Economic Bulletin Issue 6 / 2015.
Date: 2015-11-05
Institutions: European Central Bank
Subjects: economic policy, economic situation, euro area, monetary policy, serial publication

Economic Bulletin 

Issue 6 / 2015

30°53%100%3,5E7,5E6EEEE80°6E6EEContents

Economic and monetary developments

Overview 

1  External environment 

2  Financial developments 

3  Economic activity 

4  Prices and costs 

5  Money and credit 

6  Fiscal developments 

Boxes

1  Recent developments and outlook for non-oil commodity prices 

2 

3 

 Liquidity conditions and monetary policy operations in the period  
from 22 April to 21 July 2015 

 The usefulness of TARGET2 transaction data for the analysis of  
the unsecured overnight money market 

4  Publication of TARGET balances 

5 

6 

7 

8 

 Revised trade weights for the effective exchange rates of the euro reflect  
the increasing importance of emerging market economies 

 A survey-based measure of slack for the euro area 

 Recent developments in euro area food prices 

 Country-specific recommendations for fiscal policies under the  
2015 European Semester 

articles

1  The state of the house price cycle in the euro area 

2  The fiscal impact of financial sector support during the crisis 

Statistics 

3

6

11

15

20

24

29

31

33

37

42

45

49

52

55

58

74

S1

2

ECB Economic Bulletin, Issue 6 / 2015 – Contents 
 
 
 
 
 
 
 
Economic and monetary 
developments

Overview

a review of recent data, new ECB staff macroeconomic projections and an 
interim evaluation of recent market fluctuations point to a continued, although 
somewhat weaker, economic recovery in the euro area and a slower increase 
in inflation rates compared with earlier expectations. The changed outlook is 
due to a considerable extent to external developments. While the world economy 
is gradually expanding, it remains on an uneven path. On the one hand, economic 
activity in advanced economies is being supported by low oil prices, continued 
accommodative financing conditions, a slower pace of fiscal consolidation and 
improving labour markets. On the other hand, the outlook has worsened in emerging 
market economies amid heightened uncertainty, as structural impediments and 
macroeconomic imbalances are restraining growth in some countries, while others 
are adjusting to lower commodity prices and less favourable external financing 
conditions. In parallel, inflationary pressures have been dampened by falling 
commodity prices.

renewed downside risks to the outlook for growth and inflation have also 
emerged as a result of the recent increase in volatility in financial markets. Two 
significant episodes of heightened tensions occurred over the summer. The first was 
associated with developments in Greece in late June and early July and had, overall, 
a relatively muted impact on financial markets. The second episode took place in the 
second half of August and was related to developments in China. It had a significant 
impact on stock and foreign exchange markets as well as on perceptions of risk. 
Overall, long-term nominal euro area government bond yields declined slightly 
between early June and early September. Euro area stock prices declined markedly, 
especially amid losses in the Chinese equity market in the second half of August 
and the related increase in global uncertainty. In this environment of increased risk 
aversion and weakness in emerging markets, the effective exchange rate of the euro 
has recently appreciated significantly.

Euro area real gDP grew in the second quarter of 2015 at a slightly slower pace 
than in the first quarter. The pace of growth in the second quarter was somewhat 
slower than expected. The moderation was due to weaker than expected domestic 
demand and was broad-based across countries. The latest survey indicators suggest 
that the pace of real GDP growth in the second half of 2015 will be similar to that 
recorded in the second quarter.

Looking further ahead, the euro area recovery is expected to continue, albeit at 
a somewhat weaker pace than previously anticipated. This reflects in particular 
the slowdown in emerging market economies, which is weighing on global growth 
and thus on demand for euro area exports. Domestic demand should be further 
supported by the ECB’s monetary policy measures and their favourable impact 

3

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments 
on financial conditions, as well as by the progress made with fiscal consolidation 
and structural reforms. Moreover, lower oil prices should bolster households’ real 
disposable income and corporate profitability, providing additional support for private 
consumption and investment. At the same time, the necessary balance sheet 
adjustments in a number of sectors and the sluggish pace of implementation of 
structural reforms are likely to dampen the pick-up in activity.

The September 2015 ECB staff macroeconomic projections for the euro area1 
foresee annual real gDP increasing by 1.4% in 2015, 1.7% in 2016 and 1.8% in 
2017. Compared with the June 2015 Eurosystem staff macroeconomic projections, 
the outlook for real GDP growth has been revised down, primarily due to lower 
external demand owing to weaker growth in emerging markets. In the Governing 
Council’s assessment, risks to the outlook for economic activity remain on the 
downside, reflecting in particular the heightened uncertainties related to the external 
environment. Notably, current developments in emerging market economies have the 
potential to further affect global growth adversely via trade and confidence effects.

Following an upward trend earlier this year, HICP inflation in the euro area has 
recently stabilised at low positive rates. According to Eurostat’s flash estimate, 
annual HICP inflation remained at 0.2% in August for the third consecutive month. 
While low energy prices have dampened inflation, this has been compensated for by 
higher increases in food and non-energy industrial goods prices. Recent indicators 
confirm a gradual strengthening in underlying inflation. HICP excluding food and 
energy is estimated to have increased from a trough of 0.6% at the beginning of the 
year to 1.0% in August.

on the basis of the information available, annual HICP inflation rates will 
remain very low in the near term, mainly reflecting recent developments in 
energy prices. Towards the end of 2015, however, headline inflation is expected 
to rise, also on account of base effects associated with the fall in oil prices in late 
2014. Inflation rates are foreseen to pick up further during 2016 and 2017, supported 
by the expected economic recovery, the pass-through of past declines in the euro 
exchange rate and the assumption of somewhat higher oil prices in the years ahead 
as reflected in oil futures markets. However, this increase in annual inflation rates is 
currently expected to materialise somewhat more slowly than anticipated thus far.

The September 2015 ECB staff macroeconomic projections for the euro area 
foresee annual HICP inflation at 0.1% in 2015, 1.1% in 2016 and 1.7% in 2017. 
In comparison with the June 2015 Eurosystem staff macroeconomic projections, the 
outlook for HICP inflation has been revised down, largely owing to lower oil prices. 
Taking into account the most recent developments in oil prices and recent exchange 
rates, there are downside risks to the September staff inflation projections.

The ECB’s monetary policy measures continue to be transmitted to lending 
conditions and remain supportive of broad money and credit dynamics. The 
targeted longer-term refinancing operations (TLTROs) and the expanded asset 
purchase programme (APP) have contributed to improvements in money and credit 

1 

See the article entitled “September 2015 ECB staff macroeconomic projections for the euro area”, 
published on the ECB’s website on 3 September 2015.

4

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsindicators. Banks’ funding costs stabilised at historical lows in the second quarter 
of 2015, and favourable lending conditions continued to support a gradual recovery 
in loan growth. In addition, divergences in bank lending rates across euro area 
countries have narrowed further. The overall annual flow of external financing to 
non-financial corporations is estimated to have stabilised in the second quarter of 
2015, although the dynamics of loans to non-financial corporations remain subdued. 
Stronger growth of credit to general government and a continued gradual recovery of 
credit to the private sector are supporting broad money growth.

The governing Council judges it premature to conclude on whether recent 
economic and financial market developments could have a lasting impact 
on the achievement of a sustainable path of inflation towards its medium-
term aim or whether they should be considered to be mainly transitory, and 
will continue to closely monitor all relevant incoming information. Based on 
its regular economic and monetary analysis, and in line with its forward guidance, 
the Governing Council decided at its meeting on 3 September to keep the key 
ECB interest rates unchanged and confirmed that the asset purchase programme 
continues to proceed smoothly. Looking ahead, the Governing Council will closely 
monitor the risks to the outlook for price developments over the medium term, 
focusing in particular on the pass-through of its monetary policy measures, as well 
as on global economic, financial, commodity price and exchange rate developments.

The governing Council emphasises its willingness and ability to act, 
if warranted, by using all the instruments available within its mandate 
and, in particular, recalls that the asset purchase programme provides 
sufficient flexibility in terms of adjusting the size, composition and duration 
of the programme. In the meantime, the Eurosystem will fully implement its 
monthly asset purchases of €60 billion, which are intended to run until the end of 
September 2016, or beyond, if necessary, and, in any case, until the Governing 
Council sees a sustained adjustment in the path of inflation which is consistent with 
its aim of achieving inflation rates below, but close to, 2% over the medium term. 
The Governing Council reiterates the need to firmly implement its monetary policy 
decisions and to monitor closely all relevant incoming information as concerns their 
impact on the medium-term outlook for price stability.

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ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments1 

External environment 

The world economy is gradually expanding, but remains on an uneven path. On the 
one hand, economic activity in advanced economies is being supported by low oil 
prices, continued accommodative financing conditions, waning fiscal consolidation 
and improving labour markets. On the other hand, the outlook has worsened in 
emerging market economies (EMEs) amid heightened uncertainty, as structural 
impediments and macroeconomic imbalances are restraining growth in some 
countries, while others are adjusting to lower commodity prices and tighter external 
financing conditions. Inflationary pressures are expected to remain restrained 
following the recent fall in oil prices and the still abundant global spare capacity.

Global economic activity and trade 

National accounts data and survey indicators point to modest and uneven 
growth in global economic activity in the second quarter. Economic activity 
has been sustained in advanced economies, with growth rebounding in the United 

Chart 1
Global composite output PMI

(diffusion index)

global excluding euro area 
global excluding euro area: long-term average 
emerging market economies 
advanced economies excluding euro area 

60 

55 

50 

45 

40 

35 

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Markit and ECB calculations.
Notes: The latest observation refers to August 2015. Emerging market economies are 
Brazil, China, India and Russia. Advanced economies are Japan, the United States and 
the United Kingdom.

States and United Kingdom, although it weakened 
significantly in Japan following a strong first quarter. 
The situation in EMEs is mixed. On the one hand, 
Russia is in a deep recession and survey data point 
to further weakness in Brazil. On the other hand, 
growth recovered in China in the second quarter and 
is expected to have remained resilient in non-euro 
area central and eastern European countries (CEECs). 
Surveys suggest that global manufacturing output has 
been subdued in recent months, but overall activity 
has been more robust. The global composite output 
Purchasing Managers’ Index (PMI), excluding the euro 
area, remained broadly unchanged in August compared 
to the previous month and in line with the average 
for the second quarter, indicating some resilience in 
global activity (see Chart 1). Looking further ahead the 
OECD’s composite leading indicators and the Ifo World 
Economic Climate Index point to the global recovery 
continuing at a modest pace.

Commodity prices have fallen recently, providing 
additional near-term impetus to global demand. 
Having rebounded during the second quarter, Brent 

crude oil prices have fallen by more than 20% in the third quarter, although, 
according to the futures curve, markets continue to price in a gradual increase 
in oil prices for the coming years. The recent fall has mainly reflected supply 
developments. The oil market continues to be oversupplied, with OPEC producing 
above its target and shale oil production proving to be more resilient than expected. 
In addition, the market seems to be slowly pricing in the prospect of increased supply 
from Iran. However, moderating demand, particularly in EMEs, has also played a 
role. That is also consistent with factors underlying other commodity price declines – 

6

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 2
Retail sales

(year-on-year growth, three-month average)

advanced economies (left-hand scale) 
EME oil-exporting countries (left-hand scale) 
EME oil-importing countries (right-hand scale) 

12

10

8

6

4

2

0

-2

-4

-6

2008

2009

2010

2011

2012

2013

2014

30

27

24

21

18

15

12

9

6

3

Sources: National sources, IMF and ECB calculations.
Notes: The latest observation refers to June 2015. Advanced economies include the 
euro area, Japan, the United States and the United Kingdom. Oil-importing EMEs are 
China, Indonesia, Korea, Turkey and Mexico. Oil-exporting EMEs are Russia, Brazil, 
Colombia, Chile and South Africa.

over the past year, for example, metal prices have 
fallen substantially, which may also have reflected the 
less buoyant growth in EMEs (see also Box 1). Overall, 
the fall in oil prices since the middle of last year is 
expected to benefit demand in oil-importing countries, 
outweighing the impact on oil-exporting economies. 
As oil prices have fallen since the peak of 2014, 
headline inflation in advanced economies has dipped, 
boosting real disposable incomes. Retail sales growth 
has risen in advanced economies and oil importing 
EMEs compared with a year ago, while it dropped 
sharply in major oil-exporting countries (see Chart 2).

monetary policies remain accommodative and 
are contributing to favourable global financial 
conditions. Markets continue to price in a first interest 
rate increase in the United States in the next six 
months. In Japan, policy rates are expected to remain 
low over the next two years. Disinflationary trends 
have prompted further easing of monetary policy in 
China, India and several other (mostly oil-importing) 
EMEs in recent months, while Russia continued to 

normalise interest rates after the sharp hike during the financial turmoil of December 
last year. Among the major EMEs, only Brazil has witnessed an ongoing increase 
in policy rates, as inflationary pressures remain elevated amid substantial hikes in 
administered prices, sticky pricing behaviour and further depreciation of the currency. 
As of 11 August 2015 the People’s Bank of China reformed the way it sets the daily 
fixing of the exchange rate in order to give more room to market forces, which led to 
a 3% depreciation of the renminbi against the US dollar in the week following that 
decision. Over the past two months, long-term government bond yields in advanced 
economies have broadly stabilised after the sharp increase during May and early 
June. Despite the correction, rates are still close to the low levels observed at 
the end of 2014 and term premia remain compressed. Bond yields in EMEs have 
moved broadly in tandem with those of advanced economies. However, aggregate 
private portfolio inflows to EMEs have weakened somewhat, while further currency 
depreciation in some countries is likely to have increased the cost of dollar financing. 
Moreover, the recent losses and volatility in Chinese stock markets are leading to 
heightened uncertainty. If this persists, it may ultimately translate into a tightening in 
financial conditions, particularly for vulnerable EMEs.

Looking ahead, global growth is expected to be driven by a sustained recovery 
in advanced economies. As the headwinds of private sector deleveraging and fiscal 
consolidation gradually recede, the recovery in advanced economies is expected 
to gain traction. Low oil prices, continued accommodative financing conditions, 
improving labour markets and confidence should support an improving outlook. 
In the United States, growth will be supported by stronger household spending 
following the boost to real incomes from lower oil prices, supportive financial 
conditions, continued strengthening of the labour and housing markets, moderating 
fiscal drag and the fading of household balance-sheet repair. However, the past 

7

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsappreciation of the effective exchange rate of the US dollar will dampen export 
growth in the near term, while the decline in oil prices will weigh on energy sector 
investment. In Japan, growth is expected to resume in the second half of the year, as 
households benefit from the increase in real incomes provided by the lower oil price, 
and exports benefit from improving foreign demand and the past depreciation of the 
Japanese yen. In the United Kingdom, growth is expected to continue to expand 
at a relatively robust pace. Although fiscal consolidation efforts are expected to 
dampen growth, low energy prices and accelerating wage growth should support real 
disposable incomes and private consumption. The recovery in demand and easing 
credit conditions should also spur business investment.

By contrast, the medium-term outlook for EmEs is mixed amid heightened 
uncertainty. Growth in several large EMEs has slowed markedly over the past four 
years and is expected to remain subdued in the medium term. In some countries, 
potential growth has slowed owing to structural impediments and macroeconomic 
imbalances. Other EMEs are adjusting to lower commodity prices, heightened 
political uncertainty and tighter external financing conditions. In China, growth 
rebounded in the second quarter. Recent reductions in policy rates, modest fiscal 
stimulus from the central government and efforts to loosen constraints on local 
government finances are expected to support demand. However, the recent stock 
market slump has heightened uncertainty about the outlook. Moreover, the Chinese 
political leadership has placed increasing emphasis on tackling financial fragilities 
and macroeconomic imbalances, which is likely to slow the pace of expansion in 
the medium term. CEECs outside the euro area are performing strongly, benefitting 
from strengthening domestic demand as improved labour market dynamics and the 
recent decline in oil prices are expected to support household consumption. On the 
other hand, the outlook for Brazil has deteriorated, as monetary and fiscal tightening 
are expected to act as a drag on investment and activity. In addition, supply-side 
bottlenecks and the need to tackle substantial macroeconomic imbalances and high 
inflation are holding back medium-term growth. Russia is also in the midst of a deep 
recession. Despite some easing of financing conditions since the turn of the year, 
funding costs remain elevated. Uncertainty is high and business confidence weak, 
while lower oil revenue is expected to entail a sharp fall in public expenditure.

Sharp falls in imports in a few large EmEs have driven a moderation in global 
trade in the first half of 2015. Global imports (excluding the euro area) based on 
national accounts data fell by 0.2% in the first quarter, and available data point to 
continued weakness in the second quarter. Indeed, global merchandise trade data 
from CPB Netherlands Bureau for Economic Policy Analysis showed a  
quarter-on-quarter decline in world imports of 0.9% in the second quarter. While 
imports of advanced economies and CEECs have been resilient during 2015, 
pronounced declines in a few major EMEs have driven the global aggregate. In 
the first quarter imports slumped in Russia, driven by falling domestic demand and 
exchange rate depreciation. Imports in China have also been weak in the first half of 
the year, again reflecting the moderation in domestic demand. In the near term global 
trade is expected to recover very gradually. At the same time, survey indicators point 
to rather moderate global trade growth. In particular, the global PMI for new export 
orders fell in July (see Chart 3). Looking further ahead, with the subdued outlook for 

8

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsEME imports expected to persist, world trade is expected to remain moderate and  
global imports are not expected to grow faster than global GDP.

Chart 3
World trade in goods

(three-month-on-three-month percentage changes; left-hand scale: percentage point 
contributions; right-hand scale: diffusion index)

global PMI new export orders (right-hand scale) 
global PMI excluding euro area manufacturing (right-hand scale) 
world trade 1991-2007 average (left-hand scale) 
world trade (left-hand scale) 

7.5 

5.0 

2.5 

0.0 

-2.5 

-5.0 

-7.5 

-10.0 

-12.5 

-15.0 

65 

60 

55 

50 

45 

40 

35 

30 

25 

20 

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Markit, CPB and ECB calculations.
Note: The latest observation refers to August 2015 for PMI and June 2015 for world 
trade.

Chart 4
CPI inflation

(year-on-year percentage changes)

OECD countries 
Brazil 
Russia 
China 
India 

18

16

14

12

10

8

6

4

2

0

-2

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

Sources: National sources and OECD.
Note: The latest observation refers to July 2015.

overall, the global growth outlook continues to 
suggest a modest and uneven recovery. According 
to the September 2015 ECB staff macroeconomic 
projections, for which the cut-off date for the financial 
and commodity price assumptions was 12 August, 
world (excluding the euro area) real GDP growth is 
projected to accelerate gradually from to 3.2% in 
2015 to 3.8% in 2016 and 4.0% in 2017. Euro area 
foreign demand is expected to expand from 1.4% in 
2015 to 3.3% in 2016 and 4.1% in 2017. Compared 
with the June 2015 Broad Macroeconomic Projection 
Exercise, this constitutes a downward revision to world 
growth, mostly reflecting the weaker than expected 
outlook across EMEs. Revisions to euro area foreign 
demand are more significant, as weak activity in EMEs 
is now judged to weigh more sharply on imports than 
previously anticipated.

risks to the outlook for global trade and activity 
remain tilted to the downside. Notably, current 
developments in EMEs have the potential to further 
affect global growth adversely via trade, financial and 
confidence effects. Some EMEs are also vulnerable 
to a shift in global risk sentiment, after a period of risk 
spread and volatility compression. Geopolitical risks 
also continue to weigh on the outlook, and increased 
tensions between Russia and Ukraine could have 
adverse implications for global growth.

Global price developments 

global inflation remains low following the sharp 
oil price declines. Annual consumer price inflation 
in the OECD countries remained unchanged in July, 
at 0.6%. Excluding food and energy, OECD annual 
inflation also remained stable, at 1.6% (see Chart 4). 
Headline inflation remained broadly unchanged at 
low levels in major advanced economies. Outside the 
OECD countries, broad disinflationary pressures persist 
in China and India. However, in Brazil and Russia, 
inflation has remained high, as currency depreciation 
has led to higher import prices.

9

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsLooking ahead global inflation is expected to rise only gradually. In the short 
term the recent fall in oil and other commodity prices should dampen inflationary 
pressures further, but thereafter the negative contribution from the energy 
component should gradually diminish as the effects of past oil price declines begin 
to fade. At the same time, the upward slope of the oil futures curve implies some 
recovery in oil prices, which should increase the contribution of energy prices to 
headline inflation. However, it is expected that the projected slow pick-up in world 
economic activity will result in only a gradual reduction in spare capacity, and wage 
and cost inflationary pressures are expected to remain generally muted. 

10

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments2 

Financial developments

Long-term nominal government bond yields in the euro area declined slightly 
between early June and early September, following significant increases in previous 
months. These declines took place in an environment characterised by a weakening 
growth outlook for the global economy, falling oil prices and declining inflation-linked 
swap rates. Equity prices also declined amid higher levels of volatility, especially 
in the aftermath of the currency depreciation and large losses in the equity market 
in China in August. Uncertainty associated with developments in Greece, which 
peaked in late June and early July, had a somewhat stronger impact on stock 
markets than on bond and foreign exchange markets, but overall its financial impact 
was contained and temporary. The effective exchange rate of the euro appreciated 
markedly between early June and 2 September 2015.

Two significant episodes of heightened tensions occurred during the review 
period, the first one being associated with developments in greece in late 
June and early July. This episode overall had a relatively muted impact on financial 
markets. This can be seen, for example, in spreads of ten-year government bond 
yields in lower-rated euro area countries relative to the corresponding German 
yield, which widened only marginally during that period and quickly returned to their 
previous levels. At the peak of the turmoil, spreads vis-à-vis the German ten-year  
Bund were up by a maximum of 35 basis points in most euro area countries. 
Implied bond market volatility, like other measures of risk, rose only moderately and 
temporarily during that period. However, developments in Greece had a somewhat 
more pronounced impact on equity markets, leading to increases in implied volatility 
and declining prices. Between 26 June and 7 July the Dow Jones EURO STOXX 
50 equity price index fell by more than 8%, with the implied volatility of that index 
increasing strongly. The euro weakened somewhat against the US dollar during that 
period.

The second episode of heightened financial market uncertainty started around 
mid-august and was related to developments in China. This episode, unlike the 
former, had a significant impact on stock and foreign exchange markets as well as 
on perceptions of risk. The depreciation of the Chinese renminbi which started on 
11 August was followed by a period of sharp declines in Chinese equity prices and 
strong increases in global uncertainty. These developments, combined with rapidly 
declining oil prices, were regarded as a sign that the global economic outlook was 
weakening and weighed on international financial markets and implied volatility. 
Stock prices in the euro area fell by around 16% from 11 to 24 August, while ten-year 
AAA-rated euro area government bond yields declined by around 10 basis points 
over that period. These declines were probably also fuelled by the appreciation of the 
euro, amid the unwinding of carry trade positions (that are typically closed in periods 
of heightened uncertainty) in which the euro was used as a funding currency as well 
as by declining market expectations of an imminent increase in monetary policy rates 
in the United States. Lower than usual market liquidity – a result also of seasonal 
factors – may have exacerbated fluctuations in stock and bond markets.

Ten-year aaa-rated euro area government bond yields declined slightly overall 
between early June and early September, to stand at very low levels. Initially, 

11

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsthe average of ten-year AAA-rated euro area sovereign yields increased further, from 
0.93% on 4 June to 1.08% on 10 June, thereby continuing the trend observed since 
mid-April. It then remained broadly stable until mid-July, possibly reflecting mixed 
data releases. It later declined, to stand at around 0.90% in early September. Euro 
area sovereign yield spreads against Germany declined slightly over the review 
period, amid some volatility during the episodes of heightened uncertainty. Implied 
bond market volatility remained overall stable, notwithstanding some temporary 
increases. 

Chart 6
EONIA forward curve

(percentages per annum)

2 September 2015
after the Governing Council meeting on 16 July 2015
5 June 2015

2.5

2.0

1.5

1.0

0.5

0.0

Chart 5
EONIA swap rates

(percentages per annum)

two-year EONIA swap
five-year EONIA swap
ten-year EONIA swap
thirty-year EONIA swap

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

Jan.

Apr.

July
2014

Oct.

Jan.

Apr.

July

2015

-0.5

2015

2017

2019

2021

2023

2025

Source: Thomson Reuters.
Note: The latest observation is for 2 September 2015.

Sources: Thomson Reuters and ECB calculations.

Chart 7
Euro area ten-year real, nominal and inflation-linked 
swap rates

(percentages per annum)

ten-year real swap (nominal minus inflation-linked swap)
ten-year nominal swap
ten-year inflation-linked swap

2

1

0

-1

-2

Jan.

Apr.

July
2014

Oct.

Jan.

Apr.

July

2015

Sources: Thomson Reuters and ECB calculations.
Notes: The ten-year real swap is derived from the ten-year nominal EONIA swap 
and the ten-year inﬂ ation-linked swap. The latest observation is for 2 September 2015.

EoNIa swap rates mirrored the movements in  
aaa-rated yields. EONIA swaps decreased most in 
the medium maturity segments while a small increase 
was recorded for the longest maturities (see Chart 5). In 
line with developments in bond yields, EONIA forward 
rates edged downwards up to the eight-year horizon 
over the review period, with a peak reduction of around 
20 basis points at the four-year horizon (see Chart 6).

The decline in nominal rates was mirrored in lower 
inflation-linked swap rates in July and august. 
An accounting decomposition of ten-year nominal 
EONIA swap rates into ten-year inflation-linked swap 
rates and, as a residual, ten-year real rates shows 
that around two-thirds of the fluctuations in nominal 
yields in June came from changes in the real rate, 
with one-third derived from the inflation-linked swap 
rate (see Chart 7). In July and August, by contrast, 
the two relative contributions changed significantly, 
with lower nominal yields being derived entirely from 

12

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentslower inflation-linked swap rates. (See also Section 4 for additional evidence on 
developments in inflation expectations.)

Chart 8
Corporate bond yields in the euro area

(percentages per annum)

financials
non-financials

2.5

2.0

1.5

1.0

0.5

Jan.

Apr.

July
2014

Oct.

Jan.

Apr.

July

2015

Sources: iBoxx and ECB.
Note: The latest observation is for 2 September 2015.

Chart 9
Financial and non-financial sector stock price indices

(1 January 2014 = 100)

euro area financials
US financials
euro area non-financials
US non-financials

130

120

110

100

90

Jan.

Apr.

July
2014

Oct.

Jan.

Apr.
2015

July

Source: Thomson Reuters.
Notes: Based on DataStream market indices. The latest observation is for 
2 September 2015.

Corporate bond yields were resilient in the face 
of the various financial market tensions, although 
they increased somewhat over the review period. 
Overall, corporate bond yields relative to the average 
of AAA-rated euro area sovereign yields moved in line 
with developments in the perception of risk. Accordingly, 
they tended to rise in response to the uncertainty 
associated with developments in Greece in late June 
and early July and, after moderating slightly, they 
then increased again in the second half of August in 
connection with the increase in global uncertainty  
(see Chart 8). Corporate bond yields rose by between  
5 and 20 basis points from early June to early 
September, depending on the sector and rating class.

Spreads in asset classes other than public sector 
securities purchased under the expanded asset 
purchase programme (aPP) – covered bonds and 
asset-backed securities (aBSs) – behaved in a 
broadly similar manner. However, while covered 
bond spreads were broadly unchanged overall over 
the review period, some increases were observed for 
discount margins on lower-rated ABSs.

Significant losses were recorded in euro area 
equity markets in both the financial and the 
non-financial sectors. Euro area financial and non-
financial stock prices recorded overall declines of 8% 
and 10% respectively between early June and early 
September (see Chart 9). While losses associated with 
developments in Greece were recovered by mid-July, 
equity prices declined markedly following the increase 
in uncertainty about the global outlook in the second 
half of August. Unlike in the euro area, US financial and 
non-financial stock prices were broadly stable between 
early June and mid-July, evidencing the euro area-
specific origin of the turbulences during that period. US 
stock prices then recorded broadly twice as large losses 
as for the euro area from mid-July, amid a deteriorating 
global growth outlook. Implied equity market volatility 

rose by 8 and 10 percentage points in the euro area and the United States 
respectively over the review period.

The EoNIa stabilised between early June and early September amid gradual 
increases in excess liquidity. After gradually declining following the announcement 
of the expanded APP, the EONIA then stabilised, averaging -0.12% between early 

13

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsJune and 2 September amid gradual increases in excess liquidity. Those higher 
levels of excess liquidity were largely the result of APP purchases and the targeted 
longer-term refinancing operation allotted in June. Box 2 presents more detailed 
information on liquidity conditions and monetary policy operations. The three-month 
EURIBOR fixing, after turning negative for the first time in late April, continued to 

Chart 10
Changes in the exchange rate of the euro against 
selected currencies

since 2 September 2014
since 2 June 2015

(percentages)

EER-38
Chinese renminbi
US dollar
Pound sterling
Swiss franc
Japanese yen
Polish zloty
Czech koruna
Swedish krona
Russian rouble
Turkish lira
South Korean won
Indonesian rupiah
Hungarian forint
Danish krone
Romanian leu
Taiwan dollar
Brazilian real
Indian rupee
Croatian kuna

-20

-10

0

10

20

30

40

50

60

Source: ECB.
Notes: Percentage changes relative to 2 September 2015. EER-38 is the nominal 
effective exchange rate of the euro against the currencies of 38 of the euro area’s most 
important trading partners.

edge downwards, standing at -0.033% on 2 September. 
Box 3 provides further insights on the unsecured 
overnight money market developments since 2008 
using TARGET2 transaction data while Box 4 briefly 
recalls what TARGET balances are and the factors 
behind their developments.

The effective exchange rate of the euro appreciated 
by 4.8% between early June and 2 September. In 
bilateral terms, it appreciated by 2.0% against the 
US dollar (see Chart 10). The euro also appreciated 
against the pound sterling, the Swiss franc, the Swedish 
krona and the renminbi. Meanwhile, declining oil prices 
and depressed economic activity in Russia weighed on 
the Russian rouble, resulting in the euro appreciating by 
30% against that currency. The euro also strengthened 
against the currencies of several other emerging market 
economies and commodity-exporting countries. By 
contrast, it depreciated against the Japanese yen and 
the Czech koruna. The Danish krone continued to trade 
close to its central rate within ERM II. Box 5 presents 
the recent update of the trade weights used for the 
effective exchange rates of the euro.

14

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments3 

Economic activity

The economic recovery in the euro area has continued to gradually firm, although at 
a slower pace than previously anticipated. Euro area real GDP continued to grow in 
the second quarter of 2015, albeit at a slightly slower pace than in the first quarter. 
The latest survey indicators point to a broadly similar pace of real GDP growth in 
the second half of this year as in the second quarter. Looking further ahead, the 
recovery of economic activity is expected to continue. The low level of oil prices will 
bolster real disposable income, thus supporting private consumption and corporate 
profitability. In addition, the very accommodative monetary policy stance should 
further contribute to easing overall financing conditions and enhancing access to 
credit, which will encourage more business investment. At the same time, the growth 
trend is expected to be slightly weaker than previously anticipated, reflecting in 
particular the slowdown in emerging economies, weighing on global growth and thus 
on the demand for euro area exports. Against this background, the September 2015 
ECB staff macroeconomic projections for the euro area foresee a somewhat weaker 
growth outlook compared with the June 2015 Eurosystem staff macroeconomic 
projections. 

Chart 11
Euro area real GDP and its composition

(quarter-on-quarter percentage changes and quarter-on-quarter percentage 
contributions)

GDP
changes in inventories
private consumption
government consumption
net exports
gross fixed capital formation

2

1

0

-1

-2

-3

-4

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat and ECB calculations.
Note: GDP growth in the euro area for the second quarter of 2015 is Eurostat’s ﬂ ash 
estimate.

Euro area real gDP continued to grow in the second 
quarter of 2015, albeit at a slightly slower pace 
than in the first quarter. According to Eurostat’s flash 
estimate, real GDP rose by 0.3%, quarter on quarter, 
in the second quarter of 2015, down from 0.4% in 
the first quarter (see Chart 11). This outturn in growth 
developments was somewhat lower than expected in 
the June 2015 Eurosystem staff projections. Although 
no breakdown was available by the cut-off date for this 
issue of the Economic Bulletin, available information 
suggests that private consumption and net trade made 
positive contributions to growth, with the latter reflecting 
gains in euro area export market shares on the back 
of the supportive euro exchange rate. In contrast, 
investment and changes in inventories are likely to 
have made a negative contribution to growth in the 
second quarter.

Indicators for business confidence point to a 
similar pace of growth in the near term as in the 
second quarter. Survey data available up to August 
signal ongoing moderate growth in the near term. For 

instance, both the European Commission’s Economic Sentiment Indicator (ESI)  
and the composite output Purchasing Managers’ Index (PMI) improved slightly 
between the second quarter of 2015 and the first two months of the third quarter.  
In addition, both indicators stood in July and August above their respective long- 
term average levels. While both the ESI and the PMI rose in August, business 
confidence declined in the capital and intermediate goods sectors, reflecting in 
part a weaker assessment of export order book levels amid a deterioration in the 

15

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 12
Euro area real GDP (including projections)

(quarter-on-quarter percentage changes)

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Sources: Eurostat and the article entitled “September 2015 ECB staff macroeconomic 
projections for the euro area”, published on the ECB’s website on 3 September 2015. 
Notes: Working day-adjusted data. The ranges shown around the central projections 
are based on the differences between actual outcomes and previous projections carried 
out over a number of years. The width of the ranges is twice the average absolute value 
of these differences. The method used for calculating the ranges, involving a correction 
for exceptional events, is explained in “New procedure for constructing Eurosystem and 
ECB staff projection ranges”, ECB, December 2009.

Chart 13
Euro area private consumption and consumer 
confidence

(year-on-year percentage changes; mean-adjusted)

real private consumption
consumer confidence

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

-3.0

2007

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat, European Commission and ECB calculations.

global growth outlook. Whereas slower global trade 
dampens the euro area growth momentum, the past 
depreciation of the euro and low interest rates are likely 
to support business investment, while favourable labour 
market developments and lower energy prices should 
encourage private consumption in the near term. 

The assessment of a slower economic recovery 
is reflected in the September 2015 ECB staff 
macroeconomic projections for the euro area.1 
The economic recovery in the euro area is projected 
to gradually broaden over the next two years, although 
being slower than previously anticipated, reflecting in 
particular the slowdown in emerging market economies. 
Positive contributions to growth are expected from 
domestic and, to a lesser extent than before, external 
demand. The ECB’s monetary policy measures should 
further support activity in the near and medium term, 
through a variety of channels. Bank lending rates 
are expected to remain at historical low levels and 
aggregate demand, notably fixed capital formation, 
is expected to benefit from the very accommodative 
monetary policy stance. At the same time, the 
sluggish pace of implementation of structural reforms 
and the necessary balance sheet adjustments in a 
number of sectors are likely to continue to weigh on 
growth. According to the September 2015 ECB staff 
macroeconomic projections for the euro area, annual 
real GDP in the euro area is expected to increase 
by 1.4% in 2015, 1.7% in 2016 and 1.8% in 2017 
(see Chart 12). 

Private consumption growth, which has been the 
main driver of growth for the past year, is likely 
to have lost some of its momentum recently. For 
instance, combined retail sales and new passenger car 
registrations for the euro area grew by 0.3%, quarter on 
quarter, in the second quarter of 2015, down from 1.3% 
in the first quarter. While consumption probably slowed 
in the second quarter, the European Commission’s 
indicator on consumer confidence for the euro area, 
which provides a reasonably good picture of trend 
developments in private consumption, stood in August 
above its long-term average level and points to a 
continued growth momentum (see Chart 13).

1 

See “September 2015 ECB staff macroeconomic projections for the euro area” (http://www.ecb.europa.
eu/pub/pdf/other/ecbstaffprojections201509.en.pdf?373c7ff1a4072123c81757486566b3f2).

16

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsLooking ahead, private consumption growth is expected to remain the key 
driver of the pick-up in activity. Private consumption should continue to benefit 
from the favourable impact of the decline in energy prices on real disposable income. 
Thereafter, wage income is expected to pick up, on the back of steady employment 
growth and accelerating nominal compensation per employee. Easing financing 
conditions as well as low financing costs, reinforced by the ECB’s non-standard 
measures, should further support private consumption.

Euro area total investment growth is likely to have contracted in the second 
quarter of this year. In the first quarter investment was supported by improving 
demand, benign financing conditions, the mild winter and temporary fiscal incentives 

Chart 14
Euro area capital goods production and investment 
confidence

(year-on-year percentage changes; net percentage balance; index)

European Commission’s capital goods sector confidence (left-hand scale)
PMI capital goods production (right-hand scale)
industrial production of capital goods (left-hand scale)

20

10

0

-10

-20

-30

-40

-50

70

60

50

40

30

20

10

0

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat, European Commission and Markit.

Chart 15
Euro area export market shares

(total euro area exports of goods as a share of total world merchandise exports)

export market shares

38

36

34

32

30

28

26

26.8

26.6

26.4

26.2

26.0

25.8

25.6

25.4

Dec. Feb.
2014

Apr.
2015

June

24

2002

2004

2006

2008

2010

2012

2014

2016

Sources: CPB Netherlands Bureau for Economic Policy Analysis and ECB calculations.

in some countries. In the second quarter of 2015 
euro area total investment growth is expected to have 
contracted, as dynamics in industrial production of 
capital goods decelerated and capacity utilisation in 
the manufacturing industry was broadly unchanged. 
With regard to construction investment, construction 
production growth has slowed and confidence 
indicators remain at levels associated with contraction, 
pointing to weak growth in the second quarter.

The pick-up in business investment may be 
slightly slower than previously thought, but 
remains a driver of the recovery. Survey data for 
the capital goods sector suggest broadly stabilising or 
slightly weakening confidence and lower production 
expectations up to August (see Chart 14). The 
deterioration in the external environment is also 
weighing on the short-term outlook for investment. 
Nevertheless, looking ahead, business investment 
should progressively recover in an environment of very 
accommodative monetary policy, easing credit supply 
conditions, stronger profit mark-ups, strengthening 
demand and replacement needs. 

Construction investment is expected to gradually 
recover in the second half of 2015. Following the 
end of a protracted adjustment period, construction 
investment should be supported by very benign 
financing conditions and growth in household 
disposable income. The progress in housing market 
adjustments in some countries as signalled by a 
turnaround in building permits and house prices will 
also boost residential investment over time. 

owing to the weaker external environment, 
expectations for export growth have been scaled 
down. The growth of euro area exports of goods and 
services moderated somewhat in the first quarter of 
2015. Although global growth slowed down significantly, 

17

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 16
Extra-euro area export volumes of goods to major 
trading partners

(three-month-on-three-month percentage changes; percentage point contributions)

total
United States
China
Asia excluding China
Russia
non-euro area Europe excluding Russia
Latin America
other

4 

3 

2 

1 

0 

-1 

-2 
May

Aug.
2014

Nov.

Feb.

May
2015

Sources: Eurostat and ECB calculations.
Notes: “Non-euro area Europe” also includes some non-EU countries (e.g. Norway, 
Switzerland and Turkey).

Chart 17
Euro area employment, PMI employment expectations 
and unemployment

(quarter-on-quarter growth; index; percentage of labour force)

employment (left-hand scale)
PMI employment expectations (left-hand scale)
unemployment rate (right-hand scale)

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

2005 2006 2007 2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat, Markit and ECB calculations.
Note: The PMI is expressed as deviations from 50 divided by 10.

14

13

12

11

10

9

8

7

6

trade in goods data for the second quarter of 2015 point 
to a pick-up in goods export growth, indicating gains in 
euro area export market shares in the first half of 2015, 
partly due to the supportive euro exchange rate 
(see Chart 15). These developments reflect an increase 
in exports to the United States and Asia (excluding 
China), while exports to China and Russia remained 
subdued (see Chart 16). 

Looking ahead, however, export growth is projected 
to fall well short of its pre-crisis pace, reflecting both 
a moderation in global demand and lower global trade 
elasticity to growth. Euro area imports are expected to 
continue to further strengthen over the medium term in 
line with the recovery in domestic demand. As a result, 
net exports are expected to make a broadly neutral 
contribution to real GDP growth over the next two years. 

Euro area labour markets are continuing to improve 
gradually. Headcount employment (see Chart 17) 
increased moderately, rising by 0.1% quarter on quarter 
in the first quarter of 2015 (the latest period for which 
data are available). At the sectoral level, employment 
growth was led by a rebound in construction 
employment. At the same time, services sector 
employment growth registered a further slowdown for 
the fourth consecutive quarter. Despite the increase 
in total employment, total hours worked decreased 
slightly in the first quarter, reflecting a decrease in hours 
worked per person employed. Survey results indicate 
that employment continued to improve in the second 
quarter of 2015. These indicators also point to some 
further improvements in labour market conditions in 
both industry and services in the second quarter of 
2015 and a slight decline in construction. 

Unemployment continues to recede gradually from 
elevated levels. The euro area unemployment rate 
declined to 11.1% in the second quarter of 2015, from 
11.2% in the first quarter. In July the unemployment 
rate fell further to 10.9%. The ongoing decline in the 
unemployment rate is visible across all groups of 
workers (youth workers, adult workers, males and 
females) and across most euro area economies; 
nevertheless, substantial differences remain at the age 
group and country level. 

Looking ahead, euro area labour markets are expected to improve further 
over the short and medium term. Employment growth is expected to accelerate 
somewhat over the coming quarters, on the back of the economic recovery.  

18

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsAs a consequence, the euro area unemployment rate is expected to decline further 
as the recovery broadens. 

The risks surrounding the economic outlook for the euro area remain on the 
downside. They reflect in particular heightened uncertainties related to the external 
environment. Notably, developments in emerging market economies have the 
potential to further adversely affect global growth via trade and confidence effects. 

19

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments4 

Prices and costs

HICP inflation has recently stabilised at low, positive levels. On the basis of the 
information available and current oil futures prices, annual HICP inflation rates 
will remain very low in the near term. Annual HICP inflation is expected to rise 
towards the end of the year, also on account of base effects associated with the 
fall in oil prices in late 2014. Inflation rates are foreseen to pick up further during 
2016 and 2017, supported by the expected economic recovery, the pass-through 

Chart 18
Euro area HICP inflation (including projections)

(annual percentage changes)

5 

4 

3 

2 

1 

0 

-1 

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Sources: Eurostat and ECB calculations.
Note: The latest observation is for August 2015 (ﬂ ash estimate).

