Gender inequalities in the new world of work

Abstract

German

French

Digitalisation, automation and technological change have brought about shifts in the occupational structure, the place and the timing of work, and career patterns, putting a further strain on the standard employment relationship. In the recent research on digitalisation, scant attention has however been paid to the gender impact of these changes. This article addresses this gap by developing a gender perspective on digitalisation, considering how these developments interact with existing social inequalities and gender segregation patterns in the labour market. We identify two broad areas in which digitalisation has thus far had a pronounced effect on employment: the structure of employment (including occupational change and the task content of jobs) and forms of work (including employment relationships and work organisation). We find that, despite the profound changes in the labour market, traditional gender inequalities continue to reassert themselves on many dimensions. With standard employment declining in significance, the policy challenge is to include new forms of work in effective labour protection frameworks that promote equal access of women and men to quality jobs and their equal treatment at work.

Keywords

Gender employment digitalisation technological change platform work flexible employment labour market segmentation

Introduction

The world of work is undergoing radical transformation, in large part driven by digitalisation, automation and technological change (see e.g. Autor, 2015; Frey and Osborne, 2013; Valenduc and Vendramin, 2017). The change is seen in the occupational structure, the place and the timing of work and career patterns, as well as a continuing decline in the standard employment relationship. In recent research on digitalisation, its impact on various occupational groups has been emphasised, yet very limited attention is paid to how outcomes differ between women and men. This article addresses this gap, considering the potential gender impact of digitalisation.

We argue that digitalisation, defined as the increasing integration of digital technologies in the work process, can be best understood as the most recent phase of the long-term transformation of the world of work through technological innovation (see Valenduc and Vendramin, 2017). It does not constitute a radical break with the past and can only be partially disentangled from other processes with which it interacts, such as population ageing, globalisation and economic liberalisation. Moreover, as emphasised by feminist scholarship, changing employment relations interact with enduring gender inequalities in paid and unpaid work, with one shaping and, in turn, being shaped by the other (Feldberg and Glenn, 1979; Gornick et al., 2009; Lewis, 1992; Vosko, 2000). To gain a better understanding of the impact of digitalisation on gender relations and inequalities in the labour market, we identify two broad areas in which digitalisation has thus far had a pronounced effect on employment: the structure of employment and the forms of work. We develop a gender perspective on digitalisation around these two broad areas of change, considering how these developments interact with existing social inequalities and gender segregation patterns in the labour market.

The first area of change connected to digitalisation is the structural transformation in employment in terms of shifts in the size of occupational categories and evolutions in the task content of jobs. We draw on the theory of skill- or routine-biased technological change (see e.g. Autor, 2015) which explains how the deployment of digital technologies changes labour demand by automating certain jobs, while at the same time creating demand for new types of activities and skills. The skill-bias hypothesis assumes that low-skilled workers will bear the brunt of the adjustment costs (e.g. Arntz et al., 2016). More precisely, automation is routine-biased as it mainly affects jobs with routine and hence automatable tasks, potentially affecting jobs involving a range of skill levels (de la Rica and Gortazar, 2016; Graetz and Michaels, 2015). Non-automatable tasks involve inter-personal contact and people skills, such as empathy, a feature of female-dominated personal services and care sectors, as well as creativity and critical thinking, more often required in male-dominated high-skilled professional jobs. Routine-biased technological change has driven labour market polarisation, particularly in high-income countries, as jobs involving non-automatable tasks tend to cluster in the lower and upper ends of the job hierarchy; however, it has also interacted with other factors, particularly educational expansion, demographic change, migration flows and changes in labour legislation (e.g. Hardy et al., 2016; Oesch, 2013; Salvatori, 2015). The impact of these changes on gender segregation is complex, with horizontal and vertical segregation impacted differently, as also seen in the context of changing gender norms and gender egalitarianism (Charles and Grusky, 2004; Estévez-Abe, 2005).

