2. Berlin’s labour market: Positive long-term trends, but socio-economic disparities persist

Berlin is the largest German city, one of 16 German federal states and the capital of Germany. In 2019, Berlin was home to 3 644 830 inhabitants, making it the biggest German city and metropolitan area. The city of Berlin is, next to Hamburg and Bremen, the only city-state of Germany. Geographically, Berlin is located approximately 70 kilometres west of the Polish border. It is surrounded by the significantly less densely populated federal state of Brandenburg (Figure 2.1, Panel A). Parts of Brandenburg, most note-worthy its capital Potsdam (178 000 inhabitants in 2019), fall into Berlin’s commuting zone. Berlin is divided into 12 boroughs, which are administrative units with no autonomous competences (Figure 2.1, Panel B).

Like other German federal states, Berlin has legislative power in some domains, such as education, but does not have fiscal autonomy. The German federal states do not raise their own taxes but only collect these. Public revenues are allocated to the federal states using a formula that takes into account the total population and the regional Gross Domestic Product (GDP). Importantly, the German federal system assigns large responsibility to the federal states in the field of initial education (including, to a large extent, tertiary education). Federal statutes regulate other areas within the field of education, such as Germany’s dual education system and the continuing education and training (CET) of adults. CET measures implemented on the federal state level thus complement those by the federal government, a system discussed in more detail in section 3. Politically, Berlin’s mayor also serves as the federal state’s prime minister. Berlin’s Senate acts as the city and regional government and the Senators correspond to state Ministers (OECD, 2010[1]). The different Senatsverwaltungen (“Senate Departments”) correspond to state Ministries.

Berlin’s economy and labour market reflect its highly diverse and dynamic population. Berlin, as Germany’s largest city was host to nationals from 193 countries in 2020. Its historic division into an East and a West German part further adds to its diversity even among German nationals. The high population growth – between 2000 and 2019, Berlin’s population grew by almost 8% – is partly explained by its attractiveness to internal migrants from across Germany. Berlin’s labour market reflects some of its dynamism, posing distinct opportunities and challenges to the provision of adult learning. The remainder of this section provides a high-level overview of recent trends in Berlin’s labour market and describes the socio-economic profile of its population in more detail.

Berlin’s unemployment rate has been on a steady decline over the past decade. Before the start of the COVID-19 pandemic, Berlin’s unemployment rate declined from 13.0% in 2010 to 5.5% in 2019 (Figure 2.2). Compared to other cities across the OECD, Berlin’s unemployment rate was relatively low in 2019, on a par with Stockholm (6.3%) and London (4.6%) and only slightly above the second and third biggest cities of Germany, Hamburg (3.7%) and Munich (2.6%). In absolute terms, the 7.5 percentage point decline in Berlin’s unemployment rate between 2010 and 2019 was the highest drop among the OECD cities shown in Figure 2.2.

However, Berlin’s unemployment rate remains the highest in Germany when compared to the other German federal states. Figure 2.3 shows Berlin’s unemployment rate in national comparison over the period from 2010 to 2020. Unemployment rates dropped across all German federal states over the past decade. The federal states that started at the highest level of unemployment in 2010 – Berlin, Mecklenburg-Vorpommern, Sachsen and Sachsen-Anhalt – experienced the sharpest drops in their unemployment rate, leading to convergence across German regions. However, despite Berlin’s significant labour market improvements, Berlin’s unemployment rate remains the highest in Germany. In 2020, the first year of the COVID-19 pandemic, the unemployment rate in Berlin was 6.4%, 1 percentage point higher than in 2019. All other German federal states (excluding Bremen) recorded unemployment rates below 5% in 2020 and only Hamburg (+1.2 percentage points), Brandenburg (+1.1 percentage points) and Rheinland-Pfalz (+1 percentage point) experienced an equally large or larger year-on-year increase in unemployment during the first year of the COVID-19 pandemic.

Berlin’s labour force participation rate increased significantly before the COVID-19 pandemic, both in absolute terms and in comparison to other European cities. The second important headline figure on regional labour markets is the labour force participation rate. It is defined as the share of the working-age population that is employed or unemployed and seeking for work, and thus captures the share of working-age people who are economically active. Figure 2.4 shows that Berlin’s labour force participation rate increased from 76.0% in 2010 to 79.2% in 2019 and its economic activity is now on a par with other OECD capital cities such as London (78.1% in 2019) and Madrid (76.8% in 2019). Nationally, Berlin’s labour force participation almost closed the economic activity gap to both Hamburg (79.5% in 2019) and Munich (80.8% in 2019). The 3.2 percentage point increase in Berlin’s labour force participation rate is one of the largest among all OECD cities shown in Figure 2.4. The simultaneous decline in Berlin’s unemployment rate over the same period (Figure 2.2) implies that the rise in economic activity was indeed driven by an increase in employment within this age group.

