the employment of women over the longer term. While women tend to work in industries that are less sensitive to recessionary demand drops, the rapid uptake of digital technologies in the pandemic, as well as trends in automation, might put female employment at risk going forward. This highlights the strong need for information and communication technology (ICT) upskilling and closing the digital gender gap.
Ensuring ICT skills for all is crucial for weathering the crisis, and success beyond it. The top 11 industries that entail the highest ICT or digital skill levels used on the job are either teleworkable or essential. The industry-level analysis in this chapter highlights the importance of ICT skills for successfully making it through the crisis, but also points to laggard sectors where the average ICT skill level of the workforce is comparatively low, or where firms do not demand these types of skills. Raising the digital skills of the workforce in some of these industries – in particular those with potential for remote or contactless work and delivery of services – could help increase resilience in the future.
Workers with mid-level education and skills are particularly at risk of losing their jobs, and policy should encourage upskilling and career changes. The crisis increased firms’ incentives to digitalise, and automate production. Allowing workers in industries and occupations that were already at risk before the crisis to change careers and acquire new skills, rather than upgrading their job-specific human capital, can help keep people employed. It will also help accelerate structural change and labour market reallocation, which will foster an efficient and speedy recovery.
Introduction
One of the most important concerns about the crisis is its heterogeneous impact on employment outcomes across different groups in society – with potentially adverse consequences in terms of inequality. Two salient dimensions in which differential short-term impacts are observed across population groups are gender and skills.
The labour market participation of women remains a substantial concern in most OECD countries. Analysis of micro data from several national labour force surveys shows that, in most countries, the pandemic triggered what is being called a “shecession” – a recession causing larger employment declines among women than men (Alon et al., 2021[1]). Women are impacted differently than men in several ways, with evidence pointing to a “substantial and persistent drop in their labour force participation” (Albanesi and Kim, 2021[2]). Women are particularly adversely affected by the closures of schools and childcare facilities, as they still shoulder most of the childcare responsibilities within households – spending more than twice as much time on them as men (OECD, 2020[3]). Women also represent the majority of health and care workers, making their work essential during the crisis, and somewhat shielding them from job loss; although, this also means a disproportionate burden in terms of high-risk labour during a pandemic. Many women also work in retail and hospitality, which are being hit particularly hard by lockdowns and restrictions on travel and tourism, resulting in drops in demand for these occupations. In addition, some of these industries often use temporary forms of employment, which can make it more difficult for workers to benefit from employment retention schemes or other types of support linked to employment status.
Skills are another important dimension affecting how people are impacted. For example, jobs with high potential to shift to telework are mainly held by high-skilled workers (Espinoza and Reznikova, 2020[4]). To the extent that lower-skilled workers are employed more often in non-standard forms of employment (on temporary contracts and through agencies, but also via self-employment), they are also not always well covered by firm-level schemes that support jobs and incomes (OECD, 2020[5]).
Longer-term consequences of the crisis, including changes in demand, are likely to also have differential effects by gender and skill. In particular, as telework continues to be an important tool in preventing the spread of the virus, differential propensities in the ability to telework across different worker groups can affect longer-term labour market outcomes (e.g. through accumulated tenure and labour market experience). As the immediate health crisis abates, longer-term recessionary effects and structural changes in demand stemming from possible long-term changes in behaviour, induced by the crisis (e.g. a long-term move to more telework, as discussed in Chapter 5), have the potential to exacerbate or mitigate divergences that have emerged through the crisis.
This chapter complements existing OECD work (OECD, 2020[3]; 2020[6]; 2020[7]) by exploring the gender and skill dimensions of the COVID-19 crisis from an industry perspective, comparing the share of female and low-skill (and low-education) workers in each industry with indicators of potential economic impact. For each of the two dimensions, the analysis focuses first on an indicator that captures mainly short-term restrictions on the supply side, as a measure of whether employment opportunities – across essential, teleworkable,1 and non-teleworkable industries.
