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ANALYSIS �� Patterns of Income Across Europe

June 2015

Patterns of Income Across Europe

Abstract

This paper examines the recent trends of the levels and growth of income, as measured by per capita disposable personal income, across the metropolitan areas of Europe to understand where income is growing the strongest and how the distribution of income across these regions may be changing. The data cover 272 metropolitan areas across 24 countries for which there are data on nominal disposable income.

Prepared byAnna [email protected]

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MOODY’S ANALYTICS / Copyright© 2015 1

ANALYSIS

Patterns of Income Across EuropeBY ANNA ZABRODZKA

Over the last decade differences between incomes across Europe have increased significantly. This is contrary to the stated goals of the European Union as borders were opened, a common currency was introduced, and labor market regulations were increasingly harmonized. From the very outset of

integration, the Treaty of Rome, which established the European Economic Community in 1957, decreed, among other things, equal pay for equal work. Integration and equality were also founding principles of the European Union, formally established by the Maastricht Treaty in 1993. In the last 15 years, the fight against poverty and exclusion has taken priority under the auspices of the Lisbon Strategy’s economic, social and environmental dictates. Yet despite these initiatives, regional income inequalities across Europe and within individual countries have not been remedied (see Chart 1).

This paper examines the recent trends of the levels and growth of income, as measured by per capita disposable per-sonal income, across the metropolitan areas of Europe to understand where in-come is growing the strongest and how the distribution of income across these regions may be changing. The data cover 272 metropolitan areas across 24 coun-tries for which there are data on nominal disposable income.

For this analysis the metro areas are grouped into five tiers based on size (see

Chart 2). The size tiers are used because in-vestor interest in metropolitan areas often is determined by size since it often reflects the economic role of the metro areas. For example, the 24 Tier I metro areas contain all of the areas that are generally considered gateway metro areas, which have strong global linkages to trade and investment. The Tier II areas are often centers of regional trade, transportation and commerce. Many in the smaller tiers are centers of local or re-gional government or have a highly special-ized economic base.

It is harder, however, to char-acterize size tiers by income. In fact, there is very little

correlation between size and per capita in-come (see Chart 3). A scatter plot of popu-lation versus per capita income indicates little dependence and the correlation of these two series is only 0.05, implying al-most no relationship. It is true that London and Paris, two of the largest metro areas in Europe, also are ranked among the top for per capita income. Munich and Milan, however, have similar levels of income but with a much smaller population base. And Zurich, a Tier II metro area, has the highest income on a per capita basis.

This paper, therefore, seeks to trace the pattern of personal income across the metro areas of Europe and to explore where pat-terns of income growth may be changing as the economy has emerged from the financial

1

Chart 1: Income Distribution Remains Unequal

Sources: National statistical offices, Eurostat, Moody’s Analytics

Nominal disposable income per capita, € ths, 2014

>22

14 to 17.918 to 19.920 to 21.9

<14

By metropolitanarea

22

Tier Population range Metro areas in tier

I >2.5 million 24

II 1.25 to 2.5 million 40

III 750,000 to 1.24 million 69

IV 500,000 to 749,999 72

V <500,000 67

Chart 2: Metro Area Size Class Cohorts

Source: Moody’s Analytics



crisis and the subsequent long period of eco-nomic recovery, and as the European Union has expanded.

Nordics and Swiss rule the ranksThe European metro areas with the high-

est income per capita are not within the Eu-ropean Union at all; they include the larger ones of Switzerland and Norway (see Table 1; Chart 4 summarizes Table 1 for a number of capital cities and major metro areas.) Fur-ther, non-EU metro areas took the top seven spots in 2014, and none of them are among the large Tier I metro areas. Most are smaller Tier III to Tier V metro areas, although as mentioned, Zurich, a Tier II metro area, did rank first.

This is not a surprise. Switzerland and the Nordic countries have for decades been among the richest countries and also are vanguards of equal pay in Europe, with high labor force participation rates for women and high fertility rates. According to Euro-stat, Switzerland’s poverty rate is among Eu-rope’s lowest and its distribution of wealth is relatively more even. At the same time, the cost of living in Switzerland is among the highest in the world, with the cities of Zurich and Geneva ranked second and third most expensive, respectively, according to a UBS study.1 Although taxes in Switzerland are relatively moderate, leading to higher disposable income levels, average prices in Zurich are around 20% higher than those

1 http://www.ubs.com/global/en/wealth_management/wealth_management_research/prices_earnings.html

in the other Western European cities. Fur-ther, the small size of their metropolitan areas supports the per capita measures. Moreover, Swiss metro areas also have benefited from strong appreciation of the franc against the euro, while most other currencies weakened against the single currency (see Chart 5). Only recent actions by the European Central Bank such as the start of the government bond pur-chase programmer in early 2015 started to push the euro lower against other European currencies, but most of them are still weaker than the January 2008 level. This develop-ment supported a large increase in income per capita between 2009 and 2014 after conversion to euros, with income rising by around 30% for all five Swiss metro areas.

Munich is ranked at the top for per capita income among the Tier I metropolitan areas that are within the European Union and is 16th among all metro areas in Europe of any size. In the Tier I group, Munich is followed by London (11th overall), Paris (16th over-all), and Milan (19th overall). Among these top-ranked large areas are some smaller Austrian ones as well. The middle-ranking metro areas are a mix of German, British, French, Italian and Swedish cities. Lower in

the ranks are those in Spain, Greece and Por-tugal, while Central and Eastern Europe sits near the bottom. Of the CEE metro areas, the highest-ranking is Bratislava at number 227, followed by other national capital re-gions, including Warsaw, Tallinn, Vilnius and Prague. Smaller Bulgarian and Romanian metropolitan areas are at the bottom of the list.

Interestingly, the smallest metropolitan areas have the highest average income per capita (see Chart 6). However, looking at the composition of the Tier V group, this is not surprising as a majority of the metro areas are located in Germany’s richest Bundes-lands—Bavaria and Baden-Württemberg, to-gether with a couple of Swiss, Austrian and British metro areas. However, there is only a handful of CEE, Italian and Spanish metro ar-eas in this group. Therefore, the distribution of income across Tier V is very much skewed towards the richer metro areas, resulting in

3

y=11.587x + 918.19R²=0.0029

0

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4

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8

10

12

14

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0 10 20 30 40 50

Pop

ulat

ion,

mil

Income per capita, € ths

London

Paris

ZurichSofia

Barcelona

Munich

Milan

Madrid

AthensWarsaw

Chart 3: Size Does Not Drive Income


4

Chart 4: Swiss Metro Areas Top the RankingNominal disposable income per capita, € thsMetro area Income Rank Tier

Zurich 42.5 1 II

Oslo 36.0 4 III

Munich 26.1 10 I

London 25.0 11 I

Paris 24.0 16 I

Milan 23.8 19 I

Stockholm 23.4 23 II

Edinburgh 22.2 42 II

Vienna 22.1 46 I

Helsinki 21.9 49 II

Rome 19.6 116 I

Dublin 19.1 126 II

Metro area Income Rank Tier

Berlin 18.4 144 I

Amsterdam 17.2 166 II

Madrid 18.9 179 I

Lisbon 14.2 200 I

Athens 13.6 205 I

Bratislava 11.9 215 IV

Warsaw 10.4 229 I

Tallinn 9.2 231 IV

Vilnius 9.1 232 III

Prague 8.6 233 I

Budapest 7.0 247 I

Sofia 4.6 263 II


5

50

60

70

80

90

100

110

120

130

140

08 09 10 11 12 13 14 15

CHF GBP NOK PLN HUF CZK

Chart 5: Swiss Areas Gain From Strong Franc

Sources: Bloomberg, Moody’s Analytics

Exchange rate, € per local currency, Jan 2008=100



relatively high average income per capita for the whole group.

