Economic bulletin. Issue 7 / 2021.
Date: 2022-03-11
Institutions: European Central Bank
Subjects: European Central Bank, European Monetary System, euro area, labour market, monetary policy, price stability, serial publication

Economic Bulletin 

Issue 7 / 2021 

Contents 

Update on economic, financial and monetary developments 

Summary 

1 

2 

3 

4 

5 

External environment 

Financial developments 

Economic activity 

Prices and costs 

Money and credit 

Boxes 

1 

2 

3 

4 

5 

6 

7 

8 

Scarring effects of the COVID-19 pandemic on the global economy – 
reviewing recent evidence 

Main findings from the ECB’s recent contacts with non-financial 
companies 

Labour supply developments in the euro area during the COVID-19 
pandemic 

The impact of the COVID-19 pandemic on labour productivity growth 

Economic developments and outlook for contact-intensive services in 
the euro area 

The recovery of housing demand through the lens of the Consumer 
Expectations Survey 

The prevalence of private sector wage indexation in the euro area and 
its potential role for the impact of inflation on wages 

Results of a special survey of professional forecasters on the ECB’s 
new monetary policy strategy 

Articles 

1 

2 

3 

The predictive power of equilibrium exchange rate models 

Key factors behind productivity trends in euro area countries 

The euro area housing market during the COVID-19 pandemic 

Statistics 

ECB Economic Bulletin, Issue 7 / 2021 – Contents 

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2 

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9 

12 

17 

23 

28 

28 

34 

38 

46 

52 

58 

63 

67 

74 

74 

92 

115 

  S1 

1 

Update on economic, financial and 
monetary developments 

Summary 

Economic activity 

At the global level, economic activity continued to expand, albeit at a measurably 
moderating pace, amid a combination of factors, most prominently persistent supply 
bottlenecks. The Purchasing Managers’ Index data for August and September 2021 
suggest that momentum moderated in advanced economies, although it remained 
above its historical average, while it was softer in emerging market economies. At 
the same time, global supplier delivery times remained at record highs in September, 
owing primarily to strong demand. World trade growth also continued to soften, albeit 
from still high levels. Price pressures remain elevated on account of increasing food 
and energy inflation, reflecting the rebound from the low price levels recorded 
immediately after the onset of the coronavirus (COVID-19) pandemic. Most of the 
price pressures are judged to be of a temporary nature. 

The euro area economy continued to recover strongly, although momentum 
moderated to some extent. Output is expected to exceed its pre-pandemic level by 
the end of the year. The grip of the pandemic on the economy has visibly weakened, 
with a high share of people now vaccinated. This is supporting consumer spending, 
especially on entertainment, dining, travel and transportation. But higher energy 
prices may reduce purchasing power in the months to come. 

The recovery in domestic and global demand is also supporting production and 
business investment. That said, shortages of materials, equipment and labour are 
holding back the manufacturing sector. Delivery times have lengthened considerably, 
and transport costs and energy prices have surged. These constraints are clouding 
the outlook for the coming quarters. The labour market continues to improve. 
Unemployment has fallen and the number of people in job retention schemes is 
down significantly from the peak last year. This supports the prospect of higher 
incomes and increased spending. But, both the number of people in the labour force 
and the hours worked in the economy remain below their pre-pandemic levels. 

To sustain the recovery, targeted and coordinated fiscal support should continue to 
complement monetary policy. This support will also help the economy adjust to the 
structural changes that are under way. An effective implementation of the Next 
Generation EU programme and the “Fit for 55” package will contribute to a stronger, 
greener and more even recovery across euro area countries. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Summary 

2 

 
Inflation 

Euro area inflation increased to 3.4% in September and is expected to rise further 
this year. But while the current phase of higher inflation will last longer than originally 
expected, inflation is expected to decline in the course of next year. The upswing in 
inflation largely reflects a combination of three factors. First, energy prices – 
especially for oil, gas and electricity – have risen sharply. In September, energy 
inflation accounted for about half of overall inflation. Second, prices are also going 
up because recovering demand related to the reopening of the economy is outpacing 
supply. These dynamics are especially visible in the prices of consumer services, as 
well as the prices of goods affected most strongly by supply shortages. And finally, 
base effects related to the end of the VAT cut in Germany are still contributing to 
higher inflation. The influence of all three factors is expected to ease in the course of 
2022 or to fall out of the year-on-year inflation calculation. As the recovery continues, 
the gradual return of the economy to full capacity will underpin a rise in wages over 
time. Market and survey-based measures of longer-term inflation expectations have 
moved closer to 2%. These factors will support underlying inflation and the return of 
inflation to the ECB’s 2% target over the medium term. 

Risk assessment 

The recovery continues to depend on the course of the pandemic and further 
progress with vaccinations. The Governing Council sees the risks to the economic 
outlook as broadly balanced. In the near term, supply bottlenecks and rising energy 
prices are the main risks to the pace of recovery and the outlook for inflation. If 
supply shortages and higher energy prices last longer, these could slow down the 
recovery. At the same time, if persistent bottlenecks feed through into higher than 
anticipated wage rises or the economy returns more quickly to full capacity, price 
pressures could become stronger. However, economic activity could outperform 
current expectations if consumers become more confident and save less than 
currently expected. 

Financial and monetary conditions 

Growth and medium-term inflation dynamics still depend on favourable financing 
conditions for all sectors of the economy. Market interest rates have increased. 
Nevertheless, financing conditions for the economy remain favourable, not least 
because bank lending rates for firms and households remain at historically low 
levels. 

The forward curve of the benchmark euro short-term rate (€STR) steepened 
significantly during the review period, which in part reflected market participants’ 
repricing of an earlier rise in policy interest rates. At the same time, longer-term 
nominal risk-free rates – as well as sovereign bond yields – rose on the back of a 
marked rise in inflation compensation. Equity prices for non-financial corporations 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Summary 

3 

 
and corporate bond spreads remained broadly unchanged, while bank equity prices 
increased. The euro depreciated in trade-weighted terms. 

Money creation in the euro area continued to normalise in September 2021, 
reflecting an improving situation regarding the pandemic and policy support 
measures. Eurosystem asset purchases remained the dominant source of money 
creation. 

While there was a pick-up in September, lending to firms remains moderate. This 
continues to reflect the fact that firms generally need less external funding, since 
these have high cash holdings and are increasingly retaining their earnings. Lending 
to households remains strong, driven by demand for mortgages. The most recent 
euro area bank lending survey shows that credit conditions for firms stabilised and 
were supported – for the first time since 2018 – by a reduction in banks’ risk 
perceptions. By contrast, banks are taking a slightly more cautious approach to 
housing loans and have tightened their lending standards for these loans 
accordingly. Bank balance sheets continue to be supported by favourable funding 
conditions and remain solid. 

Monetary policy decisions 

Against this background, at its monetary policy meeting in October, the Governing 
Council continued to judge that favourable financing conditions can be maintained 
with a moderately lower pace of net asset purchases under the pandemic 
emergency purchase programme (PEPP) than in the second and third quarters of 
this year. 

The Governing Council also confirmed its other measures to support the ECB’s price 
stability mandate, namely the level of the key ECB interest rates, the Governing 
Council’s forward guidance on their likely future evolution, Eurosystem purchases 
under the asset purchase programme (APP), the Governing Council’s reinvestment 
policies and its longer-term refinancing operations. 

The Governing Council stands ready to adjust all of its instruments, as appropriate, 
to ensure that inflation stabilises at the ECB’s 2% target over the medium term. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Summary 

4 

 
 
 
1 

External environment 

At the global level, economic activity is decelerating owing to combination of factors, 
most prominently persistent supply bottlenecks. The Purchasing Managers’ Index 
data for August and September suggest that momentum moderated in advanced 
economies, although it remained above its historical average, while it was softer in 
emerging market economies. At the same time, global supplier delivery times 
remained at record highs in September, owing primarily to strong demand but also 
reflecting persistent supply constraints. World trade growth also continued to soften, 
albeit from still elevated levels. Price pressures are still elevated on account of 
increasing food and energy inflation, reflecting the rebound from the low price levels 
recorded immediately after the onset of the coronavirus (COVID-19) pandemic. Most 
of the price pressures are judged to be of a temporary nature. 

Global economic activity is decelerating owing to a combination of factors, 
most prominently persistent supply bottlenecks. The moderating growth 
momentum partly reflects a normalisation from the post-COVID-19 rebound as base 
and re-opening effects fade and stimulus wanes. At the same time, adverse 
idiosyncratic factors in selected major economies – such as COVID-19 resurgences, 
labour shortages and a property sector slowdown – imply rising downside risks to the 
outlook. These are reinforced by broad-based supply chain disruptions. Box 1 
presents an assessment of the scarring effects of the pandemic at a global level. It 
concludes that global potential output has declined during the pandemic, albeit less 
than during the Great Recession and mostly on account of temporary factors. 

The slowdown in the global recovery is reflected in survey data. The global 
composite output Purchasing Managers’ Index (PMI) – excluding the euro area – 
confirmed that growth momentum moderated in advanced economies in August and 
September (Chart 1), although it remained well above its historical average. Growth 
momentum in emerging market economies remained softer than in advanced 
economies, especially in manufacturing. Specifically, industrial production 
momentum continued to soften in advanced economies in July, while it contracted for 
the third consecutive month in emerging markets. 

Supply bottlenecks show no signs of normalisation. Global supplier delivery 
times remained at record highs in September. According to internal estimates, 
demand factors account for about two-thirds of the lengthening of delivery times. 
Supply constraints are proving to be rather persistent, given that pre-existing 
bottlenecks, such as the semiconductor shortage, are being compounded by other 
factors, namely global strains in energy markets, rising labour shortages and, in 
some regions, pandemic-related disruptions, such as factory and port closures. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
External environment 

5 

 
Chart 1 
Global composite output PMI (excluding the euro area)   

(diffusion indices) 

Emerging market economies (excluding China)
Advanced economies (excluding the euro area)
Global (excluding the euro area)

65

60

55

50

45

40

35

30

25

20

01/14 07/14 01/15 07/15 01/16 07/16 01/17 07/17 01/18 07/18 01/19 07/19 01/20 07/20 01/21 07/21

Sources: Markit and ECB calculations. 
Note: The latest observations are for September 2021. 

World trade growth continues to soften, albeit from still-elevated levels. 
Although world (excluding the euro area) merchandise import volumes were still 
above their pre-pandemic levels and picked up slightly in August, trade growth 
momentum (measured in three-month-on-three-month terms) remained in negative 
territory (Chart 2). The volume of merchandise trade has decreased since the peak 
recorded in March. The moderation in global trade activity is confirmed by the global 
manufacturing new export orders PMI, which in the third quarter was, on average, 
just above the expansionary threshold. At the same time, new data from the World 
Trade Organization (WTO) point to a steady increase in commercial services trade in 
the second quarter, although it remains 20 percentage points below its pre-pandemic 
level. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
External environment 

6 

 
 
Chart 2 
Surveys and global trade in goods (excluding the euro area) 

(left-hand scale: three-month-on-three-month percentage changes; right-hand scale: diffusion indices) 

Global merchandise imports (left-hand scale)
Average global merchandise imports, 1991-2020 (left-hand scale)
Global PMI, manufacturing new export orders (right-hand scale)

10

8

6

4

2

0

-2

-4

-6

-8

-10

75

70

65

60

55

50

45

40

35

30

25

01/14 07/14 01/15 07/15 01/16 07/16 01/17 07/17 01/18 07/18 01/19 07/19 01/20 07/20 01/21 07/21

Sources: Markit, CPB Netherlands Bureau for Economic Policy Analysis and ECB calculations. 
Note: The latest observations are for August 2021 for global merchandise imports and September 2021 for the PMIs. 

Global price pressures remain elevated. Annual consumer price index (CPI) 
inflation in the countries of the Organisation for Economic Co-operation and 
Development (OECD) increased marginally in August to 4.3% on account of 
increasing food and energy inflation, still reflecting the rebound from the low price 
levels recorded after the COVID-19 outbreak. Meanwhile, core inflation remained 
stable at 3.1%, unchanged since June. Most of the current price pressures are still 
seen as temporary. Nevertheless, inflation expectations for 2022 slightly increased in 
advanced economies in September. At the same time, input and output prices from 
PMIs for advanced economies remained close to historically high levels, amid 
record-high freight rates, while price pressures again rose in emerging markets. 

Oil prices climbed on the back of demand and supply factors. Oil prices have 
increased to well above pre-pandemic levels since the Governing Council meeting in 
September, supported by the ongoing global economic recovery and substitution 
from gas to oil amid high gas prices. On the supply side, OPEC+ failed to reach its 
targets in August and September, mainly owing to capacity problems in Nigeria and 
Angola. Moreover, at its October meeting OPEC+ indicated that it would stick to its 
existing plan, resisting calls to further lift its production targets to stabilise energy 
prices. In the United States, the recovery in shale oil production has been sluggish 
and supply was further interrupted by Hurricane Ida. Food and metal prices have 
also increased since the last Governing Council meeting, with higher energy input 
costs more prominently supporting copper and aluminium prices. 

In the United States, the economic recovery is moderating amid supply chain 
constraints and the surge in Delta variant cases. COVID-19 cases increased at 
the start of the third quarter, leading to a plunge in consumer confidence and lower 
spending, especially in vulnerable industries. In addition, household disposable 
income fell in real terms in August, as unemployment benefits fell back to pre-
COVID-19 levels. These two factors, together with the ongoing challenges along 
supply chains, are expected to weigh on activity in the second half of the year. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
External environment 

7 

 
 
Annual headline CPI remained high at 5.4% in September, while inflation less food 
and energy remained unchanged at 4.0%. In month-on-month terms, inflation 
increased in September after having decreased in August. Supply chain disruptions 
represent an upside risk to future inflation. However, although households’ short-term 
inflation expectations have risen recently, longer-term expectations have remained 
well anchored so far. 

In the United Kingdom, the economy is slowing following a strong rebound in 
the second quarter of 2021. UK GDP rebounded sharply by 5.5% in the second 
quarter of 2021, which also reflected an improved trade balance. However, the 
combination of the Delta variant spike, labour shortages and broader supply-side 
disruptions led to a moderation of growth to 0.4% month on month in August. Retail 
sales and business and consumer confidence surveys also signal a slowdown in the 
third quarter. Annual UK CPI, the Bank of England’s target inflation rate, eased to 
3.1% in September, while core CPI inflation dropped to 2.9%. The slowdown in the 
annual rate of headline inflation in September was mainly driven by a lower 
contribution from restaurant and hotel prices, which was only partially offset by an 
increase in transport prices. 

In Japan, a firmer recovery is no longer expected until nearer the end of the 
year amid headwinds from lingering supply bottlenecks. A surge in new 
infections in the early summer and the expansion of the latest state of emergency 
weighed on mobility and consumption in August. Given that the number of cases has 
steadily decreased, consumption is expected to recover to some extent in 
September, as indicated by the latest survey data. As a result, the economy is 
expected to move towards a broader recovery by the end of the year. Annual 
headline inflation returned to positive territory in September (0.2%), partly reflecting a 
higher energy price contribution and increasing food prices. However, core inflation 
declined marginally to -0.8% in September. 

In China, GDP decelerated in the third quarter. Economic activity rose by 0.2% in 
quarter-on-quarter terms in the third quarter (4.9% year on year), compared with 
1.2% in the previous quarter. The slowing momentum is related to the COVID-19 
outbreak, power shortages and the property sector slowdown. Monthly indicators for 
September point to a gradual pick-up in retail sales since July. By contrast, industrial 
production and investment continued to decelerate. Rising uncertainties related to 
real estate activity and energy constraints are increasing downside risks for the near-
term growth outlook. CPI inflation decreased to 0.7% year on year in September, 
pointing to subdued inflation, which was due largely to base effects and ongoing food 
price deflation amid normalising pork prices. By contrast, producer price index (PPI) 
inflation increased to 10.7% year on year, the highest rate of increase in around 25 
years, mainly on the back of strong price increases in coal and other energy-
intensive industries. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
External environment 

8 

 
 
 
2 

Financial developments 

Against the backdrop of rising inflationary pressures as the dominant theme in the 
financial markets during the review period, the forward curve of the benchmark euro 
short-term rate (€STR) steepened significantly, mainly reflecting market participants 
pricing in an earlier rise in policy interest rates. Likewise, longer-term nominal risk-
free rates – and with them sovereign bond yields – rose on the back of a marked rise 
in inflation compensation. Equity prices for non-financial corporations and corporate 
bond spreads remained broadly unchanged in the review period, while bank equity 
prices increased. The euro depreciated in trade-weighted terms. 

The benchmark €STR and euro overnight index average (EONIA) averaged -57 
and -49 basis points respectively over the review period (9 September to 27 
October 2021). 1  Excess liquidity increased by approximately €20 billion to around 
€4,424 billion, mainly reflecting asset purchases under the pandemic emergency 
purchase programme (PEPP) and the asset purchase programme (APP), as well as 
the €97.57 billion take-up of the ninth operation under the third series of targeted 
longer-term refinancing operations (TLTRO III). However, the growth in excess 
liquidity was substantially curtailed by €79.24 billion in early repayments of funds 
borrowed under previous TLTRO III operations and a net decline in other assets of 
around €107 billion over the review period. 

The €STR forward curve steepened considerably, with market participants, in 
response to rising near-term inflationary pressures, pricing in an earlier rise in 
policy interest rates. The forward curve implies that the €STR remains at its current 
level (-57 basis points) until June 2022, after which it increases and reaches 26 basis 
points at the end of 2027. On the back of a significant increase in market-based 
measures of inflation compensation, the timing of the €STR exceeding its current 
level by 10 basis points, as reflected in the forward curve, has shifted from the 
beginning of 2024 to late 2022, and now stands more than a year earlier than at the 
beginning of the review period. 

Long-term sovereign bond yields broadly mirrored the development of 
nominal risk-free rates (Chart 3). Over the review period, the GDP-weighted euro 
area and German ten-year sovereign bond yields increased by 20 and 19 basis 
points to 0.20% and -0.18% respectively, while ten-year sovereign bond yields in 
Spain and Portugal increased by 16 and 18 basis points respectively. These 
increases broadly mirrored developments in long-term risk-free interest rates, with 
the ten-year nominal overnight index swap (OIS) rate increasing by 16 basis points 
to 0.08%. Over the same period, ten-year US government bond yields increased by 
25 basis points to 1.55%, while ten-year UK government bond yields rose by 24 
basis points to 0.98%. The marginally smaller increase in euro area sovereign bond 
yields relative to the United States and the United Kingdom can be attributed to 
market participants pricing in an earlier monetary policy normalisation in the United 
States and the United Kingdom than in the euro area. Euro area bond markets 

1    The methodology for calculating the EONIA changed on 2 October 2019; it has since been calculated 
as the €STR plus a fixed spread of 8.5 basis points. See the box entitled “Goodbye EONIA, welcome 
€STR!”, Economic Bulletin, Issue 7, ECB, 2019. The EONIA will be discontinued on 3 January 2022. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Financial developments 

9 

 
 
smoothly absorbed the EU’s successful auction of its first Next Generation EU green 
bond, the largest green bond issue on record. 

Chart 3 
Ten-year sovereign bond yields 

(percentages per annum) 

GDP-weighted euro area average                                                                                           
United Kingdom                                                                                     
United States                                                               
Germany
Ten-year euro area overnight index swap rate

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

01/15 08/15 03/16 10/16 05/17 12/17 07/18 02/19 09/19 04/20 11/20 06/21

12/20 02/21 04/21 06/21 08/21 10/21

Sources: Refinitiv and ECB calculations. 
Notes: The vertical grey line denotes the start of the review period on 9 September 2021. The latest observation is for 27 October 
2021. 

Long-term euro area sovereign bond spreads relative to OIS rates edged up 
slightly. The German ten-year sovereign bond spread became slightly less negative, 
ending the review period at -0.25%. The French and Italian ten-year bond spreads 
over the corresponding OIS rate widened by 4 and 6 basis points to 0.09% and 
0.82% respectively. Overall, changes in individual sovereign spreads to risk-free 
rates were limited, as also reflected in the aggregate ten-year euro area GDP-
weighted sovereign bond spread, which widened by 4 basis points to 0.12%. This 
metric remains close to the very low levels observed at the beginning of the review 
period and significantly below the levels prevailing before the start of the coronavirus 
(COVID-19) crisis. 

Equity prices of non-financial corporations remained broadly unchanged over 
the review period, while bank equity prices recorded strong gains. The negative 
impact of higher discount rates on equity prices was counterbalanced by stronger 
earnings growth expectations in both the euro area and the United States. Non-
financial stock prices remained broadly unchanged, continuing to stand close to 
record high levels. While higher discount rates weighed on equity prices more 
generally, bank equity prices increased by 6% in the euro area and by 12% in the 
United States. Over the second half of September, the impact of a potential default 
by Chinese property developer Evergrande had weighed on global capital markets, 
as reflected in temporarily higher price volatility, but was later perceived as 
sufficiently contained by the Chinese authorities. 

Both financial and non-financial corporate bond spreads remained broadly 
unchanged over the review period, standing below their pre-pandemic levels. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Financial developments 

10 

 
 
Spreads (relative to the risk-free rate) on investment-grade financial and non-
financial bonds stood at 49 and 39 basis points respectively at the end of the review 
period, well below their pre-pandemic levels. Overall, investors and rating agencies 
appear to remain optimistic about the profitability and credit outlook for euro area 
corporates. 

In foreign exchange markets, the euro depreciated in trade-weighted terms 
(Chart 4), reflecting a broad-based weakening against several major 
currencies. Over the review period the nominal effective exchange rate of the euro, 
as measured against the currencies of 42 of the euro area’s most important trading 
partners, weakened by 0.9%. The euro depreciated against the US dollar (by 1.8%), 
continuing its recent downward trend and reflecting market expectations of a faster 
normalisation of US monetary policy relative to the euro area. The euro also 
weakened against other major currencies, including the Chinese renminbi (by 2.8%), 
the Swiss franc (by 1.8%) and the pound sterling (by 1.2%), and strongly depreciated 
(by 5.5%) against the Russian rouble, which displayed broad-based strength in the 
context of the recent increase in energy prices. Over the same period the euro 
appreciated significantly against the Turkish lira (by 9.9%) and the Brazilian real (by 
2.9%), amid their recent broad-based volatility. The euro also continued appreciating 
against the currencies of several non-euro area EU Member States, including the 
Hungarian forint (by 3.8%), the Polish zloty (by 1.9%) and the Czech koruna (by 
1.1%). 