Berlin’s labour force participation rate is close to the German average and only experienced a small drop in the first year of the COVID-19 pandemic. The key labour market risk of the COVID-19 pandemic is its potential to make searching for jobs more difficult, potentially driving the unemployed into long-term unemployment and ultimately into economic inactivity. Such a dynamic has been well documented in the United States during the early stages of the pandemic (Coibion, Gorodnichenko and Weber, 2020[2]). Thus, the COVID-19 pandemic risks leaving longer-lasting scars on the labour force. Berlin’s labour force participation rate only experienced a small drop of 0.6 percentage points between 2019 and 2020 (Figure 2.5). Exactly half of the German federal states also showed a drop in economic activity, with Bremen experiencing the largest year-on-year decline of 4.2 percentage points. In 2020, the national-level labour force participation rate stood at 79.2%, only 0.6 percentage points above that of Berlin (78.6%). Over the 2010 to 2020 period, Berlin’s 2.6 percentage point increase in labour force participation was around the median among Germany’s federal states, with Saarland (+4.3 percentage points) and Bayern (+3.9 percentage points) showing the highest growth rates in their regional labour forces.

Berlin has indeed enjoyed a two decades long boom in employment, in particular in service sectors with many high skill jobs. Between 2000 and 2019, total employment in Berlin grew by an annual rate of almost 1.3%, compared to 0.7% in Germany and 0.6% in the European Union (Figure 2.6). This is equivalent to the creation of almost 450 000 new jobs in Berlin since 2000. The three sectors that recorded the fastest employment growth in Berlin were trade, transport, accommodation and food services (annual growth of 2.4%), financial, insurance, real estate and business activities (annual growth of 1.6%), and public administration, education, health, and arts (annual growth of 1.4%). Those three sectors alone recorded more than 500 000 new jobs. In contrast, employment in industrial jobs or construction fell by 37 000 and 22 000, respectively, making them the sectors with the biggest absolute job losses.

Berlin’s workforce has become only slightly more productive over the past decade as labour force productivity grew significantly less than in some OECD regions. Stagnation in labour productivity can pose a risk for Berlin’s competitiveness as a place of business. Figure 2.7 shows relative changes in regional gross value added per worker against the 2008 base year. In international comparison, Berlin’s productivity growth until 2018 was sluggish and only stood at 106% of its productivity per worker in 2008. In other OECD cities such as Oslo (133%), Copenhagen (115%), Barcelona (113%) or Amsterdam (112%), productivity per worker grew significantly more over the same period. As a result, gross value added per worker in Berlin stood at USD 81 107 (constant prices, constant PPP, base year 2015) in 2019, a value much lower than in other major OECD cities that also showed sluggish productivity growth over the past decade, such as Vienna (USD 99 038), Hamburg (USD 104 051) or Stockholm (USD 110 507).

Berlin’s labour market has significantly tightened over the past decade. Following a decade of rapid employment growth driven by the service sector, Berlin’s labour market has now entered a new period. Recruitment of suitably qualified workers will become increasingly difficult for employers, ultimately putting upward pressure on wages in sectors that experience shortages in labour supply. Such development elevates the importance of the local adult learning system for two reasons: First, it can increase the supply of qualified workers that can meet the skills needs of Berlin employers. Second, as wages are likely to rise disproportionally in high-skill sectors, there is a risk of aggravated social divisions if low and medium-educated workers are not trained and upskilled to remain attractive to local employers.

Labour demand indicators indeed suggest that Berlin’s labour market will soon face a period of greater difficulties in recruitment and upward pressure on wages in sectors that experience labour shortages. Figure 2.8 and Figure 2.9 show the number of job vacancies and the number of unemployed per job vacancy in Berlin and Brandenburg in comparison to the other German federal states. Figure 2.8 shows that the total number of job vacancies in Berlin has increased between 2010 and 2020, rising from 39 800 in 2010 to 113 600 in 2019, followed by a drop to 92 400 during 2020, the first year of the COVID-19 pandemic. Most other German federal states experienced a similar increase in absolute vacancies, with differences mostly explained by different population sizes across regions. However, such absolute rise in labour demand needs to be compared to the supply side of the labour market, best measured by the available workforce. Figure 2.9 shows that, relative the jobseeker-per-vacancy ratio dropped significantly in Berlin. In 2010, approximately nine unemployed workers were available for each open position. By 2019, that ratio had dropped to slightly above one unemployed worker per vacancy and then rose to approximately two unemployed workers per job vacancy in 2020, the first year of the COVID-19 pandemic. The other German federal states experienced a similar tightening of their labour market, albeit at a less rapid pace than Berlin.