Different paths of recovery since finan-cial crisis

Although the ranking has not changed notably compared with the precrisis years, the pace of recovery has varied significantly across regions. Regardless of the size, met-ropolitan areas located in CEE have recorded the strongest income growth between 2009 and 2014, while metro areas in the south—Spain, Italy and Greece—still report lower nominal per capita income today than five years ago (see Table 1). This holds for all five size tiers.

In general the rate of income growth in metropolitan areas reflects the countries where they are located. The three Baltic countries were the leaders in the recovery process, as measured by GDP. While each lost around 20% of its GDP during the downturn, each managed to create robust economic growth over the last five years thanks to deep structural reforms and have almost recovered all lost output (see Chart 7). Income growth followed this pattern; Estonia and Lithuania recorded the high-est increases in income per capita between 2009 and 2014. Thus, it is not surprising that Tallinn and Vilnus are among the top metro areas when ranked by income growth, although they were still overtaken by some small Polish cities.

In general Central and Eastern Euro-pean economies managed to ensure strong growth following the financial crisis, relative

to their western peers. Therefore, almost all of the CEE metro areas exhibited very strong income growth during the last five years with the exception of Czech metropolitan areas and Slovenia’s Ljubljana. In the Czech Repub-lic, despite relatively strong public finances, the government introduced proportionately strict austerity measures following the 2008-2009 financial crisis, which resulted in a pro-longed recession across the country, curbing wage growth and consumption. However, within the region, the Czech Republic is one of the most developed countries, with per capita income in its Tier I metro areas higher than in most other CEE peers. Meanwhile, Slovenia’s economy has been dampened by its banking crisis in recent years.

Some of the recent strong income growth in CEE can be explained by the rise of the information and communications technol-ogy industry as an important employer (see Chart 8). Because of the still-low wages paid in the region com-pared with its west-ern peers, the ICT boom in Central and Eastern Europe has largely been driven by outsourcing and off-shoring. As the largest country in the region, Poland is the biggest draw, with most firms initially locating their offices in Warsaw. Over the last decade, hundreds of foreign

corporations including IBM, Google, HP, and Microsoft have opened offices in Poland’s capital. Other capitals such as Budapest and Tallinn, which is home to the Skype research and development centre now belonging to Microsoft, have experienced a similar influx of foreign corporates.

Peripheral Europe: Some areas emergeNot surprisingly, Greek, Spanish and Ital-

ian metro areas are on the other end of the scale, with per capita income much lower in 2014 than in 2009. The strict austerity mea-sures designed to control ballooning public debt and deficit levels, combined with long-lasting recessions, have resulted in massive drops in wages. Athens and Thessaloniki have recorded the sharpest drops in disposble income per capita, and in the current cirum-stances no significant turnaround is expected in the near future, especiallly considering continued political tensions regarding Greek

6

0 5 10 15 20

Tier V

Tier IV

Tier III

Tier II

Tier I

20142009

Chart 6: Small Areas Are Richest on Average


Avg income per capita, € ths, by metro area size tiers

7

Chart 7: Baltic Countries Recovered Output


Real GDP, % change

-30 -20 -10 0 10 20 30

GreecePortugal

SpainItaly

FranceHungary

IrelandU.K.

SwitzerlandGermanySweden

LatviaPoland

LithuaniaEstonia

2009-20142008-2009

8

y=0.0822x + 2.6568R²=0.0153

0123456789

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-6 -4 -2 0 2 4 6 8 10 12

ICT

em

ploy

men

t, %

of t

otal

, 20

14

Income per capita, CAGR % change, 2009-2014

Helsinki

Chart 8: ICT Industry Supports Income Growth


Reading

DublinSofia

Warsaw

Tallinn

Prato

Santander

Stockholm

Budapest

All EU metropolitan areas



debt. However, it seems that the downward trend has bottomed out, and unless Greece leaves the euro area, the only way now is up, although the pace of improvement will likely be very sluggish.

Even in Ireland, which is viewed as an example of economic recovery within the peripheral euro area, per capita income is still lower than in 2009, although it returned to growth at the end of 2013. Also Dublin’s per capita income has been subdued despite the fact that in the last few years it attracted most of the foreign investment flooding into Ireland thanks to the country’s competitive corporate tax rate. The capital’s information and telecommunications sector gained on this development, with Google and Twit-ter setting up offices in the metro area, creating highly paid jobs for skilled workers. Therefore, per capita income will likely grow robustly in coming years, although it will take some time before the precrisis level is achieved.

In some areas industrial structure played an important role in determining the pace of income growth. For example, in Spain the large dependence upon construction dur-ing the precrisis years in many metropolitan areas was very damaging (see Chart 9). The collapse of the industry was particularly pronounced as a result of the housing bubble that developed before the crisis. Spain’s southern metro areas Alicante, Malaga and Murcia recorded the largest drops in dispos-able income per capita. These areas suffered greatly with a loss of jobs in both travel and tourism as well as construction. They each

had a large share of employment in construction before the crisis and so far they have not man-aged to attract any other significant industry to propel their recovery.

On the other hand, Spanish metro areas that have a much more diverse industrial

composition such as Oviedo, Valencia and Madrid fared much better, although dis-posable income is still lower than in 2009. Only one metro area—Vitoria-Gasteiz—which has the highest concentration of manufacturing jobs among Spanish metro areas—around 25% of total employment—managed to have its per capita income in-crease slightly between 2009 and 2014. The area’s manufacturing operations include Mercedes-Benz, Michelin, Gamesa and Heraclio Fournier.

Meanwhile, the main Portuguese metro-politan areas—Lisbon and Coimbra—already have managed to recover, with nominal per capita income in 2014 higher than in 2009 (see Table 1). This economic rebound has been driven mainly by strong external de-mand, while domestic demand remains very weak. Portuguese exports jumped 33% over the last five years, while imports grew only 4.4%, resulting in Portugal’s first current account surplus in two decades. Low wages improved the competitiveness of Portuguese goods, which has boosted exports. Moreover, lower labour costs and a large, multilingual pool of young people in the labour force have attracted foreign companies to relo-cate call centres, resulting in a service sector boom in Lisbon.

In Italy incomes have also been subdued throughout the country, even in the main economic centres such as Turin, which is focused on car manufacturing, and Milan, an important financial hub. Still the loss of per capita income between 2009 and 2014 in the northern cities has been much smaller

than that in the south, which is focused on tourism and agriculture. Interestingly, Rome recorded one of the largest declines during that period, most likely because of weak tourism during the post-crisis recovery peri-od. There were only a few small Tier IV metro areas, along with Padua, a Tier III manufac-turing centre, where incomes managed to recover from the slump.

Divergence, not convergenceOne idea behind an integrated Europe

was that income inequalities should diminish over time, with the lowest-income regions slowly converging with the highest. But the dispersion rates in 2014 and a decade earlier show that the gap has widened.

