Regions and Cities at a Glance 2020

Regions and Cities at a Glance 2020 provides a comprehensive assessment of how regions and cities across the OECD are progressing in a number of aspects connected to economic development, health, well-being and net zero-carbon transition. In the light of the health crisis caused by the COVID-19 pandemic, the report analyses outcomes and drivers of social, economic and environmental resilience. Consult the full publication here.

OECD REGIONS AND CITIES AT A GLANCE - COUNTRY NOTE

GERMANY

A. Resilient regional societies

B. Regional economic disparities and trends in productivity

C. Well-being in regions

D. Industrial transition in regions

E. Transitioning to clean energy in regions

F. Metropolitan trends in growth and sustainability

The data in this note reflect different subnational geographic levels in OECD countries: • Regions are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3). Small regions are classified according to their access to metropolitan areas (see https://doi.org/10.1787/b902cc00-en). • Functional urban areas consists of cities – defined as densely populated local units with at least 50 000 inhabitants – and adjacent local units connected to the city (commuting zones) in terms of commuting flows (see https://doi.org/10.1787/d58cb34d-en). Metropolitan areas refer to functional urban areas above 250 000 inhabitants. Disclaimer: https://oecdcode.org/disclaimers/territories.html

Regions and Cities at a Glance 2020 Austria country note 2  A. Resilient regional societies

Berlin and Hamburg have the highest potential for remote working A1. Share of jobs amenable to remote working, 2018 Government regions (map)

LUX GBR AUS SWE CHE NLD ISL DNK FRA FIN NOR BEL LTU EST IRL GRC DEU AUT LVA SVN OECD30 PRT HRV POL ITA USA CZE HUN CAN ESP ROU SVK BGR TUR COL 0 10 20 30 40 50 % The shares of jobs amenable to remote working in the German regions range from close to 45% in Hamburg and Berlin to less than 30% in Mecklenburg-Vorpommern and Sachsen-Anhalt (Figure A1). Such differences depend on the task content of the occupations in the regions, which differ in the extent of being amenable to remote working. As in all other OECD countries, occupations available in cities tend to be more amenable to remote working than in other less densely populated areas.

Hamburg has the highest fiber optic availability across large regions (Bundesländer) in Germany with 70% of the buildings connected to the network (Figure A2).

A2- Internet infrastructure Share of buildings connected, 2019 Fiber Cable ADSL Hamburg Schleswig-Holstein Bavaria Saxony Mecklenburg-… North Rhine-Westphalia Lower Saxony Hesse Saxony-Anhalt Berlin Brandenburg Baden-Württemberg Rhineland-Palatinate Thuringia Bremen Saarland

0 20 40 60 80 100 % Low coverage High coverage

Figure [A1]: The lower percentage range (<25%) depicts the bottom quintile among 370 OECD and EU regions, the following ranges are based on increment of 5 percentage points. Further reading: OECD (2020), Capacity to remote working can affect lockdown costs differently across places, http://www.oecd.org/coronavirus/policy-responses/capacity-for-remote-working-can-affect-lockdown-costs-differently-across-places-0e85740e/  3 Ageing challenges eastern regions and regions far from metropolitan areas more strongly The elderly dependency rate, defined as the ratio between the elderly population and the working age (15-64 years) population, is high and has further increased in all types of regions in Germany since 2000. Regions far from metropolitan areas show the highest elderly dependency rate (36%) (Figure A3). In almost 20% of the small regions in Germany, there were two elderly for every five working- age residents in 2019 (Figure A4).

A3. Elderly dependency rate A4. Elderly dependency rate, 2019 By type of small regions in Germany (TL3) Small regions (TL3)

Metropolitan regions Regions near a metropolitan area % Regions far from a metropolitan area

25 OECD average of regions

20 2000 2010 2019

German regions have more hospital beds per capita than the OECD average

A5 - Hospital beds per 1000 inhabitants All regions in Germany have significantly Large regions (TL2) more hospital beds per capita than the ‰ OECD average, although the availability of 12 hospitals has fallen in most regions since 2000 (Figure A5). Regional disparities in 8

hospital beds are above the OECD average,

Vorpommern

- Holstein

with Berlin having the lowest availability of Palatinate

- Westph.