The second area of changes brought about by digitalisation includes the organisation of work and forms of employment. Digital technologies allow a better coordination of workers across space and time, enabling increasing reliance on flexible and non-standard work. For instance, matching clients with workers to perform even the smallest tasks in a one-off transaction through digital labour platforms is supporting the expansion of self-employment and other atypical forms of work outside a (regulated) employment relationship (Drahokoupil and Fabo, 2016; see also Transfer 2/2017). Technology also promotes changes in work practices, such as increasing flexibility in the place and timing of work. These developments are often linked to increasing employment precariousness and to risks related to an increasingly blurred boundary between work and non-work, but are also expected to create new opportunities for achieving a better work-life balance and increased labour market participation of marginalised groups of workers. We address these issues from both a gender and a segmentation theory perspective, both of which see labour market vulnerability, inter alia gender-related, as supporting and reinforcing placement of certain workers in the secondary and more precarious labour market segments (Rubery, 1978; Rubery and Piasna, 2016). As a result, female employment might expand in the new digital economy due to the continued existence of disadvantages (e.g. women are more likely to work in non-standard employment, with lower wages) which make women a more attractive (i.e. more flexible and cheaper) source of labour. But this expansion will not necessarily lead to the creation of secure and adequately paid employment (Leschke and Jepsen, 2011).

In the first two sections, we tackle the structural transformation of employment in Europe, the first area of change, considering the changes in occupational structure and in the task content of jobs. In the following two sections, we look at the second area of change, analysing the changing forms of employment and work organisation. We conclude with some policy recommendations for effective responses to the challenges and risks posed by digitalisation.

Occupational structure

To investigate the changing pattern of gender segregation, including gender differences in skill use, we analyse patterns of net job growth and destruction in Europe between 2011 and 2015, the first years of recovery following the ‘Great Recession’ of 2008. We use the ISCO occupation classification, based on a hierarchy of tasks and duties performed in a job. We investigate the extent to which changes in occupational structure impact male- and female-dominated segments differently and the degree to which they challenge the current structure of occupational segmentation by gender. Though unable to separate the effects of technological change from other factors, we assess the extent to which job creation and destruction in individual categories correspond to the expectations of skill-biased technological change. The occupational analysis also allows us to see the extent to which women have been able to upgrade to occupations involving higher skills.

In 2015, women represented 46 per cent of the EU workforce, an increase of just one percentage point since 2008 and mainly reflecting the destruction of predominantly male-dominated jobs in manufacturing and construction in the first period of the crisis. Despite austerity policies and cuts in public sector employment, jobs in the female-dominated service sector (health care and education) proved to be more resilient. Employment gains in 2011–2015 were then shared by men and women relatively evenly (Eurofound, 2016b).

Changes in non-manual occupations (ISCO 1–5 in Figure 1) seem to correspond to the skill-biased technological change, with several high-skilled professional occupations expanding and clerical jobs (within ISCO 4) disappearing. Moreover, and apparently related to population ageing, health-related jobs expanded both in higher- and medium-skilled categories. Reinforcing the existing structure of gender segregation, these female-dominated sectors expanded mainly by employing even more women.

Figure 1.

Change in employment by gender, EU-28, 2011–2015, ISCO 1–5.

At the top of the occupational spectrum, managers (in ISCO 1) recorded job losses (see Figure 1). These disproportionately affected men, thus driving up the share of female employment within these male-dominated occupations. The majority of jobs added in production and specialised services management, the only expanding managerial subcategory, went mostly to men. Moving down the occupational hierarchy, changes in ISCO 2 subcategories conform to the expectations of skill-biased technological change, with high-quality and skill-intensive jobs associated with the knowledge-based economy expanding (i.e. science/engineering and ICT professionals). However, the advancement of women in these categories is, at best, mixed, with both categories remaining male-dominated (see gender breakdown in Table A1). Growing more dynamically than science/engineering, the ICT professionals expanded mainly by employing even more men, with the share of women declining slightly. By contrast, science and engineering professions grew by creating jobs for women, while male employment dropped.

Job creation in the medium-skilled, non-health categories took place mainly by increasing female employment, pushing up the share of women in these gender-mixed categories. At the lower end of the skill spectrum in the non-manual categories, the destruction of female-dominated jobs in the personal care category (within ISCO 5) is surprising in the light of the expectations of the routine-biased theory. Interestingly, the personal service occupation (also within ISCO 5, 61 per cent female in 2015) expanded mainly by adding men – another sign of changing gender segregation.

In manual job categories (ISCO groups 7–9 in Figure 2), changes in male employment do not correspond to skill-biased technological change, with significant male job creation in the blue-collar categories. Moreover, much of the job destruction recorded in 2011–2015 was related to the collapse of the construction sector in a number of EU countries after 2008. However, expansions in female-dominated service categories conform to the expectation of job creation at the lower end of the occupational hierarchy.

