Employment impact of digitalisation

This research digest does not aim to give a comprehensive overview of the scale and scope of the employment impact of digitalisation in Europe. Rather, it gives an illustrative indication of the nature of its impact and examples of, for instance, occupations or professions that are likely to be affected by increasing or decreasing demand, as well as indicating the associated skills needs. Although the distinction is not always straightforward, this research digest focuses on the macro aspects of employment rather than the micro aspects (for example, related to work organisation, working conditions or job quality).

A full list of references used to compile this research digest can be found at the end of the page. 

Author: Irene Mandl

 

Overview

 


 

 

Policy pointers

  • Digitalisation requires a proactive approach relying on the anticipation of change and the delivery of skills needed for workers and businesses to succeed in the digital age.

  • Safety nets for those workers affected by digitally driven job loss need to be in place. This includes the need to review social protection and welfare systems.

  • As some digitisation technologies and platform work result in decreased contractual stability, policymakers should monitor employment quality and, if necessary, review rights and obligations linked to the different types of employment relationships/employment statuses.

 


Digitalisation: General and comparative perspectives

Introduction

The employment impact of digitalisation can have different dimensions. From an employment-level perspective, job loss related to automation when machines replace human input is widely discussed. At the same time, job creation is triggered by the emergence of new occupational profiles tailored to the exploitation of the new technologies, as well as by increasing demand for technology-based products and services due to lower prices or new markets, client groups or areas of demand. Lower prices in the context of digitalisation can increase the real income of the population. This, when spent on any products or services (including those unrelated to technology), can lead to job creation elsewhere in the economy. There can be substantial variations in terms of whether job loss or job creation dominates, whether numerical employment developments affect the same countries, regions, sectors, occupations and demographic groups, and whether they take place in parallel or at different points in time, depending on the specific technologies (or combination of them), their operational deployment in the economy and public policies.

While the European Company Survey (ECS) 2019 cannot establish causality as regards the digitalisation intensity and employment levels in European establishments with at least 10 employees, it found that 40–43% of ‘highly digitalised’ establishments and establishments with limited computer use but high use of robots and other digital technology increased their staff numbers in the three years preceding the survey, compared with only 30–34% of establishments with limited digitalisation or with high computer use but limited use of other digital technology (Figure 1). However, a higher share of establishments with limited digitalisation maintained their staff numbers, while the shares of those reducing staff were almost the same across the different establishment types.

Figure 1: Change in number of employees since 2016 by digitalisation intensity of establishments, EU27 and the UK, 2019 (%)

Similarly, a higher share of ‘highly digitalised’ establishments planned to increase their employment numbers in the next three years (42%) than of ‘limited digitalisation’ establishments (22%) (Figure 2). In keeping with the pattern from 2016 to 2019, the latter are more likely to report an expectation of stable employment prospects (71% versus 52%), and there is little difference between the establishment types as regards job loss.

Figure 2: Expected change in the number of employees in the next three years by digitalisation intensity of establishments, EU27 and the UK, 2019 (%)

Note: See Figure 1 notes.

Digitalisation affects not only whole jobs (understood as bundles of tasks) but also the composition of jobs, by altering the task profiles within them. This means, for example, that some tasks within an existing occupational profile disappear (as they are automated or no longer needed in a digital workplace), some are altered (for example, manual/physical work processes are digitised) and new ones are created (such as analysis of the data created through the deployment of technology). Changes in task profile can range from marginal to substantial. In the latter case, the changes might be considered to result in the emergence of a new occupational profile and the disappearance of the previous one. This is important to bear in mind, as it may lead to a situation in which the workers holding the previous jobs are unable to perform the new tasks. This highlights the blurring of boundaries at the employment level (job loss in one area and job creation in another) mentioned above.

Another element to consider is employment quality. In general, standard employment – that is, employment under a permanent full-time contract subject to labour law – is considered to be of better quality for workers than non-standard employment (fixed-term, part-time or self-employment). Some caution is required in making such general assessments, as some workers consciously and intentionally opt for non-standard employment, as it better suits their personal requirements or preferences. For example, they might opt for part-time work for a better work–life balance or to better align work with other responsibilities such as caring or education, or they might opt for self-employment because they want to benefit from greater flexibility and autonomy.