Chart 4 
Changes in the exchange rate of the euro vis-à-vis selected currencies 

(percentage changes) 

Since 9 September 2021
Since 27 October 2020

EER-42
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

-15

-10

-5

0

5

10

15

Source: ECB. 
Notes: EER-42 is the nominal effective exchange rate of the euro against the currencies of 42 of the euro area’s most important 
trading partners. A positive (negative) change corresponds to an appreciation (depreciation) of the euro. All changes have been 
calculated using the foreign exchange rates prevailing on 27 October 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Financial developments 

11 

 
 
 
 
3 

Economic activity 

Economic activity in the euro area continued its recovery path in the third quarter of 
2021 after having expanded by 2.1%, quarter on quarter, in the second quarter. The 
lifting of pandemic-related restrictions, high vaccination rates and reduced fear of 
contagion enabled contact-intensive market services and tourism to rebound strongly 
in the summer months. At the same time, production in the manufacturing sector has 
continued to be held back by shortages of materials, equipment and labour, as well 
as by rising transport costs and energy prices. 

Output growth is expected to remain dynamic in the period ahead, albeit slowing 
towards the end of the year. Consumer spending, particularly for services, continues 
to rebound and consumer confidence is strong, although the impact of the higher oil 
prices may reduce households’ purchasing power. At the same time, the labour 
market continues to improve, which supports the prospect of higher incomes and 
increased spending. Moreover, the recovery in domestic and global demand is 
supporting business investment, but supply-side constraints continue to weigh on 
production and trade, particularly in the capital goods sector. 

To sustain the recovery, targeted and coordinated fiscal support should continue to 
complement monetary policy. This support will also help the economy adjust to the 
structural changes that are under way. An effective implementation of the Next 
Generation EU programme and the “Fit for 55” package will contribute to a stronger, 
greener and more even recovery across euro area countries. 

The risks to the euro area growth outlook are seen as broadly balanced. 
Nevertheless,, the pandemic-related uncertainties remain high. The pace of the 
recovery could be slowed by downside risks relating to supply bottlenecks and rising 
energy prices. However, greater than expected dissaving by consumers could lead 
to a stronger expansion than currently envisaged. 

Following two quarters of falling output, euro area real GDP rebounded in the 
second quarter of 2021 and is estimated to have strengthened further in the 
third quarter. 2  In the second quarter economic activity rebounded, with GDP rising 
by 2.1%, quarter on quarter. This outcome more than offset the 0.7% cumulative fall 
over the two previous quarters. However, GDP was still 2.7% below the pre-
pandemic peak seen at the end of 2019 (Chart 5). The expenditure breakdown 
shows that domestic demand was the main contributor to growth alongside a small 
positive contribution from net trade. At the same time, changes in inventories 
contributed negatively to growth in the second quarter, following two quarters of 
strong positive contributions. The rise in activity in the second quarter was broad-
based across countries. 

2    Real GDP grew by 2.2% in the third quarter according to Eurostat’s flash estimate that was published 

after the Governing Council meeting on 28 October. This estimate is broadly in line with the September 
2021 ECB staff macroeconomic projections for the euro area. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Economic activity 

12 

 
 
Chart 5 
Euro area real GDP, composite output PMI and ESI 

(left-hand scale: quarter-on-quarter percentage changes; right-hand scale: diffusion index) 

Real GDP (left-hand scale)
Composite output PMI (right-hand scale)
ESI (right-hand scale)

16

12

8

4

0

-4

-8

-12

-16

90

80

70

60

50

40

30

20

10

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2020

2021

Sources: Eurostat, European Commission, Markit and ECB calculations. 
Notes: The two lines indicate monthly developments; the bars show quarterly data. The European Commission’s Economic Sentiment 
Indicator (ESI) has been standardised and rescaled to have the same mean and standard deviation as the Purchasing Managers’ 
Index (PMI). The latest observations are for the second quarter of 2021 for real GDP, September 2021 for the ESI and October 2021 
for the PMI. 

The combination of hard data, survey results and high-frequency indicators 
point to continued strong GDP growth in the third quarter of this year, before a 
moderation in the fourth quarter. This outcome would reflect the increased 
vaccination rates and declining infection rates in the third quarter that enabled the 
observed relaxation of containment measures. Growth in the third quarter is likely to 
have been mainly driven by the services sector, as part of the manufacturing sector 
remained affected by supply-side bottlenecks. These bottlenecks, together with 
labour shortages, are likely to curb output growth towards the end of the year, while 
any offsetting impact from the services sector might be less pronounced given that 
the fourth quarter typically is a less tourism-intense period. Box 5 provides a more in-
depth review of the role of contact-intensive services in the recovery. Companies 
operating in the non-financial sector broadly confirm this overall narrative about the 
short-term outlook (see Box 2). 

Turning to the most recent monthly data, industrial production fell by 1.6%, month on 
month, in August after a similar-sized increase in July. The more timely composite 
output Purchasing Managers’ Index (PMI) rose to 58.4 in the third quarter of 2021, 
up from 56.8 in the second quarter, reflecting falling manufacturing output (to 58.6) 
and rising activity in services (to 58.4). However, in October the PMI declined further, 
reaching 54.3, driven by developments in both services business activity and 
manufacturing output. Manufacturing supply bottlenecks, as captured by the PMI 
suppliers’ delivery times index, intensified in October. A record high level of stocks of 
purchases in manufacturing was recorded in October, suggesting additional 
inventory building to deal with potential supply shortages. The European 
Commission’s Economic Sentiment Indicator (ESI) also increased from the second 
to the third quarter, remaining well above the pre-pandemic level seen in February 
last year. This rise was broad-based across its components, with the largest increase 
recorded for services. At the same time, high-frequency indicators related to 

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Economic activity 

13 

 
 
consumption stabilised at around pre-pandemic levels in the third quarter, signalling 
stronger demand for services (e.g. recreation, restaurants and hotels) than for goods 
(e.g. passenger cars). 

The unemployment rate in the euro area declined in August, still supported by 
job retention schemes. 3  The rate stood at 7.5% in August, 0.1 percentage points 
lower than in July (Chart 6) and around 0.1 percentage points higher than before the 
pandemic in February 2020. The number of workers in job retention schemes is 
declining and represented around 2% of the labour force in August. Employment 
increased by 0.7% in the second quarter of 2021, following a decrease of 0.1% in the 
first quarter.4  Total hours worked increased by 2.3% in the second quarter, following 
a 0.3% decline in the first quarter of 2021. These recent developments reflect the 
impact of the relaxation of pandemic-related restrictions following the vaccination 
campaigns. However, total hours worked in the second quarter of 2021 remained 
4.1% below the level recorded in the fourth quarter of 2019. Similarly, labour force 
participation in the second quarter of 2021 was still lower than pre-crisis levels by 
around 1.4 million people.5 

Chart 6 
Euro area employment, the PMI employment indicator and the unemployment rate 

(left-hand scale: quarter-on-quarter percentage changes, diffusion index; right-hand scale: percentages of the labour force) 

Employment (left-hand scale)
PMI assessment of employment (left-hand scale)
Unemployment rate (right-hand scale)

1.2

0.8

0.4

0.0

-0.4

-0.8

-1.2

-1.6

-2.0

-2.4

-2.8

-3.2

9.0

8.8

8.6

8.4

8.2

8.0

7.8

7.6

7.4

7.2

7.0

6.8

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

2020

2021

Sources: Eurostat, Markit and ECB calculations. 
Notes: The two lines indicate monthly developments; the bars show quarterly data. The PMI is expressed as a deviation from 50 
divided by 10. The latest observations are for the second quarter of 2021 for employment, October 2021 for the PMI and August 2021 
for the unemployment rate. 

Short-term labour market indicators have continued to improve. The monthly 
composite PMI employment indicator, encompassing industry and services, 
increased to 56.1 in October from 54.4 in September, thus remaining above the 

3    For an overview of the use of government-supported job retention schemes during the pandemic, see 
Chart 11 in the article entitled “Hours worked in the euro area”, Economic Bulletin, Issue 6, ECB, 2021. 
For a broader assessment of the euro area labour market during the pandemic, see the article on “The 
impact of the COVID-19 pandemic on the euro area labour market”, Economic Bulletin, Issue 8, ECB, 
2020. 

4    See also Box 4 entitled “The impact of the COVID-19 pandemic on labour productivity growth” in this 

issue of the Economic Bulletin. 

5    More recent but preliminary monthly unemployment figures for July and August 2021 suggest that the 

labour force participation rate increased in the third quarter of 2021. See also Box 3 entitled “Labour 
force participation during the pandemic” in this issue of the Economic Bulletin. 

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14 

 
 
 
threshold level of 50 that indicates an expansion in employment. The PMI 
employment index has recovered significantly since its all-time low in April 2020 and 
stood in expansionary territory in October 2021 for the ninth consecutive month. 

Household spending continued to rebound in the third quarter, reflecting high 
vaccination rates and reduced fear of infection. Following a few weaker readings, 
consumer confidence rose again to -4.0 in September (after -5.3 in August). Retail 
sales in July and August stood on average 0.1% above their level in the second 
quarter, reflecting the rebalancing from goods to services. Spending on holidays 
continued to rise during the summer, in line with improving business confidence in 
the accommodation and travel services sectors. Looking ahead, households remain 
confident about their financial situation despite rising energy prices. The extent to 
which the current surge in energy prices may slow down the recovery in private 
consumption depends on whether the price increases are the consequence of higher 
aggregate demand or disruptions in energy supply. The European Commission’s 
consumer survey suggests that the current rise in consumer prices has so far largely 
been driven by higher economic activity, given elevated levels of household 
expectations regarding both economic activity and their own financial situation. This 
stands in stark contrast with, for example, the response at the time of the Iraqi 
invasion of Kuwait in 1990, when severe disruptions in oil supply led households to 
immediately revise down their expectations regarding income and activity. 

Corporate investment continued to be hampered by supply-side disruptions 
during the third quarter. In July and August 2021 capital goods production fell on 
average by 1.2% relative to the second quarter. The PMI for new orders of capital 
goods continuously declined during the summer months, amid persistent supply 
chain bottlenecks, although it remained at high levels. At the same time, the ESI is 
near its all-time high and the assessment of order books according to the 
Commission’s industry survey has increased recently, indicating ongoing strong 
demand for capital goods. On balance, the available indicators suggest that business 
investment growth remained in positive territory in the third quarter. Developments in 
supply-side disruptions will remain a crucial source of uncertainty for investment 
dynamics over the next few quarters. Since the beginning of 2021 there has been an 
increasing divergence between capital goods production and supplier delivery times 
supporting the view that the supply-side disruptions, which have caused a 
lengthening of intermediate input delivery times, are likely hampering capital goods 
production and business investment. To the extent that supply bottlenecks are 
expected to be resolved only gradually, they could continue to weigh on future 
business investment. 

Housing investment continued to be affected by supply bottlenecks in the 
third quarter, in an environment of buoyant demand. Housing investment had 
already exceeded the pre-crisis level recorded in the last quarter of 2019 by more 
than 2% in the second quarter of 2021.6  Although robust demand continued to 
support the recovery, supply constraints increasingly hampered construction activity 

6    For a comprehensive assessment of the recent drivers and near-term outlook of the euro area housing 
market, see the article entitled “The euro area housing market during the COVID-19 pandemic” in this 
issue of the Economic Bulletin. 

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Economic activity 

15 

 
 
in the third quarter. In July and August building activity was, on average, around 1% 
below its level in the second quarter. According to the PMI for construction output, 
available up to September, supply bottlenecks materialised in historically long 
delivery times and high input prices. Furthermore, the European Commission’s 
construction survey, available up to September, suggests that the main limits to 
production were shortages of materials and workers, with only negligible constraints 
from demand. The uncertain evolution of the balance between supply-side 
headwinds and demand-side tailwinds entails high uncertainty around the short-term 
prospects for housing investment. On the one hand, persistent bottlenecks may lead 
to further rises in construction costs, thus denting profitability for firms, and put 
upward pressure on house prices, hence reducing affordability for households. On 
the other hand, favourable financing conditions and income support measures, as 
well as a large stock of accumulated savings, could further sustain demand, as 
shown by households’ intentions to purchase and renovate houses, which are well 
above their pre-pandemic levels.7 

The recovery in euro area trade continues at two speeds, with exports of 
goods remaining subdued and exports of services rebounding somewhat. Euro 
area total exports increased by 2.7%, quarter on quarter, in the second quarter of 
2021. However, goods exports momentum slowed at the end of the second quarter, 
and available data for July and August point to continued weak trade developments 
in the third quarter of 2021. Volumes of euro area exports and imports fell in July, 
and the decline was broad-based across major trading partners and goods 
categories. The weakness can be attributed to declining foreign demand for euro 
area products, visible in global imports (excluding the euro area) and order-based 
forward-looking indicators that have also moderated. Moreover, shipping and input-
related bottlenecks continued to exert a drag, in particular on euro area goods trade, 
in the third quarter.8  Leading indicators for services trade have also moderated 
somewhat yet continue to signal some rebound in the third quarter of 2021, driven by 
tourism. 

The euro area economy is expected to continue its recovery path, supported 
by monetary and fiscal policies. At the same time, the pandemic may produce 
more lasting shifts in demand that could lead to lingering supply and demand 
imbalances across sectors. To support the recovery, ambitious, targeted and 
coordinated fiscal policy should continue to complement monetary policy. The results 
of the latest round of the Survey of Professional Forecasters (conducted in early 
October) show that GDP growth forecasts have been revised upwards for 2021, 
while remaining broadly unchanged for 2022 and 2023, relative to the previous round 
conducted in early July. 

7    This evidence is confirmed by recent data from the ECB’s new Consumer Expectations Survey. See 
Box 6 entitled “The recovery of housing demand through the lens of the Consumer Expectations 
Survey” in this issue of the Economic Bulletin. 

8    See Box 4 entitled “The impact of supply bottlenecks on trade” Economic Bulletin, Issue 6, ECB, 2021. 

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4 

Prices and costs 

According to Eurostat’s final release for September, euro area annual HICP inflation 
increased further to 3.4% in September, up from 3.0% in August 2021. Inflation is 
expected to rise further this year. While the current phase of higher inflation will last 
longer than originally expected, inflation is expected to decline in the course of next 
year. The upswing in inflation largely reflects a combination of three factors. First, 
energy prices have risen sharply and accounted for about half of overall inflation in 
September. Second, prices are going up because recovering demand related to the 
reopening of the economy is outpacing supply. These dynamics are especially visible 
in the prices of consumer services, as well as the prices of goods affected most 
strongly by supply shortages. Finally, base effects related to the end of the VAT cut in 
Germany are still contributing to higher inflation. The influence of all three factors is 
expected to ease in the course of 2022 or to fall out of the year-on-year inflation 
calculation. As the recovery continues, the gradual return of the economy to full 
capacity will underpin a rise in wages over time. Market and survey-based measures 
of longer-term inflation expectations have moved closer to 2%. These factors will 
support underlying inflation and the return of inflation to our target over the medium 
term. 

Annual HICP inflation increased further in September, owing to higher growth 
in energy and services prices (Chart 7). According to Eurostat’s final release for 
September, the increase in headline HICP inflation to 3.4%, up from 3.0% in August, 
mainly reflects higher inflation for energy, amounting to an annual rate of change of 
17.6% in September compared with 15.4% in August. However, HICP inflation 
excluding food and energy (HICPX) also increased further from 1.6% in August to 
1.9% in September, as a result of stronger increases in services prices which stood 
at 1.7% in September, up from 1.1% in August. At the same time, the increase in 
non-energy industrial goods (NEIG) prices slowed to 2.1% in September, down from 
2.6% in the previous month. In September, annual growth in food prices remained at 
2.0%, while there was a decline in the dampening effect on inflation of changes in 
HICP weights. Using 2020 HICP weights, the September outcomes for headline 
HICP inflation and HICPX would have been 0.3 and 0.2 percentage points higher 
respectively, compared with 0.5 and 0.6 percentage points higher in August.9 

9    For a detailed overview of the role of the changes in HICP weights on the measurement of inflation in 
2021, see Box 6 entitled “2021 HICP weights and their implications for the measurement of inflation”, 
Economic Bulletin, Issue 2, ECB, 2021. 

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Chart 7 
Headline inflation and its components 

(annual percentage changes; percentage point contributions) 

HICP
Energy
Food
NEIG
Services
Weight changes effect

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

01/20

05/20

09/20

01/21

05/21

09/21

Sources: Eurostat and ECB calculations. 
Notes: Contributions of HICP components are computed using HICP weights for 2020. The impact of the changes in weights is 
estimated by the ECB. The latest observations are for September 2021. 

The surge in the energy components of HICP energy is broad-based, reflecting 
increases in the prices of energy commodities. Globally, energy commodity 
prices dropped sharply at the onset of the pandemic in 2020. They then started to 
recover at the end of 2020 and continued to increase in 2021. As prices have risen 
from the low levels recorded in 2020, these dynamics have resulted in a strong 
upward base effect, to a large extent explaining the current buoyant annual growth 
rates. However, current prices for energy commodities have surpassed pre-
pandemic levels. The all-time high for HICP energy of 17.6% in September can be 
attributed to increases in the gas, electricity and fuel HICP components (Chart 8). 
Until May, the surge was driven mainly by an increase in the fuel component, 
reflecting the rebound in oil prices. Over more recent months, the contribution of the 
fuel component remained high, accompanied by increases in the gas and electricity 
components. The extent to which these increases translate into changes in 
consumer prices varies greatly from one country to another, and the impact is often 
lagged. 

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Chart 8 
Energy inflation decomposition 

(annual percentage changes; percentage point contributions) 

Energy
Fuel
Electricity
Gas
Other

20

15

10

5

0

-5

-10

-15

2019

2020

2021

Sources: Eurostat and ECB calculations. 
Notes: “Fuel” refers to the HICP component “liquid fuels and fuels and lubricants for personal transport equipment”. “Other” includes 
“solid fuels” and “heat energy” COICOP5 items. The latest observations are for September 2021. 

Most indicators of underlying inflation continued to increase (Chart 9). While 
the temporary VAT cut in Germany from July to December 2020 continues to have a 
small upward impact on HICPX inflation rates in the second half of 2021, the impact 
of changes in weights currently has a small dampening effect. Net of these special 
effects, HICPX inflation would stand at 1.9%. HICPXX inflation, which also excludes 
travel-related items, clothing and footwear, increased to 1.9% in September from 
1.8% in August. The model-based Persistent and Common Component of Inflation 
(PCCI), which is less affected by the changes in weights and the temporary VAT rate 
reduction in Germany, declined marginally from 1.7% in August to 1.6% in 
September. The Supercore indicator lies at the lower end of the range of measures 
of underlying inflation and increased for the fourth consecutive month, edging up to 
1.6% in September from 1.3% in August. Most measures of underlying inflation 
remain below the target of 2%.10 

10    Trimmed means (which remove around 5% or 15% from each tail of the distribution of annual price 

changes) stand above the target of 2% because they include some energy items that currently have 
very high inflation rates. For further information on these and other measures of underlying inflation, 
see Boxes 2 and 3 in the article entitled “Measures of underlying inflation for the euro area”, Economic 
Bulletin, Issue 4, ECB, 2018. 

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Chart 9 
Measures of underlying inflation 

(annual percentage changes) 

Range of underlying indicators
HICPX
HICPX excluding VAT changes in Germany and weight changes effects
HICPXX
HICPXX excluding VAT changes in Germany and weight changes effects
PCCI
Supercore

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

01/20

04/20

07/20

10/20

01/21

04/21

07/21

Sources: Eurostat and ECB calculations. 
Notes: The range of indicators includes HICP excluding energy, HICP excluding energy and unprocessed food, HICPX (HICP 
excluding energy and food), HICPXX (HICP excluding energy, food, travel-related items, clothing and footwear), 10% and 30% 
trimmed means and the weighted median. The latest observations are for September 2021. 

Pipeline price pressures for HICP non-energy industrial goods continued to 
build up in August, proving to be longer lasting than initially thought (Chart 
10). Producer prices at different stages of the pricing chain continued to rise in July 
and August compared with June. At the earlier input stages, the annual rate of 
change in producer prices for intermediate goods rose from 12.7% in July to 14.2% 
in August, while for intermediate goods the annual rate of change in import prices 
increased from 14.5% in July to 15.5% in August. Focusing on the later stages of the 
pricing chain, domestic producer price inflation for non-food consumer goods – a key 
measure of pipeline pressures in NEIG inflation – continued to increase gradually for 
the sixth consecutive month, rising from 2.0% in July to reach the historically high 
value of 2.2% in August. The annual rate of change of import prices for non-food 
consumer goods also increased to 2.2% in August, up from 1.3% in July, mainly 
reflecting an exchange rate depreciation rather than selling prices in trading partner 
countries. As pipeline pressures materialise in NEIG prices with a delay, further 
upward pressure can be expected in the near term. 

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Prices and costs 

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Chart 10 
Indicators of pipeline price pressures 

(annual percentage changes) 

Import prices – intermediate goods (right-hand scale)
Import prices – non-food consumer goods
Domestic producer prices – intermediate goods (right-hand scale)
Domestic producer prices – non-food consumer goods

9

6

3

0

-3

2017

18

12

6

0

-6

2018

2019

2020

2021

Sources: Eurostat and ECB calculations. 
Note: The latest observations are for August 2021. 