Chart 19
Contribution of components to euro area headline 
HICP inflation

(annual percentage changes; percentage point contributions)

HICP 
non-energy industrial goods 
food 
services 
energy 

5

4 

3 

2 

1

0

-1

-2

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015

Sources: Eurostat and ECB calculations. 
Note: The latest observations are for August 2015 (ﬂ ash estimates). 

of past declines in the euro exchange rate and the 
assumption of somewhat higher oil prices in the years 
ahead as currently reflected in oil futures markets. The 
September 2015 ECB staff macroeconomic projections 
for the euro area foresee annual HICP inflation at 0.1% 
in 2015, 1.1% in 2016 and 1.7% in 2017. In comparison 
with the June 2015 Eurosystem staff macroeconomic 
projections, the outlook for HICP inflation has been 
revised down, largely owing to lower oil prices.

HICP inflation has recently stabilised at low, positive 
rates, following the rebound from the negative rates 
seen earlier this year (see Chart 18). According to 
Eurostat’s flash estimate, annual HICP inflation was 
0.2% in August, unchanged from July and June. This 
recent stabilisation reflects two sets of offsetting factors 
(see Chart 19). On the one hand, the renewed decline in 
oil prices has driven down energy inflation. On the other 
hand, HICP inflation excluding energy and food has 
edged up somewhat over the past few months, driven 
by non-energy industrial goods inflation, while services 
inflation has remained broadly stable. Furthermore, in 
August food inflation picked up substantially on the back 
of a higher contribution from unprocessed food prices, 
while processed food inflation remained broadly stable, 
dampened by dairy prices.

Energy and food price inflation have reinforced the 
pattern of euro area inflation in recent quarters. 
Energy price inflation has been negative since 
July 2014, when oil prices started to decline sharply. 
After peaking in mid-June 2014, the price of crude oil 
fell in euro terms by around 50% until January. On the 
back of rising oil prices following this trough in early 
2015, energy price inflation started to pick up during the 
first half of this year. However, since July oil prices have 
seen a renewed downward trend, exerting downward 
pressure on energy price inflation. Similarly, the 
contribution from food price inflation declined markedly 
in 2014 and has rebounded somewhat since the start 

20

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsof this year, supported by significant base effects. Nevertheless, food price inflation 
remains relatively low by historical standards (see Box 7).

Chart 20
Measures of underlying inflation

(annual percentage changes)

HICP excluding food and energy
measure based on a dynamic factor model
diffusion index (right-hand scale)
range of underlying inflation measures

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

90

80

70

60

50

40

30

20

10

0

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat and ECB calculations.
Notes: In the range of underlying measures, the following have been considered: HICP 
excluding energy; HICP excluding unprocessed food and energy; HICP excluding 
energy and food; trimmed mean (10%); trimmed mean (30%); the median of the HICP; 
and the measure based on the dynamic factor model. The diffusion index is calculated 
as the share of individual HICP items which has seen an increase in the annual rate 
of change over the past three months. The latest observations are for August 2015 for 
HICP excluding energy and food (ﬂ ash estimate) and July 2015 for all other indicators.

Chart 21
Producer prices for non-food consumer goods and 
extra-euro area import prices

(annual percentage changes)

Producer Price Index – non-food consumer goods (left-hand scale)
extra-euro area import prices (total industry excluding energy
and construction, right-hand scale) 

4

3

2

1

0

-1

-2

-3

8

6

4

2

0

-2

-4

-6

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015

Sources: Eurostat and ECB calculations.
Note: The latest observations are for July 2015 (Producer Price Index) and June 2015 
(import prices).

recent indicators point to a gradual strengthening 
in underlying inflation. HICP inflation excluding 
food and energy (a measure of underlying inflation) 
remained stable at 1.0% in August. Most other 
indicators of underlying inflation, looking at the broad 
set presented in Box 4 of the previous issue of the 
Economic Bulletin,1 continued to post higher levels 
in July 2015 than early this year (see Chart 20). The 
pass-through of the euro’s strong depreciation between 
May 2014 and April 2015 to non-energy consumer 
prices and the continued recovery in domestic demand 
in the euro area should provide the impetus for a 
further pick-up in underlying inflation. However, it is still 
premature to conclude that underlying inflation is on an 
upward trend, as the indirect effects of the declines in 
oil and non-oil commodity prices and low global inflation 
could temporarily exert renewed downward pressure. 
The recent appreciation of the euro could also reduce 
some of the upward impact of the euro’s previous 
strong depreciation.

The effects of the exchange rate depreciation can 
increasingly be seen in the rise in goods inflation. 
Non-energy industrial goods inflation recorded a broad-
based rise from -0.1% in February 2015 to 0.6% in 
August. The upward trend reflects the pass-through 
of the marked increases in import prices for non-food 
consumer goods, which are related to the depreciation 
of the euro and the rise in the consumption of durable 
goods observed over recent quarters.

The impact of the weaker euro on domestic pipeline 
pressures should become more evident over the 
coming quarters. Whereas extra-euro area import 
prices remained at elevated levels (see Chart 21), the 
domestic sources of pipeline price pressures remain 
weak. Indeed, pipeline pressures for food prices point 
to a modest dynamic along the price chain, while the 
annual rate of change in producer prices for non-food 
consumer goods was 0.2% in July, posting a small 
increase for the second consecutive month. Survey 
indicators have weakened recently. The Purchasing 
Managers’ Index (PMI) for non-food input prices in the 

1 

See the box entitled “Has underlying inflation reached a turning point?”, Economic Bulletin, Issue 5, 
ECB, July 2015.

21

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsretail sector declined further in August, interrupting the upward movement observed 
until June. Although the increase in the annual rate of change in producer prices for 
non-food consumer goods has been subdued, the recent upturn should continue as 
the pass-through of the strong depreciation of the euro gains further traction.

Domestic price pressures have stabilised for the time being. The GDP deflator, 
which is indicative of domestic inflationary pressures, increased marginally in the first 
quarter of 2015, reflecting continued weak labour cost developments and a moderate 
strengthening in profit margins. Growth in unit labour costs decreased slightly in the 
first quarter of 2015 as productivity grew at a stronger rate than compensation per 
employee. Profit growth (measured in terms of gross operating surplus) strengthened 
somewhat, reflecting the impact of the ongoing improvement in real GDP growth and 
a pick-up in the rate of growth in profit per unit of output.

The high level of economic slack continues to depress services price inflation. 
The annual rate of services price inflation has hovered just above 1.0% in recent 
months. Most of the fluctuations around this level reflect the impact of travel-related 
items amid ongoing subdued developments in both wage growth and profit margins. 
The weakness in wage and profit margin growth could be attributed to a number 
of factors, including the high level of economic and labour market slack and higher 
wage and price flexibility in some countries following structural reforms in labour and 
product markets over recent years.

Chart 22
Market-based measures of inflation expectations

(annual percentage changes)

one-year rate two years ahead 
one-year rate four years ahead 
one-year rate nine years ahead 
five-year forward five years ahead inflation-linked swap rate 
ten-year spot inflation rate 

3.0

2.5

2.0

1.5

1.0

0.5

Jan.

Apr.

July
2014

Oct.

Jan.

Apr.

July

Oct.

2015

Sources: Thomson Reuters and ECB calculations. 
Note: The latest observation is for 2 September 2015.

market-based measures of inflation expectations 
have recently declined, following a continuous 
upward trend since January, while survey-based 
measures have remained relatively stable. The 
upward trend in inflation expectations signalled by 
inflation-linked swaps since early 2015 came to a 
halt in late June. Overall, long-term forward inflation-
linked swap rates rose by about 0.4 percentage point 
in the first six months of 2015. Between late June and 
early September, these rates declined amid signs of a 
moderation in global economic activity and decreasing 
commodity prices. However, other factors may also 
have contributed to the fall, such as low levels of 
market liquidity during the summer months and lower 
inflation risk premia incorporated in the inflation-linked 
swap rates. The five-year inflation rate five years 
ahead declined by 15 basis points between early 
July and 2 September, reaching 1.7% on this date 
(see Chart 22). Over the same period, short to  
medium-term inflation swap rates decreased more 
markedly than the longer-term rates. Survey-based 

22

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 23
Survey-based measures of inflation expectations

(annual percentage changes)

realised HICP inflation 
SPF (Q3 2015) 
Consensus Economics Forecast 

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

2012

2014 

2016 

2018 

2020

Sources: Eurostat, Reuters, ECB Survey of Professional Forecasters, ECB calculations 
and Consensus Economics. 
Notes: Realised HICP data are included up to August 2015 (ﬂ ash estimate). SPF data 
are based on the Q3 2015 survey results. The long-term forecast refers to ﬁ ve years 
ahead. Consensus Economics forecasts are based on the August 2015 forecasts for 
2015 and 2016 and the April 2015 forecasts for 2017 and 2018.

measures of inflation expectation have remained 
relatively stable so far and continue to suggest that 
inflation exceptation will return to a level below, but 
close to, 2% over the medium term (see Chart 23).

overall, HICP inflation for the euro area is projected 
to rise from the end of 2015 (see Chart 18). On the 
basis of the information available in mid-August,  
the September 2015 ECB staff macroeconomic 
projections for the euro area forsee HICP inflation at 
an average of 0.1% in 2015, rising to 1.1% in 2016 and 
1.7% in 2017.2 The initial pick-up is mainly related to 
base effects due to past falls in oil prices. In 2016 and 
2017 headline inflation is envisaged to rise significantly 
as further reductions in economic and labour market 
slack gradually push up underlying inflation. Moreover, 
rising external price pressures in view of the protracted 
exchange rate pass-through to consumer prices and 
upward effects from the assumed rise in energy and 
non-energy commodity prices, combined with strong 
upward base effects from the recent oil price declines, 
will contribute to higher inflation. Compared with the 

June 2015 Eurosystem staff macroeconomic projection exercise, the outlook for 
HICP inflation has been revised downwards over the whole projection horizon, 
but only marginally for 2017. The downward revision mainly reflects the direct and 
indirect effects of the lower oil prices. 

Taking into account the most recent developments in oil prices and recent 
exchange rates, there are downside risks to the September 2015 ECB staff 
inflation projections. Since the cut-off date for the technical assumptions in the 
September projections, oil prices have declined, while the effective exchange rate 
of the euro has strengthened, partly in connection with the financial turmoil in some 
emerging market economies.

The recovery in house prices is relatively broad-based, although house 
price growth remains heterogeneous across euro area countries. The ECB’s 
residential property price indicator for the euro area continues to increase at a 
relatively muted pace, rising by 1.0% (year-on-year) in the first quarter of 2015. 
House price growth in the euro area as a whole is expected to strengthen further 
in the period ahead, reflecting improving prospects for households’ income and 
employment, favourable financing conditions and the correction of previous 
overvaluations of house prices.3

2 

3 

See the article entitled “September 2015 ECB staff macroeconomic projections for the euro area”, 
published on the ECB’s website on 3 September 2015.

See the article entitled “The state of the house price cycle in the euro area” in this issue of the 
Economic Bulletin.

23

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments5 

Money and credit

In an environment of very low interest rates, money and loan growth have continued to 
recover. Portfolio substitution and improved credit dynamics are driving broad money 
growth. In addition, the targeted longer-term refinancing operations (TLTROs) and 
the expanded asset purchase programme (APP) have contributed to improvements 
in money and credit indicators. Banks’ funding costs stabilised at historical lows in the 
second quarter of 2015. Favourable lending conditions continued to support a further 
recovery in loan growth, which is materialising gradually. In addition, divergences in 

Chart 24
M3, M1 and loans to the private sector

(percentage changes; adjusted for seasonal and calendar effects)

M3
M1
loans to the private sector

20

15

10

5

0

-5

2008

2009

2010

2011

2012

2013

2014

2015

Source: ECB.

Chart 25
M3 and its components

(contribution to M3 annual growth rate; adjusted for seasonal and calendar effects)

M3
M1
marketable instruments
other short-term deposits

12

10

8

6

4

2

0

-2

-4

-6

-8

2008

2009

2010

2011

2012

2013

2014

2015

Source: ECB.

bank lending rates across countries have narrowed 
further. While bank lending is the main source of external 
funding for non-financial corporations (NFCs), the overall 
annual flow of external financing to NFCs is estimated to 
have stabilised in the second quarter of 2015. Overall, 
recent developments confirm that the ECB’s monetary 
policy measures are gradually being transmitted to 
lending conditions and are ultimately supportive of broad 
money and credit dynamics.

The latest monetary data confirm the robustness of 
money growth dynamics. In July annual M3 growth 
stood at 5.3%, compared with 5.0% for the second 
quarter of 2015 (see Chart 24). This increase in M3 
growth was mostly driven by the narrow monetary 
aggregate M1. Annual growth in M1 continued to 
accelerate in July 2015, reaching an annual rate of 
12.1%, compared with 10.9% in the second quarter 
of 2015. Recent developments in narrow money are 
consistent with the prospect of a continued recovery in 
economic activity. 

money-holders are focusing on overnight deposits. 
M1 made a sizeable contribution to M3 growth in July, 
as through the second quarter of 2015 (see Chart 25). 
The environment of very low interest rates is providing 
incentives for money-holders to invest in overnight 
deposits within M3. The M1 contribution also reflects 
inflows related to the sales of public sector bonds, 
covered bonds and asset-backed securities by the 
money-holding sector in the context of the expanded 
asset purchase programme (APP). In the first half 
of 2015 the low levels of remuneration for less 
liquid monetary assets contributed to the ongoing 
contraction of short-term deposits other than overnight 
deposits, which continued to be a drag on M3 growth. 
Further support for M3 growth came from marketable 
instruments (i.e. M3 minus M2), the contribution of which 
was small, but positive. 

24

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 26
Counterparts of M3

(annual flows; EUR billions; adjusted for seasonal and calendar effects)

M3
credit to the private sector (1)
credit to general government (2)
net external assets (3)
longer-term financial liabilities (excluding capital and reserves) (4)
other counterparts (including capital and reserves) (5)

1,600

1,400

1,200

1,000

800

600

400

200

0

-200

-400

-600

-800

2008

2009

2010

2011

2012

2013

2014

Source: ECB.
Notes: M3 is shown for reference only (M3 = 1+2+3-4+5).

money creation continued to move towards 
domestic sources in July, partly linked to the 
effects of the TLTros and the aPP. Among the 
counterparts of M3 (see Chart 26), shifts away from 
longer-term financial liabilities and increased credit to 
general government have been the main contributors 
to annual money growth in recent months, while the 
contribution from credit to the private sector has turned 
marginally positive. The annual rate of contraction in 
MFIs’ longer-term financial liabilities (excluding capital 
and reserves) held by the money-holding sector 
continued to be strong and stood at -6.5% in July 2015, 
unchanged from the second quarter of 2015. The 
reductions in MFIs’ longer-term financial liabilities are 
in part triggered by the attractiveness of TLTROs as an 
alternative to long-term market-based bank funding, 
as well as by the APP purchases. Credit from MFIs 
(including the Eurosystem) to general government 
increased in July, mainly reflecting the increase in 
the holdings of general government securities by the 
Eurosystem under its public sector purchase programme 

(PSPP), while banks continued to reduce their holdings of government securities. 
The latter offers room for a rebalancing of MFI investments towards private sector 
assets. At the same time the contribution from the MFI sector’s net external 
asset position, which reflects a sizeable surplus in the euro area current account, 

Chart 27
Banks’ composite cost of debt financing

(percentages per annum)

euro area
Germany
France
Italy
Spain

7

6

5

4

3

2

1

0

2008

2009

2010

2011

2012

2013

2014

2015

Sources: ECB, Merrill Lynch Global Index and ECB calculations.
Notes: Average of deposit rates on new business (i.e. the composite cost of deposits) 
and cost of unsecured market debt funding weighted with their corresponding 
outstanding amounts.

moderated further in the second quarter of 2015 
and turned negative in July 2015. The large outflows 
from net external assets in July occurred despite the 
persistently positive contribution of the current account 
surplus.

Banks’ funding costs edged up in the second 
quarter of 2015, but remain at low levels. Looking at 
the last few years, the composite cost of bank funding 
shows a declining trend (see Chart 27), against the 
backdrop of net redemptions of MFIs’ longer-term 
financial liabilities. Since its announcement, the 
expanded APP has significantly contributed to a further 
reduction in bank funding costs. The recent rise in 
banks’ cost of debt funding was driven by an increase in 
bank bond yields, while deposit rates remained stable at 
historically low levels. In this context, the July 2015 euro 
area bank lending survey showed that banks’ access to 
retail deposit and debt securities funding deteriorated in 
the second quarter of 2015, while access to the money 
market and securitisation improved, albeit to a lesser 
extent than in the previous quarter. There are no signs 

25

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 28
Factors contributing to a tightening of credit standards 
for loans to NFCs and net demand

that banks’ deposit costs are moving into negative 
territory as a result of the ECB’s negative deposit 
facility rate. 

(average net percentages per category)

credit standards
net demand
risk perceptions
competition
cost of funds and balance sheet constraints

125

100

75

50

25

0

-25

-50

2008

2009

2010

2011

2012

2013

2014

2015

Source: ECB.
Notes: “Cost of funds and balance sheet constraints” are an unweighted average of 
“cost related to capital position”, “access to market ﬁ nancing” and “liquidity position”; 
“risk perceptions” are an unweighted average of “general economic situation and 
outlook”, “industry or ﬁ rm-speciﬁ c situation and outlook/borrower’s creditworthiness” 
and “risk on collateral demanded”; “competition” is an unweighted average of “bank 
competition”, “non-bank competition” and “competition from market ﬁ nancing”.

Chart 29
Composite indicator of the cost of borrowing for NFCs

(percentages per annum; three-month moving averages)

euro area
Germany
France
Italy
Spain
Netherlands
cross-country standard deviation (right-hand scale)

7

6

5

4

3

2

1

1.70 

1.45 

1.20 

0.95 

0.70 

0.45 

0.20 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

Source: ECB.
Notes: The indicator for the total cost of bank borrowing is calculated by aggregating 
short and long-term rates using a 24-month moving average of new business volumes. 
The cross-country standard deviation is calculated over a ﬁ xed sample of 12 euro area 
countries. Latest observation: July 2015.

Bank lending conditions are supported by 
favourable developments in credit demand and 
supply factors. The July 2015 bank lending survey 
shows that increased competition between banks 
contributed to an easing of credit standards on loans to 
both enterprises and households in the second quarter 
of 2015. This development coincided with an increase 
in firms’ net demand for loans (see Chart 28). Banks 
also eased their terms and conditions on new loans 
across all categories in the second quarter, mainly 
driven by a further narrowing of margins on average 
loans. Overall, this suggests that the dampening effect 
of credit supply factors has receded further and that the 
impact of demand factors on NFC loan growth is turning 
slightly positive, which in turn will further support the 
growth of loans to the private sector. In this connection, 
the July survey indicates that in the second quarter 
banks used TLTRO funds to substitute for market-
based funding and to bolster credit supply.

Bank lending rates declined further in the second 
quarter, notably due to the ECB’s non-standard 
measures. Significant declines in the nominal cost of 
bank borrowing for NFCs and households have been 
recorded. In particular, since the third quarter of 2014, 
when the ECB stepped up its efforts in the context of 
further monetary policy accommodation, banks have 
progressively passed on the improvement in their 
funding costs in the form of lower bank lending rates: 
the composite costs of borrowing for households 
and non-financial corporations in the euro area have 
declined by around 70 basis points and 75 basis points 
respectively (see Charts 29 and 30). Recent data for 
July suggest that bank lending rates have reached 
historical lows.

Divergences in bank lending rates across countries 
are significant, but have narrowed further. Despite 
some encouraging developments in credit supply 
conditions for the euro area as a whole, credit 
standards remain heterogeneous across countries 
and sectors. In this respect, the credit easing package 
adopted in June 2014 and the APP have contributed 
to a narrowing of the cross-country dispersion of 

26

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 30
Composite indicator of the cost of borrowing 
for households for house purchase

Chart 31
MFI loans to non-financial corporations

(percentages per annum; three-month moving averages)

(annual rate of growth)

euro area
Germany
France
Italy
Spain
Netherlands
cross-country standard deviation (right-hand scale)

euro area
Germany
France
Italy
Spain
Netherlands
country dispersion

7

6

5

4

3

2

1

1.10

0.95

0.80

0.65

0.50

0.35

0.20

40

30

20

10

0

-10

-20

2008

2009

2010

2011

2012

2013

2014

2015

2008

2009

2010

2011

2012

2013

2014

2015

Source: ECB.
Notes: The indicator for the total cost of bank borrowing is calculated by aggregating 
short and long-term rates using a 24-month moving average of new business volumes. 
The cross-country standard deviation is calculated over a ﬁ xed sample of 12 euro area 
countries. Latest observation: July 2015.

Source: ECB.
Notes: Adjusted for loan sales and securitisation. The country dispersion 
is calculated as a minimum/maximum over a ﬁ xed sample of 12 euro area countries. 
Latest observation: July 2015.

Chart 32
MFI loans to households

(annual rate of growth)

euro area
Germany
France
Italy
Spain
Netherlands
country dispersion

30

20

10

0

-10

2008

2009

2010

2011

2012

2013

2014

Source: ECB.
Notes: Adjusted for loan sales and securitisation. The country dispersion is calculated 
as a minimum/maximum over a ﬁ xed sample of 12 euro area countries. Latest 
observation: July 2015.

borrowing costs. Those euro area countries displaying 
relatively low growth rates for loans to NFCs have also 
experienced particularly strong decreases in bank 
lending rates for such loans. 

The growth of loans to the private sector is 
recovering gradually. Adjusted for sales and 
securitisation, annual growth of MFI loans to the private 
sector increased to 1.4% in July 2015, up from 0.9% 
in the second quarter of 2015. Following an extended 
period of accelerating contraction, the annual growth 
of loans to NFCs has been gradually recovering since 
the beginning of 2014, reaching 0.9% in July 2015 
(see Chart 31). Despite these improvements, the 
dynamics of loans to NFCs remain subdued. The 
growth of loans to households improved further 
through the second quarter and stood at 1.9% in July 
(see Chart 32). As noted above, these developments 
have been supported by the significant decreases in 
bank lending rates which have been widespread in 
the euro area since summer 2014, as well as by an 
improvement in both the supply of and demand for 
bank loans. At the same time, the subdued economic 

27

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart 33
NFCs’ external financing in the euro area

(EUR billions; four-quarter flows)

total 
MFI loans 
quoted shares 
loans from rest of the world 
trade credit 
debt securities 
loans from non-MFIs 
unquoted shares 
other

1,500 

1,250 

1,000 

750 

500 

250 

0 

-250 

-500 

2008

2009

2010

conditions and relatively tight lending conditions still 
weigh on loan supply in some parts of the euro area. 

The overall annual flow of external financing to  
NFCs is estimated to have stabilised in the second 
quarter of 2015, after strengthening further in the 
previous quarter. NFCs’ external financing in the 
second quarter stood at levels similar to those observed 
in early 2012 (see Chart 33) and in 2004-05, before the 
strong credit growth took place. NFCs’ external financing 
was supported by the strengthening of economic activity, 
further declines in the cost of bank lending, the easing of 
bank lending conditions and the very low cost of market-
based debt. At the same time, related to low opportunity 
costs, NFCs, in particular listed firms, maintained 
historically high cash balances as precautionary liquidity 
buffers and possibly also to finance possible mergers 
and acquisitions in the future. 

Sources: Eurostat and ECB. 
Notes: MFI loans and loans from non-MFIs (includes other ﬁ nancial intermediaries and 
insurance corporations and pension funds) corrected for loans sales and securitisation. 
“Other” is the difference between the total and the instruments included in the chart and 
includes inter-company loans and the rebalancing between non-ﬁ nancial and ﬁ nancial 
accounts data. Latest observation: Q1 2015.

2011

2014

2012

2013

recent data show that net issuance of debt 
securities by NFCs moderated in may and June 2015. 
This development followed the surge in both the direct 
and indirect issuance of debt securities by NFCs and 
their conduits in the first quarter of 2015 after the launch 
of the public sector purchase programme (PSPP). Firms 
increased their recourse to market-based financing between January and April 2015 in 
order to benefit from the very favourable market conditions and low interest rates. 

The overall nominal cost of external financing for euro area NFCs increased 
slightly in the second quarter of 2015, after reaching historically low levels in 
February. This increase was driven by the decline in financial asset prices in the 
second quarter, resulting in an increase in the cost of both market-based debt and 
equity for euro area NFCs, which in July and August 2015 stood on average at around 
45 basis points higher than observed in February. The increase in the cost of market-
based financing was partly compensated for by the continued declines in bank lending 
rates in the second quarter of 2015.

28

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developments6 

Fiscal developments 

The improvement in the euro area fiscal balance is projected to continue, on account 
of the cyclical recovery and low interest rates. Based on the available information, 
however, the structural improvement is thought to have come to a halt. Looking 
ahead, additional consolidation efforts will be needed in many countries to set their 
high public debt ratio firmly on a downward path.

The average euro area fiscal deficit is projected to continue to decline. Based 
on the September 2015 ECB staff macroeconomic projections for the euro area, the 
general government deficit ratio for the euro area is expected to decline from 2.4%  
of GDP in 2014 to 1.7% of GDP in 2017 (see Table 1). The budgetary improvement 
is expected to stem entirely from the projected cyclical improvement and lower 
interest costs in the baseline scenario. Compared with the June 2015 projections, 
the outlook for the headline deficit is broadly unchanged for 2015, reflecting the  
fact that only a few new budgetary measures have been adopted in the meantime,  
while for the outer years the deficit improvement is projected to be more moderate, 
on account of a slightly less favourable economic recovery. 

Table 1
Fiscal developments in the euro area

(percentages of GDP)

a. Total revenue 

b. Total expenditure 

of which:

c. Interest expenditure 

d. Primary expenditure (b - c)

Budget balance (a - b)

Primary budget balance (a - d)

Cyclically adjusted budget balance

Structural balance

Gross debt

Memo item: real GDP (percentage changes)

2012

45.9

49.5

3.0

46.5

-3.6

-0.6

-3.5

-3.2

89.0

-0.7

2013

46.4

49.3

2.8

46.5

-2.9

-0.1

-2.2

-2.2

90.6

-0.2

2014

46.5

49.0

2.6

46.3

-2.4

0.2

-1.9

-1.8

91.7

0.9

2015

46.3

48.4

2.4

46.0

-2.1

0.3

-1.8

-1.8

91.6

1.4

2016

45.9

47.9

2.3

45.6

-2.0

0.3

-1.9

-1.8

90.7

1.7

2017

45.7

47.5

2.2

45.3

-1.7

0.5

-1.7

-1.7

89.4

1.8

Sources: Eurostat and September 2015 ECB staff macroeconomic projections.
Notes: The data refer to the aggregate general government sector of the euro area, including Lithuania (including the period before 2015). Owing to rounding, ﬁ gures may not add up. 

The structural balance is expected to remain broadly unchanged until 2017. 
This reflects improvements on the expenditure side, mainly in the form of lower 
interest expenditure, which are projected to be broadly offset by cuts in direct taxes 
and social contributions on the revenue side in several countries. The euro area 
fiscal deficit in structural terms is projected overall to decline marginally from 1.8%  
of GDP in 2014 to 1.7% of GDP in 2017, which is slightly above the level forecast  
in the June projections. 

government debt is projected to decline gradually over the projection horizon, 
albeit remaining at a high level. The euro area debt-to-GDP ratio is projected to 
decline from its peak of 91.7% of GDP in 2014 to reach 89.4% of GDP by the end of 
the projection horizon. The improvement in the debt outlook, though not happening 
as quickly as expected in the June projections, is mainly explained by favourable 
developments in the interest-growth differential and a gradually improving primary 

29

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsbalance. The deficit-debt adjustments are expected to turn favourable, particularly 
as the debt-increasing impact of financial sector support fades. Further details on 
the fiscal impact of, and fiscal risks related to, financial sector support are spelt out 
in the article entitled “The fiscal impact of financial sector support during the crisis” in 
this issue of the Economic Bulletin. As the projected debt level remains high in many 
euro area countries, further consolidation efforts are needed to set the debt ratio 
firmly on a downward path. This is all the more important in view of the substantial 
long-term challenges resulting from an ageing population and rising healthcare costs. 

The projected shortfall from structural efforts is expected to widen the gap 
with respect to the requirements of the Stability and growth Pact (SgP). The 
aggregate 2015 fiscal stance is expected to be broadly neutral. This is appropriate 
within the current weak economic environment, in which fiscal policies should 
support the economic recovery, while ensuring debt sustainability. In the country-
specific recommendations this June, the European Council, however, identified risks 
of non-compliance with the structural effort requirements of the SGP in 12 of the  
17 euro area countries under review (see Box 8). Therefore, it will be important that, 
in particular, those countries that still need to consolidate increase their emphasis 
on debt sustainability and achieve progress towards their medium-term budgetary 
objectives. The draft budgetary plans for 2016 should clarify how governments 
whose structural efforts fall short of their commitments under the SGP intend to 
follow up on the country-specific recommendations to ensure compliance with the 
EU’s fiscal rules through faster deficit reduction. 

30

ECB Economic Bulletin, Issue 6 / 2015 – Economic and monetary developmentsChart a  
Food commodity prices

(index: 1 January 2007=100)

food 
maize 
wheat 
soybeans 

300

250

200

150

100

50

0

2007

2008

2009

2010

Sources: Hamburg Institute of International Economics (HWWI) and Bloomberg.

Box 1  
Recent developments and outlook  
for non-oil commodity prices

global prices for non-oil commodities have come under scrutiny in recent 
months, as continuing downward pressures have resulted in lows not seen 
over the past five years. Despite broadly similar trends in commodity prices for 
food and metals, supply and demand factors specific to individual markets warrant  
a careful analysis of price developments for key commodities. This box discusses  
the main drivers of recent developments in, and the outlook for, the prices of selected  
non-oil commodities.

Food commodity prices have been declining since 
mid-2012 as a result of oversupplied markets on the 
back of consecutive good harvests. Ample supply 
and high inventories have contributed to a fall in  
wheat and maize prices, especially since the beginning 
of 2015 (see Chart A). The restrictions on wheat  
exports from Russia in the first half of 2015 did not 
counter this decline. Concerns about weakening 
demand for ethanol inputs and slowing Chinese import 
activity have also contributed to the decline in maize 
prices. Soybean prices have likewise fallen as a result  
of plentiful supply. 

2011

2012

The outlook for global food commodity prices 
remains subdued. Wheat and maize production  
levels are expected to decline only marginally in the 
2015-16 season, while the production of oilseeds 
(particularly soybeans) is expected to increase. Price 
risks on the upside could result from adverse weather 
conditions, while, on the downside, a stronger than 
expected slowdown in the production of biofuels could further weaken demand  
for some agricultural commodities.

2014 2015

2013

metal commodity prices have been declining since mid-2011 owing to 
continued supply increases and weakening demand growth, particularly  
in China. In comparison with food commodities, metals tend to be more sensitive to 
developments in global economic activity. As China imports a substantial proportion 
of global metal output, metal prices are particularly responsive to Chinese economic 
growth. Accordingly, market concerns about the strength of Chinese demand have 
accelerated the decline in metal prices since the beginning of 2015 (see Chart B). 
Ongoing increases in supply and high inventories also explain part of this fall.  
The continued growth in production is supported by large investments in production 
capacity made in previous years, which were motivated by high prices at the time. 

31

ECB Economic Bulletin, Issue 6 / 2015 – Box 1Chart B
Metal commodity prices

(index: 1 January 2007=100)

non-ferrous metals 
aluminum 
copper 
iron ore, scrap 

250 

200 

150 

100 

50 

0 

2007

2008

2009

2010

2011

2012

2013

2014 2015

Sources: Hamburg Institute of International Economics (HWWI) and Bloomberg.

The decline during 2015 has been broad-based across 
different metal commodities, with prices for iron ore (the 
main input for steel) declining by 26%, aluminium by 
13% and copper by 20% since the start of the year. 

oversupply and the slowdown in the growth of 
emerging market economies are likely to dampen 
metal prices in the short term. Growth in demand for 
commodities in China is expected to remain weaker 
than in the past, consistent with the gradual rebalancing 
of the country’s growth path. More generally, growth in 
emerging market economies, whose output tends to 
be more commodity-intensive than that of advanced 
economies, is slowing. The outlook for metal prices is 
therefore one of only gradual rises, as the supply side is 
expected to rebalance only slowly. The main downside 
risks relate to a sharper than expected slowdown in 
the demand growth of emerging market economies, 
particularly China, and a higher resilience of supply to 
declining prices. 

32

ECB Economic Bulletin, Issue 6 / 2015 – Box 1Box 2 
Liquidity conditions and monetary 
policy operations in the period 
from 22 April to 21 July 2015

This box describes the ECB’s monetary policy operations during the third 
and fourth reserve maintenance periods of 2015, which ran from 22 april 
to 9 June 2015 and from 10 June to 21 July 2015 respectively. During the period 
under review, the interest rates on the main refinancing operations (MROs), the 
marginal lending facility and the deposit facility remained unchanged at 0.05%, 
0.30% and -0.20% respectively.1 On 24 June 2015, the fourth targeted longer-term 
refinancing operation (TLTRO) was settled for an amount of €73.8 billion, slightly 
more than markets expected but below the €97.8 billion allotted in March. This 
brought the total allotment in the first four TLTROs to €384 billion.2 In addition, 
the Eurosystem continued buying public sector securities, covered bonds and  
asset-backed securities as part of its expanded asset purchase programme (APP) 
with a targeted rate of €60 billion per month.3

Liquidity needs

In the period under review, the average daily liquidity needs of the banking 
system, defined as the sum of autonomous factors and reserve requirements, 
increased by €24.6 billion compared with the previous review period to stand 
at €606.8 billion. The increase was explained mainly by higher average autonomous 
factors, which rose by €23.7 billion to €496.8 billion (see the table).

The increase in autonomous factors resulted mainly from an increase in 
liquidity-absorbing factors, including a significant change in the level of 
government deposits. The average level of government deposits increased 
by €19.5 billion to €85.6 billion. After the deposit facility rate was cut to -0.20% 
in September 2014, government deposits followed a downward trend, as 
the introduction of the negative deposit facility rate and the decision on the 
remuneration of government deposits4 gave national treasuries incentives to 

1  MROs continued to be conducted as fixed-rate tender procedures with full allotment. The same 
procedure remained in use for the three-month longer-term refinancing operations (LTROs). 
The interest rate in each LTRO was fixed at the average of the rates on the MROs over the LTRO’s 
lifetime.

2 

For information on the TLTRO allotments, see similar boxes in previous issues of the Bulletin  
or the ECB’s website: http://www.ecb.europa.eu/mopo/implement/omo/html/index.en.html.

3  Detailed information on the expanded APP is available on the ECB’s website: http://www.ecb.europa.

eu/mopo/implement/omt/html/index.en.html.

4 

 Available at: https://www.ecb.europa.eu/ecb/legal/pdf/oj_jol_2014_168_r_0015_en_txt.pdf.

33

ECB Economic Bulletin, Issue 6 / 2015 – Box 2reduce their cash holdings with the Eurosystem. However, the downward trend 
in government deposits halted in the second maintenance period of 2015, and 
deposits increased again in the third and fourth maintenance periods. Low market 
rates and abundant liquidity reduced the alternatives for treasuries to place cash. 
In addition, the average level of banknotes in circulation rose by €23.8 billion, 
reflecting a long-term upward trend in demand for banknotes and recent  
country-specific developments.

among the liquidity-providing factors, net foreign assets continued to rise 
in the period under review but were offset by changes in other autonomous 
factors. The depreciation of the euro at the beginning of 2015 led to a revaluation 
of net foreign assets as of the second quarter of 2015. On average over the two 
reserve maintenance periods, net foreign assets were €42.2 billion higher than in 
the first and second maintenance periods, at €649.8 billion. However, the effect was 
offset by an increase of €45.4 billion in other autonomous factors. Apart from this, 
revaluations in other financial assets of the Eurosystem were the main driver of a 
€22.9 billion increase in assets denominated in euro, which only partly offset the 
increase in liquidity-absorbing factors.

The volatility of autonomous factors remained elevated during the period 
under review. That primarily reflected strong fluctuations in government deposits 
and somewhat high levels of volatility in demand for banknotes. As a result of 
the quarterly revaluations at the end of June, net foreign assets and net assets 
denominated in euro declined, which added to the volatility, but by less than in the 
previous review period.

The average absolute error in weekly forecasts of autonomous factors 
increased slightly in the period under review, rising to €6.4 billion, mostly as a 
result of higher forecasting errors for government deposits. This shows that it 
remained difficult to anticipate the investment activities of treasuries in the presence 
of increasingly negative short-term money market rates and high levels of excess 
liquidity.

Liquidity provision

The average amount of liquidity provided through open market operations – 
tender operations and outright purchases – increased by €165.3 billion in the 
period under review, rising to €937.2 billion. This increase was driven almost 
entirely by outright purchases, while tender operations remained almost unchanged.

The average level of liquidity provided through tender operations increased 
only slightly, rising to €513.2 billion, albeit with significant substitution 
among the operations. The two three-year LTROs which matured in the previous 

34

ECB Economic Bulletin, Issue 6 / 2015 – Box 2review period and the reduced participation in regular operations (i.e. MROs and 
three-month LTROs) in this review period were, on average, compensated for by 
TLTRO allotments. While the average outstanding amount of TLTROs increased 
by €87.9 billion, the average for MROs decreased by €41.0 billion, the average for 
three-month LTROs decreased by €6.3 billion, and the average for the three-year 
LTROs (which stood at €38.6 billion in the previous review period) declined to zero.

average liquidity provided through outright portfolios increased by 
€163.4 billion to €424.0 billion owing to the implementation of the expanded 
aPP. The increases in the average liquidity provided by the public sector purchase 
programme, the third covered bond purchase programme and the asset-backed 
securities purchase programme (which rose by €135.4 billion, €31.7 billion and 
€3.7 billion respectively) more than offset the declines caused by the maturing of 
some bonds held under the Securities Markets Programme and the previous two 
covered bond purchase programmes.

Excess liquidity

Excess liquidity increased further during the review period, rising to an 
average of €329.2 billion, on account of the aPP and the TLTros. That increase 
was fairly evenly distributed across the two maintenance periods, with the respective 
increases being €73.0 billion and €79.2 billion. While the monthly APP purchases 
support a steady upward trend in excess liquidity, swings in autonomous factors may 
contribute to significant fluctuations around this trend within a maintenance period.

average daily current account holdings increased by €95.8 billion to 
€339.4 billion on account of the higher level of excess liquidity. Average use of 
the deposit facility also increased further, rising from €55.5 billion to €101.3 billion. 
Average recourse to the deposit facility increased only slightly as a percentage of 
excess liquidity, standing at 46%, compared with 42% in the previous review period.

Interest rate developments

reflecting the increase in excess liquidity and growing acceptance of trading 
at negative rates, money market rates decreased further in the period under 
review. The EONIA decreased to averages of -0.098% and -0.119% in the third and 
fourth maintenance periods respectively, compared with an average of -0.045% in 
the previous review period. In the secured segment, overnight rates decreased to 
levels close to the deposit facility rate. In particular, average overnight repo rates 

35

ECB Economic Bulletin, Issue 6 / 2015 – Box 2Eurosystem liquidity situation

22 april 

28 January 

maintenance 

maintenance 

to 21 July

to 21 april

period

period

Fourth 

Third 

Liabilities – liquidity needs (averages; EUr billions)

autonomous liquidity factors

1,690.3

(+88.7)

1,601.6

1,696.2

(+11.0) 1,685.2

(+58.0)

Banknotes in circulation

Government deposits

Other autonomous factors

monetary policy instruments

Current accounts

Minimum reserve requirements

Deposit facility

Liquidity-absorbing ﬁ ne-tuning operations

1,034.5

(+23.8)

1,010.7

1,042.7

(+15.3) 1,027.4

(+11.5)

85.6

(+19.5)

570.2

(+45.4)

339.4

(+95.8)

111.2

(+0.9)

101.3

(+45.8)

0.0

(+0.0)

66.1

524.8

243.6

109.1

55.5

0.0

96.3

(+19.8)

76.5

(+6.3)

557.1

(-24.2)

581.3

(+40.3)

381.4

(+78.0)

303.4

(+41.6)

112.3

103.1

0.0

(+1.9)

(+3.4)

(+0.0)

110.3

(-0.2)

99.7

(+31.1)

0.0

(+0.0)

autonomous liquidity factors

1,193.8

(+65.1)

1,128.7

1,183.2

(-19.8) 1,203.0

(+40.8)

assets – liquidity supply (averages; EUr billions)

Net foreign assets

Net assets denominated in euro

monetary policy instruments

Open market operations

Tender operations

MROs

Special-term reﬁ nancing operations

Three-month LTROs

Three-year LTROs

Targeted LTROs

Outright portfolios

First covered bond purchase programme

Second covered bond purchase programme

Third covered bond purchase programme

Securities Markets Programme

Asset-backed securities purchase programme

Public sector purchase programme

649.8

(+42.2)

544.1

(+22.9)

607.6

521.1

642.9

540.3

(-12.8)

(-7.0)

655.7

547.3

(+29.8)

(+11.0)

937.2

(+165.3)

513.2

(+2.0)

89.7

0.0

90.5

0.0

(-41.0)

(+0.0)

(-6.3)

(-38.6)

333.0

(+87.9)

424.0

(+163.4)

24.4

11.1

87.1

136.6

7.5

(-2.1)

(-0.8)

(+31.7)

(-4.5)

(+3.7)

157.4

(+135.4)

0.2

(-0.1)

771.9

511.2

130.7

0.0

96.9

38.6

245.1

260.7

26.5

11.9

55.4

997.5

(+111.9)

525.7

(+23.2)

82.4

(-13.4)

0.0

(+0.0)

83.8

(-12.6)

0.0

(+0.0)

359.5

(+49.2)

471.8

(+88.6)

23.3

10.8

95.1

(-2.0)

(-0.5)

(+14.9)

885.6

502.5

95.9

0.0

96.3

0.0

310.3

383.1

25.3

11.3

80.2

141.1

134.6

(-3.7)

138.3

8.8

(+2.4)

6.4

(+90.0)

(-2.4)

(-23.0)

(+0.0)

(-12.1)

(+0.0)

(+32.6)

(+92.6)

(-0.7)

(-0.2)

(+16.7)

(-2.5)

(+1.7)

199.1

(+77.5)

121.6

(+77.6)

0.3

(+0.2)

0.1

(-0.1)

3.8

22.0

0.3

Marginal lending facility

Aggregate liquidity needs

Autonomous factors

Excess liquidity

MROs

Marginal lending facility

Deposit facility

EONIA average

other liquidity-based information (averages; EUr billions)

606.8

(+24.6)

496.8

(+23.7)

329.2

(+139.5)

582.2

473.1

189.7

622.9

(+29.9)

513.3

(+30.7)

371.9

(+79.2)

593.0

482.6

292.7

(+17.2)

(+17.4)

(+73.0)

Interest rate developments (percentages)

0.05

0.30

(+0.00)

(+0.00)

-0.20

(+0.00)

0.05

0.30

-0.20

0.05

0.30

(+0.00)

(+0.00)

0.05

0.30

(+0.00)

(+0.00)

-0.20

(+0.00)

-0.20

(+0.00)

-0.107

(-0.063)

-0.045

-0.119

(-0.021)

-0.098

(-0.037)

Source: ECB.
Note: Since all ﬁ gures in the table are rounded, in some cases the ﬁ gure indicated as the change relative to the previous period does 
not represent the difference between the rounded ﬁ gures provided for these periods (differing by €0.1 billion).

in the GC Pooling5 market declined to -0.18% and -0.17% for the standard and 
extended collateral baskets respectively, down 5 and 7 basis points compared with 
the previous review period.

