copy the linklink copied!Chapter 1. Trends, challenges and opportunities in the Hamburg Metropolitan Region

This chapter analyses the main trends, challenges and opportunities in the Hamburg Metropolitan Region (referred to as HMR for short), with a particular focus on three dimensions: i) economic performance, innovation and digitalisation; ii) quality of life, transport, housing, environmental sustainability and tourism; and iii) the institutional framework. In doing so, the chapter benchmarks the HMR with all other metropolitan regions in Germany and with carefully selected, comparable metropolitan regions across OECD countries.

    

copy the linklink copied!Overview of the Metropolitan Region of Hamburg in Germany

The HMR has almost 5.4 million inhabitants (about 6% of the total population in Germany). It is comprised of the Free and Hanseatic City of Hamburg (referred to as Hamburg for short) – the second largest city in Germany, with a population of more than 1.8 million – and parts of 3 surrounding federal states: Lower Saxony, Mecklenburg-Western Pomerania and Schleswig-Holstein (Statistikamt Nord, 2017[1]; Destatis, n.d.[2]).

The HMR is 1 of 11 metropolitan regions in Germany, together with the Capital Region of Berlin-Brandenburg, Bremen-Oldenburg in the Northwest, FrankfurtRheinMain, Hannover-Braunschweig-Göttingen-Wolfsburg, Central Germany, Munich, Nuremberg, Rhein-Neckar, Rhein-Ruhr and Stuttgart.1 Metropolitan regions make up more than half (almost 55%) of the total area of Germany and are home to about two-thirds of its entire population. They do not necessarily constitute a distinct tier of government in the German federal system: only Metropolitan Region (MR) Frankfurt, MR Rhein-Neckar, MR Ruhr and MR Stuttgart, which are organised as associations (Verbände), are distinct tiers according to German basic law. MR Berlin-Brandenburg – with its joint planning authority comprising the two constituent federal states Berlin and Brandenburg – can be seen as a distinct tier of government as well. Metropolitan regions were defined by the Standing Conference of Ministers responsible for Spatial Planning (Ministerkonferenz für Raumordnung), a joint committee comprising the Federal Minister of the Interior, Building and Community and ministers from individual federal states who are responsible for spatial planning.

The HMR is politically defined, and its definition has changed over time. Co-operation within THE HMR dates back to the 1950s: between 1955 and 1962, joint regional planning between the federal states of Hamburg and Schleswig-Holstein, as well as between Hamburg and Lower Saxony was established, including bilateral promotional funds as a common financial instrument to grant funding to projects put forward by municipalities in the region. In 1991, an intergovernmental agreement was signed between Hamburg, Lower Saxony and Schleswig-Holstein about trilateral co-operation within the metropolitan region of Hamburg. From 1992 onwards, the first Regional Development Concept (Regionales Entwicklungskonzept) was developed, and in 1995, the formal recognition as a metropolitan region by the Standing Conference of Ministers responsible for Spatial Planning followed. In 1997, the trilateral Joint Regional Planning “Metropolregion Hamburg” was established, including the installation of political and executive bodies, in particular, the Planning Council (from 2006 onwards Regional Council), Steering Committee and thematic working groups. A first regional expansion followed in the same year, and the ensuing years saw a first operative programme and a first administrative agreement about co-operation within the metropolitan region. While the region followed initially a decentralised approach, with a joint secretariat at three decentralised locations (Hamburg, Bad Segeberg, and Lüneburg), the secretariat was centralised in Hamburg from 2009 onwards.

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Figure 1.1. The metropolitan region of Hamburg with constituent federal states
Figure 1.1. The metropolitan region of Hamburg with constituent federal states

Source: Provided by the Metropolitan Region of Hamburg.

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Figure 1.2. The metropolitan region of Hamburg with constituent districts
Figure 1.2. The metropolitan region of Hamburg with constituent districts

Source: Provided by the Metropolitan Region of Hamburg.

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Figure 1.3. Metropolitan regions in Germany as of April 2018
Figure 1.3. Metropolitan regions in Germany as of April 2018

Source: Provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development.

From 2006 onwards, the metropolitan region saw several geographical expansions as well as an inclusion of a larger group of stakeholders. In 2006, the remainder of the district of Dithmarschen was included, and in the same year, districts became stakeholders. In 2012, with the involvement of the federal state of Mecklenburg-Western Pomerania, the districts Ludwigslust and Nordwestmecklenburg were included. Likewise, the district Ostholstein and the unitary cities Lübeck and Neumünster, part of the already constituent federal state of Schleswig-Holstein, were included. The latest regional expansion followed in 2017, with the inclusion of Mecklenburg-Western Pomerania’s state capital of Schwerin and the district of Parchim (Ludwigslust and Parchim have merged to form a single district Ludwigslust-Parchim). At the same time, several stakeholders joined, including the Chamber for Commerce Hamburg, Chambers for Commerce and Industry Flensburg (IHK Flensburg), Lübeck (IHK Lübeck), Lüneburg-Wolfsburg (IHK Lüneburg-Wolfsburg), Kiel (IHK Kiel), Schwerin (IHK Schwerin), Stade (IHK Stade); the Chambers of Crafts Hamburg, Lübeck and Schwerin; the United Business Associations of Hamburg and Schleswig-Holstein (UV Nord); and the Federation of German Trade Unions, District North (DGB Nord). A Second Strategical Framework was adopted in 2017, covering the years 2017-2020, as a successor to the First Strategical Framework (2011-2013).

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Box 1.1. History of metropolitan regions in Germany

The notion of urban regions and agglomerations with international standing as drivers of economic growth in Germany dates back to the Spatial Policy Orientation Framework (Raumordnungspolitischer Orientierungsrahmen) and the Spatial Policy Report (Raumordnungsbericht) by the federal government in 1993. The HMR was first mentioned in the Regional Development Concept for the Metropolitan Region of Hamburg in 1991.

In 1995, the first six European metropolitan regions, including the HMR, Berlin-Brandenburg, Frankfurt, Munich, Rhein-Ruhr and Stuttgart, were recognised by the Standing Conference of Ministers responsible for Spatial Planning. In 1997, the Metropolitan Region Sachsendreieck (the predecessor of today’s Central Germany) followed. The federal government recognised metropolitan regions as “engine[s] of societal, economic, social and cultural success, which enhance the European integration process” (Federal Ministry for Regional Planning, Building and Urban Development, 1995[3]). They were seen as to increase participation in globalisation processes beyond urban centres, born out of a tension between exploiting spatial agglomeration benefits on the one hand and avoiding negative externalities associated with increasing populations in urban centres on the other.

In 2005, after a decision by the Standing Conference of Ministers responsible for Spatial Planning, four additional metropolitan regions were recognised, including Northwest, Hanover, Nuremberg and Rhein-Neckar. The metropolitan region Rhein-Ruhr consists of two parts: Rheinland (which was established in 2017 and has taken over the part that had hitherto been played by the Köln/Bonn Region) and Ruhr. There are 11 metropolitan regions in Germany today.

In Germany, metropolitan regions are organised within the Association of European Metropolitan Regions in Germany (Initiativkreis Europäische Metropolregionen in Deutschland). At the European level, the HMR and the metropolitan regions Berlin-Brandenburg, Central Germany, Frankfurt, Nuremberg, Rhein-Neckar, Rheinland and Stuttgart are part of the Network of European Metropolitan Regions and Areas (METREX).

Source: Federal Ministry for Regional Planning, Building and Urban Development (1995[3])Raumordnungspolitischer Handlungsrahmen: Beschluss der Ministerkonferenz für Raumordnung in Düsseldorf am 8. März 1995.

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Box 1.2. Germany’s federal structure

Germany is a federal republic comprised of 16 federal states (Bundesländer). Besides the federal government (Bundesregierung) in Berlin (where the Federal Ministry of the Interior, Building and Community is located), there exists 16 individual state governments (Landesregierungen) – located in 16 state capitals – which enjoy large autonomy when it comes to, for example, education, energy or spatial policy. Two types of federal states can be distinguished: city-states (Stadtstaaten), which are states that are geographically limited to a single city, and territorial states (Flächenstaaten), which span a wider geographical area.2 There are three city-states in Germany: Berlin, which also hosts the federal government, Bremen and Hamburg; with the remaining states being territorial states. Federal states are, in turn, subdivided into unitary cities (Kreisfreie Städte) and county districts (Landkreise), with a (largely formal) distinction similar to city and territorial states. There are 107 unitary cities and 294 county districts in Germany, yielding 401 districts in total, with numbers and size varying by federal state. For example, there are 37 districts in Lower Saxony but only 6 in Mecklenburg-Western Pomerania. While executive, legislative and judicial powers lie with the federal and respective state governments, government at the district level is limited to executive powers. Below county districts and unitary cities, there are municipalities (Gemeinden). Note that in some federal states, there is an additional layer between county districts and unitary cities on the one hand and municipalities on the other such as, for example, bureaus (Ämter) in Mecklenburg-Western Pomerania.

The German federal system

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A relatively fragmented administrative structure

The HMR has a relatively fragmented administrative structure (Table 1.1). It spans across 4 federal states: 1 city-state (Hamburg, with 100% of its territory lying within the HMR) and 3 territorial states (Lower Saxony, 26%; Mecklenburg-Western Pomerania, 30%; and Schleswig-Holstein, 51%). No other metropolitan region in Germany spans more federal states – the mean number of federal states is two. The HMR is also one of the few metropolitan regions in Germany (besides Berlin-Brandenburg and Northwest) that includes a city-state. At the same time, the HMR consists of only 20 districts, less than the average number of districts that metropolitan regions in Germany typically contain, which is 22. Two of these districts are located in other metropolitan regions at the same time: Cuxhaven (Northwest) and Heidekreis (Hanover). To the extent that federal states differ in the way and level at which administrative processes are implemented at the regional and local levels, a relatively more fragmented administrative structure may require more co-ordination and co-operation amongst its stakeholders.

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Box 1.3. Implications of administrative fragmentation

Administrative fragmentation can be measured by the number of local governments (in total or per capita) within a specific geographical area (including across different regions/states or across different countries). Administrative fragmentation can have two, potentially opposing, effects on economic performance: on the one hand, more fragmentation may enhance economic performance as it may give greater choice over public service provision and put competitive pressure on local governments to align public goods and services with residents’ preferences. On the other hand, however, it may lead to duplication of efforts and reduced economies of scale. In a context of tight public finances, administrative fragmentation further complicates the efficient delivery of transport, housing, schools, hospitals and other public services that are central to residents’ well-being.

A growing body of evidence suggests that administrative fragmentation has, indeed, adverse effects on economic performance on average (see Martinez-Vasquez et al. (2017[4]), for example). Ahrend et al. (2014[5]) study the impact of administrative fragmentation on labour productivity in 5 OECD countries (Germany, Mexico, Spain, the United Kingdom and the United States), exploiting observations on wages of more than 2 million residents across 430 OECD functional urban areas (i.e. geographical areas that are defined by commuting behaviour rather than administrative boundaries and, therefore, vary in the number of local governments). The authors show that doubling the number of local governments within a metropolitan area reduces labour productivity by about 6%, potentially outweighing spatial agglomeration benefits. This holds true even when controlling for a wide range of other, productivity-driving differences such as city size, average human capital, the presence of a port or capital city status.

The OECD (2015[6]) finds that, during the period 2000 to 2010, metropolitan areas with low administrative fragmentation experienced growth in gross domestic product (GDP) per capita that was more than twice as strong as those with high fragmentation. Bartolini (2015[7]) shows that fragmentation harms growth in GDP per capita most in and around urban areas (where people are more likely to commute across administrative boundaries). In fact, the suboptimal provision of public transport infrastructure (where, for example, transport modes such as subways end at administrative borders for no apparent economic reason) is an often-cited symptom of fragmentation.

Besides amalgamation of municipalities, one way to overcome adverse effects of administrative fragmentation is to create an overarching entity dedicated to policy co-ordination between local governments (often referred to as a metropolitan governance body). About two-thirds of 275 OECD metropolitan areas studied in the OECD Metropolitan Governance Survey have such entities in place (most operating on a voluntary basis), although with varying competencies (most co-operate in regional development, transportation and spatial planning) (Ahrend, Gamper and Schumann, 2014[8]; OECD, 2015[9]). Ahrend et al. (2014[5]) show that the presence of metropolitan government bodies can reduce the penalty associated with administrative fragmentation, on average, by half. Several transmission channels can explain this positive relationship. Metropolitan co-ordination can help exploit synergies across different policy sectors (transport, spatial planning and housing, for example). It can also help reduce costs, reap economies of scale and improve the quality of public service delivery, thereby contributing to higher productivity.

Source: Martinez-Vazquez, J., S. Lago-Peñas and A. Sacchi (2017[4]), “The impact of fiscal decentralization: A survey”, Journal of Economic Surveys, Vol. 31/4, pp. 1095-1129; Ahrend, R. et al. (2014[5]), “What Makes Cities More Productive? Evidence on the Role of Urban Governance from Five OECD Countries”, https://dx.doi.org/10.1787/5jz432cf2d8p-en; Bartolini, D. (2015[7]), “Municipal Fragmentation and Economic Performance of OECD TL2 Regions”https://dx.doi.org/10.1787/5jrxqs60st5h-en; Ahrend, R., C. Gamper and A. Schumann (2014[8]), “The OECD Metropolitan Governance Survey: A Quantitative Description of Governance Structures in large Urban Agglomerations”, https://dx.doi.org/10.1787/5jz43zldh08p-en; OECD (2015[9])Governing the Cityhttps://dx.doi.org/10.1787/9789264226500-en; Ahrend and Lembcke (2015).

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Table 1.1. Administrative structure of metropolitan regions in Germany

Founded

Number of federal states

of which city-states

of which territorial states

Number of districts

of which unitary cities

of which county districts

Number of districts belonging to other regions

HMR

1995

4

1

3

20

3

17

2

Berlin-Brandenburg

1995

2

1

1

19

5

14

0

Northwest

2005

2

1

1

16

5

11

1

Frankfurt

1995

3

0

3

25

7

18

1

Hanover

2005

1

0

1

18

3

15

1

Central Germany

1997

3

0

3

12

6

6

0

Munich

1995

1

0

1

33

6

27

0

Nuremberg

2005

2

0

2

34

11

23

0

Rhein-Neckar

2005

3

0

3

15

8

7

1

Rhein-Ruhr

1995

1

0

1

31

20

11

0

Stuttgart

1995

1

0

1

20

3

17

0

Note: Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018).

A monocentric region with a wide periphery

Table 1.2 shows the descriptive statistics on the geographical area, structure and population of the HMR relative to the average of all other metropolitan regions in Germany.3 Amongst the 11 metropolitan regions in Germany, the HMR is the second largest (about 8% of the total area of Germany), with the largest being MR Berlin-Brandenburg (about 8.5%) and the smallest MR Rhein-Neckar (about 1.6%). Despite its large total area, however, the share of area used for settlement – residential, commercial and industrial – or transport in the HMR is relatively small, at about 12.5%. In this respect, the region ranks 8th out of 11, with the largest share being in MR Rhein-Ruhr (about 34%) and the smallest in MR Berlin-Brandenburg (about 11.2%). For the HMR, the low ratio of area used for settlement or transport to total area is also reflected in its low population density; with 186 inhabitants per square kilometre, the region ranks 10th out of 11 (MR Rhein-Ruhr, with 991 inhabitants per square kilometre, and MR Nuremberg, with 161 inhabitants per square kilometre, are the most and least densely populated metropolitan regions respectively). Given its relatively large total area, low share of area used for settlement and transport, and low overall population density with a densely populated urban core, the HMR can be characterised as a monocentric region with a wide second ring. This second ring includes all districts that are not immediately adjacent to the urban core (which are, in turn, referred to as first ring).