Figure 2.

Change in employment by gender, EU-28, 2011–2015, ISCO 7–9.

More specifically, male job destruction in some male-dominated categories (builders, metal and machinery workers, drivers, and miners) was accompanied by job creation in other male-dominated segments (electrical and electronics workers) or male job creation in the more gender-balanced occupation of assemblers. By contrast, blue-collar women were less affected by job destruction. Agricultural labourers represented an exception here, as this male-dominated category adjusted by reducing female employment. Cleaners and helpers and food preparation assistants were the two female-dominated low-skill categories that recorded net job creation, corresponding to skill-biased technological change. Moreover, they showed signs of structural change challenging gender segregation: the female-dominated food preparation occupation expanded by adding similar numbers of men and women.¹

In general, the transformation of the occupational structure in the EU between 2011 and 2015 did not seem to benefit categories dominated by one gender: there is only a weak relationship between the share of women and job growth across 37 occupational categories, r = 0.187, p = 0.268 (as listed in Table A1). In fact, some of the sectors adding most jobs exhibited a balanced gender profile. At the same time, there are signs of upgrading in the female occupational structure, with the share of women in high-skilled occupations, both in female-dominated professional groups (within ISCO 2) and male-dominated manual occupations (craft and related crafts workers, ISCO 7), increasing. By contrast, the share of women in some low-skilled categories (sales, personal services and care in ISCO 5) and elementary occupations (ISCO 9) declined (see Table A1).

However, none of these developments signal a major break with the traditional division between ‘female’ and ‘male’ jobs. Female-dominated segments still constituted the main drivers of female job growth. In particular, health care and education remained the main sources of female employment, as well as the main drivers of job growth among women after 2011, thereby contributing to growing segregation as the share of women further increased in these most female-dominated sectors (Eurofound, 2016b). On the other hand, there were signs of a possible transformation of gender employment patterns across several occupational categories that added new jobs in the period 2011–2015. The shift in gender segregation was most pronounced in the female-dominated food preparation and personal services segments, contributing significantly to male employment growth, as well as in the male-dominated science and engineering segments, where female employment grew.

The task content of jobs

The risk of a job being automated should be linked to the tasks performed within the job (Arntz et al., 2016; Autor, 2015; Autor et al., 2003). Digitalisation can be expected to impact men and women differently due to gender differences in the task content of jobs, even within the same occupations. However, there is no systematic international comparative evidence on the interaction of gender and the risk of a job being automated.² Keister and Lewandowski’s analysis (2017) of the expansion of routine work in Eastern Europe identifies two clearly gendered groups of workers performing routine work. The first group consists of manufacturing workers, mostly male, with secondary education and wages in the middle of the distribution. The other one comprises service workers, mostly women, with secondary education and earning low wages. These two routine occupations match the vulnerable job categories also identified in high-income countries (Acemoglu and Autor, 2011; Goos et al., 2014).

To assess the extent to which women and men across Europe are exposed differently to the risk of automation, we identify the gender gap in the intensity of repetitive and complex tasks as well as on-the-job learning. Repetitive tasks are used as a proxy for routine tasks. We rely on information reported in the 2015 European Working Conditions Survey. Table 1 shows these differences along the occupational hierarchy, using the broad ISCO occupational categories for the EU-28. The analysis indicates that women across most occupational categories were more likely to perform repetitive and routine tasks and less likely to perform complex ones. In general, women thus appear to be more at risk of being pushed out by robots and algorithms. The differences are most pronounced in manual categories (ISCO 7–8 in particular), though the gender gaps in non-manual categories are also significant. On average, women were also less likely to upgrade their skills on the job (i.e. workers reporting ‘learning new things’). However, the differences in most categories were smaller than the gender gaps in repetitive and complex task intensity. Finally, clerical support workers (ISCO 4) stand out: the skill intensity gender gap appears smaller in this category and women reported more on-the-job learning than men.

Table 1.

Task content of jobs by gender and occupation, EU-28, 2015.