Data from the ECS 2019 show that around 80% of highly digitalised establishments offer permanent contracts to 80% or more of their employees, compared with around 70% of establishments with limited digitalisation (Figure 3).

Figure 3: Shares of employees with permanent contracts by digitalisation intensity of establishments, EU27 and the UK, 2019 (%)

Note: See Figure 1 notes.

Digitalisation can also affect wage levels in the labour market. Increases in aggregate demand and productivity due to technology adoption can lead to an increase in wages. At the same time, increased productivity and job loss can result in an overall decrease in the national, regional, sectoral or occupational wage level. Whichever of the two pathways materialises depends on how productivity gains are distributed between capital and labour, and on the labour market (supply of and demand for labour). As regards the latter, and in relation to skills, the European Centre for the Development of Vocational Training (Cedefop) has found that workers in jobs with advanced information and communications technology (ICT) skills needs benefit from an hourly earnings premium of about 3.7% compared with those in jobs with basic ICT skills needs. The hourly wages of those employed in jobs that do not require any ICT skills are about 8% lower.

Digitalisation also affects the demand for and supply of skills. New skills are needed for newly emerging jobs or tasks, whereas other skills will become less relevant in the labour market as certain jobs and tasks experience decreased demand. Cedefop has found, for example, that more than 70% of employees in the EU need at least basic ICT skills to satisfy job demands, but about 30% of them are at risk of falling into the digital skills gap. High demand for advanced digital skills is expected in the future, although little information is available about the precise types of ICT skills that will be required.

Skills needs in the digital age relate not only to digital skills (such as being able to handle ICT or conduct data analytics) but also to occupational skills (for example, driving skills will be less in demand if autonomous vehicles become more widespread) and transversal skills (such as creativity, decision-making and social skills such as communication), with requirements driven by the digital transformation of job profiles and work organisation.

Opportunities

  • Increased labour market opportunities for highly qualified workers
  • Good working conditions for highly qualified workers

Digitalisation is expected to lead to positive employment growth in occupational profiles associated with products or services benefiting from increased demand due to the adoption of new technologies, and in those affected by changed production/service provision processes (for example, reshoring of activities previously offshored from Europe to locations with lower production costs). In manufacturing, this is widely discussed in relation to higher-skilled profiles; it is likely to result in skills upgrading in manufacturing employment. Enhanced labour market opportunities are anticipated for:

  • traditional engineering profiles, such as engineers and quality control staff
  • newer profiles, such as industrial data scientists, big data statisticians and data security analysts
  • workers with a multidisciplinary skill set, such as managers with advanced data analysis/statistical competencies
  • workers with advanced management capabilities and soft skills

In addition to resulting in such job creation, it is also expected that the increased adoption of advanced technologies will make highly qualified workers more productive. Owing to increased demand for them in the labour market, it is likely that these employees will benefit from good employment and job security, good working conditions (including remuneration) and interesting work content.

Risks

  • Job loss for low-skilled routine workers
  • Limited capabilities of workers and the education system to adapt to changing skills needs
  • Labour shortages in science, technology, engineering and mathematics (STEM) and as regards workers with multidisciplinary skill sets
  • Increase in involuntary atypical employment

Job loss for workers in low-skilled routine work in manufacturing, such as blue-collar production work and basic clerical work, as observed during recent decades in the EU, will further increase, notably as a result of the potential for automating a large number of the tasks inherent in such jobs. The strongest impact is expected in those sectors most affected by technology adoption, such as automotive, machinery and consumer goods manufacturing. A similar trend is likely to be seen in some services sectors (for example, online retail or banking activities), as a result of the increased use of a combination of automation and digitisation technologies.

Although new labour market opportunities are emerging at the same time, offering alternatives for those workers losing their jobs, the question arises as to whether workers are well equipped to master an occupational change and whether educational curricula are prepared to provide the required skills on time.

The increasing demand for higher-skilled and specialised staff, in combination with expected shortcomings in the education and training sector, poses the risk of increased labour shortages, for example as regards graduates in STEM or workers with an advanced/multidisciplinary skill set.

As digitalisation goes hand in hand with employment flexibilisation to at least some extent, it might contribute to an increase in involuntary atypical forms of employment, including less secure employment relationships (such as shorter fixed-term contracts or lower-hours part-time employment), subcontracting and outsourcing.