5 

The GC Pooling market allows repurchase agreements to be traded on the Eurex platform 
against standardised baskets of collateral.

36

ECB Economic Bulletin, Issue 6 / 2015 – Box 2Box 3 
The usefulness of TARGET2 transaction 
data for the analysis of the unsecured 
overnight money market

Despite the importance of money markets, granular information on 
transactions is generally not readily available. Overnight transactions mainly take 
place over the counter, and when recording these, the few trading platforms typically 
focus on certain jurisdictions. For the euro area, daily information on unsecured 
overnight lending is collected for a panel of banks, and the weighted average  
of their rate contributions gives rise to the euro overnight index average (EONIA)  
as the reference rate for the overnight unsecured segment. However, the panel bank 
contributions are not at the level of individual transactions, but daily aggregates  
of their lending activity. As of mid-2016, money market transaction data will be 
collected for the euro area under the Money Market Statistical Reporting Regulation, 
under which, initially, 53 banks will report.1

TargET2 (the Trans-European automated real-time gross settlement Express 
Transfer system) offers an unparalleled source of granular overnight money 
market information. While information on overnight unsecured loans in TARGET2 
is not directly available, it can be accessed by screening the set of transactions that 
occur through the payment system for the settlement of the two legs of an interbank 
loan.2 This method has long been used worldwide and allows for the reconstructing of 
significant parts of the unsecured overnight money market activity.

The large coverage of banks in TargET2 provides a comprehensive picture 
of the unsecured overnight money market. A key benefit of using TARGET2 data 
for analysis is the high number of banks (around 1,000) participating in the payment 
system.3 Although a small fraction of overnight market trading settles privately outside 
TARGET2, TARGET2 data still provide a close representation of the euro area 
overnight market. This is evidenced by the fact that the total lending as measured 
by TARGET2 data for the second quarter of 2014 is broadly similar to that resulting 
from the Euro Money Market Survey (€2.0 trillion),4 which captures trading outside 
TARGET2, but covers a much lower number of banks (154).

1 

2 

3 

4 

See Regulation (EU) No 1333/2014 of the European Central Bank of 26 November 2014 concerning 
statistics on the money markets (ECB/2014/48): https://www.ecb.europa.eu/ecb/legal/pdf/oj_
jol_2014_359_r_0006_en_txt.pdf

For more information, please see the box entitled “Using TARGET2 payment data to analyse money 
market conditions”, Monthly Bulletin, ECB, May 2013 or the report on the Macro-prudential Research 
Network (MaRs): https://www.ecb.europa.eu/press/pr/date/2014/html/pr140623.en.html

Furthermore, each transaction in TARGET2 contains the same fields and information, allowing 
comparability. See the TARGET Annual Report 2014: https://www.ecb.europa.eu/press/pr/date/2015/
html/pr150601.en.html

See the Euro Money Market Survey and Study: https://www.ecb.europa.eu/stats/money/mmss/html/
index.en.html

37

ECB Economic Bulletin, Issue 6 / 2015 – Box 3aggregate statistics for the overnight unsecured money market based on 
TargET2 data confirm some well-known crisis-related developments. For 
instance, the overnight unsecured money market shrank from a peak  
of €2.5 trillion per reserve maintenance period in mid-2008 to slightly over €0.5 trillion 
by the time of the settlement of the second three-year longer-term refinancing 
operation in March 2012. The total number of banks active in the overnight 
unsecured market in a given reserve maintenance period dropped from a peak of 
around 600 in August 2008 to around 330 by June 2015 (see Chart A). In parallel, 
money market stress became apparent from rate developments. For example, the 
average spread of the interest rate paid for overnight market funds over the deposit 
facility rate varied significantly over time. After falling towards the deposit facility rate 
with the introduction of the fixed rate full allotment procedure and the associated 
increase in excess liquidity, the spread occasionally reached high levels, especially 
during the euro area sovereign debt crisis that started in spring 2010 (see Chart B). 

Chart a
Overnight unsecured money market volume 
and number of banks

Chart B
Weighted average overnight spread

(EUR billions; number of banks)

(basis points)

volume in MP (left-hand scale)
number of banks in MP (right-hand scale)

maintenance period weighted rate

800

150

FRFA
Oct.
2008

Sov. debt
crisis
May 2010

3-year LTRO
Dec. 2011

Extended APP
announcement
Jan. 2015

700

600

500

400

300

200

100

50

0

-20

3,000

2,500

2,000

1,500

1,000

500

0

2008

2009

2010

2011

2012

2013

2014

2015

2008

2009

2010

2011

2012

2013

2014

2015

Sources: TARGET2 and ECB calculations.
Note: Figures are computed at maintenance period (MP) level. 

Sources: TARGET2 and ECB calculations.
Notes: Figures are computed at maintenance period level. The overnight spread 
is over the deposit facility rate. FRFA = ﬁ xed rate full allotment. APP = asset purchase 
programme.

money market data derived from TargET2 transactions also provide 
information on the dispersion of rates and volumes across banks. Chart C 
presents volume-weighted kernel densities of the spread of overnight rates over 
the deposit facility rate in selected periods. In mid-2008, the bulk of trading took 
place at interest rates close to the main refinancing operation rate (i.e. at a spread 
of 100 basis points) under the variable rate tender procedure, and in 2011 and 2014 
at rates closer to the deposit facility rate, with excess liquidity resulting from the full 
allotment procedure. However, the densities also reveal considerable dispersion of 

38

ECB Economic Bulletin, Issue 6 / 2015 – Box 3Chart C
Distribution of overnight spread in selected periods

Chart D
Overnight borrowing volume by credit risk group

(basis points)

(EUR billions)

x-axis: overnight spread vs. deposit facility rate
y-axis: density

2014 MP1
2011 MP11
2008 MP6

Group 1
Group 2
Group 3

Group 4
Group 5
Group 6

0.08

0.06

0.04

0.02

0.00

0

2,000

1,500

1,000

500

0

25

50

75

100

125

150

2008

2009

2010

2011

2012

2013

2014

Sources: TARGET2 and ECB calculations.
Notes: Figures are computed at maintenance period (MP) level. The overnight spread 
is over the deposit facility rate.

Sources: TARGET2, DBRS, Fitch Ratings, Moody’s, Standard & Poor’s and ECB 
calculations.
Note: Figures are computed at maintenance period level. 

interest rates at different points in time, with a particularly large tail towards higher 
spreads at the end of 2011 when the sovereign debt crisis reached its height.5 

Individual bank transaction data can be matched with characteristics of the 
trading banks, such as their size, geographical location or credit risk, to obtain 
a better understanding of developments in the overnight unsecured market.  
To explain the dispersion in rates, TARGET2 data are matched with the credit ratings 
of the trading banks. For this purpose, the ratings by four agencies6 are grouped into 
six credit risk groups from the lowest risk group (1) to the highest risk group (6), as 
presented in the table. Both borrowing and lending banks are assigned to credit risk 
groups.7

The credit rating data matching indicates that trading volume is largely 
determined by the credit risk of the borrowing banks. Chart D presents a 
breakdown of the total borrowing volume by credit risk group.8 Banks with the highest 
credit standing (Group 1) strongly reduced their borrowing by end-2012, for at least 
two important reasons. First, this group of non-stressed banks is known to have 

5 

As the densities are volume-weighted and trades at higher spreads are relatively small in volume, the 
density does not capture the full extent to which rates are dispersed.

6  DBRS, Fitch Ratings, Moody’s and Standard & Poor’s are the four external credit assessment 

institutions (ECAIs) accepted by the Eurosystem.

7 

8 

The assignment to a specific credit risk group is based on the availability of at least one longer-term 
rating. When two or more ratings are available, the group is determined by the average of the ratings 
after they have been converted into a numeric scale. The number of banks represented in each group 
is not homogeneous, as it reflects the representativeness of each group in the euro area. Over time, 
banks may change group owing to rating migration. See also the table. 

The total share of overall volume for banks not included in the sample is around 25%. This share stays 
relatively constant over time, therefore not biasing the sample.

39

ECB Economic Bulletin, Issue 6 / 2015 – Box 3accumulated a lot of excess liquidity and therefore had smaller borrowing needs 
despite market access. Second, a significant portion of banks were downgraded 
during the financial crisis, potentially moving the banks and their lending volume 
to a new credit risk group. However, credit risk group migration could not, by itself, 
explain the reduction in total volume for all banks. Furthermore, banks with a lower 
rating (Groups 2 to 3) kept on borrowing contained amounts throughout the period. 
Considering the higher liquidity needs of such banks, these limited amounts  
reflect the fact that banks can also seek funds elsewhere, i.e. at a longer maturity, 
in the secured segment of the market, from non-bank counterparties or from the 
Eurosystem. The fact that the secured market has gained importance in recent years 
can also be partly attributed to a substitution of unsecured trading.9 

The dispersion of interest rates is also determined by counterparty credit 
risk, with spreads across bank credit rating groups varying over time. Chart E 
presents weighted average borrowing rates per credit risk group. Lower-rated banks 
generally pay higher interest rates, which explain part of the rate dispersion observed 
in Chart C. However, Chart E also shows that there was little dispersion across the 
average borrowing rates of credit risk groups during 2008-10 and 2014-15. This 
indicates how banks with limited market access do not influence overall unsecured 
money market rates, as they often need to obtain liquidity from other sources, as 
mentioned above. It was only as of the end of 2011 and into 2012 that considerable 
rate differentials across credit risk groups emerged, but against small volumes for the 
more risky borrowers. Overall, only banks of a certain perceived quality could obtain 
funds in the unsecured interbank market.

Chart E
Overnight borrowing spread by credit risk group

(basis points)

Group 1
Group 2
Group 3

Group 4
Group 5
Group 6

FRFA
Oct. 2008

Sov. debt crisis
May 2010

3-year LTRO
Dec. 2011

Extended APP
announcement
Jan. 2015

150

100

50

0

2008

2009

2010

2011

2012

2013

2014

2015

Sources: TARGET2, DBRS, Fitch Ratings, Moody’s, Standard & Poor’s and ECB calculations.
Notes: Figures are computed at maintenance period level. The overnight spread is over the deposit facility rate. FRFA = ﬁ xed rate full 
allotment. APP = asset purchase programme.

9 

See the Euro Money Market Survey and Study: https://www.ecb.europa.eu/stats/money/mmss/html/
index.en.html 

40

ECB Economic Bulletin, Issue 6 / 2015 – Box 3Chart F 
Overnight lending and borrowing volume by credit risk group

6

5

4

3

2

1

e
d
a
r
g

t
n
e
m
t
s
e
v
n
i
-
n
o
N

e
d
a
r
g

t
n
e
m
t
s
e
v
n
I

Borrowers by
credit risk
group 

Group 1
Group 2
Group 3

Group 4

Group 5

Group 6

Size daily
average volumes

€25 bn

€10 bn 

€1 bn 

2008 (MP6) 

2011 (MP11) 

2014 (MP1) 

Sources: TARGET2, DBRS, Fitch Ratings, Moody’s, Standard & Poor’s and ECB calculations.
Notes: The size of the bubble represents average daily lending volumes. The shares within the bubbles represent the borrowing per 
credit risk group. The ﬁ gures are based on transactions by counterparties identiﬁ ed in the TARGET2 database and having at least 
one public credit rating assessment. 

Definition of credit risk groups

Range of classes by credit rating agency (long-term)

DBRS

Fitch Ratings

Moody’s

Standard & Poor’s

Grade

Group 1

AAA to AA (low)

AAA to AA-

Aaa to Aa3

AAA to AA-

Group 2

A (high) to A (low)

A+ to A-

A1 to A3

A+ to A-

Group 3

BBB (high) to BBB (low)

BBB+ to BBB-

Baa1 to Baa3

BBB+ to BBB-

Group 4

BB (high) to BB (low)

BB+ to BB-

Ba1 to Ba3

BB+ to BB-

Group 5

B (high) to B (low)

B+ to B-

B1 to B3

B+ to B-

Group 6

CCC (high) to D

CCC+ to D

Caa1 to D

CCC+ to D

T
N
E
M
T
S
E
V
N

I

-

N
O
N

T
N
E
M
T
S
E
V
N

I

Eurosystem 
harmonised 
rating scale 
for ECAIs

CQS1

CQS2

CQS3

Sources: TARGET2, DBRS, Fitch Ratings, Moody’s, Standard & Poor’s and ECB calculations.
Notes: CQS (credit quality step) refers to the Eurosystem harmonised rating scale for ECAIs (external credit assessment institutions). 
See the box entitled “Eurosystem credit assessment framework for monetary policy operations”, Monthly Bulletin, ECB, April 2014.

Investment grade banks dominate overnight market lending. Chart F provides 
a who-to-whom breakdown of the overnight lending and borrowing volumes per 
credit risk group, which in comparison with Chart D adds information on the source 
of the funds in three maintenance periods. It shows that the bulk of trading took 
place among banks with the highest rating (Group 1) in 2008, but that this volume 
declined after 2008 as demand from those banks evaporated amid high levels 
of excess liquidity. However, the bulk of the supply remained in the hands of the 
investment grade banks, who lent contained amounts to a variety of lower-rated 
banks in 2011 and 2014.

41

ECB Economic Bulletin, Issue 6 / 2015 – Box 3 
 
Box 4  
Publication of TARGET balances

The ECB will, as of this month, publish data on the individual TargET 
balances of the euro area national central banks (NCBs) on a monthly basis.1 
These data will be included in the ECB’s statistical data warehouse (SDW) under 
the framework of the monetary financial institution balance sheet statistics.2 The 
publication of individual TARGET balances is part of the ECB’s commitment to 
transparency. This Box briefly recalls what TARGET balances are and the factors 
behind their evolution.3

TargET balances are the net claims and liabilities of the euro area NCBs 
vis-à-vis the ECB which arise through cross-border payments settled in 
central bank money of the respective national banking sectors or the NCBs 
themselves and are executed via the common euro area payment platform 
known as TargET.4 When a bank makes a payment to another bank via TARGET, 
the current account of the payer’s bank at its NCB is debited and the current account 
of the recipient bank at its NCB is credited. If both banks hold their current accounts 
at the same NCB there is no net impact on the aggregate account of banks at the 
NCB and there are no implications for TARGET balances. However, in the case 
of cross-border transactions, the NCB of the paying bank sees a reduction in that 
bank’s account at the NCB, and the NCB of the recipient bank sees an increase 
in the recipient bank’s account. Such positions are balanced by a TARGET liability 
for the first NCB and a TARGET claim for the second NCB. TARGET liabilities and 
claims also result from cross-border transactions by NCBs themselves, such as 
the purchase or sale of securities held for investment purposes. At the end of each 
day, such intra-Eurosystem claims and liabilities are aggregated and netted out 
throughout the Eurosystem. This leaves each NCB with a single net bilateral position 
vis-à-vis the ECB, in the form of a positive or negative TARGET balance. By design, 
all the TARGET balances (including the ECB’s balance) add up to zero.5

These TargET balances constitute a normal feature of the decentralised 
implementation of monetary policy in the euro area. They reflect cross-border  

1 

TARGET  stands  for  Trans-European  Automated  Real-time  Gross  Settlement  Express  Transfer 
System.  The  current  system  is  called  TARGET2.  It  fully  replaced  TARGET  in  May  2008,  but  for 
convenience both TARGET and TARGET2 are referred to in this box as “TARGET”.

2  Data  are  published  on  the  first  working  day  of  every  month,  with  a  one  month  time  lag.  For  more 
details,  see  the  ECB’s  website  under  “Statistics  >  Monetary  and  financial  statistics  >  TARGET 
balances of participating NCBs”.

3 

4 

5 

For more details, see the article entitled “TARGET balances and monetary policy operations” in the 
Monthly Bulletin, Issue 5, ECB, May 2013.

A few non-euro area NCBs in the European Union have joined TARGET on a voluntary basis. This 
article focuses on the euro area countries.

The  ECB’s  TARGET  balance  vis-a-vis  the  NCBs  as  depicted  in  the  chart  reflects  the  net  result  of 
claims  and  liabilities  stemming  from  activities  carried  out  directly  by  the  ECB,  for  instance  in  the 
context of the US dollar-euro liquidity swap arrangements.

42

ECB Economic Bulletin, Issue 6 / 2015 – Box 4Chart
TARGET balances

(EUR billions)

a)

ECB
DE

LU
NL

FI
BE

IE

800

700

600

500

400

300

200

100

0

-100

-200

May
2008

May
2009

May
2010

May
2011

May
2012

May
2013

May
2014

May
2015

ECB
GR

AT
IT

PT
ES

FR

b)

300

200

100

0

-100

-200

-300

-400

-500

May
2008

May
2009

May
2010

May
2011

May
2012

May
2013

May
2014

May
2015

ECB (right-hand scale)
EE
LV

LT
SI
CY

SK
MT
Extra euro area NCBs

c)

40

30

20

10

0

-10

-20

-30

May
2008

May
2009

May
2010

May
2011

May
2012

May
2013

May
2014

May
2015

Source: ECB.
Notes: “Extra euro area NCBs” represents the claims of those non-euro area EU NCBs 
that participate in TARGET on a voluntary basis. The NCBs included in this group change 
over time. As of January 2015, it refers to Bulgaria, Denmark, Poland and Romania.

financial flows within the euro area, which arise from 
cross-border financial transactions largely initiated by 
private entities such as credit institutions, corporates or 
individuals and the resultant cross-border distribution 
of central bank liquidity. Individual TARGET claims and 
liabilities of the NCBs vis-à-vis the ECB and of the ECB 
vis-à-vis the NCBs have therefore existed since the 
start of Economic and Monetary Union. In fact, the sum 
of all claims on the balance sheets of euro area NCBs 
stood at around EUR100 billion prior to mid-2007.

The emergence of large TargET balances during 
the crisis broadly reflects the distribution of non-
cash central bank liquidity within the Eurosystem. 
The Eurosystem implements its monetary policy 
in a decentralised manner, whereby the aggregate 
Eurosystem liquidity provision via its NCBs corresponds 
to the aggregate liquidity needs of the euro area 
banking sector. Liquidity can be redistributed across 
banking sectors through the euro area interbank 
market. During the crisis, the liquidity needs of euro 
area banks increased significantly, with substantial 
dispersion across countries. At the same time, interbank 
markets dried up, which prevented the distribution of 
liquidity via private markets. From the start of the crisis, 
the Eurosystem accommodated the euro area banking 
sector’s liquidity needs, providing ample extra liquidity 
through its refinancing operations. As of October 2008, 
the Eurosystem has fully satisfied banks’ demand for 
central bank liquidity in fixed-rate full-allotment tenders 
subject to the availability of eligible collateral. 

The significant increase in the recourse to central 
bank funding during the crisis, and its uneven 
distribution across countries, were associated 
with a corresponding rise in TargET claims and 
liabilities. These increased until the end of 2012 
(see chart) as a result of banks in some countries 
facing net payment outflows in conjunction with 
reduced access to short-term funding markets while 
banks in other countries benefited from large financial 
inflows. Subsequently, as market financing conditions 
gradually improved and banks’ use of Eurosystem 
refinancing operations declined, TARGET balances 
generally decreased in absolute terms. They widened 
again somewhat in late 2014. This in part reflected 
the relatively higher participation of banks in some  
countries with TARGET liabilities in the Targeted  
Longer-Term Refinancing Operations (TLTROs) 

43

250

200

150

100

50

0

-50

-100

ECB Economic Bulletin, Issue 6 / 2015 – Box 4launched in June 2014, as these operations tended to be more attractive for such 
counterparties. The expanded asset purchase programme (APP) that started in 
March 2015 may affect TARGET balances when the buying and selling parties are 
operating in different jurisdictions, which might explain more recent developments. 

Interpreting TargET balances within an integrated financial system like the 
euro area requires caution. For instance, these balances also reflect money 
transfers within large, cross-border banking groups where the central bank money 
needed by the group is procured centrally at one NCB and then redistributed among 
group members via TARGET. These balances also reflect payment flows caused by 
remote participants.6 Other factors that highlight the need for caution include differing 
preferences between euro area countries regarding holding banknotes and access 
to the Eurosystem monetary policy instruments of banks outside the European 
Economic Area via subsidiaries in a country connected to TARGET.7 In addition, 
cross-country purchases of securities in the context of the APP may affect TARGET 
balances but do not indicate financial stress. Thus, TARGET balances do not, and 
are not meant to, provide a complete picture of the net financial flows between 
countries.

6 

An institution established in a country in the European Economic Area but which is not participating in 
TARGET (e.g. a bank located in the United Kingdom) may open an account at another NCB of its choice 
participating in TARGET.

7  More information on this is provided in the box entitled “TARGET2 balances of national central banks in 

the euro area”, Monthly Bulletin, Issue 10, ECB, October 2011. 

44

ECB Economic Bulletin, Issue 6 / 2015 – Box 4Box 5 
Revised trade weights for the effective 
exchange rates of the euro reflect the  
increasing importance of emerging  
market economies

The ECB has updated recently the trade weights underlying the calculation of 
the effective exchange rates (EErs) of the euro. This is carried out every three 
years to capture medium-term changes in the pattern of euro area manufacturing 
trade in a timely fashion.1 In the most recent exercise, carried out in August 2015, 
average trade weights for the three-year period from 2010 to 2012 were added to the 
series, while weights for previous time periods (from 1995 to 2009) were updated to 
reflect revised trade data.

Chart a
Evolution of trade weights of the EER-38 group 
of trading partners

(percentages)

China 
United States 
United Kingdom 
other advanced European economies
other advanced economies
other emerging market economies
CEE countries

25

20

15

10

5

0

1995-97

1998-00

2001-03

2004-06

2007-09

2010-12

Source: ECB.
Notes: The item “other advanced European economies” comprises Denmark, Norway, 
Sweden and Switzerland; “other advanced economies” comprises Australia, Canada, 
Hong Kong, Iceland, Israel, Japan, Korea, New Zealand, Singapore and Taiwan; “CEE 
countries” comprises Bulgaria, Croatia, the Czech Republic, Hungary, Poland and 
Romania; and “other emerging market economies” comprises Algeria, Argentina, Brazil, 
Chile, India, Indonesia, Malaysia, Mexico, Morocco, Philippines, South Africa, Russia, 
Thailand, Turkey and Venezuela.

The updated and revised series shows a continued 
rise in the importance of emerging market 
economies, in particular China, as trading partners 
of the euro area (see Chart a and the table). The 
weight of China in a basket of 38 of the euro area’s 
most important trading partners (the EER-38 group), 
which stood at around 4% in the period 1995-97, rose 
further from about 15% in the period 2007-09 to around 
18% in the period 2010-12. The importance of other 
emerging economies as trading partners of the euro 
area also increased – albeit at a more moderate pace – 
with Turkey, Russia and Indonesia recording the largest 
gains. Conversely, the trade weights of advanced 
economies in the EER-38 group declined over the same 
period. In 1995 the two largest trading partners  
of the euro area were the United Kingdom and the 
United States, with trade weights of around 18% and 
17% respectively. In 2012 their shares had declined  
to about 10% and 13% respectively. 

The importance of central and eastern Europe 
(CEE) in euro area trade has also increased, in 
line with growing economic integration in Europe. 
Since the period 1995-97 the combined weight of CEE 

1 

For an overview of the methodology used to calculate the euro EERs, see Schmitz, M., De Clercq, M., 
Fidora, M., Lauro, B. and Pinheiro, C., “Revisiting the effective exchange rates of the euro”, Occasional 
Paper Series, No 134, ECB, 2012.

45

ECB Economic Bulletin, Issue 6 / 2015 – Box 5Trade weights for the EEr-38 group of trading partners

(percentages)

Country

China

United States

United Kingdom

Switzerland

Japan

Poland

Czech Republic

Sweden

Russia

Turkey

Korea

Indonesia

Hungary

Denmark

Romania

Taiwan

Brazil

Hong Kong

Mexico

Singapore

Canada

Thailand

Malaysia

South Africa

Norway

Australia

India

Israel

Morocco

Bulgaria

Chile

Algeria

Argentina

Croatia

Philippines

Venezuela

New Zealand

Iceland

Total

1995-97

1998-00

2001-03

2004-06

2007-09

2010-12

Change 2010-12 
versus 1995-97

4.4

16.8

18.4

5.3

19.4

17.7

7.6

18.7

16.6

11.2

15.5

14.3

14.8

13.5

12.0

17.7

12.7

10.3

6.7

9.6

2.4

2.2

4.8

2.5

2.2

2.9

1.5

1.5

2.6

0.7

2.3

1.5

2.0

0.9

1.8

1.5

1.2

1.2

0.9

1.3

0.8

1.0

1.1

0.6

0.3

0.3

0.3

0.6

0.5

0.4

0.2

0.1

0.0

6.0

8.8

2.8

2.4

4.4

1.8

2.2

2.7

1.3

2.2

2.3

0.8

2.4

1.4

1.7

1.2

1.6

1.6

1.0

1.2

0.9

1.2

0.7

0.8

1.0

0.6

0.3

0.3

0.3

0.5

0.4

0.5

0.2

0.1

0.1

5.8

7.6

3.3

3.0

3.9

2.2

2.3

2.8

1.5

2.5

2.3

1.1

2.1

1.2

1.5

1.3

1.4

1.6

1.0

1.2

0.9

1.0

0.7

0.7

0.9

0.6

0.4

0.3

0.3

0.3

0.5

0.5

0.2

0.1

0.1

5.4

6.7

4.0

3.4

4.1

3.0

3.0

3.2

1.8

2.6

2.2

1.4

1.8

1.2

1.5

1.2

1.4

1.4

1.0

1.1

1.0

1.1

0.7

0.6

0.7

0.6

0.4

0.4

0.3

0.3

0.5

0.4

0.2

0.1

0.1

5.3

5.7

5.0

4.1

3.9

3.5

3.1

3.2

2.1

2.5

2.1

1.6

1.5

1.4

1.3

1.2

1.2

1.3

1.1

1.0

0.9

1.1

0.7

0.6

0.7

0.6

0.5

0.4

0.4

0.3

0.5

0.3

0.2

0.1

0.1

5.5

5.3

5.1

4.1

3.6

3.5

3.3

3.2

2.4

2.3

1.7

1.7

1.5

1.4

1.4

1.3

1.3

1.2

1.1

1.0

1.0

1.0

0.8

0.7

0.7

0.6

0.5

0.4

0.4

0.4

0.4

0.3

0.2

0.1

0.1

100.0

100.0

100.0

100.0

100.0

100.0

13.2

-4.1

-8.1

-1.2

-4.3

2.7

1.9

-1.2

1.0

1.1

0.3

1.0

0.7

-0.9

1.0

-0.8

-0.1

-0.6

0.5

-0.6

-0.3

-0.1

-0.2

0.0

-0.3

0.0

-0.3

-0.4

0.0

0.2

0.1

0.1

-0.2

-0.2

-0.1

-0.1

0.0

0.0

0.0

Source: ECB 
Note: Countries are listed in the order of their trade weights for the period 2010-12.

economies has risen above that of, for example, the United States and the  
United Kingdom, although it remained stable in the 2010-12 reference period,  
at 14% of the EER-38 group, reflecting the downturn in intra-European trade 
over that period. In particular, CEE countries had a weight of 18% in euro area 
manufacturing imports, which was second only to that of China and reflects their 
crucial role in the pan-European contribution to global value chains.

China’s position as the euro area’s largest trading partner mainly results from 
its importance in imports and third-market competition (see Chart B). China has 
become the main source of euro area manufacturing imports, with a share of 21% 
over the period 2010-12. On the export side, to also account for competition faced by 
euro area companies in foreign markets from exporters based in third countries,  

46

ECB Economic Bulletin, Issue 6 / 2015 – Box 5Chart B
Comparison of trade weights of the EER-38 group of trading partners, 2010-12

(percentages)

total weight
import weight
simple export weight
double export weight

25

20

15

10

5

0

China

United States

United Kingdom

other advanced 
European economies

other advanced 
economies

other emerging 
market economies

CEE countries

Source: ECB.
Notes: The simple export weights are the shares of each country or group of countries in euro area manufacturing exports. Double export weights capture the competition faced by 
euro area exporters in foreign markets from domestic producers and exporters from third countries.

the overall trade weights are adjusted through “double-weighting”. In particular, 
owing to the importance of China, as well as some other advanced and emerging 
market economies, as competitors of euro area exporters, this adjustment results 
in a significant increase in their trade weights beyond the levels implied by direct 
export linkages. In the case of China, the “double” export weight amounted to 16%, 
compared with a simple export weight of 7%, over the period 2010-12. 

Using the updated weighting scheme, it emerges that the depreciation of 
the euro – in both nominal and real effective terms – since 2010 was slightly 
more pronounced than previously indicated. Between the beginning of 2010 
and the end of July 2015 the updated daily nominal EER of the euro vis-à-vis the 
EER-38 group of trading partners depreciated by 12.0%, compared with 11.6% 
based on the previous indicator. The revision was primarily due to the increased 
trade weight of the Chinese renminbi (see Chart C). The improvement in euro area 
price competitiveness since early 2010, as reflected in the real depreciation of the 
euro, was also slightly more sizeable according to the updated indicators. From the 
beginning of 2010 to June 2015 the updated CPI-deflated real EER-38 declined by 
16.1%, whereas the previous index decreased by 15.3% (see Chart D). 

47

ECB Economic Bulletin, Issue 6 / 2015 – Box 5Chart C
Contributions to the change in the nominal EER-38 
since 2010

Chart D
Previous and revised real EER-38

(percentages; percentage points)

(CPI-deflated; index: Q1 1999 = 100)

change (revised weights)
change (previous weights)
contribution (revised weights)
contribution (previous weights)

revised real EER-38 
previous real EER-38 

4

2

0

-2

-4

-6

-8

-10

-12

-14

106

104

102

100

98

96

94

92

90

88

86

84

1

2

3

4

5

6

7

8

9

2010 

2011 

2012 

2013 

2014 

2015 

1 Chinese renminbi
2 US dollar
3 pound sterling

4 Swiss franc
5 Japanese yen
6 Russian rouble

7 Swedish krona
8 Turkish lira
9 nominal EER-38

Source: ECB.
Notes: A negative value indicates a depreciation of the euro, while a positive value 
indicates an appreciation of the single currency. The chart covers the period from 
1 January 2010 to 31 July 2015.

Source: ECB.
Note: A downward movement indicates a depreciation of the euro, while 
an upward movement indicates an appreciation of the single currency.

48

ECB Economic Bulletin, Issue 6 / 2015 – Box 5Box 6 
A survey-based measure of slack for  
the euro area

measures of economic slack, such as the output gap, are an important element 
of economic policy analysis, as they represent the interaction between 
demand and supply. However, slack is unobserved and has to be estimated. 
Indeed, estimates of slack are very uncertain, tend to be revised and, therefore, need 
to be interpreted with caution.1 This box presents a tool that draws on information 
about demand from available survey indicators, in order to estimate slack in the 
economy. The measure described tends to be revised less frequently, and can thus 
complement output gap estimates of structural models.

Chart a
Estimates of the euro area output gap

(percentage shares of potential output)

European Commission
IMF
OECD

4

3

2

1

0

-1

-2

-3

-4
1999

2002

2005

2008

2011

2014

Sources: European Commission Spring 2015 Economic Forecast, IMF World Economic 
Outlook April 2015 and OECD Economic Outlook May 2015.

Several methods exist to assess the degree of 
slack in the economy. Estimates of slack based on a 
production function have the advantage that they are 
consistent with economic theory and are able to explain 
developments in potential output via its components 
(labour, capital and total factor productivity). Estimates 
of this type are produced by the European Commission2 
and the Organisation for Economic Co-operation and 
Development (OECD)3, while the International Monetary 
Fund (IMF) uses different approaches depending on the 
country assessed. Chart A depicts the recent estimates 
by these institutions for the euro area. The output gap 
in the euro area is estimated to stand between -2.1% 
and -2.7% in 2015, suggesting that a considerable 
amount of slack remains. One drawback of such 
estimates, however, is that they tend to be revised quite 
significantly, due to changes to data, parameters and 
the model setup.4

To assess the amount of slack, analysts also turn to surveys such as capacity 
utilisation in the manufacturing sector, or the perceived degree of insufficient 
demand as a constraint on businesses. These surveys have the advantage of 
being revised less frequently, thus performing better in real time, and are known  

1 

2 

3 

See, for example, the box entitled “Recent evidence on the uncertainty surrounding real-time estimates 
of the euro area output gap”, Monthly Bulletin, ECB, November 2011

See Havik, K. et al., “The production function methodology for calculating potential growth rates and 
output gaps”, European Economy – Economic Papers, No 535, European Commission, November 
2014.

See Beffy, P. O. et al., “New OECD methods for supply-side and medium-term assessments: a capital 
services approach”, OECD Economics Department Working Papers, No 482, OECD, July 2006.

4  On the revisions and uncertainty of estimates by international institutions, see Section 2.2 of Anderton, 
R. et al., “Potential output from a euro area perspective”, Occasional Paper Series, No 156, ECB, 
November 2014.

49

ECB Economic Bulletin, Issue 6 / 2015 – Box 6Chart B
Capacity utilisation and demand limiting production 
in manufacturing

Chart C
Insufficient demand limiting production

(percentage point deviations from historical average)

(percentage shares of total responses)

capacity utilisation in manufacturing (left-hand side)
insufficient demand in manufacturing (right-hand side)

manufacturing
construction
services

6

3

0

-3

-6

-9

-12

1999

2002

2005

2008

2011

2014

-20

-10

0

10

20

30

40

70

60

50

40

30

20

10

0
1999

2002

2005

2008

2011

2014

Source: European Commission.

Source: European Commission.

for better identifying turning points in the cycle. Survey indicators are also released 
with a short delay following the reference period. However, survey measures based 
on manufacturing capture less than a fifth of the economy and do not take into 
account the development of slack in the labour market. Surveys of capacity utilisation 
or insufficient demand in manufacturing indicate that, in mid-2015, slack in the euro 
area is close to its historical average (see Chart B).

a new survey-based measure of slack maps the results of the European 
Commission’s “factors limiting production” survey to gDP dynamics. In this 
survey, managers are asked about the main factors currently limiting their production. 
The answer “insufficient demand limiting production” was selected as the indicator 

Chart D
Slack in the euro area

(percentage point deviation from trend)

output gap by European Commission
output gap by IMF
output gap by OECD
survey-based measure of slack

4

3

2

1

0

-1

-2

-3

-4

-5
1999

2002

2005

2008

2011

2014

Sources: ECB staff calculations, European Commission Spring 2015 Economic Forecast, 
IMF World Economic Outlook April 2015 and OECD Economic Outlook May 2015.
Note: The grey area represents a +/-2 standard deviation uncertainty band around the 
survey-based measure.

of slack for the model. The survey indicator, which 
combines information on the manufacturing, construction 
and services sectors, where available (see Chart C), 
is used in a bivariate unobserved components model. 
In the model, actual output is equal to the sum of 
(unobserved) trend output and the measure of slack. 
The growth rate of trend output is modelled as a random 
walk, and slack is determined by developments in the 
aggregate survey indicator.

For most of the period since 1999, the survey-based 
measure shows an estimate of slack similar to the 
latest estimates by the European Commission, the 
oECD and the ImF. However, according to the survey-
based measure, the amount of slack in the period 
2014-15 is declining relatively fast. As a result, the 
amount of slack according to this measure is currently 
smaller than that estimated by international institutions 
(see Chart D). Since the survey-based measure draws 

50

ECB Economic Bulletin, Issue 6 / 2015 – Box 6information from firms’ assessments of insufficient demand limiting their production, 
the decline in slack could suggest that growth in the euro area since 2014 reflects an 
improvement in demand, rather than in supply conditions.

The survey question relating to “insufficient 
demand limiting production” helps to pin down 
developments in slack in the model, and this also 
results in smaller revisions. Recursive estimates 
show that, over most of the period 2000-14, the survey-
based measure produces smaller differences between 
quasi real-time and ex post estimates than output gap 
estimates by international institutions. Using GDP 
vintages to create real-time estimates shows that, for 
the most volatile period of 2007-12, revisions are the 
smallest for the (annualised) survey-based measure 
of slack. Revisions of the European Commission’s 
output gap estimates are somewhat larger. The largest 
revisions are seen for the OECD and IMF estimates 
(see Chart E).

Chart E
Revisions of estimates of slack

(percentage points)

output gap by European Commission 
output gap by IMF 
output gap by OECD 
survey-based measure of slack 

4

3

2

1

0

2007

2008

2009

2010

Sources: OECD, IMF, European Commission and ECB staff calculations.

2011

2012

average

overall, the survey-based measure of slack 
indicates a smaller amount of slack in the euro 
area in 2015 than the published estimates of 
international institutions. While the measure of slack 
indicated by the survey-based approach is surrounded by uncertainty, and cannot be 
broken down into labour, capital and total factor productivity developments, it tends 
to be revised less frequently. Thus, using such measures to complement output gap 
estimates of production function-based models could be worthwhile.

51

ECB Economic Bulletin, Issue 6 / 2015 – Box 6Box 7  
Recent developments in euro area food 
prices

Chart a
Contribution of food price developments to HICP 
inflation 

(annual percentage changes; percentage points)

HICP 
food 
energy 
HICP excluding food and energy 

5 

4 

3 

2 

1 

0 

-1 

-2 

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat and ECB calculations.
Note: Latest observations are for August 2015 (ﬂ ash estimates).

Chart B
Unprocessed food price inflation 

(annual percentage changes; percentage points)

unprocessed food prices 
vegetables 
fruit  
meat and fish 

6 

4 

2 

0 

-2 

-4 

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat and ECB calculations.
Note: No breakdown is yet available for the latest observations, which are for 
August 2015 (ﬂ ash estimates).

Developments in food prices have reinforced the 
pattern of euro area inflation driven by energy 
prices in recent quarters. This holds in respect of 
both the decline in the contribution to headline inflation 
in 2014 and its rebound during the first few months 
of 2015 (see Chart A) – a pattern of decrease and 
recovery visible in prices of both unprocessed and 
processed food. As food price inflation can be subject 
to considerable volatility, this box examines the factors 
behind recent developments in order to gain a better 
understanding of the rebound.

recent movements in unprocessed food price 
inflation are largely explained by developments in 
fruit and vegetable prices. These account for most of 
the decline in the annual growth rate (from around 5% in 
mid-2013 to a historic low of almost -3% in mid-2014)  
and most of the rebound to positive growth rates 
(of around 2% in mid-2015; see Chart B). Seasonal 
factors typically play an important role here. Much of 
the decline in fruit and vegetable prices in early 2014 
reflects the mild winter of 2013-14 in conjunction with 
the unwinding of earlier upward impacts resulting from 
adverse weather conditions. The rebound to positive 
annual growth rates in the first half of 2015 thus reflects 
both the relatively subdued price developments one 
year earlier and the relatively strong price movements in 
2015 (see Chart C). 

recent movements in processed food price 
inflation are explained by different factors. First, 
the upturn in early 2015 partly reflects a somewhat 
larger contribution from tobacco prices, which are often 
subject to the impact of tax measures (see Chart D). 
Second, like energy prices, processed food prices can 
be heavily influenced by developments in international 
commodity prices. Nevertheless, for the euro area,  
EU internal market prices measured at the farm gate 
rather than international commodity prices are typically 
more relevant for the pass-through to consumer food 
prices. Both international food prices and EU internal 

52

ECB Economic Bulletin, Issue 6 / 2015 – Box 7Chart C
Intra-annual pattern of euro area fruit and vegetable 
prices

Chart D
Processed food price inflation 

(index: January = 100)

(annual percentage changes; percentage points)

developments since January 2015 
developments since January 2014 
average developments over the past ten years (2004-14) 
standard deviation

processed food prices 
tobacco 
milk, cheese and eggs prices  
other 

115 

110 

105 

100 

95 

8

6

4

2

0

-2

Jan.

Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.

Jan.

2008

2009

2010

2011

2012

2013

2014

2015

Sources: Eurostat and ECB calculations.
Note: The green shaded area indicates a one standard deviation increase/decrease in 
the index levels for each month relative to their level in January of the previous year 
over the period 2004-14.

Sources: Eurostat and ECB calculations.
Note: No breakdown is yet available for the latest observations, which are for 
August 2015 (ﬂ ash estimates).

market prices (see Chart E) have declined since 2014 and showed tentative signs 
of stabilisation in early 2015. Downward pressure on commodity prices and also 
prices of processed food continues to come in particular from the respective dairy 
components, which are currently also subject to special influences. Barring further 
decreases in commodity prices, a fading out of such 
factors should make the rebound in processed food 
prices more sustained.

Chart E
Developments in EU internal market prices 

(EUR; index: January 2008 = 100, non-seasonally adjusted)

EU internal market prices 
dairy prices 

130

120

110

100

90

80

70

60

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015

Sources: European Commission and ECB calculations.
Note: The latest observations are for July 2015.

a special factor currently affecting food price 
inflation (both in terms of unprocessed and 
processed food products) is the russian ban on 
imports from the European Union. The Russian ban 
became effective in mid-2014 and may have prevented 
a stronger recovery in food prices. Indeed, anecdotal 
evidence at the time pointed to a negative impact 
on prices of unprocessed food such as apples and 
processed food such as dairy products. However, the 
European Commission responded by activating support 
measures for perishable fruit and vegetables which 
may have mitigated the downward pressure on prices 
resulting from an excess supply of such goods.1 The 
extension of the Russian embargo and the mitigating 
support measures should continue to dampen prices of 
unprocessed foods. 