	Repetitive hand or arm movements			Short repetitive tasks of less than 1 minute			Short repetitive tasks of less than 10 minutes			Complex tasks			Learning new things
	men	women	gender gap W-M	men	women	gender gap W-M	men	women	gender gap W-M	men	women	gender gap W-M	men	women	gender gap W-M
Managers	28.5%	37.2%	8.7	19.3%	17.5%	−1.8	28.6%	30.6%	2.0	77.2%	67.6%	−9.6	84.9%	82.3%	−2.6
Professionals	32.5%	29.2%	−3.4	13.6%	16.5%	2.9	24.8%	29.2%	4.4	84.2%	79.4%	−4.7	90.7%	90.7%	0.0
Technicians and associate professionals	28.0%	36.0%	7.9	16.6%	19.5%	2.9	30.4%	40.2%	9.8	78.9%	73.1%	−5.8	86.2%	84.6%	−1.7
Clerical support workers	40.0%	47.2%	7.3	25.9%	25.2%	−0.7	44.9%	45.2%	0.3	62.7%	65.5%	2.8	69.8%	75.4%	5.6
Service and sales workers	32.3%	40.1%	7.8	27.8%	30.2%	2.4	44.5%	44.7%	0.2	48.6%	41.6%	−7.0	63.4%	63.3%	−0.1
Craft and related trades workers	51.6%	69.4%	17.8	30.7%	35.8%	5.1	49.2%	60.3%	11.1	75.1%	53.9%	−21.2	75.1%	68.9%	−6.2
Plant and machine operators, and assemblers	55.1%	72.6%	17.5	26.1%	46.1%	20.0	49.1%	59.4%	10.2	48.5%	38.2%	−10.3	53.2%	52.9%	−0.3
Elementary occupations	51.7%	55.6%	3.9	28.9%	37.8%	8.9	51.2%	55.2%	4.0	36.5%	26.5%	−10.0	48.1%	30.2%	−18.0

Sources: European Working Conditions Survey (Eurofound), own calculations.

However, the static analysis in Table 1 cannot capture possible changes in task content over time. It is likely that the structural changes identified in the previous section will result in an upgrading of tasks performed by women. That would be consistent with evidence from Germany and the US showing that, while women tend to work in jobs with a higher intensity of routine-cognitive tasks, they exhibit a faster growth in the share of non-routine, analytic and inter-personal tasks (Autor and Price, 2013; Black and Spitz-Oener, 2010). The current division of tasks thus renders women more vulnerable to automation, but trends point to a narrowing of the gender gap through upgrading.

Forms of employment

Along with the changes in the structure of employment and the task content of jobs, an important aspect of work subject to change pressure due to digitalisation is the employment relationship itself. The decline in the standard employment relationship (a statistical norm and point of reference in much of employment regulation) has been underway for decades, paralleled by a rising precariousness of work. Digitalisation has accentuated these processes, bringing about the increasing fragmentation of the employment relationship (Rubery, 2015). With outsourcing, offshoring and the use of online platforms as an intermediary between worker and employer, the traditional employment relationship is shifting towards a complex and multi-faceted network of relations between ‘independent contractors’, clients and intermediaries (Bergvall-Kåreborn and Howcroft, 2014). Not only are ‘jobs for life’ disappearing, but also work for a single employer is being substituted by ‘portfolio careers’.

Despite the gradual dismantling of standard employment with its deeply gendered version of labour protection, the rising precariousness of employment is traditionally also highly gendered (e.g. Rubery, 2011; Vosko, 2000). The gender bias has proven very resilient in spite of the dramatic transformation of employment systems over the last few decades, amplified in the aftermath of the 2008 crisis by the recession and austerity policies with an unequal gender impact (see e.g. Karamessini and Rubery, 2013). Although available empirical evidence remains inconclusive as to the overall gender balance among workers in the platform economy (e.g. Berg, 2016; Huws et al., 2016; Ipeirotis, 2010),³ the recent increase in the fragmentation of work appears to affect women more than men. One measure of this development is the holding of multiple jobs, especially if this involves juggling self-employment without employees (hereafter solo self-employment) with other forms of employment, or multi-employer work as an own-account worker. Such a situation is probably the closest reflection of work in multiple ‘gigs’ in the digital economy and through crowdsourcing platforms, even if it admittedly also includes traditional jobs not related to the use of new technology. The solo self-employment is among the most precarious forms of work, particularly among women, as it is associated with low income, inadequate, if any, benefits, lack of representation and high job insecurity (Wall, 2015).