Concluding commentary

Digitalisation offers a wide range of labour market opportunities for higher-skilled workers and those capable of adapting to changed skills needs. Low-skilled routine workers, however, are at risk of losing their jobs and will need to master the transition to the digital age.

This is a challenge not only for the affected workers but also for employers – faced with skills shortages and the need to support workers in adapting to altered production and service provision processes – and institutions.

The education system needs to anticipate future skills needs and, jointly with the business sector, adapt curricula to provide the required content using the most suitable delivery mechanism, including by improving newer training methods, such as online learning or the use of augmented reality (AR) and virtual reality (VR).

Public authorities and social partners need to facilitate this process by assisting with future skills needs assessments, linking education systems and businesses, encouraging lifelong learning among the workforce, and providing financial and other support for training to workers and employers. Furthermore, as not all workers will be able to master the transition to the digital age, safety nets must be established to support those affected by digitally driven job loss. This might require a review and adaptation of social protection and welfare systems, including as regards their financial sustainability, given that digitalisation is not the only megatrend affecting the economy: demographic change also has an important role to play.

 


Automation

Automation is one of the ‘vectors of change’ identified as part of the broader notion of ‘digitalisation’ in Eurofound’s conceptual framework. It is the replacement of human input, in full or in part, by machine or software input. Advanced robotics, both for services and for manufacturing, is grouped with autonomous vehicles under the automation vector, since the ultimate aim of their application is to substitute machine for human input.

Introduction

For years automation has been widely discussed in relation to its impact on job loss because of its very nature: technology replaces human input. Early skyrocketing estimates of ‘robots stealing our jobs’ have been corrected downwards, notably because of increasing recognition that the potential for fully automating jobs is more limited than that for automating individual tasks within jobs. Accordingly, although automation is likely to alter task profiles within jobs, its impact on job loss will be less severe than initially forecast. In mid-2021, estimates of job loss likely to be caused by automation ranged from 10% to 15%.

In general, the employment impact of automation is stronger in manufacturing than in services because of its higher share of routine tasks and less reliance on tasks such as communication and customer service. Although automation technologies such as robots are already a common feature in manufacturing, they are only beginning to become more widespread in services because of the less structured work environment and less repetitive work content. That said, in some services sectors such as logistics and transport, the employment impact of automation is already visible, and it is starting to emerge in others, such as banking and financial services.

Cedefop has found the highest shares of employees in the EU27 with high automation risk among subsistence farm workers, machine and plant operators, assemblers, handicraft and printing workers, construction workers and other manufacturing workers (all > 15%). In contrast, the lowest shares (< 5%) are among street services workers, managers and care workers (Figure 4).

Figure 4: Shares of employees with high automation risk by occupation, EU27, 2020 (%)

 

Source: Cedefop European Skills and Jobs Survey/Skills Panorama

Overall, automation leads to the suppression or transformation of jobs characterised by a high share of routine tasks, which mainly – but not exclusively – affects low-skilled workers. In manufacturing, occupations such as production line operators and supervisors, production or specialised services managers, and forklift operators are expected to face substantial changes that will require upskilling or reskilling of the affected workers. Other jobs dominated by manual but less routine tasks, such as some machine operators and specialised assemblers, will become more complex, again suggesting the need for continuous skills development. Similarly, in some services sectors, automation is expected to complement rather than replace human labour. Examples are medical and care professions and emergency and rescue workers.

New employment opportunities and skills needs will arise in relation to the deployment and maintenance of automation technologies. Examples are robot developers, robotic systems integrators, and specialists in programming and robot maintenance.

Particularly in smaller companies, automation is expected to modify the job profiles of roles such as chief executives, business administrators and human resources managers. These types of staff have to learn how to manage the integration of automation technologies into the workplace and to exploit the opportunities and mitigate the challenges related to changes in tasks and processes.

Opportunities

  • Automation as a mitigation strategy to tackle labour shortages
  • Emergence of new – highly skilled – occupations
  • Further increase in job creation in the highly skilled segment of the labour market due to the combination of automation and artificial intelligence (AI)
  • Indirect job creation effects on other industries, such as insurance

Replacing human resources with machines can be an attractive approach to tackling labour shortages. An example of this is the shortages among professional drivers (mainly truck drivers) in some EU countries, such as Germany, which could be partly mitigated through autonomous vehicles.