Standard deviation is used as a tool to measure dispersion of per capita income around the mean value of areas under con-sideration. A large standard deviation indi-cates that numbers in the set are far from the mean and from each other, while a small standard deviation indicates that numbers are close to the mean and to each other. In 2014, the average disposable per capita income across the 272 metro areas was €17,300, with a standard deviation of €6,500 (see Chart 10).

Even when we exclude the outliers—Scan-dinavian and Swiss metro areas at the top and Romanian and Bulgarian metro areas at the bottom—the dispersion has increased from €5,000 to €5,200, although the jump in this case is much smaller. Further, inequal-ity has been increasing despite previously mentioned strong income growth rates in the poorest metropolitan areas (see Chart 11). Incomes have nearly doubled in CEE over the last 10 years, but with growth rates in the Nordic and Swiss metro areas also at around 40%, their incomes are increas-ing too fast for the poorer regions to catch up. However, also within the CEE there are differences among the metropolitan areas. Although the standard deviation is relatively small, it rose from €1,600 in 2004 to €2,100 in 2014 (see Chart 12). However, the differ-ence between the richest (Warsaw, Poland) and poorest (Galati, Romania) cities in the region stabilized and even fell marginally in 2014 compared with 2008.

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y=-0.9403x + 8.1069R²=0.3537

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-6 -4 -2 0 2 4

Con

stru

ctio

n em

ploy

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Income per capita, CAGR % change, 2009-2014

Chart 9: Construction Loss Hurts Spain’s Areas

Sources: National statistics offices, Eurostat, Moody’s Analytics

Athens

Palma de Mallorca Granada

Alicante

Thessaloniki

HeilbronnHague

Euro zone metropolitan areas

Oviedo

PlauenSevilla

Schweinfurt

Brussels

Madrid

Düsseldorf



Fractured euro zoneEven within the single-currency area,

arguably the most integrated part of the EU, income inequality has increased over the last decade, with the rate of dispersion accelerating after the 2008-2009 financial crisis. The standard deviation rose to €3,700 in 2014 from €2,900 in 2004, an increase of almost 30% and the difference between the richest and poorest metro areas also jumped strongly (see Chart 13). As mentioned earlier, one reason for the increase was the destruc-tive effect the sovereign debt crisis had on the southern members of the euro zone, including Italy, Portugal, Spain and Greece. In 2004, Milan and Rome ranked among other high-income Tier I metropolitan areas for per capita income such as Munich, Paris and Stuttgart. In 2014, no Italian city ranked among the top 10 euro zone metro areas.

Meanwhile, the Greek, Spanish and Por-tuguese metro areas have fared worse than

their peers. Already near the bottom of the euro zone ranking in 2004, their situation has worsened considerably since the last financial crisis. Between 2009 and 2014 income per capita plummeted by almost 5% on an an-nual basis. Since 2009, incomes have declined in nearly all metropolitan areas of the euro zone periphery, plus a few Dutch metro areas, because of the extended recession.

On the other hand, disposable income per capita in French and German metro areas managed to recover well from the slump. Germany’s economy has been propelling the euro zone out of recession. But the French economic expansion has disappointed even as income continued to grow. Interestingly, France is unique in that high unemployment rates, a loss of competitiveness, and low profitability do not drive down wages, as would be expected, but they do curb con-sumption. The conservative structure of the local labour market—which creates too many

efficiency wages2, strong trade unions, and a minimum wage that is too high compared with the mean wage—works to keep wages elevated. The minimum wage, for example, does not correspond to the prevailing eco-nomic situation in the country. Since 2008, France’s GDP has mostly flatlined, while the minimum wage has steadily climbed.

Within-country inequalitiesMany European countries are too small

to consider within-country differences since they have only two to four metropolitan areas; this does not create a useful measure of standard deviation. There are seven coun-tries with more than 10 metropolitan areas: France (34), Germany (67), Italy (20), Neth-erlands (12), Poland (18), Spain (23), and the U.K. (35). Among them, the country with the

2 An efficiency wage is a wage that is above the equilibrium wage, which would equal marginal revenue product.

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0 2 4 6 8 10

Standarddeviation

Max-min

200420082014E

Chart 12: CEE Becomes Increasingly Unequal


Per capita income, € ths

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0 5 10 15 20

Standarddeviation

Max-min

200420082014E

Chart 13: Inequality Widened Across Euro Zone

Sources: National Statistics Offices, Eurostat, Moody’s Analytics


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0 2 4 6 8

Standarddeviation ex

outliers

Total standarddeviation

200420082014E

Chart 10: Income Inequality Has Widened


Per capita income one standard deviation from the mean, € ths

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Oslo

Stockholm

Zurich

Prague

Bratislava

Tallinn

Warsaw

Vilnius

Sofia

Bucharest

Chart 11: Robust Income Growth in CEE


Disposable income per capita, % change, 2004 to 2014



highest standard deviation is Italy. However, unlike for other European countries, this number actually decreased between 2004 and 2014. Meanwhile in Poland, although the standard deviation is the lowest among the above-mentioned countries, it almost doubled over the last decade.

The remainder of this paper will discuss divergent patterns of income growth within two countries. It first examines Germany and the special circumstances it has faced with its reunification since the fall of the Berlin Wall. The subsequent section focuses on the U.K. and the shifting industrial structure of its regions since the 1980s.

Germany’s east-west divideDespite being the driving force of the euro

zone economy, Germany has experienced one of the greatest divergences of regional income trends in the recent years among European countries. The standard deviation of per capita income across its metro areas increased from €1,700 in 2004 to €2,200 last year, and the gap between the richest (Heilbronn) and poorest (Neubrandenburg) jumped by €2,500 in the last five years (see Chart 14). On November 9, 2014 Germany celebrated the 25th anniversary of the fall of the Berlin Wall, which started the country on the path towards reunification. East Germany had weaker economic foundations in 1989, and 25 years later, despite some €2 trillion in investment, eastern Germany still lags the richer west. Moreover, the fiscal transfer system through which wealthier German states subsidize the poorer ones, mainly in

the east, is set to expire in 2019. As of 2012, three German states provided subsidies: Hes-sen, whose biggest city is Frankfurt am Main; Badem-Würtemberg, containing the City of Stuttgart; and Bavaria, where Munich is lo-cated. The remaining 13 states were all recipi-ents, with Berlin receiving the largest share of funds. In February 2013, Bavaria and Hessen challenged this redistribution scheme in the German Supreme Court. A ruling is pending.

Despite all of its efforts at redistribution, Germany’s economy continues to display significant inequalities. According to the German Institute for Economic Research, or DIW Berlin, the country has the euro zone’s highest national Gini coefficient—a common gauge of wealth inequality that ranges from 0 to 1, with one denoting maximum inequal-ity. In 2012, Germany’s coefficient stood at 0.78, compared with 0.68 in France and 0.61 in Italy. The DIW found that in the same year, households in the former West Ger-many were more than twice as wealthy as those in the former East, with average assets of €94,000 compared with €41,000. This disparity was due mainly to regional differ-ences in house prices, and since homeowner-ship rates in Germany are low, the disparity may not be that telling. Breaking down key economic indicators by metropolitan area, however, suggests that the east-west gap remains wide.