Gender differences in exposure to employment effects
Figure 7.1 displays the female share of employment against the share of employment in jobs that – under normal circumstances – involve regular face-to-face contact with customers. It is striking that several of the most female-dominated industries are also those in which significant in-person contact is required. Most notably, women make up at least 70% of the workforce in health and long-term care (as discussed more in depth in OECD (2020[3])). From a health perspective, this means that women face greater risks when doing their job than men. From a job security perspective, however, it seems as though female employment might be more shielded from the negative employment effects of the COVID-19 crisis, as the three industries with the highest shares of women are either deemed as essential (Care and social work, and Health services) or have continued operating via remote learning (Education).
The exposure of female employment in different industries, discussed in the following analysis, also shows that women tend to work in service industries (most importantly, the retail and hospitality sectors) that have been heavily affected by the pandemic and associated containment measures, and in certain essential industries that had greater job security during the pandemic. This suggests that their employment has been affected heterogeneously over the short term. The longer-term economic impact of the crisis might look different: due to high employment shares in essential and domestic service industries, female employment is potentially safer from longer-term recessionary effects.
While representing the majority of the essential workforce during the pandemic, women have simultaneously been facing additional childcare responsibilities, due to closures of childcare facilities and schools. Women with young children have therefore faced additional burdens in terms of both paid and unpaid work. However, their jobs are fundamental for maintaining basic functions in the health system and other parts of the economy, making their contribution to the workforce critical. Further, as women drop out of the labour force to take on more unpaid work, progress made towards gender equality in the labour market before the crisis is jeopardised, in terms of both participation and career prospects. Mothers dropping out of the labour force is a concern particularly in countries relying heavily on public childcare.
Figure 7.1. Share of women and share of jobs involving regular face-to-face contact by industry
Note: Female share of employment is the cross-country (unweighted) average of female labour share by industry in 2019, for 20 European countries and the United States. “Face-to-face share” indicates the share of jobs in each industry that involve face-to-face contact with customers. A job is defined as involving face-to-face contact if the job includes tasks such as dealing with external customers, assisting and caring for others, or providing consultation and advice to others, and face-to-face communication occurs at least several times a week. Indicators are constructed by matching the tasks associated with different occupations in O*NET, then matching these to the occupation structure of NAICS17 three-digit industries using the US Bureau of Labour Statistics industry-occupation matrix for February 2020. Koren and Petö’s three-digit industry-level measures have been aggregated to SNA A38 industries to match the book definition. The reported Agriculture industry does not include Agriculture, but does include Forestry and logging; Fishing, hunting and trapping; and Services to agriculture. Three A38 industries could not be matched to the three-digit NAICS information: Pharmaceuticals, Scientific R&D, Public administration and defence, as in Figure 3.5.
Sources: Measures from Koren and Petö (2020[8]), “Business disruptions from social distancing”, https://cepr.org/file/9913/download?token=xJIvOgjM aggregated to A38 industry classification for the face-to-share share; OECD calculations based on European Labour Force Surveys and United States Current Population Survey for the female share of employment (accessed in October 2020).
Potential labour demand impacts
Figure 7.2 shows the share of women employed in each industry, with bars colour-coded according to the classification into essential, non-essential but teleworkable, and non-essential and non-teleworkable industries. While no clear picture emerges from this simple comparison of industries, it is also important to consider that some of these industries (such as Wholesale and retail, but also Education, Health services, and Care and social work) are much larger than others (such as many of the male-dominated specialised manufacturing industries), as shown in Annex B. Taking the size of industries within the three broad groupings (essential, non-essential but teleworkable, and non-essential and non-teleworkable) into account, averages weighted by industry employment2 show that female employment shares are lowest in non-essential, non-teleworkable industries (38.5%), followed by non-essential, teleworkable industries (50.5%), and are indeed highest in the essential industries, where women account for 56.9% of employment on average.3
When considering the absolute numbers of women affected across these three groupings, most women (41%) work in non-essential, non-teleworkable industries. This is driven by the large size of the retail sector, in which half the workforce is female and which accounts for more than 14% of total private sector employment on average, in OECD countries. Essential industries employ another 38% of all female workers. The remaining 20% are employed in non-essential, teleworkable industries. Given that a large proportion of retail businesses were permitted to stay open in most countries during lockdowns, due to being deemed essential (e.g. supermarkets and food stores such as bakeries and butchers), and that in many countries several other retailer types were allowed to operate during further phases of containment measures (although often only with hygiene and capacity protocols), the direct employment effects on these industries are likely to be more varied than for those that are non-essential and non-teleworkable. In fact, there is evidence that working hours might have increased more for women than for men (Givord and Silhol, 2020[9]), due to heightened demand for work in some female-dominated industries (including not only Care and health services, but also Education and parts of Retail).
