3. The impact of the future of work on Berlin’s labour market

Automation threatens almost half of all jobs in Berlin

Automation will cause one of the most significant transformation of labour markets in OECD countries in decades. On the one hand, the automation of production processes offers new opportunities and enhances productivity, thus raising prosperity and living standards. On the other hand, it poses new and unequal risks to workers because it is a skills-biased technological change. It tends to benefit some workers (mainly high-skill) but potentially replaces or strongly changes the jobs of other workers (mainly low-skill or middle-skill). Automation will result in a replacement of certain tasks and jobs, creating a risk that some workers may not enjoy the benefits that automation can generate but instead might struggle to find new jobs given the changing labour market and skill needs (OECD, 2018[1]). Consequently, automation could aggravate existing socio-economic inequalities by leading to lower wages for some jobs and further job polarisation across types of skills (Acemoglu and Autor, 2011[2]).

Across the OECD, about 46% of jobs face risks of automation. Around 14% of jobs are highly automatable (i.e. face a probability of automation of over 70%). Another 32% of jobs face a significant risk of being strongly affected by automation, which means they are likely to see significant change in their tasks and the required skill sets for such tasks. On average, automation tends to have smaller metropolitan areas in the OECD due to their stronger focus on service-sector jobs. However, several metropolitan areas in Spain, Italy, or Germany, face an even higher automation risk to jobs than the OECD average (Figure 3.1).

The risk of job automation affects almost every second job in Berlin. Compared to other major OECD metropolitan areas, Berlin faces relatively high risks of automation (Figure 3.1). In total, 14% of jobs in Berlin are highly automatable and a further 33% are likely to be changed significantly by automation (see Box 3.1 for a detailed explanation of the computation of risks of automation). Among peer metropolitan areas, only Barcelona, Madrid, Milan and Hamburg record a higher share of jobs that are likely to be automated or significantly transformed. At the other end of the spectrum, the labour markets in Oslo and London appear more resilient with respect to the impact of automation, which will only affect around 28% and 29% of jobs respectively.

Figure 3.1. Almost every second job in Berlin faces risks of automation

Share of jobs at significant and high risk of automation, Berlin and selected peers, 2018

Note: The metropolitan regions show the corresponding TL2 regions. ‘High risk of automation’ refers to the share of workers featuring a risk of automation of 70% or above. ‘Significant risk of change’ reflects the share of workers with a risk of automation between 50% and 70%.

Source: OECD Calculations on EU-LFS and Census data.

Across German states, Berlin records the lowest share of jobs at risk of automation. In Berlin, the share of jobs at high risk of automation and the share of jobs that will be significantly changed are both lower than in any other German state (Figure 3.2). However, they are still above the OECD average. Jobs in Thüringen and Mecklenburg-Vorpommern face the highest risks of automation, with more than 50% of jobs facing either high automation risk or risk of significant change. Overall, automation risks are greater in German states than in the OECD, partly due to the greater share of jobs in manufacturing in Germany.

Figure 3.2. Automation risks are greater in the rest of Germany than in Berlin

Percentage of jobs at significant and high risk of automation across Germany, 2020

Note: "High risk of automation" refers to the share of workers facing a risk of automation of 70% or more. "Risk of significant change" refers to the share of workers facing a risk of automation between 50% and less than 70%. The OECD average estimates are from Nedelkoska and Quintini (2018).

Source: OECD calculations on EU-LFS data.

The occupational profile of local labour markets help explain why the risk of automation differs within Germany and across OECD metropolitan areas. Occupational differences mainly reflect different industrial structures of regions or metropolitan areas. For example, sectors such as agriculture, construction, food and beverage services, manufacturing, or transport have a higher probability of losing jobs to automation (Box 3.1). German regions that face a higher risk of automation than Berlin tend to rely more strongly on employment in such sectors. Almost 60% of employees in Berlin work in a sector with low automation risks, whereas only around 38% work in high-risk sectors (Figure 3.3). In contrast, in Thüringen, the share of employees in industries with high automation risks amounts to 25%, while only 40% of employees work in industries with low automation risks. Employees in Berlin face lower risks of automation because many work in occupations and industries that involver fewer routine tasks, including jobs in professional and scientific services, finance, or real estate.

Figure 3.3. Berlin has the largest share of employment in industries at low risk of automation in Germany

Employment shares in industries with the lowest and highest risk of automation across Germany, 2019

Note: The figure shows the employment shares in the 20 industries with highest average risk of automation (high risk industries) and the 20 industries with lowest average risk of automation (low risk industries). Industries are classified according to 3-digit ISIC Rev. 4. Automation risks are presented in Nedelkoska and Quintini (2018). The figure includes economic activities from ISIC Rev. 4 codes A to J, L to N, and S95.