Eastern metro areas are clearly lagging their western peers when measured by in-come per capita (see Chart 15). But inequali-ties also appear within western Germany, with just a few metropolitan areas such as

Munich, Stuttgart and Düsseldorf earning much higher incomes than other western metro areas. Gerald Braun, author of the “Atlas of Industrialization in East Germany3,” blames misplaced subsidy policies after re-unification. Most of the funds went towards infrastructure upgrades while productivity, efficiency and entrepreneurship were ne-glected. When the country split after World War II, many firms moved headquarters and production from the Soviet East to the West. After reunification, many state-owned East German industries were sold to West Ger-man companies by the Treuhand agency, which was created in 1990 to privatize the East German economy. A map of company headquarters shows little enterprise develop-ment in East Germany since then.3

The Ifo Institute found that German wage inequality, which has increased consider-ably over the last two decades, stemmed largely from a steady decline in the number of workers covered by collective bargaining agreements. This has hit low-wage workers, and hence eastern Germany, especially hard. To remedy the situation, the German gov-ernment plans to begin rolling out this year a nationwide minimum wage of €8.50 per hour. Some worry that this could significant-ly reduce demand for labour, particularly for younger service workers in the former East Germany who are less likely to be protected by union agreements than are manufacturing workers in the west.

3 http://www.hie-ro.de/index.php/en/projects/current/10-industrieatlas-en

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Standarddeviation

Max-min

200420082014E

Chart 14: Germany Not a Role Model of Equality



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Chart 15: Eastern Metro Areas Still Much PoorerDisposable income per capita in 2014, € mil

Sources: Deutsche Bundesbank, Federal statistical office, Eurostat, Moody’s Analytics

>23.020.5 to 23.0

<20.5



While the east’s economy is growing, the pace is slow. Eastern metropolitan areas will not catch up to those in the west soon. After the Hartz labour reforms, which were introduced in the early 2000s, jobless rates rose across the country, peaking for most metropolitan areas in 2005. Labour market conditions have steadily improved since then, and even weathered the 2009 recession without much damage. Between 2009 and 2014, unemployment rates fell across the board, with those in the former East German metro areas of Leipzig, Plauen and Dresden falling the most; they had very high initial levels (see Chart 16). Disposable income per capita rose in the eastern metropolitan areas over the last five years, though the growth has been relatively weak, with Berlin, Leipzig, Halle and Rostock recording the smallest growth among all German metro areas (see Chart 17). Some of the increase in per capita income is due to the shrinking populations of

former East German cities, with Plauen and Magdeburg experiencing one of the sharpest contractions between 2009 and 2014 (see Chart 18). An exception is the capital, Berlin, whose population has grown steadily. Still the weak increase in income per capita in the German capital is surprising considering its strong economic expansion in the last few years. Berlin recently emerged as one of the leaders in the IT industry. The metro area is traditionally the educational, cultural and legislative heart of Germany, but is now experiencing an explosion in high-tech and IT startups. The opening of a Google-backed startup hub this year called the Factory, which houses 22 companies including Sound-Cloud and Twitter, will support this trend.

Meanwhile, small Tier V metro areas in the southwest of Germany, Heilbronn in Baden-Württemberg and Würzburg, Sch-weinfurt and Aschaffenburg in Bavaria have been the leaders in per capita income growth

among the German metro areas. Germany’s Tier V metro areas are usually very special-ized industrial centres. At the same time, the main German metropolitan areas Munich and Hamburg recorded average growth in their incomes while Frankfurt disappointed.

The U.K.’s South East outperforms The United Kingdom also exhibits in-

equality among its regions, although the increase has been moderate compared with that in other countries (see Chart 19). The standard deviation of disposable income has increased only marginally from an equiva-lent of €1,800 in 2004 to €2,300 in 2014 (see Chart 20), although the income gap between the richest (London) and the poor-est (Bradford) rose by almost €1,000 in the last five years. The current situation stems from a large jump in inequality that occurred between the late 1970s and the early 1990s, when the erosion of labour unions occurred

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-5 -4 -3 -2 -1 0

LeipzigPlauen

DresdenBerlin

MagdeburgHanover

HamburgCologneStuttgartFrankfurt

MunichRuhrgebiet

Chart 16: Unemployment Fell Across GermanyChange in unemployment rate, 2009 to 2014, ppts


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PlauenRuhrgebiet

MunichHamburgCologne

MagdeburgStuttgartHanoverDresdenFrankfurt

LeipzigBerlin

WesternGermany

EasternGermany

Chart 17: Growth in the East Fails to OutperformNominal disposable income per capita, CAGR, %, 2009 to 2014


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-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

PlauenRuhrgebietMagdeburg

DresdenHanover

LeipzigStuttgart

HamburgCologneFrankfurt

BerlinMunich

West Germany

East Germany

Chart 18: Most Eastern Metros Struggle to GrowPopulation, CAGR % change, 2009 to 2014


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Chart 19: U.K. South East OutperformsDisposable income per capita in 2014, € ths


>19.517.8 to 19.5

<17.8



because of reforms introduced by Prime Min-ister Margaret Thatcher. Labour unions were particularly strong in the Midlands, where the economies of cities such as Birmingham and Manchester depended upon the coal and steel industries. When the Thatcher ad-ministration crushed the labour unions and the industries went into decline, the loss of the economic base resulted in sharply rising rates of poverty.

On the other hand, government policies and a shift from heavy industry to a services-based economy supported the growth of London as the country’s economic and financial center. During the postwar period, London was a second-class city with falling population that ultimately was transformed into the service and banking center that it is today. Most other metro areas in the U.K. failed to successfully go through this transi-tion process and have yet to discover a new economic purpose, at least until recently (Chart 21). Today there are some metro areas that are becoming important specialized centres. Manchester, for example, is devel-oping as an important media and telecom-munications hub. Its MediaCity is one of the biggest digital hubs in Europe, leading the BBC—the U.K.’s public broadcasting service—to relocate jobs numbering in the thousands from London. Following large-scale public and private investment, Manchester is at the forefront of cloud computing, with major global technology companies.

Still, according to the latest data from the Office of National Statistics, London and the South East are now the only two regions

with wages above the national average. Ex-cluding Scotland, the north-south divide is growing, with workers in the North East now the worst paid in Britain. Since the financial crisis, growth of disposable income per capita has been broadly similar across British metro areas (see Chart 22). But because the rates of growth are similar, the poorer regions are not catching up to their richer peers.

Meanwhile Scotland, which started from a very low level of income, has recorded strong improvements. In the 1980s, there was an economic boom in the so-called Silicon Glen, which is a corridor between Glasgow and Edinburgh, where many large technology firms relocated. Edinburgh is the financial services centre of Scotland, while Glasgow is the fourth largest manufacturing centre in the U.K. Additionally, Aberdeen has benefited strongly from the discovery and extraction of oil and gas in the North Sea. Aberdeen is home to the headquarters of im-portant international petroleum corpora-tions Shell and BP. Since the financial crisis, income growth has been strong across Scotland, rising above 20% between 2009 and 2014 for all three areas.

SummaryIn the coming

years the ranking of European metropoli-

tan areas by per capita income will likely be little changed, with Swiss and Scandinavian cities still topping the list and Romania and Bulgaria at the bottom. Those in Central and Eastern Europe should experience the fast-est growth of per capita income as they try to catch up to their more developed peers, although this transition process will continue for decades. Meanwhile, in the euro area the divergence in incomes in the short term will likely increase but at a smaller rate than in the last five years. As the troubled single-currency members emerge from the long-lasting im-pacts of the recession, incomes are expected to start rising. However, austerity measures have taken a toll on their labour markets, and growth is projected to underperform in com-ing years before the euro area is able to return to its long-term potential rate of growth.