Figure 7.2. Share of women by industry
Note: Female share of employment is the cross-country (unweighted) average of female labour share by industry in 2019, for 20 European countries and the United States.
Sources: Espinoza and Reznikova (2020[4]), “Who can log in? The importance of skills for the feasibility of teleworking arrangements across OECD countries”, https://doi.org/10.1787/3f115a10-en for teleworkability; OECD calculations based on European Labour Force Surveys and United States Current Population Survey for the female share of employment (accessed in October 2020).
Figure 7.3. Share of women and cyclicality of demand by industry
Note: Female share of employment is the cross-country (unweighted) average of female labour share by industry in 2019, for 20 European countries and the United States. See note to Figure 3.5 for details on cyclicality index.
Sources: OECD calculations based on European Labour Force Surveys and United States Current Population Survey for the female share of employment, based on OECD (2020[10]), Structural Analysis (STAN) Database, http://oe.cd/stan for the cyclicality index (accessed in October 2020).
Because relatively more women than men are employed in essential industries, which almost by definition entail a relatively low elasticity of demand, female-dominated industries might also be less at risk of long-term declines in demand. Figure 7.3 investigates this graphically, plotting the degree of cyclicality of industries (introduced in Chapter 3) against the female share of employment. There is a clearly discernible negative relationship between the degree of cyclicality and the share of female workers, driven by three essential industries: Health services, Education, and Care and social work. Several of the industries with the lowest cyclicality of demand (Public administration and defence, Real estate, Scientific R&D) are also easily teleworkable. An important consideration, however, is that until the health crisis is over, many of the service activities that require face-to-face contact (e.g. Hotels and food services), in which women have disproportionally high employment shares will not be able to resume. Because of this, the stabilising effect of domestic household demand on female employment will strongly depend on the evolution of the pandemic and the strategies of governments to deal with the health crisis. In addition, for some of these activities, employers may decide to switch to automated solutions (Chernoff and Warman, 2020[11]), putting employment in these sectors at risk despite their traditional resilience to business cycle fluctuations.
Factors affecting female labour supply: Household demographic structure and childcare
Concluding that women’s employment is more resilient in the crisis rests on the assumption that women can maintain their labour supply through the crisis and its aftermath. Several features are relevant to understand how well the workforce is able to adjust to the challenges posed by the COVID-19 crisis and maintain labour supply at pre-crisis levels, either through telework or continued activity in essential industries. The closure of schools and childcare facilities during lockdowns represents one such challenge that particularly affects working parents, who need to adjust their daily schedules to accommodate both work and childcare obligations.
Especially with regards to telework, women, much more than men, face multiple demands throughout the crisis, particularly in terms of caring for children (but also other vulnerable relatives), which might prevent them from participating in the labour market to the same extent or intensity as before the crisis. Following the school and childcare facility closures through the first wave – and, to a lesser degree, also further waves – of the pandemic, subsequent sections explore the patterns of intra-household division of labour, childcare arrangements, and working-time arrangements. They highlight differences that exist across countries in these dimensions, which are important determinants of female labour supply.
It should also be noted that while the closure of schools and childcare facilities may have been temporary, and has been avoided by many countries during later waves of the pandemic, quarantine rules at schools continue to force parents to care for their children at home, with few alternatives given that the nature of quarantine itself prohibits other forms of private arrangements for childcare.4
Countries in which households with children follow a more traditional division of labour (i.e. one partner working full-time and the other staying at home, all or most of the time) are likely to adjust more easily to this situation. Conversely, in economies with a more widespread use of childcare facilities, workers with small children face larger disruptions in their daily routines when these facilities suddenly become unavailable. Given that the majority of childcare is still provided by mothers (OECD, 2020[3]), this will likely disproportionately affect women’s time availability, also for work purposes.