1 

In early August 2014, in line with the Common Agricultural Policy (CAP), the European Commission 
announced emergency market support measures for perishable fruit and vegetables, which have 
been used to purchase fruit and vegetables at full price from farmers, or to compensate them for not 
harvesting their produce. On 30 July 2015 these measures were extended for another year, until 
30 June 2016.

53

ECB Economic Bulletin, Issue 6 / 2015 – Box 7The expiration of EU milk output quotas in march 2015 is a special factor that 
has affected prices of dairy products in particular. During the past few years, 
in anticipation of the abolition of these quotas, EU dairy farmers increased their 
production for world export markets and thus raised their exposure to changes in 
global demand. Presently, increased competition and excess supply in the wake of 
lower demand from Russia and some emerging economies are weighing on prices.  
If there is no pick-up in global demand, some of the downward pressure on food 
price inflation may fade only gradually.

overall, rising food prices contributed to the rebound in inflation in early 2015 
supported by significant base effects. Food price inflation, however, remains 
rather low by historical standards. Special factors, such as the Russian ban on EU 
food imports, the oversupply of dairy products stemming from the abolition of milk 
quotas and lower than expected global demand, are presently limiting increases in 
food prices.

54

ECB Economic Bulletin, Issue 6 / 2015 – Box 7Box 8 
Country-specific recommendations for 
fiscal policies under the 2015 European 
Semester

on 25-26 June the European Council endorsed country-specific 
recommendations for economic and fiscal policies for 26 non-programme 
EU member States.1 These recommendations were adopted by the economic and 
finance ministers on 14 July to formally conclude the 2015 European Semester. The 
Council’s fiscal policy recommendations aim to ensure that countries comply with 
the EU’s Stability and Growth Pact (SGP). To this end, they give opinions on the 
2015 updates to stability and convergence programmes, which governments had to 
submit to the Council and the European Commission by mid-April.2 These opinions 
take account of the Commission’s communication on flexibility within the SGP which 
was released in January this year.3 In terms of follow-up work, the country-specific 
recommendations for fiscal policies issued under the 2015 European Semester will 
need to be reflected in the draft budgetary plans for 2016 which countries have to 
submit to the Eurogroup and the Council by mid-October. Against this background, 
this box reviews the recommendations for fiscal policies that were addressed to 
the 17 euro area countries under the 2015 European Semester and identifies the 
implications for their budgetary plans for 2016.

The European Council’s country-specific recommendations identify risks of 
non-compliance with the structural effort requirements of the SgP in 12 of 
the 17 euro area countries under review. Overall, although the five euro area 
non-programme countries (Ireland, Portugal, Slovenia, Spain and France) currently 
subject to an excessive deficit procedure (EDP) are required to make, on average, 
structural efforts equivalent to 0.7% and 0.9% of GDP in 2015 and 2016 respectively 
in order to ensure compliance with the SGP, the average figures in a “no policy-
change scenario” are actually expected to be slightly negative in both years.4 In turn, 
the 12 euro area countries under the SGP’s preventive arm are required to progress 
towards their medium-term budgetary objectives with structural efforts amounting to 
0.2% of GDP on aggregate over 2015-16, yet the figures for this period are expected 

1 

2 

This includes all EU Member States except Cyprus and Greece.

These programmes outline governments’ budgetary plans for at least the current and subsequent three 
years. For an overview, see European Commission, “The 2015 stability and convergence programmes 
– an overview”, Institutional Paper, No 2, July 2015, Brussels. 

3  On  13  January  the  European  Commission  issued  a  Communication  entitled  “Making  the  best  use  of 
the  flexibility  within  the  existing  rules  of  the  Stability  and  Growth  Pact”  (http://ec.europa.eu/economy_
finance/economic_governance/sgp/pdf/2015-01-  13_communication_sgp_flexibility_guidelines_en.pdf). 
See also the box entitled “Flexibility within the Stability and Growth Pact”, Economic Bulletin, Issue 1, 
2015. 

4  Under  the  SGP’s  corrective  arm,  the  Council  abrogated  Malta’s  excessive  deficit  by  the  2014  EDP 
deadline.  At  the  same  time,  it  decided  against  initiating  an  EDP  for  Finland,  although  the  breach  of 
the 3% of GDP reference value in 2014 is not forecast to be temporary, with the deficit not expected to 
return to below the reference value until 2016.

55

ECB Economic Bulletin, Issue 6 / 2015 – Box 8to be slightly negative.5 The fiscal policy recommendations therefore call on eight 
Member States (Belgium, Estonia, Italy, Latvia, Lithuania, Malta, Austria and Finland) 
to make structural efforts commensurate with the preventive arm of the SGP. Two 
Member States (Belgium and Italy) also face large consolidation gaps with respect 
to the debt rule. The required improvement in the structural balance under the 
debt rule in 2015 is equivalent to 2.1% of GDP for Italy (resulting from cumulated 
consolidation shortfalls since 2013) – which compares with a forecast for structural 
efforts amounting to 0.3% of GDP – and 1.1% of GDP for Belgium (resulting from 
cumulated consolidation shortfalls since 2014) – which compares with a forecast for 
structural efforts amounting to 0.5% of GDP. These requirements are not reflected in 
the 2015 country-specific recommendations for Italy and Belgium, as the Commission 
has concluded that the deviation from the debt rule can be explained by relevant 
factors, such as unfavourable economic conditions and the implementation of 
structural reforms. In many countries, interest expenditure was lower than initially 
budgeted. At the same time, instead of using the savings from lower than expected 
interest payments to accelerate deficit adjustment, many Member States increased 

Table

Country-specific fiscal developments in 2015 and 2016 

Country

SgP commitment              
(change in the structural 
balance; percentage of 
gDP – if not at mTo)

European Commission  
spring 2015 forecast
 (change in the structural 
balance; percentage of 
gDP)

annual consolidation 
gap (difference between 
European Commission 
spring 2015 forecast 
and commitment – 
if not at mTo)

Preventive arm  

Belgium

Germany

Estonia

Italy

Latvia

Lithuania

Luxembourg

Malta

Netherlands

Austria

Slovakia

Finland

Corrective arm (EDP)  

EDP deadline

2015

Ireland*

Portugal*

Slovenia*

2016

Spain

2017

France

2015

2016

2015

2016

2015

2016

0.6

at MTO

-0.2

0.25

-0.4

0.2

at MTO

0.6

at MTO

-0.06

0.0

0.1

1.9

0.5

0.5

0.8

0.5

0.6

at MTO

0.4

0.1

0.3

0.5

at MTO

0.6

at MTO

0.3

0.25

0.5

0.6

0.6

0.6

1.2

0.8

0.5

-0.2

-0.5

0.3

-0.3

-0.7

-1.0

0.5

-0.1

-0.4

0.2

-0.2

0.5

-0.8

0.1

-0.4

0.3

0.2

-0.3

-0.3

-0.2

-0.3

0.7

-0.2

0.6

-0.1

-0.6

0.0

-0.4

0.3

-0.6

-0.5

-0.2

0.0

-0.1

at MTO

-0.4

at MTO

-0.4

0.0

0.1

-0.9

at MTO

-0.1

at MTO

-0.3

0.2

-0.3

-1.4

-1.3

-0.4

-1.2

-0.2

-0.7

-0.3

-0.6

0.2

at MTO

0.0

at MTO

-0.8

-0.2

-0.9

-0.3

-1.2

-1.1

-1.4

-0.8

memo: change 
in interest 
expenditure;  
percentage of gDP 
between 2014 and 
2016 (European 
Commission spring 
2015 forecast)

2015-2016  
(cumulated)

-0.4

-0.3

0.0

-0.4

-0.2

-0.2

0.0

-0.3

-0.1

-0.1

-0.4

-0.1

-0.6

-0.4

-0.3

-0.3

-0.1

Sources: Country-speciﬁ c recommendations for 2015 (http://www.consilium.europa.eu/en/press/press-releases/2015/06/19-country-speciﬁ c-recommendations/) and the European 
Commission’s 2015 spring forecast.
Notes: The countries mentioned in the table include euro area countries that are not subject to a ﬁ nancial assistance programme (i.e. all Member States except Cyprus and Greece). 
The structural effort commitments under the preventive and corrective arm of the SGP are as outlined in the 2015 country-speciﬁ c recommendations. Ireland was subject to an 
EDP prior to the “six-pack reform” and is thus required to deliver an annual average structural effort in the absence of annual targets. * Structural effort requirements for 2016 
as applicable under the preventive arm, assuming a timely abrogation of the EDP by the 2015 deadline. Structural effort requirements under the SGP’s preventive arm exclude 
requirements under the debt rule. Figures are rounded up, except where they refer to SGP commitments.

5 

See also the box entitled “The effectiveness of the medium-term budgetary objective as an anchor of 
fiscal policies”, Economic Bulletin, Issue 4, Frankfurt.

56

ECB Economic Bulletin, Issue 6 / 2015 – Box 8 
primary spending (i.e. government expenditure excluding interest payments) relative 
to their initial plans. Countries faced with high general government debt ratios 
(Belgium, France, Italy, Ireland and Portugal) are therefore advised to use any so-
called windfall gains, i.e. savings from lower than anticipated interest payments, for 
deficit reductions. Moreover, of the countries that have already met their medium-
term budgetary objectives, Germany has been advised to further increase public 
investment in infrastructure, education and research, while the Netherlands has been 
advised to shift public expenditure towards supporting investment in research and 
development. 

The 2015 European Semester, which is the fifth surveillance cycle since 
its inception, followed a streamlined approach aimed at issuing fewer and 
more targeted recommendations. This reorganisation entailed, inter alia, earlier 
publication of European Commission analyses and recommendations in order 
to enhance dialogue with Member States. Overall, the 2015 country-specific 
recommendations appropriately reflect countries’ risks of non-compliance with the 
EU’s fiscal rules, while identifying the major areas for reform. To this end, they follow 
the broad fiscal policy guidance issued for this European Semester in the 2015 
Annual Growth Survey, namely “[..] pursuing fiscal responsibility”.6  In general, the 
European Semester process can only be effective in increasing fiscal sustainability if 
non-compliance with the EU’s fiscal rules is appropriately identified and addressed. 
In this context, the so-called five presidents’ report published in June7 proposes 
the establishment of a new European fiscal board, which would provide public and 
independent assessments of countries’ budgetary policies vis-à-vis their obligations 
under the EU’s fiscal framework. These would then feed into the decisions taken 
by the European Commission in the context of the European Semester. The report 
also proposes a more integrated European Semester, with national parliaments 
being involved more. Looking ahead, in the light of persistent vulnerabilities such 
as high government debt and sizeable structural deficits in euro area countries, 
any potential reforms of the European Semester should not weaken the ambition of 
recommendations for budgetary policies.

Fiscal policies should support the recovery, while remaining compliant  
with the Stability and growth Pact. Full and consistent implementation is key for  
confidence in our fiscal framework. The draft budgetary plans for 2016 should 
therefore clarify how governments whose structural efforts fall short of their 
commitments under the SGP intend to follow up on the country-specific 
recommendations in order to ensure compliance with the EU’s fiscal rules by 
reducing their deficits faster. With bond yields subject to volatility, the budgetary plans 
of high-debt countries in particular should allow for risks related to a reversal of the 
current low interest rate environment.

6 

7 

See European Commission, “Annual Growth Survey 2015”, November 2014, Brussels (http://ec.europa.
eu/europe2020/pdf/2015/ags2015_en.pdf).

See  the  report  prepared  by  Jean-Claude  Juncker,  in  close  cooperation  with  Donald  Tusk,  Jeroen 
Dijsselbloem, Mario Draghi and Martin Schulz, entitled “Completing Europe’s Economic and Monetary 
Union”, 2015, Brussels.

57

ECB Economic Bulletin, Issue 6 / 2015 – Box 8Articles 
The state of the house price cycle  
in the euro area 

This article discusses the current state of the euro area house price cycle and 
compares it with historical patterns. It finds that the recovery in house prices in the 
euro area has been rather muted thus far and appears to be weaker than the typical 
increase observed historically during the initial phase of an upturn in the house 
price cycle. At the same time, corrections of previous overvaluations, together with 
favourable income and financing conditions, suggest that the current recovery has a 
better chance of being sustained than the short-lived upturn observed relatively  
soon after the crisis. A gradual and sustained recovery in the house price cycle 
would support economic developments. At the same time, the accompanying credit 
dynamics have thus far remained muted, limiting the build-up of systemic risks to 
the euro area financial system. The new macroprudential toolkit is also helping to 
mitigate possible risks in a targeted and granular way.

1 

Introduction

House prices have been a key indicator in assessing the state of the euro area 
economy since the financial crisis. This reflects the general importance that the 
housing sector tends to have for the timing and amplitude of the business cycle and, 
in particular, its specific importance in the aftermath of a boom-bust episode. In  
a number of euro area countries, house prices had increased at unsustainable rates 
and to unsustainable levels prior to the crisis, and the inevitable adjustments had 
subsequently led to declines in house prices or muted developments at best. 

after some ups and downs during a protracted period of adjustment, there  
are now increasing signs that house prices in the euro area are finally on the 
rise again. Measured in terms of the annual rate of change in residential property 
prices, the cycle reached a low point in early 2013 before the rates of change became  
successively less negative and moved into positive territory in the second half 
of 2014. This recovery does not appear to have been very strong thus far, but the 
upward path of the growth rate is relatively broad-based across euro area countries. 
Against this background, it is instructive to analyse the various factors underlying  
the nascent recovery in house prices, as well as its strength and sustainability  
going forward.

Understanding the state and nature of the house price cycle is important from 
both a macroeconomic and financial stability point of view. This is because it is 
linked to the business and financial cycles, with house prices affecting credit markets, 
as they determine the value of collateral that households can borrow and banks can 
lend against. Tellingly, the collateral channel had shown its importance in the context 

58

ECB Economic Bulletin, Issue 6 / 2015 – Articlesof the financial crisis and the banking sector problems that emerged in its aftermath 
in some euro area countries. Going forward, house price developments deserve 
particular scrutiny in a low interest rate environment given the complex links between 
residential property prices, economic activity and credit dynamics.

This article discusses the recent developments in, and current outlook for, house 
prices across the euro area, focusing on price developments in residential real estate 
and abstracting from those in commercial real estate (Section 2). It then compares 
the ongoing house price and credit cycles with previous ones (Section 3), before 
elaborating on the interaction between house price developments, the real economy 
and the banking sector (Section 4). 

2 

Recent developments and current outlook  
for house prices

recent developments in euro area residential property prices suggest that the 
corner has been turned and a recovery is underway. The annual rate of change 
in house prices started to increase in mid-2013, turning mildly positive in the second 
half of 2014. There were similar signs in 2009-10, relatively soon after the financial 
crisis, but the recovery could not be sustained, given the unfolding of the euro area 
sovereign debt crisis. In this context, it is instructive to assess the sustainability of 
the nascent, but still subdued, recovery for the euro area as a whole by looking at 
the different factors underpinning it.

Chart 1
Euro area residential property prices by groups 
of countries

(year-on-year percentage changes and percentage point contributions)

euro area nominal residential property prices
contribution from Germany and Austria
contribution from countries most affected by the financial crisis
contribution from other countries (excluding Germany and Austria)

8.0

6.0

4.0

2.0

0.0

-2.0

-4.0

-6.0

First, the recovery in euro area house prices 
appears to be relatively broad-based across groups 
of countries. With contributions to euro area house 
price growth from Germany and Austria remaining 
solidly positive, the upturn in the annual growth rate 
since early 2013 essentially reflects a gradual easing 
of the negative contributions from the countries most 
affected by the financial crisis (Ireland, Greece, Spain, 
Italy, Cyprus, Portugal and Slovenia). To a somewhat 
lesser extent, this also holds for the group of other euro 
area countries (excluding Germany and Austria) (see 
Chart 1). These broad-based contributions towards 
positive euro area house price growth differ from those 
in the upturn of 2009-10,  
when the countries most affected by the financial crisis 
continued to contribute negatively and the upturn 
mainly reflected a strong rebound in house price growth 
in the other countries. 

2006

2007

2008

2009

2010

2011

2012

2013

2014

Sources: ECB calculations based on national data. 
Note: The countries most affected by the ﬁ nancial crisis are Ireland, Greece, 
Spain, Italy, Cyprus, Portugal and Slovenia. Last observations were for Q1 2015 for 
all countries except Belgium, Slovakia and Finland, where the last observation was for 
Q4 2014.

Second, the current recovery in euro area house 
price growth seems less contingent on prices in 
metropolitan areas than in 2009-10. Aggregating 
selected countries for which data are available shows 

59

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 2
Euro area residential property prices 
at the metropolitan and aggregate level

(Ratio Q1 2010 = 100 and year-on-year percentage changes)

euro area ratio of property prices index in big cities vis-à-vis 
the national aggregate index (left-hand scale)
interquartile range of the euro area ratio (left-hand scale)
property prices: capital city/big cities annual 
growth rate (right-hand scale)
property prices: aggregate annual growth rate (right-hand scale)

130

125

120

115

110

105

100

95

90

85

12

10

8

6

4

2

0

-2

-4

-6

2008

2009

2010

2011

2012

2013

2014

Sources: ECB calculations based on BIS and national sources.
Notes: The euro area ratio is an aggregation of country ratios including Austria, 
Belgium, Germany, Estonia, Spain, France and Italy up to 2010 and also Ireland, 
Slovenia and Finland since 2010, and using GDP weights. The shaded area refers to 
the interquartile range of country ratios (25th to 75th percentile). Different coverage 
and data sources may account for differences from the aggregate property price 
developments reported in Chart 1. The last observations were for Q4 2014.

that house prices in metropolitan areas have been 
growing faster since the financial crisis than in the 
economy as a whole (see Chart 2). There may be 
geographical reasons for this, such as less availability 
of land and a correspondingly lower elasticity of 
housing supply in metropolitan areas than rural areas. 
However, this may also imply that house price dynamics 
in metropolitan areas could pick up faster in response 
to the economic cycle and then provide false signals if 
the latter is not sustained. In recent quarters the growth 
rate differential between growth in metropolitan and 
economy-wide house prices has remained relatively 
stable, while the upturn in house prices observed 
in 2009-10 was characterised by a much stronger 
acceleration in metropolitan areas than elsewhere, 
which subsequently also unwound much faster. As with 
the individual countries, house price dynamics in the 
euro area as a whole also seem to have been more 
balanced recently in terms of location, thus providing a 
better starting point for a sustained recovery than a few 
years ago.

Third, the current recovery is taking place in an 
environment where earlier imbalances in house 
prices have resulted in substantial corrections. 
In several countries, in particular those most affected 
by the financial crisis, large rises in house prices in 

the run-up to 2007 were followed by falls in the 2007-13 period, i.e. preceding the 
current upturn. This suggests some correction of the imbalances that had built 
up in the pre-crisis period, when a number of countries saw very strong growth in 
house prices – double-digit in some cases (see Chart 3). It also explains why house 
prices are currently rising again at a faster pace in some of these countries, such as 
Ireland and Spain, in an environment of improving macroeconomic and favourable 
financing conditions. Countries which have experienced relatively strong house 
price growth since 2007, especially recently (such as Germany and Austria), are the 
ones that saw more moderate growth in the period up to 2007 and did not require a 
correction thereafter. The notion of a correction of imbalances also comes through 
when looking at developments in house prices as a ratio to income (measured in 
terms of GDP). Countries which had the largest increases in this ratio in the period 
up to 2007 by and large also saw the smallest increases or biggest declines in the 
period 2007-13 (see Chart 4). In some countries, such as Spain, Ireland and Greece, 
the declines were even larger than the previous increases.

measuring the degree of house price imbalances and the corresponding need 
for corrections is surrounded by considerable uncertainty. This reflects the 
complex interaction between the housing, rental and mortgage markets, different 
structural characteristics between these markets across countries, as well as data 

60

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 3
House price growth in the run-up to, and unwinding of, 
the financial crisis

Chart 4
House price-to-GDP ratios in the run-up to, and 
unwinding of, the financial crisis

(average annual percentage changes; x-axis: 2007-2013, y-axis: 2001-2007)

(average annual percentage changes; x-axis:2007-2013, y-axis: 2001-2007)

16

12

8

4

0

-4

-8

-12

-16

-16

ES

IE

GR

CY

MT
IT

FR

BE

NL

EA

PT

FI

DE

AT

45 degree
line

-12

-8

-4

0

4

8

12

16

10

8

6

4

2

0

-2

-4

-6

-8

-10

-10

ES

IE

MT

CY

FR

BE

IT

FI

EA

NL

DE

AT

GR

PT

45 degree
line 

-8

-6

-4

-2

0

2

4

6

8

10

Source: ECB calculations based on national data.
Notes: The countries most affected by the ﬁ nancial crisis are Ireland, Greece, Spain, 
Italy, Cyprus and Portugal which are shown with a yellow dot. Data for Estonia, Latvia, 
Lithuania, Luxembourg, Slovenia and Slovakia are not available for the full sample. For 
Cyprus, the pre-crisis average is computed for the period 2002-07.

Sources: Eurostat and ECB calculations.
Notes: See Chart 3.

Chart 5
Euro area house price-to-income ratios

(percentage point deviations from long-term average and percentage point 
contributions)

house price-to-income ratio (basic)
ratio adjusted using annuity formula
ratio adjusted using quadratic formula for interest rate
contribution from countries most affected by the financial crisis
contribution from other countries

20

15

10

5

0

-5

-10

-15

2006

2007

2008

2009

2010

2011

2012

2013

2014

Sources: Eurostat, ECB and ECB calculations.
Notes: The long-term average is computed over the period since Q1 1996. Disposable 
income per household is used as the proxy for income. The interest rate is the average 
mortgage interest rate. Footnote 2 in the main text explains the adjustment methods. The 
countries most affected by the ﬁ nancial crisis are Ireland, Greece, Spain, Italy, Cyprus, 
Portugal and Slovenia and their contribution to the euro area ratio is shown in green. 

constraints and measurement issues.1 The house 
price-to-income ratio is one of the available valuation 
indicators and represents a crude measure of housing 
affordability. When the indicator lies above its long-term 
average, house prices may be seen as overvalued – 
at least from the perspective of prospective new 
buyers – which should lead to downward pressures 
on prices. Since 2010 the imbalance in the house 
price-to-income ratio for the euro area as a whole has 
progressively unwound. In 2014 it was only around 3% 
above its fundamental level, suggesting that house 
prices were broadly back in line with fundamentals. 
This reflects in particular the unwinding in the group 
of countries most affected by the financial crisis, 
which between 2007 and 2010 had seen relatively 
little adjustment (see Chart 5). Considering that the 
affordability of housing and house prices are also 
determined by the costs of servicing mortgage debt, 
the basic house price-to-income indicator can be 
augmented with interest rates, either by an annuity-
based or a regression-based approach.2 These 
augmented affordability measures suggest that 

1 

2 

For a discussion, see the box entitled “Statistical valuation metrics for residential property markets”, 
Financial Stability Review, ECB, May 2015.

The annuity-based measure augments the house price-to-income ratio as follows: r/(1-(1+r)^(-T))* 
(house price index)/(income index) where T is the mortgage length and r is the nominal mortgage 
interest rate. Typically, a mortgage length of 20 years and a fixed mortgage interest rate are assumed. 
The degree of over/undervaluation is then calculated as the deviation in percentage terms of the 
augmented ratio from its long-term average. Alternatively, house prices can be regressed on income 
and mortgage interest rates and the residuals are taken as the valuation estimates. 

61

ECB Economic Bulletin, Issue 6 / 2015 – Articlesaverage house prices may currently even be moderately below historically normal 
valuation levels. However, the boost to housing affordability coming from the current 
low interest rate environment may not be fully sustainable if interest rates were to 
normalise further out.

Besides starting from a more balanced position than in 2009-10, there are 
also other factors supporting a more sustained recovery in house prices. One 
is related to the improving income and employment prospects for households that 
are associated with the ongoing economic recovery, which should boost demand 
for housing and lead to stronger house price growth. European Commission survey 
figures indicate that households’ intentions to purchase a house in the next two years 
have shown some signs of improvement since the end of 2012, but are still below 
the longer-term average since 1999. This is likely to continue, since expectations for 
economic growth one and two years ahead are currently higher than in 2009-10 and 
there is a prospect of financing conditions remaining favourable in an environment 
where non-standard monetary policy measures have been designed to keep interest 
rates low for some time to come. 

Bank lending rates and credit standards have become increasingly favourable 
in recent quarters. This clearly works as a second supporting factor: since the 
end of the last recession in early 2013 the improvement in financing conditions 
and the upturn in the house price cycle have coincided. Notwithstanding some 
heterogeneity across countries, lending rates in the euro area for loans for house 
purchase declined by more than 90 basis points from that point (see Chart 6), while 
credit standards became successively less tight and eventually eased over the same 
period (see Chart 7). Improving housing market prospects have been an important 
element in the easing of credit standards, suggesting that a sustained improvement 

Chart 6
Mortgage lending rates in the euro area 
and selected countries

Chart 7
Credit standards for loans for house purchases 
and housing market prospects

(percentages)

(net percentages; a higher value indicates tightening)

euro area
Germany
France

Italy
Spain
Netherlands

Belgium
Austria
recessions

housing market prospects
credit standards
recessions

6

5

4

3

2

1

50

40

30

20

10

0

-10

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: ECB. 
Notes: Shaded areas delimit recessions as identiﬁ ed by the CEPR Euro Area Business 
Cycle Dating Committee. The rates refer to the composite lending rate for house 
purchases across different periods of interest rate ﬁ xation, weighted with a 24-month 
moving average of new business volumes. The most recent observations are for Q2 2015.

Source: ECB euro area bank lending survey.
Notes: Shaded areas delimit recessions as identiﬁ ed by the CEPR Euro Area Business 
Cycle Dating Committee. Backward-looking three months. Weighted net percentages 
are obtained as tightened minus eased. The most recent observations are for Q3 2015.

62

ECB Economic Bulletin, Issue 6 / 2015 – Articlesin housing market prospects due to factors other than credit standards could 
be reinforced if the latter were to be relaxed further. The chances of these other 
factors that determine credit standards remaining favourable are good, because the 
tightening of credit standards in 2011-12 was initially more a result of the unfolding 
sovereign debt crisis – through the impact of the increasing cost of funds and 
balance sheet constraints triggered by the crisis – than of housing market prospects 
becoming less favourable.

The prevailing low interest rate environment could also further sustain housing 
demand and stimulate house prices from an investment portfolio perspective. 
Housing can be viewed not only as a consumption good, but also as an investment 
good, the return on which can be assessed and compared with alternative investments. 
In the current prevailing low interest rate environment, housing could become 
comparatively more appealing as an investment for households and/or investors if it 
promises higher expected returns compared with, for example, bank deposits, securities 
such as government bonds, or equity investments. Estimates of the return on residential 
housing are only available for selected euro area countries (Germany, Austria, the 
Netherlands and France) and are surrounded by considerable uncertainty.3 Broad as 
such a comparison naturally is, it indicates that annual returns on residential housing 
in these countries have generally been higher in recent years (such as 2011 and 2014) 
than the prevailing nominal long-term yields on government bonds, nominal yields on 
bank deposits and the dividend yield of the corresponding national equity markets, and 
that these differentials widened further between 2011 and 2014 (see Chart 8). Overall, 
the relative return on housing in recent years compares favourably with 2008, when 
returns on housing were lower than deposit yields and government bond yields.

Chart 8
Returns on housing investment relative to those of alternative assets

(percentage points)

composite deposit rate
10-year government bond yield
dividend yield (equity)

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

2003

2008

2011

2014

2003

2008

2011

2014

2003

2008

2011

2014

2003

2008

2011

2014

Germany

Austria

Netherlands

France

Sources: IPD, DataStream and ECB calculations.
Notes: The income return on residential housing is the net annual income receivable as a percentage of the capital employed. The composite deposit rate is the MFI interest rate on 
deposits from households with agreed maturity (total original maturity, new business coverage).

3 

The estimates of income return are from the IPD Quarterly Research Database and reflect residential 
property portfolios for institutional investors. These portfolios are likely to invest predominantly in 
the prime or close-to-prime market, a sector which is likely to have a different dynamic to the entire 
residential market. In addition, alternative asset classes are characterised by a different level of risk 
and a corresponding risk premium, and the total return of each asset class is ultimately determined 
by the stream of income obtained (in terms of rents, dividends and interest payments) plus the 
appreciation/depreciation of the underlying asset, which is not explicitly considered in this analysis.

63

ECB Economic Bulletin, Issue 6 / 2015 – Articles3 

Stylised facts of house price cycles in the euro area

assessing the current state of the house price cycle and the prospects for a 
further recovery draws on comparisons with historical patterns. In particular, 
it is informative to analyse how cycles in euro area house prices have evolved in 
relation to both the business and credit cycles, as the states of these cycles can 
condition each other. The analysis looks at the corresponding variables – house 
prices, GDP and MFI loans to households – in real terms in order to account for 
differences in average inflation rates over the past few decades. 

The real house price cycle is broadly aligned with both the business and credit 
cycles. This alignment is summarised in the overall strong degree of co-movement 
between the real house price cycle and the business and credit cycles respectively 
(see Chart 9). The maximum correlation between annual real house price growth and 
annual real GDP growth is about 60%, while that between annual real house price 
growth and annual real household loan growth is about 70%. In both cases, it is found 
at broadly coincident level. The alignment is particularly apparent during recessions 
(see Chart 10). All euro area recessions since 1980 have been accompanied by 
decreasing, and eventually negative, real house price growth, which started to recover 
only after the trough in real economic activity had been passed. In turn, periods of 
negative real house price growth are typically preceded by a strong deceleration in 
real loans, although the growth rate for the latter has rarely turned negative. Unlike 
in the run-up to recessions, real loan growth does not appear to lead the house price 
cycle in the recovery phases after a recession. Against this background, the expected 
recovery in real GDP growth in the coming quarters and the recent mild upturn in 
the household credit cycle support the view that the real house price cycle is likely to 
continue to recover at moderate levels in the short to medium term.

Chart 9
Cross-correlations of real GDP growth and real household 
loan growth with respect to real house price growth

Chart 10
Euro area real GDP, real house prices and real loans 
to households

(percentages)

(year-on-year percentage changes)

real household loan growth
real household loan growth
real GDP growth
real GDP growth

real residential property prices
real loans to households

real GDP
recessions

80

70

60

50

40

30

20

10

0

-10

-20

-30

-40

real house price growth lags

real house price growth leads

+12 +10
+12 +10

+8
+8

+6
+6

+4
+4

+2
+2

0
0

-2
-2

-4
-4

-6
-6

-8
-8

-10
-10

-12
-12

14

12

10

8

6

4

2

0

-2

-4

-6

-8
1981

1985

1989

1993

1997

2001

2005

2009

2013

Sources: Eurostat, ECB and ECB calculations.
Notes: Correlations between annual growth rates of real house price growth (deflated 
by the HICP) with, respectively, real GDP annual growth rates and real MFI loans to 
household annual growth rates (deflated by the HICP), using data from Q1 1980 to 
Q1 2015.

Sources: Eurostat and ECB.
Notes: Real house prices and real MFI loans to households obtained by deﬂ ating the 
respective nominal index with HICP. Shaded areas delimit recessions as identiﬁ ed 
by the CEPR Euro Area Business Cycle Dating Committee. The most recent 
observations are for Q1 2015.

64

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesUpturns in the house price cycle are, on average, stronger and longer-lasting 
than downturns. Looking at ten euro area countries from the first quarter of 1970 to 
the fourth quarter of 2014 (a sample dictated by the availability of the data), 37 major 
real house price increases and 43 major real house price decreases can be 
identified.4 On average, major upturns see real house prices grow by around 50% 
over a period of about five years, while major downturns are characterised by a 
smaller amplitude and shorter duration, decreasing by about 16% on average over 
a period of around three years. A subset of these major episodes includes outright 
boom-bust instances, which may last longer. Developments during major upturns 
and downturns can be used to derive benchmark paths against which to assess the 
latest downturns and upturns in euro area real house prices. 

The fall in euro area real house prices after 2007 was broadly in line with 
historical patterns. This assessment can be gleaned by comparing developments in 
real house prices in the euro area and in selected countries around the most recent 
peak in euro area aggregate real house prices with a benchmark downturn path 
(see Chart 11). This benchmark path is derived on the basis of the aforementioned 
historical episodes, depicted by interquartile ranges of historical increases and 
decreases (i.e. abstracting from the extreme developments found in the upper and 
lower quartiles). The comparison points to significant country heterogeneity in post-
peak house price developments. The decline was much more marked in countries 
which had also seen a much stronger house price boom preceding the peak (such 
as Spain) than in those which experienced more stable house prices both before and 
after the peak (such as Germany). As regards the credit cycle, real household loans 

Chart 11
Real house prices around the 2007 peak

Chart 12
Real MFI loans to households around the 2007 peak

(indices, normalised to 100 at T=peak, with T=Q3 2007 for the euro area and 
euro area countries shown)

(indices, normalised to 100 at T=peak, with T=Q3 2007 for the euro area and euro area 
countries shown)

euro area
Germany
France

Italy
Spain
Netherlands

euro area
Germany
France

Italy
Spain
Netherlands

120

110

100

90

80

70

60

50

T-20

140

130

120

110

100

90

80

70

60

50

40

T-12

T-4

T+4

T+12

T+20

T-20

T-12

T-4

T+4

T+12

T+20

Sources: BIS, ECB, national sources and ECB calculations.
Notes: Grey area delimits interquartile range of developments around peaks for the 
sample considered (Q1 1970-Q4 2014). 

Sources: BIS, ECB, national sources and ECB calculations.
Notes: Grey area delimits interquartile range of developments in real household loans 
around peaks of real house prices for the sample considered (Q1 1970-Q4 2014). 

4 

Identification of “major” house price cycles follows Claessens et al.: “What happens during recessions, 
crunches and busts?”, Economic Policy, Vol. 24, 2009, pp. 653-700. A quarterly version of the Bry-
Boschan algorithm identifies local maxima (peaks) and minima (troughs) for peak-to-peak and trough-
to-trough cycles that last at least five quarters and phases that last at least two quarters. Episodes of 
ongoing cyclical increases and decreases in real house prices, as well as those which lasted less than 
a year and those in the lower quartile (i.e. episodes characterised by minor changes) are excluded. 

65

ECB Economic Bulletin, Issue 6 / 2015 – Articlestend to stabilise or even increase for several quarters after the cycle for real house 
price peaks (see Chart 12). Marked heterogeneity also characterises the pattern 
of real household loan cycles by country along the latest euro area real house 
price peak. Overall, the fact that the latest downturn in euro area real house prices 
appears to be broadly in line with historical declines may lead to an expectation that 
the subsequent recovery is also likely to be in line with upturns in recent decades. 

However, the current euro area real house price upturn has been somewhat 
weaker than the typical increase observed historically during the initial phase 
of the upturn. This also appears to be the case for most euro area countries 
(see Chart 13). Indeed, since the latest euro area trough in 2013, only some 
countries (such as Germany) have seen an upturn in real house prices, while in 
others (such as Spain and the Netherlands) there has only been a broad stabilisation 
followed by a mild increase in prices, or even a further decline (as in France and 
Italy). These different patterns reflect several factors, including the heterogeneous 
current state of the business cycle across countries and country-specific policy 
measures affecting housing and mortgage markets. As regards the credit cycle, 
real household loans appear to increase markedly both before and after real house 
price increases start (see Chart 14). As with recent real house price increases, the 
latest real household loan increases also appear to be weaker than during typical 
real house price upturns. This is also the case for most euro area countries. Thus, 
the most recent household loan developments only seem to have heralded, so far, 
relatively moderate increase in real house prices in the short to medium run.

Chart 13
Real house prices around the 2013 trough

Chart 14
Real MFI loans to households around the 2013 trough

(indices, normalised to 100 at T=trough, with T=Q4 2013 for the euro area and euro 
area countries shown)

(indices, normalised to 100 at T=trough, with T=Q4 2013 for the euro area and euro 
area countries shown)

euro area
Germany
France

Italy
Spain
Netherlands

euro area
Germany
France

Italy
Spain
Netherlands

160

150

140

130

120

110

100

90

170

160

150

140

130

120

110

100

90

80

70

80

T-20

T-12

T-4

T+4

T+12

T+20

60
T-20

T-12

T-4

T+4

T+12

T+20

Sources: BIS, ECB, national sources and ECB calculations.
Notes: Grey area delimits interquartile range of developments in real house prices 
around troughs for the sample considered (Q1 1970-Q4 2014). 

Sources: BIS, ECB, national sources and ECB calculations.
Notes: Grey area delimits interquartile range of developments in real household loans 
around troughs of real house prices for the sample considered (Q1 1970-Q4 2014). 

66

ECB Economic Bulletin, Issue 6 / 2015 – Articlesoverall, the downward adjustment of euro area real house prices after 2007 and  
their recent recovery are in line with historical cyclical patterns. At the same 
time, this recovery seems to have been somewhat weaker than normal, which may 
be related to a relatively weaker credit cycle at the current juncture. The euro area 
house price and credit cycles also remain characterised by heterogeneity across 
countries. Such heterogeneity in the timing and strength of cyclical patterns may also 
reflect differences in structural features of housing and mortgage markets across 
countries (see Box 1). 

Box 1
House price cycles and structural features of housing markets 

Differences in the cyclical development of house prices across countries may reflect 
differences in structural characteristics. Such characteristics, or changes therein, can dampen 
or amplify the impact on house prices of variations in housing supply and demand conditions over 
the business cycle. This box discusses selected structural features that are of importance for 
housing markets in the euro area.

Empirical studies point to different categories of structural features that correlate with the 
variability of the house price cycle.5 The most prominent categories relate to parameters set by 
the fiscal and financial frameworks and to those that govern the responsiveness of housing supply 
to demand shocks. However, it is difficult to assess the precise impact of these parameters, as it 
typically depends on the combination of structural characteristics in place. 

Examples of structural characteristics in the fiscal framework mainly include housing-related 
taxes and subsidies. For instance, everything else being equal, higher transaction taxes for 
buying property should dampen housing market activity and thereby also the house price cycle, but 
may be doing so less in the case of higher tax deductibility of the interest on the mortgage liability 
that typically comes with a property purchase. In around half the euro area countries, mortgage 
interest payments are eligible for some degree of tax deductibility, and recurrent property taxes are 
levied in almost all euro area countries.6 Property transaction taxes are charged in most countries, 
although there are some exceptions, such as Estonia, Slovakia and Lithuania. Changes to the fiscal 
treatment of housing have occurred in a number of countries in the most recent period, including 
Spain, Ireland, Portugal, Finland, Luxembourg, the Netherlands, Italy, Greece and Portugal, all of 
which increased property tax rates or restricted the degree of mortgage interest tax deductibility.

Examples of mortgage market characteristics relate to the general conditions under which 
loans for house purchase are taken up and granted. For instance, higher loan-to-value and  
loan-to-income ratios typically make house prices more sensitive to the business and credit cycles,  

5 

6 

European Commission, “House price imbalances and structural features of housing markets”, Quarterly 
Report on the Euro Area, Vol. 10, Issue 3, October 2011; and Tsatsaronis, K. and Zhu, H., “What 
Drives Housing Price Dynamics: Cross-Country Evidence”, Quarterly Review, Bank for International 
Settlements, March 2004.

“Tax Reforms in EU Member States 2014. Tax Policy Challenges for Economic Growth and Fiscal 
Sustainability”, European Economy, European Commission, Vol. 6, 2014; and “Possible Reforms of 
Real Estate Taxation: Criteria for Successful Policies”, European Economy, European Commission, 
Occasional Papers, No 119, October 2012.

67

ECB Economic Bulletin, Issue 6 / 2015 – Articlesas households are less constrained by credit and income when purchasing houses. Acknowledging 
the various measurement problems associated with a typical loan-to-value ratio, data collected 
before the crisis show that relatively high ratios used to be observed in the Netherlands and France, 
whereas they were on the lower side in Italy and Germany.7 Also, the possibility of mortgage equity 
withdrawal may in general magnify the response of house prices to increased housing demand in a 
boom, as it tends to make housing investment even more attractive owing to its collateral services 
when house prices are anticipated to rise. However, in contrast to the United States, this possibility 
did not appear to be widespread in euro area countries in the past. 

Examples of the structural characteristics of housing supply responsiveness relate mainly 
to zoning regulations and building approval processes. For instance, housing supply may be 
relatively inelastic in regions where geographical conditions or local land use regulations inhibit the 
development of urban land into residential property. If so, a rise in housing demand leads, all things 
being equal, to a larger increase in house prices than in regions where this additional housing 
can be supplied relatively quickly with respect to the conversion process for land and the required 
permits.8 This type of structural characteristic may be more important for urban than for rural areas.

In conclusion, there are a number of structural characteristics that can affect the amplitude 
and timing of the house price cycle. These characteristics have remained sufficiently diverse 
across euro area countries to account for heterogeneities in house price cycles across countries, 
even in cases where countries would face broadly similar macroeconomic conditions. 

4 

The implications of house price developments for the 
macroeconomy and financial stability

a recovery in house prices may have implications for both macroeconomic 
developments and financial stability.9 This reflects the inherent interlinkages 
between house prices, the real economy and the financial sector, as well as the 
multiple channels through which developments in house prices can influence the 
economic decisions of households and banks. However, as the current recovery 
of house prices is taking place in a low interest rate environment, it is important to 
weigh the positive impact on the macroeconomy and the financial sector against the 
associated potential risks to financial stability.

7 

8 

9 

“Housing finance in the euro area”, Occasional Paper Series, ECB, No 101, March 2009.

Saiz, A., “The Geographic Determinants of Housing Supply”. The Quarterly Journal of Economics, 
August 2010, pp. 1253-1296; Gyourko, J., “Housing Supply”, Annual Review of Economics, September 
2009, pp. 295-318; Andrews, D., “Real House Prices in OECD Countries. The Role of Demand 
Shocks and Structural and Policy Factors”, Organisation for Economic Cooperation and Development, 
Economics Department Working Papers No 831; Catte, P., Girouard, N., Price, R. and André, C., “The 
Contribution of Housing Markets to Cyclical Resilience”, Organisation for Economic Cooperation and 
Development, Economic Studies, Vol. 38, 2004/1; and Glaeser, E.., Gyourko, J. and Saiz, A., “Housing 
Supply and Housing Bubbles”, Journal of Urban Economics, September 2008, pp. 198-217.