In general, metropolitan areas that man-aged to adapt to the changing industrial structure have experienced relatively strong

20

0 2 4 6 8 10

Standarddeviation

Max-Min

200420082014E

Chart 20: U.K.’s Inequality Holds Steady



21

0.0 0.2 0.4 0.6 0.8 1.0 1.2

LiverpoolGlasgow

LeedsManchesterBirmingham

ReadingBristol

EdinburghAberdeen

CambridgeSouthampton

London

Rest of U.K.

North West

Midlands

Chart 21: North West and Midlands Barely GrowPopulation, CAGR % change, 2009 to 2014


22

3.0 3.5 4.0 4.5

SouthamptonGlasgow

CambridgeBirmingham

LondonBristol

ManchesterReading

EdinburghLeeds

LiverpoolAberdeen

Rest of U.K.

North West

Midlands

Chart 22: Income Growth Has Been Fairly Equal


Nominal disposable income per capita, CAGR % chg, 2009-2014



per capita income growth. For example, the expansion of the ICT sector in the CEE has given a boost to income growth in the CEE regional capitals. Also Dublin managed to benefit from the rise of this industry, while in Lisbon a boom in call centres has been driv-

ing the recovery of the local economy. On the other hand, metropolitan areas in Spain will lag as they have not managed to attract new industries to propel their recovery since the construction sector collapsed during the financial crisis. Surprisingly Berlin, despite

becoming an important IT hub in recent years, has so far experienced relatively weak per capita income growth, although its fast-rising population does grow the base, which could explain the weak income growth as measured on a per capita basis.

Table 1: Ranking of European Metropolitan Areas by Tiers and Income Growth (Cont.)Ranked by change in disposable income per capita between 2009 and 2014

Rank by 2014 per capita income

Income per capitaChange in income

per capita Population

Metropolitan area Country2014 € ths

2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

Tier I metro areas 18.99 17.67 16.38 1.49 103.21 100.22 96.98236 Katowice Poland 8.13 5.95 4.05 6.44 2.76 2.82 2.87229 Warsaw Poland 10.39 7.78 5.00 5.97 3.33 3.23 3.14247 Budapest Hungary 6.97 5.33 6.41 5.51 3.01 2.95 2.84161 Manchester U.K. 17.66 14.45 16.46 4.10 2.73 2.65 2.5611 London U.K. 25.03 20.58 22.20 3.99 14.19 13.38 12.61123 Ruhrgebiet Germany 19.27 17.36 16.59 2.11 5.03 5.10 5.2410 Munich Germany 26.14 23.70 22.50 1.98 2.81 2.62 2.5126 Hamburg Germany 23.11 20.96 19.40 1.97 3.19 3.10 3.0727 Stuttgart Germany 23.06 21.05 20.26 1.83 2.68 2.61 2.62149 Lille France 18.28 16.75 14.44 1.75 2.60 2.58 2.5616 Paris France 24.00 22.11 20.25 1.65 12.06 11.79 11.4450 Frankfurt Germany 21.90 20.24 19.19 1.58 2.59 2.50 2.4946 Wien Austria 22.10 20.73 17.98 1.29 2.70 2.58 2.49144 Berlin Germany 18.35 17.35 15.85 1.13 5.05 4.86 4.84200 Lisbon Portugal 14.17 13.57 11.85 0.87 2.80 2.81 2.7379 Brussels Belgium 20.61 19.75 17.10 0.85 2.97 2.82 2.67233 Prague Czech Rep. 8.62 8.78 5.72 -0.37 2.57 2.47 2.3219 Milan Italy 23.80 24.26 22.42 -0.39 4.36 4.04 3.92210 Valencia Spain 12.88 13.20 11.27 -0.49 2.51 2.55 2.37179 Madrid Spain 16.86 17.34 14.54 -0.57 6.36 6.37 5.87192 Barcelona Spain 15.44 16.01 13.44 -0.72 5.41 5.49 5.14216 Naples Italy 11.73 12.21 11.32 -0.79 3.17 3.07 3.06116 Rome Italy 19.56 21.94 20.28 -2.27 4.43 3.82 3.63205 Athens Greece 13.59 17.48 14.75 -4.91 3.89 4.01 3.99

Tier II metro areas 17.74 16.10 14.66 2.03 64.66 62.73 60.50245 Gdansk Poland 7.12 5.25 3.57 6.28 1.31 1.27 1.2481 Greater Gothenburg Sweden 20.59 15.31 14.48 6.10 1.63 1.57 1.52109 Greater Malmö Sweden 19.72 14.92 14.31 5.74 1.29 1.23 1.16246 Krakow Poland 7.04 5.34 3.43 5.67 1.47 1.45 1.4223 Greater Stockholm Sweden 23.39 18.11 16.84 5.25 2.20 2.02 1.87244 Bucharest Romania 7.27 5.76 2.30 4.77 2.26 2.25 2.221 Zurich Switzerland 42.48 33.90 29.45 4.62 1.45 1.35 1.26

148 Blackburn - Blackpool - Preston U.K. 18.31 14.74 16.16 4.44 1.47 1.45 1.44

147 Liverpool U.K. 18.32 14.84 16.36 4.30 1.51 1.50 1.49186 Birmingham U.K. 16.23 13.44 15.51 3.85 2.48 2.39 2.30128 Glasgow U.K. 19.02 15.79 17.11 3.79 1.83 1.80 1.77263 Sofia Bulgaria 4.57 3.99 1.91 2.73 1.69 1.65 1.6144 Bielefeld Germany 22.14 19.55 18.45 2.52 1.28 1.28 1.2921 Nuremberg Germany 23.52 21.10 19.64 2.20 1.31 1.27 1.27








2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

22 Düsseldorf Germany 23.51 21.10 20.40 2.18 1.52 1.50 1.5176 Nantes France 20.68 18.63 15.78 2.11 1.36 1.28 1.2238 Toulouse France 22.45 20.29 16.96 2.05 1.33 1.24 1.1767 Cologne Germany 21.03 19.15 18.64 1.89 1.94 1.88 1.8649 Helsinki Finland 21.96 20.03 16.57 1.85 1.60 1.52 1.4473 Marseille France 20.81 19.01 16.70 1.82 2.00 1.97 1.9274 Rouen-Le Havre France 20.73 18.95 16.64 1.82 1.26 1.25 1.2487 Hanover Germany 20.39 18.67 17.47 1.78 1.28 1.26 1.27137 Dresden Germany 18.56 17.04 15.81 1.73 1.33 1.33 1.3518 Lyon France 23.92 22.11 19.14 1.59 1.81 1.73 1.6671 Bordeaux France 20.90 19.32 16.61 1.58 1.53 1.45 1.38124 Grenoble France 19.12 17.72 15.86 1.53 1.25 1.21 1.16110 Copenhagen Denmark 19.69 18.36 16.57 1.42 1.97 1.87 1.81171 Utrecht Netherlands 17.36 16.98 15.54 0.43 1.26 1.22 1.17183 Rotterdam Netherlands 16.48 16.19 14.19 0.36 1.42 1.40 1.39166 Amsterdam Netherlands 17.53 17.32 16.10 0.24 2.46 2.36 2.28102 Turin Italy 20.00 20.16 19.48 -0.16 2.33 2.22 2.16202 Bari Italy 14.00 14.16 13.17 -0.23 1.31 1.26 1.22218 Porto Portugal 11.57 11.78 10.01 -0.36 1.26 1.29 1.28135 Brescia Italy 18.66 19.35 19.18 -0.73 1.29 1.21 1.14211 Palermo Italy 12.61 13.10 11.05 -0.77 1.27 1.24 1.23222 Sevilla Spain 10.87 11.83 10.11 -1.68 1.94 1.90 1.79126 Dublin Ireland 19.10 20.91 18.32 -1.79 1.83 1.78 1.60223 Murcia Spain 10.82 11.85 9.70 -1.80 1.47 1.45 1.31227 Malaga Spain 10.43 11.46 9.51 -1.86 1.63 1.57 1.42224 Alicante Spain 10.71 11.99 10.67 -2.23 1.85 1.84 1.66