Source: SBS data by NUTS 2 regions and NACE Rev. 2, Eurostat; Statistics Germany Table 13311-0002.

Promisingly, the bulk of recent job creation in Berlin has mostly taken place in occupations with a low to medium risk of automation. Since 2011, the vast majority of new jobs appeared in high skill occupations that are less vulnerable to automation (Figure 3.4). For example, the number of jobs for information and communication technology professionals increased by around 70 000. Similarly, Berlin’s economy created 55 000 jobs for teaching professionals, which consist of occupations that are not only high skilled but also face a relatively low risk of automation. Encouragingly, those low skill occupations that are more robust in light of automation, such as personal service workers, fared better than low skill occupations that are highly vulnerable. The recent patterns of job creation help reduce the exposure of Berlin’s labour to the risk of automation. However, the data on job creation also reveal that the opportunities for the low skilled are shrinking, as little to no growth in employment occurred in occupations that provide employment for people with low levels of educations.

Figure 3.4. The majority of newly created jobs in Berlin are in high-skilled occupations with lower automation risks

Job creation compared to risk of automation in Berlin, 2011 to 2020

Note: The change in number of jobs (in thousands) is calculated between 2011 and 2020. Occupations (ISCO-08 code indicated in the bubble) are ranked from low to high risk of automation along the horizontal axis. Changes in the number of jobs for each occupation are reported along the vertical axis. The size of the bubble represents the relative share of occupations in Berlin, in 2020.

Source: OECD calculations on EU-LFS data.

Box 3.2. Which industries have the highest risk of automation?

The following table presents the 20 industries at highest average risk of automation and the 20 industries at lowest risk. The industries with high risk of automation belong mostly to the primary and the secondary sector. Few service industries face a high risk of automation, though exceptions include food and beverage services, land transport, waste collection and treatment, and services to buildings and landscape. In contrast, almost all industries with relatively low probability of automation belong to the service sector.

Figure 3.5. Mean probability of automation

Note: The figure only includes the 20 industries with highest average risk of automation and the 20 industries with lowest average risk of automation. The classification is ISIC Rev. 4, 2-digit.

Source: Survey of Adult Skills (PIAAC) 2012, 2015; Nedelkoska and Quintini (2018[4]), Automation, skills use and training.

Automation and digitalisation make digital skills ever more relevant in Berlin. Digital skills are essential for people to maximise their opportunities, work efficiently in a job; and are crucial for ensuring productivity and growth in Berlin. Digital skills are particularly important for those groups that are most at risk of redundancy because new jobs increasingly require basic or advanced digital skills and the ability to work in a technology-rich environment. To enhance employability of vulnerable groups in the labour market, Berlin could look at interesting local initiatives such as the Local Digital Skills Partnership in Lancashire in the UK that aim to equip workers with highly sought after digital skills in close collaboration with the local business community (Box 3.3).

Labour markets in the OECD are polarising, partly reflecting a shift in labour supply

Even before the COVID-19 pandemic started, most OECD economies experienced dramatic shifts in their labour markets. Over the last decades, labour markets across the OECD have become increasingly polarised. The share of employment in middle-skill jobs has declined strongly relative to jobs with higher or lower skill levels (OECD, 2017[8]). High-skill jobs include managers, professionals and technicians; middle-skill jobs compose clerks, craft and related trades workers, machine operators and assemblers; and low-skill jobs include elementary occupations, service workers, and shop and market sales workers. In almost all OECD countries, job polarisation has been characterised primarily by a shift towards high-skill occupations (OECD, 2019[9]).

Job polarisation is not only part of labour market transformations but also poses a social challenge in OECD societies. It raises public concern about growing inequality in OECD countries. Middle-skill jobs were historically associated with a middle-class lifestyle and socio-economic mobility for future generations. In recent years, however, the overall skill distribution on the labour market has shifted towards higher-skill jobs as growth in high-skill occupations has outpaced growth in middle- and low-skill occupations, which has changed the relationship between skills and income classes. Consequently, middle-skill workers are now more likely to be in lower-income classes than middle-income classes (OECD, 2019[9]). Furthermore, the wage structure in many OECD countries is now also showing a growing divide between top earners and others, instead of experiencing growth at both ends of the wage structure.