See, for example, “Structural factors in the EU housing markets”, ECB, 2003; “Housing Finance in the 
euro area”, Structural Issues Report, ECB, 2009; “House price developments in the euro area and the 
United States”, Monthly Bulletin, ECB, November 2011; and Hartmann, P., “Real Estate Markets and 
Macro prudential Policy in Europe”, Journal of Money, Credit and Banking, 2015.

68

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 15
Changes to household net worth in the euro area

(year-on-year percentage changes and percentage point contributions)

total wealth
contribution from net financial wealth
contribution from non-financial investment
contribution from revaluations and other changes in non-financial assets

10

8

6

4

2

0

-2

-4

-6

2007

2008

2009

2010

2011

2012

2013

2014

Sources: ECB and ECB calculations. 
Note: Most recent observation is for Q1 2015.

The impact of house prices on the macroeconomy 
is typically felt via wealth and collateral effects 
on consumption, as well as incentive effects on 
housing investment. In fact, housing wealth in the 
euro area represents, on average, 37% of households’ 
net worth. Thus, changes in house prices have a direct 
impact on households’ net worth through holding gains/
losses on existing non-financial assets, typically the 
most important source of changes in households’ 
net worth (see Chart 15). A prolonged period of 
rising house prices, or the expectation that there 
will be one, could be perceived by households as a 
permanent increase in wealth, which, in turn, could 
lead to stronger consumption via a propensity to save 
less or borrow more, and thereby to higher economic 
growth.10 Box 2 discusses the macroeconomic effects 
of a housing demand shock in the euro area and the 
United States. Beyond wealth effects, higher house 
prices increase the value of the collateral against which 
households can borrow, and thus increase not only 

their borrowing propensity, but also their borrowing capacity and, in turn, potentially 
their spending. From an investment perspective, rising house prices may lead to 
an increase in the demand for residential investment if the value of new dwellings 
increases relative to their construction costs (i.e. if the so-called Tobin’s q for housing 
investment increases), thereby leading to higher profitability and increased incentives 
for new constructions. In this context, house prices, like other asset prices, represent 
a potentially important component in monetary policy transmission, to the extent that 
changes in interest rates and other (non-standard) monetary policy measures affect 
house prices, thereby influencing private consumption and residential investment via 
the aforementioned channels.

Box 2
Macroeconomic effects of housing demand shocks 

This box compares the macroeconomic effects of house prices in the euro area with those 
in the United States11. The analysis is based on a vector autoregressive model (VAR) that includes 
macroeconomic variables such as real GDP, consumer prices and the short-term interest rate, with 
house prices and real housing investment representing the housing sector. In this framework,  

10  The magnitude of the positive effect on consumption from higher house prices is an empirical matter. 
However, several findings in the literature point to greater effects on consumption from housing wealth 
than from financial wealth, and to a greater impact in the United States than in the euro area.

11  Results are obtained with an updated version of the model presented in Jarocinski, M. and Smets, F., 
“House prices and the stance of monetary policy”, Review, Federal Reserve Bank of St. Louis, 
July 2008, pp. 339-366. The data in this analysis are quarterly and the sample is from Q1 1990 to 
Q4 2014. Four lags are included, as is the typical practice with quarterly data. The estimation is 
Bayesian, with standard priors for VARs. The role of the priors is to improve the econometric properties 
of the model, given that its size is large relative to the sample size. The priors used follow Sims. C. and 
Zha, T., “Bayesian Methods for Dynamic Multivariate Models”, International Economic Review, 1998. 
By shrinking the coefficients, they overcome the overparameterisation of the VAR. A “loose” version of 
these priors is used.

69

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 
Impulse response to a housing demand shock 
in the euro area and the United States

(year-on-year percentage changes)

x axis: quarters after shock
y axis: percentage point impact

euro area
confidence intervals (euro area)
United States

a housing demand shock is identified by 
assuming that it causes a positive co-movement 
between house prices and housing investment 
on impact. In this analysis, a shock is simulated 
by assuming that it corresponds to the size of 
one standard deviation, i.e. the shock of the 
standard size encountered in the sample period 
(Q1 1990-Q4 2014).

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

-0.05

-0.10

-0.15

1

3

5

7

9

11

13

15

17

19

21

Source: ECB simulations.

a housing demand shock raises real gDP 
growth in the euro area for about two 
years. After a housing demand shock, the 
impact on real GDP growth builds up to peak 
after four quarters at about 0.25 percentage 
point. The effect then diminishes and fades in 
approximately two years (see the chart).

The results for the euro area are broadly 
consistent with other findings. Estimations 
for industrialised countries provide evidence of 
significant responses from house price shocks 

to real GDP.12 Results for OECD countries13 show that, economically, housing demand shocks 
have small but significant impacts on real GDP. Empirical studies investigating the macroeconomic 
effects of house price shocks in the euro area are, however, rather scarce compared with the 
research focusing on specific countries, but tend to corroborate these findings, notwithstanding 
some differences in the magnitude and profile of the responses to the shocks.

The US economy responds to a housing demand shock in a similar way, but with a 
stronger magnitude14. The VAR results suggest that the responses to the shock estimated 
for the United States are somewhat stronger on impact and more prolonged compared with 
those in the euro area. One possible explanation for the difference between the euro area and 
the United States is the stronger housing collateral channel in the latter. Since housing is more 
common as a form of collateral for loans to households in the United States, borrowing has a 
tighter link to house prices. Therefore, changes in house prices should have a stronger impact 
on credit conditions and, consequently, consumption, investment and GDP growth.

12 

Interestingly, the effects of house price shocks do not become stronger in periods of house price 
booms. See Goodhart, C. and Hoffman, B., “House Prices, Money, Credit and the Macroeconomy”, 
Working Paper Series, ECB, No 888, 2008. Their analysis is performed for 17 industrialised countries 
based on a fixed-effects panel VAR.

13  See Cardarelli, R., Monacelli, T., Rebucci, A. and Sala, L., “Housing finance, housing shocks and 

business cycle: VAR evidence from OECD countries”, unpublished manuscript, 2008.

14  See Musso, A., Neri, S. and Stracca, L., “Housing, Consumption and Monetary Policy: How different 

are the US and the Euro Area?”, Working Paper Series, ECB, No 1161, 2011.

70

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 16
Real estate-related loans of euro area MFIs

(percentage of GDP and percentage of total loans to households and non-financial 
corporations)

total real estate-related lending to households and 
non-financial firms (right-hand scale)
loans to non-financial firms for construction (left-hand scale)
loans to non-financial firms for real estate activities (left-hand scale)
loans to households for house purchase (left-hand scale)

57

56

55

54

53

52

51

50

49

48

47

46

45

60

55

50

45

40

35

30

25

20

15

10

5

0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: ECB.
Note: Most recent observation is for Q4 2014.

Chart 17
House prices and loans to households in 2014

The wealth and collateral aspects of house price 
developments also have an important bearing on 
the health of the financial sector. This is reflected 
by the high importance of real estate-related lending 
relative to banks’ balance sheets and overall economic 
output. In fact, as at the end of 2014, real estate-related 
loans to households and non-financial firms in the euro 
area accounted for nearly 57% of euro area banks’ total 
loans to the non-financial private sector, some 17% of 
the euro area banking sector’s total assets and 53% 
of euro area GDP, with loans to households for house 
purchase representing the largest share (see Chart 16). 
Banks typically decide on the volume of loans granted 
on the basis of borrowers’ ability to service debts 
through income (i.e. loan-to-income ratios) and the 
value of the property used as collateral for the loan (i.e. 
loan-to-value ratios). Thus, the aforementioned positive 
wealth effects and underlying favourable changes in 
collateral values would translate into lower probabilities 
of default and losses given default. However, in the 
event of unsustainable property price developments, 
the financial sector may load risks onto its balance 
sheet, especially if house price developments not only 
have a bearing on the loan volume granted, but, more 
generally, also translate into laxer credit standards.

(year-on-year percentage changes, x-axis: loans to households, y-axis: house prices)

15

10

5

0

-5

-10

-15

-10

EE

LT

AT

NL

DE

EA

FI

IT

IE

LV

PT

SI

ES

CY

GR

-5

0

LU

FR

SK

BE

MT

Positive effects of rising house prices on economic 
growth and the banking sector should thus be 
assessed against the related potential risks for 
financial stability. In the current low interest rate 
environment, greater appetite for risk may have the 
potential to push up real estate prices to values that are 
not justified by their fundamental values, a development 
that could be amplified by herding behaviour by 
investors in an environment of over-optimistic beliefs. 
In fact, residential property markets have been at the 
heart of many previous episodes of financial distress 
once buoyant house price developments started to be 
accompanied by strong credit growth and leverage. 
However, there are currently few signs of the ongoing 
recovery in residential property markets translating 
into either widespread house price imbalances or rapid 
housing loan growth at the level of the euro area as a 
whole or the respective national levels (see Chart 17), especially when compared 
with the situation prior to the crisis when most euro area countries recorded double-
digit household loan growth. Any observed decoupling of loan growth from house 
prices in the current housing market recovery phase may reflect specific factors, 

10

15

5

Sources: ECB and ECB calculations.
Notes: Bank lending data are adjusted for securitisation. Countries most affected by the 
ﬁ nancial crisis are Ireland, Greece, Spain, Italy, Cyprus, Portugal and Slovenia and are 
depicted with a yellow dot.

71

ECB Economic Bulletin, Issue 6 / 2015 – Articlessuch as the presence of foreign buyers in certain (mainly high-priced) market 
segments, especially in some large cities, or the purchase of housing with cash. In 
this context, countries with stronger growth in average house prices in the past few 
years, such as Germany and Austria, show signs of regional buoyancy, where house 
prices may exceed their longer-term fundamental value.15

macroprudential policies seem to be the most appropriate to mitigate real 
estate-related risks to financial stability, enabling granular and targeted 
policy action to be taken. The current very accommodative monetary conditions 
may not only stimulate economic risk-taking – necessary to ensure attainment of 
the price stability objective – but also lead to unintended side effects in the form 
of encouraging financial risk-taking.16 Against this background, there is a need to 
monitor risk-taking behaviour and, specifically, residential real estate price growth, 
particularly if they are accompanied by increased leverage, as such developments 
could amplify the risk of an abrupt residential property price correction. If these 
were to be widespread, they would lead to instability in the financial system, 
thereby hampering monetary policy transmission and, ultimately, price stability. 
Macroprudential policy, comprising a set of granular measures, provides the most 
appropriate instruments for mitigating risks to financial stability and containing 
systemic risks in order to support and complement monetary policy, which is geared 
towards fulfilling the ECB’s price stability mandate.

Several euro area countries have implemented macroprudential real estate 
instruments since the beginning of 2014, or plan to do so. In fact, the new 
macroprudential toolkit offers national authorities numerous property-related 
instruments. In terms of their objectives, these instruments may help to alleviate 
future cyclical challenges by smoothing the credit cycle and to increase the 
resilience of banks to potential house price excesses. Instruments targeting 
banks work via regulatory capital requirements, either directly (by imposing higher 
capital requirements) or indirectly (by targeting variables which affect capital 
requirements for real estate exposures). In this context, Belgium, for example, has 
decided to adjust risk weights under the Capital Requirements Regulation and 
Capital Requirements Directive IV, which has been in force since 1 January 2014. 
Instruments targeting borrowers work directly on the terms and conditions of the 
loans by making the volume of the loan granted dependent on the value of the 
underlying property or the debt-servicing capacity of the borrower.17 Some countries 
have opted to introduce or adjust loan-to-value caps (e.g. Estonia, Ireland and the 
Netherlands), as well as loan-to-income (e.g. Ireland) or debt service-to-income 
(e.g. Estonia and Lithuania) limits under national legislation.

15  See  the  February  2015  issue  of  the  Deutsche  Bundesbank’s  Monthly  Report  and  Schneider,  M., 
Wagner,  K.,  and  Waschiczek,  W.,  The  OeNB  property  market  monitor,  April  2015.  These  studies 
suggest that prices in German cities in 2014 could have been overvalued by between 10% and 20%, 
and prices in Vienna at the end of 2014 by 19%. 

16  For a more detailed discussion, see the box entitled “Accommodative monetary policy and euro area 

financial stability”, Financial Stability Review, May 2015.

17  For a more detailed description of macroprudential real estate instruments, see the ESRB Handbook on 

Operationalising Macro-Prudential Policy in the Banking sector, March 2014, pp. 49-76.

72

ECB Economic Bulletin, Issue 6 / 2015 – Articles5 

Conclusions

The ongoing upturn in euro area house prices appears to be sustainable. 
However, it seems that the current recovery is weaker than the typical increase 
observed historically during the initial phase of an upturn in house prices after a 
trough, and that it has not so far been accompanied by a significant increase in euro 
area real household loans. The ongoing recovery in house prices should be further 
supported by improving prospects for households’ income and employment, as well 
as favourable financing conditions. The prevailing low interest rate environment and 
its implications on yields will play an important role in this.

Substantial corrections in earlier house price imbalances have taken place in 
several euro area countries. Since 2007, when the downturn for euro area house 
prices started, the large heterogeneity in house price dynamics reflected, among 
other things, country-specific boom-bust cycles, demand and supply conditions, 
and structural factors. Housing markets in countries which have already corrected 
previous excesses in house price growth are likely to benefit more from the current 
favourable environment in terms of low interest rates and improving macroeconomic 
conditions, and vice versa. 

risks to financial stability appear to be limited at the current juncture, not 
least as the ongoing recovery in house prices has not translated into rapid 
credit growth so far. The new macroprudential toolkit has allowed several 
countries to take steps to rein in any potential house price and credit exuberance, 
with the numerous property-related instruments boding well for the alleviation of any 
future cyclical challenges, while also helping to increase the resilience of banks and 
their borrowers.

73

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesThe fiscal impact of financial sector 
support during the crisis 

During the financial crisis, most euro area governments provided financial assistance 
to ailing financial institutions with the aim of safeguarding financial stability and 
preventing a credit crunch. Over the period 2008-14 accumulated gross financial 
sector assistance amounted to 8% of euro area GDP, of which 3.3% has been 
recovered. The fiscal costs of the assistance to financial institutions are comparable 
to those of other systemic banking crises in the past, as they led to a deterioration 
in the euro area budget balance and debt by a cumulated 1.8% and 4.8% of GDP 
respectively. However, on average the measures account for a relatively small part 
of the overall strong increase in general government debt since the outbreak of the 
crisis. At the same time, outstanding government guarantees (amounting to 2.7% of 
euro area GDP at the end of 2014) and further potential losses of asset management 
vehicles to which impaired assets had been transferred still pose additional fiscal risks 
to governments. Looking ahead, it is important both to reduce the likelihood of financial 
institutions facing severe balance sheet problems by enhancing bank capital, banking 
regulation and supervision, and to promote bank resolution policies that include private 
sector involvement, thereby protecting taxpayers. In this sense, the recent steps 
towards a genuine European banking union are encouraging and should not only help 
to prevent and/or resolve future banking crises in the euro area, but also to reduce 
their potential fiscal impact on government deficits and debt. 

1 

Introduction

Since the outbreak of the financial crisis, most euro area governments have 
provided substantial financial assistance to financial institutions with the 
aim of safeguarding financial stability and preventing a credit crunch. These 
measures contributed to the increase in euro area general government debt, which 
rose by 27 percentage points between 2008 and the end of 2014, when it stood at 
92% of GDP. The direct net fiscal costs of financial sector assistance (less than 5% 
of GDP) were only one factor explaining the overall sharp rise in government debt in 
the euro area. Yet they played a much more important role in a number of euro area 
countries. A preliminary assessment of the fiscal impact of financial sector support 
was provided at an early stage of the financial crisis.1 Given that further support 
has been provided since then and in view of the gradual recovery of the euro area 
financial sector and the recent reforms supporting the prevention of banking crises, 
it is now a good time to reassess the fiscal costs of financial sector support. 

1 

See for example “The impact of government support to the banking sector on euro area public 
finances”, Monthly Bulletin, ECB, July 2009; van Riet, A. (ed.), “Euro area fiscal policies and the crisis”, 
Occasional Paper Series, No 109, ECB, 2010; and Stolz, S. and Wedow, M., “Extraordinary measures 
in extraordinary times: public measures in support of the financial sector in the EU and the United 
States”, Occasional Paper Series, No 117, ECB, 2010. 

74

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesThis article addresses the following questions: First, which financial assistance 
measures have been used? Second, how costly were the financial sector assistance 
measures for taxpayers, how much of the costs have been recovered to date and 
how much have the measures contributed to the sharp increase in government 
debt? Third, what are the remaining fiscal risks related to financial sector support? 
In addressing these questions, the article provides a detailed overview of the 
government measures to assist financial institutions since the beginning of the 
crisis.2 Compared with other systemic banking crises in advanced economies, the 
direct fiscal costs of financial assistance measures in the euro area are of a similar 
magnitude, while the overall increase in general government debt is considerably 
larger. However, the estimated fiscal costs of government intervention in the banking 
sector vary substantially across studies depending on the methodology used for their 
derivation and the definition of fiscal costs. This article mainly follows the bottom-up 
approach which sums up all of the government measures related to the financial 
crisis, although some of these measures may be difficult to quantify, especially if they 
were carried out by entities classified outside the general government sector.3 The 
article focuses on government measures to support the financial sector and does not 
look at the central bank liquidity measures adopted during the financial crisis.4 

The article is structured as follows: Section 2 looks at the support measures 
used by euro area governments over the period 2008-14, with a particular focus on 
asset acquisitions and capital transfers. Section 3 assesses their fiscal impact on 
budget balances and general government debt and discusses the recovery rates on 
acquired assets. Section 4 outlines the remaining fiscal risks related to government 
guarantees and asset management vehicles. The article ends with some policy 
conclusions in Section 5. 

2 

Financial sector assistance measures 

most euro area governments have supported the financial sector with a set 
of measures, notwithstanding considerable differences across countries and 
changes over time. For example, governments supported financial institutions by 
purchasing their illiquid financial assets and by providing them with direct loans. 
The acquired assets included equities, debt securities and other assets, which 

2 

3 

The analysis is based on data compiled by the Working Group on Government Finance Statistics of 
the European System of Central Banks (ESCB).The statistical framework is described in more detail in 
Maurer, H. and Grussenmeyer, P., “Financial assistance measures in the euro area from 2008 to 2013: 
statistical framework and fiscal impact”, Statistics Working Paper Series, No 7, ECB, 2015. 

In the literature, there are two main approaches to estimating the fiscal costs of financial sector 
support (see also ECB, 2009, op. cit.). First, the bottom-up approach sums up all of the government 
interventions; see for example Laeven, L. and Valencia, F., “Systemic banking crises database: 
an update”, Working Paper Series, No 163, IMF, 2012. Second, the top-down approach assumes 
that changes in the government debt-to-GDP ratio since the crisis are related to the financial crisis, 
although this approach also includes debt changes which are unrelated to financial sector support. 
This approach is followed inter alia by Reinhart, C. and Rogoff, K., “Recovery from Financial Crises: 
Evidence from 100 Episodes”, American Economic Review, Vol. 104(5), 2014, pp. 50-55.

4  Central bank liquidity measures are discussed, for example, in “The ECB’s non-standard measures – 

impact and phasing-out”, Monthly Bulletin, ECB, July 2011; “The ECB’s response to the financial crisis”, 
Monthly Bulletin, ECB, October 2010; and “The implementation of monetary policy since August 2007”, 
Monthly Bulletin, ECB, July 2009.

75

ECB Economic Bulletin, Issue 6 / 2015 – Articlesgovernments exchanged against cash or other collateral at their market value. 
Sometimes governments had to inject capital into ailing financial institutions by 
acquiring assets well above their market value. These recapitalisations aimed to 
cover the banks’ accumulated losses and eventually resulted in government losses. 
Some governments were also forced to nationalise (systemic) banks. Moreover, some 
of the impaired assets were acquired by newly created asset management vehicles. 
In exchange for the transferred assets, financial institutions were provided with cash 
and/or bonds issued by the vehicles usually enjoying a state guarantee. Governments 
also provided explicit guarantees to financial institutions, such as time-restricted 
guarantees for interbank loans or bonds, and raised the coverage threshold for 
guaranteed bank deposits. Furthermore, the higher government debt resulting from 
most of these interventions led to indirect fiscal costs in the form of additional interest 
payments. At the same time, government interventions also implied indirect revenues 
in the form of fees for guarantees granted, dividends on acquired equity and interest 
receipts on the loans provided and debt securities bought.

Chart 1
Assets acquired over the period 2008-14

(percentages of 2014 GDP)

net acquired assets
sales/repayments of aquired assets

6

5

4

3

2

1

0

Acquired financial
assets (total)

Equity
instruments

Provision of
new loans

Debt securities 
and other assets

Source: ESCB.
Note: The sum of the net acquired assets (blue) and the sales/repayments (yellow) 
corresponds to the gross amount of the respective acquired assets.

among the measures mentioned above, the 
acquisition of financial assets was used by euro 
area governments in particular in the early years 
of the financial crisis. Between 2008 and 2014 
governments acquired financial assets in an amount of 
5.3% of euro area GDP in gross terms (see Chart 1), 
two-thirds of which were purchased in the first three 
years of the crisis. When taking into account the fact 
that part of the assets has in the meantime been 
divested without losses, the net acquired assets 
amounted to 2.9% of GDP in 2014. Among the acquired 
assets, debt securities and equities together accounted 
for the bulk (around 90%) of the acquired assets, 
while new loans were used to a much lower degree 
over the period 2008-14. The net acquisition of financial 
assets was particularly pronounced in Germany, 
Ireland, Greece, Cyprus, Luxembourg, Portugal and 
Slovenia, with acquisitions being well above 5% of GDP 
(see column (A) of Table 2). 

moreover, euro area governments supported distressed financial institutions 
via capital transfers. Besides financial assets purchased at above-market prices, 
capital transfers reflected called government guarantees and other types of debt 
assumption. Capital transfers amounted to 2.1% of GDP over the period 2008-14. 
These deficit-increasing capital transfers played a role in particular in Ireland (above 
25% of GDP), Greece, Cyprus and Slovenia, while in other countries their extent was 
more limited (see column (C) of Table 2). 

76

ECB Economic Bulletin, Issue 6 / 2015 – Articles3 

Fiscal impact of financial assistance measures 

general government debt in the euro area increased from 65% of gDP in 
early 2008 to 92% of gDP at the end of 2014. Only a relatively small part of this 
rise in government debt, which was particularly pronounced in the first years of the 
financial crisis, was driven by the support provided by euro area governments to 
ailing financial institutions in order to secure financial stability, although for some 
individual countries the impact on government debt 
was substantial. To better understand how government 
finances were affected by the financial crisis in 
general and by financial sector support interventions 
in particular, it is useful to decompose the government 
debt increase into its main driving factors (see Chart 2). 

Chart 2
Change in government gross debt and its drivers

(percentages of GDP)

change in government debt
primary balance (-)
interest rate-growth differential
deficit-debt adjustment

10

8

6

4

2

0

-2

-4

2007

2008

2009

2010

2011

2012

2013

2014

Sources: Eurostat and ECB.
Notes: Deﬁ cit-debt adjustment is the difference between the government deﬁ cit 
and the change in government debt. For more details, see “From government deﬁ cit 
to debt: bridging the gap”, Monthly Bulletin, ECB, April 2007.

First, the debt-increasing impact of a positive 
interest rate-growth differential, which captures 
the difference between the interest rate paid to 
service government debt and the growth rate of the 
economy, was particularly strong in the early years of 
the crisis, following the cyclical downturn in the euro 
area triggered by the financial crisis. More recently, 
as a consequence of low interest rates and a cyclical 
recovery, its relative importance has started to decline. 

Second, the rise in government debt was also 
driven by a sharp deterioration of the primary 
balance. This was inter alia the result of higher primary 
expenditure, reflecting the role of automatic stabilisers, 
such as higher unemployment benefits, as well as 
discretionary fiscal policy measures. The latter also 

include part of the financial sector assistance measures, in particular the costs 
of capital transfers to cover accumulated losses of ailing banks. Moreover, lower 
government revenues following the cyclical downturn and the rebalancing process 
also contributed to the worsening of the primary balance via so-called second-round 
effects, as the decline in employment, corporate profits and asset values, triggered 
by the financial crisis, resulted in lower revenues. 

Third, the debt increase was driven by deficit-debt adjustments. These had 
several causes. Among them are those financial sector support measures that did 
not affect the primary balance, but did affect general government debt, such as 
governments’ acquisition of equities at market prices or the provision of government 
loans to the financial sector. As the governments acquired assets, these measures 
resulted in an increase in gross debt, while net debt remained largely unaffected. 
Deficit-debt adjustments were particularly pronounced until 2010 for the euro area as 
a whole. 

77

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesTo quantify the impact of the financial sector assistance measures on the 
government accounts, it is necessary to look at the individual transactions 
and the financing requirements resulting from them. Depending on the nature 
and the magnitude of the assistance measures, government finances are affected 
differently (see Table 1).

Table 1
Accounting framework for general government assistance to the financial sector 

Examples

government debt

balance

Impact on general

Impact on fiscal 

Direct impact of interventions

Acquisition of ﬁ nancial assets

Acquisition of equities (market price)

Acquisition of other assets, e.g. debt securities

Capital transfers to banks

Provision of loans

Sales of shares

Repayments of loans

Acquisition of ﬁ nancial assets above market 
price

Capital injections covering bank losses 

Debt assumptions

Called government guarantees

Indirect impact of interventions

Miscellaneous revenues 
and expenditures

Guarantee fees

Dividends

Interest payable/ receivable

Reclassiﬁ	cation	of	entities	and	other	ﬂ	ows	(without	transactions)

Entities reclassiﬁ ed from ﬁ nancial 
sector to general government

Liabilities of entities (i.e. the non-consolidated 
liabilities)

Assets of entities (i.e. only the consolidated 
assets)

Provision of guarantees

Government guarantees

Guarantees on liabilities (contingent liabilities)

Guarantees on assets

Source: ESCB.

↑

↑

↑

↓

↓

↑

↑

↑

↑

↓

↓

↑↓

↑

(↓)

0

0

0

0

0

0

0

↑

↑

↑

↑

↓

↓

↑↓

0

0

0

0

•	 While all instruments, except for outstanding government guarantees, increase 

government debt, most of them do not affect the budget balance (see Table 1). In 
particular, net acquisitions of financial assets by governments, including equities, 
loans and debt securities at market prices, are debt-increasing.

•	 Acquisitions of financial assets usually only affect gross government debt, while 

net government debt remains broadly unchanged. 

•	 By contrast, all financial sector support measures that include an irreversible 
annual expenditure for the government are recorded in the cumulated budget 
balance (see Table 1 and column (B) of Table 2). They include capital transfers, 
such as the debt of banks taken over by the government, called government 
guarantees, financial assets purchased at above-market prices and  
deficit-increasing capital injections into banks to cover past losses. In addition, 
the general government budget balance is also affected by revenues that are 
linked to financial support transactions, such as fees received by governments 
for granting guarantees, dividends from acquired equities as well as interest 
received on financial instruments acquired. The sum of the net acquisition of 

78

ECB Economic Bulletin, Issue 6 / 2015 – Articlesfinancial assets and the cumulated impact on the budget balance represents the 
government’s net fiscal costs of the financial assistance measure (see Table 2). 

The reclassification of entities from the financial sector to the general 
government sector (notably in the case of bank nationalisations) increases 
government debt. 

The provision of government guarantees has no direct impact on public finances, 
unless the guarantees are called. 

•	

•	

Financial assistance measures led to a worsening of the euro area budget 
balance by a cumulated 1.8% of gDP between 2008 and 2014. How their impact 
on government accounts is treated in the Stability and Growth Pact is described in 
the box later in this article. The impact on the budget balance differed considerably 
across countries. The deficit impact was particularly strong in Ireland, where it led to 
a cumulated worsening of the budget balance of almost 25% of GDP (see Table 2). 
The budget balances of Greece, Cyprus and Slovenia were also substantially 
affected by the support measures, with a cumulated deficit impact of between 8% 
and 13% of GDP during 2008-14. In most other countries, the cumulated deficit 
impact was more limited, ranging from 0.4% of GDP in Belgium to 4.4% of GDP in 
Spain. For France, Italy and Luxembourg, the cumulated revenues from financial 
assistance measures even slightly exceeded the expenditures. 

Table 2
Fiscal impact of financial sector support over the period 2008-14

(percentages of 2014 GDP)

Net fiscal costs

EDP debt 
impact

memo item: Change 
in government debt

Net 
acquisitions 
of financial 
assets 
(a)

Total
(a)+(B)

BE

DE

IE

gr

ES

Fr

IT

CY

LV

LT

LU

NL

aT

PT

Sl

Ea

3.7

8.0

31.1

22.1

5.0

0.0

-0.1

18.8

5.2

1.3

5.5

4.8

3.5

11.3

18.1

4.7

3.3

6.7

7.0

9.6

0.6

0.1

0.0

10.3

1.9

0.2

5.6

4.1

0.4

8.4

6.1

2.9

Cumulated impact on budget 
balance 
(deficit (+), surplus (-))

total
(B)

0.4

1.3

24.1

12.5

4.4

-0.1

-0.1

8.5

3.3

1.1

-0.1

0.7

3.1

2.9

12.0

1.8

due to capital 
transfers
(C)

(end of 
2014)
(D)

(2008-14)

1.1

1.8

25.7

14.9

4.8

0.1

0.0

9.1

3.3

0.9

0.1

0.7

3.6

2.6

11.5

2.1

4.6

8.2

22.6

22.2

5.0

0.1

0.1

19.4

5.5

0.9

5.3

5.5

8.4

11.0

18.2

4.8

19.7

11.0

85.7

73.7

62.2

31.1

32.4

53.4

31.6

25.0

16.0

26.1

19.7

61.7

58.2

27.0

Sources: ESCB and Eurostat.
Notes: Estonia, Malta, Slovakia and Finland are not included in the table as no ﬁ nancial support was provided to the ﬁ nancial sector. 
The difference between the cumulated budget balance (B) and capital transfers (C) includes net miscellaneous ﬁ nancing costs or 
revenues, such as fees on guarantees, dividends, and interest payable or receivable linked to acquired ﬁ nancial instruments. As 
regards column (D), in comparison to the net ﬁ scal costs, the excessive deﬁ cit procedure (EDP) debt impact also includes the impact 
of reclassiﬁ cations of ﬁ nancial entities (e.g. a bad bank) inside the government (without transactions), other ﬂ ows and ﬁ nancial 
transactions not recorded in EDP debt. 

79

ECB Economic Bulletin, Issue 6 / 2015 – Articlesgeneral government debt in the euro area increased by 4.8% of gDP over the 
period from 2008 to 2014 owing to financial sector assistance. The impact on 
government debt resulted from the sum of the net fiscal costs of financial sector 
support (4.7% of GDP) and the impact of reclassifications and other flows (0.1% of 
GDP).5 The debt increase as a result of financial sector support corresponds to less 
than one-fifth of the increase in government debt over the same period (see Table 2). 
The debt impact of financial sector support varied considerably across countries. 
Financial sector support led to a substantial increase in government debt of around 
20% of GDP in Ireland, Greece, Cyprus and Slovenia. It also had a high impact 
in Germany, especially owing to measures taken at the onset of the crisis, and in 
Austria and Portugal, mainly as a result of more recent interventions. By contrast, 
government debt in Italy and France was hardly affected by financial sector support.

Compared with past financial crises in advanced economies, the deterioration 
in euro area government finances was worse, despite a similar amount of 
financial sector assistance. According to a recent study from the International 
Monetary Fund (IMF), which measured the fiscal costs of 60 systemic banking 
crises between 1970 and 2011, the median increase in overall government debt 
was around 12% of GDP, of which 7 percentage points were accounted for by the 
direct fiscal costs of financial sector support.6 Regarding the recent crisis (2007-11), 
the IMF analysis looks at a sample of 25 systemic banking crises, mainly involving 
advanced economies. The median increase in government debt was 18% of GDP, 
of which 4.2 percentage points were due to direct fiscal costs, compared with an 
increase in government debt for the euro area of almost 22% of GDP, of which only 
4.6 percentage points were explained by direct financial sector support. These 
differences suggest that the indirect macroeconomic costs of the financial crisis in 
the euro area have been even more pronounced compared with previous systemic 
banking crises. 

The recovery rates, which represent the share of acquired assets which 
governments were able to successfully dispose of, are improving, but are 
still relatively low by historical standards. Recovery rates are derived from the 
difference between the acquired financial assets in gross terms and the assets in 
net terms. Up to now, eight years after the crisis started, only a small fraction of the 
fiscal costs of the euro area has been recovered (see Chart 3). Out of 8% of GDP 
of accumulated gross financial sector assistance measures,7 which corresponds to 
€800 billion, 3.3% of GDP has been recovered through sales of acquired assets and 
other miscellaneous revenues derived from the assets acquired and the guarantees 
granted (e.g. dividends, fees, interest receipts). This corresponds to a recovery rate 

5  While the difference is usually very small, there are some exceptions. For example, in Ireland, some 

financial assistance measures have been financed through the disposal of assets held by government 
pension funds.

6 

7 

See “From Banking to Sovereign Stress – Implications for Public Debt”, IMF, 2015, which analyses how 
certain country and banking sector characteristics impact the fiscal costs of banking crises.

To get an idea of governments’ financial exposure arising from financial sector support, the net fiscal 
costs have to be supplemented by the amount of outstanding government guarantees and include 
the indirect revenues. In the literature, however, it is sometimes argued that gross fiscal costs (which 
encompass recovered assets) are a better metric to reflect the taxpayers’ money spent at the time of 
the support. This indicator could also serve as a basis for determining the financial buffers needed 
for government assistance in the context of possible future crises (see for example Laeven, L. and 
Valencia, F., op.cit.).

80

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesChart 3
Fiscal costs and recovery rates over the period 2008-14

(percentages of 2014 GDP)

fiscal costs (gross)
fiscal costs (net)

45

40

35

30

25

20

15

10

5

0

-5

BE

DE

IE

GR

ES

FR

IT

CY

LV

LT

LU

NL

AT

PT

SI

EA

Sources: ESCB and Eurostat.
Notes: Estonia, Malta, Slovakia and Finland are not included in the chart as no ﬁ nancial sector support was provided in these countries. As regards Greece, see also footnote 9. 
The recovery rate is the difference between the acquired ﬁ nancial assets in gross and net terms.

of slightly more than 40% of gross fiscal costs, which is relatively low by international 
comparison. For example, Sweden was able to recover almost 95% of its budgetary 
outlays five years after the crisis in 1991.8 The recovery rates to date are particularly 
low in Ireland, Cyprus and Portugal, while they are relatively high in the Netherlands.9

The recovery rates, however, differ for different types of acquired assets. 
Looking at the different instruments, most (92%) of the loans provided to banks had 
been paid back by the end of 2014 (see Chart 1). By contrast, governments still hold 
a large share of the acquired debt securities and equities, although they were able to 
sell 43% of the debt securities and 34% of the equities by end-2014. Consequently, 
the accumulated net acquisition of both debt securities and equities amounted to 
1.4% of GDP by the end of 2014. 

at the same time, the recovery rates also need to be carefully analysed, together 
with the accumulated losses related to the assistance measures. In fact, a low 
recovery rate might be due to very different scenarios and does not necessarily 
indicate high accumulated losses. For example, a limited recovery rate could indicate, 
in the best case, that a government retains ownership of a well-performing bank which 
would generate substantial gains in the event of a subsequent privatisation. In the 
worst case, a limited recovery rate could indicate that the interventions led to major 
irreversible losses, as in the case of Cyprus, with a holding loss for the government on 
equity instruments amounting to 10.5% of GDP owing to the restructuring of one of its 
largest banks. For the period 2008-14 the accumulated losses on average for the euro 
area amounted to 1.8% of GDP, thus indicating that almost 25% of the gross fiscal 
needs are currently accounted for as a loss which cannot be recovered. 

8 

See also Table 1 in ECB, July 2009, op. cit.

9  With regard to Greece, the substantial difference between gross and net fiscal costs is mainly 

explained by the following facts. Until 2012 the Hellenic Financial Stability Fund provided loans to the 
Greek banking system. These loans were subsequently used to bridge the time until recapitalisations 
in the form of equity acquisitions could take place (2013). Although this basically represents a shift in 
instruments, the gross fiscal costs include both loans and recapitalisations, while the net fiscal costs 
are adjusted for the loans cancelled after recapitalisations.

81

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesBox 
Treatment of financial sector support in the Stability and Growth Pact 

In the Stability and growth Pact (SgP), financial sector support is treated in a special manner 
given its importance for safeguarding financial stability. At times of severe banking problems, 
Member States are generally not required to compensate for the fiscal costs arising from financial 
sector support, provided the measures are of a temporary nature.10 Given that the fiscal costs to 
secure financial stability are largely beyond the control of governments, this seems broadly justified. 
In fact, it is important that the SGP does not provide disincentives to effective public backstops. A 
similar approach has also been taken in the EU-IMF adjustment programmes, in which nominal fiscal 
targets are set explicitly by excluding the impact of financial sector support on the deficit. 

Under the preventive arm of the SgP, capital injections are excluded from the calculation 
of the structural effort, provided they are temporary. The structural effort is the main indicator 
under the preventive arm to assess whether the adjustment path towards the medium-term 
objective (MTO) is fulfilled. The structural effort is calculated based on the change in the structural 
balance, which excludes temporary and one-off measures such as capital injections. 

Under the corrective arm of the SgP, an excessive deficit procedure (EDP) may not be 
opened in the case of financial sector support. If a Member State were to be temporarily in 
breach of the deficit criterion as a result of financial sector support, but the deficit would remain 
close to 3% of GDP (i.e. not more than 0.5% of GDP above the reference rate), opening an EDP 
can be avoided. Likewise for the debt, when non-compliance with the debt reduction benchmark 
results from financial sector support, a debt-based EDP will not be opened. In both cases, financial 
sector support measures (including contingent liabilities) are accounted for as relevant factors, as 
under the debt-based EDP assessments for Belgium and Italy published in February 2015. 

In addition, financial sector support would not lead to the stepping-up of an existing EDP. 
Compliance with the EDP requirements is assessed based on the structural effort, which excludes 
temporary and one-off measures. Thus, Member States providing financial sector support are not 
forced to make additional fiscal consolidation efforts, provided that the necessary structural efforts 
are made. For example, in the context of the 2013 extension of the EDP deadline for Slovenia, 
the Council recommendation also referred to deficit targets, netting out the expected fiscal costs 
of financial sector support. Moreover, if a country is not able to correct its excessive deficit by 
the agreed deadline as a result of financial sector support, the procedure will not be stepped 
up. Instead, the abrogation of the procedure would be delayed for some time (usually one year). 
However, when deciding on the abrogation of an EDP, the decision is based on the nominal 
(headline) deficit, which is not adjusted for financial sector support.

10  See the box entitled “The fiscal implications of financial sector support”, Monthly Bulletin, ECB, 

June 2013.

82

ECB Economic Bulletin, Issue 6 / 2015 – Articles4 

Fiscal risks related to government interventions 
in financial institutions 

In addition to the above-mentioned fiscal costs, financial sector support 
can entail significant broader fiscal risks. If one were to include the amount of 
explicit contingent liabilities related to government guarantees granted to financial 
institutions, which have no immediate direct impact on government finances but pose 
a potential fiscal risk should they be called, the government’s exposure would be 
much higher.11 Moreover, governments also provided implicit guarantees to financial 
institutions, partly motivated by the too-big-to-fail argument and in order to avoid 
spillover effects. However, these implicit guarantees are difficult to quantify and are 
therefore not discussed further in this article. 

at the outbreak of the financial crisis, many euro area governments provided 
explicit guarantees to financial institutions to support financial stability. Most 
explicit government guarantees were granted to ailing financial institutions for 
different kinds of liabilities and assets, such as issued bonds, interbank deposits, 
senior unsecured debt, asset-backed securities and dated subordinated debt. 
Furthermore, in a few cases, for example in Ireland, Spain and France, government 
guarantees were also granted for the financing of asset management vehicles (on 
average these amounted to about 20% of outstanding government guarantees at the 

Chart 4
Financial exposure of governments arising from 
interventions in financial institutions

(percentages of GDP)

net fiscal costs
government guarantees

12

10

8

6

4

2

0
2008

2009

2010

2011

2012

2013

2014

Source: ESCB.
Notes: The ﬁ nancial exposure of governments arising from interventions in ﬁ nancial 
institutions comprises the ﬁ nancial assistance measures recorded under direct net 
ﬁ scal costs and the amount of outstanding government guarantees. Government 
guarantees and net ﬁ scal costs are expressed as a percentage of annual GDP. 
Government guarantees do not include deposit insurance schemes.

end of 2014). Explicit guarantees were mainly granted 
in the early years of the crisis. The level  
of government guarantees granted, expressed in terms 
of GDP, exceeded the net fiscal costs of financial 
sector support until 2012 (see Chart 4). Government 
guarantees in the euro area peaked at almost 
8% of GDP in 2009 (excluding government guarantees 
on retail deposits) and declined to 2.7% of GDP by the 
end of 2014.

Since 2012 the amount of outstanding government 
guarantees has declined strongly. This can be 
explained by three factors. First, a large share of 
guarantees has expired since then and, because 
financial stability has been re-established, it was not 
necessary to prolong them. There were no or only 
limited new guarantees granted to financial institutions 
in 2014. Second, in a few cases guarantees were 
called and the amounts were then recorded as general 
government debt. Third, some financial entities to 
which governments granted guarantees were recently 
reclassified inside the general government. This implied 
that the liabilities of the reclassified entities became part 

11  The immediate direct impact of state guarantees on government finances relates to the collection of 

fees that financial institutions usually have to pay in exchange for the state guarantee, which results in 
government revenues. However, the collected fees have been rather limited for most countries. For the 
euro area, they amounted to an annual average of less than 0.05% of GDP during 2008-14.

83

ECB Economic Bulletin, Issue 6 / 2015 – Articlesof general government debt, while the amount of government guarantees to these 
entities declined accordingly. 

While most euro area countries provided explicit government guarantees, 
their outstanding amount varied across countries. By far the highest level of 
government guarantees in terms of GDP was observed in Ireland, which peaked at 
190% of GDP in 2008, but substantially declined thereafter to around 13% of GDP 
by the end of 2014 (see Table 3). Government guarantees at end-2014 were still 
sizeable in Greece and to a lower degree in Belgium, while they had been almost 
fully phased out in the Netherlands and Austria. For the latter, the decrease in 
guarantees is explained by the fact that one financial institution is now classified 
inside the general government, which implies that the respective government 
guarantees are no longer recorded, whereas gross government debt has increased 
by the amount of the outstanding liabilities of this entity.