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Table 1.2. Geographical area, structure and population of metropolitan regions in Germany

 

Total area (km²)

Share of total area in Germany (%)

Area for settlement, transport (km²)

Share of area for settlement, transport in region (%)

Total population (million)

Share of total population in Germany (%)

Inhabitants per km²

HMR

28 469

8

3 562

12.5

5.3

6.4

186

Berlin-Brandenburg

30 546

8.5

3 426

11.2

6

7.3

197

Northwest

13 751

3.8

2 207

16

2.8

3.3

200

Frankfurt

14 755

4.1

2 592

17.6

5.7

6.9

385

Hanover

18 580

5.2

2 669

14.4

3.8

4.7

206

Central Germany

9 114

2.6

1 475

16.2

2.5

3

275

Munich

25 548

7.1

3 131

12.3

6

7.3

235

Nuremberg

21 783

6.1

2 608

12

3.5

4.3

161

Rhein-Neckar

5 637

1.6

1 091

19.4

2.4

2.9

422

Rhein-Ruhr

11 744

3.3

3 988

34

11.6

14.2

991

Stuttgart

15 427

4.3

2 415

15.7

5.4

6.5

347

Notes: Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest;

Frankfurt = FrankfurtRheinMain; Hanover = Hanover Braunschweig Göttingen Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

The HMR ranks upper midfield for economic development in Germany

The HMR generates about 6.2% of German GDP, with MR Rhein-Ruhr (13.1%) being the largest and MR Central Germany (2.2%) the smallest regional economies amongst the German metropolitan regions (Table 1.3). Regarding aggregate labour market performance, the HMR ranks 6th out of 11 for unemployment, regardless of whether the unemployment rate is measured as the share of the unemployed within the total population, which is 3.2%, or as the share of the unemployed within the labour force, which is 6.6%. MR Munich performs best, with the lowest unemployment rate (1.9%, 3.1%), whereas MR Berlin-Brandenburg (5.1%, 9.3%) and MR Central Germany (4.7%, 9.4%) perform worst with highest levels of unemployment. In terms of employment rates, the HMR ranks 6th for the share of the employed within the labour force, but 10th out of 11 for the share of the employed within the total population. Most other metropolitan regions in Germany, however, are not vastly different in this respect. In terms of GDP per capita (as a measure of overall economic activity), the HMR is placed in the upper middle field, ranking 4th out of 11 (Munich ranks first, Central Germany last).

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Table 1.3. Economic development of metropolitan regions in Germany

 

GDP (EUR 

billion)

Share of GDP in Germany (%)

GDP per capita (EUR)

Change 2005-15 (%)

Employed (million)

Employment rate (% total population)

Employment rate (% labour force)

Unemployed (thousand)

HMR

209.7

6.2

39 604

19

2.6

48.2

81

172

Berlin-Brandenburg

190.6

5.6

31 669

39

2.9

48.7

83.1

309.7

Northwest

92.2

2.7

33 517

32

1.4

51.3

81.4

97.8

Frankfurt

255.2

7.6

44 930

21

2.9

51.6

80.4

150.5

Hanover

133.5

4

34 881

28

1.8

46.8

80.6

137.9

Central Germany

73.5

2.2

29 345

42

1.3

50.3

82.2

118.1

Munich

299

8.9

49 797

30

3.4

56.9

82.5

112.5

Nuremberg

128.7

3.8

36 696

39

1.9

55.5

84

81.7

Rhein-Neckar

92.6

2.7

38 914

29

1.2

52.4

79.9

63.2

Rhein-Ruhr

440.8

13.1

37 879

28

6

51.3

78.8

543.4

Stuttgart

239.5

7.1

44 738

39

3

55.6

82.5

112.9

Notes: Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest;

Frankfurt = FrankfurtRheinMain; Hanover = Hanover Braunschweig Göttingen Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklunghttp://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015

The HMR shows lower labour productivity than directly comparable metropolitan regions across the OECD

The HMR is at the lower end of labour productivity of directly comparable metropolitan regions across the OECD, showing labour productivity like that of Barcelona (Spain). Table 1.4 shows descriptive statistics on population and economic development in the HMR alongside selected comparator regions across the OECD. Comparator regions are divided into principal comparators, which follow strict comparison rules, and other comparators, which follow more lenient rules. Principal comparators include Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden), and Rotterdam (Netherlands), while other comparators include Athens (Greece), Birmingham (UK), Busan (Korea), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Montreal (Canada), Naples (Italy), Oslo (Norway), Rome (Italy), Stockholm (Sweden), Vancouver (Canada).

Barcelona (Spain) and Boston (US) have slightly larger populations (about 7.5 million and 6.8 million respectively) than the HMR (almost 5.4 million), whereas Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands) have relatively smaller populations (about 2.5 million, 1.9 million and 3.6 million respectively). Boston (US) and Rotterdam (Netherlands) have larger average incomes (about EUR 59 000 and EUR 41 000 respectively) than the HMR (about EUR 40 000), whereas Barcelona (Spain) has a smaller average income (about EUR 34 000). In Copenhagen (Denmark) and Gothenburg (Sweden), income levels (about EUR 39 000) are very similar to the HMR. Finally, all regions have slightly different levels of labour productivity, measured in terms of GDP per employed, with the HMR being at the lower end, performing only slightly better than Barcelona (Spain). The other comparator regions are broadly in line with the HMR and principal comparators, with slightly larger populations and lower average income levels (Annex Table 1.A.1).

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Box 1.4. International comparison of the HMR’s economic performance

While it is natural to compare the HMR with other metropolitan regions in Germany as these are set within a similar institutional context, comparing the HMR with other regions across the OECD faces the challenge of balancing comparability with heterogeneity, i.e. choosing regions that are comparable but not too comparable in order to allow for learning from differences. The HMR’s economic performance has, therefore, been benchmarked against the performance of other metropolitan regions across the OECD in two steps:

  • A set of primarily comparable regions was selected based on three criteria. First, primarily comparable regions should have about the same population as the HMR. Second, they should have a comparable level of development. Finally, candidates should, ideally, have a (major) port, although this is used as a weaker criterion. Based on these criteria, Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands) were selected as primarily comparable regions.4

  • As an extended set of comparable regions, Athens (Greece), Birmingham (UK), Busan (Korea), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Montreal (Canada), Naples (Italy), Oslo (Norway), Rome (Italy), Stockholm (Sweden) and Vancouver (Canada) were chosen. Here, the selection criteria were less stringent: the population in each region should be below 10 million and the ratio of population in the core functional urban area to that in the surrounding administrative area should be between 40% and 80% (given that this ratio is about 60% for the HMR).5

To compare regions across OECD countries, the OECD defines regions as administrative tiers of subnational government. Two categories of relevance to this review are distinguished: larger regions (OECD TL2 level), which, in case of Germany, correspond to the 16 federal states (Bundesländer) and smaller regions (OECD TL3 level), which correspond to the 401 districts (Kreise), including both county districts and unitary cities.

Note that these categorisations may differ by country as administrative tiers of subnational government may differ. In France, for example, regions at the OECD TL2 level correspond to the 13 Régions de France métropolitaine, whereas regions at the OECD TL3 level correspond to the 96 Départements de France métropolitaine.

When conducting comparisons with other metropolitan regions across the OECD, wherever possible, the HMR is constructed from smaller regions (OECD TL3 level), taking into account exact geographical boundaries of the HMR. The majority of the other regions are constructed from larger regions (OECD TL2 level). In some instances, several OECD TL2 or TL3 regions are combined to provide a more accurate picture of the respective region.

Source: Based on OECD (2018[11]), Territorial Grids, OECD, Paris, http://www.oecd.org/cfe/regional-policy/territorial-grid-2018.pdf.

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Table 1.4. Comparison of economic development with metropolitan regions across the OECD

 

Total population (million)

Share of total population in country (%)

Inhabitants per km²

GDP

(EUR

billion)

Share of GDP in country (%)

GDP per capita (EUR)

Change 2000-16 (%)

HMR

5.3

6.4

186

209.7

6.2

39 604

18.9

Barcelona (Spain)

7.5

16.1

233

254.2

19.1

34 233

9.4

Boston (US)

6.8

2.1

336

400.5

2.7

58 793

20.1

Copenhagen (Den-mark)

2.5

43.1

359

112

50.3

38 710

10.9

Gothenburg (Sweden)

1.9

19

68

76.8

19.8

38 823

29.4

Rotter-dam (Netherlands)

3.6

21.1

1 301

148

21.4

40 707

10.4

Average principal comparators

4.5

20

459

198.3

23

42 253

16

Median principal comparators

3.6

19

336

148

20

38 823

11

Average other

5.4

24

316

173.9

27

33 579

13

Median other

5.2

16

376

167.7

18

31 387

9

Notes: Metropolitan regions are composed of OECD TL2 or TL3 regions.

Principal comparators = Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden), Rotterdam (Netherlands). See Footnote 4 for exact compositions.

Other comparators = Athens (Greece), Birmingham (UK), Busan (Korea), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Montreal (Canada), Naples (Italy), Oslo (Norway), Rome (Italy), Stockholm (Sweden), Vancouver (Canada). See Footnote 5 for exact compositions.

USD converted into EUR as of 4 February 2019.

Source: Own calculations based on OECD Regional Statistics (n.d.[12]), Regional Social and Environmental Indicators: Internet Broadband Access, http://stats.oecd.org (accessed on 18 December 2018), latest available data from 2016 except for Marseille (France), which are from 2015.

In sum, the HMR has a more fragmented administrative structure than other metropolitan regions in Germany. As a monocentric region with a densely populated urban core and a wide second ring, the region takes a midfield to upper midfield position compared with other metropolitan regions in Germany when it comes to the level of economic development, measured in terms of unemployment, employment and GDP. The HMR has about the same average income as principal comparators across the OECD but ranks relatively low for labour productivity.

copy the linklink copied!Economic performance, innovation and digitalisation

Labour productivity lags behind regions in Southern Germany

Although the HMR started at a slightly higher level of labour productivity compared to all other metropolitan regions in Germany in 2005, this gap has almost closed in 2015. Figure 1.4 plots the evolution of labour productivity, measured as GDP per employed (i.e. output divided by the number of individuals generating it) in the HMR compared to all other metropolitan regions in Germany, pooled together, during the period 2005 to 2015, the latest year for which comparable data at the district level are currently available. Other regions, which had, on average, labour productivity slightly below that of the HMR in 2005, grew faster and, by 2015, had reached almost the same level as the HMR.6 A similar picture arises for GDP per capita (as opposed to employed) as a measure of overall economic activity: the HMR grew from a level of EUR 33 246 in 2005 to a level of EUR 39 549 in 2015 (+19%); other regions grew from a level of EUR 29 740 to a level of EUR 39 105 (+31%).

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Figure 1.4. Labour productivity: The HMR vs. other metropolitan regions in Germany
Figure 1.4. Labour productivity: The HMR vs. other metropolitan regions in Germany

Notes: GDP per employed in EUR 1 000.

The figure takes into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Other metropolitan regions in Germany = MR Berlin-Brandenburg, Northwest, MR Frankfurt, MR Hanover, Central Germany, MR Munich, MR Nuremberg, MR Rhein-Neckar, MR Rhein-Ruhr, MR Stuttgart.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; weighted by district employment level.

Note that as the structure and composition of metropolitan regions have changed over time (for example, because districts have entered or left a region), it is difficult to compare regions over time. To minimise bias, regions are compared based on their most recent structure and composition and then projected back in time. That is, the structure and composition of the HMR in 2018, including the city of Schwerin (which entered the HMR in 2017) is taken to be the same as in 2005. The same logic applies to all other regions in Germany.

In particular, compared to metropolitan regions in Southern Germany, the HMR experienced relatively sluggish growth in labour productivity during the period 2005 to 2015. Figure 1.5 shows that MR Munich, which was already at a higher level in 2005, has almost doubled its initial gap in GDP per employed by 2015; MR Stuttgart, which was initially at a lower level, has leaped ahead comfortably; and MR Rhein-Neckar, which was also initially at a lower level, is now at about the same level as the HMR. Again, a similar picture arises for GDP per capita: while GDP per capita increased by about 19% in the HMR between 2005 and 2015, it increased by about 30% in MR Munich, 39% in MR Stuttgart, 29% in MR Rhein-Neckar and 39% in MR Nuremberg.

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Figure 1.5. Labour productivity: The HMR vs. metropolitan regions in Southern Germany
Figure 1.5. Labour productivity: The HMR vs. metropolitan regions in Southern Germany

Notes: GDP per employed in EUR 1 000.

The figure takes into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; weighted by district employment level.

The initial gap in labour productivity between the HMR and metropolitan regions in Southern Germany amounted to slightly less than EUR 4 000 in 2005 but has risen over time by about 50%. By 2015, it amounted to more than EUR 6 000. Figure 1.6 plots the annual difference in GDP per employed between the HMR and metropolitan regions in Southern Germany, pooled together, showing the divergence in productivity over time in terms of this difference. This figure can be interpreted as the amount that the HMR is missing out by not growing as fast as metropolitan regions in Southern Germany, and the size of this amount is growing over time, as shown by the downward-sloping (dotted) trend line. The slope of this trend line indicates that, if this downward trend continues, the HMR will be missing out about EUR 1 000 in GDP per employed relative to metropolitan regions in Southern Germany every 4 years.

It is unlikely that relative sluggishness in GDP per capita and GDP per employed is driven by a demographic factor alone (i.e. a relative increase in the population in the HMR which then, mechanically, reduces GDP per capita or GDP per employed): for example, MR Munich, which clearly outperformed the HMR, experienced a constant and strong population growth of about 6.3% between 1997 and 2010, increasing to about 8.5% until 2015. the HMR, on the contrary, experienced a lower and more volatile population growth, of about 4.1% between 1997 and 2006, about 2.1% if the years 2000 to 2010 are considered, and about 4.3% between 2010 and 2015 (Federal Institute for Research on Building, Urban Affairs and Spatial Development, 2008[13]; 2012[14]; n.d.[10]). One key factor for explaining sluggish relative growth in the HMR is the low level of productivity gains compared to other metropolitan regions in Germany, which may arise due to differences (both stock and flow) in human capital and research and development.

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Figure 1.6. Difference in labour productivity between the HMR and metropolitan regions in Southern Germany
Figure 1.6. Difference in labour productivity between the HMR and metropolitan regions in Southern Germany

Notes: Difference in GDP per employed in EUR 1 000.

Solid line = Difference between labour productivity in the HMR and labour productivity in metropolitan regions in Southern Germany (MR Munich, MR Nuremberg, MR Rhein-Neckar, MR Stuttgart), pooled together. Dashed line = Trend in difference in GDP per employed.

The figure takes into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10])INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklunghttp://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; weighted by district employment level.

Large differences in labour productivity within the HMR persist

The HMR is not a homogeneous entity when it comes to labour productivity. Figure 1.7 plots GDP per employed in the Free and Hanseatic City of Hamburg vs. the first ring (including, for brevity, all districts directly bordering the city, which are Harburg, Herzogtum Lauenburg, Pinneberg, Segeberg, Stade and Stormarn) vs. the second ring (including, for brevity, all districts and unitary cities not directly bordering the city, which are Cuxhaven, Dithmarschen, Heidekreis, Lübeck, Lüchow-Dannenberg, Lüneburg, Ludwigslust-Parchim, Neumünster, Nordwestmecklenburg, Ostholstein, Rotenburg [Wümme], Schwerin, Steinburg and Uelzen). There is a discrepancy in labour productivity between the city of Hamburg and the rest of the region and little convergence over time: the productivity difference that prevailed in 2015 was almost the same as in 2005. Yet, districts in the second ring have caught up with districts in the first ring, which might be explained by changes in the spatial economic structure brought by, for example, the expansion of wind power in the second ring and the closure of conventional power plants in the core region; infrastructure pertaining to connectivity has changed little during that period.

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Figure 1.7. Labour productivity: Hamburg City vs. First ring vs. Second ring
Figure 1.7. Labour productivity: Hamburg City vs. First ring vs. Second ring

Notes: Difference in GDP per employed in EUR 1 000.