Wage pressures remain subdued overall. The latest available indicators of wage 
growth, such as growth in compensation per employee or growth in compensation 
per hour, continued to be strongly affected by measures to cushion the effects of the 
pandemic, such as job retention schemes. In spring 2020 these schemes pushed 
down compensation per employee and pushed up compensation per hour, which 
gave rise to strong base effects in the second quarter of 2021. Accordingly, 
compensation per employee increased by 8% in the second quarter of 2021, while 
compensation per hour decreased by 3.9%. By contrast, negotiated wages were not 
directly affected by developments in hours worked and the recording of benefits from 
job retention schemes in 2020, making them a more reliable indicator of wage 
pressure throughout the pandemic. At the same time, one-off pandemic-related 
payments recently introduced some volatility into this measure. Overall, growth in 
negotiated wages has remained low in recent months, and it is uncertain whether the 
upward impacts of recent increases in consumer price inflation will pass through to 
wages.11 

Market-based indicators of longer-term inflation expectations continued to 
increase further, with the five-year forward inflation-linked swap (ILS) rate five 
years ahead having surpassed 2% for the first time in seven years. Market-
based indicators of longer-term inflation expectations reached new highs. Over the 
review period, the five-year forward ILS rate five years ahead rose above 2%, a level 
not seen since August 2014, and stood at 2.1% on 27 October 2021. While ILS rates 
rose across the maturity spectrum, the increase was most pronounced in short and 
medium-term maturities, in line with a transient but more persistent increase in near-
term inflation. Markets priced in a stronger and longer transitory rise in near-term 

11    For an overview of the extent to which wages are indexed to inflation in the euro area and on the role 

this might play in second-round effects, see Box 7 in this issue of the Economic Bulletin: “The 
prevalence of wage indexation in the euro area and its potential role for the impact of inflation on wage 
developments”. 

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Prices and costs 

21 

 
 
 
inflation compensation than at the beginning of the review period. The increase in 
market-based indicators of inflation expectations, particularly in the near term, can 
mainly be attributed to ongoing supply-demand imbalances, namely, more persistent 
supply bottlenecks and rising energy prices. Nevertheless, over the next five years, 
inflation options markets still signal around a 40% risk-neutral probability of average 
inflation in the euro area staying below 2%, with the probability of inflation exceeding 
3% having increased to 11%. According to the ECB Survey of Professional 
Forecasters (SPF) for the fourth quarter of 2021, inflation expectations have been 
revised upwards across the forecast horizon (Chart 11). HICP inflation expectations 
stand at 2.3%, 1.9% and 1.7% for 2021, 2022 and 2023 respectively. Compared with 
the previous round, this is an increase of 0.4 percentage points for 2021 and 2022 
and 0.2 percentage points for 2023. Respondents attributed the upward revisions 
mainly to higher energy prices and the impact of supply chain tensions. Regarding 
the near-term inflation outlook, many respondents reported that they expected a 
further increase in the inflation rate in the last months of 2021, but continue to expect 
a sharp fall in inflation in the course of 2022. SPF five years ahead inflation 
expectations increased from 1.8% to 1.9%, moving closer to the 2% target. 

Chart 11 
Survey-based indicators of inflation expectations and market-based indicators of 
inflation compensation 

(annual percentage changes) 

HICP
Consensus Economics – HICP (14 October 2021)
Market-based indicators of inflation compensation (20 October 2021)
SPF Q4 2021
SPF Q3 2021
SPF Q2 2021

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

2019

2020

2021

2022

2023

2024

2025

2026

Sources: Eurostat, Thomson Reuters, Consensus Economics, ECB (SPF) and ECB calculations. 
Notes: The market-based indicators of the inflation compensation series are based on the one-year spot inflation rate and the one-year 
forward rate one year ahead, the one-year forward rate two years ahead, the one-year forward rate three years ahead and the one-
year forward rate four years ahead. The latest observations for market-based indicators of inflation compensation are for 20 October 
2021. The ECB SPF for the fourth quarter of 2021 was conducted between 1 and 11 October 2021. The Consensus Economics cut-off 
date is 14 October 2021. 

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5 

Money and credit 

Money creation in the euro area continued to normalise in September 2021, 
reflecting an improving situation regarding the coronavirus (COVID-19) pandemic 
and policy support measures. Eurosystem asset purchases remained the dominant 
source of money creation. Growth in loans to the private sector stabilised at 
pre-pandemic levels and benefited from favourable financing conditions. Moreover, 
according to the euro area bank lending survey, credit standards remained broadly 
unchanged for loans to firms and tightened somewhat for housing loans in the third 
quarter of 2021, while demand for loans by firms and households continued to 
increase. 

In September 2021 broad money growth moderated further. The annual growth 
rate of M3 declined to 7.4% in September, down from 7.9% in August (Chart 12), as 
it continued to be affected by negative base effects linked to the exceptional increase 
in liquidity between March and September 2020. The quarterly pace of money 
growth moved closer to its longer-term average, with shorter-run dynamics of M3 
continuing to benefit from the significant support provided by monetary, fiscal and 
prudential policies. On the components side, the main driver of M3 growth was the 
narrow aggregate M1, which includes the most liquid components of M3. Having 
already started to moderate in the second quarter of 2021 from the high growth rates 
observed during the first year of the pandemic, the annual growth rate of M1 
remained stable at 11.0% in September, mainly as a result of strong growth in 
overnight deposits. In the same month, although the contribution of other short-term 
deposits remained negative, notably owing to time deposits, marketable instruments 
continued to make a small contribution to annual M3 growth, reflecting the low level 
of interest rates and investors’ search-for-yield behaviour. 

Money creation continued to be driven by Eurosystem asset purchases. As in 
previous quarters, the largest contribution to M3 growth came from the Eurosystem’s 
net purchases of government securities under the asset purchase programme (APP) 
and the pandemic emergency purchase programme (PEPP) (red portion of the bars 
in Chart 12). Further support for M3 growth came from credit to the private sector 
(blue portion of the bars). Bank credit to general government continued to make a 
negative contribution to money creation, owing to sales of government bonds and 
reduced issuance of government securities (light green portion of the bars). Net 
external monetary flows also had a slight negative impact on money creation, 
coinciding with a weakening of the effective euro exchange rate (yellow portion of the 
bars). However, other counterparts supported broad money growth (dark green 
portion of the bars), as favourable conditions for targeted longer-term refinancing 
operations provided incentives for the substitution of bank funding away from longer-
term liabilities. 

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developments 
Money and credit 

23 

 
Chart 12 
M3 and its counterparts 

(annual percentage changes; contributions in percentage points; adjusted for seasonal and calendar effects) 

M3 
Net external monetary flows
General government debt securities held by the Eurosystem
Credit to general government from MFIs excluding the Eurosystem
Credit to the private sector
Inflows from longer-term financial liabilities and other counterparts

16

14

12

10

8

6

4

2

0

-2

-4

01/19

04/19

07/19

10/19

01/20

04/20

07/20

10/20

01/21

04/21

07/21

Source: ECB. 
Notes: Credit to the private sector includes monetary financial institution (MFI) loans to the private sector and MFI holdings of 
securities issued by the euro area private non-MFI sector. As such, it also covers the Eurosystem’s purchases of non-MFI debt 
securities under the corporate sector purchase programme and the PEPP. The latest observations are for September 2021. 

Loan growth to the private sector increased in September 2021. Lending to firms 
and households continued to benefit from favourable financing conditions and the 
ongoing economic recovery. Loan growth to the private sector rose to 3.2% in 
September, up from 3.0% in August, driven by lending to firms and reflecting a 
positive base effect (Chart 13). The annual growth rate of loans to firms reached 
2.1% in September, up from 1.5% in August, supported by a rise in longer-term 
loans. Despite that increase, the high cash balances and the availability of other non-
bank funding sources might still weigh on firms’ demand for bank loans. At the same 
time, the growth rate of loans to households edged down slightly to 4.1% in 
September (Chart 13). The growth in household borrowing was due mainly to a rise 
in mortgage lending, as consumer credit growth remained weak. This is attributable 
to the fact that the recovery has become less credit intensive, with households 
instead tending to finance their consumption with their disposable income and 
savings accumulated during the pandemic. Overall, loan developments are masking 
considerable differences across euro area countries, which, among other things, is a 
reflection of the uneven impact of the pandemic and progress of the economic 
recovery across countries. 

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Chart 13 
Loans to the private sector 

(annual percentage changes) 

Loans to the private sector 
Loans to non-financial corporations
Loans to households 

8

6

4

2

0
01/19

04/19

07/19

10/19

01/20

04/20

07/20

10/20

01/21

04/21

07/21

Source: ECB. 
Notes: Loans are adjusted for loan sales, securitisation and notional cash pooling. The latest observations are for September 2021. 

According to the October 2021 euro area bank lending survey, credit standards 
for loans to firms and to consumers remained broadly unchanged, while those 
for housing loans tightened somewhat in the third quarter of 2021 (Chart 14). 
Following a strong tightening in the earlier stages of the pandemic, credit standards 
for loans to firms remained broadly unchanged for the second consecutive quarter. 
This reflects an overall improvement in the euro area economy as containment 
measures have been gradually lifted and monetary, fiscal and supervisory authorities 
continue to provide support. Banks reported that risk perceptions and competition 
from other banks had had a slight net easing impact on credit standards, while 
banks’ cost of funds and balance sheet constraints had had a broadly neutral impact, 
owing to banks’ solid capital ratios and favourable funding costs. For housing loans, 
the net tightening of credit standards was related to banks’ risk tolerance and their 
cost of funds and balance sheet constraints, whereas these factors had a broadly 
neutral impact on consumer credit. For the fourth quarter of 2021, euro area banks 
expect a moderate net tightening of credit standards for loans to firms and a further 
tightening of credit standards for loans to households for house purchase. 

The survey shows that demand for loans continued to increase in the third 
quarter of 2021, albeit more among households than firms. This increase is 
attributable to improved consumer confidence, the historically low level of interest 
rates and housing market prospects. Banks also indicated that firms’ financing needs 
for both fixed investment and inventories and working capital had contributed 
positively to loan demand. For the fourth quarter of 2021, banks expect a further rise 
in demand for loans by firms and no change in demand for loans by households for 
house purchase. 

The survey also suggests that, on balance, the ECB’s unconventional 
monetary policy measures supported banks’ credit intermediation activities. 
Banks indicated that the ECB’s asset purchase programmes (APP and PEPP), along 
with the third series of targeted longer-term refinancing operations (TLTRO III) had 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Money and credit 

25 

 
 
had a positive impact on their liquidity position and market financing conditions. 
Furthermore, together with the negative deposit facility rate, banks reported that 
these measures had had an easing impact on bank lending conditions and a positive 
impact on lending volumes. At the same time, banks suggested that the ECB’s asset 
purchases and the negative deposit facility rate had had a negative impact on their 
net interest income, while a large percentage of banks reported that the TLTRO III 
operations and the two-tier system had supported bank profitability. 

Chart 14 
Changes in credit standards and net demand for loans (or credit lines) to enterprises 
and households for house purchase 

(net percentages of banks reporting a tightening of credit standards or an increase in loan demand) 

Credit standards – loans to non-financial corporations
Credit standards – loans to households for house purchase
Demand – loans to non-financial corporations
Demand – loans to households for house purchase

75

50

25

0

-25

-50

-75

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

2019

2020

Q2

2021

Q3

Source: Euro area bank lending survey. 
Notes: For the bank lending survey questions on credit standards, “net percentages” are defined as the difference between the sum of 
the percentages of banks responding “tightened considerably” or “tightened somewhat” and the sum of the percentages of banks 
responding “eased somewhat” or “eased considerably”. For the survey questions on demand for loans, “net percentages” are defined 
as the difference between the sum of the percentages of banks responding “increased considerably” or “increased somewhat” and the 
sum of the percentages of banks responding “decreased somewhat” or “decreased considerably”. The latest observations are for the 
third quarter of 2021. 

Bank lending rates have stabilised close to their historical lows. In August 2021 
the composite bank lending rate fell slightly, to 1.44%, for loans to non-financial 
corporations and remained broadly unchanged, at 1.32%, for loans to households for 
house purchase (Chart 15). The decline in lending rates to firms was widespread 
across euro area countries. Moreover, the spread between bank lending rates on 
very small loans and those on large loans remained stable at low levels, mainly 
reflecting declines in rates on very small loans. There is still considerable uncertainty 
regarding the longer-term economic consequences of the pandemic, but policy 
support measures have prevented a broad-based tightening of financing conditions, 
which would have amplified the adverse impact it has had on the euro area 
economy. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Money and credit 

26 

 
 
Chart 15 
Composite bank lending rates for non-financial corporations and households 

(percentages per annum) 

Non-financial corporations
Households for house purchase

4

3

2

1

1.75

1.50

1.25

1.00

2014

2015

2016

2017

2018

2019

2020

2021

01/21

03/21

05/21

07/21

Source: ECB. 
Notes: Composite bank lending rates are calculated by aggregating short and long-term rates using a 24-month moving average of 
new business volumes. The latest observations are for August 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Update on economic, financial and monetary 
developments 
Money and credit 

27 

 
 
 
Boxes 

1 

Scarring effects of the COVID-19 pandemic on the global 
economy – reviewing recent evidence 

Prepared by Julia Doleschel and Ana-Simona Manu 

The recession caused by the coronavirus (COVID-19) pandemic weighed on 
potential output across advanced economies and emerging market 
economies, but whether it will have a temporary or lasting impact remains to 
be seen. Taking a production function approach perspective, the decline in potential 
output can be explained by (i) smaller contributions from production factors 
(employment and capital), and/or (ii) lower technological gains (i.e. the efficiency with 
which inputs are combined). Although strong policy responses, particularly in 
advanced economies, cushioned the overall economic impact of the COVID-19 
pandemic, there is still a risk of long-term output scarring. This relates, for instance, 
to hysteresis effects, as many workers have been at least temporarily excluded from 
the labour market, potentially resulting in skills losses and/or a permanent exit. While 
job retention schemes during the pandemic preserved employment and shielded 
productive but fragile firms, they might have partly hindered labour reallocation and 
hampered productivity. This box reviews evidence on the scarring effects of the 
COVID-19 shock and compares recent data relevant for determining the evolution of 
potential output with developments in the aftermath of the Great Recession. 

The falloff in global investment stemming from the COVID-19 shock was short-
lived and led to a slower accumulation of physical capital stock, although this 
was less pronounced than during the Great Recession. The surge in uncertainty 
regarding the economic outlook coupled with lockdown measures led to a reduction 
in new investment. In the first two quarters of 2020, world (excluding euro area) 
gross fixed capital formation declined by around 10% compared with the last quarter 
of 2019. This led to a slowing of aggregate growth in capital stock by 0.5 percentage 
points, although this was largely recovered in subsequent quarters (Chart A). By 
comparison, the Great Recession saw a much greater cumulative decline in global 
capital accumulation, although it occurred at a slower pace. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

28 

 
Chart A 
Cumulative change in capital input 

(percentage points) 

COVID-19 (Q4 2019-Q1 2021)
Great Recession
COVID-19 trough

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

Aggregate

AEs excluding euro area

EMEs excluding China

Sources: National sources and ECB calculations. 
Notes: The blue bars refer to the change between the fourth quarter of 2019 and the first quarter of 2021, while the red diamonds 
depict the change between the fourth quarter of 2019 and the trough in the second quarter of 2020. The yellow bars refer to the 
change between the average for the period 2005-07 and the average for the period 2008-10. Advanced economies (AEs) include 
Canada, Japan, the United Kingdom and the United States. Emerging market economies (EMEs) include Brazil, India, Mexico, Russia, 
Turkey and South Korea. 

By contrast, the contraction in hours worked during the pandemic was much 
stronger than during the Great Recession owing to both larger employment 
losses and reduced working hours per employee. Widespread lockdown 
measures prevented individuals from working as businesses closed or reduced 
operations, while school-related closures increased the need for childcare at home. 
In 2020 aggregate working hours declined by 8% compared with 2019, 
approximately 2.5 times more than during the Great Recession. This was due to a 
decline of equal proportion in employment and number of hours worked per 
employee.1  The labour market deterioration was widespread across countries, but 
somewhat more pronounced in emerging market economies. By comparison, during 
the Great Recession, the reduction in total hours worked occurred mostly as a result 
of employment losses and was larger in advanced economies (Chart B). As the 
recovery takes hold, unemployed and marginally attached2  workers are partly 
reabsorbed into employment, as indicated by the decline in the unemployment rate 
and the rise in the participation rate (Chart C). 

1   

In order to isolate changes in hours worked from demographic trends, the preferred measure is the 
total number of hours worked over the year divided by the working age population. For emerging 
market economies, this figure was already on a downward path prior to the Great Recession, implying 
a larger reduction in working hours during the reference period shown in the chart for the Great 
Recession compared with the decline that occurred between 2009 and 2008 only. 

2    The US Bureau of Labor Statistics defines marginally attached workers as “persons who are not in the 
labor force, want and are available for work, and had looked for a job sometime in the prior 12 months. 
They are not counted as unemployed because they had not searched for work in the prior 4 weeks, for 
any reason whatsoever. The marginally attached are a group that includes discouraged workers.” 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

29 

 
 
 
Chart B 
Changes in total hours worked 

(percentage changes; percentage point contributions) 

Total hours worked
Employment
Average hours worked per employee

0

-2

-4

-6

-8

-10

COVID-19

Great
Recession

COVID-19

Great
Recession

COVID-19

Great
Recession

Aggregate

AEs excluding euro area

EMEs

Sources: The Conference Board, World Bank and ECB calculations. 
Notes: In order to isolate changes in hours worked from demographic trends, the measure used is the total number of hours worked 
over the year divided by the working age population. COVID-19 refers to the change between 2019 and 2020, while the Great 
Recession refers to the change between the average for the period 2005-07 and the average for the period 2008-10. Advanced 
economies (AEs) include Australia, Canada, Denmark, Japan, Norway, New Zealand, Sweden the United Kingdom and the United 
States. Emerging market economies (EMEs) include Brazil, China. India, Mexico, Russia, South Korea and Turkey. 

Chart C 
Labour market developments 

a) Employment rate 

(percentages of working age population) 

COVID-19
Great Recession

76

74

72

70

68

66

t

58

56

54

52

50

t+4

AEs excluding euro area

48

t

t+8

t+4

EMEs

t+8

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

30 

 
   
 
 
 
b) Actual and long-term unemployment rate   

(percentages of labour force aged 15-64) 

COVID-19
Great Recession

14

12

10

8

6

4

2

14

12

10

8

6

4

2

4

3

2

1

0

t

t+4

t+8

t

AEs excluding euro area

t+4

EMEs

t+8

t

t+4

t+8

AEs excluding euro area 
long-term unemployment

c) Labour force participation rate 

(percentages of labour force aged 15-64) 

COVID-19
Great Recession

78

76

74

72

t

65

60

55

t+4

t+8

50

t

AEs excluding euro area

t+4

EMEs

t+8

Sources: National sources, Organisation for Economic Co-operation and Development and ECB calculations. 
Notes: t=Q4 2019 for COVID-19 and Q3 2008 for the Great Recession. The advanced economies (AEs) aggregate is calculated as the 
weighted average across Australia, Canada, Denmark, Switzerland, Japan, New Zealand, Norway, Sweden, the United Kingdom and 
the United States (Australia, Canada, Switzerland, the United Kingdom and the United States for long-term unemployment). The 
emerging market economies (EMEs) aggregate includes Brazil, India, Mexico, Turkey, Russia and South Korea. Long-term 
unemployment is defined as being unemployed for 12 months or more (over 27 weeks for the United States). For Brazil and India, data 
refer to the labour force participation rate for all ages. The latest observations refer to the second quarter of 2021. 

The weakness in the labour market translates partly into lower potential 
employment. People who entered early retirement schemes or left the labour 
market to meet childcare needs (i.e. who are interested in working, but not searching 
for work) are considered to lower the potential labour force participation rate and 
thus potential employment. This effect is likely to be temporary, at least in part, and 
to last until schools re-open fully, which will release parents from childminding, 
allowing them to re-integrate into the labour market.3  Because mothers, in particular 
of younger children, were more prone to take over childcare responsibilities, the 
impact on potential employment is likely to be driven by a decline in labour force 

3    See, for example, Furman, J., Kearney, M.S. and Powell III, W., “The role of childcare challenges in the 
US jobs market recovery during the COVID-19 pandemic”, NBER Working Paper, No 28934, June 
2021; Bauer, L., “Mothers are being left behind in the economic recovery from COVID-19”, Up Front, 
Brookings Institution, 6 May 2021; International Labour Organisation, “Fallout of COVID-19: Working 
moms are being squeezed out of the labour force”, 27 November 2020. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

31 

 
 
 
 
participation by women. Where available, data suggest that the decline in labour 
force participation of mothers contributed by around 20% to the decline of total 
prime-age labour force participation.4  At the same time, recessions can incentivise 
people to prolong their education or return to education for some years as a 
preferred alternative to being underemployed or unemployed. This would add 
downward pressure on labour force participation in the short term but raise 
productivity in the longer term. 

Lasting damage to the labour market may arise from hysteresis effects if 
people remain out of work for longer, loose their skills or become discouraged. 
This phenomenon is likely to be concentrated in the hardest hit industries (for 
example, contact-intensive industries). However, strategic investment directed 
towards sectors that exhibited stronger bottlenecks coupled with the fact that some 
sectors are likely to expand as a result of new opportunities for business created by 
the recession (for example, chemicals, pharmaceuticals and logistics) could mitigate 
such adverse effects. Long-term unemployment in advanced economies increased 
by around 1.5 percentage points, which is less than during the Great Recession 
(Chart C, panel b). To the extent that the increase in long-term unemployment 
translates into a higher rate of structural unemployment5  and participation rates do 
not fully recover, lower labour input is likely to remain a long-term drag on potential 
output going forward. 

Total factor productivity (TFP) has also been suppressed by the COVID-19 
shock, although its “residual” nature and inherited cyclicality requires 
cautious interpretation. In 2020 aggregate trend TFP growth fell by 0.2 percentage 
points compared with 2019, a decline similar to that seen during the Great 
Recession (Chart D).6  Despite the significant uncertainty related to quantification of 
this unobserved variable, the balance of economic arguments suggests that the 
pandemic impaired the global efficiency of combining factors of production. Lower 
productive efficiency could be related to a less dynamic entry of new firms, amid 
heightened uncertainty about the economic environment and limited exit of inefficient 
firms in view of generous policy support. Moreover, supply chain disruptions 
increasingly lead to a less efficient reallocation of resources. Adjustments to the new 
remote working systems are also costly. On the positive side, progress with 
automation and digitalisation could improve efficiency, but such effects are likely to 
take longer to materialise fully. 