A third factor relevant to childcare is household and family structure. In countries with traditionally larger households, families will likely find it easier to cope with additional childcare responsibilities during the crisis, as duties can be divided between more members of the household. Countries in which smaller or single-parent households are more widespread may face greater challenges in reconciling work and childcare responsibilities.5
To capture the different facets related to the structure of families and childcare, the following graphs show the distribution of work in couple households with one or more children under the age of 14, the percentage of young children (0-2 years old) enrolled in childcare, and the percentage of children (6‑11 years old) living with a single parent.
Figure 7.4. Distribution of work in couple households with at least one child under 14 years old
Note: Data refer to 2014 except for Denmark, Finland and Sweden (2012), and Chile, Germany and Turkey (2013). The definition of part-time employment varies slightly across countries (for details, consult the original source).For the United States, the data refer to children aged 0-17. Average refers to the unweighted average across the OECD reported countries.
Source: OECD (2020[12]), Family Database, www.oecd.org/els/family/database.htm (accessed in June 2020).
Figure 7.5. Percentage of children 0-2 years old in early childhood education and care services
Note: Data refer to 2017 except for the United States (2011) and Switzerland (2014). Data generally include children enrolled in early childhood education services (ISCED 2011 level 0) and other registered ECEC services, with the exception of a few countries as listed in the original data source.
Source: OECD (2020[13]), “Enrolment rate in early childhood education (indicator)”, https://doi.org/10.1787/ce02d0f9-en (accessed on 26 June 2020).
While the three indicators cover inherently related aspects of the same topic, and there is a certain degree of overlap in country rankings, some interesting differences nevertheless emerge among countries. For example, the Netherlands is among the top three childcare facility users, with almost 60% of infants and children under the age of two enrolled in early education and childcare facilities. While this would suggest that closures of care centres may be particularly challenging for parents in the Netherlands, the country also has one of the lowest proportions of children living in single-parent households (slightly over 10%) and a high proportion of couple households with children where at least one partner works only part-time (over 70%). This suggests that households in the Netherlands might be able to cope better with school and day care centre closures than countries ranking in the middle of most indicators, such as Belgium, which has a relatively high share of single-parent households (23%), alongside a relatively high share of early education and childcare facility use (over 55%), and a low share of households in which one parent is not working (around 10%).
Figure 7.6. Percentage of children 6-11 years old living with a single parent
Note: Data refer to 2016 except for Iceland (2015). Average refers to the unweighted average across the reported countries.
Source: OECD calculations based on OECD (2020[12]), Family Database, www.oecd.org/els/family/database.htm (accessed in June 2020).
Of course, these numbers can merely give a rough indication, as a fraction of households will not face a trade-off between work and childcare if one or both parents are working in sectors affected by the lockdowns that are non-essential and not teleworkable. It is also possible that the crisis can lead to a reversal of traditional roles in some couple households, with men taking over more childcare responsibilities if the woman works in an essential (or teleworkable) industry and the man does not (Alon et al., 2020[14]). This, however, appears to be the exception rather than the norm; a study using timely data during the crisis (Eurofound, 2020[15]) finds that, on average, the unequal burden on women of care work continues; among individuals with children who report working from home, women spend around one hour more per day on unpaid household work.
Importantly, the absence of women from their jobs may have long-term consequences for workforce gender equality, as levels of accumulated work experience are an important determinant of wages and promotions. Returns to experience tend to be higher in industries employing more women: they are higher in services than manufacturing, and higher in manufacturing than agriculture (Islam et al., 2018[16]). Absence from work because of COVID-19 related reasons may therefore exacerbate the already disadvantaged position of women.