Automation will give rise to new occupations and increased demand for workers in existing occupations, such as robot developers, integrators and maintainers, autonomous transport planners, analysts, and fleet and supply chain managers. This is expected to provide additional job opportunities for higher-skilled workers, as well as improved employment conditions – such as greater contractual stability and better working conditions – as workers will have more bargaining power.

In combination with AI, automation is expected to result in the creation of four types of jobs, all of which require skills related to dexterity, creativity, social interaction, intelligence and general knowledge of automated operations:

  • engaging with automation technologies: jobs in which automation and AI complement the work of humans, such as those of healthcare professionals
  • developing automation technologies: jobs that involve developing AI and advanced robotics, such as those of software developers, data analysts involved in the collection and curation of data to train AI applications, and sociologists involved in assessing the social and ethical implications of the deployment of automation and AI
  • supervising automation technologies: jobs related to the monitoring, licensing and testing of AI and automation technologies
  • responding to paradigm shifts: jobs related to reshaping work, life and social structures, such as cybersecurity jobs or those of town planners tasked with drafting strategies for autonomous vehicles

There are also likely to be indirect job creation and enrichment opportunities in other sectors. An example is the insurance industry, in which clerical staff will be required to familiarise themselves with automation technologies and related workflows, in order to be able to draft insurance policies that adequately capture inherent risks and liabilities. Similarly, the data generated through sensors and cameras in autonomous vehicles may have a positive employment impact in the telecommunications sector.

Risks

  • Decline in jobs with a high level of routine tasks
  • Limited adaptability of some workers affected by automation
  • Decrease in contractual stability

Automation brings a realistic risk of a decline in jobs with a high level of routine task content. This is likely to have a more severe effect on manufacturing than on services sectors, on blue-collar workers than on white-collar workers and on low-skilled workers than on highly skilled workers.

Although some occupations will be transformed rather than disappear, the question arises as to whether the job holders will be able to manage the transition to the new job profile given the likelihood of the need for skills upgrading. An often-cited example is that of professional drivers who might be subject to being replaced by autonomous vehicles while experiencing job enrichment through enhancement of other tasks, such as loading, monitoring or customer relationship management. As these tasks require a substantially different skill set from driving, it remains to be seen whether the emerging job profile will be covered by the current drivers.

Furthermore, the fragmentation of jobs into tasks driven by automation can result in a decrease in contractual stability and an increase in atypical employment if assignments become shorter. This, in turn, can negatively affect career prospects of workers, as employers might be less inclined to invest in skills development of staff with whom they have less stable employment relationships.

Concluding commentary

Automation can be used strategically to tackle labour shortages and foster skills upgrading in the European labour market. Newly emerging job opportunities, either directly or indirectly caused by automation, should be anticipated, and public and private sector partnerships should be leveraged to stimulate job creation related to automation (and its combination with other technologies, such as AI). Employers and workers need to be equipped with the required skills to realise the emerging employment opportunities. This relates not only to digital skills but also to transversal and soft skills, including managerial skills.

Workers facing the risk of losing their jobs or having them substantially transformed because of automation need to be supported in re- and upskilling. Governments and social partners need to continue their efforts to raise awareness and create a culture of lifelong learning among companies, workers and society, and support affected businesses and individuals through advice, matching them with adequate training offers, and financial contributions to education and training.

Policy also needs to address the potential decrease in employment stability, reduced career development, and threat of unemployment and inactivity.

 


Digitisation

Digitisation is one of the ‘vectors of change’ forming part of ‘digitalisation’ in Eurofound’s conceptual framework. It refers to the process through which aspects of the physical world are rendered into data and virtual models, and vice versa. Three main technologies fall under this vector of change: 3D printing; Augmented Reality (AR)/Virtual Reality (VR); and the internet of things (IoT).

Introduction

Owing to their nature – bringing what were previously physical processes into the digital sphere – digitisation technologies require a workforce that is versatile in managing and analysing vast amounts of data and able to design suitable production/service provision and quality assurance systems. This holds particularly true when different digitisation technologies are combined, such as 3D printing and IoT applications.