4    Despite a generally lower level of participation rates. See, for example, the ILO-UN Women study, 

which found that, based on data for 84 countries, the labour force participation rate of prime-age men is 
95%, while prime-age women have a labour force participation rate of 52%. 
5    As measured by the non-accelerating inflation rate of unemployment (NAIRU). 
6    See Box 4 of this issue of the Economic Bulletin for a description of euro area developments in labour 

productivity during the COVID-19 pandemic. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

32 

 
 
Chart D 
Trend total factor productivity growth 

(percentages) 

1.2

1.0

0.8

0.6

0.4

0.2

0.0

2008

2009

2019

2020

Great Recession

COVID-19

Sources: National sources and ECB calculations. 
Notes: The global trend for TFP growth is computed by aggregating the trend TFP growth rates across nine economies (Brazil, China, 
India, Japan, Mexico, Russia, South Korea, Turkey and the United States) using GDP PPP weights. The country-specific trend TFP 
growth rates are obtained using a standard Cobb-Douglas Production Function Approach. 

Overall, the level of global potential output has declined during the pandemic, 
albeit less than during the Great Recession and mostly on account of 
temporary factors. While investment was impaired, the drop was rather short-lived. 
Furthermore, the weakness in labour markets temporarily lowered potential 
employment and there are indications that TFP growth declined. Looking ahead, the 
prospects for global potential output depend on the adjustment to the post-pandemic 
economic landscape and, importantly, on the phasing-out of policy support 
measures. Far-reaching monetary and fiscal policy stimulus helped avert a wave of 
bankruptcies and mass unemployment. Accordingly, a premature withdrawal of 
policy support could be costly and hamper the economy’s potential output (for 
example via inefficient bankruptcies7  and capital retirement).Employing the right mix 
of labour market policies will be key to promoting an equitable and sustained 
recovery from the COVID
permanently detached from the labour market. 

19 crisis and will help workers avoid becoming 

-

7    See, for example, Financial Stability Board, “COVID-19 support measures: Extending, amending and 

ending”, 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Scarring effects of the COVID-19 pandemic on the global economy – reviewing recent 
evidence 

33 

 
 
 
 
2 

Main findings from the ECB’s recent contacts with non-
financial companies 

Prepared by Gabe de Bondt, Friderike Kuik and Richard Morris 

This box summarises the results of contacts between ECB staff and representatives 
of 68 leading non-financial companies operating in the euro area. The exchanges 
mainly took place between 4 and 13 October 2021. 1 

Contacts reported strong growth in activity overall, but many said that supply 
constraints were increasingly limiting their ability to meet demand. 
Manufacturers described healthy order books and long delivery times, but shortages 
of inputs made it difficult for production to keep pace with orders. The acute shortage 
of semiconductors continued, and the spread of the Delta variant of the coronavirus 
(COVID-19) in Asia had further affected chip supplies. This resulted in a sharp drop 
in automotive production and in demand for related intermediate inputs. By contrast, 
contacts reported growing demand for many non-durable consumer goods and 
continuing robust demand for household durables, in turn sustaining demand for 
most intermediate goods. Faced with strong order books, most contacts said that 
manufacturing activity was being hampered by shortages of materials and 
components, also related to congestion at container shipping ports. Activity in the 
construction sector was also hindered by shortages of materials and labour, despite 
strong or recovering demand for both residential and non-residential investment. 
Developments in retail and transport services reflected the continued strong demand 
for manufactured goods. Contacts elsewhere in the services sector generally 
reported steady or robust growth in activity. Leisure travel, hospitality and recreation 
services had recovered quite strongly over the summer, but activity in these sectors 
remained markedly below pre-pandemic levels. Demand for IT and 
telecommunications services remained solid, while media and advertising services 
were steadily recovering from pandemic-induced lows. 

Looking ahead, most contacts were optimistic about the outlook for activity in 
the fourth quarter of 2021 and beyond. Strong order books would sustain the 
output of manufacturers for a number of months or quarters, while the further 
loosening of travel restrictions would give an added boost to related services 
industries. The recent rise in energy prices was, however, creating additional 
uncertainty regarding production in energy-intensive industries. Moreover, higher 
inflation could subdue real disposable income and final consumer demand. Supply 
chain disruption was likely to persist for several months before gradually easing in 
the course of 2022, while more substantial capacity investments would take effect in 
2023. 

Contacts reported growing employment but also an increasing lack of 
available labour and high attrition, resulting in many unfilled positions. 
Numerous companies observed a scarcity of applicants, which acutely affected 

1    For further information on the nature and purpose of these contacts, see the article entitled “The ECB’s 

dialogue with non-financial companies”, Economic Bulletin, Issue 1, ECB, 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Main findings from the ECB’s recent contacts with non-financial companies 

34 

 
 
those firms seeking to hire or rehire on a large scale. This was attributed to people 
moving to jobs in other industries, returning to their home countries (in the case of 
foreign workers) or adjusting their work-life balance – factors that could persist to 
varying degrees. There were also structural drivers related to job preferences and 
ageing. Pent-up demand to change jobs together with reduced geographical 
limitations (owing to home working) also led to higher attrition rates. Not only were 
some long-standing shortages (such as of software engineers and lorry drivers) 
becoming more acute, but shortages were also increasingly observed across a 
range of professions, albeit to differing degrees across geographical areas.   

Chart A 
Summary of views on developments in and the outlook for activity and prices 

(percentage of respondents) 

Previous quarter
Current quarter

a) Activity

60

50

40

30

20

10

0

b) Prices

60

50

40

30

20

10

0

Significant
decrease

Decrease No change Increase Significant
increase

Significant
decrease

Decrease No change Increase Significant
increase

Source: ECB. 
Notes: The scores for the previous quarter reflect the ECB staff assessment of what contacts said about developments in activity 
(sales, production and orders) and prices in the third quarter of 2021. The scores for the current quarter reflect the assessment of what 
contacts said about the outlook for activity and prices in the fourth quarter of 2021. 

Contacts in the industrial sector reported significant increases in selling 
prices, while price developments in the services sector were less dynamic. 
This was not dissimilar to the situation described three months earlier, in which many 
input and selling prices were already rising at the fastest rate seen for many years. 
However, the persistence of the high or rising input costs, together with the recent 
surge in energy prices, did lead many contacts to raise their price expectations for 
next year and to anticipate a stronger pass-through to consumer prices. In particular, 
most contacts in business-oriented sectors described a favourable environment for 
passing higher costs on to their customers, given the latter’s focus on securing 
supply, and many anticipated a significant further pass-through to consumers. At the 
same time, many of the contacts in more consumer-oriented sectors considered that 
strong competition among retailers and from online merchants would limit the pass-
through to final consumer prices. 

Industrial input prices were still expected to ease at some point in 2022, but 
contacts expected wage inflation to pick up. The prices of some commodities had 
already peaked towards the middle of this year, and while the prices of others were 
still increasing, most contacts still expected these to stabilise or ease in the coming 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Main findings from the ECB’s recent contacts with non-financial companies 

35 

 
 
quarters. However, the transmission of pipeline pressures through the value chain 
would persist for some time. If not reversed, the recent surge in gas and electricity 
prices would further add to costs next year as contracts are rolled over. Most 
contacts anticipated higher wage increases in upcoming wage negotiations, broadly 
mirroring the recent pick-up in consumer prices. Besides pressure to sustain real 
incomes, wage negotiations would reflect improved business profits (in some cases 
reflected in bonuses rather than wages), a certain degree of catch-up in cases where 
wages had been restrained during the pandemic, and tighter labour market 
conditions. However, for some contacts the main concern was not negotiated wage 
increases but the higher wages that needed to be offered to attract new staff. 

Chart B 
Summary of views on the persistence of supply constraints and input price pressures 

(percentage of respondents) 

Business-oriented sectors
Consumer-oriented sectors
Energy sector

35

30

25

20

15

10

5

0

Less than
half a year

Half a year
to one year

One year
or more

Not sure/
no answer

Source: ECB. 
Notes: The chart presents the ECB staff interpretation of what contacts said about the likely duration of the supply constraints and 
input price pressures currently faced by their industry, including those related to the scarcity of inputs, transport delays, energy costs 
and labour shortages. The views are expressed as a percentage of the respondents who said that their firm or industry was 
experiencing supply constraints and/or input price pressures. Business-oriented sectors comprise intermediate and capital goods, 
construction, transport and business services. Consumer-oriented sectors comprise consumer goods (including food), retail and 
consumer services. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Main findings from the ECB’s recent contacts with non-financial companies 

36 

 
 
Chart C 
Summary of views on the extent to which current cost pressures will be passed 
through to consumer prices 

(percentage of respondents) 

Business-oriented sectors
Consumer-oriented sectors
Energy sector

50

40

30

20

10

0

A lot

A little

Not at all

Not sure/
no answer

Source: ECB. 
Notes: The chart presents the ECB staff interpretation of what contacts said about the extent to which the unusual cost pressures that 
they were facing would be passed through to consumer prices. The views are expressed as a percentage of the respondents who said 
that their firm or industry was experiencing supply constraints and/or input price pressures. Business-oriented sectors comprise 
intermediate and capital goods, construction, transport and business services. Consumer-oriented sectors comprise consumer goods 
(including food), retail and consumer services. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Main findings from the ECB’s recent contacts with non-financial companies 

37 

 
 
 
3 

Labour supply developments in the euro area during the 
COVID-19 pandemic 

Prepared by Katalin Bodnár and Derry O’Brien 

In this box we look at what has contributed to the evolution of labour force 
participation during the pandemic, as well as its outlook. This is relevant from a 
policy perspective as a depressed participation rate over a prolonged period could 
point to scarring effects in the labour market, whereas a continued recovery may 
help to address existing labour shortages, contain emerging wage pressures and 
support the economic recovery overall. 

Labour supply in the euro area remains adversely affected by the pandemic. 
The labour force participation rate, one of the main indicators of labour supply, in the 
euro area was 64.3% in the second quarter of 2021, up from its low of 62.9% in the 
second quarter of 2020 (Chart A).1  But it was still about 0.6 percentage points below 
its pre-pandemic level in the fourth quarter of 2019, corresponding to roughly 1.4 
million fewer workers in the labour force.2  More recent but still preliminary monthly 
unemployment data for July and August suggest that the labour force recovered 
slightly in the third quarter of 2021.   

Chart A 
Labour supply in the euro area 

(thousands; percentage of working age population) 

Labour force
Labour force participation rate (right-hand scale)

166,000

165,000

164,000

163,000

162,000

161,000

160,000

159,000

65.0

64.5

64.0

63.5

63.0

62.5

62.0

61.5

Q4

2019

Q1

Q2

Q3

Q4

Q1

Q2

2020

2021

Sources: Eurostat Integrated European Social Statistics and ECB staff calculations. 

Recent labour force developments have tended to partly mirror the stringency 
of pandemic containment measures. Across the euro area, the numbers in the 

1   

In this box the terms “labour supply” and “labour force” are used interchangeably. The labour force is 
the sum of workers who are either employed or unemployed (and aged between 15 and 74). The 
labour force participation rate is calculated as the ratio between the labour force and the working age 
population (those aged between 15 and 74).     

2    These data should be interpreted with some caution as they may be subject to larger than usual 

revisions related to the ongoing implementation of the Integrated European Social Statistics (IESS) 
Directive. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

38 

 
 
 
labour force fell sharply in the second quarter of 2020 (Chart B), predominantly due 
to declines in employment as the number of workers in unemployment decreased 
only marginally. However, the widespread support from job retention schemes is 
likely to have stemmed flows from employment into inactivity, preventing a much 
larger decline in the labour force. Some easing in the stringency of the containment 
measures in the third quarter of 2020 saw a quick but partial rebound in the labour 
force, although this was partly driven by an increase in unemployed workers. The 
stringency of containment measures subsequently continued to be mirrored 
somewhat in movements in the labour force, explaining a temporary further dip in the 
labour force numbers in the first quarter of 2021. These labour force developments 
were closely aligned with opposite movements in the numbers of discouraged 
workers, i.e. those who are available to work but are not seeking a job (e.g. because 
they think they would not find one) and are thus considered to be inactive. Such a 
close alignment of labour force and discouraged workers runs contrary to the pre-
pandemic pattern and may be partly explained by the fact that the working age 
population remained broadly unchanged during the pandemic, whereas it had been 
increasing before then. Already before the pandemic, the growth of the working age 
population was projected to slow down because of population ageing, as the cohorts 
entering working age are smaller than the cohorts leaving it. 3  Net immigration to the 
euro area was expected to counterbalance this and keep the working age population 
increasing. However, based on the statistics on population by citizenship, the foreign 
population in the euro area has stopped increasing since the start of the pandemic 
crisis, while it was rising before. While limitations to statistics make it challenging to 
assess how migration was affected, there is some evidence that some foreign 
workers returned to their home countries.4  As a result of a moderation in net 
immigration, the working age population flattened out in the last quarters.   

3    See “"The macroeconomic and fiscal impact of population ageing”, Box 1 in “Evolution of the ECB’s 
analytical framework”, Occasional Paper Series, No 277, European Central Bank, September 2021. 
4    See the box entitled “Main findings from the ECB’s recent contacts with non-financial companies” in this 

issue of the Economic Bulletin and “International Migration Outlook 2021”, OECD, 2021 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

39 

 
 
Chart B 
Labour force and discouraged workers in the euro area 

(thousands, cumulative change since the fourth quarter of 2019) 

Labour force
Employed
Unemployed
Discouraged workers (inverted)

2,000

0

-2,000

-4,000

-6,000

-8,000

-10,000

Q4

2019

Q1

Q2

Q3

Q4

Q1

Q2

2020

2021

Sources: Eurostat Integrated European Social Statistics and ECB staff calculations. Note: Series are seasonally adjusted. 

Developments in labour supply have been quite heterogeneous across larger 
euro area countries during the pandemic. The number of workers in the labour 
force remains well below pre-pandemic levels in Germany and Italy; it is close to pre-
pandemic levels in France and Spain and is noticeably higher in the Netherlands 
(Chart C). Such cross-country heterogeneity, which has also been reflected in the 
corresponding labour force participation rates, may reflect several factors. First, 
countries are at different stages of population ageing and the working age population 
is already decreasing in some countries (for example, Germany), while it is still 
increasing in others (for example, Spain). Second, the labour force participation rate 
differs considerably across demographic groups, meaning that a shift in the structure 
of the working age population can affect the aggregate labour force. The share of 
older cohorts has been rising in all euro area countries, but because of differences in 
levels across the shares of older (and other) cohorts in their populations, the impact 
of the rising trend on the labour force has been quite heterogeneous.5  Finally, the 
impact of the pandemic on migrant workers and, in turn, on labour supply, was also 
heterogenous across countries. 

5    For more details, see the article entitled “Labour supply and employment growth”, Economic Bulletin, 

Issue 1, ECB, 2018. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

40 

 
 
 
Chart C 
Labour force in the euro area and the largest euro area countries 

(thousands, cumulative changes since the fourth quarter of 2019) 

Euro area
Germany
France
Italy
Spain
Netherlands
Other

1,000

0

-1,000

-2,000

-3,000

-4,000

-5,000

-6,000

Q4

2019

Q1

Q2

Q3

Q4

Q1

Q2

2020

2021

Sources: Eurostat Integrated European Social Statistics and ECB staff calculations. 

Understanding how structural and cyclical drivers affect the labour force 
participation rate of the different demographic groups helps us to better 
assess the impact of the pandemic and prepare forecasts. The labour force 
participation rate of young workers and prime-age men showed the greatest 
correlation with the business cycle before the pandemic (Chart D, panel a). At the 
same time, the labour force participation rate of older workers and prime-age women 
was more independent from the business cycle. The labour force participation rate of 
those with a medium level of education strongly co-moved with GDP growth, 
whereas movements in the rates for those with higher and lower levels of education 
were less cyclical or a-cyclical. Structural drivers also differed. The labour force 
participation rate of older workers was trending upwards before the pandemic, 
reflecting their rising educational level and the effect of past pension reforms, among 
other factors.6  At the same time, the labour force participation rate of prime-age 
women was also trending upwards, although less strongly.7 

The coronavirus (COVID-19) shock affected some demographic groups’ labour 
force participation rate differently from what past cyclicality would suggest. 
The assessment, however, depends on the counterfactual scenario used, i.e. the 
assumption of what would have happened in the absence of the pandemic shock. 
Using counterfactual scenarios that take the pre-pandemic trends into consideration, 
the labour force participation rate gap – i.e. the difference between the observed 
labour force participation and the no-pandemic-shock counterfactual – is the widest 
for older workers and for workers with lower and medium levels of education, 
respectively (Chart D, panel b). The apparently strong effect of the shock on the 

6    For more details, see the article entitled “Drivers of rising labour force participation – the role of pension 

reforms”, Economic Bulletin, Issue 5, ECB, 2020. 

7    For more details, see the article entitled “Hours worked in the euro area”, Economic Bulletin, Issue 6, 

ECB, 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

41 

 
 
 
labour force participation rate of older workers is not in line with past cyclical 
patterns. This reflects the special nature of the shock. Fear of infection may be one 
of the factors behind the findings for older workers.8  By contrast, younger workers’ 
labour force participation rate may have benefited from labour shortages and less 
fear of infection at workplaces.9 

8    There is some evidence that in the United States, early retirement increased. See, for example, Faria-

e-Castro, M. “The COVID Retirement Boom”, Federal Reserve Bank of St. Louis, Economic Synopses, 
Number 25, 2021.   

9    Other aspects of heterogeneity may also be important, but an analysis of their role is subject to data 

constraints. One such aspect is the effect of the pandemic on immigration. Pre-pandemic increases in 
the labour force were strongly supported by immigration in some euro area countries. Net immigration 
flow may have slowed down, while some emigration may have occurred in the wake of the pandemic 
shock. However, data come with a time lag and are surrounded by a larger than usual uncertainty. 
Moreover, the effect on the labour force participation rate may be ambiguous. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

42 

 
 
Chart D 
Cyclicality of the participation rate of different population groups, and the labour force 
participation gap in the second quarter of 2021 

a) correlation of the cyclical part of the labour force participation rate with the output gap 
between the first quarter of 2000 and the last quarter of 201910 

(correlation coefficient) 

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

Higher

Medium

Lower

Women

Men

Women

Men

Women

Men

Working
age
population

Educational level

Aged 15-24

Aged 25-54

Aged 55-74

Aged 15-74

b) gap between the labour force participation rate and different counterfactual scenarios 

(percentage points) 

Forecast based on the pre-pandemic GDP projection
Linear extrapolation of the pre-pandemic trend
Compared with the fourth quarter of 2019

1.0

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

Higher

Medium

Lower

Women

Men

Women

Men

Women

Men

Working
age
population

Educational level

Aged 15-24

Aged 25-54

Aged 55-74

Aged 15-74

Source: Eurostat and ECB staff calculations 
Notes: On panel a, the correlation is calculated between the output gap and the one-quarter lagged value of the Hodrick-Prescott 
filtered value of the labour force participation rate for the period 2005-19. The output gap is calculated using the potential output 
estimates of the European Commission. The Hodrick-Prescott filter is applied to the labour force participation rate by age groups, 
disregarding the pandemic observations and using instead a counterfactual scenario for the years 2020-21, in order to mitigate end-
point uncertainty and the impact of the pandemic shock on pre-pandemic trend due to smoothing. On panel b, the first counterfactual 
scenario is derived from regressions linking the change of the labour force participation rate to GDP growth and past levels of the 
labour force participation rate and forecasting the path using the pre-pandemic GDP projections (2019 December Broad 
Macroeconomic Projection Exercise). 

Recovery to pre-pandemic levels of the labour force participation rate will take 
time, even if discouraged workers return quickly. Past crises have tended to 
weigh on the labour force participation rate over a prolonged period. After accounting 
for the upward trending behaviour in the labour force participation rate (Chart E), the 
current gap to pre-crisis dynamics is more evident and is estimated to be 

10   The correlation with the aggregate unemployment rate as an alternative business cycle indicator 

provides a very similar picture. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

43 

 
 
 
 
around -0.75 percentage points. This estimate is surrounded by some uncertainty.11 
It is not yet clear whether the pandemic affects the trend of the labour force 
participation rate and how big the scarring effects will be. The above estimations 
suggest that the groups that had made the largest contribution to the upward trend of 
the aggregate labour force participation rate before the pandemic are the main cause 
of that rate being below its counterfactual path. Should these changes persist, future 
increases in the labour force participation rate may be more limited than expected 
before the pandemic. 

A gradual return of the aggregate labour force participation rate could also be 
seen as broadly consistent with past regularities. The high persistence tends to 
be mainly driven by the stickiness of non-market work activities. 12  This may be 
stronger if the pandemic, to a greater extent than past crises, has prompted workers 
to reassess their career goals and to learn new skills by returning to education. 
Discouraged workers can rejoin the labour force relatively quickly, but even if the 
number of discouraged workers returned to pre-pandemic norms, there would still be 
a shortfall. A recovery in the participation rate will also depend on the strength of the 
recovery in labour demand. 

11    First, the data may be subject to larger than usual revisions due to the ongoing implementation of the 
IESS. Second, both the trend-cycle decomposition and the extrapolation embed some degree of 
uncertainty. Several alternative methods were tested to assess the robustness of the estimate for this 
gap. Given the persistence of the labour force participation developments, the estimate for the 
counterfactual is not very sensitive to the methodology used, and the estimated gap remains in the 
range of -0.7 to -0.9 percentage points. 