Berlin’s service sector drove the increase in labour demand. The employment growth in the service sector is also reflected by a sectoral decomposition of a direct labour demand measure, the number of job vacancies. Figure 2.10 shows the total number of job vacancies disaggregated by the sector of economic activity in Berlin between 2010 and 2020. The service sector, both business and other services, experienced by far the largest rise in absolute job openings. Vacancies in the business service sector rose from 12 000 in 2010 to 26 000 in 2019. Similarly, job vacancies in other services – which includes services related to human health and social services, education services, arts, entertainment and recreation as well as accommodation and food services – increased from 13 000 in 2010 to 43 000 in 2019. The construction sector (+11 100 vacancies between 2010 and 2019) and the information and communication sectors (+4 400) also experienced a rise in labour demand.

Already before the COVID-19 pandemic, a growing number of businesses in Berlin struggled to fill vacancies. The share of Berlin’s and Brandenburg’s companies that reported difficulties in finding a suitable candidate during the hiring procedure increased from 30.0% in 2010 to 40.6% in 2019 (Figure 2.11). The increased difficulties in hiring reported by companies follows the trend in Germany as a whole: Over the same period, the share of German companies reporting difficulties during the hiring procedure rose from 29.1% to 44.0%. The drop in the share of vacancies companies had difficulties to fill observed between 2019 and 2020 across all German federal states but Schleswig-Holstein and Hamburg can likely be ascribed to the selective decrease in hiring for positions that are relatively harder to fill.

An insufficient number of applicants and an insufficient professional qualification of candidates are increasingly the main reasons why companies struggle to fill job vacancies. Figure 2.12 shows the self-assessed reasons companies mention when asked about why they experience difficulties to find suitable candidates for job openings. In 2010, Berlin’s companies stated that they struggled filling 17.7% of job vacancies due to insufficient professional qualification of the candidate. In 2019, the share of vacancies they struggled to fill for the same reason rose to 26.4%. Similarly, the share of vacancies Berlin’s companies struggled to fill due to an insufficient number of applications rose from 17.1% in 2010 to 26.1% in 2019. Other reasons, such as the unwillingness of applicants to accept the workload (+5.7 percentage points between 2010 and 2019) and excessive salary demands (+9.0 percentage) points also increased significantly but were mentioned less frequently. The drop in the share of vacancies companies struggled to fill between 2019 and 2020 can once again likely be ascribed to selective hiring during the COVID-19 pandemic.

The above analysis shows that Berlin’s labour market has reached a point where the demand for skilled workers may exceed their supply. For this reason, Berlin’s policy makers have to make sure their entire labour force is equipped with the right skills to succeed in the local labour market. One of the key indicators for a workforce’s skill level is the level of initial education. However, to support upskilling in the labour force, identifying relevant population characteristics and geographical variation within the regional labour market can also help in targeting training and education measures. The remainder of this chapter describes the socio-economic profile of Berlin’s population and labour force to characterise the regional labour supply side, with a particular focus on Berlin’s young adults and its diverse migrant population.

The average level of education in Berlin is higher than in other German regions, but lower than in major cities across the OECD. Figure 2.13 shows that among Berlin’s 25 to 64 year old population, 13% fell into the low education category in 2020, defined as individuals whose formal education is below upper secondary education. Forty-three percent had attained a medium level of education, defined as an attainment of upper secondary but non-tertiary education. Forty-two percent of Berlin’s population had attained tertiary education and are therefore considered highly educated. Panel A of Figure 2.13 shows that Berlin’s share of highly-educated individuals is the highest among Germany’s federal states. On the other hand, the share of low-educated individuals falls right on the median among Germany’s regions. However, academic research has long established that highly educated individuals are drawn to cities in relatively larger number for better income opportunities (see Brinkman (2015[3]) for a summary). Thus, it is insightful to compare the level of education in Berlin’s population to large cities across the OECD. Panel B shows that Berlin’s share of highly educated is relatively low when compared to other OECD cities. Vienna (42.7%), Brussels (49.3%), Madrid (50.2%), Stockholm (53.7%), Oslo (54.9%) and London (68.4%) all have a larger share of highly educated than Berlin among its 25 to 64 year olds. Berlin, on the other hand, has the largest share of individuals educated at a medium level (43%) compared to the cities displayed.

The share of early leavers from education and training is high in Berlin compared to other German regions. The level of education among young adults can be a good indicator for the future of regional labour supply. Figure 2.14 shows the share of individuals aged 18 to 24 who finished no more than lower secondary education and are not involved in further education or training in Berlin, compared to other German regions. In 2018, this number stood at 13.6% in Berlin, second only to Bremen, where an even larger share of young adults fell into the low education category (14.6%). The German average stood at 10.3% in the same year. Over the 2010 to 2018 period, Berlin saw little improvement in its rate of early leavers from education and training. The share of early leavers from education and training only decreased by 0.8 percentage points over the observed period compared to a 1.5 percentage point decline in Germany as a whole.