Hamburg = Free and Hanseatic City of Hamburg; First ring = Harburg, Herzogtum Lauenburg, Pinneberg, Segeberg, Stade, Stormarn; Second ring = Cuxhaven, Dithmarschen, Heidekreis, Lübeck, Lüchow-Dannenberg, Lüneburg, Ludwigslust-Parchim, Neumünster, Nordwestmecklenburg, Ostholstein, Rotenburg (Wümme), Schwerin, Steinburg, Uelzen.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10])INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; weighted by district employment level.

The HMR’s economic performance is improving relative to other metropolitan regions across the OECD

Although the HMR started at lower labour productivity compared to both principal and other comparators across the OECD in 2005, it has shown relatively strong growth, and by 2015, has substantially reduced its initial gap to other comparators. A large gap, however, still exists in 2015 between the HMR and principal comparators (although smaller than in 2005). Figure 1.8 plots the evolution of GDP per employed for the HMR alongside the principal comparators of Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands) as well as other comparators across the OECD, pooled together, during the period 2005 to 2015. The HMR did, therefore, perform relatively well internationally. There is some evidence that other regions show stronger uptake in growth from 2013 onwards. GDP per capita in principal comparators grew by 6% between 2005 and 2015, and only by 5% in other comparators. Growth in the HMR, as well as other regions, was stronger than in their countries, on average: in Germany, for example, growth in labour productivity during 2005 to 2015 was about 8%, while it was about 13% in the HMR. For principal comparators, average country-level growth in labour productivity was about 6%, while for metropolitan regions within countries it was about 7%.

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Figure 1.8. Labour productivity: The HMR vs. metropolitan regions across the OECD
Figure 1.8. Labour productivity: The HMR vs. metropolitan regions across the OECD

Notes: GDP per employed in EUR 1 000.

Principal comparators = Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden), Rotterdam (Netherlands).

Other comparators = Athens (Greece), Birmingham (UK), Busan (Korea), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Montreal (Canada), Naples (Italy), Oslo (Norway), Rome (Italy), Stockholm (Sweden), Vancouver (Canada).

USD converted into EUR as of 4 February 2019.

Source: Own calculations based on OECD Regional Statistics (n.d.[12]), Regional Social and Environmental Indicators: Internet Broadband Access, http://stats.oecd.org (accessed on 18 December 2018), latest available data from 2016 except for Marseille (France), which are from 2015.

The HMR scores lower for human capital than other regions in Germany

The HMR performs less well when it comes to the educational profile of its workforce compared to other metropolitan regions in Germany, in particular, those in the south. Differences in human capital may be an important factor driving differences in productivity across regions over time. Annex Table 1.A.2 shows, amongst others, shares of employed by degree, shares of employed in two human capital-intensive sectors (the high-tech and creative sectors, which also include crafts, service companies and freelancers) and shares of students enrolled in university as a percentage of the overall population with recent rates of change, by metropolitan region in Germany. With about 14.4%, the HMR is ranked 8th out of 11 for the share of employed with a tertiary degree (defined as a master school, college or university degree), clearly being outperformed by MR Munich, which is the frontrunner at about 18.9%. The bottom-placed is Northwest with a share of only 10.5%. However, the HMR ranks fourth in terms of growth, suggesting moderate catch-up.

The HMR has relatively low shares of high-tech employment and employed with a tertiary degree

The picture is even less favourable for the share of employed in the high-tech sector (Figure 1.9, left panel): with about 4.8%, the HMR ranks only 10th out of 11, the frontrunner being MR Stuttgart in the south (about 14.1%) and the bottom-placed MR Berlin-Brandenburg (about 3.6%). Almost three times as many employees in the labour force work in the knowledge-intensive high-tech sector in MR Stuttgart than in the HMR. While there are positive growth rates in MR Munich (about 3.1%) and MR Stuttgart (about 1.5%), both of which are in the south, the share of the employed in this sector has stagnated, even showing a slight drop in the HMR (about -0.2%). With about 2.4%, the HMR ranks 9th when it comes to the share of employed with a tertiary degree (Figure 1.9, right panel), with MR Munich in the south being first (about 18.9%) and Northwest being last (about 10.5%). The HMR performs fairly well when it comes to employment in the creative sector, in third place (about 3.3%) with a positive growth trend (about 3%) (Annex Table 1.A.2).

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Figure 1.9. High-tech employment and share of employed with a tertiary degree in 2015
Figure 1.9. High-tech employment and share of employed with a tertiary degree in 2015

Note:s Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg;Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

The HMR ranks midfield to lower midfield for quality of education

Indicators of human capital presented so far have been related to the quantity of education. Quality of education, however, is just as important. It is difficult to measure the quality of education, and even more so to compare it between metropolitan regions in Germany, considering differences in their education systems. Due to the federal structure of Germany, education policy is the responsibility of federal states. To some extent, federal states co-ordinate education policy in the Standing Conference of the Ministers of Education and Cultural Affairs of the Federal States (Kultusministerkonferenz), a voluntary body without legislative power comprising the ministers responsible for education policy from the federal states. Differences in education policy between federal states, however, still remain. It is even more difficult to compare quality of education between metropolitan regions in Germany when these regions are composed of different federal states. Each of the four federal states of which the HMR is composed has their own education policy. MR Munich and MR Stuttgart, on the contrary, are comprised of (parts of) only one federal state (Bavaria and Baden-Württemberg respectively), making it a homogeneous educational space.

The Educational Monitoring 2018 (Bildungsmonitor 2018) by the Initiative New Social Market Economy (Initiative für Neue Soziale Marktwirtschaft), an organisation related to the German Employers’ Association, benchmarks the 16 federal states in Germany against each other based on 93 indicators (collected from various sources), covering the entirety of the educational system from primary schooling to tertiary education in each federal state (Institut der deutschen Wirtschaft Köln, 2018[15]). An excerpt from the latest edition of the report is shown in Table 1.5.

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Table 1.5. Excerpt from Educational Monitoring 2018 on selected benchmarks

Federal state

Overall ranking

Quality of schools

Vocational training and labour market orientation

Tertiary education and “STEM”

Research orientation

Baden-Württemberg

4

8

2

3

8

Bavaria

3

2

1

9

5

Berlin

13

15

15

5

2

Brandenburg

14

5

14

15

16

Northwest

16

16

7

1

7

Hamburg

5

14

4

12

6

Hesse

10

12

12

10

12

Mecklenburg-Western Pomerania

7

7

8

13

3

Lower Saxony

8

11

9

14

9

North-Rhine Westphalia

15

13

16

7

11

Rhineland Palatinate

9

10

5

8

15

Saarland

6

9

10

6

3

Saxony

1

1

6

2

1

Saxony-Anhalt

12

4

11

11

13

Schleswig-Holstein

10

6

13

16

14

Thuringia

2

3

3

4

10

STEM = Science, Technology, Engineering and Mathematics.

Source: Initiative für Neue Soziale Marktwirtschaft (2018[16]) Der INSM-Bildungsmonitor 2018, http://www.insm-bildungsmonitor.de (accessed on 18 December 2018), selected benchmarks.

Hamburg, Lower Saxony, Mecklenburg-Western Pomerania and Schleswig-Holstein – the four federal states, parts of which constitute the HMR – ranked in the middle of federal states in 2018, at positions five, eight, seven and ten respectively. Bavaria and Baden-Württemberg (which can be used as proxies for the metropolitan regions Munich and Stuttgart respectively) ranked third and fourth. Quality of schools, which is measured by indicators for reading in fourth and ninth grade as well as for mathematics in grade four, is particularly low in Hamburg: it ranks 14th out of 16 federal states – Lower Saxony ranks 11th, Mecklenburg-Western Pomerania 7th and Schleswig-Holstein 6th. On the other hand, Munich ranks second and Baden-Württemberg eighth for quality of education. It should be noted, however, that the demographic background of students differs substantially between federal states: for example, in Hamburg, the share of students who are not German citizens and who have a migration background is 10.5% and 29.1% respectively. Corresponding figures for Baden-Württemberg are 9.8% and 19.5%, those for Bavaria 7.3% and 13.8% respectively (Kemper, 2017[17]).

The constituent federal states that make up the HMR are placed eighth and below (with the exception of Hamburg itself, which comes out fourth) for vocational training and labour market orientation of education, measured by the number of graduates from vocational training schools, the vocational training ratio and the number of “NEET” (Not in Education, Employment or Training). Bavaria and Baden-Württemberg lead the table, being first and second respectively. Related to the concept of vocational training and labour market orientation of education is tertiary education and its relation to natural science or “STEM” (Science, Technology, Engineering and Mathematics) subjects (which can be considered as catering more to economic needs): based on the number of graduates from tertiary education, in particular from subjects such as engineering, the federal states that constitute the HMR perform poorly: Schleswig-Holstein is ranks last and all other constituent federal states of the HMR rank 12th or lower, whereas Baden-Württemberg and Bavaria are placed 3rd and 9th respectively out of 16.

Research orientation of education draws a similar picture in terms of relative ranking (which may be one reason for the low rate of technology transfers between universities and the private sector), suggesting differences in quality of education between the HMR and other regions in Germany, in particular, those in the south. It is conceivable that initiatives at the federal level such as, for example, the implementation of the Digital Pact (Digitalpakt), which aims to ensure basic training in digital skills in high schools, have the potential to reduce, to some extent, inequalities in quality of education between federal states.

The fragmented educational space in the HMR may have implications going well beyond between-region comparability of quality of education: to the extent that frictions in education policy can lead to a decrease in labour mobility (for example, parents may not move for new jobs across federal state borders within the metropolitan region because their children would be subject to a different school system), aggregate labour productivity may be further reduced as labour does not flow to where it could be most productive.

It should be noted that the Educational Monitoring 2018 is just one source of comparable data on quality of education in Germany, its advantage being that it covers education from primary to tertiary schooling. Other sources include, for example, the Institute for Quality Development in the Educational System (Institut zur Qualitätsentwicklung im Bildungswesen), which, in its regularly published educational trend reports, benchmarks primary-school students in subjects German and mathematics at the end of grade four. In its latest edition for 2016, in mathematics (global), the share of students who did not reach the minimum requirements at the end of grade four is 21.2% in Hamburg, 16.3% in Lower Saxony, 14.8% in Mecklenburg-Western Pomerania and 13.2% in Schleswig-Holstein. In Bavaria and Baden-Württemberg, these shares are 8.3% and 15.5% respectively; the German average is 15.4% (Stanat et al., 2017[18]).

More dynamic labour markets elsewhere may increase skills shortages in the HMR

Differences in human capital between metropolitan regions in Germany affect labour market dynamics and can further widen already existing differences in aggregate labour productivity between regions.

The unemployment rate in the HMR followed closely that of all other metropolitan regions in Germany, pooled together, during the period 2005 to 2015: unemployment decreased from about 12% in 2005 to about 7% in 2015, as Germany was heading towards full employment. Although the HMR has a higher unemployment rate (by about two percentage points) than metropolitan regions in the south, there is little evidence that regions in the south do much better when it comes to reducing unemployment. During the period 2005 to 2015, the HMR and metropolitan regions in the south reduced unemployment by approximately equal amounts, from about 12% unemployment in the HMR and 10% in the south in 2005 to about 7% in the HMR and 5% in the south in 2015 (Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), weighted by district employment level, own calculations).7

As the unemployment rate continues to decrease, the demand for workers requiring a professional qualification (Fachkraft) is rising. This is evidenced by the ratio of the number of vacancies requiring a professional qualification to the number of unemployed having a professional qualification, multiplied by 100. This ratio is rising across all metropolitan regions in Germany. In the HMR, it was about 21 in 2005 and about 35 in 2015 (+ 67%); in other metropolitan regions in Germany, it was about 16 in 2005 and 34 in 2015 (+113%) (Federal Institute for Research on Building, Urban Affairs and Spatial Development, n.d.[10]). According to the generic statistical definition of the Federal Employment Agency (Bundesagentur für Arbeit), a candidate with a professional qualification has completed at least two years of vocational training or a comparable qualification and has sound knowledge and skills that enable the candidate to engage in specialist activities. As of 2015, the majority (more than two-thirds) of all job vacancies matched this candidate profile, with little differences between metropolitan regions in Germany or even within the HMR.

However, the rise in the ratio of vacancies to unemployed is much more pronounced in regions in the south, especially in MR Stuttgart and MR Munich, pointing towards a higher rate of job creation, in particular of jobs demanding this type of labour, in the regions. Figure 1.10 shows this development.

A relatively tighter labour market in regions in the south, in particular in case of jobs requiring a professional qualification (Fachkraft), has three implications for HMR:

  1. 1. Higher demand for candidates with a professional qualification in the south may increase (discretionary) wages, attracting such individuals, which may increase skill shortages (Fachkräftemangel) in HMR. In part, this is already reflected in a higher average monthly disposable household income in regions in the south, which is indicative of higher labour productivity more generally (Table 1.3).

  2. 2. A higher rate of job creation in regions in the south may point towards systematic differences in business and industry structure, including differences in capital productivity arising from a different research and development environment.

  3. 3. Greater job creation in regions in the south may perpetuate and enhance existing differences in labour productivity between regions if coinciding with labour productivity growth (e.g. by attracting skilled workers).

In sum, although HMR has experienced (average) growth in aggregate labour productivity (measured in terms of GDP per employed) over the past decade, other metropolitan regions in Germany, especially those in the south, have experienced stronger growth, having either increased an initial advantage or decreased an initial disadvantage over HMR. These regions are further widening the gap in aggregate labour productivity with HMR. One of the reasons for differences in aggregate labour productivity between metropolitan regions in Germany over time may be differences in the stock and flow of human capital, as evidenced by shares of employees with different educational endowments and shares of employees working in sectors with different educational prerequisites. The HMR does not score highest in either of them relative to regions in the south, and most recent rates of change suggest that differences may continue to persist or even widen. This development is exacerbated by a fragmented educational structure, with schools and universities that – when it comes to standardised achievement – score, on average, only midfield to lower midfield (a supply-side issue of skilled labour), as well as by higher rates of job creation in other regions that may attract qualified candidates and thereby lead to skill shortages (a demand-side issue of skilled labour).

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Figure 1.10. Ratio vacancies-unemployed (professional qualification): The HMR vs. metropolitan regions in Southern Germany
Figure 1.10. Ratio vacancies-unemployed (professional qualification): The HMR vs. metropolitan regions in Southern Germany

Notes: Ratio of vacancies to unemployed for job profile “professional qualification”, multiplied by 100.

The figure takes into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; weighted by district unemployment level.

The HMR ranks midfield for research, development and innovation (RDI) but is improving

Besides differences in labour productivity, differences in capital productivity may be another reason why the HMR experienced sluggish growth in GDP per capita compared to other metropolitan regions in Germany. Such differences may arise due to systematic differences in business and industry structure: to the extent that firms in regions in the south invest more into research and development and are more innovative (e.g. when it comes to novel production processes and methods), capital in these regions is put to more productive use and relative output increases.

The HMR tends to have smaller firms

The HMR tends to have a higher share of smaller firms and a lower share of larger firms than other metropolitan regions in Germany, especially those in the south. Table 1.6 shows the distribution of firms by firm size, the number of DAX firms,8 the share of research and development personnel as a percentage of overall employees, firm investment into research and development (including the rate of change between 2003 and 2009) and the number of public research institutes – defined as major public research institutes (i.e. institutes by the four major research associations Fraunhofer Gesellschaft, Helmholtz Association, Leibnitz Association and Max Planck Society in Germany plus federal research institutes) – by metropolitan regions in Germany. There is a clear gradient in the share of firms by size in the HMR compared to other metropolitan regions in Germany: while the HMR ranks 5th and 6th out of 11 for the share of very small and small firms respectively, it ranks only 9th for the share of medium-sized and even 10th for the share of large firms, which typically engage more in research and development.