12   For evidence on the role of non-market-work activities in driving the delayed cyclical recovery, see for 

example, for the United States, Cajner, T., Coglianese, J. and Montes, J., “The Long-Lived Cyclicality of 
the Labor Force Participation Rate”, Finance and Economics Discussion Series, Board of Governors of 
the Federal Reserve System, No 2021-047, 2021; see also for 30 OECD countries, Duval, R., Eris, M. 
and Furceri, D., “The effects of downturns on labour force participation”, OECD Economics Department 
Working Papers, No 875.l, 2011. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Labour supply developments in the euro area during the COVID-19 pandemic 

44 

 
 
Chart E 
Trend and cyclical component of the labour force participation rate   

(percentage of working age population and percentage point deviation from trend) 

Labour force participation rate
Trend of the labour force participation rate
Counterfactual
Cyclical component of the labour force participation rate (right-hand scale)

66

65

64

63

62

61

60

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

2015

2016

2017

2018

2019

2020

2021

Sources: Eurostat and ECB staff calculations.   
Notes: The data refer to the 15-74 age group. The trend is derived using a Hodrick-Prescott filter (λ=1600), with forecast data for the 
post-2019 period included in the estimation sample to alleviate the end-of-sample issues and to exclude an effect of the pandemic on 
trend. The cyclical component is the deviation of the actual labour force participation rate and the Hodrick-Prescott filtered trend. The 
counterfactual is derived as an aggregation of the counterfactual paths of different population groups. The latter is derived from 
regressions linking the change in the labour force participation rate to GDP growth and past levels of the labour force participation rate, 
and forecasting the path using the pre-pandemic GDP projections (2019 December Broad Macroeconomic Projection Exercise). 
Eurostat population projections are used for the aggregation. The latest observations are for the second quarter of 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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45 

 
 
 
4 

The impact of the COVID-19 pandemic on labour 
productivity growth 

Prepared by Paloma Lopez-Garcia and Bela Szörfi1 

The growth of euro area labour productivity, measured by real GDP per hour 
worked, increased at the onset of the coronavirus (COVID-19) pandemic before 
declining in the course of the subsequent economic recovery.  2  This contradicts 
the general notion of productivity being procyclical and reflects the unique nature of 
this crisis.3  This box discusses the recent patterns in labour productivity and 
considers the extent to which some of these developments might fade or consolidate 
after the crisis. 

Between the last quarter of 2019 and the first quarter of 2021, euro area labour 
productivity growth remained positive and even accelerated compared with 
the period prior to the pandemic (Chart A). Average growth in annual real GDP 
per hour worked rose to 1.7% during this period, more than twice the average pre-
pandemic (2014-19) rate, while real GDP and total hours worked declined by annual 
averages of 5.7% and 7.4% respectively. The fall in employment was much smaller, 
due mainly to the different job retention schemes set up in various euro area 
countries – on average, employment fell by an annual 1.6% over the same period. In 
the second quarter of 2021, however, these developments reversed, with hours 
worked and employment rebounding sharply, causing productivity growth to slow. 
Nevertheless, productivity is now more than 2% higher than the pre-pandemic level 
seen in the fourth quarter of 2019. 

1    We would like to thank ECB colleagues Vasco Botelho, Rodrigo Barrela, Paul Reims and Charles 

Hoffreumon for their input. 

2    Total factor productivity (TFP) is another measure of productivity. Developments in TFP growth outside 

the euro area are discussed in Box 1. 

3    A paper by Basu and Fernald, for instance, starts with the sentence: “Productivity rises in booms and 
falls in recessions.” See Basu, S. and Fernald, J., “Why Is Productivity Procyclical? Why Do We 
Care?”, NBER Working Paper Series, No 7940, October 2000. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
The impact of the COVID-19 pandemic on labour productivity growth 

46 

 
 
Chart A 
Real GDP per hour worked in the euro area 

(year-on-year percentage changes) 

Real GDP
Employment (inverse scale)
Productivity
Hours worked per person (inverse scale)

20

15

10

5

0

-5

-10

-15

-20

2014-19

Q1 2020

Q2 2020

Q3 2020

Q4 2020

Q1 2021

Q2 2021

Sources: Eurostat and ECB staff calculations 

The pandemic and the associated containment measures have affected 
aggregate labour productivity growth in many different ways. The discussion in 
this box is organised by grouping the different channels into those with an impact on 
within-firm productivity growth and those with an impact on the reallocation of 
resources across firms within and across sectors of activity. Within-firm productivity 
growth depends on input quality, managerial practices, innovation and technology 
adoption. Resource reallocation results from the expansion or contraction of firms 
and from the process of creative destruction whereby new, productive firms displace 
obsolete ones. Reallocation can take place across sectors or within sectors. 

Within-firm productivity growth is benefiting from the acceleration in digital 
uptake brought about by the pandemic. Containment measures obliged firms to 
adjust quickly to remote working and establish new channels for sales and customer 
contact, which had an impact on their working and business arrangements. It can 
therefore be said that the pandemic has accelerated the trend for digitalisation that 
had already started well before the crisis. Chart B shows how productivity, measured 
as sector value added per hour worked, increased soon after the first wave of 
lockdowns in sectors such as information and communication (ICT) and finance, 
where staff could work remotely and firms could take advantage of new digital 
solutions. Some of those productivity gains were retained after the economies 
started to re-open. This was reinforced by events in manufacturing and in the 
wholesale, retail and accommodation sectors, where productivity gains started later 
but remained strong well into 2021. Survey-based evidence suggests that these 
sector-specific developments reflect rapid productivity gains from digital adoption, 
particularly in those firms and sectors that are relatively less technology-intensive.4 
Although part of the shift to remote working might reverse over time, some is likely to 

4    See Box 6 entitled “The long-term effects of the pandemic: insights from a survey of leading 

companies”, Economic Bulletin, Issue 8, ECB, 2020. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
The impact of the COVID-19 pandemic on labour productivity growth 

47 

 
 
 
persist, at least in some areas, and could potentially open the door to substantial 
gains in terms of productivity and employee well-being.5 

Chart B 
Real value added per hour worked, different sectors 

(Q1 2019=100) 

Finance and real estate
Professional and business activities
Wholesale, retail, transport, food services
ICT
Manufacturing

110

108

106

104

102

100

98

96

03/19

06/19

09/19

12/19

03/20

06/20

09/20

12/20

03/21

06/21

Sources: Eurostat and ECB staff calculations 
Note: The latest observation is for the second quarter of 2021. 

Looking ahead, the pandemic might, however, also have a negative impact on 
within-firm productivity growth. An increase in firm exits as supporting policies are 
withdrawn could lead to the destruction of jobs, which could potentially lead to a 
deterioration of skills if the reallocation of displaced workers to other firms is slow. 
Human capital accumulation might also be affected by lockdown-induced 
interruptions to education and training. Supply chain disruption might persist, and 
firms might need to find new suppliers, new transport routes or new production 
locations. In addition, it will be important for favourable financial conditions to be 
maintained to make new productivity-enhancing projects viable and to prevent 
corporate debt overhangs, together with high uncertainty, weakening investment 
going forward. 

The impact of the shock has been asymmetric, which has triggered a 
productivity-enhancing reallocation of resources across sectors, at least in the 
short run. The containment measures have had a major impact on service sectors 
relying on face-to-face interactions. These are, on average, relatively less productive 
than other less hard-hit sectors like manufacturing or technology-intensive sectors 
that have even benefited from the increased demand for online solutions. This 
redistribution of activity across sectors of varying productivity might serve to enhance 
productivity. A shift-share analysis using quarterly euro area data confirms that 
during the pandemic, the reallocation of resources across sectors has contributed 

5    The COVID-19 pandemic could also exacerbate the inequality between firms if only the most 

productive and largest companies adopt the latest digital technologies. The reason is that digital 
technologies are characterised by scalability, large fixed costs and low marginal costs, and benefit from 
network effects. See Haskel, J. and Westlake, S., Capitalism without Capital: The Rise of the Intangible 
Economy, Princeton University Press, Princeton, NJ, November 2017. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
The impact of the COVID-19 pandemic on labour productivity growth 

48 

 
 
 
between 30 and 40% of aggregate productivity growth (Chart C, panel a). 6  This 
development contrasts sharply with the pre-crisis period when sector reallocation 
contributed little – and negatively – to aggregate productivity growth. Intra-sector 
developments in the areas least affected by the shock explain the rest of aggregate 
productivity growth. There are, however, two possible caveats looking ahead. First, it 
is not clear to what extent the contribution of sector reallocation will persist over time 
– the impact already seems to be declining in the second quarter of 2021, and this 
may accelerate as containment measures are gradually removed.7  And second, 
reallocating jobs and capital across sectors is always harder, and takes longer, than 
within sectors, which might weigh on the recovery. 

There are also signs that the creative destruction process could be 
productivity-enhancing. The exit of low-productivity firms is regarded as a silver 
lining in crises.8  However, the exogenous and horizontal nature of this shock – 
affecting all firms in a given sector – was no guarantee that productivity-enhancing 
creative destruction would take place. Evidence shows that compared with other 
crises, exits have been muted as a result of the different policies supporting 
corporates enacted by governments.9  But ECB simulations show that the firms most 
likely to exit as a result of the pandemic are less productive than other, more resilient 
firms within their sectors (Chart C, panel b). 10,11  Regarding firm entry, recent data 
show that firm entry declined at the onset of the crisis but recovered thereafter, to a 
higher level than before the crisis in some euro area countries, as well as in the 
United Kingdom and the United States.12 

6    A shift-share analysis decomposes labour productivity growth into three terms: (i) intra-sector 

productivity growth, holding the economic weight of sectors constant (intra-sector contribution); (ii) 
change in sector economic weights holding sector productivity constant (inter-sector contribution); and 
(iii) the interaction between a change in sector economic weights and labour productivity growth 
(interaction or covariance).   

7    See Bloom, N., Bunn, P., Mizen, P., Smietanka, P. and Thwaites, G., “The Impact of Covid-19 on 

Productivity”, NBER Working Paper Series, No 28233, December 2020. 

8    While the crisis might increase the exit of low-productivity firms and thereby support productivity 

growth, they might also undermine it depending on the nature of the shock and market distortions. See 
Foster, L., Grim, C. and Haltiwanger, J., “Reallocation in the Great Recession: Cleansing or Not?”, 
Journal of Labor Economics, Vol. 34, No S1, 2016, pp. S293-S331. 

9    See Criscuolo, C., “Productivity and Business Dynamics through the lens of COVID-19: the shock, risks 

and opportunities”, working paper presented at the ECB Forum on Central Banking 2021. 

10    This claim is based on a simulation of firm-level imbalances reflecting firms’ financial conditions before 
the crisis and sector value added dynamics according to the Eurosystem staff projections for the euro 
area dated December 2020. Firms at risk are defined as those with negative working capital and at the 
top 25% of the leverage distribution within their country-sector.   

11    These results are confirmed by survey data matched with administrative data for Spain (Fernández-

Cerezo, A., González, B., Izquierdo, M. and Moral-Benito, E., “Firm-level heterogeneity in the impact of 
the COVID-19 pandemic”, Working Papers, No 2120, Banco de España, May 2021) and in a multi-
country framework by Criscuolo, C., op. cit. 

12    See Criscuolo, C., op. cit. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
The impact of the COVID-19 pandemic on labour productivity growth 

49 

 
 
Chart C 
Impact of the COVID-19 pandemic on resource reallocation and productivity 

a) Contribution of inter- and intra-sector developments to aggregate productivity growth 

(percentage points) 

Intra-sector (less exposed)
Intra-sector (more exposed)
Inter-sector
Covariance
Total labour productivity growth

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

2014-19
cumulative

Q4 2018-Q4 2019

Q4 2019-Q4 2020

Q4 2019-Q1 2021

Q4 2019-Q2 2021

b) Productivity of firms at risk of exit and of healthy firms in the same country and sector 
according to ECB simulations 

(EUR thousands) 

Firms at risk of exit
Healthy firms

80

70

60

50

40

30

20

10

0

Germany

Spain

France

Italy

Sources: Panel a: own calculations based on Eurostat data; panel b: ORBIS-iBACH and ECB staff calculations. 
Notes: Panel a: sector labour productivity measured as real value added per hour worked. The intra-sector contribution is divided into 
the sectors least and most affected by the pandemic. The more exposed sectors are arts and entertainment, accommodation and 
food, transport and retail and wholesale trade. Panel b: productivity defined at the firm level as real value added per employee. Based 
on simulation of firm-level imbalances reflecting firms’ financial conditions before the crisis and sector value added dynamics according 
to the Eurosystem staff projections for the euro area dated December 2020. The numbers refer to the quarter with the maximum 
number of firms at risk according to the simulations. Firms at risk of exit are defined as those with negative working capital and at the 
top 25% of the leverage distribution within their country-sector. 

Looking ahead, productivity growth will depend heavily on the consolidation 
of widespread digital uptake and the design of the exit strategies from policy 
support. The accompanying article on productivity trends in this issue of the 
Economic Bulletin highlights a slowdown in the spread of innovation and technology 
in the euro area. Seen in this light, the observed acceleration in digital uptake could 
be a positive outcome from the crisis. However, the long-term productivity trend will 
depend heavily on institutions, infrastructure, skills and methods of production and 
management developing and digitising in tandem. Furthermore, digital adoption 
needs to be widespread, across sectors and firms. And finally, the massive policy 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
The impact of the COVID-19 pandemic on labour productivity growth 

50 

 
 
 
support granted to the corporate sector has been crucial in mitigating the initial 
impact of the shock. Once the recovery takes hold on a sustainable basis, however, 
policy support needs to be lifted gradually to avoid impairing the efficient reallocation 
of resources by setting wrong incentives. Accordingly, the design and timing of the 
exit strategies will determine how far aggregate productivity growth will be impacted 
by further after-effects from the shock. 

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51 

 
 
5 

Economic developments and outlook for contact-
intensive services in the euro area 

Prepared by Malin Andersson, Niccolò Battistini and Grigor Stoevsky   

This box takes stock of the development of economic activity in euro area 
contact-intensive services that were adversely affected by the pandemic. 
During the first wave of the coronavirus (COVID-19) pandemic, value added in the 
manufacturing and services sectors behaved very similarly, contracting by more than 
15% in the second quarter of 2020 compared with pre-pandemic levels. However, 
since then the recovery paths of the two sectors have been markedly different. The 
rebound in services activity was interrupted around the turn of the year, as some 
consumer services (henceforth “more contact-intensive services”) were largely shut 
down as a result of the resurgence of the pandemic and the tightening of COVID-19 
restrictions, while other consumer services (henceforth “less contact-intensive 
services”) and manufacturing continued to recover (Chart A, panel a).1 

After a bumpy 2020, more contact-intensive services began to rebound in 
spring 2021 and have been driving GDP growth since then. Preliminary flash 
GDP and production data imply a continued recovery in services value added in the 
third quarter of 2021. As the euro area economy reopened, tourism was a key driver 
of the strong upswing in more contact-intensive services activity. Together with the 
progress of vaccination campaigns, this has led to confidence in more contact-
intensive sectors returning to and even overshooting pre-pandemic levels in recent 
months (Chart A, panel b). In addition, strong rises in credit card spending suggest 
more frequent use of more contact-intensive services like hospitality. However, 
despite this restored confidence, activity in these sectors has not fully regained its 
pre-crisis levels and there is still ample slack. Although perceived capacity utilisation 
has risen strongly since the second quarter of 2021, in the fourth quarter it amounted 
to only around 75% in the travel subsector and 85% in both the accommodation and 
food subsectors, which are the three most-affected subsectors. By the end of the 
third quarter, value added in more contact-intensive services was estimated to be 
approximately 8% below its pre-pandemic level. 

1    More contact-intensive services, representing 22% of the total economy before the pandemic, refer to 
wholesale and retail trade, transport, accommodation and food services (NACE2 Rev2 classification: 
G, H, I) as well as arts and entertainment (R, S, T, U). Less contact-intensive services, representing 
33% of the total economy before the pandemic, cover information and communication (J), financial and 
insurance activities (K), real estate (L), professional, scientific and technical activities (M), and 
administrative and support service activities (N). 

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52 

 
 
Chart A 
Services sector developments 

a) Value added by sector 

(Q4 2019 = 100) 

Services
More contact-intensive services
Less contact-intensive services
Industry

105

100

95

90

85

80

75

70

Q4

2019

Q1

Q2

Q3

Q4

Q1

2020

Q2

2021

Q3

b) Services sector confidence 

(net percentage balances) 

Total services
More contact-intensive services
Less contact-intensive services
Other services

50

25

0

-25

-50

-75

-100

01/20

04/20

07/20

10/20

01/21

04/21

07/21

10/21

Sources: ECB calculations using Eurostat and ECFIN data. 
Notes: Panel a): data for the third quarter of 2021 are based on quarterly data for value added in (1) manufacturing and other industry 
(referred to as “manufacturing” in the text), (2) construction and (3) other sectors. These data are imputed from available monthly data 
up to September for (1) industrial production excluding construction, (2) construction production and (3) residual with respect to real 
GDP according to the flash release. Panel b): the grey area refers to the interval between the maximum and minimum values of 17 
other NACE services subsectors series covered by the European Commission’s Economic Sentiment Indicator. Yellow lines refer to 
three selected more contact-intensive services sectors: accommodation, travel, and food and beverage. Red lines refer to three less 
contact-intensive services sectors: telecommunication, information and programming. The latest observations are for the second 
quarter of 2021 (value added) and October 2021 (confidence). 

This notable slack in more contact-intensive services is confirmed by evidence 
that demand has been a more important factor limiting activity during the 
pandemic than it was before the pandemic. The limits on activity in more contact-
intensive services are perceived as stemming largely from “other” factors, which are 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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53 

 
 
 
mostly related to preventive pandemic containment measures,2  and also to some 
extent from demand and labour input, while financial constraints play a broadly 
similar role in the fourth quarter of 2021 as they did before the pandemic (Chart B). 
Labour is perceived as an increasingly limiting factor to activity in more contact-
intensive services, such as in the accommodation and the food and beverage 
subsectors. Unlike the manufacturing sector, the services sector is not experiencing 
shortages of materials, but it is more sensitive to pandemic-related constraints. From 
a cyclical perspective, the entire services sector is now in an expansionary phase. A 
majority of surveyed firms in more contact-intensive services report rising past and 
expected demand, suggesting an ongoing expansion (Chart C). These subsectors 
saw very strong expansions following the two pronounced dips during the earlier 
pandemic waves, which is also reflected in accelerating price pressures. In 
comparison, the less contact-intensive services subsectors exhibited much more 
contained cyclical variation over the past 18 months. 

Chart B 
Limits on activity in the services sector in the fourth quarter of 2021 

(deviations from fourth quarter of 2019 in net percentage balances) 

Equipment
Demand
Labour force
Financial constraints
Other

 Travel

 Accommodation

 Food and beverage

Selected more contact-intensive services

 Information

Programming

 Telecommunication

Selected less contact-intensive services

Services sector

-20

-10

0

10

20

30

40

50

60

Source: ECFIN data. 
Notes: Values for both more and less contact-intensive services refer to the average of the values for the three subsectors shown 
immediately above them. The latest observations are for the fourth quarter of 2021 (survey undertaken in October). 

2   

It is worth noting that in recent months mobility indices related to recreational activities (e.g. Google 
mobility index for retail and recreation) – which broadly reflect the extent of voluntary social distancing 
– recovered to pre-crisis levels, while indices related to more regulated activities (e.g. Google mobility 
index for workplaces) remained below pre-crisis levels. Based on data for the United States, much of 
the shift in mobility appears voluntary and lifting constraints may yield a quick recovery, provided the 
reduction in COVID-19 risk is deemed credible (see Maloney, W. and Taskin, T., “Determinants of 
Social Distancing and Economic Activity during COVID-19: A Global View”, Policy Research Working 
Paper Series, No 9242, World Bank Group, May 2020). The reduction in voluntary social distancing 
and the improvement in confidence would therefore point to a continued recovery. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Economic developments and outlook for contact-intensive services in the euro area 

54 

 
 
 
Chart C 
Cycles across selected services sectors 

(percentage balances; x-axis: demand over past three months; y-axis: demand over next three months) 

a) Total

b) Less contact-intensive

c) More contact-intensive

Recovery

Expansion

Recovery

Expansion

50

25

0

-25

-50

-75

50

25

0

-25

-50

-75

Recovery

Expansion

50

25

0

-25

-50

-75

Contraction

Downturn

-100

-100

Contraction

Downturn

-100

Contraction

Downturn

-60

-40

-20

0

20

40

-40

-20

0

20

40

60

-100 -75 -50 -25

0

25

50

Sources: ECB calculations using ECFIN data. 
Notes: Yellow dots refer to the period from January 2011 to January 2019. Blue lines refer to the period from February 2020 to 
September 2021. “Less contact-intensive ” and “more contact-intensive” refer to the selected subsectors exhibited in Chart B. The 
latest observations are for October 2021 (red dot). 

The stringency of containment measures has been an important factor 
affecting activity in more contact-intensive services since spring 2021. Model-
based results suggest that the estimated sensitivity of aggregate economic growth to 
changes in the stringency of restrictions in the middle of 2021 increased from its 
average level at the turn of the year, but remained below the level reached in the 
third quarter of 2020 (Chart D). 3  The pattern observed for the total economy was 
largely driven by services, in particular more contact-intensive services subsectors, 
while the responsiveness of industry declined throughout recent quarters. Activity in 
more contact-intensive services appears to be more responsive to a loosening of 
containment measures than to a tightening, as shown by the relatively higher 
average estimated elasticities in the third quarter of 2020 and the second and third 
quarters of 2021, when the stringency of containment measures was relaxed. 
Overall, this evidence suggests that the continued reopening of the economy over 
the coming quarters will remain a key factor in the recovery of more contact-intensive 
services. 

3    For further details on the model, see the box entitled “The impact of containment measures across 
sectors and countries during the COVID-19 pandemic”, Economic Bulletin, Issue 2, ECB, 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Economic developments and outlook for contact-intensive services in the euro area 

55 

 
 
 
Chart D 
Estimated elasticities of sectoral activity to the stringency of containment measures 

(impact of a one point decrease in the Oxford Stringency Index on real gross value-added quarter-on-quarter growth; percentage 
points) 

Q1 to Q2 2020
Q3 2020
Q4 2020 to Q1 2021
Q2 to Q3 2021

Industry

More contact-intensive services

Less contact-intensive services

Rest of the economy

Total economy

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Sources: Eurostat and authors’ calculations. 
Notes: Estimated sectoral elasticities for the third quarter of 2021 are based on quarterly data for value added in (1) manufacturing and 
other industry, (2) construction and (3) other sectors. These data are imputed from available monthly data for (1) industrial production 
excluding construction, (2) construction production and (3) residual with respect to real GDP according to the flash release. “Rest of 
the economy” refers to agriculture and public services. 