As illustrated in Figure 3, holding multiple jobs was traditionally more frequent among men. But over the period 2002–2015, a much steeper increase was noted among women. As a result, the gender gap considerably narrowed, with 4.42 million men and 4.28 million women having more than one job in the EU in 2015. This increase in multiple jobs was particularly visible among employees who were solo self-employed in their second job. This category increased by 45 per cent over the analysed period among women, while it remained stable among men. Similarly, an increasing number of women whose primary employment status was solo self-employment reported having more than one job. Among women, the group juggling more than one own-account work increased by 72 per cent between 2002 and 2015, with a visible acceleration of this trend between 2010 and 2015. Though this group was still bigger among men, the overall increase here was much slower (19 per cent between 2002 and 2015). Fragmentation of careers is thus more intense among women, and they are ‘catching up’ with men in particular in juggling fragmented ‘gigs’, i.e. multiple self-employed jobs.

Figure 3.

Working in more than one job, by gender and employment status in main and other paid job(s) (in thousands), EU-28, 2002–2015.

Moreover, fragmentation of work among women increasingly affects high-skilled professionals. Between 2002 and 2015, the incidence of women holding multiple jobs increased most among professionals, technicians and associate professionals, and to a smaller degree also among clerks and manual high-skilled workers (Figure 4). The expansion among professionals becomes even more remarkable considering the overall growth of this occupational group (Figure 5). Thus, between 2002 and 2015, the number of professionals, technicians and associate professionals having more than one job increased by 456,600 among men and by a striking 704,400 among women in the EU-28. While this is consistent with the findings that workers engaged in new forms of work linked to digitalisation are better skilled and better educated than the average worker in a respective country (Codagnone et al., 2016), the increase of multiple job holders among a more task-routine job category of service and sales workers provides some evidence of increasingly fragmented careers among groups more at risk of being negatively affected by digitalisation.

Figure 4.

Employed persons having a second job by gender and occupation in first job (as a % of occupational category), EU-28, 2002–2015.

Figure 5.

Employed persons having a second job by gender and occupation in first job (in thousands), EU-28, 2002–2015.

One consequence of work fragmentation is the growing risk of deepening gender-related workforce segmentation because of differences in women’s position, relative to men, in the occupational structure, the family and welfare policy, all of which render them more vulnerable to market pressures (Rubery, 2013). Fragmentation adds to the constant competitive pressures exerted on workers by employing organisations, with competition for work no longer subject to any geographical constraints. Workers compete with a growing precarious workforce, for various reasons compelled to take up low paying and unstable jobs (Graham et al., 2017). Without regulations setting minimum employment standards, such a situation leads to deteriorating working conditions and increased segmentation (Cappelli et al., 1997; Rubery and Piasna, 2016). While it can be argued that such downward pressure on labour standards will apply across the entire workforce engaged in new forms of work, gender differences are likely to be reproduced because of women’s more vulnerable position vis-à-vis employers (Rubery, 2007; Vosko et al., 2009). This means that the organisation of social reproduction in a society is key to understanding the gendering of precarious employment and the constraints forcing women to enter such forms of work (O’Reilly and Fagan, 1998; O’Reilly and Spee, 1998; Rubery and Fagan, 1995).

Nevertheless, non-standard forms of employment are often expected to benefit women more than men and thus contribute to levelling gender inequality in the labour market. Such expectations, however, acknowledge an unequal position of women in the labour force and in households, but fail to question it. For instance, women, especially those with care obligations, are believed to benefit from working fragmented gigs, insofar as such flexible work offers the possibility of combining it with unpaid work and opens up opportunities to those weakly attached to the labour market to find paid work (Eurofound, 2015; Eurofound and the International Labour Office, 2017). Nevertheless, the predictability and inflexibility of care provision (see e.g. Golden, 2005) is hardly compatible with precariousness in employment and unsteady workflow. Indeed, in the EU-28, women more often than men reported that they worked in temporary jobs because they could not find a permanent position (63.6 per cent of women and 61.6 per cent of men, Eurostat, data for 2015). Short hours of work also do not seem to fit well with women’s preferences and needs as in 2015, 25.7 per cent of women working part-time reported they would rather work in full-time jobs, up from 23.6 per cent in 2008 (in EU-28, Eurostat).