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Table 1.6. Firm size and research and development infrastructure in metropolitan regions in Germany

Share of very small firms

(‰ total firms)

Share of small firms

(‰ total firms)

Share of medium-sized firms

(‰ total firms)

Share of large firms

(‰ total firms)

Number of DAX firms

Firm investment into R&D

(EUR million)

Change 2003-09

HMR

892.2

85.2

19.2

3.3

1

1 575

39

Berlin-Brandenburg

901

77.9

17.9

3.2

1

1 551

-10

Northwest

869.6

102.6

23.8

4

0

457

-3

Frankfurt

894.7

81.3

19.9

4.2

3

5 201

26

Hanover

868.4

103.3

24

4.2

2

3 183

-10

Central Germany

871.7

99.4

24.7

4.2

0

1 489

33

Munich

904.6

75.2

16.8

3.4

7

6 624

6

Nuremberg

880.7

93.2

22

4.1

1

1 951

20

Rhein-Neckar

890.2

85.6

20.3

3.9

4

2 438

16

Rhein-Ruhr

892.2

82.4

21

4.3

10

4 453

23

Stuttgart

891.6

83.6

20.8

4

1

8 492

46

Notes: Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Sources: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (2012[14]), Regionales Monitoring 2012; Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklunghttp://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level on shares of firms by firm size from 2014, on research and development investments from 2009, on public research institutes from 2010, and on broadband access from 2010.

A similar picture arises when it comes to the presence of DAX firms’ headquarters, which, with relatively large research and development budgets, are more likely to engage in research and development activities. The HMR hosts one DAX firm (Beiersdorf), ranking 7th out of 11, together with MR Berlin-Brandenburg and MR Stuttgart. MR Rhein-Ruhr, the largest metropolitan region in Germany in terms of GDP, hosts ten DAX firms, the largest number amongst all metropolitan regions. MR Munich, MR Rhein-Neckar and MR Nuremberg, which recorded high GDP per capita growth over the past decade, are home to seven, four and two DAX firms respectively.

The HMR ranks lower midfield for research and development in Germany

The HMR ranks only 10th out of 11 when it comes to the share of research and development personnel as a percentage of overall employees (about 4.6%) (Figure 1.11). More than 4 times as many employees work in research and development in MR Stuttgart (the frontrunner, about 19.7%) and slightly fewer in MR Munich (in second place with about 16.2%) and MR Rhein-Neckar (third place, about 15.2%). Northwest ranks last, with only about 2.7%. The share of research and development personnel is also reflected in firm investment into research and development as a percentage of regional GDP. The HMR ranks 9th out of 11 (about 0.8%), MR Stuttgart (about 3.5%) raking first again and Northwest (about 0.5%) last again. However, the HMR experienced the second largest growth in the share of research and development personnel (about 39.0%) between 2003 and 2009, outranked only by MR Stuttgart (about 45.7%). Finally, with the presence of 13 institutes, the HMR ranks midfield for the number of public research institutes in the region. Note, however, that this indicator accounts only for major research institutes by the main research associations in Germany plus the federal government; if smaller institutes are also accounted for, the number for HMR increases to 30, making the HMR upper midfield in this category.

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Figure 1.11. R&D personnel: The HMR vs. metropolitan regions in Germany
Figure 1.11. R&D personnel: The HMR vs. metropolitan regions in Germany

Notes: Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (2012[14]), Regionales Monitoring 2012; Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklunghttp://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level on research and development personnel and investments from 2009; own calculations.

The HMR scores lower for innovation performance than regions in Southern Germany

The HMR performs similarly, or worse than, other metropolitan regions in Germany with respect to innovation indicators. Figure 1.12 illustrates the performance of the HMR on different indicators relative to other metropolitan regions in Germany, pooled together, using the latest edition of the Regional Innovation Scoreboard.9 The HMR only rarely scores higher than average, and if so, only slightly, as in case of most-cited publications and marketing or organisational innovators. It scores considerably lower than average when it comes to research and development expenditure by the business sector, innovative small- and medium-sized enterprises collaborating with others, public-private co-publications, patent and design applications, and employment in medium and high-tech manufacturing and knowledge-intensive services.10

The largest differences in innovation performance between the HMR and other regions in Germany can be found with regions in the south. Figure 1.13 replicates Figure 1.12 for metropolitan regions in Southern Germany. All regions in the south (with the exception of MR Munich when it comes to research and development expenditure by the public sector, publications and lifelong learning) clearly outperform the HMR, some by a very large margin. MR Stuttgart, and to a lesser extent MR Rhein-Neckar, MR Munich and MR Nuremberg perform considerably better than the HMR when it comes to research and development expenditure by the business sector, patent and design applications, employment in medium- and high-tech manufacturing and knowledge-intensive services, and exports of medium- and high-tech manufacturing.

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Figure 1.12. Regional Innovation Scoreboard 2017: The HMR vs. other metropolitan regions in Germany
Figure 1.12. Regional Innovation Scoreboard 2017: The HMR vs. other metropolitan regions in Germany

Notes: Metropolitan regions are composed of NUTS2 regions. See Footnote 11 for the exact composition. Other metropolitan regions in Germany = MR Berlin-Brandenburg, Northwest, MR Frankfurt, MR Hanover, Central Germany, MR Munich, MR Nuremberg, MR Rhein-Neckar, MR Rhein-Ruhr and MR Stuttgart.

Source: Own calculations based on European Commission (2017[19]), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019), latest available data from 2017.

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Figure 1.13. Regional Innovation Scoreboard 2017: The HMR vs. metropolitan regions in Southern Germany
Figure 1.13. Regional Innovation Scoreboard 2017: The HMR vs. metropolitan regions in Southern Germany

Note: Metropolitan regions are composed of NUTS2 regions. See Footnote 11 for the exact composition.

Source: Own calculations based on European Commission (2017[19]), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019), latest available data from 2017.

Differences between the HMR and other regions in Germany are not as systematic as those between the HMR and regions in the south. For example, the HMR scores lower than MR Hanover when it comes to research and development expenditure (regardless of whether public or private), employment in medium- and high-tech manufacturing and knowledge-intensive services, and exports of medium- and high-tech manufacturing – the latter items are most likely driven by the strong presence of the automobile sector in MR Hanover. The HMR also scores lower than MR Hanover when it comes to lifelong learning, patent applications and innovative small- and medium-sized enterprises (SMEs) collaborating with others, which is a recurring theme, possibly made worse by the fragmented administrative structure in the HMR that makes public-private or private-private collaboration across state boundaries more difficult. Note that some SMEs may deliberately not apply for patents so as not to lose industrial knowledge to competitors, while others may spontaneously engage in (non-formal) collaborations (for example, production networks) to fulfil orders too large for one firm to handle.

The more systematic differences between the HMR and metropolitan regions in Southern Germany are somewhat expected: although differences in overall firm size between the HMR and regions in the south are not large, regions in the south concentrate more human capital within their boundaries, with the potential of more research and development being undertaken. They also concentrate several large and dominant firms that operate in certain research-intensive industries and that tend to invest more into research and development activities than others, with regional supply chains potentially profiting.11 This is a mutually reinforcing process: as firms invest more into research and development, and thereby become more productive, they are able to pay higher wages, which, in turn, attracts more human capital that can be used to raise productivity.

The HMR ranks midfield for innovation performance compared to principal comparators across the OECD

Principal comparators across the OECD are as good as or better than the HMR on most indicators of innovation performance, with the exception of marketing or organisational innovators as well as exports of medium- and high-tech manufacturing, where the HMR seems to have a competitive edge. Figure 1.14 replicates Figure 1.12, illustrating the relative performance of the HMR for each indicator of innovation performance relative to principal comparators, including Barcelona (Spain), Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands) as well as other comparators across the OECD.12 Average All denotes the median innovation performance of all regions participating in this round of the European Commission’s Regional Innovation Index, which are EU28 regions and regions in neighbouring accession countries.

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Figure 1.14. Regional Innovation Scoreboard 2017: The HMR vs. metropolitan regions across the OECD
Figure 1.14. Regional Innovation Scoreboard 2017: The HMR vs. metropolitan regions across the OECD

Notes: Metropolitan regions are composed of NUTS2 regions. See Footnote 13 for the exact composition. Principal comparators = Barcelona (Spain), Copenhagen (Denmark), Gothenburg (Sweden), Rotterdam (Netherlands).

Other comparators = Athens (Greece), Birmingham (UK), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Naples (Italy), Oslo (Norway), Rome (Italy), Stockholm (Sweden).

Source: Own calculations based on European Commission (2017[19]), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019), latest available data from 2017.

The picture is more diverse for other comparators across the OECD: although other regions fare better when it comes to education performance (i.e. population with tertiary education, lifelong learning or scientific co-publications), the HMR seems to be better in putting innovation into practice, as evidenced by higher scores for European Patent Office (EPO) patent applications, trademark applications and, to a lesser extent, design applications. Overall, the HMR can be located between the principal and other comparators across the OECD when it comes to innovation performance. It fares better (except for population with a tertiary degree) than the median of all other participating countries.

The HMR’s relative innovation performance has improved over time

The HMR has slightly improved its overall index of innovation performance relative to metropolitan regions in Southern Germany, in particular, MR Munich and MR Nuremberg, during the period 2009 to 2017. The European Commission’s Regional Innovation Scoreboard includes, besides composite indicators for specific aspects of innovation performance, an overall index that combines all aspects. It is standardised, with values greater than 100 implying that the respective region is above the EU27 average in terms of overall innovation performance. Figure 1.15 shows that the improvement of the HMR relative to MR Munich and MR Nuremberg during the period 2009 to 2017 was, in equal parts, due to a rise in the HMR (from about 105 to about 111) and a reduction in both MR Munich and MR Nuremberg (from about 123 and 128 to about 118 and 120 respectively).

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Figure 1.15. Regional Innovation Index: The HMR vs. metropolitan regions in Southern Germany
Figure 1.15. Regional Innovation Index: The HMR vs. metropolitan regions in Southern Germany

Note: Metropolitan regions are composed of NUTS2 regions. See Footnote 11 for the exact composition.

Source: Own calculations based on European Commission (2017[19]), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019), latest available data from 2017.

The HMR ranked only midfield for overall innovation performance relative to principal comparators across the OECD. However, Figure 1.16 shows that, in 2017, the HMR had a score of 111, whereas Barcelona (Spain) had a score of 91. Yet, the HMR was outperformed, by a considerable margin, by Rotterdam (Netherlands), which scored 131, and even more so by Copenhagen (Denmark), which scored 159. The innovation performance of Copenhagen (Denmark) is closely shadowed by that of Gothenburg (Sweden).

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Figure 1.16. Regional Innovation Index: The HMR vs. principal comparators across the OECD
Figure 1.16. Regional Innovation Index: The HMR vs. principal comparators across the OECD

Note: Metropolitan regions are composed of NUTS2 regions. See Footnote 13 for the exact composition.

Source: Own calculations based on European Commission (2017[19]), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019), latest available data from 2017.

The HMR’s comparative advantage in innovation potential: The case of renewable energy generation and storage

The HMR plays a leading role in electricity generation from renewable energy, especially wind power. Figure 1.17 is a heatmap plotting the so-called local wind power adequacy, obtained from the German Meteorological Service (Deutscher Wetterdienst) for Germany in 2014. It is defined as the average annual energy yield of a wind turbine in kilowatt hours per square metre of rotor area.13 Federal states in the north of Germany, in particular those located at or close to the coast of the North and Baltic Seas – including Hamburg, Lower Saxony, Mecklenburg-Western Pomerania and Schleswig-Holstein, large parts of which constitute the HMR – have a higher local wind power adequacy than other regions in Germany, as indicated by brighter spots on the heatmap. A wind turbine of a given generation capacity can, within a given time frame, produce more energy in the northern parts of the country than in its southern parts. In other words, generating energy from wind is relatively more efficient in the north than in the south. Even larger wind power adequacy is considered to be offshore Lower Saxony and Schleswig-Holstein, in the North Sea, further elevating the potential for electricity generation from wind power in the region.

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Figure 1.17. Local wind power adequacy in Germany
Figure 1.17. Local wind power adequacy in Germany

Note: The local wind power adequacy is the average annual energy yield of a wind turbine in kilowatt hours per square metre of rotor area. Brighter spots on the heatmap imply a higher average annual energy yield, making investments into wind power generation capacities more worthwhile, whereas darker colours imply a lower yield.

Sources: Own calculations based on German Meteorological Service (2018[20]), Karten zur Windkraftnutzungseignung in 80 Meter über Grundhttps://www.dwd.de/DE/klimaumwelt/ku_beratung/energie_bau/windenergie/windenergie_node.html (accessed on 24 December 2018); Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

This potential has already been (partially) exploited over the past two decades. Figure 1.18 shows installed electrical capacity from wind power, measured in megawatts, at the district level in Germany in 2017. As can be seen, there is a clear gradient in the amount of installed electrical capacity in Germany, with districts in northern parts of the country having more megawatts installed than those in southern parts. The correlation between local wind power adequacy and installed electrical capacity, however, is not perfect (for example, there are regions in Bavaria that have a low wind power adequacy but a high installed electrical capacity), which is, in part, due to energy policy in Germany over the past two decades. Starting in 2000, the passing of the first Renewable Energies Act (Erneuerbare-Energien-Gesetz – there have been six revisions since, the most recent being in 2018) guaranteed favourable, fixed feed-in-tariffs when producing electricity from renewables. This led to a massive expansion in installed electrical capacity from wind, biomass and solar photovoltaic: gross electricity consumption from renewables was about 6.3% in 2000; by the end of 2017, it had reached 36%, about half of which was from wind power (Federal Ministry for Economic Affairs and Energy, n.d.[22]; n.d.[23]). The trend towards renewables in electricity generation has been fuelled further by the decision of the federal government in 2011 to completely phase out nuclear power by 2022 (which affects three nuclear power plants in the HMR, namely Brunsbüttel and Krümmel, which are already being decommissioned, and Brokdorf, which goes off grid by the end of 2021).

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Figure 1.18. Installed electrical capacity of wind power in Germany at the district level (megawatts) as of December 2017
Figure 1.18. Installed electrical capacity of wind power in Germany at the district level (megawatts) as of December 2017

Sources: Open Power System Data (2018[24]), Data Package Renewable Power Plants, https://doi.org/10.25832/renewable_power_plants/2018-03-08; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

The German energy transition (Energiewende) yields significant opportunities for the HMR. With its already dominant position in electricity generation from renewables and the potential of storage using innovative technologies such as hydrogen, the HMR may take a leading position for renewables (especially wind power) and become a knowledge provider for the transition towards renewables in general, having learned from the phasing-out and decommissioning of conventional technologies. Renewables could also play a key role in reducing urban-rural disparities in economic development, with urban areas as consumers and rural areas (where renewable power plants are sited) as producers of locally generated renewable energy (contrary to, for example, conventional energy generated outside the HMR). At the same time, however, the German energy transition, as a dynamic and ongoing process, poses several challenges for the region.

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Box 1.5. Challenges for the HMR due to the German energy transition

The revision of the Renewable Energies Act in 2017 sees a change in paradigm from a system of fixed feed-in-tariffs towards a system of auctioning (Federal Ministry for Economic Affairs and Energy, n.d.[25]). This implies that, instead of paying a fixed feed-in-tariff to everybody, volumes of renewable energy generation capacities are now being auctioned to those bidders that can produce these volumes most cost-effectively. While this is generally a favourable development for the HMR (which has a competitive advantage and can produce renewable energy very cost-effectively due to its location), at the same time, many early-generation installations in the HMR (as an early adopter of the technology) are about to drop out of the original Renewable Energies Act and thus fixed feed-in-tariffs within the next two years (the law was passed in 2000 and has a lifetime of 20 years).