Tier III Metro Areas 17.52 16.31 15.45 1.44 67.27 65.85 64.07239 Lódz Poland 7.42 5.39 3.59 6.62 1.09 1.12 1.15256 Kielce Poland 5.87 4.28 2.88 6.53 0.77 0.78 0.79232 Vilnius Lithuania 9.10 6.73 4.14 6.20 0.80 0.82 0.84235 Wroclaw Poland 8.19 6.13 3.93 5.96 1.21 1.19 1.172 Lausanne Switzerland 39.22 29.84 24.41 5.62 0.76 0.70 0.65234 Poznan Poland 8.31 6.33 4.48 5.60 1.17 1.14 1.11248 Bydgoszcz Poland 6.85 5.28 3.85 5.33 0.77 0.77 0.76119 Norwich U.K. 19.44 15.30 16.83 4.91 0.88 0.85 0.825 Bern Switzerland 35.28 27.88 25.63 4.82 1.01 0.97 0.96172 Doncaster U.K. 17.32 13.90 15.71 4.50 0.80 0.79 0.77155 Cardiff U.K. 17.88 14.37 16.26 4.47 1.12 1.10 1.0657 Bournemouth U.K. 21.53 17.33 19.29 4.43 0.76 0.73 0.71140 Newcastle U.K. 18.49 14.98 16.45 4.30 1.16 1.14 1.11138 Leeds U.K. 18.55 15.04 17.10 4.28 1.10 1.07 1.0542 Edinburgh U.K. 22.22 18.04 19.74 4.26 0.86 0.82 0.78129 Nottingham U.K. 19.01 15.45 17.30 4.24 1.15 1.12 1.0917 Reading U.K. 23.99 19.53 21.71 4.20 0.89 0.85 0.81139 Stoke-on-Trent U.K. 18.55 15.18 16.83 4.09 1.11 1.09 1.0680 Bristol U.K. 20.60 16.88 18.62 4.07 1.11 1.06 1.02108 Coventry U.K. 19.75 16.25 18.56 3.98 0.89 0.85 0.8269 Exeter U.K. 20.95 17.26 17.83 3.96 0.76 0.74 0.72150 Leicester U.K. 18.26 15.08 17.51 3.90 1.05 1.00 0.96241 Riga Latvia 7.35 6.31 3.55 3.09 1.01 1.05 1.08








2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

4 Oslo Norway 36.04 31.00 26.84 3.06 1.23 1.12 1.02271 Iasi Romania 3.03 2.68 1.42 2.50 0.82 0.82 0.8137 Bonn Germany 22.47 19.86 18.71 2.49 0.90 0.88 0.8864 Bremen Germany 21.23 18.90 16.82 2.35 1.24 1.23 1.24253 Kosice Slovakia 6.54 5.82 4.20 2.34 0.80 0.79 0.78111 Saarbrücken Germany 19.69 17.61 16.46 2.26 0.79 0.81 0.8532 Heidelberg Germany 22.71 20.34 18.95 2.22 0.95 0.93 0.93122 Brest France 19.31 17.38 14.77 2.12 0.91 0.90 0.88

103 Braunschweig-Salzgitter-Wolfsburg Germany 19.94 17.95 16.56 2.12 0.98 0.98 1.00

53 Rennes France 21.66 19.63 16.70 1.99 1.04 0.99 0.93158 Toulon France 17.76 16.22 14.28 1.84 1.04 1.01 0.9788 Nice France 20.36 18.59 16.38 1.84 1.09 1.08 1.0612 Linz Austria 24.68 22.59 18.89 1.79 0.77 0.75 0.7459 Mannheim-Ludwigshafen Germany 21.45 19.67 17.99 1.75 0.89 0.88 0.89134 Angers France 18.74 17.20 14.95 1.73 0.81 0.78 0.7683 Strasbourg France 20.48 18.90 17.38 1.63 1.11 1.10 1.07153 Leipzig Germany 18.04 16.69 15.45 1.57 0.99 0.97 0.98121 Montpellier France 19.35 18.00 15.20 1.45 1.12 1.04 0.99156 Saint-Etienne France 17.87 16.70 14.84 1.37 0.76 0.75 0.74162 Liege Belgium 17.66 16.52 14.06 1.35 0.81 0.79 0.76143 East Jutland (Arhus) Denmark 18.39 17.33 15.51 1.20 0.86 0.83 0.79165 Mulhouse France 17.55 16.69 15.61 1.02 0.76 0.75 0.73112 Padua Italy 19.68 18.89 18.75 0.82 0.95 0.91 0.86175 Eindhoven Netherlands 17.15 16.50 15.60 0.77 0.75 0.74 0.73117 Antwerp Belgium 19.49 19.00 16.22 0.50 1.03 0.99 0.95243 Brno Czech Rep. 7.29 7.24 4.51 0.15 1.17 1.16 1.1528 Bologna Italy 22.98 23.25 22.76 -0.24 1.02 0.97 0.93250 Ostrava Czech Rep. 6.76 6.85 4.18 -0.27 1.22 1.24 1.26199 Oviedo Spain 14.25 14.50 11.99 -0.34 1.05 1.08 1.06136 Venice Italy 18.58 18.96 18.47 -0.40 0.86 0.82 0.80208 A Coruña Spain 13.25 13.63 10.77 -0.57 1.13 1.14 1.11197 Zaragoza Spain 14.94 15.62 13.04 -0.89 0.97 0.97 0.90213 Catania Italy 12.13 12.74 11.39 -0.97 1.12 1.08 1.06219 Santa Cruz de Tenerife Spain 11.57 12.14 11.41 -0.97 1.02 0.98 0.89114 Verona Italy 19.60 20.60 18.26 -0.98 0.94 0.89 0.84182 The Hague Netherlands 16.50 17.40 16.75 -1.06 0.84 0.80 0.77225 Granada Spain 10.62 11.22 9.04 -1.09 0.92 0.92 0.86212 Vigo Spain 12.26 13.10 10.72 -1.32 0.95 0.95 0.92176 Bilbao Spain 17.10 18.30 14.96 -1.34 1.13 1.16 1.13228 Cádiz Spain 10.40 11.13 9.02 -1.35 1.25 1.23 1.16220 Las Palmas de Gran Canarias Spain 11.06 11.85 10.83 -1.38 1.11 1.07 0.97226 Córdoba Spain 10.53 11.41 9.50 -1.59 0.79 0.80 0.78127 Genoa Italy 19.03 20.67 19.11 -1.64 0.88 0.86 0.86105 Florence Italy 19.87 21.92 20.75 -1.95 1.05 0.96 0.93209 Palma de Mallorca Spain 13.00 14.37 13.66 -2.00 1.12 1.08 0.94217 Thessaloniki Greece 11.61 14.01 10.20 -3.67 1.09 1.14 1.13