A large share of Berlin’s young population is not in education and unemployed or inactive (NEET). A complementary indicator to the share of early leavers from education and training is the NEET rate, which is defined as the share of 18 to 24 year olds who are not currently in education and unemployed or inactive (NEET). For example, OECD research has shown that differences in literacy skill growth across the OECD are strongly related to the NEET rate. Conversely, reductions in NEET rates result in decreased disparities in achievement on literacy tests and decrease intergenerational transmission of educational advantages (OECD, 2021[4]). In 2019, the NEET rate among Berlin’s young adults stood at 10.2%, well above the German average of 7.7% and third only to the two other city states of Hamburg (11.0%) and Bremen (12.0%) among Germany’s federal states. Despite the relatively high NEET rate in Berlin, the trend between 2010 and 2019 shows a relatively large drop of 5.1 percentage points, the third highest improvement across German federal states. Thus, recent cohorts of 18 to 24 year olds are more likely to be in education or employment than previous cohorts.

A distinct feature of Berlin’s population is its diversity. In 2020, Berlin was host to nationals from 193 countries. Figure 2.16 shows that the share of Berlin’s population with a migration background stood at 33.1% in 2019. Only the federal states of Bremen (36.5%), Hessen (34.4%), Hamburg (33.9%) and Baden-Württemberg (33.8%) had marginally higher populations with migration background in relative terms. In all other German regions, a significantly lower share of the regional populations had a migration background in 2019. For the purposes of this report, migration background is defined as either not having German citizenship, or at least one parent not holding German citizenship by birth.

Migrants are spread highly unevenly across Berlin’s boroughs. Similar to the settlement patterns in the whole of Germany, Berlin’s migrants are mostly settled in Berlin’s former Western parts for historical reasons (Figure 2.17). In Berlin-Mitte, which was divided into an Eastern and a Western part before the German reunification, 54% of the population had a migration background in 2021, compared to 18% in Treptow-Köpenick. Other boroughs with large migrant populations are Neukölln and Friedrichshain-Kreuzberg. Individuals with a migration background from outside the EU constitute the largest share of the total population with a migration background in all of Berlin’s boroughs.

The labour market attachment of Berlin’s population with a migration background is lower in all boroughs. Figure 2.18 shows the labour force participation and the unemployment rate in Berlin’s boroughs among the population aged 15 to 64 by migration background in 2019. Panel A shows that the labour force participation of Berlin’s population with a migration background is lower in all of its boroughs. On average, 72% of those with a migration background were economically active, compared to 83% among Germans without a migration background. Similarly, Panel B shows that the unemployment rate among Germans without a migration background stood at 4% in Berlin, compared to 9% among those with a migration background. Taken together, Figure 2.18 shows that Berlin’s population with a migration background has a significantly lower labour market attachment. The uneven spatial distribution of Berlin’s population with a migration background across the city therefore provides a partial explanation for differences in headline labour market indicators.

One reason for the relatively low labour force participation and the relatively high unemployment rate among people with a migration background is the lower average level of education in that segment of the population. Figure 2.19 shows the educational attainment in Berlin’s population by migration background. In 2019, 24.8% of Berlin’s population aged 25 or older with a migration background fell into the low education category, compared to 8.3% among those without a migration background. The share of the population with a migration background holding a medium level of formal education stood at 33.2%, compared to 51.2% among Germans without a migration background. 42.0% of those with a migration background had attained a high level of education, which is higher than the rate of the German population, which stands at 40.5%. The higher incidence of low education among Berlin’s population with a migration background constitutes a likely reason for the lower labour market attachment of this group. However, as explained in more detail in Box 2.1, migrants also face distinct challenges on the labour market. The non-transferability of degrees across borders, lacking local language skills and lacking citizenship can all have a negative effect on employment prospects also for those with high educational attainment.


[3] Brinkman, J. (2015), “Big Cities and the Highly Educated: What’s the Connection?”, Federal Reserve Bank of Philadelphia Research Department.

[2] 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] Ludolph, L. (2021), The Value of Formal Host-Country Education for the Labour Market Position of Refugees: Evidence from Austria, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3904543.

[4] OECD (2021), OECD Skills Outlook 2021: Learning for Life, OECD Publishing, Paris, https://dx.doi.org/10.1787/0ae365b4-en.

[1] OECD (2010), Higher Education in Regional and City Development - Berlin, Germany, https://www.oecd.org/germany/45359278.pdf (accessed on 19 January 2022).

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