Skills-biased technological change has been driving a labour market polarisation across the OECD. This is particularly noticeable in large cities, which tend to be at the forefront of labour market transformations. Across OECD metropolitan areas, labour markets are increasingly polarising into high and low skilled jobs. In contrast, middle-skill jobs are rapidly disappearing in many places. All the 17 OECD metropolitan areas considered, including Berlin, have lost middle-skill jobs in relative terms since 2000 (Figure 3.6). On average, the share of workers in such jobs decreased by more than 7 percentage points between 2000 and 2018. Most of those metropolitan areas have replaced middle-skill jobs with both high-skill and low-skill jobs, with the former recording the largest relative increase in jobs. In fact, 16 metropolitan areas have mostly replaced middle-skill jobs with high-skill jobs.

Figure 3.6. Job polarisation in Berlin is less pronounced than in most other OECD metropolitan areas

Change in the share of employment for high skill, middle skill and low skill jobs in Berlin and selected peers, 2000-18

Note: The data correspond to the TL2 regions that compose the respective metropolitan area.

Source: OECD calculations on Labour Force Surveys.

Compared to other OECD metropolitan areas, middle-skilled jobs disappeared at a lower rate in Berlin. Since 2000, the share of middle-skilled jobs fell by 2.7 percentage points (Figure 3.6). A significant rise in high-skilled jobs (+ 4.9 percentage points) more than compensated for the loss in middle-skilled jobs. Contrary to most comparable OECD metropolitan areas, Berlin did not experience a simultaneous increase in the share of low-skilled jobs. Instead, the share of low-skilled jobs fell by 2.2 percentage points. In summary, Berlin’s labour market has experienced a shift towards high-skilled jobs, with both middle-skilled and low-skilled jobs falling at similar rates.1

The green transition: an opportunity for Berlin?

The green transition to a low-carbon economy is another major development that will shape labour markets over the coming decades. Reaching the political objective of reducing emissions and achieving a net-zero economy, requires decisive actions by many countries around the world. Those actions, which include the phasing out of fossil fuels and a move towards renewable energies, will inevitably also affect the labour market. Jobs and sectors that support the green transition could thrive, while others that are emission-intensive such as the chemical industry or parts of manufacturing could see job losses or at least a significant structural transformation.

The opportunities and challenges that the green transition brings will differ vastly across local labour markets. Due to differences in their economic structure and the share of jobs across sectors, some local labour markets will face significant risks while others could benefit from growing green sectors. Currently, a lack of clear empirical evidence on green jobs and skills across local areas hampers the assessment of where the green transition might create new economic opportunities other than in the renewable energy sector. However, by looking at a subset of jobs that are emission-intensive, one can assess the extent to which jobs across OECD regions might be put at risk by a move towards net-zero economy.

In Berlin, employment risks due to the net-zero transition appear low. Figure 3.7 presents data on the share of jobs in four manufacturing sectors that entail, on average, high levels of emissions. Those sectors are transport, coal and other mining, chemical and plastic products, and other manufacturing. In Berlin, these sectors only account for around 0.8% of total employment, the lowest share across all German federal states. Similarly, the share of employment in those sectors is much lower that the OECD average of 2.2%. In the extent to which those jobs could be classified as “brown jobs” that face heightened risk from the move to a net-zero economy, Berlin’s labour market seems relatively well-shielded. The relevant dominance of the service sector in Berlin’s economy provides further protection against adverse shocks from the green transition, as the major impact is likely to be on manufacturing jobs. Looking a step further, the green transition could in fact provide new economic opportunities for Berlin supported by its young workforce and dynamic entrepreneurship scene.

Figure 3.7. Employment risks due to the net-zero transition are small in Berlin

Percent of total regional employment in industries at risk due to the net-zero transition, large regions (TL2), 2017

Note: The y-axis shows the employment share in industries put at risk until 2040. For details on the methodology, see OECD (2021, p. 106[10]).

Source: OECD estimates based on EU-LFS data.

Non-standard work is on the rise in Berlin

Many labour markets across the OECD have undergone a gradual transition away from traditional open-ended contracts. Non-standard forms of work, which include temporary, part-time, or self-employed work, have been rising (see Box 3.6 for information on the definition of non-standard work). Changing consumer preferences and new technological developments are two important factors explaining the increase in non-standard work forms. The latter allows firms to adopt more job flexibility and outsourcing of tasks, including the hiring of temporary help or freelance contractors. The former, have caused a shift among firms to more just-in-time delivery and customised services.