Women also went into the pandemic at a disadvantage in terms of specific skills which seem to be particularly relevant for bridging the crisis, such as technical skills for telework. Using skill-related indicators (Grundke et al., 2017[17]; OECD, 2018[18]),6 Figure 7.7 shows that men generally have higher levels of the skills that entail extra wage premia in digital intensive industries relative to women. Across countries, industries, occupations, men – on average – have higher numeracy and advanced numeracy skills, as well as higher task-based self-organisation and management, and communication skills.
Women, on average, already had lower levels of some of the skills needed for the digital era7before the pandemic. These divides, as well as other pre-existing inequalities between men and women (such as the gender wage gap), are likely to be exacerbated by the COVID-19 crisis, which has both accelerated the digital transformation and impacted female labour market participation. Further, since especially skills related to management and communication, and self-organisation, are acquired and improved through learning-by-doing and experience, the gender skill gap is at risk of widening if women are given less opportunity to work in such roles and perform these tasks during and after the pandemic.
Figure 7.7. Differences in skill scores between men and women
Note: Differences in standardised skill scores between men and women are conditional on the covariates from the wage regressions (which include age and education of the individual; country-, industry-, and occupation effects; firm size and part-time status). The skill measures are taken from Grundke et al. (2017[17]). For each skill variable, OLS regressions of workers’ skill endowment on the covariates from the wage regressions are estimated on the pooled set of 31 countries for which PIAAC data was available. For each of these regressions, the bars show the coefficients of the “male” variable, which takes value 1 if the individual is male and 0 if female. Shaded bars signal that differences between men and women are not significant at the 5% level. Bars above the x-axis reflect skills that men are relatively more endowed with; bars below the x-axis show skills that women are relatively more endowed with.
Source: OECD (2018[18]), Bridging the Digital Gender Divide: Include, Upskill, Innovate, www.oecd.org/digital/bridging-the-digital-gender-divide.pdf.
As discussed in detail in Chapter 5, the COVID pandemic has triggered an acceleration of the digital transformation of economies and societies. While this may in itself be good news, women display a relative shortage of skills that are considered particularly important for the digital era. Combined with the perceived “masculinity” of technologies,8 and the fact that some women and girls feel insecure and at times aggressed in the digital space (OECD, 2019[19]),9 these factors raise concerns about the erosion of female workers’, citizens’, and consumers’ rights, and opportunities both during and after the COVID pandemic.10 From a longer-term perspective, while job loss due to the pandemic and the lasting impact it is likely to have on labour demand patterns is certainly bad news for all workers, being made redundant may be especially detrimental to women. Given the gender gap in digital skills, finding a new and possibly different job may turn out to be more challenging for women than for men. OECD estimates (OECD, 2019[19]) suggest that, on average, women need to bridge greater skills shortages – and hence undergo more training – to move to another occupation, than men do. The following section will investigate the role of skills for coping with the possible short- and long-term impacts of the pandemic.
Skills and education differences in exposure to employment effects
Another salient dimension along which impacts of the COVID-19 crisis differ is skills and educational background. ICT skills in particular determine how well workers are able to adjust to remote organisation of their work, and the extent to which employers can rely on their workforce being able to efficiently work remotely. Recent OECD work (Andrieu et al., 2020[20]) using real-time data on job vacancy postings in the United Kingdom also points towards the longer-term importance of digital skills, which were in demand throughout the crisis, but accelerated especially towards the end of the observation period in September 2020.
Figure 7.8. Average ICT skill level by industry
Note: Skill levels are scaled to range from a minimum of 0 to a maximum of 100. The ICT skills indicator consists of a number of different self-reported tasks carried out on the job in a sample of workers in each industry: frequencies of excel use, programming language use, transactions through internet (banking, selling/buying), email use, simple internet use, word use, real-time discussions through ICT computers, reading/writing letters, emails or memos, level of computer use required for the job, and frequency of working physically over long periods.
Source: Cammeraat, Samek and Squicciarini (2021[21]), “Management, Skills and Productivity”, https://doi.org/10.1787/007f399e-en, with estimates of the task-based skill indicators constructed following Grundke et al. (2017[17]), “Skills and global value chains: A characterisation”, https://doi.org/10.1787/cdb5de9b-en.