4 -

Anhalt

- Württemberg

hospital beds per 1 000 inhabitants in 2017, -

Baden Rhineland

less than half the level in Mecklenburg- Mecklenburg

Brandenburg N.Rhine

Thuringia

Saxony Saarland Schleswig Bremen Hamburg

Saxony Hesse Bavaria Lower Saxony Berlin Germany OECD Vorpommern. 0 2017 2000

Figure notes. [A3]: OECD (2019), Classification of small (TL3) regions based on metropolitan population, low density and remoteness https://doi.org/10.1787/b902cc00-en. [A4]: Small (TL3) regions contained in large regions. TL3 regions in Germany are composed by 401 Kreise.

B. Regional economic disparities and trends in productivity

Regional economic gaps have declined since 2000, partially due to lower growth of the most productive regions Differences between German regions in terms of GDP per capita have decreased over the last eighteen years. However, regional disparities among remain above the median of OECD countries, with Hamburg having more than twice the GDP per capita than Mecklenburg-Vorpommern (Figure B1). With a productivity growth of 1.5% per year over the period 2000-18, Thuringia, the region with the lowest level of productivity, is catching up with other regions and Hamburg, the frontier region in terms of productivity in Germany. Hamburg experienced a decline in the same period, the lowest productivity growth in Germany (Figure B2). After a period of stagnating productivity compared to metropolitan regions, regions far from a metropolitan area of at least 250,000 inhabitants have narrowed their gap to metropolitan regions since 2007, and even exceeded the productivity level of regions near a metropolitan area in 2017 (Figure B3).

B1. Regional disparity in GDP per capita Top 20% richest over bottom 20% poorest regions Ratio Small regions Large regions

2018 2000 Country (number of regions considered) Note: A ratio with a value equal to 2 means that the GDP of the most developed regions accounting for 20% of the national population is twice as high as the GDP of the poorest regions accounting for 20% of the national population.

B2. Gap in regional productivity B3. Gap in productivity by type of region GDP per worker Productivity level of metropolitan TL3 regions=100 USD Per cent 140 000 92

120 000 90

100 000 88

80 000 86

60 000 84 Hamburg (Highest productivity in 2000) Regions near a metropolitan area Thuringia (Lowest productivity in 2000) Regions far from a metropolitan area 40 000 82  5 C. Well-being in regions

Germany faces large regional disparities in the well-being dimensions of community, safety and education C1 Well-being regional gap

Top region Bottom region Berlin Regions (Bundesländer)

Thuringia Mecklenburg- Thuringia Bavaria Berlin Vorpommern Bavaria

top top 20% Bavaria Baden- Saxony-Anhalt Württemberg Baden- Württemberg

(1 to 440) to (1 Saarland

Mecklenburg- Bremen Brandenburg Vorpommern

middle middle 60% Bremen Saxony-Anhalt Saxony-Anhalt Lower Saxony Baden- Saxony-Anhalt Württemberg

Berlin Ranking Ranking OECD of regions Baden-

Württemberg bottom bottom 20%

Community Safety Education Jobs Health Environment Access to Life Housing Civic Income services Satisfaction Engagement

Note: Relative ranking of the regions with the best and worst outcomes in the 11 well-being dimensions, with respect to all 440 OECD regions. The eleven dimensions are ordered by decreasing regional disparities in the country. Each well-being dimension is measured by the indicators in the table below.

Most German regions rank in the middle 60% of OECD regions in 9 out of 11 well-being dimensions, however, they perform among the top 30% of OECD regions in access to services. In contrast, outcomes across Bundesländer are very unequal in the dimensions of community, safety and education. While Thuringia ranks in the top 5% of OECD regions in terms of education, Bremen is close to the median of OECD regions (Figure C1). The average of the top 20% German regions ranks above the average of the top 20% OECD regions in 5 out of 13 well-being indicators, particularly in terms of unemployment rates and household income (Figure C2).

C2. How do the top and bottom regions fare on the well-being indicators?