Non-standard work brings new opportunities as well as challenges for local labour markets. It offers opportunities to find employment or increase flexibility for some workers but it also worsens working conditions for others. On the one hand, non-standard work arrangements can enhance the compatibility of work and family life and increase worker flexibility more generally. Thus, it can encourage labour force participation, especially among those who would otherwise have stayed out of the labour market. For instance, it can help women to combine professional and personal responsibilities. Moreover, it can facilitate school-to-work transitions by providing a stepping-stone for young people (OECD, 2018[14]). However, on the other hand, non-standard work is often associated with worse working conditions in terms of reduced job security, higher income volatility, and slower career progression.

Non-standard work employment has increased in most OECD countries since 2000. Temporary contracts have become more common in OECD countries, especially among young workers (Figure 3.9 Panel A). Compared to 1980, the share of OECD workers under the age of 26 in a fixed-term contract has risen from 17% to 25% in 2016. Moreover, the share of employees in part-time work has also increased significantly (Figure 3.9 Panel B). While a large part of this trend is due to the entry of women into the labour market that historically struggled to combine family and professional life, part-time work has also increased amongst men.

Figure 3.9. Non-standard employment has been rising across OECD countries

Share of temporary and part-time work, OECD countries, 2000-16

Source: OECD (2018), "Labour Market Statistics: Employment by permanency of the job & Full-time and part-time employment - common definition: incidence", OECD Employment and Labour Market Statistics (database), https://doi.org/10.1787/lfs-data-en.

Part-time work has increased in Berlin, Germany and the OECD

In Berlin, part-time employment has grown at a similar pace as Germany overall. In 2019, around 26% of employment in was part time, an increase of 6 percentage points from 2002 (Figure 3.10). In Germany overall, a slightly higher share of total employment, 27%, is part time. Compared to the EU27 countries, part-time employment in Berlin and Germany is relatively high. On average, 18% of 16 to 64 year olds have a part-time job in the EU27. A number of reasons could help explain this difference. For example, women make up a large share of part-time employees. As such, part-time employment can be a means of ensuring a work-life balance and making family and professional life compatible. In countries such as the Netherlands, Switzerland, Germany, the United Kingdom or Norway, women’s increased labour market participation partly explains the rise in part-time employment, with women being more than twice as likely as men to work part time and on average, almost one quarter of women – often mothers – work part-time (OECD, 2020[16]). However, part-time employment often comes with disadvantages too.

Figure 3.10. More than a quarter of 16 to 64 year olds in Berlin have part-time work

Share of part-time employment in total employment by federal state, 2002 and 2019, 16 to 64 year olds

Note: 2002 earliest value available.

Source: Eurostat, table lfst_r_lfe2eftpt.

Part-time employees face higher job security and tend to earn lower hourly wages in OECD countries (OECD, 2018[17]). Poverty rates tend to be higher for part-time workers than for standard employees. While on average 10% of part-time workers live in a household with an annual disposable income of less than 50% of the national median, this is only the case for 3% of standard employees (OECD, 2020[16]). Furthermore, part-time workers are less likely to participate in training, which has a negative impact on their future earnings. Lower training participation also means that part-time workers are less likely to adapt to the future of work and changing skills requirement. As pointed out in the previous section, automation and digitalisation change labour market needs and skill profiles that employers seek. Part-time workers are less able to react to these developments by using learning opportunities to retrain or upskill.

Another dimension of non-standard work is self-employment, which has been increasing significantly in Berlin. More than 13% of all workers in Berlin are self-employed, making it the federal state in Germany with the highest rate of self-employment (Figure 3.11). Self-employment is not only much more common in Berlin than elsewhere in Germany but also grown over the past 15 years, while it fell in Germany overall. The share of self-employed workers grew from around 11.7% to 13.4% between 2004 and 2019 in Berlin, whereas it decreased from 9.8% to 8.5% nationally. As of 2019, the proportion of self-employment among workers in Berlin was similar to the average of the EU27 countries. However, contrary to the development in Berlin’s economy, the EU27 self-employment rate markedly decreased from 2004 to 2019. One factor that contributes to the rise of self-employment in Berlin is the emergence of the digital economy. Some self-employed workers in the digital economy have been able to benefit from new markets and opportunities by finding high-value added work as independent professionals or freelancers. However, for others, self-employment in the digital economy takes on precarious forms, as some work for a single client that is effectively their employer, without having the benefits of a formal employer-employee relationship including social security or work regulation that protects employees.