An important risk posed by the new forms of work in the digital economy is that for the most part they are not covered by traditional labour and social protections. In some cases, such protections are considered impractical or even unnecessary, based on the assumption that work offered through platforms might indeed be precarious, but not the workers who perform it as they rely on other sources of income or because they have a preference for high job flexibility (see discussion in Campbell and Price, 2016). However questionable in the first place, such claims also find little support in the existing empirical evidence. For instance, a recent survey of adults working via online platforms in the UK revealed that as many as 81 per cent are main breadwinners in their households and, for nearly one in three, platform work constitutes the main source of income (Huws and Joyce, 2016). Accordingly, the most often reported reason for engaging in such work is a need to earn money, along with a necessity to work from home due to care obligations (Berg, 2016).

Social protection systems are rarely adapted to non-linear and unstable career patterns, as experienced by women due to their unequal share of unpaid care work (Leschke and Jepsen, 2011). Insecure, fragmented and often informal forms of work, such as those the digital economy is creating, can be expected further to worsen access to social protection, as well as undermine its fiscal support and endanger the sustainability of social protection systems. This will have particularly negative consequences for women due to their greater reliance on social protection across the life course. Women are also in a more precarious position when they become unemployed, exerting additional pressure to accept any work such as low wage and unstable jobs offered through platforms.

Similarly, a lack of collective representation and institutionalised wage setting in new forms of work can be expected to affect negatively women’s earnings, as they were found to be less likely to bargain for pay on an individual basis (Graham et al., 2017; Rubery, 2011). This creates a vicious cycle of growing disadvantage and labour market segmentation.

Finally, the employer becomes ‘invisible’ to the worker when the contact between the two parties can be mediated by the Internet platform or suppliers (Bergvall-Kåreborn and Howcroft, 2014; Graham et al., 2017; Marchington et al., 2005). This could potentially lead to less gender-based discrimination, as the Internet affords anonymity and employers using algorithms to select workers can be expected to hire rationally on the basis of information on skills or past performance. However, research on online labour markets revealed that gender stereotypes play a role in the observed discrimination in hiring decisions regarding types of work and contracts for women (Silberzahn et al., 2014; Uhlmann and Silberzahn, 2014). Moreover, the requirement of constant availability and instantaneous responsiveness can discriminate against workers who juggle online work with other activities, most notably care (i.e. mainly women). Rejecting work might have a major detrimental effect on employment opportunities on a particular platform, for instance leading to the termination or suspension of the accounts of workers who rejected assignments (Berg, 2016; De Stefano, 2016).

Little in terms of a positive impact of digitalisation on gender equality can thus be expected when taking into account the increasing precariousness of new forms of work, characterised by more employer-led flexibility and atypical employment (Wajcman, 2004).

Work organisation

In addition to the transformation of forms of employment, recent technology-enabled innovations, including the widespread use of ICT, emails or outsourcing, are linked to changes in the organisation of work (Drahokoupil, 2015; Huws, 2013). There has been an erosion of formal rules governing work, including those related to place and time, and the boundary between work and non-work activities is now in a state of dissolution. These changes have important implications for gender equality, as women’s position in the labour market is to a great extent shaped by their dual role as workers and carers – a role that puts constraints on their labour market availability both in terms of time and place.

Understanding why gender inequalities persist in the labour market allows us to formulate expectations as to the impact of digitalisation and technological change on women’s position in employment. One important issue is the gendered division of housework and care activities, which are unpaid and undervalued, but also never completed (Wajcman, 2008). Women remain responsible for the bulk of unpaid work (Eurofound, 2016a), constraining the extent and timing of their availability for paid work. As a result, men’s and women’s work is valued differently by employers, and the greater availability of men to work longer hours and overtime increases their propensity to receive higher wages. Thus, the limited time availability of women for paid work negatively affects their bargaining position vis-à-vis employers (Huws, 2012). The requirement of constant availability has not been eased by digitalisation; quite the contrary. As shown by Berg (2016), digital platform workers spend long hours waiting and looking for work, and once they find a task they generally must be available to execute it straightaway.