As regards 3D printing, experts do not share a common expectation concerning its potential quantitative employment impact. Although some assume that it will reduce overall labour demand, others believe that it will ‘only’ decrease demand for manual tasks, such as assembly. Accordingly, opinions also differ on whether specific skills needs driven by 3D printing (such as machinery operation, or process, material or chemical engineering) will be relevant only for machine operators (who may need to adapt to new techniques such as part finishing and machine calibration, or recording and maintaining data) and managers (who may have to coordinate cross-functional teams in a spirit of change and innovation), or for a wider range of staff.

The changing demand for skills will also affect designers, who will have to focus more on function than on manufacturability. This means that designers will also have to be very familiar with the possibilities and limitations of 3D printing compared with traditional manufacturing.

There is limited evidence on the impact of wearable devices and AR/VR on employment. One of the reasons for this is that they are typically used to complement human capabilities rather than replace them. Accordingly, the tasks of workers engaged with wearable devices are likely to change, as production/service provision processes will be reorganised to make them more efficient and cheaper.

IoT will raise the level of importance of at least basic information technology (IT) skills among all staff, and social and intellectual skills (notably communication, cooperation, problem-solving, decision-making and creativity) will become more relevant in the manufacturing sector.

Opportunities

  • Job creation for technology experts and data analysts
  • New occupational profiles related to 3D printing, wearables and the internet of things (IoT)

Digitisation technologies trigger job creation for technology experts, software developers and instructors; content and design experts tasked with virtual environment design; and job profiles in which data analysis is required, including data scientists, statisticians, machine learning engineers and AI experts. Up-to-date skills related to big data, privacy and data security will be in demand.

As a result of the growing use of 3D printing, new occupational profiles are emerging – for example, related to machine loading and machine cleaning. In connection with wearable devices, there is expected to be a growing need for staff who can manage and maintain the devices as well as for dedicated data managers. The implementation of IoT will result in increased demand for IT managers and infrastructure specialists.

Risks

  • Decrease in contractual stability
  • Disappearance of some occupational profiles in the services sector in the event of increased spread of augmented reality (AR)

Digitisation facilitates the shifting of production processes to the cloud. This, in turn, facilitates subcontracting/outsourcing of an increasing number of tasks in the (traditional) production process and might trigger global competition for jobs. As a result, the contractual stability, income and working hours of workers might be negatively affected.

If operational deployment spreads, AR devices could replace some occupational profiles, such as tourist guides or service personnel in retail.

Concluding commentary

Overall, relatively little is known about the potential employment impact of digitisation technologies. Accordingly, further research and the establishment of dedicated tools to monitor developments related to digitisation in the labour market would be helpful for informed policymaking. Given the virtual nature of digitisation, and hence its potential for cross-border applications and impacts, such a mechanism might be most effective at supranational level.

Owing to the transfer of established work processes to the digital sphere, digitisation technologies are expected to give rise to labour market opportunities for newly emerging job profiles. This is likely to be particularly related to the design and maintenance of digitised processes as well as anything related to data handling, analysis and quality control. Policy could (further) support businesses in their digital transformation, including in the identification of suitable external staff and the re- and upskilling of incumbent staff. In educating and training ‘digitisation staff’, emphasis should be not only on their data analytics skills, but also on issues such as data ownership and privacy and cybersecurity.

Digitisation technologies do not necessarily automate pre-existing production/service processes or value chains. Consequently, the risk of workers losing their jobs appears to be relatively low. That said, in some sectors and occupations, discussions regarding job loss caused by digitisation have emerged. To avoid sudden larger-scale unemployment, policy should aim to find solutions early on and use the still small dimension of the labour market challenge as a testing ground for workers’ transitions.

Similarly, there are initial indications that digitisation might negatively affect employment quality. Contractual stability and working conditions might decrease because of enhanced subcontracting and outsourcing. Policy should ensure that employment and working conditions do not deteriorate, for example by reviewing and if necessary adapting employment rights and obligations linked to employment status.

 


Platforms

Platform work is a form of employment in which organisations or individuals use an online platform to access other organisations or individuals to solve problems or provide services in exchange for payment.