As the public health crisis abates and economies reopen further, activity in 
more contact-intensive services is expected to continue to expand, but the 
medium-term outlook remains uncertain. The need to replace obsolete productive 
capacity, expand labour input and reallocate resources across subsectors could 
durably alter the trend growth of more contact-intensive services. At the same time, if 
containment measures are long-lasting or policy support is withdrawn abruptly, there 
may be an increase in insolvencies.4,5  Moreover, changes in preferences, such as 
moving away from business travel and long-distance tourism towards hybrid working 
solutions and local vacation destinations, could cause permanent shifts in 
consumption patterns necessitating the sectoral reallocation of activity.6 

To summarise, the COVID-19 pandemic has had a marked impact on more 
contact-intensive services in the euro area. While these subsectors made a 
significant contribution to economic growth in the middle of 2021, their growth 
potential will be determined by the evolution and containment of the pandemic in the 
near term, coupled with a number of structural factors in the medium term. The likely 

4    For instance, bankruptcies in the accommodation and food services sector increased by 23% between 
the fourth quarter of 2019 and the second quarter of 2021, according to data from Eurostat. In other 
more contact-intensive services bankruptcies were lower in the second quarter of 2021 than at the 
onset of the crisis, in an environment where policy support remained strong. 

5   

In a study by the Federal Reserve Bank of New York, each additional week of closure in more contact-
intensive services subsectors reduces the probability of a business reopening by 2 percentage points. 
Recent evidence from Banca d’Italia suggests that the COVID-19 pandemic has not significantly 
affected the number of active Italian firms in the tourism sector, but their relatively higher recourse to 
bank lending than before the pandemic will undermine their recovery when policy support is eventually 
withdrawn. 

6    See IMF, “Managing Divergent Recoveries”, World Economic Outlook, April 2021 and de Vet, J.M, et 

al., “Impacts of the COVID-19 pandemic on EU industries”, Policy Department for Economic, Scientific 
and Quality of Life Policies, European Parliament, Luxembourg, 2021. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Economic developments and outlook for contact-intensive services in the euro area 

56 

 
 
 
need for sectoral reallocation and the possible increase in company insolvencies 
might impair a quick and full recovery of more contact-intensive services. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
Economic developments and outlook for contact-intensive services in the euro area 

57 

 
 
6 

The recovery of housing demand through the lens of the 
Consumer Expectations Survey 

Prepared by Desislava Rusinova1 

This box reports past trends and future expectations related to housing 
demand based on the ECB’s new Consumer Expectations Survey (CES). 2  The 
decisions of households to purchase a house or flat depend on many factors, 
including their working status and financial situation, income and wealth, and their 
expectations regarding the general level of prices, housing prices and mortgage 
credit conditions. The CES can provide micro-level insights into the purchasing 
decisions of households as well as some of the determining factors. For instance, 
the share of CES respondents who have purchased a house/flat in the past 12 
months can be seen as an indicator of recent housing demand for different 
socioeconomic groups. Similarly, the share of respondents who intend to buy a 
house/flat in the next 12 months provides a forward-looking indicator of housing 
demand. House purchases reported by CES respondents increased between the 
second and third quarters of 2020 and then plateaued (Chart A, panel a). At the 
same time, expected purchases in the next 12 months remained relatively stable 
throughout the period (dark blue bars). 

1    The author would like to thank Pedro Neves, Niccolò Battistini, Johannes Gareis, Virginia di Nino and 

Moreno Roma for their input and comments. 

2    More detail on the CES is available in “ECB Consumer Expectations Survey: an overview and first 

evaluation”, Occasional Paper Series, ECB, forthcoming. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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58 

 
 
Chart A 
Recent and expected housing demand according to CES data 

a) Share of respondents who have bought a house or flat in the past 12 months or plan to buy 
a house or flat in the next 12 months and residential investment 

(left-hand scale: percentages; right-hand scale: index: Q2 2020 = 100) 

Plan to buy house/flat, next 12 months
Bought house/flat, past 12 months
Euro area residential investment (right-hand scale)

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

150

140

130

120

110

100

90

80

70

60

50

Q2

Q3

2020

Q4

Q1

Q2

2021

Q3

b) Share of respondents who intend to buy a house/flat in the next 12 months, by income 
quintile 

(percentages) 

Plan to buy house/flat, next 12 months

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

1st quintile

2nd quintile

3rd quintile

4th quintile

5th quintile

Source: CES. 
Notes: Weighted averages of the six largest euro area countries. Panel a: shares of respondents who replied “yes” to whether they 
had purchased a house or flat in the past 12 months or whether they intend to buy a house or flat in the next 12 months. Quarterly 
averages are used to smooth sample composition effects as the sample is relatively small. The red line shows real residential 
investment from the national accounts, normalised to 100 in the second quarter of 2021. Panel b: shares of respondents planning to 
buy a house/flat by income quintile; averages over the period February-August 2021. 

The intention to buy a house/flat differs according to household income. 
Households from higher income quintiles are much more likely to intend to buy a 
house or flat in the next 12 months than lower-income households (Chart A, panel b) 
and are therefore likely to provide stronger support for housing demand. Moreover, 
these households are also the most likely to have actually bought a house or flat in 
the past 12 months. In addition to level of income, expected income dynamics also 
play an important role in decisions to invest in housing: respondents intending to buy 
a house in the next 12 months tend to have substantially higher income growth 
expectations than those who do not intend to do so. 

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59 

 
 
 
While accumulated savings are likely to have already been used for house 
purchase, these may still boost housing demand over the near term. The 
amount of accumulated savings, or savings behaviour in general, can have an 
impact on the decision to purchase a house or flat. In March 2021, the CES collected 
information about respondents’ accumulated savings since the beginning of the 
coronavirus (COVID-19) pandemic, distinguishing between net savers, net 
dissavers, and a neutral category (those who have saved approximately as much as 
they have dissaved).3  An analysis of these data shows that net dissavers generally 
had the highest share of home buyers in the past 12 months (Chart B, panel a). 
When also asked about the reasons for dissaving, 36% of dissavers indicated that 
“An increase in my household spending because of a major purchase that I/we had 
planned for (e.g. house, car, etc.)” had been important or very important. This 
suggests that, at least to some extent, accumulated savings may have already been 
used for house purchases by March 2021, and some households are net dissavers 
because they have paid for house purchases. Looking forward, net savers had the 
highest share of expected house buyers. Hence, although some transactions driven 
by excess savings during the pandemic may have already materialised, the large 
stock of accumulated savings may still boost housing demand over the near term. 
About 44% of net savers declare that “a desire to put aside enough money to make a 
major purchase in the future (e.g. house, car, etc.)” has been an important or very 
important reason for accumulating savings since January 2020. 

3    The descriptions are based on the following response options in the questionnaire: net savers – “the 

household has added more money than it has withdrawn since January 2020”; net dissavers – “the 
household withdrew more money than it added”; and neutral – “the household added as much money 
as it withdrew”. The responses of non-savers – “the household has neither added nor withdrawn 
money” and “we do not have any money in savings or financial investments” – are less relevant for the 
purposes of this box and are not considered. 

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60 

Chart B 
The link between savings, housing demand and housing attractiveness 

a) Intention to buy a house by saving category, March 2021 

(percentages) 

Will buy house/flat, next 12 months
Bought house/flat, past 12 months

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Net savers

Net dissavers

Neutral

b) Attractiveness of housing as an investment by income quintile 

(balance indicator) 

5th quintile
4th quintile
3rd quintile
2nd quintile
1st quintile

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

04/20

06/20

08/20

10/20

12/20

02/21

04/21

06/21

08/21

Source: CES. 
Notes: Balance indicator calculated as the sum of “good” and “very good” responses minus the sum of “bad” and “very bad”, divided by 
the number of respondents in the respective group. Weighted average of the six largest euro area countries. 

The attractiveness of housing as an investment has increased since the start 
of the pandemic and remains high, particularly for households in the higher 
income quintiles. A useful indicator of potential housing demand for investment 
purposes is the degree of attractiveness of housing as an investment in the 
respondents’ own neighbourhood, which is measured on a five-step scale. The 
balance indicator for housing attractiveness rose across all income categories over 
the survey period, before levelling off in spring 2021. The increase was particularly 
pronounced among respondents in the highest income quintiles (Chart B, panel b). 4 

4    The choice of neighbourhood is likely to depend on income, so higher-income households are likely to 

invest in higher-quality neighbourhoods, which in turn makes them a better investment. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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61 

 
 
 
 
Expectations for credit conditions and housing prices point to a dynamic 
housing market. As factors increasing housing demand, CES respondents pointed 
to, among other things, expectations of easier access to credit in the future and 
declining mortgage rates. At the same time, in the period between April 2020 and 
May 2021, respondents almost continuously increased their expectations for house 
price growth. This suggests continued strong demand in the housing sector but also 
possible affordability issues for the lower income quintiles, as expected growth in 
housing prices has been much stronger than expected growth in household income 
and the general price index. 

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62 

 
 
7 

The prevalence of private sector wage indexation in the 
euro area and its potential role for the impact of inflation 
on wages 

Prepared by Gerrit Koester and Helen Grapow 

Shocks to inflation can have longer-lasting effects in the presence of second-
round effects and second-round effects are more likely in the presence of 
wage indexation. Second-round effects can occur if households and/or firms 
attempt to compensate the loss of real income incurred by higher inflation when 
setting wages and/or prices. The potential effects of wage indexation mechanisms on 
wage setting and inflation developments depend not only on the prevalence of wage 
indexation to inflation, but also on the inflation indicator used for indexation. This box 
investigates the prevalence of wage indexation mechanisms in the private sector.1 
As wage-setting mechanisms differ considerably across euro area countries, 
regulations across countries are analysed and a euro area indicator is derived by 
aggregating characteristics of national wage indexation schemes using country 
shares in euro area private sector employment as weights. 

Across euro area countries, four different wage indexation regimes can be 
identified, ranging from automatic wage indexation schemes to regimes with 
no formal role for inflation in wage setting (Chart A). The first, more general 
automatic wage indexation scheme functions in a way that inflation developments 
automatically feed into wage setting – therefore aiming to neutralise the effects of 
inflation on the purchasing power of labour incomes. Under the second regime, 
inflation plays a formal role in wage negotiations, for example in the form of an 
explicit inflation benchmark guiding wage negotiations. However, this may only apply 
to some sectors of the economy and the effects of inflation on wage setting is also 
often less direct and less automatic than under automatic wage indexation. The third 
regime deploys inflation indexation for minimum wages only – therefore aiming to 
neutralise the effects of inflation on purchasing power for employees on the lowest 
labour incomes. All other regimes which have no formal role for inflation indexation in 
wage setting fall into the fourth category. 

1    This box focuses primarily on inflation effects through the indexation of private sector wages to inflation. 
Additional effects could stem from the public sector, for example, through the indexation of public sector 
pensions to inflation or through other special indexation schemes. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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the impact of inflation on wages 

63 

 
 
Chart A 
Prevalence of wage indexation to inflation in the euro area   

(share of total private sector employees in the euro area in percentages; 2021) 

Inflation including energy
Inflation excluding energy
Backward-looking
Backward- and forward-looking
Mostly forward-looking

25

20

15

10

5

0

Automatic indexation
(private sector and minimum wages)

Formal role for inflation
in wage setting

Automatic indexation
only for minimum wages

Sources: Eurosystem and ECB staff calculations. 

With regard to the inflation indicator, wage indexation can be forward- or 
backward-looking and can include or exclude energy prices. Backward-looking 
indicators imply a lagged adjustment of wages to observed inflation, while forward-
looking indicators need to rely on forecasts. There is evidence that wage indexation 
based on backward-looking inflation measures makes the effect of inflationary 
shocks longer-lasting and inflation stabilisation harder than wage indexation that is 
based on forward-looking measures.2  In some cases, backward- and forward-
looking indicators are combined – linking wage growth to inflation forecasts but 
incorporating, for example, ex post compensation for inflation forecast errors. Where 
the inflation measure used for wage indexation includes energy, global oil price 
shocks can have persistent effects on domestic underlying inflation through wage 
indexation and the possibly resulting wage-price spiral. 

In the euro area, general automatic wage indexation schemes only apply to a 
very small share of employees. All in all, only around 3% of private sector 
employees in the euro area have their wages and minimum wages automatically 
indexed to inflation.3  For most of the employees covered by these regimes, the 
inflation measure is backward-looking and includes energy (first two columns of 
Chart A).4 

2    See, for example, Crowley, J., “The Effects of Forward-Versus Backward-Looking Wage Indexation 

Price Stabilization Programs”, IMF Working Papers, Issue 38, IMF, April 1997.   

3    Automatic wage indexation applies to a large share of private sector employees in Belgium, Cyprus, 

Malta and Luxembourg. 

4   

In Belgium, the relevant inflation index excludes petrol, tobacco and alcohol.   

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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the impact of inflation on wages 

64 

 
 
 
Indexation regimes with a formal role for inflation developments in wage 
negotiations apply to around 18% of employees in the euro area. In these 
cases, they consider mostly forward-looking inflation measures that exclude energy.5 

Around 18% of euro area private sector employees work in countries where 
only the minimum wages are automatically indexed to inflation. These 
indexation mechanisms are usually backward-looking with an inflation measure that 
includes energy. While usually only a relatively small share of employees in euro 
area countries earns the minimum wage,6  the indexation of minimum wages to 
inflation usually acts as a floor in other wage agreements. Furthermore, increases in 
minimum wages often play an important role as a general benchmark for sectoral 
wage agreements.7 

For more than half of the private sector employees in the euro area, inflation 
does not play a formal role in wage setting but can be an important factor in 
wage negotiations. That said, where there is no formal role for inflation, inflation 
developments have often been of little importance (compared with regimes where 
inflation does play a formal role), for example, during times of high uncertainty or 
large external shocks, with the focus instead being on job security. 

Since the Great Financial Crisis, indexation regimes with a formal role for 
inflation in wage setting have become somewhat less prevalent (Chart B). 8  The 
share of private sector employees with a formal role for inflation in wage setting has 
decreased in the euro area by around 6 percentage points since 2008, with the 
decline in the share of workers covered by contracts with backward-looking inflation 
indexation in Spain playing a key role. The remaining share of contracts for which a 
formal role for inflation is foreseen now refer predominantly to forward-looking 
inflation measures that exclude energy.9  As a consequence of the fall in the share of 
indexation regimes with a formal role for inflation in wage setting, the share of private 
sector employees for which inflation plays no formal role in wage setting has 
increased in the euro area since the Great Financial Crisis. As for more general 
automatic indexation regimes that cover private sector wages and regimes that only 
index the minimum wages to inflation, there have been no significant changes in 
their importance since the Great Financial Crisis, and the type of inflation measures 
used for the indexation has not altered much either. 

5    The most prominent example is Italy, where the Italian National Institute of Statistics’ annual three-year 
forecast of the Consumer Price Index excluding energy is the central benchmark for wage agreements 
at the sectoral level. 

6    For the share of minimum wage earners, see “Eurostat’s minimum wage statistics” and the box entitled 
“Recent developments in social security contributions and minimum wages in the euro area”, Economic 
Bulletin, Issue 8, ECB, 2019. 

7    For further details on France, see Fougère, D., Gautier, E and S. Roux, “Wage floor rigidity in industry-

level agreements: Evidence from France”, Labour Economics, Vol. 55, September 2018. 

8    For further details on wage indexation regimes at the time of the Great Financial Crisis, see the box 
entitled “Wage indexation mechanisms in euro area countries”, Monthly Bulletin, ECB, May 2008. 

9    These regimes are prevalent in Italy. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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the impact of inflation on wages 

65 

 
 
Chart B 
Prevalence of wage indexation in the euro area in 2008 compared with 2021 

(share of total private sector employees in the euro area in percentages; 2021) 

Inflation including energy
Inflation excluding energy
Backward- and forward-looking
Mostly forward-looking

25

20

15

10

5

0

2008

2021

Formal role for inflation in wage setting

Sources: Eurosystem and ECB staff calculations. 
Note: Only regimes with a formal role for inflation in wage setting are shown in this chart, as there were no significant changes to the 
other regimes shown in Chart A. 

Overall, the likelihood of wage-setting schemes triggering second-round 
effects based on inflation indexation is relatively limited in the euro area, 
particularly when it comes to energy inflation. In some countries, the recent hikes 
in energy inflation could therefore be expected to lead to automatic increases 
predominantly in minimum wages, since the corresponding indexation mechanisms 
usually consider an inflation measure that includes energy. These minimum wage 
increases could then be passed through to generally higher wage agreements, given 
that minimum wage increases can serve as a benchmark for wage structures in the 
overall economy. For wage indexation regimes with a formal role for inflation in wage 
negotiations, only very limited direct effects from the recent energy inflation hikes are 
to be expected, since these regimes predominantly use an inflation measure that 
excludes energy. Overall, unless the shock to inflation leads to a significant increase 
in wage indexation, a broadly based and automatic pass-through of recent inflation 
hikes to wage growth seems rather unlikely given the prevailing mechanisms.10 

10    See the discussion in Nickel, C. et al., “Understanding low wage growth in the euro area and European 

countries”, Occasional Paper Series, No 232, ECB, September 2019. 

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the impact of inflation on wages 

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8 

Results of a special survey of professional forecasters on 
the ECB’s new monetary policy strategy 

Prepared by Aidan Meyler, Marta Saez Moreno, Rodolfo Arioli and 
Franziska Fischer 

Along with the Survey of Professional Forecasters (SPF) for the fourth quarter 
of 2021, participants were asked to complete an additional special survey on 
the ECB’s new monetary policy strategy. The aim of that survey was to gain an 
insight into how the participants in the regular SPF have assessed the new strategy 
and into whether it has already had, or will have, an impact on their forecasts. The 
questionnaire, together with the aggregate results, is available on the “Background 
on the survey of professional forecasters” webpage. This box summarises some of 
the findings. 

A vast majority of respondents considered the ECB’s new monetary policy 
strategy to be an improvement and, on balance, thought that it made it more 
likely that the ECB would meet its primary objective of price stability in the 
euro area (Chart A). Two-thirds of respondents were of the opinion that the new 
monetary policy strategy was either “somewhat better” or “much better”, with only a 
small minority viewing it as “somewhat worse”. The so-called net percentage 
balance1  was clearly positive at +45%. Almost 40% of respondents thought that the 
new strategy would make it either “somewhat more likely” or “much more likely” that 
the ECB would meet its mandate, and just over half thought it would be “about the 
same” (i.e. neither more nor less likely). Only a few respondents thought it would 
make it “somewhat less likely” and none thought it would be “much less likely”. The 
net percentage balance was positive at +19%. 

1    The net percentage balance is calculated as (a) the portion of respondents saying “much better” plus 

half of the portion saying “somewhat better” minus (b) half of the portion saying “somewhat worse” and 
the portion saying “much worse”. This score is bounded in the range ±100%, with +100% meaning that 
all respondents stated “much better” and -100% meaning that all respondents stated “much worse”. A 
positive (negative) net percentage balance generally indicates that more respondents thought that it 
was better (worse). 

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Chart A 
What is your overall assessment of the ECB’s monetary policy strategy compared 
with the situation before and will it make it more or less likely that the ECB will meet 
its mandate and primary objective? 

a) What is your overall assessment of the ECB’s new monetary policy strategy compared with 
the situation before? 

(percentage of responses) 

70

60

50

40

30

20

10

0

59%

18%

18%

0 %

6%

Much worse

Somewhat worse

About the same

Somewhat better

Much better

b) In your opinion, will the new strategy make it more or less likely that the ECB will meet its 
mandate and primary objective of price stability in the euro area? 

(percentage of responses) 

70

60

50

40

30

20

10

0

55%

35%

0%

6%

4%

Much less likely

Somewhat less likely

About the same

Somewhat more likely

Much more likely

Source: Special SPF survey in the fourth quarter of 2021. 
Note: There were 51 responses for each question. 

Respondents identified the clearer 2% and symmetric inflation target as the 
key elements of the new strategy. They also viewed those elements as the key 
improvements, particularly as they made the target more understandable for the 
general public. Respondents also considered that they facilitated the understanding 
of the ECB’s reaction function. Some respondents stated that it was the practical 
implementation in terms of actual policy changes that would ultimately determine the 
success of the new strategy. When asked about negative or missing aspects, a 
relatively common theme was that participants saw some ambiguity in various 
aspects of the new strategy, such as the inclusion of owner-occupied housing and 
the possible extent and duration of an overshooting of the inflation target that might 
be tolerated. A number of respondents also thought that monetary policy might be 
distracted from its primary objective by the consideration of other aspects, such as 
climate change. Some respondents were of the opinion that the new strategy did not 

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68 

 
 
 
adequately clarify some dimensions of asset purchases (in particular possible limits 
on holdings). 

When asked explicitly about the importance of specific elements of the new 
strategy and whether these were better or worse, most respondents 
considered them all to be at least “somewhat important” and all at least 
“somewhat better” (Chart B). For example, with regard to the “move away from 
‘below, but close to, 2%’ to ‘2%’”, a clear majority of respondents considered it to be 
either “very important” (44%) or “somewhat important” (50%). In terms of whether 
that change was viewed as an improvement or deterioration, again a clear majority 
thought it had made the strategy either “much better” (31%) or “somewhat better” 
(60%). 

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strategy 

69 

 
Chart B 
Respondents were asked for their opinion of the following elements/statements in 
terms of their unimportance/importance and whether they represent a 
deterioration/improvement in the strategy. 

a) What is your assessment of the following elements/statements in terms of their 
unimportance/importance? 

(percentage of responses) 

Largely irrelevant
Somewhat irrelevant
Neutral
Somewhat important
Very important

A: 2%

B: Symmetry

C: Medium-term

D: HICP

E: Owner-occupied housing

F: Proportionality

G: ELB

H: Policy rates

I: Other instruments

J: Climate

K: Analytical framework

L: Communication

M: Article 3

N: Financial stability

0

20

40

60

80

100

b) What is your assessment of the following elements/statements in terms of whether they 
represent a deterioration/improvement in the strategy? 

