This chapter analyses the main trends, challenges and opportunities for the labour market in Berlin. It focuses on four dimensions: i) the effects of automation; ii) the consequences of digitalisation and the transition to a low-carbon economy; iii) the rise of non-standard work; and (iv) the changing demand for skills during the COVID-19 pandemic. In doing so, the chapter benchmarks Berlin with other regions in Germany and with selected comparable metropolitan areas across OECD countries.
Future-Proofing Adult Learning in Berlin, Germany
OECD Reviews on Local Job Creation
3. The impact of the future of work on Berlin’s labour market
Abstract
In Brief
The COVID-19 pandemic has accelerated megatrends such as automation and digitalisation. A wide body of evidence shows that firms are more likely to invest in automation following economic crisis periods. Amid lockdowns and social distancing measures, firms and employees in Berlin have embraced remote-working and digital tools, thus speeding up the process of digitalisation.
Automation could transform Berlin’s labour market. Risks of automation mainly affect workers in low- or medium-skill occupations because automation is a skills-biased technological change that mainly benefits and complements high-skilled workers. Combined with COVID-19, the impacts of automation could be a double whammy on disadvantaged groups, entrenching inequality in Berlin.
Automation threatens almost half of all jobs in Berlin. According to OECD estimates, 14% of jobs in Berlin are highly automatable, with a probability of automation of over 70%. At 32%, the share of jobs that have a significant risk of being strongly affected by automation is even higher. Those jobs are likely to see significant changes in their tasks and the required skill sets for these tasks, necessitating learning and training opportunities for those workers that are most at risk.
Many people have in non-standard work arrangements in Berlin, consisting of self-employed, part-time and temporary work. The rise of non-standard work offers new opportunities for some, such as greater compatibility of family and professional life, or an easier transition into the labour market for youth, but creates new challenges for others. In most cases, individuals in non-standard work arrangements have worse social protection, have less access to training and adult learning opportunities, and are generally more vulnerable to economic shocks such as the current COVID-19 pandemic.
COVID-19 and digitalisation have led to a change in the demand for skills in Berlin. Over the course of the pandemic, Berlin experienced a surge in the relative demand for advanced digital skills, such as programming or data analysis. Additionally, basic digital skills appear to be taken for granted in most new jobs. Despite this growing importance, many Berliners lack digital skills, highlighting the need for support programmes that allow workers to gain experience and competence in dealing with an increasingly digital work environment.
Introduction
Across the OECD, megatrends such as digitalisation, automation and artificial intelligence (AI) are contributing to one of the most profound transformation of local labour markets in decades. As elsewhere, these trends will have a strong and lasting impact on Berlin’s labour market. While the effects of these trends are already in motion, the COVID-19 pandemic is accelerating digitalisation and automation. With social distancing rules in place, millions of workers have adopted teleworking. It seems likely that a non-negligible degree of teleworking or hybrid working arrangements might prevail beyond the pandemic. Thus, COVID-19 appears to be a catalyst for long-lasting change in the way firms operate and people work as they embrace technological change to find innovative solutions that allow them to work.
Against this backdrop, the changing future of work will come with greater risks for some people and sectors in Berlin than for others. The pandemic has been exacerbating pre-existing structural issues within Berlin’s labour market, such as skills gaps, skills imbalances and a polarisation into well-paying and precarious jobs. Sound adult learning and skills development strategies are an essential policy tool that can help address these issues and can ensure that Berlin is prepared for the future of work, and the new types of jobs and alternative work arrangements such as part-time work that are on the rise.
In managing the economic consequences of the crisis and the subsequent recovery, policy makers in Berlin need to provide new solutions. For a strong and sustainable economic recovery, those solutions need to not only address the direct economic effects of the pandemic but also address structural labour market challenges. In analysing these trends and challenges, this chapter has the following structure. First, it examines the impact of automation on Berlin’s local labour market. Second, it describes how job polarisation affects the availability of different types of jobs. Finally, it shows trends in the growth of non-standard work, which creates both opportunities and new challenges for individuals.