As shown in Figure 7.8, the top 11 industries in terms of ICT skill levels used on the job are all either teleworkable (top 4) or essential. This underlines the importance of ICT skills for successfully making it through the crisis, but also points to laggard sectors where the average ICT skill level of the workforce is comparatively low, or where firms do not demand these types of skills. Raising the digital skills of the workforce in some of these industries – in particular those with potential for remote organisation of work or remote and/or contactless delivery of services – could help increase resilience in the future. As discussed above, these industries also have particularly low shares of female employees. Raising the digital skills of women in particular could yield a double dividend by reducing both the gender gap and the skills gap, aiding in the recovery phase as well as resilience in the future.
Looking at other types of skills, the picture changes to some extent. Numeracy skills are much more evenly distributed across sectors, with an average level – using the same scale as the above figure – of 52 in non-essential, non-teleworkable industries, 53 in essential industries, and 57 in teleworkable industries. Readiness to learn and creative problem solving11 is equally similar across sector groups, with an average of 49 in non-teleworkable industries, 50 in essential industries, and 56 in teleworkable industries. This suggests that these broader types of general skills do not, from an industry perspective, represent a relevant dimension across which impacts of the crisis differ to a large extent.
Focusing on skill types as well as skill levels, recent research using data from job postings in the United Kingdom (Andrieu et al., 2020[20]), shows that medium-skilled workers experienced the most pronounced decline in posted vacancies during the crisis. Considering educational background instead of skills, empirical analysis at the industry level confirms this pattern for both the short- and the long-term measure of exposure to the crisis: Workers with low levels of education (defined here as lower secondary education or less) make up 18% of the workforce in non-essential, non-teleworkable industries, 13% in essential industries, and only 5% in teleworkable industries. Workers with medium levels of education (higher than lower secondary but below tertiary) represent the largest share of the workforce. They account for 58%, 46%, and 25% of workers in non-essential, essential, and teleworkable industries, respectively. Highly educated workers (tertiary education or higher) are the mirror image of the other two groups, with shares of 25%, 41%, and 71% of workers in non-essential, essential, and teleworkable industries, respectively.12
Figure 7.9 depicts the correlation of the industry share of medium-educated workers with the cyclicality of demand index. The clearly positive relationship, with a correlation of 0.4, suggests that medium-education workers are particularly at risk of losing their employment over the medium term, as the recession following the health crisis unfolds. Low-education workers also tend to work more in industries that are more sensitive to the business cycle, but the relationship is weaker (correlation of 0.2) and this group also represents a smaller fraction of overall workers. Highly educated workers, on the other hand, tend to work relatively more in anticyclical industries, indicating that they are relatively more shielded from the longer-term impacts of the crisis.
Figure 7.9. Cyclicality of demand and share of medium-education workers by industry
Note: See note to Figure 3.8 for details on cyclicality index.
Sources: OECD calculations based on European Labour Force Surveys and United States Current Population Survey for the share of employment with medium education; and based on OECD (2020[10]), Structural Analysis Database (STAN) Database, http://oe.cd/stan for the cyclicality index (accessed in October 2020).
The overall image arising from the analysis on skills is that the workers most at risk of being displaced over the short and long term from the economic impacts of the crisis are those in the middle of the skill – and consequently the wage – distribution. In this sense, the crisis is once again an accelerator of already existing pre-crisis trends, where precisely this group of workers had already been identified as being most at risk of losing their jobs, due to advances in automation and digitalisation. At the firm level, for example, the crisis is accelerating firms’ incentives to automate production, in view of minimising the negative effects of further COVID-19 waves, or of future pandemics (Chernoff and Warman, 2020[11]).
A second key insight is that ICT and digital skills are a key dimension separating winners and losers of the crisis. This is true in both the short and long term. On the one hand, familiarity with the technology to telework has been crucial for maintaining labour supply through the crisis. On the other hand, longer-term trends in digitalisation – not only on the labour market, but in many other aspects of everyday life, as discussed in Chapter 5 – make digital readiness a key skill ensuring inclusiveness and participation in society more generally.