Note that wind turbines have high maintenance costs (see Vitina et al. (2015[26]), for example), which industry estimates to require a minimum price of between EUR 25 and EUR 35 per megawatt hour to be realised at the stock exchange (the average price in 2017 was EUR 33 per megawatt hour, which has recently increased to more than EUR 40 due to higher CO2 prices). It is not clear as to whether early-generation installations, which have relatively high maintenance costs, would still be profitable when transitioning from a system of fixed feed-in-tariffs to a more competitive system of auctioning. When it comes to repowering (that is, replacing old installations with new ones at existing locations), it should be noted that the siting process and locational decision-making are stricter today than 20 years ago. It is, therefore, questionable whether old installations can simply be replaced with new ones at existing locations.

In general, the transition towards renewable energy generation poses the challenges of how to most efficiently use green energy, for example, by jointly optimising electricity, heating and transport sectors (so-called sector coupling); how to effectively store green energy (so-called Power-to-X), for example, by using modern hydrogen technologies; and how to control electricity consumption and shift demand in times of underproduction towards times of overproduction (so-called demand-side integration) (Stötzer et al., 2015[27]).

Finally, the German energy transition implies that, in the medium to longer term, energy will be produced in the and consumed in the south, especially by industrial hubs in the Rhein-Ruhr and Rhein-Neckar regions. To transport energy from the north to the south, however, new high-tension power lines have to be built. Network expansion from the north to the south of Germany is progressing slowly, partly due to long planning requirements in Germany. This means that network interventions will increasingly become necessary and power generation from renewable energy will have to be temporarily reduced. In other words, the region is currently producing less renewable energy than would normally be required for more developed networks.14

Source: Own elaborations based on expert interviews.

The HMR takes a leadership position for digital infrastructure

Besides differences in labour and capital productivity arising from differences in human capital endowments and the overall research and development environment, differences in infrastructure – physical or digital – may be another reason why the HMR experienced sluggish growth in GDP per capita compared to other metropolitan regions in Germany, especially those in the south. Infrastructure is an important component of overall (total factor) productivity, which, in turn, determines how productive labour and capital as input factors are when generating output. Digital infrastructure, in particular, has the potential to raise total factor productivity by increasing the efficiency of production processes and methods. It also has the potential to allow for savings in production costs, which can then be reinvested into productive capital. To the extent that relative changes (over time) in digital infrastructure between metropolitan regions increase the advantage of certain regions over others, such differences may explain some of the divergences in GDP per capita between regions.

Digitalisation, as a transformation process, is more than just digital infrastructure, but digital infrastructure is the prerequisite for digitalisation to occur. According to the latest data available from the Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), most households in metropolitan regions in Germany already had access to basic broadband (i.e. two megabits) in 2010, with coverages ranging between 89% to 98% approximately. In the past decade, the extension and improvement of broadband infrastructure was relatively successful in the HMR compared to other metropolitan regions in Germany: in 2010, Table 1.6’s year of reference, the share of households with 2 Mbit and 50 Mbit broadband access in the HMR was 93.7% and 31.1% respectively; in 2017, the HMR reached shares of 99.6% (nearly universal broadband coverage) and 36.2% respectively, putting the HMR into a leading position amongst German metropolitan regions for high-speed Internet access (i.e. households with optic fibre connections).15

In comparison with principal comparators across the OECD, the HMR performs very favourably when it comes to basic broadband access: in 2016, Rotterdam (Netherlands) reported a share of about 95%, Copenhagen (Denmark) of about 92%, Gothenburg (Sweden) of about 89%, Boston (US) of about 83%, and Barcelona (Spain) of about 82% (OECD Regional Statistics, n.d.[12]).16

However, this should not hide the fact that there are disparities within the HMR. Figure 1.19 shows the share of households with high-speed broadband access in 2017, using district-level data from the Federal Ministry of Transport and Digital Infrastructure, within the HMR, by constituent district.

While the urban core and the districts to the north report nearly full coverage of high-speed broadband access, more remote areas (particularly in the east) report much lower coverages, some below 60% (Nordwestmecklenburg, Lüchow-Dannenberg, and Ludwigslust-Parchim report the lowest coverages with only 52%, 51% and 45% respectively, whereas Neumünster, Hamburg and Lübeck report the highest coverages with 98%, 97% and 95% respectively). Differences in broadband access coverage may be a result of different priorities of federal states involved in the HMR, all of which follow an independent digitalisation strategy: with its Digitale Stadt strategy, Hamburg focuses on digital public services, whereas Lower Saxony (Masterplan Digitalisierung), Mecklenburg-Western Pomerania and Schleswig-Holstein focus on broadband expansion and individual digital public services. Note that undersupply of high-speed broadband Internet access in rural areas is not a phenomenon which is exclusively observable in the HMR but, instead, a German-wide phenomenon (Wernick and Bender, 2016[28]).

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Figure 1.19. Mean coverage of broadband access over all technologies ≥ 50 Mbit/s in the HMR at the district level (%)
Figure 1.19. Mean coverage of broadband access over all technologies ≥ 50 Mbit/s in the HMR at the district level (%)

CUX = Cuxhaven, DAN = Lüchow-Dannenberg, HEI = Dithmarschen, HH = Hamburg, HK = Heidekreis, HL = Lübeck, IZ = Steinburg, LG = Lüneburg, LUP = Ludwigslust-Parchim, NMS = Neumünster, NWM = Nordwestmecklenburg, OD = Stormarn, OH = Ostholstein, PI = Pinneberg, ROW = Rotenburg (Wümme), RZ = Herzogtum Lauenburg, SE = Segeberg, SN = Schwerin, STD = Stade, UE = Uelzen, WL = Harburg.

Sources: Own calculations based on Federal Ministry of Transport and Digital Infrastructure (2017[29]), Broadband, Glassfiber, 3G, 4G Metropolregion Hamburg, latest available data on from 2017; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

Digitalisation is more than just digital infrastructure: it is part of an innovation cycle in which firms design, test and implement new digital technologies into their production processes and methods. There exists no comparable data on the absorptive capacity of firms in metropolitan regions in Germany. However, lessons learned from secondary data analyses on research and development as well as innovation performance more generally can also be applied to the case of digitalisation (as a specific case of innovation): a higher share of smaller firms (with a smaller scale, and hence incentive, to fully take advantage and reap benefits of digitalisation) in a diversified (yet fragmented) economy may put the HMR at a relative disadvantage for absorptive capacity of new digital technologies, for example, relative to metropolitan regions in Southern Germany. These regions are characterised by a few large, dominant firms in research-intensive industries, which can serve as incubators (and examples) for the streamlining of new digital technologies into operations and facilitators for knowledge exchange along supply chains.

copy the linklink copied!Quality of life, infrastructure and environmental sustainability

Quality of life is high

Quality of life is relatively high in the HMR and in many dimensions exceeds that in other regions across the OECD.17 Figure 1.20 plots quality-of-life data in the HMR relative to principal and other comparators across the OECD, pooled together.18

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Figure 1.20. Well-being 2018: The HMR vs. metropolitan regions across the OECD
Figure 1.20. Well-being 2018: The HMR vs. metropolitan regions across the OECD

Notes: Metropolitan regions are composed of OECD TL2 regions.

Principal comparators = Barcelona (Spain), Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands). See Footnote 4 for exact compositions.

Other comparators = Athens (Greece), Birmingham (UK), Dublin (Ireland), Lisbon (Portugal), Manchester (UK), Marseille (France), Milan (Italy), Naples (Italy), Oslo (Norway), Rome (Italy), and Stockholm (Sweden). See Footnote 5 for exact compositions.

Summary scores are normalised for ease of comparison, with values ranging between zero and ten.

Source: Own calculations based on OECD Regional Well-Being Statistics (2018[30]), Regional Well-Being in Hamburg, Catalonia, Massachusetts, Copenhagen (Denmark) District, Lombardy, South Holland, http://www.oecdregionalwellbeing.org (accessed on 19 December 2018), unweighted.

The HMR performs equally well or better than other comparators across the OECD when it comes to well-being. However, comparing the HMR with principal comparators, including Barcelona (Spain), Boston (US), Copenhagen (Denmark), Gothenburg (Sweden) and Rotterdam (Netherlands), a more diverse picture emerges: while the HMR does perform better on some indicators such as education, jobs, income and accessibility to services, it performs worse on others, notably the environment, housing and overall satisfaction with life. Note again that, in this analysis, the HMR is represented by the federal state of Hamburg alone, whereas other OECD TL2 regions refer to the first administrative tier of subnational governments. Thus, there seems to be room for improvement for the HMR, especially when it comes to the natural and built environment as well as, to a certain extent, overall subjective well-being of residents.

The happiest people in the region do not necessarily live in the city of Hamburg but instead live in the districts to the south of the core or in the most northern district. Figure 1.21 plots average life satisfaction in 2016 (the latest year for which comparable data at the individual level are currently available), taken from the German Socio-Economic Panel Study (SOEP), within the HMR by constituent district. Respondents in districts in the east tend to be, on average, less satisfied with their lives than respondents in districts in the west, with some exceptions (they are below the average life satisfaction in Germany in 2016, which was about 7.4).19 Even when adjusting life satisfaction for differences in economic conditions between districts, differences in life satisfaction between constituent districts in the region remain, pointing towards other important aspects of quality of life that matter for liveability in the region. In line with the international benchmarking, three are discussed in more detail: transport, housing and environmental sustainability with its potential for tourism.

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Figure 1.21. Mean life satisfaction: The HMR at the district level (0 to 10 scale)
Figure 1.21. Mean life satisfaction: The HMR at the district level (0 to 10 scale)

Notes: The item on life satisfaction asks respondents: “How satisfied are you with your life, all things considered?”. Answer possibilities range from zero (“completely dissatisfied”) to ten (“completely satisfied”). CUX = Cuxhaven, DAN = Lüchow-Dannenberg, HEI = Dithmarschen, HH = Hamburg, HK = Heidekreis, HL = Lübeck, IZ = Steinburg, LG = Lüneburg, LUP = Ludwigslust-Parchim, NMS = Neumünster, NWM = Nordwestmecklenburg, OD = Stormarn, OH = Ostholstein, PI = Pinneberg, ROW = Rotenburg (Wümme), RZ = Herzogtum Lauenburg, SE = Segeberg, SN = Schwerin, STD = Stade, UE = Uelzen, WL = Harburg.

Sources: Own calculations based on SOEP (2016[31]), Data for Years 1984-2016, Version 32, German Socio-Economic Panel Study, German Institute for Economic Research, Berlin, latest available data from 2016; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

Large differences in transport and mobility within the region

There is a large number of daily commuters in the HMR (about 761 000 daily commuters, whereby 350 000 alone enter the city of Hamburg every day) and a general perception that the growth of daily commuters in recent years has not been met by growth in transport infrastructure, leading to congestion. Transport relates to productivity in the sense that transport infrastructure enables labour and capital to flow, more or less quickly, to where they are most productive. But there is also a quality-of-life aspect to transport, especially public transport: long commuting times from home to work (and back) have been shown to have a very detrimental effect on life satisfaction (Stutzer and Frey, 2008[32]; Dickerson, Hole and Munford, 2014[33]).

Bottlenecks may be further exacerbated by several factors such as fragmented tariff zones and structures across the region – the regional transport association, Hamburger Verkehrsverbund (HVV), serves only part of the region, covering seven districts spread across three federal states; and competition between passenger and freight on both road and rail, especially inbound towards the harbour in the city of Hamburg. There is also a demographic component to public transport, pertaining to the difficulty of maintaining public transport services in rural areas with demographic change. Three dimensions of transport are examined in further detail below: connectivity, accessibility of public transport and congestion.

The HMR’s level of connectivity resembles that of other metropolitan regions in Germany

Although there is some heterogeneity when it comes to average driving time to the nearest connection point, differences between metropolitan regions in Germany are, in general, not large. Figure 1.22 illustrates connectivity in the HMR relative to other metropolitan regions in Germany, by plotting the average driving time by car in minutes to the nearest connection point of a motorway (Autobahn) and an intercity or intercity express train station (IC/ICE Train Station) in 2015, the latest year for which comparable data are currently available. Some regions, especially those with higher population densities and smaller geographical areas such as MR Rhein-Neckar or MR Rhein-Ruhr, have lower average driving times than the HMR, but the HMR shows a similar overall pattern as, for example, MR Munich or MR Stuttgart. It seems that, across metropolitan regions in Germany, connectivity to train stations is worse than to motorways. Differences in connectivity, however, are not large, despite the fact that the size of geographical areas is quite different between regions.

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Figure 1.22. Average driving time to the nearest access point in metropolitan regions in Germany (minutes)
Figure 1.22. Average driving time to the nearest access point in metropolitan regions in Germany (minutes)

Note: Values are average driving times by car to the nearest access point in minutes. Reachability calculations of motorised individual traffic are based on route searches in a road network model. Calculations of car speeds used for different road types depends on the state of completion as well as settlement-structural and topographic conditions.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

Large differences exist within the HMR in terms of connectivity, accessibility of public transport and congestion

More pronounced, however, are differences in connectivity between districts within the HMR. Figure 1.23 plots the average driving time by car in minutes to the same connection points by district. Note that, unfortunately, no calculations are available for unitary cities. The general pattern – train stations being less well connected than motorways – that is observable between metropolitan regions in Germany, is also observable within the HMR itself, with few exceptions. There are large discrepancies in connectivity to train stations between districts, with districts such as Cuxhaven, Stade or Rotenburg (Wümme) showing long average driving times of more than 40 minutes (59, 56 and 44 minutes respectively), almost or more than twice as much as average (26 minutes). Lüchow-Dannenberg, one of the most eastern districts in the region, also shows a long average driving time to the nearest motorway connection point (53 minutes, with an average of 13 minutes).

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Figure 1.23. Average driving time to the nearest access point in the HMR at the district level (minutes)
Figure 1.23. Average driving time to the nearest access point in the HMR at the district level (minutes)

Note: Values are average driving times by car to the nearest access point in minutes. Reachability calculations of motorised individual traffic are based on route searches in a road network model. Calculations of car speeds used for different road types depends on the state of completion as well as settlement-structural and topographic conditions. No driving-time calculations are available for unitary cities.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

So far, average driving time to the nearest connection point of a motorway and intercity or intercity express train station was measured by driving time in a personal vehicle. Large differences within the HMR, however, also exist when looking at average distances to the nearest public transport stop. Figure 1.24 plots the population-weighted average linear distance (which implies that the actual, non-linear distance is slightly longer) to the nearest public transport stop with at least ten departures per day at the district level within the HMR. Districts can be broadly categorised into those having a public transport stop in less than 400 metres, between 400 and 600 metres, and in more than 600 metres of distance to places of residence. The largest distance can be found in Ludwigslust-Parchim, a district in the eastern part of the region (about 895 metres), the smallest in Neumünster, a district in the northern part (about 191 metres). The average distance is approximately 429 metres. For wider, rural areas where regular public transport services may prove difficult to sustain, novel concepts in the area of on-demand public transport (for example, ride-sharing or call bus systems, which are already operating in the districts of Ludwigslust-Parchim and Nordwestmecklenburg) may be an alternative to uphold services.

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Figure 1.24. Average distance to the nearest public transport stop in the HMR at the district level (metres)
Figure 1.24. Average distance to the nearest public transport stop in the HMR at the district level (metres)

Note: Population-weighted Euclidean distance in metres to the nearest public transport stop according to publicly available timetable query. Only stops with at least ten departures per day are considered.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

A final way to look at transport in the region is congestion. There seems to be a tendency for relative travel time to increase in proximity to the urban core. This tendency, however, is unevenly distributed: districts in the west of the urban core show relatively higher congestion than others. Figure 1.25 plots coefficients for relative travel time by car under respective, normal traffic conditions (as of December 2018) from the centroids (i.e. geographical midpoints) of the different constituent districts of the HMR to the city of Hamburg, reflecting average commuting behaviour in a monocentric region from the second and first ring towards the urban core. Coefficients for relative travel time are measured in minutes per kilometre, i.e. dividing travel time in minutes by travel distance in kilometres. Districts are then ranked from highest to lowest coefficient. There is a discrete jump in relative travel time from Neumünster, a district in the northern part of the region, to Pinneberg and Stade, which are two districts to the west of the urban core. As distance to the urban core (the denominator) is low for adjacent districts, a higher travel time (the nominator) under normal traffic conditions from close-by districts to the urban core must be driving the coefficients of relative travel time to the city of Hamburg. This is especially true for Pinneberg and Stade; other districts adjacent to the urban core such as, for example, Harburg or Lüneburg, show lower relative travel time.20 Congestion, therefore, is affecting some districts adjacent to the urban core more strongly than others.