How does automation affect Berlin’s labour market?
Labour markets in OECD countries have undergone significant structural changes over recent decades. Driven by new technologies, products and consumption patterns, a range of new types of jobs have emerged. On the flipside, employment in some traditional industries has declined. As a result, the type of skills that firms seek and workers need to thrive professionally has changed. Global megatrends such as automation and digitalisation that have accelerated during the COVID-19 pandemic will further drive this structural transformation of the economy, leading to a significantly different future of work. The pandemic being a catalyst for change, as firms and employees have embraced new ways of working and collaborating, ranging from sudden rise in remote working to a significant uptake of digital technology and services. These developments, especially since they are likely to continue at least partly, will have a considerable impact on how people work as well as what type of skills they need to have.
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
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.
Box 3.1. Estimating the risk of automation across OECD countries and metropolitan areas
Frey and Osborne (2017[3]) (FO) estimated the number of occupations at high risk of automation in the United States using a two-step methodology. They conducted a workshop with a group of experts in machine learning, whom they provided with a list of 70 occupations and their corresponding O*NET1 task descriptions. Experts were asked “Can the tasks of this job be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment?”. This allowed for the coding of each occupation as automatable or non-automatable. FO then used a machine learning algorithm to find out more about the links between the coding to automate and the list of O*NET variables. They were able to identify those variables (and their associated bottlenecks) with higher prediction power. High scores on these bottlenecks are likely to mean that an occupation is safe from automation. They could then compute a “probability of computerisation” for each occupation in the US, leading to the aggregate estimate that 47% of US jobs have a probability of automation of more than 70%.
Building on this approach, Nedelkoska and Quintini (2018[4]) (NQ) calculated the risk of automation across 32 OECD countries. The approach is based on individual-level data from the OECD Survey of Adult Skills (PIAAC), providing information on the skills composition of each person’s job and their skillset. While drawing on FO, this methodology presents four main differences: (i) training data in the NQ model is taken from Canada to exploit the country’s large sample in PIAAC; (ii) O*NET occupational data for FO’s 70 original occupations were manually recoded into the International Standard Classification of Occupations (ISCO); (iii) NQ uses a logistic regression compared to FO’s Gaussian process classifier; (iv) NQ found equivalents in PIAAC to match FO’s bottlenecks. PIAAC includes variables addressing the bottlenecks identified by FO, but no perfect match exists for each variable. No question in PIAAC could be identified to account for job elements related to “assisting and caring for others”, related to occupations in health and social services. This implies that risks of automation based on NQ could be slightly overestimated.
Table 3.2. Automation bottleneck correspondence
Note: Please refer to the source below for further details on the definition of the PIAAC variables.
Source: Nedelkoska and Quintini (2018[4]), “Automation, Skills Use and Training”, OECD Social, Employment and Migration Working Papers, No. 202.
Recent studies have pointed out the difficulty in predicting the risk of automation, as different models and variables come into play. Frey and Osborne’s original examination of the impact of automation on jobs was focused on machine learning and mobile robotics, but these are not the only key technological developments likely to impact the future of skills. Others have identified the rise of various forms of telepresence and virtual/augmented/mixed forms of reality, as well as the expansion of digital platforms as trends that will have important impacts on the future. The inherent unpredictability of technological progress means that within the growing literature, estimates of the jobs at risk of automation can vary widely, and the timeframes within which these impacts are predicted to occur are similarly broad, ranging from 10 to 50 years. Both the shape disruption will take, and its extent, are uncertain. What is certain is that workers will need to learn new skills and develop new competencies to adapt to changes are on their way (Crawford Urban and Johal, 2020[5]).
Source: Based on OECD (2020[6]), Preparing for the Future of Work in Canada.
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
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
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
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).
Box 3.3. Lancashire Digital Skills Partnership, UK
The Lancashire Digital Skills Partnership (LDSP) seeks to improve Lancashire’s digital skills in an inclusive way by bringing together public, private and non-profit stakeholders. LDSP is part of the Lancashire Enterprise Partnership's Skills and Employment Hub (LEP) and was formed in collaboration with the UK Department for Digital, Culture, Media and Sport (DCMS).