Conclusion and policy implications
In view of the differential effects the crisis is likely to have on female employment – not least through the additional burdens women face in terms of unpaid household and care work – it is crucial that immediate support as well as recovery measures incorporate a gender perspective, as highlighted in a number of gender-focused works on COVID-19 (OECD, 2020[3]; 2020[6]; 2020[7]; 2020[22]).13 The industry-based analysis conducted in this chapter corroborates this view. Besides focusing on the immediate crisis period, it adds a longer-term perspective, highlighting the important role of ICT skills for increasing the resilience of female employment to shocks such as COVID-19.
Even when the demand for female employment has remained stable for women employed in essential or teleworkable industries, upholding labour supply can be an additional challenge due to the closure of schools and day care centres. Special consideration and financial support should be granted to self-employed single parents, and in particular female entrepreneurs, who were unable to work due to childcare obligations. Prioritising the re-opening of childcare services during lockdowns, and the provision of affordable and universal childcare during the recovery, are important to keep the disruptions on the labour supply of mothers and fathers as small as possible.
In parallel, it is important to foster a more gender-equal division of unpaid home and care work – for example through incentives at the firm level (e.g. through well-paid parental leave for fathers, or offering more flexible working-time arrangements) – as well as removing disincentives such as excessive overtime, and those inherent in the tax structure for second earners14 (OECD, 2020[7]). More generally, these measures should be part of a broader strategy for fighting stereotypes about gender roles and the type of tasks women should perform (both in their careers as well as at home), complemented by policies fostering women’s participation in labour markets.
Support policies targeting working parents should take into account not only periods of mandatory school closures, but also periods of reduced working hours due to quarantines of children or other care obligations. When providing emergency childcare facilities, these could be extended to women in non-essential jobs who are unable to continue working due to childcare obligations. Supporting female employment through the crisis not only provides short-term economic relief for affected women, but also has long-term implications for gender equality on the labour market. Similarly, while enabling female labour market participation, both during and after the crisis, is an important goal in its own right, it is also crucial in order to preserve job matches – thereby improving firm performance, resource allocation and overall efficiency in the long run – and to contribute to a stronger economic recovery.
In terms of direct immediate support measures introduced during the crisis, employment support schemes that do not cover atypical forms of employment, such as temporary or agency work, self-employment, or – especially important from a gender perspective – part-time work, risk deepening pre-existing inequalities across worker groups that were already in more precarious forms of employment before the crisis. It is therefore crucial to extend employment retention and furlough schemes to these types of workers, as already done by a number of countries (OECD, 2020[5]).
The ICT and digital skills gap impacts inclusiveness and employment disparity, including along the gender dimension, and analysis shows that during the crisis this divide has become even more significant. This finding underscores the strong need for digital upskilling, especially for low-skilled workers and women, as well as older parts of the population, who lag behind the most in terms of basic digital knowledge. Closing the digital gender divide should also be a priority to ensure women in teleworkable jobs can continue to work, especially given that many firms are likely to keep some form of telework arrangements even after the crisis subsides.
While sustaining the jobs of those who are unable to work due to restrictions on economic activity is important, this crisis can also be taken as an opportunity to offer training, skill upgrading, or fully-fledged occupational changes to workers whose jobs are at risk over the longer term. Allowing workers in industries and occupations that were already at risk before the crisis – for example, due to pre-existing trends in automation – to change careers and acquire new skills, rather than upgrading their job-specific human capital, can also help accelerate structural change and efficient labour market reallocation. Accompanying measures can include fostering mobility, reducing regulatory barriers on occupational licensing, and promoting firm entry and business dynamism more generally, through the policy options discussed in Chapter 4.
References
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Notes
← 1. “Teleworkable” and “teleworkability” are terms used throughout the report to describe jobs and tasks that are able to be done through telework. Originating mainly in the COVID-19 crisis, given extensive literature and research arising from the rapid global uptake of telework, the terms are now of demonstrated accepted and common use in many official documents. As such, they are used within this context in this document.