Introduction

Although there are workers who do platform work as their main job (estimates differ across countries, but most available research finds that 1–2% of the workforce or population do platform work as their main job), in mid-2021 the majority of platform workers engaged in this form of employment occasionally (about 10%) and conducted it alongside another activity (a job, caring, studying, etc.). A key feature of platform work is that tasks are assigned on an individual basis rather than bundled, as is common for jobs in the traditional economy. For these two reasons, platform work tends to be more fragmented than more traditional employment relationships, which makes it difficult to provide information on its numerical employment impact.

That said, there is common agreement among policy actors and research that platform work radically changes employment relationships. Traditional employment generally involves two parties – employer and employee or client and self-employed/freelance provider – whereas in platform work a third party – the platform – is involved. This is not a completely new phenomenon (for example, temporary work agencies or employee-sharing models also involve several parties), but digitalisation pushes it to a new level. The online character of the platform at least theoretically allows for a quick and easy global division of work, by mediating across a ‘virtual crowd’ of clients and workers. In addition, the algorithm on which the business model of platforms is based has high potential to disrupt traditional task assignment and management practices. However, there are considerable differences between the various types of platform work applied in Europe, and the employment impact very likely differs depending on the scale of the tasks and skill level they require, the format of service provision (on location versus online) and the mechanisms of the platform (for example, approach to task assignment, work organisation, monitoring and control, rating systems).

Platform work has been controversial almost since its emergence on the European labour market because of the working conditions with which some platform workers are confronted. This is mainly a result of the difficulties in framing the employment status of platform workers within existing labour law, in combination with a substantial imbalance of power between platforms and workers in the labour market.

As well as affecting employment quality, platform work is likely to have an impact on the skills available on the European labour market. Again, differences between types of platform work need to be taken into account, as higher-skilled tasks engaging workers for a longer assignment will have different effects than small-scale, low-skilled tasks.

Opportunities

  • Potential to provide workers, including women, people with disabilities, young people and migrant workers, with income generation opportunities
  • Greater opportunities to engage in self-employment activities

Platform work presents opportunities to access the labour market and generate income for some groups of workers, for whom this is more challenging in the traditional labour market because of personal characteristics (for example, skills and capabilities, health status, location) and the labour market situation (notably, demand for labour and quality of employment offered). This holds particularly true for small-scale, low-skilled tasks realised on location, as these are characterised by low entry barriers to platform work. For these types of tasks, there is also the theoretical potential that platform work could contribute to legalising undeclared work, as the data collected through the platform could serve to formalise the employment relationship. However, in mid-2021, there was a lack of strong evidence as regards such an employment impact.

For workers engaged in medium- to higher-skilled tasks for which the platforms limit their involvement to matching (rather than managing the task completion), platform work offers the opportunity to test their self-employment skills, such as self-organisation and self-management or dealing with clients. This can contribute to increasing entrepreneurial spirit and innovation in the economy.

Risks

  • Precarious employment, unfavourable working conditions and lack of social protection
  • Contribution to labour market segmentation
  • Unfair competition and social dumping
  • Limited opportunities for career and skills development; risk of deskilling in some types of platform work
  • Algorithmic task assignment, management and control resulting in worse employment and working conditions

Platform work is widely associated with precarious employment, unfavourable working conditions and a lack of social protection, which is particularly problematic in those types of platform work where it is likely that employment status will be misclassified if workers are considered self-employed. This is most prominently discussed in the context of small-scale, low-skilled tasks for which the platform not only mediates between supply and demand but also manages the performance of the tasks. In this case, it is questionable whether platform work acts as a stepping stone to standard employment or, rather, contributes to labour market segmentation.

The unclear regulatory situation as regards platform work also poses a risk of unfair competition and social dumping, since platforms may have an unfair competitive advantage over traditional economic activities based on fewer employment responsibilities.

Opportunities for career and skills development within the platform economy are limited. Hierarchical progression is almost non-existent. There is little room for workers to improve their capabilities, particularly those engaged in low-skilled tasks; on the contrary, as platform workers tend to be higher skilled, there is some inherent risk of deskilling if workers conduct these low-skilled tasks over a long period of time. Furthermore, higher-skilled tasks result in limited upskilling, as workers tend to opt for tasks for which they already have the required skills, to realise better relative income.

Algorithmic task assignment, management and control, as ‘tested’ in platform work, has the potential to spill over into traditional employment relationships. It is likely that this will negatively affect employment and working conditions in the wider economy and labour market.