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Figure 1.25. Travel time to the city of Hamburg (centroid) from districts (centroids) in minutes per kilometre
Figure 1.25. Travel time to the city of Hamburg (centroid) from districts (centroids) in minutes per kilometre

Note: Values are relative travel times from geographical centroids (midpoints) of districts to geographical centroids (midpoints) of the city of Hamburg in minutes (travel time) per kilometre (travel distance, i.e. not Euclidean distance) under respective, normal traffic conditions. Calculations are based on “HERE” API.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

This congestion analysis highlights the importance of co-ordinating traffic management around the core with close-by districts and, potentially, of developing joint steering and infrastructure concepts (as is being done in other metropolitan regions in Germany such as MR Central Germany, MR Rhein-Neckar or MR Rhein-Ruhr). Integrating transport infrastructure into (ideally joint) city and urban planning – possibly developed in parallel with novel, digital technologies for traffic and mobility management where infrastructure already exists – becomes even more important as large infrastructure projects, such as the Fehmarn-Belt (which connects regions), have traffic externalities that affect several stakeholders simultaneously and require co-ordination across administrative boundaries. Finally, the circumventions of the city of Hamburg is promising to reduce bottlenecks in passenger transportation and freight around the urban core of the region in the longer term but is unlikely to be realised quickly because of relatively long planning processes in Germany.

A rising demand for housing remains partly unmet

Where people settle and how they live is a major determinant of overall quality of life in a region. Over the last decade, affordable housing and floor space more generally, has become an important issue in urban regions in Germany, including the HMR. There is a fair amount of heterogeneity when it comes to satisfaction with housing within the HMR, with residents living in southern districts reporting, on average, higher satisfaction with housing than those living in other districts, including the urban core.21 Figure 1.26 plots average satisfaction with housing in 2016, obtained from the German Socio-Economic Panel Study (SOEP), within the HMR at the district level. Residents report to be most satisfied in Heidekreis (about 8.8 out of 10), the most southern district in the region; and least satisfied in Lüchow-Dannenberg (about 6.6). With a score of 7.8, residents in the city of Hamburg are as satisfied with their housing as people in Germany on average.

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Figure 1.26. Mean housing satisfaction in the HMR at the district level (0 to 10 scale)
Figure 1.26. Mean housing satisfaction in the HMR at the district level (0 to 10 scale)

Notes: The item asks respondents: “How satisfied are you with your place of dwelling?”, with response options ranging from zero (“totally unhappy”) to ten (“totally happy”).

CUX = Cuxhaven, DAN = Lüchow-Dannenberg, HEI = Dithmarschen, HH = Hamburg, HK = Heidekreis, HL = Lübeck, IZ = Steinburg, LG = Lüneburg, LUP = Ludwigslust-Parchim, NMS = Neumünster, NWM = Nordwestmecklenburg, OD = Stormarn, OH = Ostholstein, PI = Pinneberg, ROW = Rotenburg (Wümme), RZ = Herzogtum Lauenburg, SE = Segeberg, SN = Schwerin, STD = Stade, UE = Uelzen, WL = Harburg.

Source: Own calculations based on SOEP (2016[31]), Data for Years 1984-2016, Version 32, German Socio-Economic Panel Study, German Institute for Economic Research, Berlin, latest available data from 2016; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

An increasing demand for floor space remains partly unmet

During the years 2011 to 2015, there has been quite some change in the amount of floor space, defined as the total developed area in both residential and non-residential dwellings in square metres, available per resident. Figure 1.27 shows that, while districts in the wider second ring of the HMR, especially those in the west and northeast, have increased the amount of floor space available per resident, districts in or around its urban core have seen either only a small increase or, as with the city of Hamburg and some districts in the south, even a decline. The largest increase in floor space was in Cuxhaven (about 1.5%), the largest decrease in Schwerin (-1.4%). This is interesting against the fact that the housing market forecast for 2015 to 2030 – based on population growth as well as patterns of demographic change and migration – by the Federal Institute for Research on Building, Urban Affairs and Spatial Development identifies districts in and around the urban core as those with the highest future increase in demand for floor space. Figure 1.28 shows this prognosis for the percentage change in demand for floor space available per resident overall.

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Figure 1.27. Change in floor space in square metres per resident 2011-15 (%)
Figure 1.27. Change in floor space in square metres per resident 2011-15 (%)

Notes: Percentage change in floor space between 2011 and 2015 in both residential and non-residential dwellings available per resident in square metres.

CUX = Cuxhaven, DAN = Lüchow-Dannenberg, HEI = Dithmarschen, HH = Hamburg, HK = Heidekreis, HL = Lübeck, IZ = Steinburg, LG = Lüneburg, LUP = Ludwigslust-Parchim, NMS = Neumünster, NWM = Nordwestmecklenburg, OD = Stormarn, OH = Ostholstein, PI = Pinneberg, ROW = Rotenburg (Wümme), RZ = Herzogtum Lauenburg, SE = Segeberg, SN = Schwerin, STD = Stade, UE = Uelzen, WL = Harburg.

Sources: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), latest available data at the district level from 2015; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

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Figure 1.28. Forecasted change in demand for floor space in square metres per resident 2015-30 (%)
Figure 1.28. Forecasted change in demand for floor space in square metres per resident 2015-30 (%)

Notes: Forecasted percentage change in floor space between 2015 and 2030 in both residential and non-residential dwellings available per resident in square metres.

CUX = Cuxhaven, DAN = Lüchow-Dannenberg, HEI = Dithmarschen, HH = Hamburg, HK = Heidekreis, HL = Lübeck, IZ = Steinburg, LG = Lüneburg, LUP = Ludwigslust-Parchim, NMS = Neumünster, NWM = Nordwestmecklenburg, OD = Stormarn, OH = Ostholstein, PI = Pinneberg, ROW = Rotenburg (Wümme), RZ = Herzogtum Lauenburg, SE = Segeberg, SN = Schwerin, STD = Stade, UE = Uelzen, WL = Harburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015; Institute for Cartography and Geodesy (2016[21]), Verwaltungsgebiete 1:250,000.

As can be seen, during the period 2015 to 2030, demand for floor space available per resident overall is forecasted to increase between 12% and 15% in the first ring around the urban core (more so than in the urban core itself), which is where floor space increased only modestly during the period 2011 to 2015. While demand for single-family houses seems to be rising across the HMR in the future, demand for apartment buildings seems to be increasing in and around its urban core but decreasing at its fringes. Districts in the north of the city of Hamburg are forecasted to show the strongest percentage increase in the demand for single-family houses, whereas districts in its south (including the city of Hamburg itself) are forecasted to have the strongest percentage increase in the demand for apartment buildings.

Spatial demand mismatches and management

If the planning system does not respond to changing patterns of demand, unmet future demand for space and housing in the HMR can have three implications:

  1. 1. It is likely to increase competition between different types of land use (such as housing, commercial, industrial or public open space) for the same space, thereby increasing its price.

  2. 2. Lack of space for industrial real estate and for commercial developments, or a prohibitive price for each of them (resulting from increased competition), is likely to put the HMR at a disadvantage relative to other regions in Germany and across the OECD.

  3. 3. A lack of affordable housing is likely to increase the number of commuters towards the urban core. Pertaining to this final point, besides highlighting the importance of joint planning of housing and transport infrastructure, it advocates for adequate long-term planning to ensure availability of affordable housing in the urban core of the region and its adjacent districts; residential floor space needs to be provided where long-term demand will be. Finally, competition over space may be reduced by developing novel residential housing concepts (for example, building more densely) or changing the nature of existing residential housing in and around the urban core.

Spatial planning needs to adapt to increasing demand for floor space and enable additional housing supply and greenfield development in areas where long-term demand will be. Issues of spatial demand mismatches and management are exacerbated by spatial planning occurring at different administrative levels and with different regulations in the four constituent federal states of the HMR, which, despite a spatial planning framework in the HMR as a whole, have different spatial planning strategies. While Hamburg and Schleswig-Holstein have one spatial planning body each at the state level, Mecklenburg-Western Pomerania has several regional planning associations (Regionale Planungsverbände); Lower Saxony, on the contrary, leaves regional planning entirely to the discretion of districts (8 of the 20 constituent districts of the HMR are part of Lower Saxony). Moreover, each federal state has a separate spatial planning strategy and follows a different concept. Joint spatial planning of transport and housing infrastructure that goes beyond voluntary participation – for example, as has been implemented in MR Rhein-Neckar with its joint planning association – or the arbitration of spatial planning via the HMR, is promising to reduce spatial demand mismatches in the future.

A diverse natural environment

The natural environment is an important capital endowment for the HMR. Being one of the largest metropolitan regions in Germany but also one of the least populated, the natural environment in the wider second ring beyond its urban core is likely to play an important role for the everyday quality of life of people living in the region.

The HMR has a diverse natural environment: it ranks second after MR Berlin-Brandenburg (which is the largest metropolitan region in Germany by total area size) when it comes to open space per resident in square metres, defined as non-developed area including, for example, agricultural, recreational, forest and water areas (Federal Institute for Research on Building, Urban Affairs and Spatial Development, n.d.[10]). When it comes to these different subtypes of land use, a diverse picture emerges: except for water areas per resident in square metres, where the HMR again ranks second after MR Berlin-Brandenburg, the region takes a midfield position when it comes to the other types – recreational, close-to-nature, forest and water areas – suggesting that not a single type of land use dominates the region but a diversity of types prevail. Such landscape diversity has been shown to be positively related to life satisfaction of residents in their surroundings (Kopmann and Rehdanz, 2013[34]). Note, however, that metropolitan regions in Germany differ quite substantially in size and location (including surrounding areas), rendering comparisons of natural endowments between regions difficult.

The HMR takes a midfield position for recreational areas per resident in square metres, including green spaces, parks, sports and leisure areas; for close-to-nature areas, including moors, heathers, and areas covered by rocks and dunes; and for forests. When it comes to water, the HMR also ranks second after MR Berlin-Brandenburg, which has the most water areas within its boundaries. The diversity of the HMR’s natural environment is also reflected in the fact that five UNESCO Biosphere Reserves are located within the HMR: Flusslandschaft Elbe, Hamburgisches Wattenmeer, Niedersächsisches Wattenmeer, Schaalsee and Schleswig-Holsteinisches Wattenmeer und Halligen, with two (Hamburgisches Wattenmeer and Schaalsee) being located entirely within the HMR and the remainder in part (UNESCO, n.d.[35]). Such biosphere reserves do not only play an important role in preserving biodiversity but can also provide an example for sustainable living in the region and be economically-relevant, natural capital endowments as they provide recreational green areas and areas for research and education. Hamburgisches Wattenmeer, Niedersächsisches Wattenmeer and Schleswig-Holsteinisches Wattenmeer und Halligen are already top tourist destinations; Flusslandschaft Elbe and Schaalsee, which are situated in the wider second ring of the HMR, have the potential to become so in the future, both qualitatively and quantitatively in terms of number of tourists (for example, for sustainable environmental tourism).

Highly attractive for tourism but differences within region persist

Holding several UNESCO World Heritage Sites (Altstadt Wismar, Hansestadt Lübeck, Wattenmeer and Hamburg’s Speicherstadt and Kontorhausviertel with Chilehaus), the HMR is a very attractive tourist destination. With about 43.9 beds per 1 000 residents, the HMR leads the table of metropolitan regions, followed closely by Northwest (about 42.3), MR Hanover (about 40.6), MR Munich (about 39.4) and MR Berlin-Brandenburg (about 36.8). Table 1.7 shows the performance of the HMR relative to other metropolitan regions in Germany when it comes to tourism, measured in terms of beds in tourism establishments per 1 000 residents (as a supply-side component of tourism) and overnight stays in tourism establishments per resident (as a demand-side component) in 2015, including rates of change during the period 2011 to 2015. It also shows the shares of foreigners in overnight stays in percentage (as an item measuring the international reputation of a tourism destination and recognisability of a brand) and the average number of overnight stays per trip in tourism establishments (as an item measuring the intensive margin of tourism). Although MR Berlin-Brandenburg and Northwest have grown the fastest when it comes to beds in tourism establishments during the period 2011 to 2015 (about 5.7% and 5.6% respectively), the HMR – which comes from a higher baseline level – has also experienced moderate growth in this supply-side measure (about 3.4%) during that time.

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Table 1.7. Tourism in metropolitan regions in Germany

Beds per 1 000 residents

Change 2011-15 (%)

Overnight stays per resident

Change 2011-15 (%)

Share of foreigners in overnight stays (%)

Average number of overnight stays per trip

HMR

43.9

3.40

6.4

12

14.8

2.6

Berlin-Brandenburg

36.8

5.60

7.1

17

29.7

2.6

Northwest

42.3

5.70

4.6

9.00

11.4

2.8

Frankfurt

34.2

-0.40

4.8

8.00

21.1

2.3

Hanover

40.6

3

4.4

7.60

15.3

2.4

Central Germany

22

3.60

3.1

13

11.6

2.2

Munich

39.4

0.50

6.8

11

25.7

2.3

Nuremberg

30

-2.80

4.2

4.10

17.3

2.3

Rhein-Neckar

27.8

0.10

3.8

13

19.2

2.2

Rhein-Ruhr

14.9

2.90

2.4

8.70

19.6

2

Stuttgart

26

3.10

3.4

13

19.6

2.3

Notes: Beds and overnight stays refer to tourism establishments with at least ten beds. Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), latest available data at the district level from 2015.

However, not all parts of the HMR are benefitting equally from tourism: its urban core and adjacent districts, as well as more urban areas, are profiting particularly. While the city of Hamburg and districts adjacent to the urban core as well as more urban areas experienced growth in the supply of beds in tourism establishments, defined as establishments with at least ten beds, more rural regions actually experienced a decline, as show in Figure 1.29.

This divergence may be further exacerbated by different tourism strategies in each of the four federal states which constitute the HMR, including the lack of a coherent joint marketing strategy for the HMR as a whole and the lack of a joint tourism strategy that connects the urban core to the remainder of the region.

It should be noted that the official statistics on tourism may conceal tourist hotspots within the HMR: first, some of the region’s most successful tourist destinations are located at the coasts of the North and Baltic Seas and thus in more peripheral, rural regions; although these hotspots attract a considerable number of tourists every year, the official statistics on tourism conceal this fact as the districts in which these tourist hotspots are located are much larger and the remaining parts of these districts less successful in attracting tourists. Second, the official statistics on tourism only count tourists in establishments with at least ten beds; there is a considerable number of smaller establishments in the region and these smaller establishments are often located in more rural areas. Finally, the official statistics exclude campsites, which are similarly more often located in rural areas, for example, close to the North and Baltic Seas. Figures presented here, therefore, likely underestimate the tourism potential in rural areas of the region.

Despite having the highest supply of beds, the HMR does not appear to realise the highest demand for overnight stays, although it is not too far off matching supply with demand in tourism. Table 1.7 shows that MR Berlin-Brandenburg clearly outperforms all other metropolitan regions in Germany when it comes to the number of overnight stays in tourism establishments as a demand-side measure: it leads the table for level (about 7.1 overnight stays per resident in 2015) and for growth (about 16.6% increase during the period 2011 to 2015). With about 6.4 overnight stays per resident in 2015, the HMR ranks third and is not too far behind MR Munich in second place (about 6.8%). The HMR and MR Munich also experienced similar growth rates during the period between 2011 and 2015 (about 11.7% and 10.5% respectively).