Tier IV metro areas 14.83 13.35 11.92 2.23 43.77 43.36 42.88251 Radom Poland 6.76 4.25 3.52 9.72 0.62 0.63 0.63254 Lublin Poland 6.32 4.47 2.94 7.17 0.71 0.72 0.72








2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

240 Czestochowa Poland 7.35 5.21 3.84 7.11 0.52 0.53 0.54255 Bialystok Poland 6.01 4.29 2.94 6.96 0.51 0.51 0.51252 Opole Poland 6.64 4.80 3.15 6.71 0.60 0.62 0.63231 Tallinn Estonia 9.17 6.74 4.45 6.33 0.58 0.56 0.54238 Bielsko-Biala Poland 7.62 5.86 4.00 5.40 0.67 0.66 0.65237 Kaunas Lithuania 7.98 6.30 4.13 4.85 0.58 0.63 0.67259 Rzeszów Poland 5.27 4.17 2.86 4.78 0.63 0.62 0.6178 Cheshire West and Chester U.K. 20.66 16.54 18.79 4.55 0.62 0.61 0.61160 Middlesbrough U.K. 17.67 14.22 15.43 4.43 0.56 0.56 0.55264 Timisoara Romania 4.40 3.56 2.16 4.37 0.68 0.68 0.677 Bergen Norway 30.33 24.51 20.70 4.36 0.51 0.48 0.45151 Kingston upon Hull U.K. 18.22 14.73 16.48 4.34 0.59 0.59 0.58270 Plovdiv Bulgaria 3.18 2.58 1.48 4.25 0.68 0.69 0.70249 Szczecin Poland 6.82 5.56 4.00 4.18 0.74 0.74 0.73169 Kirklees U.K. 17.46 14.24 16.54 4.16 0.64 0.62 0.59266 Brasov Romania 3.53 2.91 1.74 3.96 0.60 0.60 0.60189 Bradford U.K. 15.86 13.11 15.49 3.89 0.53 0.52 0.49125 Belfast U.K. 19.11 15.83 17.33 3.84 0.68 0.67 0.6545 Cambridge U.K. 22.11 18.34 20.17 3.81 0.64 0.61 0.58163 Swansea U.K. 17.60 14.61 16.44 3.79 0.52 0.51 0.50272 Galati Romania 2.72 2.30 1.27 3.46 0.59 0.61 0.62268 Cluj-Napoca Romania 3.34 2.82 1.72 3.41 0.69 0.69 0.69178 Sheffield U.K. 16.87 14.34 16.84 3.30 0.56 0.54 0.52267 Craiova Romania 3.52 3.09 1.74 2.60 0.69 0.70 0.7263 Osnabrück Germany 21.27 18.88 16.78 2.41 0.51 0.51 0.5147 Münster Germany 22.10 19.63 18.15 2.39 0.52 0.50 0.4958 Freiburg Germany 21.51 19.20 18.01 2.30 0.63 0.61 0.6084 Kiel Germany 20.45 18.28 16.66 2.27 0.64 0.63 0.63118 Aachen Germany 19.47 17.45 16.84 2.21 0.55 0.54 0.55261 Debrecen Hungary 5.17 4.64 3.35 2.19 0.53 0.54 0.5560 Augsburg Germany 21.43 19.26 18.20 2.16 0.65 0.63 0.6377 Besançon France 20.66 18.56 17.09 2.16 0.54 0.53 0.51190 Cagliari Italy 15.65 14.12 10.62 2.08 0.60 0.66 0.78265 Constanta Romania 3.72 3.36 2.02 2.08 0.72 0.72 0.72130 Tampere Finland 18.87 17.03 14.29 2.07 0.50 0.49 0.4662 Clermont-Ferrand France 21.27 19.28 17.01 1.99 0.65 0.63 0.6290 Caen France 20.35 18.47 15.73 1.96 0.69 0.68 0.6714 Graz Austria 24.26 22.03 18.42 1.95 0.61 0.59 0.5735 Dijon France 22.57 20.50 17.87 1.94 0.53 0.52 0.5251 Karlsruhe Germany 21.86 19.90 18.99 1.89 0.73 0.71 0.70154 Erfurt Germany 17.88 16.32 14.91 1.84 0.52 0.52 0.5468 Tours France 20.98 19.16 16.79 1.82 0.60 0.59 0.58113 Pau France 19.61 17.95 15.62 1.78 0.67 0.65 0.63107 Avignon France 19.80 18.15 16.44 1.75 0.55 0.54 0.5375 Amiens France 20.71 19.03 16.89 1.70 0.57 0.57 0.56198 Taranto Italy 14.32 13.18 12.13 1.68 0.57 0.56 0.56131 Nancy France 18.84 17.38 15.80 1.63 0.73 0.73 0.7261 Orléans France 21.33 19.81 17.85 1.50 0.67 0.66 0.64157 Le Mans France 17.87 16.60 14.88 1.49 0.57 0.56 0.55181 Charleroi Belgium 16.72 15.58 13.06 1.43 0.65 0.64 0.63








2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

91 Reims France 20.34 18.98 16.81 1.40 0.57 0.57 0.57141 Aalborg Denmark 18.48 17.28 15.44 1.35 0.58 0.58 0.58180 Nimes France 16.79 15.71 13.79 1.34 0.75 0.71 0.67206 Messina Italy 13.41 12.64 11.77 1.20 0.65 0.66 0.6772 Ghent Belgium 20.85 19.67 16.54 1.18 0.63 0.61 0.58262 Miskolc Hungary 4.77 4.50 4.00 1.17 0.66 0.69 0.73215 Bratislava Slovakia 11.87 11.38 7.38 0.85 0.62 0.60 0.59194 Breda Netherlands 15.32 14.93 14.12 0.52 0.62 0.61 0.61204 Enschede Netherlands 13.69 13.50 12.69 0.28 0.63 0.62 0.62242 Pilsen Czech Rep. 7.29 7.29 4.69 0.02 0.58 0.57 0.56191 Heerlen Netherlands 15.58 15.67 14.17 -0.13 0.60 0.61 0.62195 Den Bosch Netherlands 15.06 15.21 14.45 -0.20 0.65 0.64 0.63196 Arnhem Netherlands 14.97 15.32 15.23 -0.47 0.72 0.71 0.69173 Pamplona Spain 17.20 17.73 15.36 -0.61 0.64 0.63 0.58221 Ljubljana Slovenia 10.96 11.32 8.77 -0.64 0.53 0.52 0.50146 Donostia-San Sebastián Spain 18.34 19.01 15.96 -0.71 0.70 0.71 0.68168 Cork Ireland 17.46 18.52 15.28 -1.17 0.67 0.66 0.6185 Modena Italy 20.44 22.12 20.47 -1.56 0.71 0.68 0.65207 Santander Spain 13.29 14.44 12.40 -1.65 0.59 0.59 0.55