Figure 3.11. Self-employment in Berlin has increased

Changes in self-employment rate across German federal states, 2004 and 2019

Note: 2004 earliest value available.

Source: Eurostat, table lfst_r_lfe2estat.

COVID-19 and digitalisation lead to surging demand for digital skills

During the COVID-19 pandemic, Berlin has experienced a surge in demand for digital skills. The shift to teleworking has led to the adoption of new technology and a proliferation of technological solutions to work elements such as meetings that were limited by social distancing measures. This push, which has forced firms and workers to experiment with new working arrangements and embrace virtual approaches to some of their tasks, is likely to have a lasting impact. It has emphasised the relevance of being able to work in a digital environment and has thus increased the demand for digital skills.

Berlin has recorded a significant jump in new jobs that require advanced information and communications technology (ICT) skills. From the start of the pandemic until the end of 2020, the share of job postings in Berlin that require advanced ICT skills rose from 26% to 33% (Figure 3.12). Such jobs entail specialised skills such as programming, coding and data analysis (see Box 3.7 for more details). While many German cities saw a rising demand for ICT skills during the pandemic, it rose particularly fast in Berlin, suggesting that Berlin might be experiencing a faster transformation of its local economy than other places in Germany. Looking at the requirements of job postings offers a timely alternative to measuring labour demand and the changing skills mix in Berlin’s economy.

Figure 3.12. Labour demand for advanced ICT skills has increased in Berlin during the pandemic

Share of job postings requiring advanced ICT skills, German cities, 2015-20

Note: For a description of the methodology applied, see Box 3.7.

Source: OECD calculations using Burning Glass Technology job postings data.

Contrary to advanced digital skills such as programming or data analysis, demand for more basic digital skills has not risen. Not all digital skills recorded significant increases in demand as firms and employees had to embrace digital ways of working. Demand for generic digital skills that encompass simple ICT skills, for instance referring to knowledge of “MS Excel”, remained mostly constant (Figure 3.13). In fact, their relative importance has fallen since 2015, which probably indicates that such skills are increasingly taking for granted. They appear to become a minimum work requirement for many jobs in Berlin.

Figure 3.13. Labour demand for generic ICT skills remained unchanged during the pandemic

Share of job postings requiring generic ICT skills, German cities, 2015-20

Note: For a description of the methodology applied, see Box 3.7.

Source: OECD calculations using Burning Glass Technology job postings data.

Comprehensive data on digital skills among Berlin’s workforce are missing but alternative measures can provide an approximation. Ideally, extensive surveys of adult workers or firms could highlight the extent to which individuals have the necessary digital skills to succeed in the local economy. However, most of those data sources are either not representative at the subnational level (e.g. the OECD Programme for the International Assessment of Adult Competencies (PIAAC) or do not offer systematic evidence on digital skills gaps as in the case of employer surveys that cover Berlin. To approximate basic digital skills, regular internet use might offer an alternative. In Berlin, as in other OECD metropolitan areas, internet use has not only risen over the past decade but has become ubiquitous in most people’s lives (Figure 3.14).

Figure 3.14. Almost everyone uses the internet regularly in OECD metropolitan areas

Share of population that used the internet at least once over the past 3 months, Berlin and selected peers, 2011-2020

Source: Community survey on ICT usage by households and individuals, available on Eurostat [isoc_r_iuse_i].

While most people in Berlin use the internet regularly, almost half of Berlin’s population does not perform basic tasks online. In comparison to other OECD metropolitan areas such as Oslo, London, Brussels or Hamburg, many more people in Berlin do not appear to use social networks, buy or sell goods online, or use online banking. While there could be several factors at play for those differences, the data might also suggest that many individuals in Berlin could lack the necessary familiarity and experience in pursuing tasks online. This, in turn, could indicate a lack of digital skills that goes beyond the minimal competency of simply using the internet for minimal tasks such as browsing or search queries. In a broader sense, such data appear to corroborate the widespread views expressed by employers in Berlin that many workers or job seekers do not meet the requirements in terms of digital skills that many jobs entail.

Figure 3.15. Only 45% of internet users in Berlin perform basic tasks online

Share of total internet users in Berlin performing basic tasks online, international comparison

Note: Performing basic tasks online calculated as the unweighted average of those who replied they used social networks, sold or bought goods online and used online banking over the past three months. The denominator are those who used the internet over the past three months. Year 2012 imputed as average of year 2011 and 2013 due to lack of available data.

Source: Community survey on ICT usage by households and individuals, available on Eurostat [isoc_r_iuse_i].