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Figure 1.29. Change in beds in tourist establishments in the HMR at the district level 2011-15 (%)
Figure 1.29. Change in beds in tourist establishments in the HMR at the district level 2011-15 (%)

Note: Changes in beds in tourist establishments that can host at least ten guests temporarily, excluding campsites.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

The HMR performs worse in terms of international guests: with a share of foreigners in overnight stays of only about 14.8% in 2015, the HMR ranks 9th out of 11 metropolitan regions in Germany, being outperformed by all other regions except Central Germany (about 11.6%) and Northwest (about 11.4%). The top-performer is clearly MR Berlin-Brandenburg where almost every third overnight guest in 2015 came from abroad. However, the new concert hall in Hamburg (Elbphilharmonie) has given the HMR a lot of international exposure and can help significantly enhance the metropolitan region’s attractiveness as an international tourist destination. The enhanced international recognition of Hamburg can also generate future positive trickle-down effects for international tourism in the neighbouring federal states, both within and outside of the HMR.

copy the linklink copied!Institutional framework

Besides differences in the administrative structure between metropolitan regions, differences in terms of organisation and competencies are also important. In collaboration with the HMR office, a survey was, therefore, sent to the offices of the remaining ten metropolitan regions in Germany, asking about their legal status, organisation and budget, and strategic co-operation between participating bodies and stakeholders in various policy domains. Table 1.8 shows the findings of this survey as of April 2019. Metropolitan regions in Germany vary when it comes to legal status and, in particular, organisation and budget as well as the extent of strategic co-operation between participating bodies and stakeholders.

  • Seven out of 12 metropolitan regions (for this exercise, we count MR Rheinland and MR Ruhr , both of which are part of MR Rhein-Ruhr, separately), including the HMR, have a legal mandate, which is given to them in all cases by state treaty. A legal mandate enshrined into state law, however, is not exclusive to those metropolitan regions that span more than one federal state: MR Ruhr and MR Stuttgart are nested respectively within a single federal state but are given its legal mandate by state treaty. When it comes to legal form, many metropolitan regions in Germany are organised as registered associations. There are, however, some exceptions: while the HMR does not have any legal form, MR Central Germany and MR Rhein-Neckar are organised as limited companies; MR Stuttgart is a corporation under public law.

  • Eleven out of 12 metropolitan regions, including the HMR, have a central governing body (only MR Rheinland has none): in most cases, it is composed of representatives of stakeholders and determined by a general assembly. MR Stuttgart is a notable exception: the central governing body is composed of regional deputies who are directly elected by the citizens within the region. In MR Frankfurt, the central governing body is composed of municipal representatives. Not every central governing body, however, is able to set its own rules.

The organisational capacities, measured in terms of full-time, part-time and voluntary staff differ substantially between regions. While MR Rheinland has only 5 (full-time) staff, MR Ruhr has about 451 (327 full-time and 124 part-time); MR Frankfurt, the second largest region in terms of the overall number of staff, has 116 full-time and 32 part-time staff; the HMR has 7 full-time and 6 part-time. The median number of paid staff is 19. Not all staff are paid by own resources, which is especially the case for the HMR (where almost all staff are paid by external sources) and, to a lesser extent, MR Nuremberg.

Stark differences in staffing are also reflected in differences in budgeting. While all metropolitan regions in Germany do have their own budget, with a budget cycle of one year in most cases (exceptions are MR Berlin-Brandenburg and MR Frankfurt, where budget is set for two years), budget amounts vary substantially. While MR Stuttgart and MR Ruhr have a budget of about EUR 350 million and EUR 90 million respectively, most other regions have budgets below EUR 10 million (with the exception of MR Frankfurt, which has a budget of about EUR 15 million). the HMR has a budget of about EUR 0.4 million. The mean budget is about EUR 43 million, the median about EUR 4.7 million. All regions state that they are able to obtain additional funding, mostly from the EU (Interreg, Horizon 2020, European Regional Development Fund [EFRD], European Social Fund [ESF], and Connecting Europe Facility [CEF]) and from the private sector or federal states.

Differences in organisational capacities, staffing and budgets can be explained by the fact that some regions have sovereign competencies, including planning and operating public services (such as public transport in case of Stuttgart). When it comes to budget, differences can be explained by the financial involvement of the federal states or stakeholders from the private sector (for example, enterprises).

Joint strategies between participating bodies and stakeholders in metropolitan regions can be in place in various policy domains and at different levels. The survey asked, in particular, about joint strategies in 15 key domains, namely economic development, urban and spatial planning, housing and infrastructure, transportation, education, health services, social services and welfare, demographic change, environment, innovation, digitalisation, tourism, culture, marketing and budget and finance.

Most metropolitan regions in Germany have joint strategies in place in various policy domains. Most strategies can be found in MR Rhein-Neckar, which has them in place in all key domains above, followed by MR Ruhr (12 out of 15 domains) and MR Stuttgart (11). On the other hand, no joint strategies at all can be found in the HMR, MR Central Germany and MR Northwest. Note that the absence of a joint strategy does not necessarily mean that there is no co-operation: in fact, in the HMR, there is co-operation on various topical issues and at various levels, although it is not officially mandated in a legally binding joint strategy document. In the HMR, business development, tourism and transportation are mixed competencies (federal states, county districts and unitary districts); marketing is a shared competency between the federal state of Hamburg and surrounding districts. Ultimately, mandated joint strategies within a region may enhance co-operation between neighbouring metropolitan regions, which may be beneficial to enhance each other’s innovativeness, competitiveness and global visibility.

The average number of joint strategies across metropolitan regions in Germany is in 6 out of 15 possible policy domains. Most joint strategies can be found when it comes to transportation, the environment, culture and marketing (7 out of 12 regions have joint strategies in place in these domains), followed by economic development and urban/spatial planning (6 regions). On the other hand, only a small number of regions show joint strategies in the areas of health (two regions) and social services/welfare (only one region has a joint strategy in place). Finally, it is important to note that not all joint strategies are legally mandated: for example, in MR Rhein-Neckar, which has most joint strategies in place, only about half of them are mandated by state treaty; the remainder is voluntary.

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Table 1.8. Organisation and competencies of metropolitan regions in Germany

HMR

Berlin-Branden-burg

Northwest

Frankfurt

Hanover

Central Germany

Munich

Nuremberg

Rhein-Neckar

Ruhr

Rheinland

Stuttgart

(Part of Rhein-Ruhr)

(Part of Rhein-Ruhr)

Legal mandate (yes/no)

Yes

Yes

Yes

Yes

No

No

No

No

Yes

Yes

No

Yes

if yes, legal basis for co-operation (e.g. state treaty)

State treaty

State treaty

State treaty

State law

State treaty

Legal basis

State treaty

Legal form (e.g. limited company)

No

Public admin./joint department (?)

Registered association

Public body

Limited company (various company partners; private, public)

Association/limited company

Registered association

Registered association

Limited company (various company partners; private, public)

Public admin.

Registered association

Corporation under public law

Central governing body (yes/no)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

if yes, determined/elected by (e.g. federal state)

State treaty

General assembly

Shareholders' meeting/

Supervisory board/Parliamentary advisory board

General assembly

General assembly

General assembly

Regional assembly

Elected by citizens

if yes, composed of (e.g. municipality representatives)

Reps. of stake-holders

Reps. of federal states

State reps., district reps.,

reps. of the economic sector

Municipal reps.

Reps. of stake-holders

Reps. of stake-holders

Reps. of stake-holders

Municipal reps.

Reps. of general assembly

Municipal reps.

Directly elected regional deputies

Ability to set own governing rules (yes/no)

Yes

Yes

No

Yes

No

No

No

Yes

Yes

Yes

No

Yes

if no, governing rules set by

(e.g. federal state)

Federal states

Federal states

Federal state

Stake-holders

Number of

full-time staff

7

70

4

116

14

11

4

17

73

327

5

98

of which paid by own resources

(= from own budget)

0

70

3.00

113

4

5

4

10

0

313

5

97

of which paid by other resources

(by stakeholders, incl. "Entsendeprinzip")

7

0

1

3

10

6

0

7

0

14

0

1

Number of

part-time staff

6

0

4

32

2

3

3

12

0

124

0

42

of which paid by own resources

(= from own budget)

1

0

4

32

0

3

3

2

0

122

0

40

of which paid by other resources

(=by stakeholders, incl. "Entsendeprinzip")

5

0

0

0

4

0

0

8

0

2

0

2

Number of voluntary staff

0

0

0

25

0

0

0

0

0

2

0

8

Total number of paid staff

13

70

6

132

15

12.5

5.5

23

73

453

5

119

Own budget (yes/no)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

if yes, budget amount (EUR million)

0.44

(?)

0.65

15.0

2.2

0.7

0.7

2.2

9.0

(VRRN + MRN GmbH)

90.0

1.0

349.4

if yes, budget cycle (years)

1

2

1

2

1

1

1

1

1

1

1

1

if yes, determined by

(e.g. federal state)

Stake-holders

Federal states

Steering Committee

Parlia-mentary chamber

Share-holders' meeting/

Supervisory board Parlia-mentary advisory board

Association-ship fees

Regional manage-ment

Member-ship fees, project funding (state and federal)

General assembly, company meeting

Regional assembly

Members

Regional assembly

Other financial resources (EUR million)

2.7

> 0

0

> 0

0

0.9

0.6

0.7

0

10.0

0

> 0

if > 0, by whom (e.g. private sector)

Federal States

EU Interreg, others

EU Interreg, Horizon 2020

EU, federal states, private sector

Private sector, federal states

Private sector

Private sector

Private sector, EFRD,

EU Interreg, ESF, CEF

EU funds, federal gvt., federal states

EU, federal state, private sector

Joint strategy for…

… economic development (yes/no)

No

No

No

No

Yes

No

Yes

Yes

Yes

Yes

No

Yes

if yes, mandated or voluntary

Voluntary

Voluntary

Voluntary

Mandated

Mandated (legal task)

Mandated

if no, which administrative level

Federal states

Federal states

Districts, district-free cities

Federal states, districts, cities

Federal states, districts, cities

… (urban/spatial) planning (yes/no)

No

Yes

No

Yes

No

No

No

Yes

Yes

Yes

No

Yes

if yes, mandated or voluntary

Mandated

Mandated

Voluntary

Mandated

Mandated

if no, which administrative level

Federal state, districts, wards

Federal states, districts, district-free cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

… housing/

infrastructure (yes/no)

No

No

No

Yes

No

No

No

No

Yes

Yes

No

Yes

if yes, mandated or voluntary

Voluntary

Mandated

Mandated (legal task)

Mandated

if no, which administrative level

Federal states

Federal states

Federal states, districts, district-free cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

… transportation (yes/no)

No

Yes

No

Yes

No

No

No

Yes

Yes

Yes

Yes

Yes

if yes, mandated or voluntary

Mandated

Voluntary

Voluntary

Mandated

Mandated (legal task)

Voluntary

Mandated

if no, which administrative level

Federal states, districts, district-free cities

Federal states, districts, district-free cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

… education (yes/no)

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

No

if yes, mandated or voluntary

Voluntary

Voluntary

Mandated (legal task)

Voluntary

if no, which administrative level

Federal states

Federal states

Federal states

Federal states

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

Federal state

… health services (yes/no)

No

Yes

No

No

No

No

No

No

Yes

No

No

No

if yes, mandated or voluntary

Federal states

Voluntary

if no, which administrative level

Federal states

Federal states

Federal states

Federal states, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Districts

… social services/welfare (yes/no)

No

No

No

No

No

No

No

No

Yes

No

No

No

if yes, mandated or voluntary

Voluntary

if no, which administrative level

Federal states

Federal states

Federal states, districts, district-free cities

Federal states, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Districts, munici-palities

… demographic change (yes/no)

No

Yes

No

No

No

No

No

No

Yes

Yes

No

No

if yes, mandated or voluntary

Mandated

Voluntary

Mandated

if no, which administrative level

Federal states, districts, district-free cities

Federal states, districts, district-free cities

Federal states, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

Federal state

… environment (yes/no)

No

No

No

Yes

Yes

No

Yes

Yes

Yes

Yes

No

Yes

if yes, mandated or voluntary

Mandated

Voluntary

Voluntary

Voluntary

Mandated

Mandated (legal task)

Mandated

if no, which administrative level

Federal states

Federal states, regions, municipalities

Federal states

Federal states, districts, cities

… innovation (yes/no)

No

Yes

No

No

No

No

No

Yes

Yes

Yes

No

Yes

if yes, mandated or voluntary

Voluntary

Voluntary

Voluntary

Mandated

Mandatory

if no, which administrative level

Federal states

Federal states

Federal states

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

… digitalisation (yes/no)

No

No

No

Yes

No

No

No

No

Yes

Yes

Yes

Yes

if yes, mandated or voluntary

Voluntary

Voluntary

Mandated

Voluntary

Voluntary

if no, which administrative level

Federal states

Federal States

Federal states

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

… tourism (yes/no)

No

No

No

No

No

No

No

Yes

Yes

Yes

No

Yes

if yes, mandated or voluntary

Voluntary

Mandated

Mandated (legal task)

Mandated

if no, which administrative level

Federal states

Regions, counties, munici-palities

Federal states

Federal states, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

… culture (yes/no)

No

No

No

No

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

if yes, mandated or voluntary

Voluntary

Voluntary

Voluntary

Mandated

Mandated (legal task)

Voluntary

Voluntary

if no, which administrative level

Federal states

Federal states, counties, munici-palities

Federal states

Federal states, districts, cities

Federal states, districts, cities

… marketing (yes/no)

No

No

No

No

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

if yes, mandated or voluntary

Voluntary

Voluntary

Voluntary

Mandated

Mandated (legal task)

Voluntary

Mandated

if no, which administrative level

Federal states

Federal states

Federal states, districts, counties

Federal states, districts, counties

… budget/finance (yes/no)

No

Yes

No

No

No

No

No

No

Yes

No

Yes

if yes, mandated or voluntary

Mandated for Joint Spatial Planning, diverse other State Contracts for Joint Boards

Mandated

if no, which administrative level

Federal states

Diverse by Federal states

Federal states

Federal states, districts, cities

Federal state, districts, cities

Federal states, districts, cities

Federal state, districts, cities

Federal state, districts, cities

Column Total

0

6

5

0

4

4

15

9

5

11

Notes: Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Author’s own elaborations based on responses from metropolitan regions to survey.

References

[5] Ahrend, R. et al. (2014), “What Makes Cities More Productive? Evidence on the Role of Urban Governance from Five OECD Countries”, OECD Regional Development Working Papers, No. 2014/5, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jz432cf2d8p-en.

[8] Ahrend, R., C. Gamper and A. Schumann (2014), “The OECD Metropolitan Governance Survey: A Quantitative Description of Governance Structures in large Urban Agglomerations”, OECD Regional Development Working Papers, No. 2014/4, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jz43zldh08p-en.

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[19] European Commission (2017), Regional Innovation Scoreboard 2017, https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en (accessed on 15 December 2019).

[21] Federal Institute for Cartography and Geodesy (2016), Verwaltungsgebiete 1:250,000.

[14] Federal Institute for Research on Building, Urban Affairs and Spatial Development (2012), Regionales Monitoring 2012.

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[10] Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklung, http://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018).

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[3] Federal Ministry for Regional Planning, Building and Urban Development (1995), Raumordnungspolitischer Handlungsrahmen: Beschluss der Ministerkonferenz für Raumordnung in Düsseldorf am 8. März 1995.

[29] Federal Ministry of Transport and Digital Infrastructure (2017), Broadband, Glassfiber, 3G, 4G Metropolregion Hamburg.

[20] German Meteorological Service (2018), Karten zur Windkraftnutzungseignung in 80 Meter über Grund, https://www.dwd.de/DE/klimaumwelt/ku_beratung/energie_bau/windenergie/windenergie_node.html (accessed on 24 December 2018).