← 2. The cross-country average of employment in each industry, computed from Annex table A B.1, is used for weighting.
← 3. Note that data on female employment shares in the Mining and Coke and petroleum industries are not reliable. Due to the small size of these industries, this does not affect the averages (imputed shares between 10% and 50% women yield the same results).
← 4. In Germany, for example, over 3 200 schools did not provide presence-based teaching for all pupils in mid-November due to coronavirus precautionary measures (Der Spiegel, 2020[35]).
← 5. The vast majority of single parents are still mothers. For example, in the United States in 2016, the share of single fathers was just under 20% (Grall, 2020[29]). In the same year in Germany, the share was even lower, at around 10% (BMFSFJ, 2018[30]).
← 6. These encompass workers’ cognitive skills, which are assessed through tests in the Survey of Adult Skills (PIAAC), namely literacy, numeracy, and problem solving; as well as indicators of the frequency with which workers perform certain tasks on the job. The latter provide information on some of workers’ cognitive abilities, namely ICT-related skills, “Advanced numeracy” STEM skills, and “Accountancy and selling”, as well as on non-cognitive skills such as “Managing and communication” and “Self-organisation”, and socio-emotional skills like “Readiness to learn and creative problem solving”.
← 7. Women are generally better endowed with literacy, ICT and accountancy, and selling skills than men, suggesting that women are not short on all the skill dimensions relevant for the digital transformation. See OECD (2018[18]) for more details.
← 8. Research indicates that the connection between masculinity and technology (Tiainen and Berki, 2019[24]) is a result of the historical and cultural construction of gender (Wajcman, 1991[28]). Existing studies show that, in girls’ minds, technologies are largely male-centric, reinforcing the idea and the social norms defining technologies to be mostly in the range of experience or thought of men (Antonio and Tuffley, 2014[31]; Harding, 1986[27]; Lie, 1995[32]). See OECD (2019[19]) for more details.
← 9. A report from the UN Broadband Commission for Digital (2015[25]) finds that close to three quarters of women online have been exposed to some form of cyber violence. Female user of the Internet are also frequently subjected to harassment and hate speech, and experience threats, violence and abuse on social media platforms, often with little accountability. The aim of violence and abuse creates a hostile online environment for women with the goal of shaming, intimidating, degrading, belittling or silencing women (Amnesty International, 2018[26]).
← 10. Aggression and violence against women has also potentially increased for women in their homes. Research using emerging information from media coverage and reports from organisations responding to violence against women points to substantial increases in domestic violence during lockdowns (Roesch et al., 2020[33]). A further analysis of 38 articles published between December 2019 and June 2020 suggests that factors that increase women’s vulnerabilities to violence were exacerbated during the social distancing and lockdown period (Sánchez et al., 2020[34]).
← 11. Similar to the ICT skills indicator, Readiness to learn and creative problem solving consists of a number of different items from the PIAAC survey: “I like to get to the bottom of difficult things”, “If I don't understand something, I look for additional information to make it clearer”, “When I come across something new, I try to relate it to what I already know”, “When I hear or read about new ideas, I try to relate them to real life situations to which they might apply”, “I like learning new things”, and “I like to figure out how different ideas fit together”. The indicator is constructed following Grundke et al. (2017[17]).
← 12. In absolute terms, almost two thirds (62.8%) of low-education workers are employed in non-essential and non-teleworkable industries; another third (32.4%) work in essential industries, and less than 5% are employed in teleworkable industries. For medium-education workers, the shares are 22%, 66% and 11.6%, respectively; and for high-education workers they are 10.7%, 63.2%, and 26.1%.
← 13. A number of further policy options for improving gender equality are outlined in extensive OECD work on the topic, including in (OECD, 2020[6]; 2020[7]).
← 14. Tax structures that imply a disproportionately higher tax burden on second earners – more often women than men – discourage labour market participation of second earners, thereby further exacerbating the pre-existing divides (Harding, Perez-Navarro and Simon, 2020[23]).