Among the new occupations booming in the digital economy, IT work generates great hope, not only in relation to its growth but also to flexibility and autonomy for self-managed workers (Baldry et al., 2007). However, in Europe, as in the US, women are significantly underrepresented among IT experts in knowledge-intensive services, in contrast to their representation among highly skilled professionals in general (Legault and Chasserio, 2012). At the policy level, the issue has mainly been viewed from the labour supply side, with a lack of adequately trained women and their low share among Science, Technology, Engineering and Math (STEM) students seen as a key driver of segregation (European Commission, 2015). The demand side is hardly mentioned. And yet, the huge dropout rate of women in these professions along the career path suggests that organisation of work deters women from pursuing careers in science and technology jobs (Valenduc, 2011). Existing studies point to problems such as long hours of unplanned and unpaid overtime, tight control by management and high job insecurity (Bergvall-Kåreborn and Howcroft, 2013; Holtgrewe, 2014; Legault and Chasserio, 2012). Moreover, the unavailability of part-time work to accommodate family commitments and restrictions on the use of maternity leave constitute major forms of discrimination against women (Hunter, 2006). Thus, pursuing a career in high-tech occupations poses an additional challenge for women who not only have to obtain suitable training but also deal with post-educational labour market forces that men are not exposed to. Not surprisingly, education plays a much smaller role in occupational matching in science and technology jobs for women than for men (Srinivas, 2011). This leaves little hope that further technological progress and upskilling alone will have a positive impact on gender equality in such male-dominated workplaces and work cultures. Instead, in line with the postulates of segmentation theory, the role of practice at the workplace and organisational level should be recognised as perpetuating gender-related labour market segmentation.

Another major change in work organisation involves the blurring of the concept of a traditional workplace, with workers able to perform their work anywhere as long as they have access to a computer with an Internet connection. Some expect a liberating and democratising effect of such work arrangements on gender relations and a way to increase women’s labour market participation. Additionally, home-based work is approached as a work-life balance-enhancing solution, benefiting women in particular (Eurofound and the International Labour Office, 2017). In a similar vein, the EU-level strategy for gender equality emphasises the promotion of female entrepreneurship (European Commission, 2015), despite a recognition by the European Parliament that ‘among the various occupational categories, the self-employed and businesswomen in particular are having great difficulty in achieving a work-life balance’ (2016: 8).

Here again, we find little support for the positive expectations attached to home-based work in available empirical evidence. Looking first at the self-employed, home-based workers were found to work irregular hours that erode work-home boundaries – an effect that persists for workers at all skill levels (Baines and Gelder, 2003; Gold and Mustafa, 2013). Insecurity of work with uncertain workflows and a need to react promptly to clients’ requests, all features of contemporary platform work, further intensify the spillover of work into family life. This, if anything, is only aggravated by mobile communication technologies and online work that allow for, or more often require, perpetual contact (Berg, 2016). Turning to teleworking women, studies show that time saved on commuting to work tends to be allocated to caring or housework, with the traditional gendered division of household labour reproduced rather than challenged by new ways of working (Hilbrecht et al., 2008). Moreover, as argued by Wajcman (2008), a tendency to perform more tasks simultaneously within a given period of time or multi-tasking differs by gender. Accordingly, there are gender differences in the quality of leisure time, with men tending to enjoy more uninterrupted spells of leisure activities, while women’s time is more fragmented and accompanied by a second activity, often a combination of leisure and unpaid (care) work. This might imply gender-specific implications of online home-based labour, with a higher risk of time squeeze and a negative work-life spillover for women.

Conclusions

Technological change, interacting with other factors such as population ageing, has brought about profound changes in the labour market, but the traditional gender inequalities continue to reassert themselves in the new world of work. To begin with, there is a considerable degree of continuity in gender employment segregation in the EU, with female-dominated segments, such as health care and lower-level services, constituting the main job creation drivers for women. However, there are some signs of change, with men entering female-dominated categories at the lower end of job hierarchy, and jobs for women being created in the skill-intensive job categories. The latter suggests that the trends may lead to a more equal form of female labour market integration. On the other hand, the analysis of the task content of jobs reveals that women are more at risk of automation as they tend to perform routine tasks more often than men, even within the same occupational category.

Furthermore, a gender-sensitive analysis of the forms of female labour market participation reveals that an apparent advance of women on the labour market may in fact go hand in hand with a reproduction, or even worsening, of gender inequalities both in the workplace and in the household. For instance, the requirement of constant availability and instantaneous responsiveness discriminates against workers who juggle online work with other activities, most notably care. Flexible work practices, such as working from home, may boost the unequal division of unpaid work, as well as further weakening women’s bargaining position vis-à-vis employers. Thus, increased work flexibility and fragmentation is expected to worsen further the position of women in employment and to exacerbate – rather than alleviate – existing inequalities.