Table 3
Outstanding government guarantees

 Peak amount

End of 2014

BE

DE

IE

GR

ES

FR

IT

CY

LV

LU

NL

AT

PT

Sl

EA

Percentage of gDP

15.4

5.5

190.0

37.9

9.9

4.7

5.3

16.9

2.8

4.8

12.1

5.0

9.5

5.9

7.6

Year

(2009)

(2009)

(2008)

(2011)

(2012)

(2009)

(2012)

(2010)

(2009)

(2013)

(2009)

(2009)

(2012)

(2010)

(2009)

9.3

0.8

12.9

28.5

5.2

2.2

1.4

5.7

0.2

4.6

0.0

0.0

3.6

0.3

2.7

Sources: ESCB and Eurostat.
Notes: Estonia, Lithuania, Malta, Slovakia and Finland are not included in the table as they did not provide government guarantees to 
ﬁ nancial institutions. The outstanding government guarantees do not include the guarantees on retail deposits and state guarantees 
for emergency liquidity assistance. The latter do not fall within the scope of the statistics on government guarantees

Several countries have experienced losses on government guarantees that 
were called. While for the euro area as a whole, the share of called guarantees 
seems to be rather limited, amounting to roughly 0.3% of total outstanding 
guarantees (less than 0.01% of GDP) in 2014, this figure might be somewhat 
misleading. In particular in cases of major restructuring of financial institutions, 
to which government guarantees were granted, the outstanding government 
guarantees decreased through the purchase of impaired assets previously 
guaranteed. In this respect, estimates of default probabilities can help form a view of 
the risk of government guarantees being called.12 

12  See “Fiscal implications of the global economic and financial crisis”, Occasional Paper Series, No 269, 
IMF, 2009. Based on the estimation of the expected default frequency-implied credit default swap 
spreads, using November 2008 market data, outlays from state guarantees were estimated to be in the 
order of 1-3% of GDP in cumulative terms for 2009-13 for advanced economies.

84

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesIn addition, many countries lifted the coverage threshold of their deposit 
insurance schemes. At the beginning of the crisis, the coverage of the national 
schemes was increased to a minimum level of €50,000 per depositor per bank. This 
threshold was then further raised to €100,000 as part of the revisions to the EU 
Directive on deposit guarantee schemes, which also led to the banks – rather than 
governments – having to cover the insurance. In contrast to the explicit guarantees 
provided for financial institutions’ assets, the deposit guarantee schemes are 
not time-restricted. Thus, they do not phase out unless it is decided to lower the 
threshold covered by the insurance. If claims were to be made, they would in the first 
instance be covered by the insurance, so the impact on budget balances would likely 
be limited.

moreover, government guarantees might imply higher fiscal costs in the long 
run as they could, like other financial sector interventions, create adverse 
incentives for financial institutions. In fact, those institutions benefiting from 
government guarantees and other interventions might be inclined to take more 
risks or postpone a speedy repair of their balance sheets (see IMF, 2015, op. cit.). 
Moreover, government guarantees might not be fully credible, in particular if they 
are sizeable while the country’s fiscal space is limited, in which case governments 
would be unable to pay if the guarantees were to be called.13 Credibility concerns, 
however, would make the resolution potentially more costly, as they would increase 
the likelihood of guarantees being called and the need for further measures in 
support of the financial sector. Thus, although government guarantees have almost 
no immediate direct impact on government finances, they might in the end result in 
higher direct fiscal costs.

Fiscal risks also relate to the newly created asset management vehicles for 
which fiscal costs could turn out higher than expected. More than half of the 
euro area countries created such vehicles in order to relieve the balance sheets 
of financial institutions by transferring impaired assets to these new entities at a 
reduced book value. Generally, asset management vehicles can be seen as an 
effective means for dealing with non-performing loans.14 However, the potential fiscal 
risks related to asset management vehicles vary considerably across countries, 
depending on their specific characteristics in terms of government ownership, 
governments’ responsibilities in case of losses and the underlying financing strategy 
of the entities.15 In particular when asset management vehicles are classified within 
the general government sector, all their liabilities are part of general government 
debt, as for example in the case of the BAMC in Slovenia (see Table 4). The future 
impact on the budgetary balance depends on how future revenues and expenditures 
evolve. If, however, the asset management vehicles are classified within the financial 

13  Partly as a result of substantial government guarantees, rating agencies have downgraded a number 
of euro area countries, which led to an increase in their financing costs (see Stolz and Wedow, 2010, 
op. cit.). 

14  See “Resolving the legacy of non-performing exposures in euro area banks”, Financial Stability Review, 

ECB, May 2015.

15  Privately owned asset management vehicles usually impose larger haircuts on the acquired assets 
than publicly owned entities, which affects the profitability of the entities and the subsequent fiscal 
risks. The haircut applied to transferred assets has varied from 0% to more than 50%. See Gandrud, C. 
and Hallerberg, M., “Bad banks in the EU: the impact of Eurostat rules”, Working Paper Series, No 15, 
Bruegel, 2014.

85

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesTable 4
Key features of selected asset management vehicles

Year of 
creation

government 
share

Financing 
(outstanding 
bond issuance)1)

percentage of GDP total 

assets1)
percentage 
of GDP

o/w bank 
loans

Haircuts on 
transferred 
assets2)

Net loss (-)/
profit (+) 
in 2014

per cent percentage of GDP

NAMA (Ireland)

SAREB (Spain)

BAMC (Slovenia)

2009

2012

2013

49%

45%

100%

7.3

4.3

4.2

8.4

4.8

4.7

7.2

3.1

3.8

57

46/63

71

0.2

-0.1

0.1

Sources: Websites of the asset management vehicles, and Gandrud and Hallerberg (2014, op.cit.).
1) Data refer to outstanding amounts as at the end of 2014.
2)  Average haircuts applied by selected asset management vehicles (see Gandrud and Hallerberg, 2014, op.cit.). As regards Ireland, 
the haircut relates to loans worth €74 billion in total transferred to NAMA by the ﬁ ve participating institutions (on payment of €31.8 
billion as consideration) by the end of 2011. For Spain, the average haircut for the assets transferred was 46% in the case of loans 
and 63% in the case of foreclosed assets. For Slovenia, the ﬁ gure given relates to assets transferred in 2013.

sector, the direct fiscal impact is likely to be more limited and mainly related to the 
risk that future dividends will not cover future losses, in which case the outstanding 
government guarantees would need to be called. In addition, ownership may be 
shared, as is the case with the asset management vehicles of Ireland (NAMA) and 
Spain (SAREB). Moreover, as demonstrated by NAMA and BAMC in 2014, asset 
management vehicles can also generate profits. 

5 

Conclusions

During the financial crisis, most euro area governments provided financial 
assistance to financial institutions with the aim of safeguarding financial 
stability and preventing a credit crunch. Accumulated gross financial sector 
assistance measures amounted to 8% of euro area GDP, of which only 3.3% has 
been recovered. The fiscal costs of the financial assistance measures over the 
period 2008-14 caused the euro area budget balance and debt to worsen by a 
cumulated 1.8% and 4.8% of GDP respectively. For the euro area as a whole, the 
financial sector support explains only a small part of the sharp increase in general 
government debt since the start of the crisis, while for some individual countries 
the impact on government debt has been substantial. Euro area countries used 
a variety of support measures, including bank recapitalisations, the provision of 
government loans, the acquisition of impaired assets, bank nationalisations and the 
granting of government guarantees. These measures impact recorded government 
debt and deficits to different extents. The size of these assistance measures is very 
heterogeneous across euro area countries. The interventions’ overall recovery rate 
is on average relatively low by international comparison. However, the recovery 
process is still ongoing. To complete the picture, it is also important to take the 
fiscal risks related to financial sector support into account, which mainly include the 
remaining government guarantees granted (amounting to 2.7% of euro area GDP 
at the end of 2014) and the potential losses (or possible holding gains) of asset 
management vehicles. 

Looking ahead, it is important to secure financial stability while limiting 
taxpayers’ involvement. This requires in the first instance reducing the likelihood 
that financial institutions will face severe balance sheet problems. Much has already 

86

ECB Economic Bulletin, Issue 6 / 2015 – Articlesbeen achieved in tightening banking legislation, strengthening banking supervision, 
and should financial institutions indeed face problems, having the appropriate 
resolution instruments at hand. One important way to reduce the potential fiscal 
costs of financial assistance measures is to ensure an appropriate contribution 
by private shareholders and bondholders. Indeed, the EU Capital Requirements 
Regulation and Capital Requirements Directive IV and the newly created Single 
Supervisory Mechanism will enhance the resilience of the banking sector and should 
help prevent the build-up of severe problems on banks’ balance sheets which could 
ultimately result in a severe banking crisis. In addition, the EU Bank Recovery and 
Resolution Directive and the Single Resolution Mechanism will ensure private sector 
involvement ahead of government assistance. Taken together, these key pillars 
of the European banking union should ensure that the risk of additional taxpayer 
support gradually diminishes. 

87

ECB Economic Bulletin, Issue 6 / 2015 – ArticlesStatistics

Contents

  1 External environment

  2 Financial developments

  3 Economic activity

  4 Prices and costs

  5 Money and credit

  6 Fiscal developments

Further information

S 2

S 3

S 8

S 14

S 18

S 23

  ECB statistics can be accessed from the Statistical Data Warehouse (SDW): 

http://sdw.ecb.europa.eu/

  Data from the statistics section of the Economic Bulletin are available from the SDW: 

http://sdw.ecb.europa.eu/reports.do?node=1000004813

  A comprehensive Statistics Bulletin can be found in the SDW: 

http://sdw.ecb.europa.eu/reports.do?node=1000004045 

  Methodological definitions can be found in the General Notes to the Statistics Bulletin:

http://sdw.ecb.europa.eu/reports.do?node=10000023

  Details on calculations can be found in the Technical Notes to the Statistics Bulletin:

http://sdw.ecb.europa.eu/reports.do?node=10000022

  Explanations of terms and abbreviations can be found in the ECB’s statistics glossary:  http://www.ecb.europa.eu/home/glossary/html/glossa.en.html

Conventions used in the tables

 -

.

...

data do not exist/data are not applicable 

data are not yet available

nil or negligible

(p)

provisional

  s.a.

seasonally adjusted

  n.s.a.

non-seasonally adjusted

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 1
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 External environment

1.1 Main trading partners, GDP and CPI

   GDP 1) 
   (period-on-period percentage changes)

   CPI
   (annual percentage changes)

G20

United
States Kingdom

United Japan China Memo item:
euro area

   OECD countries

Total

excluding food
and energy

United Japan China

United
States Kingdom
(HICP)

Memo item:
euro area 2)
(HICP)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug.  3) 

1

3.0
3.0
3.3

0.9
0.8

0.7
. 

- 
- 
- 
- 
- 
- 

2

2.2
1.5
2.4

1.1
0.5

0.2
0.9

- 
- 
- 
- 
- 
- 

3

0.7
1.7
3.0

0.7
0.8

0.4
0.7

- 
- 
- 
- 
- 
- 

4

1.7
1.6
-0.1

-0.3
0.3

1.1
-0.4

- 
- 
- 
- 
- 
- 

5

7.8
7.7
7.4

1.9
1.5

1.3
. 

- 
- 
- 
- 
- 
- 

6

-0.8
-0.4
0.8

0.2
0.4

0.4
0.3

- 
- 
- 
- 
- 
- 

7

2.3
1.6
1.7

1.8
1.4

0.6
0.5

0.6
0.4
0.6
0.6
0.6
. 

8

1.8
1.6
1.8

1.9
1.8

1.7
1.6

1.7
1.6
1.6
1.6
1.7
. 

9

2.1
1.5
1.6

1.8
1.2

-0.1
0.0

-0.1
-0.2
0.0
0.1
0.2
. 

10

2.8
2.6
1.5

1.5
0.9

0.1
0.0

0.0
-0.1
0.1
0.0
0.1
. 

11

12

0.0
0.4
2.7

3.4
2.5

2.3
0.5

2.3
0.6
0.5
0.4
0.3
. 

2.6
2.6
2.0

2.0
1.5

1.2
1.4

1.4
1.5
1.2
1.4
1.6
. 

13

2.5
1.4
0.4

0.4
0.2

-0.3
0.2

-0.1
0.0
0.3
0.2
0.2
0.2

Sources: Eurostat (col. 3, 6, 10, 13); BIS (col. 2, 4, 9, 11, 12); OECD (col. 1, 5, 7, 8).
1) Quarterly data seasonally adjusted; annual data unadjusted.
2) Data refer to the changing composition of the euro area.
3) The figure for the euro area is an estimate based on provisional national data, which usually cover around 95% of the euro area, as well as on early information on energy prices.

1.2 Main trading partners, Purchasing Managers’ Index and world trade

   Purchasing Managers’ Surveys (diffusion indices; s.a.)

   Merchandise
   imports 1)

   Composite Purchasing Managers’ Index

   Global Purchasing Managers’ Index 2) 

Global 2) United

States Kingdom

United Japan China Memo item: Manufacturing Services
euro area

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

1

2

3

4

5

52.6
53.3
54.2

55.6
53.3

53.9
53.4

54.9
54.1
53.4
52.7
53.1
. 

54.4
54.8
57.3

59.8
55.6

56.9
55.9

59.2
57.0
56.0
54.6
55.7
55.0

52.0
56.8
57.9

58.5
56.3

57.4
57.2

58.8
58.4
55.8
57.4
56.6
. 

49.9
52.6
50.9

51.3
50.9

50.4
51.3

49.4
50.7
51.6
51.5
51.5
. 

50.9
51.5
51.1

52.2
51.4

51.5
51.1

51.8
51.3
51.2
50.6
50.2
. 

6

47.2
49.7
52.7

52.8
51.5

53.3
53.9

54.0
53.9
53.6
54.2
53.9
54.1

7

50.2
52.3
53.4

53.6
52.4

52.8
50.9

52.9
51.0
51.2
50.5
50.8
50.0

8

51.9
52.7
54.1

56.2
53.6

54.3
54.2

55.5
55.1
54.0
53.4
53.9
. 

New export
orders

Global

Advanced
economies

Emerging
market
economies

9

48.5
50.7
51.5

51.6
50.4

50.3
49.3

49.8
49.1
48.7
50.0
49.1
48.9

10

3.7
3.2
3.5

2.7
1.5

-2.7
-1.1

-2.7
-1.9
-1.9
-1.1
. 
. 

11

2.5
-0.2
3.6

1.4
1.8

1.7
-1.2

1.7
0.9
-0.3
-1.2
. 
. 

12

4.4
5.2
3.5

3.4
1.3

-5.0
-1.0

-5.0
-3.4
-2.8
-1.0
. 
. 

Sources: Markit (col. 1-9); CPB Netherlands Bureau for Economic Policy Analysis and ECB calculations (col. 10-12).
1) Global and advanced economies exclude the euro area. Annual and quarterly data are period-on-period percentages; monthly data are 3-month-on-3-month percentages. All data

are seasonally adjusted.
2) Excluding the euro area.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 2
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
   
   
2 Financial developments

2.1 Money market interest rates
(percentages per annum; period averages)

   Euro area 1) 

Overnight
deposits
(EONIA)

1-month
deposits
(EURIBOR)

3-month
deposits
(EURIBOR)

6-month
deposits
(EURIBOR)

12-month
deposits
(EURIBOR)

1

0.23
0.09
0.09

-0.04
-0.05
-0.07
-0.11
-0.12
-0.12
-0.12

2

0.33
0.13
0.13

0.00
-0.01
-0.03
-0.05
-0.06
-0.07
-0.09

3

0.57
0.22
0.21

0.05
0.03
0.00
-0.01
-0.01
-0.02
-0.03

4

0.83
0.34
0.31

0.13
0.10
0.07
0.06
0.05
0.05
0.04

5

1.11
0.54
0.48

0.26
0.21
0.18
0.17
0.16
0.17
0.16

United States

3-month
deposits
(LIBOR)

6

0.43
0.27
0.23

0.26
0.27
0.28
0.28
0.28
0.29
0.32

Japan

3-month
deposits
(LIBOR)

7

0.19
0.15
0.13

0.10
0.10
0.10
0.10
0.10
0.10
0.09

2012
2013
2014

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

Source: ECB.
1) Data refer to the changing composition of the euro area, see the General Notes.

2.2 Yield curves
(End of period; rates in percentages per annum; spreads in percentage points)

   Spot rates

   Spreads

   Instantaneous forward rates

   Euro area 1), 2) 

Euro area 1), 2) United States United Kingdom

   Euro area 1), 2)

3 months

1 year

2 years

5 years 10 years

10 years
- 1 year

10 years
- 1 year

10 years
- 1 year

1 year

2 years

5 years 10 years

1

2

3

4

2012
2013
2014

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

0.06
0.08
-0.02

-0.21
-0.21
-0.28
-0.24
-0.27
-0.27
-0.25

-0.04
0.09
-0.09

-0.25
-0.25
-0.26
-0.25
-0.26
-0.29
-0.27

-0.01
0.25
-0.12

-0.20
-0.22
-0.21
-0.23
-0.23
-0.26
-0.22

0.58
1.07
0.07

-0.08
-0.08
0.03
0.06
0.19
0.08
0.14

5

1.72
2.24
0.65

0.37
0.26
0.42
0.61
0.95
0.73
0.82

6

1.76
2.15
0.74

0.62
0.51
0.68
0.85
1.21
1.02
1.09

7

1.61
2.91
1.95

1.80
1.69
1.81
1.87
2.09
1.87
1.84

Source: ECB.
1) Data refer to the changing composition of the euro area, see the General Notes.
2) ECB calculations based on underlying data provided by EuroMTS and ratings provided by Fitch Ratings.

8

1.48
2.66
1.45

1.45
1.19
1.39
1.32
1.52
1.35
1.46

9

10

-0.09
0.18
-0.15

-0.16
-0.20
-0.22
-0.25
-0.25
-0.29
-0.25

0.17
0.67
-0.11

-0.17
-0.20
-0.08
-0.14
-0.10
-0.13
-0.07

11

1.84
2.53
0.58

0.31
0.29
0.46
0.68
1.08
0.76
0.86

12

3.50
3.88
1.77

1.19
0.81
1.05
1.46
2.09
1.84
1.97

2.3 Stock market indices
(index levels in points; period averages)

   Benchmark

Broad
index

50

   Dow Jones EURO STOXX indices

   Main industry indices

United
States

Japan

materials

Basic Consumer Consumer Oil and Financials Industrials Technology Utilities Telecoms Health care Standard
& Poor’s
500

services

goods

gas

Nikkei
225

1

2

3

4

5

6

7

8

9

10

11

12

13

14

2012
2013
2014

239.7 2,411.9
281.9 2,794.0
318.7 3,145.3

2015 Feb.  353.2 3,453.8
         Mar.  373.9 3,655.3
         Apr.  383.3 3,733.8
         May  373.4 3,617.9
         June  364.0 3,521.8
         July  366.3 3,545.1
         Aug.  356.7 3,444.4

Source: ECB.

503.7
586.3
644.3

731.3
787.2
798.2
765.0
743.2
744.0
711.9

151.9
195.0
216.6

254.2
268.9
275.7
268.9
265.5
266.0
261.9

385.7
468.2
510.6

624.8
666.9
678.6
662.1
647.4
645.2
615.0

307.2
312.8
335.5

314.0
313.5
331.0
326.5
310.3
302.1
287.7

122.1
151.5
180.0

185.5
198.9
204.9
199.3
194.5
198.0
193.9

330.2
402.7
452.9

498.7
524.1
535.7
522.4
504.7
505.5
504.6

219.2
274.1
310.8

361.1
386.2
394.2
389.5
385.0
378.1
359.9

235.9
230.6
279.2

286.9
292.9
299.5
294.0
283.0
281.3
274.9

268.5
253.4
306.7

376.8
389.2
395.0
389.2
380.7
395.1
390.0

523.3
629.4
668.1

768.6
824.6
861.4
827.6
820.4
864.8
856.9

1,379.4
9,102.6
1,643.8 13,577.9
1,931.4 15,460.4

2,082.2 18,053.2
2,080.4 19,197.6
2,094.9 19,767.9
2,111.9 19,974.2
2,099.3 20,403.8
2,094.1 20,372.6
2,039.9 19,919.1

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 3
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
2 Financial developments

2.4 MFI interest rates on loans to and deposits from households (new business) 1), 2)
(Percentages per annum; period average, unless otherwise indicated)

   Deposits

Revolving Extended   Loans for consumption

   Loans for house purchase

Over- Redeem-
night

loans
and
able    an agreed overdrafts

   With

at    maturity of:

notice
of up Up to Over
2
months years years

to 3

2

Loans
credit
to sole
card   By initial period APRC 3) proprietors
and
credit   of rate fixation
unincor-
porated Floating Over 1 Over 5 Over
10
partner- rate and and up and up
to 10 years
years

Floating Over
1
rate and
up to
year
1 year

   By initial period
   of rate fixation

up to
1 year

to 5
years

ships

APRC 3) Composite
cost-of-
borrowing
indicator

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

2014 Aug.  0.22
         Sep.  0.21
         Oct. 
0.20
         Nov.  0.20
         Dec.  0.20

2015 Jan. 
0.19
         Feb.  0.18
         Mar.  0.17
0.16
         Apr. 
         May 
0.16
         June  0.15
         July (p) 0.15

0.96
0.95
0.94
0.91
0.89

0.86
0.84
0.82
0.79
0.82
0.78
0.74

1.20
1.17
1.09
1.01
0.96

1.01
0.97
0.89
0.87
0.83
0.77
0.67

1.66
1.70
1.65
1.66
1.58

1.94
1.51
1.37
1.15
1.08
1.18
1.10

7.30
7.37
7.20
7.18
7.14

7.18
7.13
7.13
7.03
6.98
6.97
6.82

17.00
17.06
16.96
17.12
17.10

17.12
17.05
17.05
17.01
17.08
17.02
17.04

5.51
5.35
5.38
5.58
5.07

5.25
5.18
5.16
4.89
5.04
4.88
5.09

6.71
6.67
6.59
6.66
6.21

6.42
6.47
6.16
6.13
6.29
6.14
6.22

7.05
6.99
6.98
6.98
6.53

6.73
6.82
6.49
6.42
6.60
6.45
6.50

3.06
2.94
2.89
2.92
2.75

2.76
2.79
2.72
2.66
2.67
2.59
2.60

2.55
2.49
2.42
2.41
2.42

2.30
2.07
2.11
2.01
2.05
2.02
2.04

2.74
2.69
2.63
2.50
2.52

2.55
2.47
2.45
2.38
2.33
2.25
2.26

2.71
2.61
2.54
2.51
2.53

2.43
2.32
2.29
2.17
2.09
2.12
2.21

2.86
2.82
2.78
2.72
2.68

2.44
2.49
2.41
2.36
2.29
2.31
2.36

2.96
2.88
2.81
2.76
2.76

2.68
2.58
2.55
2.49
2.45
2.48
2.56

16

2.73
2.66
2.60
2.53
2.49

2.40
2.37
2.30
2.24
2.17
2.18
2.22

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Including non-profit institutions serving households.
3) Annual percentage rate of charge (APRC).

2.5 MFI interest rates on loans to and deposits from non-financial corporations (new business) 1), 2)
(Percentages per annum; period average, unless otherwise indicated)

   Deposits

Revolving
loans and
Over-   With an agreed overdrafts
night

   maturity of:

   Other loans by size and initial period of rate fixation

   up to EUR 0.25 million

   over EUR 0.25 and up to 1 million

   over EUR 1 million

Floating

Over
rate 3 months
and up to and up to
1 year
3 months

Over
1 year

Floating
rate
and up to
3 months

Over
3 months
and up to
1 year

Over
1 year

Floating

Over
rate 3 months
and up to and up to
1 year
3 months

Over
1 year

Composite
cost-of-
borrowing
indicator

Up to Over
2 years 2 years

1

2

3

2014 Aug. 
         Sep. 
         Oct. 
         Nov. 
         Dec. 

0.28
0.26
0.25
0.25
0.24

0.23
2015 Jan. 
0.22
         Feb. 
0.21
         Mar. 
0.19
         Apr. 
0.18
         May 
         June 
0.18
         July (p) 0.17

0.50
0.51
0.50
0.44
0.43

0.44
0.35
0.32
0.30
0.30
0.31
0.31

1.56
1.46
1.37
1.16
1.26

1.19
1.04
1.07
0.89
0.91
1.10
0.87

4

3.74
3.72
3.64
3.57
3.49

3.49
3.43
3.39
3.34
3.28
3.25
3.19

5

4.17
4.01
4.00
3.82
3.68

3.78
3.57
3.45
3.46
3.37
3.19
3.27

6

7

4.27
4.03
3.94
3.86
3.75

3.85
3.72
3.65
3.58
3.51
3.48
3.60

3.55
3.53
3.53
3.42
3.26

2.98
3.12
3.13
2.95
2.96
2.91
2.86

8

2.56
2.46
2.43
2.38
2.35

2.31
2.24
2.16
2.18
2.15
2.09
2.07

9

10

11

12

13

3.20
3.01
2.90
2.84
2.78

2.82
2.71
2.67
2.64
2.46
2.33
2.36

2.85
2.77
2.71
2.63
2.50

2.05
2.39
2.33
2.26
2.23
2.23
2.22

1.80
1.83
1.75
1.74
1.74

1.67
1.52
1.63
1.62
1.57
1.60
1.51

2.42
2.38
2.25
2.17
2.16

2.03
1.99
2.11
1.93
1.85
1.91
1.73

2.54
2.42
2.46
2.27
2.13

2.20
2.14
2.02
2.03
2.04
2.04
2.05

14

2.71
2.68
2.60
2.51
2.46

2.46
2.37
2.37
2.33
2.27
2.26
2.19

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial

corporations sector.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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2 Financial developments

2.6 Debt securities issued by euro area residents, by sector of the issuer and initial maturity
(EUR billions; transactions during the month and end-of-period outstanding amounts; nominal values)

   Outstanding amounts

   Gross issues 1)

Total

MFIs
(including
Euro-

   Non-MFI corporations

   General government Total

   Non-MFI corporations

   General government

Financial
system) corporations

financial
other than FVCs corporations

Non- Central
govern-
ment

Other
general
govern-
ment

Financial
system) corporations

Non-
financial
other than FVCs corporations

MFIs

Central
govern-
ment

Other
general
govern-
ment

MFIs
(including
Euro-

MFIs

1

2

3

4

2012
2013
2014

1,426
1,247
1,312

2015 Jan.  1,388
         Feb.  1,400
         Mar.  1,420
         Apr.  1,410
         May  1,396
         June  1,329

2012
2013
2014

15,205
15,109
15,120

2015 Jan. 15,222
         Feb. 15,266
         Mar. 15,355
         Apr. 15,276
         May 15,356
         June 15,344

581
477
545

599
606
604
600
591
560

4,814
4,405
4,040

4,058
4,038
4,026
4,000
3,982
3,938

146
122
120

127
134
137
134
135
121

3,166
3,087
3,158

3,201
3,209
3,251
3,213
3,235
3,255

- 
- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

5

75
67
59

66
70
71
80
80
75

842
921
994

1,004
1,017
1,034
1,034
1,037
1,032

6

7
Short-term

558
529
538

543
534
543
533
532
517

5,758
6,069
6,286

6,316
6,356
6,399
6,389
6,462
6,485

66
53
50

54
56
66
62
59
56

Long-term

624
627
643

642
646
644
641
640
634

8

9

10

11

12

13

14

703
508
410

378
351
373
350
326
298

255
222
219

261
207
285
220
185
207

491
314
219

186
162
162
158
140
126

98
70
65

80
64
84
70
49
69

37
30
34

28
37
45
37
36
30

45
39
43

48
21
62
33
41
34

- 
- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

52
44
39

33
30
35
38
36
34

16
16
16

8
18
17
21
6
13

103
99
93

94
83
89
82
80
77

84
89
85

113
86
112
87
85
87

21
21
25

36
39
42
35
33
32

12
9
10

13
17
10
10
4
5

Source: ECB.
1) For the purpose of comparison, annual data refer to the average monthly figure over the year.

2.7 Growth rates and outstanding amounts of debt securities and listed shares
(EUR billions; percentage changes)

   Debt securities

   Listed shares

Total

MFIs
(including
Eurosystem)

   Non-MFI corporations

   General government

Total

MFIs

Financial
corporations
other than
MFIs

Non-

Central
financial government

FVCs corporations

Other
general
government

Financial
corporations

Non-
financial
other than corporations

MFIs

1

2

3

4

5

6
Oustanding amount

7

8

9

10

11

2012
2013
2014

16,631.5
16,355.8
16,432.4

16,609.5
2015 Jan. 
16,665.3
         Feb. 
16,775.5
         Mar. 
16,686.7
         Apr. 
16,751.9
         May 
         June  16,672.3

2012
2013
2014

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

Source: ECB.

1.3
-1.4
-0.7

-0.7
-0.9
-0.2
-0.3
-0.8
-1.1

5,395.8
4,881.7
4,585.5

4,657.1
4,643.5
4,630.4
4,600.2
4,572.2
4,498.3

-1.8
-8.9
-7.8

-7.8
-7.6
-7.1
-6.8
-7.2
-7.7

3,312.1
3,208.6
3,277.8

3,327.9
3,343.0
3,388.6
3,347.4
3,369.7
3,375.7

-0.3
-3.4
0.1

0.6
0.6
2.3
1.7
0.1
0.8

.
.
.

.
.
.
.
.
.

.
.
.

.
.
.
.
.
.

917.0
987.9
1,052.3

1,069.7
1,086.8
1,104.8
1,113.6
1,116.3
1,106.5

6,316.2
6,597.8
6,823.7

6,859.1
6,890.6
6,941.6
6,921.8
6,994.0
7,001.4

Growth rate

14.4
8.1
4.9

3.0
4.4
5.3
6.7
5.8
4.6

2.5
4.5
3.1

3.2
2.4
2.6
2.1
2.1
1.6

690.4
679.8
693.1

695.6
701.5
710.1
703.6
699.7
690.4

6.1
-1.1
1.2

1.8
0.7
1.8
1.9
1.4
-0.8

4,597.1
5,638.0
5,949.4

6,423.2
6,855.9
7,056.1
6,960.1
6,983.6
6,803.4

0.9
0.9
1.5

1.5
1.4
1.5
1.5
1.3
1.0

404.7
569.1
591.0

573.0
650.5
688.7
683.8
675.3
664.2

4.9
7.2
7.2

6.9
6.8
6.8
6.8
5.8
4.1

617.9
751.0
787.9

836.0
899.7
933.3
908.3
901.6
879.4

2.0
0.2
1.6

1.5
1.2
1.4
1.1
1.2
0.6

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

3,574.5
4,317.9
4,570.6

5,014.3
5,305.7
5,434.0
5,368.0
5,406.7
5,259.8

0.4
0.3
0.8

0.7
0.7
0.8
0.8
0.7
0.7

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2 Financial developments

2.8 Effective exchange rates 1)
(period averages; index: 1999 Q1=100)

Nominal

Real CPI

Real PPI

Real GDP
deflator

Real ULCM 2)

Real ULCT

Nominal

Real CPI

   EER-19

   EER-38

1

97.6
101.2
101.8

101.3
99.0

93.0
91.2

90.6
89.7
91.6
92.3
91.3
93.0

1.8

-8.4

2

95.0
98.2
97.9

97.2
94.9

89.2
87.5

86.9
86.1
87.9
88.5
87.5
89.0

1.8

-8.7

3

93.3
96.7
96.7

96.2
94.3

89.4
88.2

87.4
86.9
88.6
89.1
88.3
90.0

4

88.3
91.4
91.6

90.8
89.1

84.2
.

- 
- 
- 
- 
- 
- 

Percentage change versus previous month

1.9

- 

Percentage change versus previous year

-6.6

- 

5

99.7
102.5
102.8

102.2
99.7

93.7
.

- 
- 
- 
- 
- 
- 

- 

- 

6

95.8
99.1
100.7

100.3
98.1

92.6
.

- 
- 
- 
- 
- 
- 

- 

- 

7

107.0
111.9
114.7

113.7
112.3

106.4
104.4

103.8
102.4
104.7
106.0
105.1
108.1

2.9

-5.2

8

92.5
95.6
96.1

95.1
93.5

88.3
86.3

86.0
84.8
86.6
87.6
86.7
89.1

2.8

-6.6

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

2015 Aug. 

2015 Aug. 

Source: ECB.
1) For a definition of the trading partner groups and other information see the General Notes to the Statistics Bulletin.
2) ULCM-deflated series are available only for the EER-19 trading partner group.

2.9 Bilateral exchange rates
(period averages; units of national currency per euro)

Chinese Croatian
kuna
renminbi

Czech
koruna

Danish Hungarian Japanese
yen
forint

krone

1

2

3

4

5

6

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

8.105
8.165
8.186

8.173
7.682

7.023
6.857

6.762
6.686
6.916
6.959
6.827
7.063

7.522
7.579
7.634

7.623
7.665

7.681
7.574

7.647
7.590
7.559
7.572
7.586
7.558

25.149
25.980
27.536

27.619
27.630

27.624
27.379

27.379
27.439
27.397
27.307
27.094
27.041

Polish
zloty

7

4.185
4.197
4.184

4.175
4.211

4.193
4.088

Pound Romanian Swedish
krona
sterling

leu

Swiss
franc

US
Dollar

8

9

10

11

12

0.811
0.849
0.806

0.794
0.789

0.743
0.721

0.724
0.721
0.721
0.721
0.707
0.714

4.4593
4.4190
4.4437

4.4146
4.4336

4.4516
4.4442

4.4339
4.4155
4.4477
4.4671
4.4391
4.4235

8.704
8.652
9.099

9.205
9.272

9.380
9.300

9.245
9.325
9.304
9.272
9.386
9.515

1.205
1.231
1.215

1.212
1.205

1.072
1.041

1.061
1.038
1.039
1.045
1.049
1.078

1.285
1.328
1.329

1.326
1.250

1.126
1.105

1.084
1.078
1.115
1.121
1.100
1.114

7.444
7.458
7.455

7.452
7.442

7.450
7.462

289.249
296.873
308.706

312.242
308.527

308.889
306.100

102.492
129.663
140.306

137.749
142.754

134.121
134.289

303.445
299.429
306.327
311.960
311.531
311.614

7.459
7.466
7.461
7.460
7.462
7.463
Percentage change versus previous month

130.410
128.935
134.748
138.740
135.681
137.124

4.126
4.018
4.081
4.159
4.152
4.195

2015 Aug. 

3.5

-0.4

-0.2

0.0

0.0

1.1

1.0

1.0

-0.4

1.4

2.7

1.3

2015 Aug. 

Source: ECB.

-13.8

-1.0

-2.8

0.1

-0.7

0.0

0.1

-10.4

0.0

3.6

-11.1

-16.3

Percentage change versus previous year

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
2 Financial developments

2.10 Euro area balance of payments, financial account
(EUR billions, unless otherwise indicated; outstanding amounts at end of period; transactions during period)

   Total 1) 

   Direct
   investment

   Portfolio
   investment

Net
financial
derivatives

   Other investment

Reserve
assets

Assets Liabilities

Net

Assets Liabilities

Assets Liabilities

Assets Liabilities

Memo:
Gross
external
debt

1

2

3
Outstanding amounts (international investment position)

4

7

6

5

8

9

10

11

12

2014 Q2 
         Q3 
         Q4 

2015 Q1 

18,472.2
19,220.1
19,351.7

19,741.3
20,468.0
20,748.8

-1,269.2
-1,247.8
-1,397.1

7,542.8
7,797.7
7,568.9

5,630.5
5,900.7
5,998.8

5,960.8
6,306.2
6,509.3

9,449.4
9,713.4
9,915.2

21,087.3

22,381.9

-1,294.5

8,204.4

6,331.7

7,270.9

10,995.3

-70.3
-55.7
-43.6

-21.0

4,532.2
4,652.3
4,782.7

4,661.5
4,853.9
4,834.7

506.8
519.7
534.4

11,426.2
11,836.4
11,869.4

5,029.9

5,054.9

603.1

12,632.8

Outstanding amounts as a percentage of GDP

2015 Q1 

207.6

220.4

-12.7

80.8

62.3

71.6

108.3

-0.2

49.5

49.8

5.9

124.4

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

209.6
57.8

504.8
62.2

337.8
93.9
73.2
111.8
22.8
-72.5

119.2
-3.2

513.7
-23.6

429.2
107.5
-23.0
121.5
-0.5
-144.5

90.5
61.0

-8.8
85.7

-91.4
-13.6
96.2
-9.6
23.3
72.0

69.1
56.1

159.7
62.3

56.5
51.1
52.1
15.4
31.1
15.8

Transactions

44.7
73.5

74.7
52.4

67.3
18.9
-11.5
24.1
13.2
15.0

104.1
93.2

129.0
133.5

53.8
29.9
45.3
33.2
74.6
25.7

19.8
-2.7

260.3
13.8

133.7
76.0
50.5
-19.5
54.2
-21.0

20.3
10.2

27.3
7.5

7.1
9.7
10.5
5.8
4.3
-2.5

17.5
-104.7

182.8
-138.8

218.8
-1.1
-35.0
61.3
-85.6
-114.6

54.7
-74.0

178.7
-89.7

228.2
12.6
-62.0
116.8
-67.9
-138.6

2015 June 

834.4

606.1

228.4

347.3

245.2

459.8

291.1

65.3

-43.3

69.7

12-month cumulated transactions as a percentage of GDP

2015 June 

8.2

6.0

2.2

3.4

2.4

4.5

2.9

0.6

-0.4

0.7

12-month cumulated transactions

-1.3
2.9

6.0
-2.4

1.5
4.2
0.3
-3.9
-1.6
3.1

5.2

0.1

-
-

-
-

-
-
-
-
-
-

-

-

Source: ECB.
1) Net financial derivatives are included in total assets.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 7
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
   
3 Economic activity

3.1 GDP and expenditure components
(quarterly data seasonally adjusted; annual data unadjusted)

Total

   GDP

   Domestic demand

   External balance

Total

Private Government
consumption consumption

   Gross fixed capital formation

Changes in
inventories

Total Exports Imports

Total

Total
construction machinery

Intellectual
property products

1

2

3

4

5

7
Current prices (EUR billions)

6

2012
2013
2014

9,840.1
9,931.7
10,103.5

2014 Q2  2,520.6
         Q3  2,531.5
         Q4  2,544.9

9,581.2
9,598.4
9,729.9

2,428.2
2,437.5
2,444.2

2015 Q1  2,564.9

2,458.3

5,544.4
5,571.9
5,651.1

1,409.4
1,416.6
1,423.4

1,425.2

2,065.4 1,979.3
2,094.9 1,940.4
2,127.7 1,970.9

490.8
493.2
496.4

530.1
534.2
534.3

538.8

1,035.7
1,006.3
1,001.8

249.4
249.1
251.0

580.8
568.9
591.8

147.4
149.2
150.0

500.1

252.1
as a percentage of GDP

151.9

8

9

10

11

12

358.0
360.3
372.2

92.7
93.7
94.1

94.8

-7.9 258.9
-8.8 333.2
-19.7 373.5

4,288.9 4,030.0
4,362.7 4,029.5
4,494.4 4,120.8

92.4
-2.1
-6.5
94.0
-9.8 100.7

1,118.1 1,025.6
1,135.0 1,041.0
1,142.1 1,041.4

-5.8 106.6

1,144.3 1,037.6

2014

100.0

96.3

55.9

21.1

19.5

9.9

5.9

3.7

-0.2

3.7

- 

- 

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

0.2
0.4

0.4
0.3

-0.8
-0.4
0.8

0.8
0.9

1.0
1.2

0.2
0.4

0.4
0.3

-0.8
-0.4
0.8

0.8
0.9

1.0
1.2

0.3
0.4

0.6
. 

-2.3
-0.7
0.9

0.6
1.0

1.3
. 

0.3
0.3

0.6
. 

-2.3
-0.7
0.8

0.6
0.9

1.2
. 

Sources: Eurostat and ECB calculations.

Chain-linked volumes (prices for the previous year)
quarter-on-quarter percentage changes

0.5
0.4

0.5
. 

-1.3
-0.6
1.0

1.0
1.5

1.7
. 

0.2
0.1

0.6
. 

-0.1
0.2
0.6

0.6
0.7

1.1
. 

0.1
0.4

-0.7
0.7

1.1
0.0

1.3
0.8
. 
. 
annual percentage changes

0.6
. 

-3.7
-2.4
1.2

0.6
0.5

0.8
. 

-4.2
-3.3
-1.3

-2.9
-1.5

-1.3
. 

-5.1
-1.7
4.5

4.8
2.5

3.4
. 

0.8
0.2

0.5
. 

1.2
-0.3
2.8

3.5
2.7

2.2
. 

contributions to quarter-on-quarter percentage changes in GDP; percentage points

0.3
0.2

0.0
0.0

0.0
0.1

-0.1
0.1

0.1
0.0

0.0
0.0

0.0
0.3
. 
. 
contributions to annual percentage changes in GDP; percentage points

0.2
. 

0.1
. 

0.1
. 

0.1
. 

-0.7
-0.4
0.5

0.6
0.8

0.9
. 

0.0
0.0
0.1

0.1
0.2

0.2
. 

-0.8
-0.5
0.2

0.1
0.1

0.2
. 

-1.8
-1.4
-0.5

-0.3
-0.2

-0.1
. 

-1.3
-0.4
1.0

0.3
0.1

0.2
. 

0.2
0.0
0.4

0.1
0.1

0.1
. 

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

-0.1
0.0

0.1
. 

-0.8
0.1
-0.1

-0.2
-0.1

-0.1
. 

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

-0.1
0.0

-0.2
. 

1.4
0.4
0.0

0.2
-0.1

-0.2
. 

1.4
0.8

0.6
. 

2.7
2.0
3.8

4.1
4.1

4.2
. 

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

1.7
0.8

1.2
. 

-0.7
1.3
4.1

3.9
4.6

5.1
. 