Country OECD Top German regions Average 20% regions Top 20% Bottom 20% Community Perceived social netw ork support (%), 2014-18 91.1 94.1 94.0 86.1 Safety Homicide Rate (per 100 000 people), 2016-18 1.0 0.7 0.7 1.6 Education Population w ith at least upper secondary education, 25-64 year-olds (%), 2019 86.6 90.3 92.4 82.6 Jobs Employment rate 15 to 64 years old (%), 2019 76.7 76.0 79.7 73.4 Unemployment rate 15 to 64 years old (%), 2019 3.2 3.3 2.2 4.4 Health Life Expectancy at birth (years), 2018 81.1 82.6 82.1 80.2 Age adjusted mortality rate (per 1 000 people), 2018 8.0 6.6 7.5 8.3 Environment Level of air pollution in PM 2.5 (µg/m³), 2019 14.1 7.0 10.9 13.8 Access to services Households w ith broadband access (%), 2019 92.0 91.3 94.0 88.9 Life Satisfaction Life satisfaction (scale from 0 to 10), 2014-18 7.0 7.3 7.2 6.8 Housing Rooms per person, 2018 1.8 2.3 2.0 1.7 Civic engagement Voters in last national election (%), 2019 or latest year 76.2 84.2 78.2 73.4 Income Disposable income per capita (in USD PPP), 2018 26 083 26 617 28 591 23 087

Note: OECD regions refer to the first administrative tier of subnational government (large regions, Territorial Level 2); Germany is composed of 16 large regions. Visualisation: https://www.oecdregionalwellbeing.org.

Regions and Cities at a Glance 2020 Austria country note 6  D. Industrial transition in regions

Except for five eastern states, employment in manufacturing is falling all German regions D1. Manufacturing employment share, regional gap % Highest growth Highest decline Between 2000 and 2017, 11 out of 16 large 22 regions in Germany experienced a decline in Thuringia the share of employment in manufacturing. 20 With a reduction of 4.5*pp in the share of employment in manufacturing, North Rhine- 18 Westphalia, the most populous region, 16 recorded the largest decrease (Figure D1). N. Rhine-Westphalia 14

Decline in employment in manufacturing coincides with a reduction in manufacturing gross value-added (GVA) in Berlin and in North Rhine-Westphalia (Figure D2). However, the eastern regions Saxony, Brandenburg, Thuringia, Saxony-Anhalt and Mecklenburg-Vorpommern recorded increases in both GVA and employment in manufacturing.

D2. Manufacturing trends, 2000-17 Total jobs Jobs in GVA in

by region manufacturing manufacturing

Size of bubble represents Total regional Employment in GVA in the % value of the indicator employment as a manufacturing as a manufacturing as Colours represent the share of national share of regional a share of regional Legend 2000-17 change employment employment GVA

North Rhine-Westphalia 16

Bavaria 15

Baden-Württemberg 14

Lower Saxony 13

Hesse 12

Saxony 11

Rhineland-Palatinate 10

Berlin 9

Schleswig-Holstein 8

Hamburg 7

Brandenburg 6

Thuringia 5

Saxony-Anhalt 4

Mecklenburg-Vorpommern 3

Saarland 2

Bremen 1

Largest bubble size represents: 21% 25% 33%

Note figure D.2. : Regions are ordered by regional employment as a share of national employment. Colour of the bubbles represents the evolution of the share over the period 2000-17 in percentage points: red: below -2 pp; orange: between -2 pp and -1 pp; yellow: between -1 pp and 0; light blue: between 0 and +1 pp; medium blue: between +1 pp and +2 pp; dark blue: above +2 pp over the period.  7 E. Transitioning to clean energy in regions

North Rhine-Westphalia and Baden-Württemberg, which account for 35% of the German electricity production, still highly rely on coal The larger producer of electricity in Germany – North Rhine-Westphalia – highly rely on coal for electricity generation and use renewable sources only to a limited extent. North Rhine-Westphalia, Baden-Württemberg and generate 42% or more of their electricity using coal and less than one fourth using renewables. In contrast, Lower Saxony and Bavaria are advancing towards low-carbon electricity generation. In 2017, Bavaria produced only 3% of its electricity using coal and 40% using renewable sources (Figure E1). E1. Transition to renewable energy Total electricity Regional share of Regional share of Greenhouse gas generation renewables in coal in emissions from (in GWh per year) electricity generation electricity generation electricity generated (%) (%) (in Ktons of CO2 eq.)