The strategic framework has four key themes: Future Workforce (future pipeline of digital skills and talent), Skilled and Productive Workforce (digital skills in the workplace and for technology adoption), Inclusive Workforce (digital inclusion), and an Informed Approach (influence and inform Lancashire’s priorities in employment and skills).
The LDSP provided suggestions based on an analysis of Lancashire's digital landscape, defined in eight main pillars of activity: Careers education; equality and diversity; curriculum design; promoting Lancashire as a place to live and work in the digital sector; developing businesses' digital skills; digital apprenticeships; coherence across the digital strategies of local authorities and other partners; and digital inclusion.
Despite some issues with recruiting in the digital industry, e.g. a high rate of skills shortage opening, its substantial predicted development makes it a key area for the LEP. The goal is to assist local employers in finding candidates for hard-to-fill positions in specialised digital skill areas and to support job seekers with guaranteed interviews. The LDSP designs and creates new strategies and training packages in digital sectors with skills needs, together with businesses and training providers. For example, the intensive and flexible (up to 16 weeks) Skills Bootcamps give the opportunity to develop skills needed in local sectors guaranteeing interviews after the Bootcamp.
The joint venture Fast Track Digital Work Force Fund, by DCMS and the LDSP, funded eight projects, related to digital marketing, robots, data science or cyber-security, in order to provide access to the digital industry to underrepresented participants and encourage diversity and inclusion. Moreover, the LDSP has developed relationships with business partners, such as Google, AWS, openSAP, the Lloyds Banking Group or Freeformers to provide further training opportunities to the county in, for example, coding or big data.
Source: OECD (2021[7]), Future-Proofing Adult Learning in London, UK.
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
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
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.
Box 3.4. Assessing employment risks due to the-zero transition
The dynamic general equilibrium model OECD ENV-Linkages allows illustrating economic impacts of climate mitigation policy scenarios several decades into the future, linking activity and employment to GHG emissions (Château, Dellink and Lanzi, 2014[11]). Building on this model and applying it to large OECD regions, OECD analysis identifies regional employment risks across sectors under the goals of the Paris Climate Agreement (OECD, 2021[10]).
Overall, the two-digit ISIC sectors identified as being at risk of employment losses due to the net-zero carbon transition include: Mining of coal and lignite; Other mining and quarrying; Manufacture of textiles; Manufacture of coke and refined petroleum products; Manufacture of chemicals and chemical products; Manufacture of rubber and plastics products; Manufacture of other transport equipment; Water transport; Air transport. The petrochemical sectors contain most of the employment in sectors likely at risk of employment losses due to the net-zero carbon transition in OECD and partner countries: 32% of employment in sectors at risk is employed in the manufacture of rubber and plastics products and 20% is employed in the manufacture of chemicals and chemical products.
Source: OECD (2021[10]), OECD Regional Outlook 2021.
Changing skills needs in Berlin
The transformation of the world of work changes the skills firms need. As a result, it could create a discrepancy between the demand for skills and its supply, which is based on the education and the qualifications of the labour force. Since skills and their effective use are a fundamental driver of economic development and productivity, rising skills mismatches and gaps could harm Berlin’s economic prosperity and growth. Across the OECD, skills gaps help explain a significant share of cross-country variation in labour productivity (Adalet McGowan and Andrews, 2017[12]). Industry-level analysis shows that firms in industries with higher skills mismatches tend to have a lower labour productivity performance (Adalet McGowan and Andrews, 2015[13]). Crucially, skills also matter for resilience, as they allow workers to be more flexible in reacting to changing labour markets and economic crises.
The matching of workers to jobs in which they can utilise their skills in the best possible way is a vital element of functioning labour markets. To the contrary, mismatches between workers’ skills and the requirements of their jobs can have negative effects, ranging from lower job satisfaction, wages, and labour productivity to unused potential of human capital (OECD, 2018[1]). Mismatch by qualification is one source of such skills mismatches. It arises when workers’ educational attainment is above (over-qualification) or below (under-qualification) the level usually required by the tasks of their job.