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 8
S 8

ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
3 Economic activity

3.2 Value added by economic activity
(quarterly data seasonally adjusted; annual data unadjusted)

   Gross value added (basic prices)

Total Agriculture, Manufacturing Const-
energy and ruction

Trade,

Infor-
transport, mation

forestry and
fishing

utilities

accom- and com- insurance

Finance

Real Professional,

and estate business and ministration,
education,
health and
social work

support
services

Public ad- Arts, enter-
tainment
and other
services

modation munica-
and food
tion
services

Taxes less
subsidies
on
products

1

2

3

4

2012
2013
2014

8,845.3
8,924.2
9,068.0

2014 Q2  2,261.4
         Q3  2,272.1
         Q4  2,283.3

2015 Q1  2,306.1

151.3
154.5
148.9

38.0
36.9
35.8

36.6

1,728.4
1,740.7
1,763.9

440.2
442.6
445.7

449.7

467.2
460.1
461.3

115.0
114.6
115.7

116.7

5

6
Current prices (EUR billions)

7

8

9

10

11

12

1,675.9
1,684.8
1,709.3

425.5
428.6
431.6

410.3
407.2
410.9

102.6
102.9
103.2

439.1 1,016.3
440.6 1,032.5
450.0 1,054.0

113.2 262.9
112.8 263.8
112.1 265.6

437.6
as a percentage of value added

103.9

113.8 266.9

924.6
937.1
960.2

239.0
240.9
242.9

245.2

1,717.9
1,748.0
1,784.1

444.1
447.6
448.7

453.1

314.4
318.7
325.2

80.9
81.6
82.0

82.5

994.8
1,007.5
1,035.5

259.3
259.4
261.6

258.9

2014

100.0

1.6

19.5

5.1

18.9

4.5

5.0

11.6

10.6

19.7

3.6

- 

Chain-linked volumes (prices for the previous year)
quarter-on-quarter percentage changes

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

0.0
0.2
0.2

0.4

-0.6
-0.3
0.9

0.8
0.8
0.8

0.9

0.0
0.2
0.2

0.4

-0.6
-0.3
0.9

0.8
0.8
0.8

0.9

-0.1
0.9
-2.1

1.4

-3.0
2.5
3.7

4.3
4.8
0.2

0.1

0.0
0.0
0.0

0.0

0.0
0.0
0.1

0.1
0.1
0.0

0.0

0.0
0.0
0.2

0.3

-0.5
-0.5
0.4

0.3
0.4
0.1

-1.6
-1.0
0.7

0.5

-5.9
-2.9
-0.7

-0.6
-1.9
-1.3

-0.1
0.4
0.5

0.6
0.8
0.1

-0.3
0.4
-0.2

0.9

0.7
annual percentage changes

0.5

-1.3
-0.5
1.3

1.1
1.1
1.3

2.5
-0.1
1.8

1.9
2.2
1.6

0.7
-1.7
-0.5

-0.6
-0.1
0.2

0.3
0.2
0.3

0.1

0.0
1.0
1.3

1.3
1.3
1.3

0.4
0.6
0.4

0.7

-0.9
0.2
1.5

1.2
1.5
2.0

2.2
contributions to quarter-on-quarter percentage changes in value added; percentage points

-1.5

0.5

0.6

1.7

0.9

2.0

0.0
0.0
0.0

-0.1
-0.1
0.0

0.0
0.1
0.1

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0
0.1
0.0

0.1

0.0
contributions to annual percentage changes in value added; percentage points

0.0

0.0

0.1

0.2

0.0

-0.1
-0.1
0.1

0.1
0.1
0.0

0.1

-0.3
-0.2
0.0

0.0
-0.1
-0.1

-0.1

-0.3
-0.1
0.2

0.2
0.2
0.3

0.3

0.1
0.0
0.1

0.1
0.1
0.1

0.1

0.0
-0.1
0.0

0.0
0.0
0.0

0.0

0.0
0.1
0.1

0.2
0.2
0.1

0.1

-0.1
0.0
0.2

0.1
0.2
0.2

0.2

Sources: Eurostat and ECB calculations.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

0.1
0.1
0.2

0.1

0.2
0.0
0.7

0.8
0.7
0.5

0.5

0.0
0.0
0.0

0.0

0.0
0.0
0.1

0.2
0.1
0.1

0.1

-0.2
0.6
0.1

0.1

-0.6
-0.4
0.7

0.4
0.6
0.8

0.6

0.0
0.0
0.0

0.0

0.0
0.0
0.0

0.0
0.0
0.0

0.0

1.1
-0.4
1.3

-0.2

-2.6
-1.2
0.7

0.4
0.3
1.6

1.9

- 
- 
- 

- 

- 
- 
- 

- 
- 
- 

- 

S 9
S 9

ECB Economic Bulletin, Issue 6 / 2015 – Statistics3 Economic activity

3.3 Employment 1)
(quarterly data seasonally adjusted; annual data unadjusted)

Total    By employment

   status

   By economic activity

Employ-

ees employed

Self- Agricul- Manufac-
turing,
ture,
energy
forestry
and
and
utilities
fishing

Con-
struc-
tion

Trade,

Infor- Finance

Real Professional, Public adminis-

transport, mation
and
accom-
modation
com-
and food munica-
tion
services

and estate business and
support
services

insur-
ance

Arts,
tration, edu- entertainment
and other
services

cation, health
and
social work

1

2

3

4

5

6

7
Persons employed 

8

9

10

11

12

13

2012
2013
2014

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

100.0
100.0
100.0

-0.5
-0.7
0.6

0.6
0.7
0.9

0.8

100.0
100.0
100.0

-1.8
-1.3
0.6

0.4
0.6
1.1

0.6

-1.3
-0.6
0.1

-0.2
-0.1
0.2

-0.2

84.9
85.0
85.1

-0.5
-0.7
0.8

0.8
1.0
1.1

1.0

80.0
80.0
80.2

-1.8
-1.3
0.9

0.7
1.0
1.3

0.8

-1.3
-0.7
0.1

-0.1
0.0
0.2

-0.1

15.1
15.0
14.9

-0.1
-0.8
-0.4

-0.5
-0.5
-0.5

-0.2

20.0
20.0
19.8

-1.5
-1.2
-0.3

-0.8
-0.9
0.2

-0.3

-1.4
-0.4
0.1

-0.3
-0.3
0.6

0.0

3.4
3.4
3.4

-1.1
-1.2
0.9

0.7
0.5
0.6

0.1

4.4
4.4
4.4

-2.1
-1.5
0.8

0.2
0.3
1.8

1.2

-1.0
-0.4
-0.1

-0.5
-0.2
1.1

1.1

Sources: Eurostat and ECB calculations.
1) Data for employment are based on the ESA 2010.

as a percentage of total persons employed

15.4
15.3
15.2

-0.6
-1.5
-0.1

-0.1
0.1
0.3

0.4

24.8
6.4
24.8
6.2
6.0
24.9
annual percentage changes

2.7
2.7
2.7

-4.5
-4.3
-1.8

-1.9
-1.3
-1.5

-0.1

-0.6
-0.5
0.8

0.9
1.0
0.9

1.1

1.0
-0.1
1.1

0.9
1.3
1.4

1.2

2.7
2.7
2.7

-0.5
-1.2
-0.9

-1.3
-0.9
-0.6

-0.7

Hours worked
as a percentage of total hours worked

15.7
15.7
15.6

-2.2
-1.7
0.4

-0.4
0.3
0.9

0.4

-1.6
-0.2
0.5

-0.3
0.2
0.6

0.0

25.8
7.2
25.9
6.9
6.7
25.9
annual percentage changes

2.8
2.8
2.8

-7.0
-5.6
-1.4

-1.9
-1.6
-0.7

-0.2

-2.1
-1.1
0.8

0.7
0.9
1.0

0.6

0.5
-0.3
1.0

0.9
1.0
1.4

0.8

2.8
2.7
2.7

-1.3
-1.6
-1.3

-2.0
-1.5
-1.3

-1.3

Hours worked per person employed
annual percentage changes

-2.6
-1.3
0.3

0.0
-0.3
0.7

-0.1

-1.5
-0.6
0.0

-0.2
-0.1
0.1

-0.5

-0.5
-0.3
-0.1

0.0
-0.3
0.0

-0.4

-0.7
-0.4
-0.3

-0.7
-0.6
-0.7

-0.5

1.0
1.0
1.0

0.2
-0.9
0.7

0.6
0.9
0.9

1.5

1.0
1.0
1.0

-0.8
-1.9
0.2

0.0
-0.2
1.3

2.0

-1.0
-1.0
-0.5

-0.7
-1.1
0.3

0.5

12.7
12.8
13.0

0.7
0.2
2.0

2.1
2.1
2.5

2.5

12.4
12.5
12.7

-0.5
-0.5
2.0

1.9
2.0
2.7

2.1

-1.1
-0.8
-0.1

-0.3
-0.1
0.2

-0.4

23.8
24.0
24.0

-0.1
0.0
0.7

0.7
0.7
0.7

0.5

21.5
21.7
21.8

-0.6
-0.5
0.9

0.9
0.8
0.9

0.4

-0.6
-0.5
0.2

0.1
0.1
0.2

-0.1

7.0
7.1
7.1

0.6
0.0
0.6

0.2
0.7
1.8

0.4

6.3
6.4
6.3

-0.4
-0.8
0.2

0.1
0.1
1.6

0.5

-1.0
-0.8
-0.4

-0.1
-0.6
-0.2

0.2

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 10
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
3 Economic activity

3.4 Labour force, unemployment and job vacancies
(seasonally adjusted, unless otherwise indicated)

Labour

Under-
force, employ-
ment,
% of

millions 1)

   Total

labour Millions % of
force 1)
labour
force

   Unemployment

   By age

   By gender

Job
vacancy
rate 2)

   Adult

   Youth

   Male

   Female

Long-term
unemploy-
ment,
% of

labour Millions
force 1)

1

2

3

4

5

% of total
in 2013

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July 

159.111
159.334
160.315

160.475
160.966

160.084
. 

- 
- 
- 
- 
- 
- 

100.0

18.187
19.233
18.625

18.544
18.402

17.981
17.759

17.956
17.912
17.790
17.741
17.745
17.532

3.9
4.6
4.6

4.4
4.6

4.7
. 

- 
- 
- 
- 
- 
- 

11.4
12.0
11.6

11.6
11.5

11.2
11.1

11.2
11.2
11.1
11.1
11.1
10.9

5.2
5.9
6.1

5.9
6.1

5.9
. 

- 
- 
- 
- 
- 
- 

6

81.3

14.630
15.640
15.213

15.148
15.081

14.741
14.589

14.720
14.688
14.594
14.588
14.584
14.439

% of Millions

% of Millions

% of Millions

labour
force

7

10.1
10.8
10.4

10.4
10.3

10.1
10.0

10.1
10.1
10.0
10.0
10.0
9.9

8

18.7

3.557
3.593
3.412

3.397
3.322

3.240
3.170

3.236
3.224
3.196
3.153
3.161
3.093

labour
force

9

23.6
24.3
23.7

23.6
23.2

22.7
22.3

22.7
22.6
22.4
22.2
22.3
21.9

10

53.6

9.758
10.307
9.917

9.817
9.753

9.560
9.402

9.542
9.513
9.412
9.417
9.377
9.265

labour
force

11

11.3
11.9
11.5

11.3
11.3

11.1
10.9

11.0
11.0
10.9
10.9
10.9
10.7

12

46.4

8.429
8.926
8.708

8.726
8.649

8.422
8.356

8.415
8.399
8.378
8.323
8.368
8.267

% of % of total
posts

labour
force

13

14

11.5
12.1
11.8

11.8
11.7

11.4
11.3

11.4
11.4
11.3
11.2
11.3
11.2

1.6
1.5
1.7

1.6
1.8

1.7
. 

- 
- 
- 
- 
- 
- 

Sources: Eurostat and ECB calculations.
1) Not seasonally adjusted.
2) The job vacancy rate is equal to the number of job vacancies divided by the sum of the number of occupied posts and the number of job vacancies, expressed as a percentage.

3.5 Short-term business statistics

   Industrial production

   Total
   (excluding construction)

   Main Industrial Groupings

Con- ECB indicator
on industrial
new orders Total

struction
produc-
tion

   Retail sales

Food, Non-food

Fuel

beverages,
tobacco

New
passenger
car regis-
trations

Manu-

Inter- Capital Consumer Energy

facturing mediate
goods

goods

goods

1

2

3

4

5

6

7

8

9

100.0

86.0

33.6

29.2

22.5

14.7

100.0

100.0 100.0

10

39.3

11

51.5

12

9.1

13

100.0

% of total
in 2010

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

-2.4
-0.7
0.8

0.6
0.3

1.6
1.2

0.7
2.0
2.1
0.8
1.6
1.2

0.2
1.0
-0.5
0.1
-0.2
-0.4

-2.6
-0.7
1.7

1.2
0.9

1.1
1.5

0.2
1.2
1.9
0.9
2.3
1.4

-0.2
1.0
-0.3
0.2
0.2
-0.9

-4.4
-1.0
1.2

0.4
-0.4

-0.1
0.7

-0.2
-0.3
0.2
0.0
2.0
0.2

0.0
0.1
-0.1
0.1
0.1
-0.5

-1.0
-0.6
1.8

1.5
0.9

1.1
2.6

0.5
1.4
1.2
2.1
4.1
1.7

annual percentage changes

-2.5
-0.4
2.6

1.9
2.6

2.3
0.8

0.3
2.5
4.1
-0.1
0.3
2.1

-0.1
-0.8
-5.5

-3.1
-3.2

4.6
-0.9

2.6
6.9
4.5
0.9
-3.6
0.0

-5.8
-2.3
1.7

-1.4
-0.8

-1.6
-1.0

0.4
-3.5
-2.0
-1.2
0.2
-2.3

month-on-month percentage changes (s.a.)

0.0
0.6
-0.3
0.5
1.2
-1.8

-0.5
2.5
0.0
-0.4
-0.3
-0.3

1.5
1.3
-1.8
-1.0
-2.9
3.2

1.0
-1.4
0.6
-0.3
0.2
-1.9

-3.8
-0.1
3.3

-1.6
-0.8
1.3

2.3
2.9

1.0
4.7

0.6
0.9
1.5
2.8
4.3
7.0

-2.2
0.1
0.6
2.4
-0.1
2.1

0.8
1.9

2.1
2.1

2.4
2.4
1.6
2.6
2.6
1.2

0.6
0.0
-0.4
0.7
0.1
-0.6

-1.3
-0.9
0.3

-0.3
0.8

1.0
1.1

2.0
1.0
0.2
1.4
1.9
0.1

0.7
-0.6
-0.4
1.0
0.3
-0.8

-1.5
-0.6
2.4

2.0
2.9

3.1
3.1

2.9
3.5
2.9
3.6
3.4
2.3

1.1
0.5
-0.2
0.2
0.2
-0.2

-5.0
-0.9
0.4

-0.3
1.5

2.2
2.3

2.7
3.1
1.0
2.9
2.2
1.8

0.5
-0.7
-0.8
0.6
-0.2
0.0

Sources: Eurostat, ECB calculations, ECB experimental statistics (col. 8) and European Automobile Manufacturers Association (col. 13).

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

-11.1
-4.4
3.7

4.1
1.6

9.0
6.9

11.0
8.1
8.2
6.4
6.8
7.5

1.5
0.0
-0.6
0.8
-1.5
1.6

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3 Economic activity

3.6 Opinion surveys
(seasonally adjusted)

   European Commission Business and Consumer Surveys
   (percentage balances, unless otherwise indicated)

   Purchasing Managers’ Surveys
   (diffusion indices)

Economic   Manufacturing industry Consumer Construction
confidence
sentiment
indicator
indicator

confidence
indicator

Retail
trade
confid-

   Service industries

Services
ence confidence
indicator

indicator

Purchasing
Managers’
Capacity Index (PMI)
for manu-
utilisation
facturing
(%)

facturing
output

Manu- Business Composite
activity
output
for
services

Industrial Capacity
(long-term confidence utilisation
(%)

indicator

average
= 100)

1999-13

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

1

100.2

90.5
93.8
101.6

101.2
100.8

102.6
103.7

103.9
103.8
103.8
103.5
104.0
104.2

2

-6.1

-11.6
-9.1
-3.9

-4.6
-4.5

-4.0
-3.2

-2.9
-3.2
-3.0
-3.4
-2.9
-3.7

3

80.9

78.9
78.7
80.4

80.4
80.8

81.1
81.1

- 
81.2
- 
- 
81.1
- 

4

-12.8

-22.0
-18.7
-10.1

-10.0
-11.3

-6.3
-5.3

-3.7
-4.6
-5.6
-5.6
-7.2
-6.9

5

-13.8

-27.7
-29.2
-27.4

-27.3
-24.3

-24.9
-24.9

-24.2
-25.5
-25.0
-24.2
-23.8
-22.7

6

-8.7

-15.0
-12.2
-3.2

-3.9
-5.1

-1.6
-0.2

-0.8
-0.8
1.5
-1.3
1.1
3.1

7

6.6

-6.5
-5.4
4.8

4.5
5.3

5.6
7.6

6.1
7.0
7.9
7.9
8.9
10.2

8

- 

86.5
87.1
87.6

87.7
87.9

88.2
88.3

- 
88.5
- 
- 
88.1
- 

9

10

11

51.0

46.2
49.6
51.8

50.9
50.4

51.4
52.3

52.2
52.0
52.2
52.5
52.4
52.3

52.4

46.3
50.6
53.3

51.6
51.2

52.6
53.4

53.6
53.4
53.3
53.6
53.6
53.9

52.9

47.6
49.3
52.5

53.2
51.7

53.6
54.1

54.2
54.1
53.8
54.4
54.0
54.3

12

52.7

47.2
49.7
52.7

52.8
51.5

53.3
53.9

54.0
53.9
53.6
54.2
53.9
54.1

Sources: European Commission (Directorate-General for Economic and Financial Affairs) (col. 1-8) and Markit (col. 9-12).

3.7 Summary accounts for households and non-financial corporations
(current prices, unless otherwise indicated; not seasonally adjusted)

   Households

   Non-financial corporations

Saving Debt Real gross

Financial Non-financial

ratio ratio disposable investment

(gross) 1)

income

investment worth

(gross)

Net Hous-
ing
 2) wealth

share 3)

Profit Saving
ratio
(net)

Debt
ratio 4)

investment

Financial Non-financial
investment
(gross)

Finan-
cing

   Percentage of
   gross disposable
   income (adjusted)

   Annual percentage changes

   Percentage of net
   value added

Percent-
age of
GDP

   Annual percentage changes

2011
2012
2013

2014 Q2 
         Q3 
         Q4 

2015 Q1 

1

2

13.0 97.8
12.6 97.4
12.7 96.0

12.6 95.5
12.7 95.0
12.7 95.1

12.7 94.7

3

-0.1
-1.8
-0.5

0.4
1.6
1.3

2.2

4

1.9
1.7
1.4

1.5
1.7
1.7

1.9

5

6

1.8
-4.6
-3.5

-0.2
-0.7
-0.5

-0.1

0.5
0.6
0.4

2.7
2.7
2.3

3.8

7

1.1
-2.3
-2.2

-0.1
0.4
0.7

1.3

8

33.6
31.0
30.6

30.9
31.5
32.2

.

9

3.7
1.8
3.1

3.2
3.2
2.6

2.7

10

.
133.3
131.9

132.4
132.1
133.1

135.3

11

3.0
1.4
2.2

2.4
2.0
1.9

2.5

12

10.1
-6.2
-1.7

2.0
1.9
0.9

1.0

13

2.0
1.0
1.1

1.2
1.0
1.1

1.5

Sources: ECB and Eurostat.
1) Based on four-quarter cumulated sums of both saving and gross disposable income (adjusted for the change in the net equity of households in pension fund reserves).
2) Financial assets (net of financial liabilities) and non-financial assets. Non-financial assets consist mainly of housing wealth (residential structures and land). They also include

non-financial assets of unincorporated enterprises classified within the household sector.

3) The profit share uses net entrepreneurial income, which is broadly equivalent to current profits in business accounting. 
4) Based on the outstanding amount of loans, debt securities, trade credits and pension scheme liabilities.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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3 Economic activity

3.8 Euro area balance of payments, current and capital accounts
(EUR billions; seasonally adjusted unless otherwise indicated; transactions)

   Total

   Goods

   Services

   Primary income    Secondary income

   Current account

   Capital
   account 1)

Credit

Debit

Net Credit

Debit

Credit

Debit

Credit

Debit

Credit

Debit

Credit

Debit

1

2

3

4

5

6

7

8

9

835.8
842.7

865.3
870.2

282.9
287.7
294.7
292.8
291.5
285.9

778.1
784.9

783.6
801.9

255.1
260.5
268.1
269.0
272.4
260.5

57.7
57.8

81.7
68.3

27.9
27.2
26.7
23.8
19.1
25.4

491.2
505.3

509.0
520.0

166.7
170.5
171.8
174.3
172.9
172.8

428.7
430.4

432.6
439.3

139.8
142.9
149.9
145.8
147.9
145.6

175.5
177.9

182.1
182.6

59.6
60.7
61.9
59.7
62.2
60.6

157.9
164.6

165.6
166.8

54.0
55.6
56.0
56.2
55.6
55.0

144.9
135.6

149.2
141.8

48.2
48.0
53.0
50.9
46.8
44.1

12-month cumulated transactions

136.3
130.8

127.1
134.8

42.5
41.9
42.7
45.7
46.0
43.1

10

24.2
24.0

25.0
25.9

8.5
8.5
8.0
7.9
9.6
8.4

11

55.2
59.1

58.3
61.0

18.7
20.2
19.4
21.3
22.9
16.8

12

6.8
12.8

8.4
9.7

2.3
2.6
3.5
2.4
3.7
3.5

13

2.6
5.3

4.5
5.7

1.5
1.2
1.8
1.3
1.5
2.9

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

2015 June 

3,414.0

3,148.5

265.5 2,025.4
12-month cumulated transactions as a percentage of GDP

1,731.0

718.1

654.9

571.4

529.0

99.1

233.6

37.7

18.1

2015 June 

33.6

31.0

2.6

19.9

17.0

7.1

6.4

5.6

5.2

1.0

2.3

0.4

0.2

1) The capital account is not seasonally adjusted.

3.9 Euro area external trade in goods 1), values and volumes by product group 2)
(seasonally adjusted, unless otherwise indicated)

   Total (n.s.a.)

   Exports (f.o.b.)

   Imports (c.i.f.)

   Total

Memo item:

   Total

   Memo items:

Exports Imports

Intermediate Capital Consump-
tion
goods

goods

goods

Manu-
facturing

Intermediate Capital Consump-
tion
goods

goods

goods

Manu-
facturing

Oil

1

2

3

4

5

6

7

8

9

10

11

12

13

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Jan. 
         Feb. 
         Mar. 
         Apr. 
         May 
         June 

2.9
4.5

5.1
7.8

-0.7
4.3
11.0
8.6
2.5
12.3

1.2
3.1

2.5
. 

-1.5
1.5
7.0
3.1
-2.5
. 

0.4
0.0

1.5
3.8

-5.6
1.0
9.1
4.8
-0.2
6.6

2.1
2.0

4.8
. 

-0.3
4.7
10.1
2.8
-2.3
. 

486.1
498.3

503.9
517.4

163.9
168.6
171.4
173.4
170.8
173.1

114.5
117.2

117.3
. 

115.8
117.7
118.3
118.3
116.2
. 

Values (EUR billions; annual percentage changes for columns 1 and 2)

236.1
237.4

240.5
. 

78.4
80.5
81.6
81.7
80.7
. 

96.9
102.4

103.7
. 

34.0
35.1
34.6
35.4
34.3
. 

139.5
145.1

148.5
. 

47.6
49.7
51.3
51.3
50.9
. 

397.7
409.4

420.0
429.8

135.9
141.0
143.1
144.0
141.4
144.4

439.5
435.6

444.1
452.6

143.0
147.4
153.7
151.8
149.5
151.3

269.4
260.3

258.9
. 

83.1
86.1
89.8
89.1
86.8
. 

62.1
63.3

69.5
. 

22.4
23.3
23.9
23.7
22.3
. 

Volume indices (2000 = 100; annual percentage changes for columns 1 and 2)

112.6
113.7

114.5
. 

113.0
115.0
115.6
114.6
113.0
. 

114.4
119.3

118.7
. 

117.9
120.7
117.6
119.0
116.5
. 

116.4
121.0

121.9
. 

119.7
121.9
124.1
122.4
120.2
. 

114.7
116.9

117.7
. 

115.3
118.8
119.2
118.5
116.3
. 

101.5
102.0

105.1
. 

103.8
104.7
106.8
104.0
102.0
. 

101.1
101.9

105.7
. 

104.1
105.6
107.4
104.6
101.0
. 

100.4
98.5

103.5
. 

102.8
105.2
102.6
101.8
97.3
. 

Sources: ECB and Eurostat.
1) Differences between ECB’s b.o.p. goods (Table 3.8) and Eurostat’s trade in goods (Table 3.9) are mainly due to different definitions.
2) Product groups as classified in the Broad Economic Categories.

101.0
102.9

108.7
. 

35.1
36.0
37.6
36.3
36.7
. 

103.0
102.5

104.8
. 

103.1
103.6
107.8
102.8
103.5
. 

287.7
293.5

312.5
313.7

100.2
104.4
107.9
105.4
103.0
105.4

73.3
65.9

58.3
. 

19.2
18.9
20.2
19.3
19.0
. 

104.6
104.2

88.8
97.0

107.3 111.0
. 

. 

105.0 117.3
107.7 108.9
109.3 106.8
98.8
105.8
91.2
104.0
. 
. 

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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4 Prices and costs

4.1 Harmonised Index of Consumer Prices 1)
(annual percentage changes, unless otherwise indicated)

   Total

   Total (s.a.; percentage change vis-à-vis previous period)

Index:
2005
= 100

   Total

Goods Services

Total
excluding
food and
energy

Total Processed Unpro- Non-energy
industrial
goods

cessed
food

food

Energy Services
(n.s.a.)

   Memo item:
   Administered prices

Total HICP Adminis-
tered
prices

excluding
administered
prices

% of total
in 2015

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug.  2) 

1

2

3

4

5

6

100.0 100.0

69.7

56.5

43.5

100.0

115.6
117.2
117.7

117.7
117.8

116.8
118.4

117.9
118.2
118.5
118.5
117.7
117.8

2.5
1.4
0.4

0.4
0.2

-0.3
0.2

-0.1
0.0
0.3
0.2
0.2
0.2

1.5
1.1
0.8

0.8
0.7

0.7
0.8

0.6
0.6
0.9
0.8
1.0
1.0

3.0
1.3
-0.2

-0.3
-0.6

-1.4
-0.5

-0.9
-0.7
-0.4
-0.4
-0.5
. 

1.8
1.4
1.2

1.2
1.2

1.1
1.1

1.0
1.0
1.3
1.1
1.2
1.2

-
-
-

0.1
-0.2

-0.3
0.5

0.2
0.1
0.3
0.0
0.0
-0.1

7

12.2

-
-
-

0.2
0.0

0.2
0.3

0.1
0.2
-0.1
0.1
0.0
0.0

8

7.5

-
-
-

0.2
0.4

0.5
0.6

-0.1
0.4
0.2
-0.1
-0.3
0.8

9

26.3

10

10.6

11

43.5

12

87.1

13

12.9

-
-
-

0.0
0.0

0.1
0.2

0.1
0.1
0.1
0.0
0.2
0.1

-
-
-

-0.4
-3.0

-4.2
2.4

1.7
0.1
0.9
-0.1
-0.7
-2.2

-
-
-

0.3
0.2

0.2
0.4

0.0
0.0
0.3
0.0
0.2
0.1

2.3
1.2
0.2

0.2
-0.1

-0.5
0.1

-0.3
-0.1
0.3
0.1
0.1
. 

3.8
2.1
1.9

1.6
1.7

1.2
0.9

1.1
0.9
1.0
0.9
0.9
. 

   Goods

   Services

   Food (including alcoholic
   beverages and tobacco)

Total Processed
food

Unpro-
cessed
food

14

19.7

15

12.2

3.1
2.7
0.5

-0.1
0.3

0.3
1.1

0.6
1.0
1.2
1.1
0.9
1.2

3.1
2.2
1.2

1.0
0.7

0.5
0.7

0.6
0.7
0.6
0.7
0.6
0.6

16

7.5

3.0
3.5
-0.8

-2.0
-0.3

0.1
1.8

0.7
1.3
2.1
1.9
1.4
2.3

% of total
in 2015

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug.  2) 

   Industrial goods

   Housing

Transport Communi- Recreation Miscel-
and laneous

cation

personal

Total

17

36.9

3.0
0.6
-0.5

-0.4
-1.1

-2.3
-1.4

-1.7
-1.6
-1.2
-1.3
-1.3
. 

Non-energy
industrial
goods

Energy

Rents

18

26.3

1.2
0.6
0.1

0.1
-0.1

-0.1
0.2

0.0
0.1
0.2
0.3
0.4
0.6

19

10.6

20

10.7

7.6
0.6
-1.9

-1.8
-3.6

-7.7
-5.3

-6.0
-5.8
-4.8
-5.1
-5.6
-7.1

1.8
1.7
1.7

1.7
1.6

1.3
1.2

1.2
1.2
1.2
1.2
1.2
. 

21

6.4

1.5
1.5
1.4

1.3
1.4

1.3
1.2

1.2
1.3
1.2
1.2
1.1
. 

22

7.3

2.9
2.4
1.7

1.7
1.6

1.4
1.2

1.4
0.7
1.6
1.2
1.5
. 

23

3.1

-3.2
-4.2
-2.8

-3.1
-2.6

-1.9
-0.9

-1.7
-1.2
-0.8
-0.8
-0.7
. 

24

14.8

2.2
2.2
1.5

1.5
1.4

1.3
1.4

1.1
1.2
1.8
1.3
1.6
. 

25

7.5

2.0
0.7
1.3

1.3
1.4

1.2
1.2

1.3
1.2
1.3
1.1
1.0
. 

Sources: Eurostat and ECB calculations.
1) Data refer to the changing composition of the euro area.
2) Estimate based on provisional national data, which usually cover around 95% of the euro area, as well as on early information on energy prices.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
   
   
   
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
4 Prices and costs

4.2 Industry, construction and property prices
(annual percentage changes, unless otherwise indicated)

Total
(index:
2010 = 100)

   Industrial producer prices excluding construction

   Total

   Industry excluding construction and energy

Energy

Manu- Total

facturing

Intermediate Capital
goods

goods

   Consumer goods

Con-
struction

Residential
property
prices 1)

Experimental
indicator of
commercial
property
prices 1), 2)

Total

Food, Non-
beverages food

and tobacco

% of total
in 2010

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July 

1

2

3

4

5

6

7

8

9

100.0 100.0

78.0

72.1

29.3

20.0

22.7

13.8

8.9

108.7
2.8
108.5 -0.2
106.9 -1.5

106.8 -1.4
106.0 -1.9

104.5 -2.9
104.9 -2.1

104.6 -2.8
104.9 -2.3
104.8 -2.1
104.9 -2.0
104.9 -2.1
104.7 -2.1

2.0
-0.1
-0.9

-0.6
-1.6

-2.6
-1.7

-2.6
-1.9
-1.8
-1.5
-1.8
-2.0

1.4
0.4
-0.3

-0.1
-0.3

-0.6
-0.3

-0.7
-0.5
-0.4
-0.3
-0.4
-0.4

0.7
-0.6
-1.1

-0.6
-0.7

-1.5
-0.7

-1.7
-1.2
-0.8
-0.6
-0.6
-0.7

1.0
0.6
0.4

0.5
0.6

0.7
0.7

0.7
0.7
0.8
0.7
0.7
0.7

2.5
1.7
0.1

-0.1
-0.6

-0.7
-0.8

-0.7
-0.6
-0.8
-0.8
-0.8
-0.8

3.5
2.6
-0.2

-0.5
-1.2

-1.3
-1.4

-1.3
-1.1
-1.4
-1.3
-1.4
-1.4

0.9
0.3
0.3

0.3
0.2

0.2
0.1

0.3
0.3
0.1
0.0
0.1
0.2

10

27.9

6.6
-1.6
-4.4

-4.5
-5.8

-8.5
-6.5

-8.1
-6.8
-6.4
-6.3
-6.8
-6.5

11

12

13

1.5
0.3
0.3

0.4
0.2

0.3
. 

- 
- 
- 
- 
- 
- 

-1.7
-2.0
0.2

0.5
0.7

1.1
. 

- 
- 
- 
- 
- 
- 

-0.1
-1.6
1.3

2.1
2.8

. 
. 

- 
- 
- 
- 
- 
- 

Sources: Eurostat, ECB calculations, and ECB calculations based on MSCI data and national sources (col. 13).
1) Experimental data based on non-harmonised sources (see http://www.ecb.europa.eu/stats/intro/html/experiment.en.html for further details).
2) Data refer to the Euro 19.

4.3 Commodity prices and GDP deflators
(annual percentage changes, unless otherwise indicated)

Total Total
(s.a.;
index:
2010
= 100)

Total

   GDP deflators

   Domestic demand

Exports 1)

Imports 1)

consump-

Private Govern-
ment
tion consump-

Gross
fixed
capital
tion formation

Oil prices
(EUR per
barrel)

   Non-energy commodity prices  (EUR)

   Import-weighted 2)

   Use-weighted 2)

Total Food Non-food Total Food Non-food

1

2

3

4

5

6

7

8

9

10

11

12

13

14

% of total

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

102.4
103.7
104.6

104.7
104.9

105.3
. 

- 
- 
- 
- 
- 
- 

1.3
1.3
0.9

0.9
0.9

1.0
. 

- 
- 
- 
- 
- 
- 

1.5
0.9
0.5

0.5
0.3

0.0
. 

- 
- 
- 
- 
- 
- 

1.9
1.1
0.4

0.3
0.2

-0.2
. 

- 
- 
- 
- 
- 
- 

0.8
1.2
1.0

1.1
0.9

0.7
. 

- 
- 
- 
- 
- 
- 

1.2
0.4
0.4

0.5
0.6

0.5
. 

- 
- 
- 
- 
- 
- 

1.9
-0.3
-0.7

-0.5
-0.5

-0.5
. 

- 
- 
- 
- 
- 
- 

2.5
-1.3
-1.7

-1.5
-1.9

-2.9
. 

- 
- 
- 
- 
- 
- 

  100.0

35.0

65.0 100.0

45.0

86.6
81.7
74.5

78.0
61.5

49.0
57.4

52.4
56.6
58.9
56.7
51.7
43.0

-7.2
0.2
-9.0 -13.4
-1.6
-8.8

-6.2
-5.5

-0.4
-0.5

1.0
-1.4
-0.2
-0.1
-3.6
-7.5

-1.7
6.2

8.7
2.1

4.6
3.4
-0.3
3.1
11.1
4.7

-10.5
-6.9
-12.1

-8.3
-10.8

-4.9
-2.0

-1.0
-4.0
-0.1
-1.9
-11.0
-13.7

-3.1
5.8
-8.3 -10.1
0.7
-4.6

-2.1
1.3

5.6
4.0

6.2
4.9
3.8
3.3
0.5
-4.0

0.2
9.3

11.6
5.6

7.9
7.8
3.3
5.9
9.8
5.5

Sources: Eurostat, ECB calculations and Thomson Reuters (col. 9).
1) Deflators for exports and imports refer to goods and services and include cross-border trade within the euro area.
2) Import-weighted: weighted according to 2004-06 average import structure; use-weighted: weighted according to 2004-06 average domestic demand structure.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

15

55.0

-9.1
-6.9
-8.7

-3.8
-4.7

0.7
2.6

4.7
2.4
4.2
1.1
-7.1
-11.6

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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 Prices and costs

4.4 Price-related opinion surveys
(seasonally adjusted)

   European Commission Business and Consumer Surveys
   (percentage balances)

   Purchasing Managers’ Surveys
   (diffusion indices)

   Selling price expectations
   (for next three months)

Manu-
facturing

Retail trade

Services

Construction

Consumer
price trends
over past
12 months

   Input prices

   Prices charged

Manu-
facturing

Services

Manu-
facturing

Services

1

4.8

2.7
-0.3
-0.8

-0.7
-2.1

-5.5
-1.1

-4.6
-2.7
-0.6
0.0
-0.1
-2.4

2

- 

8.1
1.7
-1.4

-1.8
-4.4

-0.7
3.3

0.6
2.8
2.4
4.7
0.8
3.1

3

- 

2.1
-1.2
1.2

0.9
2.8

1.4
3.0

2.4
2.3
2.6
4.2
2.1
2.1

4

-1.8

-12.7
-17.1
-17.6

-16.9
-15.7

-17.0
-15.4

-16.3
-17.7
-13.7
-14.9
-14.0
-13.2

5

34.0

38.6
29.8
14.3

11.7
7.9

-2.4
-0.8

-3.8
-2.0
-0.6
0.1
0.9
0.3

6

57.7

52.7
48.5
49.6

51.2
48.7

45.8
54.7

50.7
52.4
56.0
55.7
54.4
49.6

7

56.7

55.1
53.8
53.5

53.7
52.6

52.5
54.4

54.2
53.6
55.4
54.1
54.3
53.2

8

- 

49.9
49.4
49.7

49.8
49.0

48.8
50.4

49.7
50.1
50.0
51.0
50.4
50.5

9

49.9

47.9
47.8
48.2

48.4
47.1

47.6
49.0

48.6
48.9
49.3
48.9
49.5
49.1

1999-13

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Mar. 
         Apr. 
         May 
         June 
         July 
         Aug. 

Sources: European Commission (Directorate-General for Economic and Financial Affairs) and Markit.

4.5 Labour cost indices
(annual percentage changes, unless otherwise indicated)

Total
(index:
2012 = 100)

1

100.0

100.0
101.4
102.6

100.5
107.9

97.6
. 

Total

   By component

   For selected economic activities

Wages and
salaries

Employers’ social
contributions

Business economy

Mainly non-business
economy

2

100.0

2.0
1.3
1.2

1.4
1.2

2.2
. 

3

74.6

2.0
1.5
1.3

1.5
1.1

2.2
. 

4

25.4

2.1
1.0
0.9

1.3
1.3

2.1
. 

5

69.3

2.4
1.2
1.2

1.3
1.1

2.3
. 

6

30.7

1.3
1.6
1.3

1.8
1.3

1.9
. 

Memo item:
Indicator of
negotiated
wages 1)

7

2.2
1.8
1.7

1.7
1.7

1.4
1.5

% of total
in 2012

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

Sources: Eurostat and ECB calculations.
1) Experimental data based on non-harmonised sources (see http://www.ecb.europa.eu/stats/intro/html/experiment.en.html for further details).

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
   
   
   
   
 
 
 
 
 
 
 
 
4 Prices and costs

4.6 Unit labour costs, compensation per labour input and labour productivity
(annual percentage changes, unless otherwise indicated; quarterly data seasonally adjusted; annual data unadjusted)

Total Total

(index:
2010
=100)

   By economic activity

Agriculture,
forestry

Manu-

Con-
facturing, struction

Trade,

Information
transport, and commu-

and fishing energy and
utilities

accom-
modation and
food services

nication insurance

Finance

Real Professional,

and estate business and ministration,
education,
health and
social work

support
services

Public ad- Arts, enter-
tainment
and other
services

1

2

3

4

5

6
Unit labour costs 

7

8

9

10

11

12

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

102.5
103.8
105.0

104.8
105.3
105.5

105.8

103.6
105.3
106.8

106.7
107.1
107.6

108.2

101.1
101.4
101.7

101.8
101.7
102.0

102.2

104.8
107.2
108.6

108.4
108.8
109.0

109.9

102.3
103.3
103.5

103.6
103.4
103.4

103.9

1.9
1.3
1.2

1.1
1.3
1.3

1.2

1.5
1.6
1.4

1.3
1.3
1.3

1.5

-0.4
0.3
0.3

0.2
0.1
0.0

0.2

2.9
2.3
1.3

1.4
1.3
1.1

1.6

0.9
1.0
0.2

0.4
0.2
-0.2

0.4

Sources: Eurostat and ECB calculations.