(percentage of responses) 

Much worse
Somewhat worse
About the same
Somewhat better
Much better

A: 2%

B: Symmetry

C: Medium-term

D: HICP

E: Owner-occupied housing

F: Proportionality

G: ELB

H: Policy rates

I: Other instruments

J: Climate

K: Analytical framework

L: Communication

M: Article 3

N: Financial stability

0

20

40

60

80

100

Source: Special SPF survey in the fourth quarter of 2021. 
Notes: There were 43-48 responses for each element/statement. Element A refers to “Move away from ‘below but close to 2%’ to 
‘2%’”; Element B refers to “Explicit reference to symmetry in the 2% inflation target”; Element C refers to “Confirmation of medium-term 
orientation”; Element D refers to “HICP remaining appropriate index for quantifying the price stability objective”; Element E refers to 
“Recommendation of roadmap to include owner-occupied housing in the HICP”; Element F refers to “Proportionality assessment”; 
Element G refers to “Especially forceful or persistent monetary policy measures when close to effective lower bound (ELB)”; Statement 
H refers to “Primary monetary policy instrument is the set of ECB policy rates”; Statement I refers to “Other instruments (forward 
guidance, asset purchases and longer-term refinancing operations) will remain an integral part of the toolkit”; Element J refers to 
“Adoption of climate-related action plan”; Element K refers to “Analytical framework (from two-pillar to integrated assessment of 
economic and monetary and financial analysis)”; Element L refers to “Communication”; Statement M refers to “Without prejudice to the 
price stability objective, the Eurosystem shall support the general economic policies in the EU with a view to contributing to the 
achievement of the Union’s objectives as laid down in Article 3 of the Treaty on European Union”; and Statement N refers to “The 
Eurosystem shall also contribute to the smooth conduct of policies pursued by the competent authorities relating to the prudential 
supervision of credit institutions and the stability of the financial system”. 

In general, there was a strong correlation between the ranking of the various 
elements/statements in terms of their perceived importance in the new 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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strategy 

70 

 
 
 
strategy and their perceived improvement on the previous strategy. Taking the 
percentage balance between positive and negative assessments as a summary 
statistic, respondents ranked the explicit reference to symmetry and the move away 
from “‘close to, but below, 2%’” to ‘2%’” as the two most important or relevant 
elements. Other elements with relatively high net percentage balances include the 
reference to especially forceful or persistent monetary policy action when the 
economy is close to the effective lower bound, the permanently expanded toolkit and 
climate-related action. It should also be noted that there was a positive net 
percentage balance for all of the elements/statements surveyed, indicating that 
respondents viewed them as important. In terms of their improvement on the 
previous strategy, the first four items (i.e. symmetry, 2%, forceful action at the 
effective lower bound and the toolkit) received the same ranking as for their 
importance. 

Around one-third of respondents stated that they had changed their 
macroeconomic expectations more generally in response to the new strategy. 
Chart C lists the variables and the direction of the revisions. For headline inflation, 
underlying inflation and labour costs, changes to near-term forecasts were limited, 
while changes to longer-term forecasts were revised upwards. For real economy 
variables (real gross domestic product (GDP) and the unemployment rate), the 
changes reported were generally to medium-term forecasts. For elements of the 
ECB’s monetary policy toolkit (interest rates, forward guidance, asset purchases and 
longer-term refinancing operations (LTROs)) respondents had generally revised their 
forecasts in response to the new strategy in the direction of an easing of the policy 
stance. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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strategy 

71 

 
Chart C 
For each variable/assumption and horizon, respondents were asked to indicate the 
direction (down, unchanged or up) in which they had changed their macroeconomic 
forecasts. 

(percentage of responses) 

Down
Unchanged
Up

n
o
i
t
a
l
f
n

i

n
o
i
t
a
l
f
n

i

P
C
H

I

i

g
n
y
l
r
e
d
n
U

l

a
e
R

P
D
G

e
t
a
r

t
n
e
m

s
t
s
o
c

e
t
a
r

e
c
n
a
d
u
g

i

s
e
s
a
h
c
r
u
p

e
t
a
r

l

-
y
o
p
m
e
n
U

r
u
o
b
a
L

n
o
i
t
a
r
e
p
o

d
r
a
w
r
o
F

t
e
s
s
A

s
O
R
T
L

e
g
n
a
h
c
x
e

≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead
≤1 year ahead
1-2 years ahead
3-4 years ahead
≥5 years ahead

i

n
a
M

i

g
n
c
n
a
n
i
f
e
r

o
r
u
E

0

20

40

60

80

100

Source: Special SPF special survey in the fourth quarter of 2021. 
Notes: There were 13-16 responses for each variable and horizon. For forward guidance, “shorter” is represented by “down” and 
“longer” is represented by “up”; for the euro exchange rate, “down (depreciation)” is represented by “down” and “up (appreciation)” is 
represented by “up”. 

Considering longer-term inflation expectations specifically, some respondents 
reported that they had changed both their point longer-term inflation 
expectations and the balance of risks surrounding them in response to the 
new strategy. While a large portion (around 60%) of respondents had kept their five-
year ahead inflation expectations unchanged in response to the new monetary policy 
strategy, over one-third had revised them upwards (Chart D). When asked how by 
much they had changed their five-year ahead inflation expectations, two-thirds said 
by 0.1 percentage points. With regard to their point expectations, more respondents 
(nearly half) indicated that they had revised upwards their assessment of the balance 
of risks to the five-year ahead inflation expectations. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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strategy 

72 

 
 
 
Chart D 
In response to the new monetary policy strategy, respondents were asked if they had 
revised or changed their assessments of… 

(percentage of responses) 

No
Yes

100

80

60

40

20

0

36%

64%

46%

54%

Five-year ahead inflation expectations

Balance of risks to five-year ahead inflation expectations

Source: Special SPF survey in the fourth quarter of 2021. 
Note: There were 50 responses for each question. 

ECB Economic Bulletin, Issue 7 / 2021 – Boxes 
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strategy 

73 

 
 
 
Articles 

1 

The predictive power of equilibrium exchange rate 
models 

Prepared by Michele Ca' Zorzi, Pablo Anaya Longaric and Michał 
Rubaszek1 

1 

Introduction 

Central banks carefully monitor the evolution of exchange rates. In the case of 
the European Central Bank (ECB) and other major central banks, the exchange rate 
is not a policy target. But the “market value” of the euro is highly relevant for 
understanding the medium-term inflation outlook via its impact – through import 
prices and through general equilibrium effects – on the real economy. 

It would therefore be very useful to be able to anticipate future exchange rate 
movements, but this has proven rather elusive, especially at short horizons. 
The view that exchange rates are largely disconnected from economic fundamentals 
at short horizons has mainly predominated since the seminal paper by Meese and 
Rogoff (1983), which showed that exchange rate models were unable to deliver 
more accurate nominal exchange rate forecasts than the simple prediction of “no 
change” associated with the random walk (RW) model.2  The difficulty of predicting 
exchange rates with systematically better results than by using the RW model, 
especially at short-horizons, was reiterated by Rogoff (2008)3  and, more recently, in 
the influential articles by Rossi (2013)4  and Cheung et al. (2019)5. 

Exchange rates have an important role beyond their key contribution to 
understanding the inflation outlook. While, in line with their monetary policy 
strategies, the ECB, the US Federal Reserve System and other major central banks 

1    This article has benefited from helpful comments by Philip Lane, Fabio Panetta, Livio Stracca, João 

Sousa, Michael Fidora, David Lodge, Arnaud Mehl, Chiara Osbat and Alexandra Buist.   

2    Meese, R. A. and Rogoff, K., “Empirical exchange rate models of the seventies: Do they fit out of 

sample?”, Journal of International Economics, Vol. 14(1-2), 1983, pp. 3-24. The authors showed that 
these results hold even when the models have the advantage of using known, realised economic 
fundamentals. A theoretical explanation of these findings is presented by Engel et al. (2008), who 
showed that, under certain conditions, model-based forecasts may be less accurate than an RW 
benchmark, even if the model reflects the true underlying data-generating process. For more details, 
see Engel, C., Mark, N.C. and West, K.D., “Exchange rate models are not as bad as you think”, in 
Acemoglu, D., Rogoff, K. and Woodford, M. (eds.), NBER Macroeconomics Annual 2007, Vol. 22 of 
NBER Chapters, National Bureau of Economic Research, 2008, pp. 381–441. 

3    Rogoff, K., comment on the paper by Engel, C., Mark, N.C. and West, K.D., “Exchange rate models are 
not as bad as you think”, in Acemoglu, D., Rogoff, K. and Woodford, M. (eds.), NBER Macroeconomics 
Annual 2007, Vol. 22 of NBER Chapters, National Bureau of Economic Research, 2008, pp.443–452, 
https://www.nber.org/system/files/chapters/c4076/c4076.pdf 

4    Rossi, B., “Exchange rate predictability”, Journal of Economic Literature, Vol. 51, 2013, pp. 1063-1119. 
5    Cheung, Y.-W., Chinn, M.D., Pascual, A.G. and Zhang, Y., “Exchange rate prediction redux: New 

models, new data, new currencies”, Journal of International Money and Finance, Vol. 95, 2019, pp. 
332–336. 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The predictive power of equilibrium exchange rate models 

74 

 
 
do not treat the exchange rate as a target variable,6  they are mindful that if large and 
persistent nominal exchange rate fluctuations occur, real exchange rate 
misalignments may develop over time that could have significant implications for the 
economic outlook. This is because over- and undervalued currencies, in an 
environment of price rigidity, could lead to competitiveness imbalances, excessive 
real exchange rate volatility and, potentially, sharp economic adjustments with 
adverse effects on consumption and production.7  This is the context in which 
currencies’ “fair value” is often discussed. In exceptional instances, major central 
banks have intervened directly or only verbally in foreign exchange markets in a 
concerted manner to influence exchange rate dynamics. It is hence not surprising 
that academics and policymakers have continued to strive in recent decades to 
improve their methodological frameworks for assessing equilibrium exchange rates 
(e.g. Bussière et al., 2010; Phillips et al., 2013; Fidora et al., 2017; Couharde et al., 
2018; and Cubeddu et al., 2019). 8 

Recent papers have argued that concepts of equilibrium exchange rates, 
besides their intrinsic interest, could be helpful for understanding and 
predicting exchange rate movements. These papers suggest that, even if the 
dynamic adjustment of exchange rates cannot be fully anticipated, it is known that 
they should eventually adjust to their equilibrium, i.e. a terminal condition defined by 
economic theory. For example, Ca’ Zorzi et al. (2016) and Ca’ Zorzi and Rubaszek 
(2020)9  show that it is sufficient to assume that the real exchange rate gradually 
converges to the simplest definition of the equilibrium exchange rate, i.e. relative 
purchasing power parity (PPP), to produce surprisingly accurate real and nominal 
exchange rate forecasts. In theory, this approach is best suited to predicting real 
exchange rates, as the concept of the equilibrium exchange rate is defined in real 
terms. But empirical evidence shows that real exchange rate adjustments occur 
primarily through currency movements rather than via relative price changes. Thus, 
at least for countries with moderate inflation rates, measures of real equilibrium 
exchange rates can also be employed to forecast nominal exchange rates.10  From a 

6    For a review of the role of the exchange rate in the case of the ECB, see the Work Stream on 

Globalisation, “The implications of globalisation for the ECB monetary policy strategy”, Occasional 
Paper Series, No 263, ECB, September 2021. 

7    The concept and measurement of the equilibrium exchange rate is particularly relevant also for central 
banks adopting managed exchange rate regimes or for countries joining the exchange rate mechanism 
and later fixing their parity irrevocably to the euro. 

8    Bussière, M., Ca’ Zorzi, M., Chudik, A. and Dieppe, A., “Methodological advances in the assessment of 

equilibrium exchange rates”. Working Paper Series, No 1151, European Central Bank, 2010; Phillips, 
S., Catao, L., Ricci, L.A., Bems, R., Das, M., di Giovanni, J., Unsal, D.F., Castillo, M., Lee, J., 
Rodriguez, J. and Vargas, M., “The External Balance Assessment (EBA) Methodology”, IMF Working 
Papers, No 13/272, International Monetary Fund, 2013; Couharde, C., Delatte, A.L., Grekou, C., 
Mignon, V. and Morvillier, F., “EQCHANGE: A world database on actual and equilibrium effective 
exchange rates”, International Economics, Vol. 156, 2018, pp. 206–230; Cubeddu, L.M., Krogstrup, S., 
Adler, G., Rabanal, P., Dao, M.C., Hannan, S.A., Juvenal, L., Buitron, C.O., Rebillard, C., Garcia-Macia, 
D. and Jones, C., “The External Balance Assessment Methodology: 2018 Update”, IMF Working 
Papers, No 19/65, International Monetary Fund, 2019. 

9    Ca’ Zorzi, M., Muck, J. and Rubaszek, M., “Real exchange rate forecasting and PPP: This time the 
random walk loses”, Open Economies Review, Vol. 27, 2016, pp. 585–609; Ca’ Zorzi, M. and 
Rubaszek, M., “Exchange rate forecasting on a napkin”, Journal of International Money and Finance, 
Vol. 104, 2020. 

10   This property is also partly embedded in theoretical sticky price models. However, in these models the 

obtained co-movement between real and nominal exchange rates tends to be smaller than in the actual 
data. 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The predictive power of equilibrium exchange rate models 

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different starting point, Ca’ Zorzi et al. (2017)11  and Eichenbaum et al. (2020)12 
suggest that more advanced macroeconomic models, known as dynamic stochastic 
general equilibrium (DSGE) models – which assume that real and nominal exchange 
rate fluctuations are driven by differences in the monetary policy stance adjusted for 
risk premia – also offer a fairly good description of exchange rate dynamics and 
perform well overall in forecasting real and, to a lesser extent, nominal exchange 
rates. The explanation for this is that, like simpler approaches, such models imply a 
gradual return of the real exchange rate toward its equilibrium PPP value, but they 
tend to underestimate the empirical regularity of a strong co-movement between real 
and nominal exchange rates. 

The key question addressed in this article is whether concepts of equilibrium 
exchange rates other than PPP might strengthen the predictability of exchange 
rates. To the extent that long-term drivers of exchange rates can be understood, and 
hence the equilibrium exchange rate can be estimated more precisely, it should in 
theory be possible to better forecast the future trajectory of the real and nominal 
exchange rate. For that purpose, this article evaluates the predictive power of three 
popular equilibrium exchange rate models. Besides the PPP model, we also 
investigate simplified versions of the behavioural equilibrium exchange rate (BEER) 
and the macroeconomic balance (MB) approaches along the lines of Ca’ Zorzi et al. 
(2022). 13 

2 

Three methods for assessing equilibrium exchange rates 

Equilibrium exchange rate models are employed to decompose the real 
exchange rate (
) 
components: 

) into its equilibrium (

) and misalignment (

𝑚𝑚𝑚𝑚𝑚𝑚

𝑒𝑒𝑒𝑒

𝑟𝑟𝑟𝑟𝑟𝑟

𝑟𝑟𝑟𝑟𝑟𝑟

𝑟𝑟𝑟𝑟𝑟𝑟

𝑒𝑒𝑒𝑒

𝑚𝑚𝑚𝑚𝑚𝑚

𝑟𝑟𝑟𝑟𝑟𝑟  =  𝑟𝑟𝑟𝑟𝑟𝑟

  +  𝑟𝑟𝑟𝑟𝑟𝑟

The split between the two components depends in part on the time horizon 
that is used. As discussed by Driver and Westaway (2005), exchange rate 
movements are driven by long, medium and short-term economic fundamentals and 
by an unexplained component.14  The approach taken in this article is to distinguish 
between movements of the equilibrium exchange rates, and movements of the 
exchange rate around the equilibrium. The former are driven by long and medium-
term economic fundamentals, while the latter are driven by short-term fundamentals 
and an unexplained component. This is consistent with theoretical general 

11   Ca’ Zorzi, M., Kolasa, M. and Rubaszek, M., “Exchange rate forecasting with DSGE models”, Journal of 

International Economics, Vol. 107, 2017, pp. 127–146. 

12   Eichenbaum, M., Johannsen, B.K. and Rebelo, S., “Monetary policy and the predictability of nominal 

exchange rates”, The Review of Economic Studies, Vol. 88, 2020, pp. 192–228. 

13  

In particular, this Economic Bulletin article updates the results that are described in detail in the study 
by Ca’ Zorzi, M., Cap, A., Mijakovic, A. and Rubaszek, M., “The reliability of equilibrium exchange rate 
models: A forecasting perspective”, International Journal of Central Banking, forthcoming. A previous 
version of this paper is available as ECB Working Paper No 2358. 

14   Driver, R. and Westaway, P., “Concepts of equilibrium exchange rates”, Bank of England Working 

Paper, No 248, Bank of England, 2005. 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The predictive power of equilibrium exchange rate models 

76 

 
 
 
equilibrium models,15  in which over the business cycle fluctuations in exchange 
rates around their equilibria are partly driven by central banks’ relative monetary 
policy stance, adjusted for risk premia.16  The definition of equilibrium exchange rates 
is hence important from a monetary policy perspective. 

The first equilibrium exchange rate model considered here is the PPP model, 
i.e. the oldest theory of real exchange rate determination, which was restored 
to prominence in modern times by Gustav Cassel and is still today a key 
benchmark for determining exchange rate parities in fixed exchange rate 
regimes. 17  In a nutshell, the PPP model starts from the law of one price, which 
states that international arbitrage helps to equalise the price of any tradable product 
denominated in a common currency. The concept of strong PPP emerges from 
applying this law to consumption baskets, i.e. the same basket of goods should cost 
the same across countries when denominated in a common currency. In contrast, 
the weak version of PPP theory states that, in equilibrium, the relative cost of the 
same basket of goods across countries is constant over time but might deviate from 
unity owing to factors such as taxes and/or transportation costs. The weak version of 
PPP is empirically more relevant and appealing from a practical perspective, as it 
implies that a long-run sample mean of the real exchange rate is a good proxy for 
the PPP-based equilibrium real exchange rate. 

The BEER model generalises PPP theory by assuming that persistent PPP 
deviations should not be treated as “disequilibria” if they are driven by 
economic fundamentals. In other words, while the PPP model suggests that the 
long-term equilibrium exchange rate is a constant, in the BEER model the medium-
term equilibrium fluctuates over time in line with changes in a set of economic 
fundamentals. In practice, the level of the BEER is estimated from a regression 
linking the real exchange rate to a set of economic variables indicated by economic 
theory considerations (Fell, 1996; MacDonald, 1998; Maeso-Fernandez et al., 2001; 
and Lee et al., 2008). 18  The literature has discussed at length the best choice of 
fundamentals for the BEER and the expected sign and magnitude of the parameters 
(for a comprehensive literature review, see Fidora et al., 2017). 19  In this analysis the 
choice of economic fundamentals is limited to three key ones: relative per capita 
GDP, net foreign assets and the terms of trade. All three variables aim to explain 
medium and long-term exchange rate movements while, as discussed above, the 
analysis does not include real interest rate differentials, fiscal variables or other 

15   Examples of these models are found in Ca’ Zorzi, M., Kolasa, M. and Rubaszek, M., 2017, op. cit.; 
Eichenbaum, M., Johannsen, B.K. and Rebelo, S., 2020, op. cit.; and Itskhoki, O. and Mukhin, D., 
“Exchange rate disconnect in general equilibrium”, Journal of Political Economy, Vol. 129(8), 2021. 
16   This notion is implicit in the uncovered interest rate parity as discussed in Engel, C., “Exchange rates, 
interest rates, and the risk premium”, American Economic Review, Vol. 106, 2016, pp. 436-474. 
17   Cassel, G., “Abnormal deviations in international exchanges” Economic Journal, December 1918, pp. 

413–415. 

18   Fell, J., “Balance of payments equilibrium and long-run real exchange rate behaviour”, mimeo, 

European Monetary Institute, 1996; MacDonald, R., “What determines real exchange rates?: The long 
and the short of it”, Journal of International Financial Markets, Institutions and Money, Vol. 8, 1998, pp. 
117–153; Maeso-Fernandez, F., Osbat, C. and Schnatz, B., “Determinants of the Euro Real Effective 
Exchange Rate: A BEER/PEER Approach”, Australian Economic Papers, Vol. 41(4), 2002, pp. 437–
461; Lee, J., Ostry, J.D., Prati, A., Ricci, L.A. and Milesi-Ferretti, G.M., “Exchange Rate Assessments: 
CGER Methodologies”, IMF Occasional Papers, No 261, International Monetary Fund, 2008. 
19   Fidora, M., Giordano, C. and Schmitz, M., “Real exchange rate misalignments in the euro area”, 

Working Paper Series, No 2108, European Central Bank, 2017. 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The predictive power of equilibrium exchange rate models 

77 

 
 
𝑔𝑔𝑔𝑔𝑔𝑔

) leads to an appreciation of the real exchange rate. The 

short-term factors. In most of the literature it is taken for granted that a rise in relative 
per capita GDP (
explanation is twofold. From a demand perspective, an increase in relative income 
should lead to stronger demand for domestic non-traded goods and hence to an 
increase in their price relative to traded goods, i.e. a real exchange rate appreciation. 
The supply perspective is based on the Balassa-Samuelson effect, which suggests 
that a relative increase in productivity in the production of tradable goods leads to an 
increase in 
  and a real exchange rate appreciation (Lee et al., 2013; and Zhang, 
2017). 20  Another frequently cited explanation for long-run trends in equilibrium real 
exchange rates emphasises the role of net foreign assets (
a rise in 
counterbalanced by a trade balance deterioration, requiring in turn a real exchange 
rate appreciation (Lane and Milesi-Ferretti, 2002). 21  The terms of trade (
) is the 
third most used explanatory variable in BEER regressions. A rise in 
to an improved trade balance, and therefore a real exchange rate appreciation is 
needed to restore the trade balance to its initial level. For the above reasons we 
estimate the level of the BEER with the specification used by Faruqee (1995) and 
Lane and Milesi-Ferretti (2004), 22  so that the value of the BEER for country i in 
period t is given by: 

  increases the interest income on the current account that must be 

). The rationale is that 

  should lead 

𝑔𝑔𝑔𝑔𝑔𝑔

𝑛𝑛𝑛𝑛𝑛𝑛

𝑛𝑛𝑛𝑛𝑛𝑛

𝑡𝑡𝑡𝑡𝑡𝑡

𝑡𝑡𝑡𝑡𝑡𝑡

𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵

𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚𝑖𝑖

= µ𝑚𝑚 + α1𝑔𝑔𝑔𝑔𝑔𝑔𝑚𝑚𝑖𝑖 + α2𝑛𝑛𝑛𝑛𝑛𝑛𝑚𝑚𝑖𝑖 + α3𝑡𝑡𝑡𝑡𝑡𝑡𝑚𝑚𝑖𝑖

where all explanatory variables are expressed relative to a trade-weighted average 
of foreign values and the estimated signs of the regression match the theoretical 
predictions. 