Skills mismatches by qualification are widespread in Berlin. Around 41% of all workers in Berlin have a job that does not correspond to their level of qualification (Figure 3.8). Twenty-two percent of workers in Berlin have a job for which they are formally overqualified. Another 19% appear to be underqualified for their job, meaning they do not have the skills and qualifications normally expected to fill out their position. Compared to selected large and economically important OECD metropolitan areas, Berlin records the second highest (out of 13) degree of skills mismatch by qualification, pointing out the strong disconnect between labour supply and demand in the local economy. This is particularly true for the over-qualification of workers, i.e. people working below their educational attainment, which exceeds the OECD and EU averages of 17% and 13% significantly.
Skills gaps and mismatches already inhibit Berlin’s economy. With the rapidly changing demand for new skills and the emergence of new types of jobs, the problem could become more severe if it is not addressed. Therefore, it is more important than ever to have a robust adult education and training system that provides on- and off-ramps for all individuals and firms to participate. Such a system will allow workers to gain new skills, retrain, or extend existing skills in bringing them up-to-date with recent developments. An important prerequisite for dealing with and alleviating skills gaps and mismatches consists of collecting sound data on the local labour market. A number of publicly available tools provide such data that policy makers can use to track regional labour demand, which helps design effective policy (Figure 3.5).
Figure 3.8. More than 40% of workers in Berlin are mismatched by level of qualification
Note: ISCED groups 0-2, 3, 4, 5-8. For Canada, ISCED groups 0-2, 3, 4, 5-6. ISCED groups 302, 303 and 304 are considered to be 3 according to the newest 2011 ISCED classification.
Source: OECD calculations on European labour force survey 2018 and Calculations based on Stats Canada Census 2016.
Box 3.5. Tools used by the Federal Employment Agency to analyse regional labour demand in Berlin
The German Bundesagentur für Arbeit (“Federal Employment Agency”) offers a range of publicly available tools that allow tracking regional labour demand by profession:
Fachkräfteradar: The tool monitors the ratio of vacancies and unemployment for a large range of occupations across German federal states and labour market regions.1
Fachkräftebedarfe: An interactive tool that presents diagrams and tables on key statistics on labour market shortages for different occupations by federal state, labour market regions and districts (Kreise).2
Engpassanalyse: The tool presents indicators on labour market shortages for different occupations and occupational groups across different areas in Germany. For each occupation/occupational group, the tool shows a composite labour market tightness measure that is based on six indicators: the time to fill a vacancy, the ratio of unemployed to vacancies, occupation specific unemployment rates, the change in foreigners as a share of all employed in those occupations who are liable to social security contributions, the exit rate from unemployment in a given occupation, and the change in median salary.3
Entgeltatlas: The tool provides information on average salaries by occupation in German federal states and a range of larger cities.4
1. See https://arbeitsmarktmonitor.arbeitsagentur.de/faktencheck/fachkraefte/karte/515/0/0/F7/ (accessed 01/02/2022).
2. See https://statistik.arbeitsagentur.de/DE/Navigation/Statistiken/Interaktive‑Angebote/Fachkraeftebedarf/Fachkraeftebedarf-Nav.html;jsessio-nid=E704DDDFFE03994804403BACEE25CA91 (accessed 01/02/2022).
3. See https://statistik.arbeitsagentur.de/DE/Navigation/Statistiken/Interaktive‑Angebote/Fachkraeftebedarf/Engpassanalyse-Nav.html;jsessionid=E704DDDFFE03994804403BACEE25CA91 (accessed 01/02/2022).
4. See https://con.arbeitsagentur.de/prod/entgeltatlas/beruf/3641 (accessed 01/02/2022).
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.