2.2
0.2
-3.8

-4.7
-4.8
-0.7

1.3

0.3
3.9
-1.1

-1.3
-0.7
-1.1

1.3

-1.9
3.7
2.7

3.5
4.3
-0.5

-0.1

2.3
4.2
-0.9

-0.6
-0.6
-1.5

1.0

-0.9
4.1
2.9

4.1
4.5
-1.6

-1.2

1.8
1.8
1.8

1.8
1.8
2.3

1.7

1.9
2.8
2.3

2.1
2.1
2.2

1.9

0.1
1.0
0.5

0.4
0.3
-0.2

0.2

3.5
3.0
1.8

2.5
1.9
1.6

2.0

1.7
1.2
0.0

0.7
0.1
-0.7

0.2

3.8
0.1
0.5

0.3
1.2
1.1

2.0

2.3
1.6
1.6

1.7
0.6
1.2

0.5

2.4
1.0
0.7

0.9
1.0
0.7

0.5

0.0
0.8
1.2

0.9
1.0
1.7

0.4

Compensation per employee 

1.7
1.0
1.2

1.1
1.1
1.1

1.2

1.5
0.7
2.0

1.8
1.9
1.9

1.4

Labour productivity per person employed

-1.4
1.5
1.1

1.3
-0.6
0.1

-1.4

-0.7
0.1
0.5

0.3
0.1
0.4

0.7

1.5
-0.1
0.8

1.0
0.9
0.2

0.9

Compensation per hour worked 

5.1
3.0
1.3

1.7
0.6
0.7

0.4

1.2
2.8
0.8

1.4
-0.3
-0.6

-1.3

3.4
1.8
1.2

1.3
1.2
0.9

1.6

2.0
1.0
1.9

1.7
1.7
1.5

1.1

Hourly labour productivity

0.7
0.6
0.5

0.4
0.2
0.3

1.2

2.1
0.2
0.9

1.0
1.2
0.2

1.3

0.0
2.6
1.0

0.9
0.6
0.9

0.0

1.2
2.0
1.4

1.7
1.3
1.8

1.3

1.2
-0.6
0.4

0.8
0.7
0.9

1.3

1.7
2.5
1.7

2.2
1.7
2.5

2.0

2.0
-0.1
0.7

1.5
1.4
1.6

1.8

1.3
-1.9
0.5

0.2
1.2
0.4

3.5

1.1
-0.1
1.0

0.9
1.6
0.8

2.9

-0.2
1.9
0.5

0.7
0.4
0.3

-0.6

1.7
1.4
1.1

1.5
1.3
0.3

2.7

0.8
2.9
1.0

1.4
1.5
0.0

-1.1

3.7
1.2
2.1

2.4
2.3
2.1

2.5

2.1
1.1
1.6

1.5
1.7
1.5

2.0

-1.5
-0.1
-0.5

-0.9
-0.6
-0.5

-0.5

3.2
2.1
1.5

1.4
1.4
1.3

2.2

-0.4
0.7
-0.4

-0.6
-0.5
-0.7

-0.1

0.6
1.7
1.3

1.1
1.3
1.5

1.6

0.9
1.7
1.3

1.2
1.3
1.4

1.6

0.3
0.0
0.0

0.0
0.0
-0.1

0.0

1.4
2.2
1.1

0.9
1.2
1.1

1.7

0.9
0.5
-0.2

-0.1
-0.1
-0.3

0.1

3.1
1.7
1.0

1.2
1.1
1.5

0.9

1.9
1.4
1.1

1.4
1.0
0.5

1.1

-1.1
-0.3
0.1

0.2
-0.1
-1.1

0.2

2.9
2.1
1.3

0.9
1.4
0.5

0.6

-0.1
0.5
0.5

0.3
0.5
-0.8

0.0

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 17
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics5 Money and credit

5.1 Monetary aggregates 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)

   M3

   M2

   M3-M2

   M1

   M2-M1

Currency Overnight
deposits

in
circulation

Deposits

Deposits
with an redeemable
at notice
agreed
of up to
maturity
of up to
3 months
2 years

Repos

Money
market
fund
shares

Debt
securities
with
a maturity
of up to
2 years

1

2

3

4

5

6

7

8

9

10

11

12

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

863.4
908.8
967.3

948.2
967.3

993.7
1,015.0

992.4
993.7
1,003.3
1,006.7
1,015.0
1,021.1

4,244.0
4,482.6
4,949.1

4,745.2
4,949.1

5,173.7
5,303.2

5,106.6
5,173.7
5,189.9
5,264.9
5,303.2
5,377.6

5,107.5
5,391.4
5,916.4

5,693.4
5,916.4

6,167.4
6,318.2

6,099.0
6,167.4
6,193.2
6,271.6
6,318.2
6,398.7

20.0
45.3
58.0

16.7
19.1

25.2
21.3

7.6
1.3
9.6
3.4
8.2
6.1

2.4
5.2
6.4

6.0
6.4

7.3
8.8

7.9
7.3
8.2
8.3
8.8
8.9

289.5
245.8
370.2

109.1
125.9

188.7
151.6

47.2
59.8
37.7
70.9
43.0
70.5

7.3
5.8
8.2

6.2
8.2

10.5
12.3

9.4
10.5
11.0
11.8
12.3
12.7

309.5
291.1
428.1

125.7
145.1

213.9
172.8

54.8
61.1
47.4
74.3
51.2
76.6

6.4
5.7
7.9

6.2
7.9

10.0
11.7

9.1
10.0
10.5
11.2
11.7
12.1

Source: ECB.
1) Data refer to the changing composition of the euro area.

1,803.3
1,691.2
1,605.0

1,647.5
1,605.0

1,529.2
1,478.8

1,535.3
1,529.2
1,518.9
1,486.1
1,478.8
1,469.8

-36.0
-111.1
-91.9

-27.1
-40.9

-63.3
-49.1

-19.3
-8.9
-8.5
-34.1
-6.5
-14.1

-1.9
-6.2
-5.4

-3.9
-5.4

-7.7
-10.8

-7.3
-7.7
-8.0
-10.3
-10.8
-11.5

Outstanding amounts

2,081.5
2,123.2
2,129.6

2,136.6
2,129.6

2,133.5
2,162.1

2,123.3
2,133.5
2,151.1
2,157.2
2,162.1
2,163.1

3,884.8
3,814.4
3,734.5

3,784.1
3,734.5

3,662.7
3,640.9

3,658.6
3,662.7
3,670.0
3,643.3
3,640.9
3,632.9

8,992.3
9,205.8
9,650.9

9,477.5
9,650.9

9,830.1
9,959.0

9,757.6
9,830.1
9,863.2
9,914.9
9,959.0
10,031.5

Transactions

114.9
43.9
3.6

5.1
-9.0

4.8
15.0

2.7
10.4
4.0
6.0
5.0
1.0

78.9
-67.2
-88.3

-22.0
-50.0

-58.5
-34.1

-16.6
1.5
-4.5
-28.1
-1.5
-13.1

Growth rates

5.9
2.1
0.2

0.3
0.2

0.3
0.7

0.0
0.3
0.5
0.7
0.7
0.8

2.1
-1.7
-2.3

-1.5
-2.3

-3.2
-4.3

-3.2
-3.2
-3.3
-4.1
-4.3
-4.6

388.5
223.9
339.8

103.8
95.1

155.4
138.7

38.2
62.6
42.8
46.2
49.7
63.4

4.5
2.5
3.7

3.0
3.7

4.6
5.2

4.1
4.6
4.9
5.0
5.2
5.4

125.0
120.0
122.2

122.4
122.2

125.7
91.1

132.4
125.7
129.5
111.7
91.1
105.7

-16.9
-12.0
0.8

-8.1
-0.5

2.3
-34.3

12.8
-7.0
4.1
-18.0
-20.4
14.4

-11.6
-9.5
0.7

9.7
0.7

5.1
-30.9

0.5
5.1
6.2
-9.5
-30.9
-19.1

483.1
417.8
427.3

419.0
427.3

436.5
438.0

443.0
436.5
451.7
442.9
438.0
456.2

-20.2
-48.8
10.7

10.0
11.2

4.9
1.5

4.4
-6.6
15.3
-8.8
-4.9
18.2

-3.9
-10.4
2.6

-1.1
2.6

5.3
6.8

3.4
5.3
9.2
7.7
6.8
7.9

180.6
86.5
104.4

68.8
104.4

96.9
97.0

108.9
96.9
103.5
92.7
97.0
90.5

-18.5
-62.8
12.5

3.4
18.4

-8.7
1.9

6.9
-13.2
7.5
-11.1
5.4
-6.4

-9.9
-37.8
18.3

-26.8
18.3

11.0
25.1

22.1
11.0
40.0
15.1
25.1
25.6

9,781.0
788.7
624.3
9,830.0
653.9 10,304.8

610.2 10,087.7
653.9 10,304.8

659.1 10,489.2
626.1 10,585.2

684.3 10,441.9
659.1 10,489.2
684.7 10,547.9
647.3 10,562.2
626.1 10,585.2
652.3 10,683.9

-55.6
-123.6
24.0

5.3
29.1

-1.6
-30.9

24.1
-26.8
26.9
-37.9
-19.9
26.2

-6.6
-16.2
3.9

-4.1
3.9

5.5
0.5

4.8
5.5
11.6
4.7
0.5
3.6

332.8
100.3
363.7

109.1
124.2

153.9
107.9

62.3
35.8
69.7
8.3
29.8
89.7

3.5
1.0
3.7

2.5
3.7

4.7
4.9

4.1
4.7
5.3
5.0
4.9
5.3

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 18
S 18

ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
   
5 Money and credit

5.2 Deposits in M3 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)

   Non-financial corporations 2) 

   Households 3) 

Total Overnight

With an Redeem- Repos
agreed
maturity
of up to
2 years

able
at notice
of up to
3 months

Total Overnight

With an Redeem- Repos
agreed
maturity
of up to
2 years

able
at notice
of up to
3 months

Financial
corpor-
ations
other than
MFIs and
ICPFs 2)

Insurance

Other
corpor- general
ations govern-
and ment 4)

pension
funds

1

2

3

4

5

7
Outstanding amounts 

6

8

9

10

11

12

13

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

1,618.7
1,710.6
1,813.6

1,789.5
1,813.6

1,847.0
1,851.9

1,851.7
2015 Feb. 
1,847.0
         Mar. 
1,844.5
         Apr. 
1,852.0
         May 
         June 
1,851.9
         July (p) 1,889.1

1,112.8
1,198.6
1,329.4

1,283.8
1,329.4

1,392.6
1,407.1

1,393.7
1,392.6
1,387.5
1,403.6
1,407.1
1,440.1

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

72.2
97.9
68.6

29.6
7.1

29.6
8.8

11.7
-9.0
1.6
5.2
2.0
33.3

4.7
6.1
4.0

6.0
4.0

4.6
4.2

4.8
4.6
4.4
4.4
4.2
5.5

99.4
90.4
90.2

33.6
16.3

49.1
29.4

13.5
-3.9
10.0
14.4
5.0
31.4

9.8
8.1
7.5

8.6
7.5

9.5
10.1

9.9
9.5
9.9
10.4
10.1
11.7

406.9
400.8
368.3

391.1
368.3

340.4
320.7

347.1
340.4
333.3
324.3
320.7
323.2

-33.2
-6.0
-25.4

-5.7
-12.1

-17.2
-19.0

-5.0
-8.0
-6.3
-9.5
-3.2
0.3

-7.5
-1.5
-6.3

-2.1
-6.3

-10.0
-14.1

-8.9
-10.0
-11.4
-13.9
-14.1
-14.3

88.1
94.7
96.5

99.2
96.5

99.0
111.9

97.0
99.0
112.8
111.9
111.9
113.0

10.0
7.7
1.4

1.9
-1.2

2.5
1.0

0.8
2.0
1.9
-0.9
0.0
1.1

13.2
8.8
1.5

3.4
1.5

3.6
4.5

1.3
3.6
5.5
4.5
4.5
4.6

10.8
16.5
19.5

15.4
19.5

14.9
12.2

13.9
14.9
10.9
12.2
12.2
12.8

-4.0
5.8
2.5

-0.2
4.0

-4.9
-2.6

2.5
0.9
-3.9
1.2
0.1
0.5

-25.2
54.6
14.5

47.4
14.5

-5.7
-23.3

-21.9
-5.7
-37.8
-24.1
-23.3
-11.1

5,308.6
5,414.0
5,556.9

5,531.9
5,556.9

5,598.3
5,649.4

5,566.5
5,598.3
5,611.5
5,624.4
5,649.4
5,667.8

2,360.4
2,542.6
2,753.4

2,686.9
2,753.4

2,843.8
2,911.5

2,810.2
2,843.8
2,859.2
2,878.1
2,911.5
2,945.3

Transactions

977.3
875.7
810.7

845.1
810.7

761.7
734.7

771.1
761.7
756.7
745.8
734.7
721.5

1,960.3
1,991.2
1,989.9

1,995.1
1,989.9

1,988.8
2,000.3

1,980.9
1,988.8
1,991.9
1,996.7
2,000.3
1,997.8

10.5
4.5
2.8

4.9
2.8

3.9
2.8

4.3
3.9
3.7
3.8
2.8
3.2

222.8
108.7
140.4

99.4
183.7
209.1

35.6
-100.1
-65.6

100.2
31.1
-1.4

-12.5
-6.0
-1.7

47.3
26.0

39.1
52.9

12.1
30.7
14.9
12.0
25.9
17.6

61.9
67.7

81.4
71.6

23.4
32.8
18.9
18.3
34.4
33.2

Growth rates

4.4
2.0
2.6

2.2
2.6

2.8
3.0

2.5
2.8
2.9
2.9
3.0
3.1

4.4
7.8
8.2

7.3
8.2

9.7
10.8

8.9
9.7
10.0
10.2
10.8
11.2

-16.0
-33.0

-43.3
-27.2

-12.7
-9.9
-5.0
-11.2
-11.1
-13.6

3.8
-10.3
-7.5

-7.0
-7.5

-11.2
-13.9

-10.3
-11.2
-11.4
-12.7
-13.9
-15.1

1.0
-6.6

-0.1
9.6

1.0
8.2
1.3
4.7
3.6
-2.4

5.4
1.6
-0.1

0.1
-0.1

0.1
0.2

-0.2
0.1
0.1
0.2
0.2
0.2

0.4
-2.0

1.1
-1.1

0.4
-0.4
-0.3
0.2
-1.1
0.4

-54.2
-57.0
-37.2

-20.8
-37.2

-31.0
-38.0

-25.5
-31.0
-35.3
-25.3
-38.0
-35.4

811.2
801.0
885.5

794.8
885.5

952.8
965.7

905.4
952.8
959.3
966.4
965.7
980.9

16.5
-17.4
46.4

-8.3
56.4

50.5
16.9

18.3
43.0
10.2
4.8
1.8
13.0

2.1
-2.2
5.5

-0.9
5.5

14.6
13.7

7.9
14.6
15.6
13.4
13.7
14.4

209.1
192.8
218.9

208.4
218.9

225.0
228.6

224.4
225.0
229.8
230.7
228.6
233.7

15.0
-14.2
6.3

-2.3
-8.2

4.6
3.9

-4.6
0.3
5.1
0.7
-1.9
4.8

7.8
-6.9
3.4

2.3
3.4

-0.8
-1.3

-0.9
-0.8
1.5
1.8
-1.3
-1.6

306.3
298.6
330.8

327.1
330.8

339.0
339.6

349.6
339.0
344.3
346.4
339.6
344.6

25.0
-8.5
20.9

12.6
-5.7

8.6
0.7

5.8
-10.7
5.4
2.0
-6.7
3.2

9.1
-2.8
7.0

3.3
7.0

5.2
5.1

8.0
5.2
7.5
8.5
5.1
4.9

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial

corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).

3) Including non-profit institutions serving households.
4) Refers to the general government sector excluding central government.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 19
S 19

ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
5 Money and credit

5.3 Credit to euro area residents 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)

   Credit to general government

   Credit to other euro area residents

Total

Loans

Debt
securities

Total

   Loans

   Total

Adjusted for
loan sales
and securi-
tisation 2)

To non- To house- To financial To insurance
holds 4) corporations corporations
financial
other than and pension
corpor-
ations 3)
MFIs and
funds
ICPFs 3)

Debt
securities

Equity and
non-money
market fund
investment
fund shares

1

2

3

4

5
Outstanding amounts

6

7

8

9

10

11

12

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

3,410.8
3,407.5
3,609.7

3,508.9
3,609.7

3,674.0
3,684.8

3,638.7
3,674.0
3,702.3
3,698.4
3,684.8
3,727.5

1,169.3
1,096.3
1,131.7

1,102.2
1,131.7

1,153.4
1,137.6

1,146.7
1,153.4
1,151.6
1,144.0
1,137.6
1,132.7

2,241.5
2,311.2
2,478.0

2,406.7
2,478.0

2,520.6
2,547.1

2,492.0
2,520.6
2,550.7
2,554.4
2,547.1
2,594.8

13,069.5 10,860.0
12,709.4 10,546.4
12,562.5 10,512.2

12,561.8 10,444.8
12,562.5 10,512.2

12,674.3 10,613.9
12,624.5 10,590.4

12,650.5 10,588.3
12,674.3 10,613.9
12,654.0 10,610.4
12,659.4 10,611.2
12,624.5 10,590.4
12,712.5 10,608.6

- 
- 
- 

- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

4,544.6
4,354.1
4,280.3

4,288.1
4,280.3

4,310.1
4,291.7

4,312.8
4,310.1
4,303.9
4,300.4
4,291.7
4,300.0

5,242.3
5,221.4
5,199.3

5,194.6
5,199.3

5,233.7
5,257.2

5,221.0
5,233.7
5,236.3
5,243.4
5,257.2
5,260.5

185.0
-24.4
73.6

40.4
47.5

38.5
59.8

-20.1
27.2
39.3
8.9
11.6
31.6

5.8
-0.7
2.1

-0.5
2.1

2.8
5.3

1.9
2.8
3.9
4.1
5.3
5.8

-4.0
-73.6
16.3

-1.4
12.8

21.5
-15.4

2.7
5.4
-1.5
-7.8
-6.0
-5.0

-0.3
-6.3
1.5

-0.7
1.5

2.0
1.6

1.5
2.0
2.4
0.9
1.6
0.8

189.0
49.2
57.3

41.8
34.7

17.0
75.2

-22.8
21.9
40.8
16.7
17.6
36.7

9.5
2.2
2.4

-0.4
2.4

3.2
7.1

2.1
3.2
4.7
5.6
7.1
8.1

-100.6
-304.5
-106.8

-18.7
1.7

34.6
-13.1

10.6
14.0
-1.9
1.2
-12.4
77.2

-0.8
-2.3
-0.8

-1.9
-0.8

-0.3
0.0

-0.5
-0.3
0.0
0.2
0.0
0.7

Transactions

-13.4
-221.2
19.2

-10.6
33.6

52.9
22.3

15.5
20.9
23.1
6.6
-7.4
35.9

Growth rates

-0.1
-2.0
0.2

-0.6
0.2

0.8
0.9

0.6
0.8
0.9
1.0
0.9
1.4

-107.6
-132.8
-58.3

-18.4
4.3

8.3
1.2

10.3
-3.0
2.2
-4.8
3.8
9.7

-2.3
-2.9
-1.3

-2.0
-1.3

-0.6
-0.1

-0.6
-0.6
-0.4
-0.2
-0.1
0.4

-69.1
-247.4
-50.7

-10.3
22.8

45.8
4.0

8.3
21.5
12.5
-1.7
-6.8
21.2

-0.6
-2.3
-0.5

-1.2
-0.5

0.1
0.6

-0.1
0.1
0.1
0.6
0.6
0.9

26.0
-3.5
-15.0

8.2
5.1

20.1
29.7

1.4
12.9
7.1
6.9
15.7
4.5

0.5
-0.1
-0.3

-0.5
-0.3

0.0
1.2

-0.2
0.0
0.0
1.0
1.2
1.3

984.3
872.6
904.6

858.8
904.6

935.5
905.0

917.4
935.5
933.1
923.2
905.0
916.7

14.5
-120.7
11.0

-4.4
6.8

11.5
-28.9

-1.5
14.2
0.6
-10.9
-18.7
12.1

1.5
-12.2
1.1

-2.5
1.1

2.3
-1.9

0.5
2.3
0.3
-0.9
-1.9
0.4

89.0
98.3
128.1

103.3
128.1

134.6
136.5

137.1
134.6
137.1
144.2
136.5
131.5

1,435.9
1,363.9
1,276.5

1,307.0
1,276.5

1,273.1
1,243.7

1,272.9
1,273.1
1,262.0
1,257.2
1,243.7
1,297.4

-2.0
9.6
11.6

4.2
6.6

6.0
2.0

-1.9
-2.6
2.7
7.0
-7.7
-5.0

-2.2
10.8
11.8

8.5
11.8

14.1
17.8

15.4
14.1
17.2
27.0
17.8
10.1

-69.9
-71.7
-90.0

-14.1
-36.7

-4.0
-24.3

-5.7
-1.1
-8.9
-4.9
-10.5
44.9

-4.6
-5.0
-6.6

-8.5
-6.6

-5.0
-6.0

-5.6
-5.0
-2.5
-5.4
-6.0
-2.8

773.6
799.1
773.8

810.1
773.8

787.3
790.3

789.3
787.3
781.7
791.0
790.3
806.5

38.5
14.6
33.9

5.7
15.6

-7.2
7.1

8.0
-6.4
-5.6
7.8
4.9
11.0

5.2
1.9
4.2

1.8
4.2

2.9
2.6

4.0
2.9
2.7
3.7
2.6
3.0

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Adjusted for the derecognition of loans on the MFI balance sheet on account of their sale or securitisation.
3) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial

corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).

4) Including non-profit institutions serving households.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 20
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
5 Money and credit

5.4 MFI loans to euro area non-financial corporations and households 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)

   Non-financial corporations 2) 

   Total

Up to 1 year

Over 1 Over 5 years

   Total

Adjusted for
loan sales
and securi-
tisation 4)

and up to
5 years

Adjusted for
loan sales
and securi-
tisation 4)

   Households 3)

Loans for
consumption

Loans for Other loans

house
purchase

1

4,544.6
4,354.1
4,280.3

4,288.1
4,280.3

4,310.1
4,291.7

4,312.8
4,310.1
4,303.9
4,300.4
4,291.7
4,300.0

-107.6
-132.8
-58.3

-18.4
4.3

8.3
1.2

10.3
-3.0
2.2
-4.8
3.8
9.7

-2.3
-2.9
-1.3

-2.0
-1.3

-0.6
-0.1

-0.6
-0.6
-0.4
-0.2
-0.1
0.4

2

- 
- 
- 

- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

-60.3
-127.5
-45.0

-19.9
6.8

11.2
10.7

12.4
-2.0
4.2
1.1
5.4
14.6

-1.3
-2.8
-1.0

-1.8
-1.0

-0.2
0.2

-0.3
-0.2
0.0
0.2
0.2
0.9

3

4

5

6

Outstanding amounts

1,127.9
1,065.6
1,081.2

1,056.5
1,081.2

1,089.9
1,084.5

1,090.4
1,089.9
1,090.4
1,085.2
1,084.5
1,086.9

6.2
-44.5
-13.6

-3.1
-7.3

-0.7
-0.1

3.1
-1.8
3.7
-6.2
2.5
2.1

0.5
-4.0
-1.3

-1.4
-1.3

-0.7
-1.1

0.5
-0.7
0.4
0.4
-1.1
-0.2

795.6
740.8
725.1

726.1
725.1

738.9
744.4

734.7
738.9
738.0
742.7
744.4
744.7

-51.4
-44.5
1.6

-6.8
8.5

7.3
11.6

-1.1
3.7
0.7
4.3
6.5
0.3

-6.0
-5.6
0.2

-3.3
0.2

2.1
2.8

0.8
2.1
1.4
2.4
2.8
3.1

2,621.0
2,547.8
2,474.0

2,505.4
2,474.0

2,481.3
2,462.8

2,487.7
2,481.3
2,475.5
2,472.5
2,462.8
2,468.3

5,242.3
5,221.4
5,199.3

5,194.6
5,199.3

5,233.7
5,257.2

5,221.0
5,233.7
5,236.3
5,243.4
5,257.2
5,260.5

Transactions

-62.3
-43.7
-46.2

-8.4
3.0

1.8
-10.2

8.4
-4.9
-2.2
-2.8
-5.2
7.2

Growth rates

-2.3
-1.7
-1.8

-1.9
-1.8

-1.2
-0.6

-1.5
-1.2
-1.2
-1.2
-0.6
-0.2

26.0
-3.5
-15.0

8.2
5.1

20.1
29.7

1.4
12.9
7.1
6.9
15.7
4.5

0.5
-0.1
-0.3

-0.5
-0.3

0.0
1.2

-0.2
0.0
0.0
1.0
1.2
1.3

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

7

- 
- 
- 

- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

34.7
14.3
41.2

9.6
13.9

24.2
38.7

6.4
11.6
15.4
9.2
14.0
14.0

0.7
0.3
0.8

0.5
0.8

1.1
1.7

0.9
1.1
1.3
1.5
1.7
1.9

8

9

10

602.0
573.5
563.3

567.1
563.3

567.9
578.5

565.2
567.9
566.9
568.3
578.5
578.7

-17.7
-18.1
-3.0

1.2
-2.2

2.2
9.1

-0.7
2.9
-0.7
1.7
8.2
0.8

-2.8
-3.0
-0.5

-1.1
-0.5

-0.1
1.8

-0.5
-0.1
-0.1
0.5
1.8
1.8

3,823.6
3,851.5
3,860.1

3,843.7
3,860.1

3,890.4
3,907.6

3,882.2
3,890.4
3,894.8
3,901.7
3,907.6
3,911.7

48.8
27.6
-3.2

13.1
9.3

17.9
21.7

3.8
7.5
7.5
6.4
7.8
4.4

1.3
0.7
-0.1

-0.2
-0.1

0.2
1.6

0.0
0.2
0.2
1.4
1.6
1.6

816.7
796.4
776.0

783.8
776.0

775.4
771.1

773.6
775.4
774.6
773.4
771.1
770.1

-5.1
-13.1
-8.8

-6.1
-2.1

0.0
-1.2

-1.7
2.4
0.3
-1.1
-0.3
-0.7

-0.6
-1.6
-1.1

-1.7
-1.1

-1.1
-1.2

-1.1
-1.1
-0.7
-1.0
-1.2
-0.5

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) In accordance with the ESA 2010, in December 2014 holding companies of non-financial groups were reclassified from the non-financial corporations sector to the financial

corporations sector. These entities are included in MFI balance sheet statistics with financial corporations other than MFIs and insurance corporations and pension funds (ICPFs).

3) Including non-profit institutions serving households.
4) Adjusted for the derecognition of loans on the MFI balance sheet on account of their sale or securitisation.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 21
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
5 Money and credit

5.5 Counterparts to M3 other than credit to euro area residents 1)
(EUR billions and annual growth rates; seasonally adjusted; outstanding amounts and growth rates at end of period; transactions during period)

   MFI liabilities

   MFI assets

Central
government
holdings 2)

   Longer-term financial liabilities vis-à-vis other euro area residents

Net external
assets

Total

Deposits

Deposits
with an redeemable
at notice
agreed
maturity
of over
3 months
of over
2 years

Debt

Capital
securities and reserves

with a
maturity
of over
2 years

   Other

   Total

Repos
with central
counter-
parties 3)

Reverse
repos to
central
counter-
parties 3)

1

2

3

4

5

6

7

8

9

10

Outstanding amounts

305.4
260.2
262.0

249.7
262.0

287.6
265.1

263.0
287.6
260.3
275.9
265.1
248.1

-4.9
-46.0
-6.9

-20.9
4.5

22.4
-22.5

-43.1
24.6
-27.3
15.6
-10.8
-17.0

-1.5
-15.1
-2.7

-11.5
-2.7

5.7
-6.0

-4.3
5.7
-5.6
-2.5
-6.0
-12.7

7,578.1
7,311.0
7,175.5

7,336.1
7,175.5

7,314.0
7,155.8

7,292.9
7,314.0
7,227.8
7,220.5
7,155.8
7,148.2

-112.8
-90.8
-162.4

-1.8
-94.1

-50.8
-80.8

-17.5
-15.2
-38.1
-17.7
-25.0
-6.2

-1.5
-1.2
-2.2

-1.1
-2.2

-2.9
-3.1

-2.5
-2.9
-3.1
-3.2
-3.1
-3.1

2,395.9
2,373.3
2,253.5

2,278.6
2,253.5

2,259.8
2,219.9

2,263.3
2,259.8
2,238.2
2,232.7
2,219.9
2,228.9

-156.3
-18.6
-120.1

-28.4
-25.5

-31.1
-39.3

-8.6
-6.0
-19.2
-6.9
-13.2
12.7

-6.1
-0.8
-5.1

-4.7
-5.1

-5.9
-5.4

-5.8
-5.9
-5.5
-5.3
-5.4
-4.4

106.0
91.5
92.2

92.4
92.2

90.5
86.5

91.8
90.5
88.7
87.4
86.5
85.7

-10.2
-14.3
2.1

2.3
1.2

-2.8
-4.0

-1.0
-1.3
-1.8
-1.3
-0.9
-0.7

-8.8
-13.5
2.3

-1.2
2.3

-0.3
-3.7

0.9
-0.3
-2.3
-3.8
-3.7
-5.1

2,680.8
2,506.3
2,375.1

2,457.0
2,375.1

2,394.8
2,331.5

2,396.3
2,394.8
2,355.3
2,343.2
2,331.5
2,316.0

Transactions

-106.4
-137.6
-154.9

-28.5
-77.4

-47.2
-48.3

-12.1
-22.7
-18.9
-23.9
-5.5
-21.5

Growth rates

-3.8
-5.1
-6.1

-2.7
-6.1

-6.8
-8.1

-5.9
-6.8
-7.3
-8.4
-8.1
-8.5

2,395.4
2,340.0
2,454.6

2,507.9
2,454.6

2,568.8
2,517.9

2,541.5
2,568.8
2,545.6
2,557.2
2,517.9
2,517.5

160.1
79.7
110.5

52.7
7.5

30.3
10.8

4.2
14.8
1.8
14.4
-5.4
3.4

7.1
3.4
4.6

4.2
4.6

3.9
4.1

4.2
3.9
3.3
4.2
4.1
3.5

1,029.8
1,153.9
1,388.8

1,419.3
1,388.8

1,511.5
1,457.0

1,450.8
1,511.5
1,450.6
1,467.0
1,457.0
1,391.7

99.5
359.2
246.0

38.4
37.8

3.6
-8.1

-21.1
28.8
-30.3
4.3
17.9
-55.1

- 
- 
- 

- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

154.4
130.6
181.2

183.5
181.2

230.9
239.8

257.8
230.9
229.0
233.7
239.8
248.5

31.3
-66.6
-18.3

26.4
-52.4

48.8
-34.0

32.3
-24.8
-2.7
-8.1
-23.2
12.7

- 
- 
- 

- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

260.8
183.8
185.3

163.6
185.3

234.8
224.5

226.3
234.8
209.3
222.9
224.5
202.2

9.4
32.2
1.5

-7.7
21.7

49.4
-10.3

23.0
8.4
-25.4
13.6
1.6
-22.3

2.5
10.3
0.8

-17.5
0.8

32.5
31.0

27.0
32.5
28.6
51.4
31.0
19.0

201.2
122.1
139.8

121.7
139.8

159.1
143.7

144.5
159.1
132.1
140.7
143.7
137.4

41.5
43.9
17.7

2.6
18.1

19.3
-15.4

11.3
14.6
-27.0
8.6
3.1
-6.4

26.1
23.5
14.5

-3.2
14.5

36.3
20.7

28.4
36.3
33.0
51.4
20.7
13.6

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

2012
2013
2014

2014 Q3 
         Q4 

2015 Q1 
         Q2 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July (p)

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Comprises central government holdings of deposits with the MFI sector and of securities issued by the MFI sector.
3) Not adjusted for seasonal effects.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 22
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
6 Fiscal developments

6.1 Deficit/surplus
(as a percentage of GDP; flows during one-year period)

   Deficit (-)/surplus (+)

Total

Central
government

State
government

Local
government

1

-4.1
-3.6
-2.9
-2.4

-2.6
-2.4
-2.4

-2.4

2

-3.3
-3.4
-2.5
-2.1

. 
. 
. 

. 

3

-0.7
-0.3
-0.2
-0.2

. 
. 
. 

. 

4

-0.2
0.0
0.0
0.0

. 
. 
. 

. 

Socual
security
funds

5

0.0
0.0
-0.1
-0.1

. 
. 
. 

. 

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

Sources: ECB for annual data; Eurostat for quarterly data.

6.2 Revenue and expenditure
(as a percentage of GDP; flows during one-year period)

   Revenue

Total

   Current revenue

Capital
revenue

Total

   Expenditure

   Current expenditure

Direct
taxes

Indirect
taxes

Net social
contributions

Compen-
Intermediate
sation of consumption

Interest

Social
benefits

employees

Memo item:
Primary
deficit (-)/
surplus (+)

6

-1.2
-0.6
-0.1
0.2

0.1
0.3
0.2

0.2

Capital
expenditure

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

1

2

3

44.8
45.9
46.6
46.7

46.7
46.6
46.7

46.6

44.4
45.5
46.1
46.2

46.2
46.2
46.2

46.2

11.7
12.2
12.5
12.5

12.5
12.5
12.5

12.5

4

12.6
12.9
12.9
13.1

13.0
13.1
13.1

13.1

Sources: ECB for annual data; Eurostat for quarterly data.

5

15.1
15.3
15.5
15.5

15.5
15.5
15.5

15.5

6

0.4
0.4
0.5
0.5

0.5
0.5
0.5

0.5

7

8

49.0
49.5
49.4
49.1

49.3
49.1
49.1

49.0

44.7
45.1
45.4
45.4

45.4
45.3
45.4

45.3

9

10.4
10.4
10.4
10.3

10.3
10.3
10.3

10.3

10

5.3
5.3
5.3
5.3

5.3
5.3
5.3

5.3

11

3.0
3.0
2.8
2.6

2.7
2.7
2.6

2.5

12

22.2
22.6
22.9
23.1

23.0
23.0
23.1

23.1

13

4.3
4.4
4.0
3.7

3.9
3.7
3.7

3.7

6.3 Government debt-to-GDP ratio
(as a percentage of GDP; outstanding amounts at end of period)

Total

   Financial instrument

   Holder

   Original maturity

   Residual maturity

   Currency

Debt   Resident creditors Non-resident
creditors

securities

Up to
1 year

Over
1 year

Up to
Over 1 Over
1 year and up to 5 years
5 years

Euro or
participating
currencies

Other
curren-
cies

Currency Loans

and
deposits

2

3

2.9
3.0
2.7
2.7

2.7
2.6
2.7

2.7

15.3
17.2
17.0
16.8

16.7
16.7
16.9

16.7

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

1

85.8
89.1
90.9
92.0

92.7
92.1
92.0

92.9

4

67.5
68.8
71.2
72.4

73.4
72.8
72.4

73.5

5

42.7
45.4
45.9
45.2

. 
. 
. 

. 

MFIs

6

24.3
26.2
26.1
25.9

. 
. 
. 

. 

7

43.1
43.6
45.0
46.8

. 
. 
. 

. 

8

12.2
11.4
10.4
10.1

. 
. 
. 

. 

9

73.6
77.7
80.5
81.8

. 
. 
. 

. 

10

20.3
19.5
19.4
19.2

. 
. 
. 

. 

11

12

29.8
31.6
32.2
32.2

35.7
38.0
39.3
40.5

. 
. 
. 

. 

. 
. 
. 

. 

13

84.0
86.9
89.0
89.9

. 
. 
. 

. 

14

1.8
2.2
2.0
2.0

. 
. 
. 

. 

Sources: ECB for annual data; Eurostat for quarterly data.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 23
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
   
   
   
   
6 Fiscal developments

6.4 Annual change in the government debt-to-GDP ratio and underlying factors 1)
(as a percentage of GDP; flows during one-year period)

Change in
debt-to-
GDP ratio 2)

Primary
deficit (+)/
surplus (-)

   Deficit-debt adjustment

Total

   Transactions in main financial assets

Revaluation Other

Total Currency Loans

and
deposits

securities

Debt Equity and
investment
fund shares

effects
and other
changes in
volume

Interest- Memo item:
Borrowing
requirement

growth
differential

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

1

2.1
3.3
1.8
1.1

0.9
1.0
1.1

0.9

2

1.2
0.6
0.1
-0.2

-0.1
-0.3
-0.2

-0.2

3

4

5

6

0.1
0.1
-0.2
0.2

-0.2
0.0
0.2

0.1

-0.3
1.1
-0.6
0.0

-0.1
0.0
0.2

0.2

0.2
0.3
-0.4
0.2

0.0
0.0
0.2

0.4

-0.2
0.3
-0.4
-0.1

0.0
0.0
0.0

-0.1

7

-0.2
-0.1
-0.1
-0.2

-0.2
-0.2
-0.2

-0.2

8

-0.1
0.5
0.3
0.1

0.1
0.2
0.1

0.1

9

0.4
-1.3
0.0
-0.1

-0.2
-0.3
-0.1

-0.2

10

0.1
0.3
0.4
0.2

0.1
0.3
0.1

0.1

11

0.8
2.7
2.0
1.1

1.3
1.2
1.1

1.0

12

3.9
5.0
2.7
2.7

2.6
2.7
2.7

2.7

Sources: ECB for annual data; Eurostat for quarterly data.
1) Intergovernmental lending in the context of the financial crisis is consolidated except in quarterly data on the deficit-debt adjustment.
2) Calculated as the difference between the government debt-to-GDP ratios at the end of the reference period and a year earlier.

6.5 Government debt securities 1)
(debt service as a percentage of GDP; flows during debt service period; average nominal yields in percentages per annum)

   Debt service due within 1 year 2) 

Total

   Principal

   Interest

Average
residual
maturity
in years 3)

Maturities
of up to 3
months

Maturities
of up to 3
months

   Average nominal yields 4)

   Outstanding amounts

   Transactions

Total

Floating

Zero
rate coupon

   Fixed rate

Issuance Redemption

1

2

3

4

2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2015 Feb. 
         Mar. 
         Apr. 
         May 
         June 
         July 

16.3
16.5
15.9

16.6
17.3
15.9

15.5

15.7
15.5
15.9
16.0
15.4
15.3

14.2
14.4
13.9

14.5
15.2
13.9

13.4

13.6
13.4
13.9
13.9
13.4
13.3

4.9
5.0
5.1

5.4
5.7
5.1

4.6

4.5
4.6
4.8
5.1
4.9
4.3

2.1
2.1
2.0

2.1
2.1
2.0

2.0

2.0
2.0
2.0
2.0
2.0
2.0

5

0.5
0.5
0.5

0.5
0.5
0.5

0.5

0.5
0.5
0.5
0.5
0.5
0.5

6

7

6.3
6.3
6.4

6.4
6.4
6.4

6.5

6.5
6.5
6.6
6.6
6.6
6.6

3.8
3.5
3.1

3.3
3.2
3.1

3.0

3.0
3.0
2.9
2.9
2.9
2.8

8

1.7
1.7
1.5

1.6
1.5
1.5

1.4

1.4
1.4
1.3
1.3
1.2
1.2

9

1.1
1.3
0.5

0.7
0.5
0.5

0.0

0.3
0.0
0.3
-0.2
0.1
-0.3

10

4.0
3.7
3.5

3.6
3.5
3.5

3.4

3.4
3.4
3.4
3.4
3.4
3.4

Source: ECB.
1) At face value and not consolidated within the general government sector.
2) Excludes future payments on debt securities not yet outstanding and early redemptions.
3) Residual maturity at the end of the period.
4) Outstanding amounts at the end of the period; transactions as 12-month average.

Maturities
of up to 1
year

11

3.1
2.8
2.7

2.7
2.8
2.7

2.8

2.7
2.8
2.8
2.8
2.8
2.8

12

1.6
1.2
0.8

1.1
0.9
0.8

0.6

0.7
0.6
0.5
0.4
0.3
0.3

13

2.2
1.8
1.6

1.6
1.6
1.6

1.7

1.7
1.7
1.7
1.6
1.4
1.5

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 24
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ECB Economic Bulletin, Issue 6 / 2015 – Statistics   
   
   
   
   
6 Fiscal developments

6.6 Fiscal developments in euro area countries
(as a percentage of GDP; flows during one-year period and outstanding amounts at end of period)

Belgium

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

1

-4.1
-4.1
-2.9
-3.2

-3.3
-3.1
-3.2

-3.5

102.0
103.8
104.4
106.5

108.9
108.3
106.6

111.1

2

-0.9
0.1
0.1
0.7

0.3
0.5
0.6

0.7

77.9
79.3
77.1
74.7

75.8
75.3
74.9

74.4

3

4

5

Government deficit (-)/surplus (+)

1.2
-0.2
-0.2
0.6

-0.3
-0.2
0.6

0.4

6.0
9.7
10.1
10.6

10.5
10.5
10.6

10.5

-12.7
-8.1
-5.8
-4.1

-5.1
-4.6
-4.0

-3.9

Government debt

111.2
121.7
123.2
109.7

114.5
112.6
107.6

104.8

-10.2
-8.7
-12.3
-3.5

-3.0
-2.3
-3.5

-4.6

171.3
156.9
175.0
177.1

177.4
175.8
177.1

168.8

6

-9.4
-10.3
-6.8
-5.8

-6.2
-5.7
-5.8

-5.8

69.2
84.4
92.1
97.7

96.4
96.8
97.7

98.0

7

-5.1
-4.8
-4.1
-4.0

-3.9
-4.0
-4.0

-3.9

85.2
89.6
92.3
95.0

95.5
95.7
95.6

97.5

8

9

-3.5
-3.0
-2.9
-3.0

-2.9
-2.8
-3.0

-2.9

116.4
123.1
128.5
132.1

134.1
132.0
132.1

135.1

-5.8
-5.8
-4.9
-8.8

-11.9
-10.2
-8.8

-0.2

66.0
79.5
102.2
107.5

109.8
104.7
107.5

106.8

Latvia

Lithuania Luxembourg

Malta Netherlands

Austria

Portugal

Slovenia

Slovakia

Finland

10

11

-3.3
-0.8
-0.7
-1.4

-0.3
0.0
-1.4

-1.9

42.7
40.9
38.2
40.0

41.0
40.4
40.0

35.0

-8.9
-3.1
-2.6
-0.7

-1.3
-0.7
-0.7

-0.8

37.2
39.8
38.8
40.9

38.6
38.1
40.8

38.1

12

0.4
0.1
0.9
0.6

1.3
0.7
0.6

0.3

19.1
21.9
24.0
23.6

22.4
22.1
22.1

21.6

13

14

15

16

17

18

19

Government deficit (-)/surplus (+)

-2.6
-3.6
-2.6
-2.1

-3.4
-2.8
-2.1

-2.5

69.7
67.4
69.2
68.0

74.8
72.2
68.5

70.3

-4.3
-4.0
-2.3
-2.3

-3.0
-2.8
-2.4

-2.0

Government debt

61.3
66.5
68.6
68.8

68.6
68.0
67.9

68.9

-2.6
-2.2
-1.3
-2.4

-1.2
-0.4
-2.4

-1.9

82.1
81.5
80.9
84.5

82.2
80.7
84.4

84.7

-7.4
-5.6
-4.8
-4.5

-4.6
-4.4
-4.5

-4.4

111.1
125.8
129.7
130.2

130.8
132.2
130.2

129.6

-6.6
-4.0
-14.9
-4.9

-12.8
-12.8
-4.9

-4.6

46.5
53.7
70.3
80.9

78.2
77.7
80.9

81.9

-4.1
-4.2
-2.6
-2.9

-2.6
-2.8
-2.9

-2.8

43.4
52.1
54.6
53.6

55.7
55.4
53.6

54.0

-1.0
-2.1
-2.5
-3.2

-2.7
-2.8
-3.1

-3.0

48.5
52.9
55.8
59.3

58.5
57.8
59.3

60.3

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

2011
2012
2013
2014

2014 Q2 
         Q3 
         Q4 

2015 Q1 

Source: Eurostat.

ECB Economic Bulletin, Issue 6 / 2015 - Statistics

S 25
S 25

ECB Economic Bulletin, Issue 6 / 2015 – Statistics© European Central Bank

Postal address 
Telephone 
Website 

60640 Frankfurt am Main, Germany
+49 69 1344 0
www.ecb.europa.eu

This Bulletin was produced under the responsibility of the Executive Board of the ECB. Translations are prepared and published by 
the national central banks.

All rights reserved. Reproduction for educational and non-commercial purposes is permitted provided that the source is 
acknowledged. 

The cut-off date for the statistics included in this issue was 2 September 2015.

ISSN 
ISSN 
EU catalogue No 
EU catalogue No 

2363-3417 (epub)
2363-3417 (online)
QB-BP-15-006-EN-E (epub)
QB-BP-15-006-EN-N (online)