Gender relations in new forms of work and employment interact with the old inequalities in the workplace linked to gender discrimination and the unequal division of caring responsibilities and housework (see Cranford et al., 2003). As long as technological change leaves social relations of gender unchanged, a continuity and reproduction of gender inequalities is to be expected. Public policies addressing the underlying sources of gender discrimination, such as the availability of affordable child care, support for equal participation in care activities, or working time regulations promoting work-life balance, are thus a necessary prerequisite for harnessing the positive potential of the new world of work. Flexible forms of work may be an opportunity for combining work and care responsibilities, but a strong legal framework is needed to guarantee that such work offers good working conditions and pay. Moreover, existing policies promoting equal treatment at the workplace (anti-discrimination legislation) and allowing for combining work and caring responsibilities (working time regulations) are linked to standard and protected forms of employment. With the declining significance of standard employment, the challenge is to include new forms of work in effective labour protection, promoting the equal access of women and men to quality jobs and their equal treatment at work.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

Appendix

Table A1.

Change in employment and gender composition by detailed occupation, EU-28, 2011–2015.

ISCO 08 1-digit	ISCO 08 2-digit	Female (%)	Female, change in p.p. (%)	Employment change in 000s
ISCO 08 1-digit	ISCO 08 2-digit	2011	2011–15	Men 2011–2015	Women 2011–2015
1	Chief executives, senior officials and legislators	25.0	1.0	−128.2	−22.0
	Administrative and commercial managers	38.4	0.8	−337.6	−166.0
	Production and specialised services managers	28.1	−0.9	594.8	169.8
	Hospitality, retail and other services managers	37.3	0.5	−121.4	−43.5
2	Science and engineering professionals	23.5	3.0	−0.1	265.3
	Health professionals	68.0	1.4	46.1	380.0
	Teaching professionals	70.5	0.1	13.0	49.7
	Business and administration professionals	47.2	1.8	642.8	851.0
	Information and communications technology professionals	15.6	0.2	427.4	87.6
	Legal, social and cultural professionals	53.8	2.3	54.5	356.2
3	Science and engineering associate professionals	15.6	0.7	−108.0	43.9
	Health associate professionals	79.2	−0.2	153.0	515.0
	Business and administration associate professionals	55.9	−0.2	49.1	−12.5
	Legal, social, cultural and related associate professionals	60.7	1.1	353.0	675.2
	Information and communications technicians	17.8	−0.9	143.2	11.8
4	General and keyboard clerks	79.0	1.5	−207.2	−295.8
	Customer services clerks	71.0	−0.8	114.2	149.9
	Numerical and material recording clerks	52.4	2.7	343.2	791.2
	Other clerical support workers	66.3	−4.0	−222.6	−740.0
5	Personal service workers	60.6	−2.1	399.9	74.4
	Sales workers	66.6	−0.1	119.2	179.8
	Personal care workers	89.1	−0.5	7.8	−255.0
	Protective services workers	14.0	0.9	−47.0	28.6
7	Building and related trades workers	2.0	0.5	−550.2	35.0
	Metal, machinery and related trades workers	3.8	0.2	−151.5	15.0
	Handicraft and printing workers	29.7	0.5	−16.5	2.5
	Electrical and electronic trades workers	3.4	0.2	208.4	14.9
	Food processing, wood working, garment and other craft and related trades workers	38.2	−0.2	−4.2	−18.7
8	Stationary plant and machine operators	31.9	1.4	−19.3	102.3
	Assemblers	40.9	−3.7	142.0	−12.8
	Drivers and mobile plant operators	4.3	0.0	−133.1	−7.2
9	Cleaners and helpers	84.8	−0.3	74.7	259.6
	Agricultural, forestry and fishery labourers	37.0	−5.0	31.4	−116.8
	Labourers in mining, construction, manufacturing and transport	27.2	−0.1	−282.3	−112.0
	Food preparation assistants	71.7	−3.5	136.1	130.3
	Street and related sales and service workers	28.2	0.3	9.3	4.7
	Refuse workers and other elementary workers	33.9	−1.3	196.1	53.5

Sources: Labour Force Survey (Eurostat), own calculations.

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