Box 3.6. Defining non-standard work
Non-standard work (NSW) arrangements are defined by what they are not: full-time dependent employment with a contract of indefinite duration – or what is generally considered the “standard” work arrangement. NSW therefore includes:
Temporary workers - workers in fixed-term contracts, including casual employees (duration is not fixed, but hours can vary), and seasonal workers;
Part-time workers;
The self-employed.
While this definition may be considered problematic – as it lumps together precarious and non-precarious forms of work – the convention is followed by a large part of academic research as well as by international organisations. For this reason, this chapter adopts this definition.
An additional challenge lies in the fact that the distinction between different forms of employment has become increasingly intricate. In particular, there is a growing grey area between self-employment and wage employment. The growing numbers of self-employed working for just one company represent a group on the border between two categories. While these blurred lines are at the heart of the current debate on the benefits and downsides of the gig economy, data that allows researchers to settle the debate is scarce
Source: OECD (2018[14]), Job Creation and Local Economic Development 2018: Preparing for the Future of Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264305342-en; OECD (2015[15]), “Non-standard work, job polarisation and inequality”, in In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264235120-7-en.
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
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), http://dx.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
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
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
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
Note: For a description of the methodology applied, see Box 3.7.
Source: OECD calculations using Burning Glass Technology job postings data.
Box 3.7. Methodology to calculate ICT skill demand based on online job postings
The report uses online job postings data provided by Burning Glass Technology to calculate the share of job vacancies that require generic or advanced ICT skills. The methodology follows a three-step procedure. First, the total number of unique monthly job postings is calculated by region. In a second step, the skill requirements listed in each job posting is used to calculate a dummy indicator of “generic” or “advanced” ICT skills for each job, in a procedure closely following previous OECD work on categorising these skills (Brüning and Mangeol, 2020[18]). The classification into generic and advanced skills is intuitive: Generic skills are simple ICT skills captured by key words such as “MS Excel” or “data”. Advanced ICT skills are more specialised skills such as programming, coding and data analysis. These skills are captured by key words such as “algorithm” or “data mining” but also indirectly when knowledge of software such as “Python” or “Oracle” is mentioned in the posting. Jobs that require both generic and advanced ICT skills are classified as requiring advanced ICT skills, implicitly making the plausible assumption that generic skills would not suffice to carry out the job.
In a final step, the total numbers of job postings that require generic or advanced ICT skills are summed up by region and divided by the total number of regional job postings calculated in the first step. It should further be noted that from July 2018 onwards, Burning Glass Technology started scraping job postings data using a different methodology as well as different data sources. To make data comparable over a longer period and to avoid structural breaks in the time series, the figures shown in this report do not make use of these updated data sources. Thus, other figures on regional online job postings published by the OECD using more recent data over a shorter period may differ slightly from the numbers shown in this report.
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
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
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].
References
[2] Acemoglu, D. and D. Autor (2011), Skills, Tasks and Technologies: Implications for Employment and Earnings, Elsevier-North, https://economics.mit.edu/files/7006.
[12] Adalet McGowan, M. and D. Andrews (2017), “Skills mismatch, productivity and policies: Evidence from the second wave of PIAAC”, OECD Economics Department Working Papers, No. 1403, OECD Publishing, Paris, https://dx.doi.org/10.1787/65dab7c6-en.
[13] Adalet McGowan, M. and D. Andrews (2015), “Labour Market Mismatch and Labour Productivity: Evidence from PIAAC Data”, OECD Economics Department Working Papers, No. 1209, OECD Publishing, Paris, https://dx.doi.org/10.1787/5js1pzx1r2kb-en.
[24] Autor, D., F. Levy and R. Murnane (2003), “The Skill Content of Recent Technological Change: an”, The Quarterly Journal of Economics, Vol. 118/3, pp. 1279-1333, https://economics.mit.edu/files/11574.
[21] Brinkman, J. (2015), “Big Cities and the Highly Educated: What’s the Connection?”, Federal Reserve Bank of Philadelphia Research Department.