[16] Initiative für Neue Soziale Marktwirtschaft (2018), Der INSM-Bildungsmonitor 2018, http://www.insm-bildungsmonitor.de (accessed on 18 December 2018).

[15] Institut der deutschen Wirtschaft Köln (2018), INSM-Bildungsmonitor 2018: Teilhabe, Wohlstand und Digitalisierung, Studie im Auftrag der Initiative Neue Soziale Marktwirtschaft (INSM), http://www.iwkoeln.de/fileadmin/user_upload/Studien/Gutachten/PDF/2018/IW_Gutachten_Bildungsmonitor_2018.pdf.

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[34] Kopmann, A. and K. Rehdanz (2013), “A human well-being approach for assessing the value of natural land areas”, Ecological Economics, Vol. 93, pp. 20-33.

[4] Martinez‐Vazquez, J., S. Lago‐Peñas and A. Sacchi (2017), “The impact of fiscal decentralization: A survey”, Journal of Economic Surveys, Vol. 31/4, pp. 1095-1129.

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copy the linklink copied!Annex 1.A. Additional tables on comparator regions
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Annex Table 1.A.1. Comparison of economic development with other metropolitan regions across the OECD – Extended set

 

Total population

Share of total population in country (%)

Inhabitants per km²

GDP

(EUR million)

Share of GDP in country (%)

GDP per capita

(EUR)

GDP per employed

(EUR)

Athens (Greece)

4 889 101

45.4

402

127 278

56.9

17 570

55 526

Birmingham (UK)

5 638 865

8.5

447

160 917

7.2

27 796

60 871

Busan (Korea)

7 946 209

15.3

635

248 470

15.9

31 207

62 947

Dublin (Ireland)

3 365 126

70.8

97

226 423

87.3

65 314

144 836

Milan (Italy)

9 744 596

16

439

386 033

21.7

38 549

82 433

Lisbon (Portugal)

3 597 784

35

478

103 285

43

26 534

61 204

Manchester (UK)

7 078 612

10.8

512

64 417

2.9

24 440

58 293

Marseille (France)

3 065 274

4.6

286

91 455

4.3

28 080

63 235

Montreal (Canada)

8 254 912

22.6

6

262 698

19.5

31 567

63 556

Naples (Italy)

5 771 239

9.6

436

112 429

6.3

19 235

59 483

Oslo (Norway)

2 084 157

39.6

138

105 467

39

46 006

85 525

Notes: Metropolitan regions are composed of OECD TL2 or TL3 regions. See Footnote 5 for exact composition.

USD converted into EUR as of 4 February 2019.

Source: Own calculations based on OECD Regional Statistics (n.d.[12]), Regional Social and Environmental Indicators: Internet Broadband Accesshttp://stats.oecd.org (accessed on 18 December 2018), latest available data from 2016.

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Annex Table 1.A.2. Labour markets and education in metropolitan regions in Germany

 

Share of employed without vocational degree (%)

Change 2014-15 (%)

Share of employed with vocational degree (%)

Change 2014-15 (%)

Share of employed with tertiary degree (%)

Change 2014-15 (%)

Share of employed in high-tech sector

(%)

Change 2014-15 (%)

Share of employed in creative sector

(%)

Change 2014-15 (%)

HMR

11.1

7.70

60.4

2.20

14.4

6.80

4.8

-0.20

3.3

3.00

Berlin-Brandenburg

8.9

9.70

57.6

2.10

18.7

8.50

3.6

-1.00

3.4

5.30

Northwest

12.5

7.60

63.4

2.80

10.5

6.20

5.4

-0.20

1.7

2.20

Frankfurt

12.5

5.70

56.4

1.80

17.9

6.40

7

0.20

3.2

3.30

Hanover

11.1

8.00

64.7

2.20

13.8

5.90

11.2

0.30

2.7

2.40

Central Germany

5.9

17.40

69.3

1.80

16.5

3.10

7.2

2.40

2.5

2.00

Munich

11.1

4.60

58.5

2.50

18.9

8.20

10.3

3.10

4.1

6.80

Nuremberg

12.2

4.50

67.1

2.10

11.9

6.50

10.1

-4.30

2

3.30

Rhein-Neckar

13.2

5.70

60.8

2.10

15.6

5.90

11.9

-1.20

3.1

4.10

Rhein-Ruhr

13.5

5.80

58

2.00

14.6

5.80

6.4

-0.80

2.6

1.70

Stuttgart

13.8

4.80

61.7

2.20

16.1

7.40

14.1

1.50

3.1

4.70

Notes: Figures take into account exact geographical borders of metropolitan regions, by aggregating observations over districts they are composed of.

Berlin-Brandenburg = Capital Region of Berlin-Brandenburg; Northwest = Bremen-Oldenburg in the Northwest; Frankfurt = FrankfurtRheinMain; Hanover = Hannover-Braunschweig-Göttingen-Wolfsburg.

Source: Own calculations based on Federal Institute for Research on Building, Urban Affairs and Spatial Development (n.d.[10]), INKAR online: Indikatoren und Karten zur Raum- und Stadtentwicklunghttp://www.bbsr.bund.de/BBSR/DE/Raumbeobachtung/InteraktiveAnwendungen/INKAR/inkar_online_node.html (accessed on 18 December 2018), latest available data at the district level from 2015.

Notes

← 1. For brevity, the Capital Region of Berlin-Brandenburg is referred to as Berlin-Brandenburg, Bremen-Oldenburg in the Northwest as Northwest, FrankfurtRheinMain as Frankfurt, and Hannover-Braunschweig-Göttingen-Wolfsburg as Hanover. Where necessary, the prefix MR (short for metropolitan region) is used to refer to the respective metropolitan region rather than the city of the same name.

← 2. Other international examples of city-states include Brussels (Belgium), Moscow (Russia) and Vienna (Austria).

← 3. When computing descriptive statistics on metropolitan regions in Germany, the exact geographical borders of metropolitan regions are always taken into account, by aggregating (and, when necessary, weighting) observations over the districts they contain.

← 4. The composition of principal comparators is as follows: Barcelona (Spain) includes Catalonia (ES51); Boston (US) includes Massachusetts (US25); Copenhagen (Denmark) includes Capital (DK01) and Zealand (DK02); Gothenburg (Sweden) includes West Sweden (SE23); and Rotterdam (Netherlands) includes South Holland (NL33).

← 5. The composition of other comparators is as follows: Athens (Greece) includes Attica (EL30), Central Greece (EL64), and Peloponnese (EL65); Birmingham (UK) includes West Midlands (UKG); Busan (Korea) includes Gyeongnam (KR02); Dublin (Ireland) includes Southern and Eastern (IE02); Lisbon (Portugal) includes Lisbon (PT17) and Alentejo (PT18); Manchester (UK) includes North West England (UKD); Milan (Italy) includes Lombardy (ITC4); Montreal (Canada) includes Quebec (CA24); Naples (Italy) includes Campania (ITF3); Oslo (Norway) includes Oslo (NO01) and South-Eastern Norway (NO03); Rome (Italy) includes Lazio (ITI4); Stockholm (Sweden) includes Stockholm (SE11) and East Middle Sweden (SE12); and Vancouver (Canada) includes British Columbia (CA59). While all of the aforementioned comparators were constructed from (one or more) OECD TL2 regions, Marseille (France) was constructed from OECD TL3 regions, including Bouches-du-Rhône (FR824) and Var (FR825).

← 6. A similar picture arises when looking at gross value added (GVA) instead of gross domestic product (GDP) per employed, with GVA taking into account regional differences in taxes and subsidies.

← 7. Note that regions in the south may already be at the point at which their unemployment rate is near the natural rate – this is the frictional rate that is always present due to normal job separation and finding – implying that no further decrease in unemployment is possible.

← 8. The DAX (Deutscher Aktienindex) is the German national stock market index listing the 30 largest German firms by market capitalisation.

← 9. The European Commission’s Regional Innovation Scoreboard provides comparable data on the innovation performance of EU member states’ and other European countries’ regions in various categories. These include framework conditions for innovation (human resources, attractive research systems and innovation-friendly environment), investments into innovation (finance and support as well as firm investments), innovation activities (innovators, linkages and intellectual assets) and impacts (both employment and sales impacts). The data set provides 18 composite indicators on regional innovation performance – one indicator per category – which are available at the EU NUTS2 level (similar to the OECD T2 level). NUTS2 regions are chosen such that they most accurately reflect metropolitan regions in terms of actual geographical coverage, i.e. NUTS2 regions with large parts of their geographical areas in the respective metropolitan region are chosen. Nevertheless, a caveat with this analysis is that, as composite indicators are only available at the NUTS2 level, metropolitan regions are not exactly equal to the actual metropolitan regions in Germany or across the OECD, and this divergence in geographical coverage limits the validity of the analysis to some extent.

← 10. For this analysis, the metropolitan regions are composed of the following NUTS2 regions: HMR includes Hamburg (DE60), Lüneburg (DE93), Mecklenburg-Vorpommern (DE80), and Schleswig-Holstein (DEF0). Berlin-Brandenburg includes Berlin (DE30) and Brandenburg (DE40). Northwest includes Northwest (DE50) and Weser-Ems (DE94). Frankfurt includes Darmstadt (DE71), Giessen (DE72), and Kassel (DE73). Hanover includes Braunschweig (DE91) and Hannover (DE92). Central Germany includes Chemnitz (DED1), Leipzig (DED3), Sachsen-Anhalt (DEE0), and Thüringen (DEG0). Munich includes Niederbayern (DE22), Oberbayern (DE21), and Schwaben (DE27). Nuremberg includes Mittelfranken (DE25), Oberfranken (DE24), Oberpfalz (DE23), and Unterfranken (DE26). Rhein-Neckar includes Karlsruhe (DE12) and Rheinhessen-Pfalz (DEB3). Rhein-Ruhr includes Arnsberg (DEA5), Duesseldorf (DEA1), Köln (DEA2), and Muenster (DEA3). Stuttgart includes Stuttgart (DE11) and Tübingen (DE14).

← 11. In terms of large, dominant firms that serve as incubators of innovation through relatively higher research and development intensities, MR Stuttgart hosts the headquarters of Daimler (car manufacturing), MR Rhein-Neckar the headquarters of BASF (chemicals) and SAP (software), and MR Munich the headquarters of BMW (car manufacturing) and Siemens (industrial, electronics), jointly with MR Berlin-Brandenburg.

← 12. For this analysis, the metropolitan regions are composed of the following NUTS2 regions: HMR includes Hamburg (DE60), Lüneburg (DE93), Mecklenburg-Vorpommern (DE80), and Schleswig-Holstein (DEF0). Principal Comparators: Barcelona (Spain) includes Cataluña (ES51); Copenhagen (Denmark) includes Hovedstaden (DK01) and Sjælland (DK02); Gothenburg includes Vaestsverige (SE23); and Rotterdam (Netherlands) includes Zuid-Holland (NL33). Other Comparators: Athens includes Attiki (EL30), Sterea Ellada (EL64), and Peloponnisos (EL65); Birmingham includes West Midlands (UKG); Dublin includes Southern and Eastern (IE02); Lisbon includes Lisboa (PT17) and Alentejo (PT18); Manchester includes North West (UKG); Marseille includes Méditerranée (FR8); Milan (Italy) includes Lombardia (ITC4); Naples includes Campania (ITF3); Oslo includes Oslo og Akershus (NO01) and Sør-Østlandet (NO03); Rome includes Lazio (ITI4); and Stockholm includes Stockholm (SE11) and Oestra Mellansverige (SE12).

← 13. The local wind power adequacy is calculated based on average weather data from 1981 to 2000 and encompasses a multitude of exogenous climatic and geographical factors related to electricity generation from wind power. Specifically, it is based on wind velocity and aptitude, taking into account between-region factors, such as coasts, and within-region factors, such as cities, forests, and local topographies. The variable is typically considered by project developers during cost-benefit analyses and siting decisions of new build projects and is thus a suitable predictor for locations where electricity generation from wind power is likely to take place in the future.

← 14. The slow grid expansion should be somewhat mitigated by the new Action Plan Power Grid (Aktionsplan Stromnetz) by the Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie), which is scheduled to pass cabinet by early 2019 and which foresees the north-south transmission line to be completed by early 2024 (Federal Ministry for Economic Affairs and Energy, n.d.[36]).

← 15. Unfortunately, the time-series on basic and high-speed broadband access from the Federal Institute for Research on Building, Urban Affairs and Spatial Development in Germany have been discontinued since 2010.

← 16. For this tabulation, OECD T2 regions were taken. Barcelona (Spain) was represented by Catalonia (ES51), Boston (US) by Massachusetts (US25), Copenhagen (Denmark) by Capital (DK01), Gothenburg (Sweden) by West Sweden (SE23), and Rotterdam (Netherlands) by South Holland (NL33). Unfortunately, data on basic broadband access in the US are only available at the state level. Thus, Massachusetts is taken to represent Boston.

← 17. For this analysis, OECD TL2 regions were taken. Unfortunately, the summary scores used are not available for smaller regions, which makes comparisons between HMR and other metropolitan regions in Germany, which are partly nested in one or more TL2 regions, difficult. In any case, indicators similar to some of these summary scores have already been looked at in previous sections. HMR is represented by Hamburg (DE6A) because quality-of-life data are not available for smaller OECD TL3 regions.

← 18. Each dimension of quality of life includes one or more regional well-being indicators. Education includes the share of the labour force with at least secondary education, jobs includes both the employment and the unemployment rate, income includes the household disposable income per capita, safety includes the homicide rate, health includes life expectancy at birth and the age-adjusted mortality rate, environment includes the estimated average exposure to air pollution based on satellite imagery, civic engagement includes voter turnout, accessibility to services includes the share of households with broadband access, housing includes the number of rooms per person, and community includes the share of people who have friends or relatives to rely on in case of need. Overall satisfaction with life is obtained from a single-item 11-point Likert scale that asks respondents in each region: “Overall, how satisfied are you with your life as a whole these days?” Answer possibilities range from zero (“not at all satisfied”) to ten (“completely satisfied”). For all indicators, the latest available data are taken, which implies that some indicators are more recent and some are slightly older. For example, in case of HMR, data on voter turnout are from 2017, whereas data on overall satisfaction with life are from 2010.

← 19. Average life satisfaction in HMR in 2016 was 7.5 (out of 10). The ranking of constituent districts, in terms of average life satisfaction, was, in descending order: Ostholstein (8.3), Harburg (8.0), Heidekreis (7.9), Rotenburg (Wümme) (7.9), Steinburg (7.8), Uelzen (7.7), Stormarn (7.6), Cuxhaven (7.6), Hamburg (7.6), Nordwestmecklenburg (7.5), Segeberg (7.5), Lüneburg (7.4), Pinneberg (7.4), Neumünster (7.3), Dithmarschen (7.3), Stade (7.2), Ludwigslust-Parchim (7.2), Lüchow-Dannenberg (7.2), Herzogtum Lauenburg (7.1), Lübeck (7.1), and Schwerin (6.7).

← 20. Note that there is a district called “Harburg” and a borough within the Free and Hanseatic City of Hamburg which has the same name. If not stated otherwise, the text refers to the district.

← 21. Average housing satisfaction in HMR in 2016 was 7.9 (out of 10). The ranking of constituent districts, in terms of average housing satisfaction, was, in descending order: Heidekreis (8.8), Lüneburg (8.8), Uelzen (8.7), Harburg (8.5), Rotenburg (Wümme) (8.3), Nordwestmecklenburg (8.3), Stade (8.1), Segeberg (8.1), Ostholstein (8.1), Stormarn (8.1), Dithmarschen (8.0), Schwerin (8.0), Cuxhaven (7.9), Hamburg (7.8), Ludwigslust-Parchim (7.8), Steinburg (7.7), Pinneberg (7.7), Herzogtum-Lauenburg (7.5), Neumünster (7.1), Lübeck (6.9), and Lüchow-Dannenberg (6.6).

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Chapter 1. Trends, challenges and opportunities in the Hamburg Metropolitan Region