Concluding commentary

A big advantage of platform work is that it offers easy access to the labour market and income generation, including for vulnerable groups. Furthermore, some types of platform work can contribute to fostering an entrepreneurial spirit and genuine self-employment. Against this background, from a policy perspective, it is worth considering whether and how platform work could be used strategically as a tool to integrate young or disadvantaged groups into the labour market, to extend working life and to reduce reluctance to engage in genuine self-employment.

However, platform work also raises considerable concerns as regards the employment and working conditions of most platform workers because of unclear regulatory frameworks. Clarifying the employment status of platform workers is important, as it has considerable consequences for their rights and entitlements. These include access to social protection and representation, and relate to working conditions such as working time, income, and health and safety standards.

Policymakers should also pay attention to the risks of platform work that are not yet as widely discussed as the employment status of platform workers. Examples include the contribution of this form of employment to labour market segmentation, deskilling, and the spread of algorithmic task assignment, management and control, which are likely to result in poorer employment and working conditions.

Related material

Related policy pointers Related research digests

 


References

Eurofound sources

Eurofound (2018), Additive manufacturing: A layered revolution , Eurofound working paper, Dublin.

Eurofound (2018), Advanced industrial robotics: Taking human-robot collaboration to the next level , Eurofound working paper, Dublin.

Eurofound (2018), ‘ Are blue-collar jobs turning white? ’, blog post, 27 September.

Eurofound (2018), Employment and working conditions of selected types of platform work , Publications Office of the European Union, Luxembourg.

Eurofound (2018), Game changing technologies: Exploring the impact on production processes and work , Publications Office of the European Union, Luxembourg.

Eurofound (2018), Industrial internet of things: Digitisation, value networks and changes in work , Eurofound working paper, Dublin.

Eurofound (2019), Advanced robotics: Implications of game-changing technologies in the services sector in Europe , Eurofound working paper, Dublin.

Eurofound (2019), Autonomous transport devices: Implications of game-changing technologies in the services sector in Europe , Eurofound working paper, Dublin.

Eurofound (2019), Platform work: Maximising the potential while safeguarding standards? , Publications Office of the European Union, Luxembourg.

Eurofound (2019), Technology scenario: Employment implications of radical automation , Publications Office of the European Union, Luxembourg.

Eurofound (2019), Virtual and augmented reality: Implications of game-changing technologies in the services sector in Europe , Eurofound working paper, Dublin.

Eurofound (2020), Game-changing technologies: Transforming production and employment in Europe , Publications Office of the European Union, Luxembourg.

Eurofound (2020), New forms of employment: 2020 update , New forms of employment series, Publications Office of the European Union, Luxembourg.

Other sources

Acatech and Forschungsunion (2013), Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , Plattform Industrie 4.0, Frankfurt.

Arntz, M., Gregory, T. and Zierahn, U. (2016), The risk of automation for jobs in OECD countries: A comparative analysis , OECD Publishing, Paris.

Autor, D. H. (2015), ‘ Why are there still so many jobs? The history and future of workplace automation ’, Journal of Economic Perspectives, Vol. 29, No. 3, pp. 3–30.

Baldwin, R. (2019), The globotics upheaval: Globalisation, robotics and the future of work , Oxford University Press, New York.

Beede, D., Powers, R. and Ingram, C. (2017), The employment impact of autonomous vehicles , ESA Issue Brief 05-17, Department of Commerce, Economics and Statistics Administration, Office of the Chief Economist, Washington, DC.

Boston Consulting Group (2015), Man and machine in Industry 4.0: How will technology transform the industrial workforce through 2025? , Boston.

Cedefop (European Centre for the Development of Vocational Training) (2017), #ESJ survey insights no. 9 – The great divide , web page, accessed 10 August 2021.

Cedefop, Skills Panorama (undated), Automation risk for occupations , web page, accessed 10 August 2021.

Dellot, B. and Wallace-Stephens, F. (2017), The age of automation: Artificial intelligence, robotics and the future of low-skilled work , RSA Future Work Centre, London.

Deloitte (2014), 3D opportunity in tooling: Additive manufacturing shapes the future , New York.

Demirkan, H. and Spohrer, J. (2014), ‘ Developing a framework to improve virtual shopping in digital malls with intelligent self-service systems ’, Journal of Retailing and Consumer Services, Vol. 21, No. 5, pp. 860–868.