[18] Brüning, N. and P. Mangeol (2020), “WHAT SKILLS DO EMPLOYERS SEEK IN GRADUATES? USING ONLINE JOB POSTING DATA TO SUPPORT POLICY AND PRACTICE IN HIGHER EDUCATION OECD Education Working Paper No. 231”, http://www.oecd.org/termsandconditions. (accessed on 8 December 2021).
[11] Château, J., R. Dellink and E. Lanzi (2014), “An Overview of the OECD ENV-Linkages Model: Version 3”, OECD Environment Working Papers, No. 65, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jz2qck2b2vd-en.
[20] Coibion, O., Y. Gorodnichenko and M. Weber (2020), “Labor Markets During the COVID-19 Crisis: A Preliminary View”, National Bureau of Economic Research, Cambridge, MA, https://doi.org/10.3386/w27017.
[5] Crawford Urban, M. and S. Johal (2020), Understanding the Future of Skills: Trends and Global Policy, https://fsc-ccf.ca/wp-content/uploads/2020/01/UnderstandingTheFutureOfSkills-PPF-JAN2020-EN.pdf.
[3] Frey, C. and M. Osborne (2017), “The future of employment: How susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114, pp. 254-280, https://doi.org/10.1016/j.techfore.2016.08.019.
[23] Ludolph, L. (2021), The Value of Formal Host-Country Education for the Labour Market Position of Refugees: Evidence from Austria, http://www.RePEc.org.
[4] Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris, https://dx.doi.org/10.1787/2e2f4eea-en.
[7] OECD (2021), Future-Proofing Adult Learning in London, United Kingdom, OECD Reviews on Local Job Creation, OECD Publishing, Paris, https://dx.doi.org/10.1787/c546014a-en.
[10] OECD (2021), OECD Regional Outlook 2021: Addressing COVID-19 and Moving to Net Zero Greenhouse Gas Emissions, OECD Publishing, Paris, https://dx.doi.org/10.1787/17017efe-en.
[22] OECD (2021), OECD Skills Outlook 2021: Learning for Life, OECD Publishing, Paris, https://dx.doi.org/10.1787/0ae365b4-en.
[16] OECD (2020), OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://dx.doi.org/10.1787/1686c758-en.
[6] OECD (2020), Preparing for the Future of Work in Canada, OECD Reviews on Local Job Creation, OECD Publishing, Paris, https://dx.doi.org/10.1787/05c1b185-en.
[9] OECD (2019), Under Pressure: The Squeezed Middle Class, OECD Publishing, Paris, https://dx.doi.org/10.1787/689afed1-en.
[1] OECD (2018), Good Jobs for All in a Changing World of Work: The OECD Jobs Strategy, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264308817-en.
[14] OECD (2018), Job Creation and Local Economic Development 2018: Preparing for the Future of Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264305342-en.
[17] OECD (2018), OECD Employment Outlook 2018, OECD Publishing, Paris, https://dx.doi.org/10.1787/empl_outlook-2018-en.
[8] OECD (2017), OECD Employment Outlook 2017, OECD Publishing, Paris, https://dx.doi.org/10.1787/empl_outlook-2017-en.
[15] OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264235120-en.
[19] OECD (2010), Higher Education in Regional and City Development - Berlin, Germany, https://www.oecd.org/germany/45359278.pdf (accessed on 19 January 2022).
Note
← 1. Technological change is a major reason for the disappearance of middle-skilled jobs. Information and Communication Technology (ICT) mainly offers a substitute for middle-skill jobs. Thus, technological developments and their capacity to replace routine tasks are drivers of job polarisation, as the impact of technology on jobs varies across the skills distribution. Across industries, occupations, and education levels, digitalisation is linked with reduced labour input of routine manual and routine cognitive tasks. Meanwhile, technological change and digitalisation are associated with an increase in non-routine cognitive tasks (Autor, Levy and Murnane, 2003[24]). As middle-skill jobs, such as clerical and production jobs, often entail routine tasks, they are easier to automate. In contrast, low-skill jobs often also involve non-routine manual tasks, which are more difficult to automate.