North Rhine-Westphalia 152 406 6% 73% 107 822 Nor. Lower Saxony 81 893 48% 15% 16 529 Low. Bavaria 73 511 40% 3% 13 139 Bav. Baden-Württemberg 69 570 24% 42% 28 139 Bad. Brandenburg 62 339 57% 37% 22 881 Bra. Schleswig-Holstein 57 448 73% 7% 4 755 Sch. Saxony 33 261 23% 70% 20 664 Sax. Saxony-Anhalt 31 029 68% 20% 8 252 Sax. Hesse 16 453 29% 50% 8 853 Hes. Mecklenburg-Vorpommern 12 960 70% 21% 3 128 Mec. Berlin 12 809 6% 39% 7 931 Ber. Saarland 12 246 4% 93% 9 511 Saa. Hamburg 10 846 7% 88% 8 220 Ham. Thuringia 10 143 79% 0% 1 737 Thu. Rhineland-Palatinate 8 894 39% 1% 2 943 Rhi. Bremen 5 905 13% 80% 4 135 Bre.

Carbon efficiency in electricity generation is very unequal across German regions. While Schleswig-Holstein releases 83 tons of CO2 per gigawatt hour of electricity produced, North Rhine-Westphalia emits around 710 tons of CO2 per gigawatt hour. For this reason, in 2017, North Rhine-Westphalia alone was responsible for 40% of Germany’s CO2 emissions from electricity generation, although it only generated 23% of the electricity (E2).

E2. Contribution to total CO2 emissions from electricity production, 2017

Share of electricity production

Share of CO2 emissions % 45 High carbon efficiency Low carbon efficiency 40 Contribution to total electricity production Contribution to total electricity production 35 higher than contribution to CO2 emissions lower than contribution to CO2 emissions 30 25 20 15 10 5 0

Figure notes: Regions are arranged in Figure E1 by total generation, and in Figure E2 according to gap between share of electricity generation and share of CO2 emissions (most positive to most negative). These estimates refer to electricity production from the power plants connected to the national power grid, as registered in the Power Plants Database. As a result, small electricity generation facilities disconnected from the national power grid might not be captured. Renewable energy sources include hydropower, geothermal power, biomass, wind, solar, wave and tidal and waste. See here for more details. Regions and Cities at a Glance 2020 Austria country note 8  F. Metropolitan trends in growth and sustainability

Compared to the OECD average, Germany has a higher concentration of people in medium-sized metropolitan areas of 250k to 500k inhabitants

In Germany, 75% of the population lives in cities of more than 50 000 inhabitants and their respective commuting areas (functional urban areas, FUAs), which corresponds to the OECD average. The share of population in FUAs with more than 500 000 people is 51%, lower than the OECD average of 60% (Figure F1). However, in Germany relatively more people live in medium-sized FUAs. F1. Distribution of population in cities by city size Functional urban areas, 2018 Germany,United percentage States of population in cities Population by city size, a global view

Above Between 250 000 Between 50 000 Other settlements other settlements above % 500 000 pop. and 500 000 pop. and 250 000 pop. below 50 000 below 50 000 500 000 100 25% 80 82.8 million 60 people - 75% 51% between 50 000 6% live in cities 40 and 250 000 75% 75% 64% 60% 51% 18% 20 between 250 000 25% and 500 000 0 Germany Europe OECD (29 countries) (37 countries)

Built-up area has increased faster than population in most metropolitan areas Built-up area per capita has increased in most functional urban areas in Germany since 2000, especially in Wurzburg, Magdeburg and Erfurt where the difference between the growth of urbanised area and change in population is most pronounced. Munich is the only functional urban area in Germany where population grew more than the built-up area (Figure F2).

F2. Built-up area per capita Functional urban areas with more than 500 000 inhabitants

m2 per capita 2014 2000 500

400

300

200

100

Source: OECD Metropolitan Database. Number of metropolitan areas with a population of over 500 000: 28 in Germany compared to 349 in the OECD.  9 Several metropolitan areas in eastern Germany are catching up in terms of GDP per capita, having recorded faster growth than other metropolitan areas since 2000 Munich metropolitan area has the highest GDP per capita in Germany (Figure F3), and is also among the top 5% of OECD metropolitan areas with more than 500 000 people. Yet, many mid-sized and especially eastern metropolitan areas recorded faster growth in GDP per capita.

F3. Trends in GDP per capita in metropolitan areas Functional urban areas above 500 000 people