Executive Office of the President (2016), Artificial intelligence, automation, and the economy , Washington, DC.

Foresight (2013), The future of manufacturing: A new era of opportunity and challenge for the UK , Government Office for Science, London.

Frey, C. B. and Osborne, M. (2013), The future of employment: How susceptible are jobs to computerisation? , working paper, University of Oxford Martin Programme on Technology and Employment, Oxford.

Frey, C. B. and Osborne, M. A. (2017), ‘ The future of employment: How susceptible are jobs to computerisation? ’, Technological Forecasting and Social Change, Vol. 114, pp. 254–280.

Frisoni, R., Dall’Oglio, A., Nelson, C., Long, J., Vollath, C., Ranghetti, D. and McMinimy, S. (2016), Research for TRAN Committee – Self-piloted cars: The future of road transport? , European Parliament, Brussels.

Heard, B. R., Taiebat, M., Xu, M. and Miller, S. A. (2018), ‘ Sustainability implications of connected and autonomous vehicles for the food supply chain ’, Resources, Conservation and Recycling, Vol. 128, pp. 22–24.

IFR (International Federation of Robotics) (2017), Executive summary World Robotics 2017 service robots , Frankfurt.

IFR (2017), ‘ Why service robots boom worldwide ’, presentation at IFR press conference, 11 October, Brussels.

Jung, T. H. and Han, D. (2014), ‘ Augmented reality (AR) in urban heritage tourism ’, Review of Tourism Research, Vol. 5.

Kalra, N. (2017), ‘ What autonomous vehicles could mean for American workers ’, blog post, 29 August.

Kianian, B., Tavassoli, S. and Larsson, T. C. (2015), ‘ The role of additive manufacturing technology in job creation: An exploratory case study of suppliers of additive manufacturing in Sweden ’, 12th Global Conference on Sustainable Manufacturing, Johor Bahru, Malaysia, 22–24 September 2014, pp. 93–98.

Mariani, M. (1999), ‘ Replace with a database: O*NET replaces the Dictionary of Occupational Titles ’, Occupational Outlook Quarterly Online, Vol. 43, No. 1, pp. 3–9.

McKinsey & Company (2017), Harnessing automation for a future that works , McKinsey Global Institute, New York.

McKinsey Global Institute (2015), The internet of things: Mapping the value beyond the hype , Toronto.

National Research Council (2009), A database for a changing economy: Review of the Occupational Information Network (O*NET) , National Academies Press, Washington, DC.

Nedelkoska, L. and Quintini, G. (2018), Automation, skills use and training , Working Paper No. 202, OECD Publishing, Paris.

Pedigo, S. and Bendix, A. (2017), The case for the driverless city , Schack Institute of Real Estate and NYU School of Professional Studies Urban Lab, New York.

Pesole, A., Brancati, U. and Fernández-Macías, E. (2020), Platform workers in Europe: Evidence from the COLLEEM survey , Publications Office of the European Union, Luxembourg.

Pilli-Sihvola, E., Miettinen, K., Toivonen, K., Sarlin, L., Kiiski, K. and Kulmala, R. (2016), Robots on land, in water and in the air: Promoting intelligent automation in transport services , 14/2015, Ministry of Transport and Communications, Helsinki.

Probst, L., Frideres, L., Pedersen, B. and Caputi, C. (2015), Service innovation for smart industry: Human-robot collaboration (PDF), Publications Office of the European Union, Luxembourg.

Royal Academy of Engineering (2013), Additive manufacturing: Opportunities and constraints , London.

Veryard, D., Aronietis, R., Poggi, R. and Viegas, J. (2017), Managing the transition to driverless road freight transport: Case-specific policy analysis , International Transport Forum, Paris.

Wisskerchen, G., Thibault Biacabe, B., Bormann, U., Muntz, A., Niehaus, G., Jiménez Soler, G. and von Brauchitsch, B. (2017), Artificial intelligence and robotics and their impact on the workplace , IBA Global Employment Institute, London.World Economic Forum (2015), Industrial internet of things: Unleashing the potential of connected products and services , Geneva.

World Economic Forum (2018), The future of jobs report 2018 , Geneva.

 

 

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