Tier V metro areas 19.72 17.76 16.06 2.20 24.97 24.75 24.70258 Tarnów Poland 5.46 3.78 2.82 7.65 0.47 0.46 0.4582 Uppsala Sweden 20.58 15.53 15.00 5.79 0.35 0.33 0.323 Geneva Switzerland 38.04 29.94 24.58 4.91 0.47 0.45 0.43269 Varna Bulgaria 3.26 2.58 1.50 4.81 0.48 0.47 0.46174 Sunderland U.K. 17.19 13.71 14.80 4.62 0.28 0.28 0.2830 Aberdeen U.K. 22.87 18.39 18.86 4.46 0.49 0.47 0.4466 Brighton and Hove U.K. 21.15 17.45 19.56 3.92 0.28 0.27 0.258 Heilbronn Germany 29.06 24.06 19.57 3.84 0.45 0.44 0.44187 Derby U.K. 16.15 13.49 15.81 3.67 0.25 0.25 0.24188 Portsmouth U.K. 16.07 13.42 14.95 3.66 0.21 0.20 0.20184 Southampton U.K. 16.38 13.87 15.13 3.38 0.24 0.23 0.2339 Würzburg Germany 22.41 19.40 18.17 2.92 0.50 0.50 0.5170 Schweinfurt Germany 20.92 18.15 16.83 2.89 0.27 0.27 0.2824 Aschaffenburg Germany 23.37 20.32 18.67 2.83 0.37 0.37 0.3754 Bayreuth Germany 21.63 18.81 17.47 2.83 0.25 0.25 0.2697 Paderborn Germany 20.16 17.57 16.07 2.79 0.30 0.29 0.2913 Iserlohn Germany 24.63 21.49 19.73 2.76 0.41 0.43 0.459 Rosenheim Germany 26.47 23.22 19.87 2.65 0.31 0.30 0.30100 Marburg Germany 20.05 17.62 16.04 2.62 0.24 0.24 0.2533 Siegen Germany 22.61 19.89 17.87 2.59 0.40 0.41 0.4396 Göttingen Germany 20.18 17.75 16.06 2.59 0.38 0.39 0.4043 Wetzlar Germany 22.17 19.60 17.82 2.50 0.25 0.26 0.2641 Konstanz Germany 22.31 19.75 18.39 2.47 0.28 0.27 0.2748 Ingolstadt Germany 22.02 19.50 18.10 2.47 0.47 0.45 0.4552 Offenburg Germany 21.71 19.24 18.26 2.44 0.41 0.41 0.41159 Görlitz Germany 17.72 15.73 14.48 2.41 0.26 0.28 0.3086 Kassel Germany 20.44 18.15 16.68 2.40 0.43 0.43 0.4331 Ulm Germany 22.76 20.29 18.88 2.33 0.48 0.47 0.47104 Hildesheim Germany 19.90 17.73 16.33 2.33 0.27 0.28 0.2929 Reutlingen Germany 22.97 20.47 19.38 2.33 0.28 0.27 0.28








2009 € ths

2004 € ths

2009 to 2014 CAGR, %

2014 mil

2009 mil

2004 mil

101 Kaiserslautern Germany 20.04 17.88 16.29 2.31 0.27 0.28 0.2865 Oldenburg Germany 21.16 18.89 16.54 2.29 0.41 0.40 0.3956 Regensburg Germany 21.60 19.29 18.25 2.28 0.45 0.43 0.42132 Zwickau Germany 18.84 16.84 15.45 2.27 0.32 0.34 0.3692 Flensburg Germany 20.31 18.16 16.14 2.27 0.28 0.28 0.2898 Giessen Germany 20.16 18.04 16.77 2.25 0.25 0.25 0.25133 Plauen Germany 18.77 16.83 15.49 2.21 0.23 0.24 0.2636 Wiesbaden Germany 22.51 20.18 19.19 2.21 0.46 0.45 0.4593 Mönchengladbach Germany 20.28 18.27 17.77 2.11 0.26 0.26 0.26142 Schwerin Germany 18.43 16.62 14.79 2.09 0.30 0.31 0.3355 Koblenz Germany 21.60 19.48 17.59 2.09 0.32 0.32 0.32170 Neubrandenburg Germany 17.42 15.74 14.76 2.05 0.26 0.28 0.2989 Poitiers France 20.36 18.46 15.80 1.98 0.43 0.43 0.4240 Darmstadt Germany 22.39 20.34 18.45 1.93 0.44 0.43 0.4334 Pforzheim Germany 22.60 20.54 19.35 1.93 0.31 0.31 0.31120 Turku Finland 19.36 17.62 14.50 1.89 0.47 0.46 0.45145 Magdeburg Germany 18.35 16.74 14.97 1.86 0.50 0.50 0.5299 Lübeck Germany 20.06 18.31 17.07 1.84 0.41 0.41 0.4115 Salzburg Austria 24.12 22.10 18.70 1.76 0.35 0.34 0.34260 Pécs Hungary 5.20 4.77 3.75 1.74 0.37 0.39 0.40115 Limoges France 19.60 18.00 16.17 1.71 0.38 0.38 0.3694 Wuppertal Germany 20.28 18.67 18.32 1.67 0.34 0.34 0.36257 Székesfehérvár Hungary 5.77 5.32 4.34 1.62 0.42 0.43 0.4320 Mainz Germany 23.60 21.78 20.10 1.62 0.41 0.40 0.38177 Perpignan France 17.09 15.80 13.80 1.58 0.47 0.45 0.43164 Halle Germany 17.59 16.32 14.46 1.52 0.42 0.42 0.44167 Rostock Germany 17.53 16.39 14.65 1.36 0.42 0.41 0.4225 Innsbruck Austria 23.19 21.75 18.95 1.28 0.30 0.28 0.27152 Odense Denmark 18.21 17.18 14.98 1.17 0.49 0.48 0.48230 Maribor Slovenia 10.11 9.61 7.11 1.02 0.31 0.32 0.32201 Tilburg Netherlands 14.06 13.58 12.98 0.70 0.47 0.46 0.45106 Vitoria-Gasteiz Spain 19.82 19.55 16.14 0.28 0.32 0.32 0.30214 Coimbra Portugal 12.08 11.95 10.39 0.23 0.32 0.33 0.34203 Groningen Netherlands 13.92 14.21 13.92 -0.41 0.43 0.42 0.426 Basel Switzerland 30.75 32.44 22.85 -1.07 0.69 0.68 0.6795 Parma Italy 20.21 21.87 21.40 -1.57 0.45 0.42 0.40185 Valladolid Spain 16.32 17.83 16.32 -1.75 0.44 0.43 0.38193 Prato Italy 15.37 18.08 19.06 -3.19 0.26 0.24 0.23

Tier I metro areas 18.99 17.67 16.38 1.49 103.21 100.22 96.98Tier II metro areas 17.74 16.10 14.66 2.03 64.66 62.73 60.50Tier III metro areas 17.52 16.31 15.45 1.44 67.27 65.85 64.07Tier IV metro areas 14.83 13.35 11.92 2.23 43.77 43.36 42.88Tier V metro areas 19.72 17.76 16.06 2.20 24.97 24.75 24.70

Sources: Eurostat, National statistical offices, Moody’s Analytics


AUTHOR BIOS �� www.economy.com

About the Author

Anna Zabrodzka

Anna Zabrodzka is an economist at the Moody’s Analytics Prague office. Her responsibilities include providing commentary and research on key economies in the euro zone and Central and Eastern Europe. Before joining Moody’s Analytics, Anna worked as a trainee for the European Central Bank, focusing on the analysis of monetary policy operations and euro money markets. Anna holds a master’s degree in quantitative economics from Goethe University in Frankfurt am Main and a BSc in economics and finance from Queen Mary University of London.

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