(1) 

The MB approach defines equilibrium exchange rates from a current account 
sustainability perspective. The methodology of the MB approach differs 
substantially from that of the PPP and BEER models. Instead of estimating an 
equilibrium exchange rate, the MB approach estimates or calibrates/postulates an 
equilibrium current account (or norm) and the current account gap, i.e. the distance 
of the current account from this norm.23  It then seeks to estimate or calibrate a 
relationship between the current account balance and the real exchange rate in 
order to establish to what degree the real exchange rate needs to adjust so as to 
close the current account gap. For that reason, this definition is closely connected 
with the debate on global (current account) imbalances and the role played by the 
exchange rate in unwinding them. Technically, to calculate the equilibrium exchange 

20   Lee, J., Milesi-Ferretti, G.M. and Ricci, L.A., “Real exchange rates and fundamentals: A cross-country 
perspective”, Journal of Money, Credit and Banking, Vol. 45, 2013, pp. 845–865; and Zhang, Q., “The 
Balassa-Samuelson relationship: Services, manufacturing and product quality”, Journal of International 
Economics, Vol. 106, 2017, pp. 55–82. 

21   Lane, P.R. and Milesi-Ferretti, G.M., “External wealth, the trade balance, and the real exchange rate”, 

European Economic Review, Vol. 46, 2002, pp. 1049–1071. 

22   Faruqee, H., “Long-run determinants of the real exchange rate: a stock-flow perspective”, IMF Staff 

Papers, Vol. 42, 1995, pp. 80–107; Lane, P.R. and Milesi-Ferretti, G.M., “The transfer problem revisited: 
Net foreign assets and real exchange rates”, The Review of Economics and Statistics, Vol. 86, 2004, 
pp. 841–857. As explained in Lane and Milesi-Ferretti, this specification represents a long-run 
cointegration relationship, necessitating panel cointegration estimators. 

23   See Williamson, J., (ed.) Estimating Equilibrium Exchange Rates, Institute for International Economics, 

1994; and Lee et al. (2008), op. cit. 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
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rate with the MB framework, three issues must be addressed. The first consists of 
projecting the level at which the current account balance would stabilise if exchange 
rates remained unchanged and output gaps were closed (
). The second relates to 
setting the current account norm (
), which is typically estimated using a panel 
data regression with similar fundamentals to those chosen for the above-mentioned 
BEER regressions. The third is estimating how changes in the real exchange rate 
affect the current account (elasticity 
(2022), 24  the equilibrium exchange rate (

). Addressing these issues as in Ca’ Zorzi et al. 

) can then be simply computed as: 

𝑛𝑛𝑛𝑛𝑛𝑛𝑚𝑚

𝑐𝑐𝑛𝑛

𝑐𝑐𝑛𝑛�

η

𝑀𝑀𝐵𝐵

𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚𝑖𝑖

(2) 

𝑀𝑀𝐵𝐵

𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚𝑖𝑖

= 𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚𝑖𝑖 −

𝑛𝑛𝑛𝑛𝑛𝑛𝑚𝑚

𝑐𝑐𝑛𝑛�𝑚𝑚𝑖𝑖 − 𝑐𝑐𝑛𝑛𝑚𝑚𝑖𝑖

η𝑚𝑚𝑖𝑖

Equation (2) reveals the critical role played by the current account elasticity 
measures the current account adjustment in response to a 1% exchange rate 
𝜂𝜂
appreciation. For example, doubling the value of this elasticity would halve the 
exchange rate adjustment required to bring back the long-run value of the current 
account (
of the current account elasticity is uncertain and dependent on some critical 
assumptions about the extent to which exchange rate changes pass through to 
export and import prices.25 

). The academic literature has shown that the value 

) to its norm (

𝑛𝑛𝑛𝑛𝑛𝑛𝑚𝑚

𝑐𝑐𝑛𝑛

𝑐𝑐𝑛𝑛�

, which 

In terms of country coverage, the analysis in this article includes the group of 
advanced economies that issue so-called G10 currencies, namely Australia 
(AU), Canada (CA), Switzerland (CH), the euro area (EA),the United Kingdom (UK), 
Japan (JP), Norway (NO), New Zealand (NZ), Sweden (SE) and the United States 
(US) and our quarterly dataset spans the period between the first quarter of 1975 
and the fourth quarter of 2020. Such a sample is comparable to those used in 
studies evaluating exchange rate trading strategies for the ten globally most traded 
currencies (e.g. Opie and Riddiough, 2020), as well as recent studies on exchange 
rate forecasting (e.g. Engel and Wu, 2021). 26  There are two additional criteria for the 
country selection, namely the presence of flexible exchange rate regimes for most of 
the sample and the availability of sufficient macroeconomic data for a meaningful 
forecast evaluation exercise.27  A full description of the data sources, estimated 

24   This consists in forecasting the underlying current account with an RW model, defining the current 
account norm based on a panel regression and choosing appropriate current account elasticities. 
25   For evidence of the large amount of uncertainty surrounding the estimation of trade elasticities, see 
Bussière at al. (2010), op. cit. For the baseline we have assumed producer currency pricing. This 
means that export prices do not react to exchange rate changes, whereas import prices are affected 
one-to-one, implying perfect pass-through. Given the large estimation uncertainty regarding the 
magnitude of foreign trade elasticities, we followed the literature and set the long-run price elasticities 
equal to minus one. These assumptions translate into a set of elasticities that are both country- and 
time-dependent as they are a function of import and export shares. For evidence of the impact on the 
forecasting performance of the three models using different currency pricing assumptions, see Ca’ 
Zorzi et al. (2022), op. cit. 

26   Opie, W. and Riddiough, S.J., “Global currency hedging with common risk factors”, Journal of Financial 

Economics, Vol. 136, 2020, pp. 780–805; and Engel, C. and Wu, S.P.Y. (2021), op. cit. 

27   For each of the ten analysed economies, the foreign sector is represented by the other nine countries 
plus Denmark, which is excluded from the analysis owing to its fixed exchange rate regime. The 
weights are computed based on the narrow effective exchange rate index published by the Bank for 
International Settlements (BIS). We take BIS weights for the year 1995 and adjust them so that they 
sum to unity. The exact values are presented in Table 1 in Ca’ Zorzi et al. (2022), op. cit., and cover 
75% to 96% of the BIS index. 

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The predictive power of equilibrium exchange rate models 

79 

 
 
 
regressions and equilibrium exchange rate estimates is available in Ca’ Zorzi et al. 
(2022), which for the purpose of this article was updated to the fourth quarter of 
2020. 

3 

In-sample adjustment 

A key question considered in our analysis is to what extent the euro, the US 
dollar and other major currencies tend to converge to their long-run exchange 
rate equilibria. For each equilibrium exchange rate model, currency and horizon 
we estimate the parameters of the local projection regression: 

ℎ

𝑒𝑒𝑒𝑒

(3) 

𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖+ℎ − 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 = 𝛼𝛼ℎ − 𝛽𝛽ℎ(𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 − 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖
in which the parameter 
eliminated at horizon 
the value of 
local projection: 

ℎ

𝛽𝛽ℎ

) + 𝜖𝜖𝑖𝑖+ℎ

ℎ

  measures the average fraction of misalignment 

. If the real exchange rate reverts to its equilibrium over time, 
𝛽𝛽ℎ

  should converge to 1 as 

  increases. In parallel, we estimate the 

) + 𝜂𝜂𝑖𝑖+ℎ
𝑛𝑛𝑟𝑟𝑟𝑟𝑖𝑖+ℎ − 𝑛𝑛𝑟𝑟𝑟𝑟𝑖𝑖 = 𝛾𝛾ℎ − 𝛿𝛿ℎ(𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 − 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖
which enables the extent to which the nominal exchange rate accomplishes the 
required adjustment to be evaluated. 

𝑒𝑒𝑒𝑒

(4) 

The evidence points to a clear tendency for exchange rates to converge to 
their equilibrium and for the pace of the convergence to be model-dependent. 
In Table 1, for each country the first line reports the fraction of the real exchange rate 
misalignment absorbed by the real exchange rate change (
reports the fraction absorbed by the nominal exchange rate change (
horizon 

  and the second line 

at each 

𝛽𝛽ℎ)

. 

𝛿𝛿ℎ) 

ℎ

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Table 1 
Fraction of the adjustment achieved by the real and nominal exchange rates relative 
to the initial misalignment across models 

PPP 

BEER 

MB 

PPP 

BEER 

MB 

1-quarter horizon 

4-quarter horizon 

Euro   

real exchange rate 

0.07** 

0.07** 

nominal exchange rate 

0.05* 

0.05* 

0.00 

0.01 

0.28*** 

0.31*** 

0.01 

0.23** 

0.24** 

0.03 

US dollar 

real exchange rate 

0.05* 

0.06** 

0.05*** 

0.20** 

0.26** 

0.18*** 

nominal exchange rate 

0.04 

0.05 

0.05*** 

0.16* 

0.22** 

0.20*** 

Panel 

real exchange rate 

0.05*** 

0.06*** 

0.04*** 

0.20*** 

0.25*** 

0.12*** 

nominal exchange rate 

0.04*** 

0.06*** 

0.04*** 

0.18*** 

0.24*** 

0.15*** 

12-quarter horizon 

20-quarter horizon 

Euro   

real exchange rate 

0.72*** 

0.79*** 

0.00 

1.10*** 

1.23*** 

0.10** 

nominal exchange rate 

0.63*** 

0.68*** 

0.05 

1.03*** 

1.14*** 

0.06 

US dollar 

real exchange rate 

0.74*** 

0.90*** 

0.48*** 

1.25*** 

1.49*** 

0.68*** 

nominal exchange rate 

0.69*** 

0.84*** 

0.52*** 

1.24*** 

1.48*** 

0.72*** 

Panel 

real exchange rate 

0.56*** 

0.69*** 

0.24*** 

0.72*** 

0.86*** 

0.26*** 

nominal exchange rate 

0.54*** 

0.68*** 

0.33*** 

0.73*** 

0.90*** 

0.38*** 

Source: Authors’ calculations. 
Notes: The table presents the estimates of adjustment parameters 
denote respectively the 1%, 5% and 10% significance levels. The panel comprises all of the G10 currencies. 

  from regressions (3) and (4). Asterisks 

  and 

𝛽𝛽ℎ

𝛿𝛿ℎ

 , 

 and 

∗∗∗

∗∗

∗

Four interesting findings are evident from Table 1: 

First, the adjustment of the real exchange rate starts almost immediately. In the 
case of the PPP model, for example, in one quarter the real exchange rate moves to 
absorb 7% of the real exchange rate misalignment for the euro and 5% for both the 
US dollar and the full panel of G10 currencies.28  The adjustment is similar and 
marginally stronger in the case of the BEER model. It is remarkable that within the 
short horizon of one quarter a small fraction of the required exchange rate 
adjustment is already accomplished. It is hence not the case – despite being often 
stated – that short-run exchange rate movements are entirely unpredictable. The 
adjustment toward equilibrium is very slow but starts well before the one-year 
horizon. 

Second, nominal exchange rate changes are at least in part predictable. The 
reason for this is that the required real exchange rate adjustment takes place 
via currency movements and not via an adjustment in relative prices across 
countries. This is apparent from the observation that the fraction of adjustment 
completed by the nominal exchange rate 
achieved by 
than for the real exchange rate: the nominal exchange rate absorbs 5% of the real 
exchange rate misalignment in the case of the euro and 4% for both the US dollar 
and on average for the panel. This is true across different models and horizons 

. 29  In the case of the PPP model, the numbers are only slightly lower 

  is almost as sizeable as the fraction 

𝛽𝛽ℎ

𝛿𝛿ℎ

28   The panel results are estimated using fixed effects for the ten countries in the sample and correcting 

errors for autocorrelation and heteroskedasticity. 

29   For the PPP model the ratio of nominal to real adjustment, 

  tends to be below unity, which means 

that a small fraction of the real exchange rate adjustment is completed by relative inflation. 

𝛿𝛿ℎ/𝛽𝛽ℎ

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irrespective of the degree of exchange rate predictability, which differs across 
models. 

Third, the pace of the adjustment toward the equilibrium varies considerably 
across models. With the PPP model, within three years the real exchange rate 
absorbs 72% of the required adjustment for the euro, 74% for the US dollar and 56% 
for the full panel of G10 currencies. However, the pace of adjustment is not the same 
for different equilibrium exchange rate concepts. It is somewhat stronger for the 
BEER model: within three years 79% of the adjustment is completed for the euro, 
90% for the US dollar and 69% for the full panel.30 

Fourth, the pace of the real exchange rate adjustment is much weaker with the 
MB model. After three years it is basically nil for the euro, 48% for the US dollar and 
only 24% for the full panel. This suggests that the in-sample explanatory power of 
equilibrium exchange rates estimated with the current account-based model is 
overall very poor. A similar pattern emerges if we focus on the adjustment of the 
nominal exchange rate (see Table 1). 

Finally, there is an elegant graphical equivalent way to show that nominal 
exchange rates over time absorb past exchange rate misalignments. Below we 
produce a set of scatter plots showing nominal exchange rate changes at different 
horizons against the initial real exchange rate misalignment, i.e. the deviation of the 
real exchange rate from its model-consistent equilibrium value. To the extent that 
there is an adjustment, this relationship should be negative, and, in the case of full 
adjustment accomplished by the nominal exchange rate and no movements in the 
equilibrium exchange rate, the slope of the regression should be equal to minus one. 
Chart 1 shows the results for the three models at different time horizons, indicating 
with yellow circles the observations for the US dollar, with blue circles those for the 
euro and with red circles those for all other G10 currencies. 

30   At the four-year horizon there is evidence of overshooting in the case of the euro and the US dollar, 

suggesting that the estimated misalignment flips in the opposite direction. 

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82 

 
 
Chart 1 
Scatter plot of nominal exchange rate changes vs. misalignments for the euro, the 
US dollar and all other G10 currencies 

(percentage changes) 

n
o
z
i
r
o
h
r
e

t
r
a
u
q
-
1

n
o
z
i
r
o
h
r
e

t
r
a
u
q
-
4

n
o
z
i
r
o
h
r
e
t
r
a
u
q
-
2
1

n
o
z
i
r
o
h
r
e
t
r
a
u
q
-
0
2

0.5

0

-0.5

-0.5

0.5

0

-0.5

-0.5

0.5

0

-0.5

-0.5

0.5

0

-0.5

-0.5

PPP

BEER

MB

0.5

0

-0.5

1

0

-1

0

0.5

-0.5

0

0.5

-1

0

1

0.5

0

-0.5

1

0

-1

0

0.5

-0.5

0

0.5

-1

0

1

0.5

0

-0.5

1

0

-1

0

0.5

-0.5

0

0.5

-1

0

1

0.5

0

-0.5

0

0.5

-0.5

0

0.5

1

0

-1

-1

0

1

Source: Authors’ calculations. 
Notes: The x-axis of the chart represents deviations of real effective exchange rates from their full-sample equilibrium, whereas the y-
axis represents subsequent real exchange rate adjustments. The US dollar and the euro are marked with yellow and blue circles 
respectively. All other G10 currencies are marked with red circles. The diagonal line represents perfect adjustment to the equilibrium. 

The chart confirms visually that for both the PPP and BEER models there is an 
adjustment mechanism at play, which ensures that the initial real exchange 
rate misalignment is absorbed through a nominal exchange rate movement. 
This is already visible at the one-year horizon and becomes particularly evident at 
longer horizons. For the MB model the adjustment is again much weaker and visible 
only for the US dollar at the five-year horizon. 

4 

Out-of-sample evidence 

The evidence that exchange rates converge to their long-term equilibria begs 
the key question: can this information be exploited to forecast real and 
nominal exchange rates? To find the answer, in the next step we evaluate the out-
of-sample forecast accuracy for horizons ranging from one to 20 quarters ahead in 

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The predictive power of equilibrium exchange rate models 

83 

 
 
 
 
 
 
the period from the first quarter of 1995 to the fourth quarter of 2020 on the basis of 
our full panel of data. In particular, for each vintage period s, the local projection 
regressions (5) and (6) are employed to calculate forecasts for real and nominal 
exchange rates: 

𝑓𝑓
𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚+ℎ

𝑒𝑒𝑒𝑒
= 𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚 + 𝛼𝛼ℎ|𝑚𝑚 − 𝛽𝛽ℎ|𝑚𝑚�𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚 − 𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚|𝑚𝑚

�

(5) 

(6) 

𝑓𝑓
𝑛𝑛𝑟𝑟𝑟𝑟𝑚𝑚+ℎ
where 

𝑒𝑒𝑒𝑒
= 𝑛𝑛𝑟𝑟𝑟𝑟𝑚𝑚 + 𝛾𝛾ℎ|𝑚𝑚 − 𝛿𝛿ℎ|𝑚𝑚�𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚 − 𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚|𝑚𝑚
, 

, and 

, 

, 

�
  are the parameters and equilibrium exchange 

rate estimates based on a sample ending in period 
𝛿𝛿ℎ|𝑚𝑚

𝛼𝛼ℎ|𝑚𝑚

𝛽𝛽ℎ|𝑚𝑚

𝛾𝛾ℎ|𝑚𝑚

𝑒𝑒𝑒𝑒
𝑟𝑟𝑟𝑟𝑟𝑟𝑚𝑚|𝑚𝑚

. 

𝑠𝑠

The chart showing sequential forecasts illustrates the predictive content of 
three equilibrium exchange rate models and immediately indicates that the 
PPP and BEER models perform relatively well. Chart 2 presents sequential 
forecasts for the US dollar and the euro. It shows that by predicting a gradual 
reversion of the real exchange rate to the corresponding equilibrium, the PPP and 
BEER models both usually deliver relatively accurate forecasts. By contrast, the MB 
model performs poorly31  as forecasts obtained from this model are only weakly 
correlated with subsequent outcomes.32 

31    A similar result is found by Yesin (2016). See Yesin, P., “Exchange rate predictability and state-of-the art 

models”, SNB Working Paper Series, No 2/2016, Swiss National Bank, 2016. 

32   This can be explained by two factors: high volatility of the MB-implied equilibria and low estimates of 

, which suggests that there is limited convergence to equilibrium for 

the adjustment parameter 
current account-based equilibrium exchange rate models. If 
equation (4) resemble those that would be obtained using an RW model with a drift, which is the case 
for the MB model. This confirms our in-sample insight of a very limited adjustment of exchange rates 
toward equilibrium exchange rate estimates when the latter are derived with the MB model. 

  is close to nil, forecasts calculated with 

𝛽𝛽ℎ

𝛽𝛽ℎ

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The predictive power of equilibrium exchange rate models 

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Chart 2 
Sequential forecasts 

(log of the real effective exchange rate) 

PPP

BEER

MB

1.3

1.2

1.1

1

0.9

1.3

1.2

1.1

1

0.9

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1.7

1.6

1.5

1.4

1.3

1.2

1.7

1.6

1.5

1.4

1.3

1.2

R
U
E

D
S
U

1.3

1.2

1.1

1

0.9

1.7

1.6

1.5

1.4

1.3

1.2

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

Source: Authors’ calculations. 
Note: The black line represents the actual outcome while the grey lines represent the 20-quarters ahead forecast calculated at 
different points in time.   

The above anecdotal evidence on the relative performance of the three models 
can also be shown more formally vis-à-vis the RW benchmark. To this end, in 
Chart 3 we plot an indicator of the performance of each model (i.e. the root mean 
squared forecast error – RMSFE) measured relative to the performance of the RW 
model. If this indicator is below unity, the model concerned delivers more accurate 
forecasts than those obtained using the simple assumption of an unchanged 
exchange rate. 

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Chart 3 
RMSFE of the euro and the US dollar at different horizons across different models 
relative to the RW benchmark 

a) RMSFE for real exchange rates 

(index) 

RW
PPP
BEER
MB

EUR

1.2

1.1

1.0

0.9

0.8

0.7

USD

1.2

1.1

1.0

0.9

0.8

0.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

b) RMSFE for nominal exchange rates 

(index) 

RW
PPP
BEER
MB

EUR

1.3

1.2

1.1

1.0

0.9

0.8

0.7

USD

1.3

1.2

1.1

1.0

0.9

0.8

0.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Source: Authors’ calculations. 
Notes: The chart shows on the y-axis the ratios of the root mean squared forecast error from the models in comparison with the RW 
benchmark. The x-axis is ordered in terms of quarterly horizons. 

The findings confirm that the PPP and BEER models perform well in relative 
terms while the MB model performs poorly for the euro area. In the cases of the 
euro and the US dollar, the PPP model (yellow line) and the BEER model (red line) 
clearly outperform the RW model (blue line) in terms of forecasting both real (Chart 
3, panel a) and nominal (Chart 3, panel b) exchange rates, as this indicator of 
forecasting performance is always below the threshold of 1. Interestingly, for the PPP 
model the result is already statistically significant at the one-year horizon. The MB 
model (green line) conversely fails to outperform the RW model in the case of the 

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The predictive power of equilibrium exchange rate models 

86 

 
 
 
euro, while it outperforms the RW model for the US dollar at horizons longer than 
one year. 

More mixed results are obtained for other G10 currencies. Table 2 presents the 
results for all countries that issue G10 currencies, with nominal and real exchange 
rates at the four-quarter and 20-quarter horizons. The results at short horizons show 
that equilibrium-based model forecasts beat the RW model forecasts approximately 
half of the time, albeit in only in a few instances in a significant way. However, at the 
longer, 20-quarter horizon, the PPP and BEER models show a stronger forecast 
performance than the RW model, while the MB model is generally inaccurate.33  The 
PPP model is the best performing among the three models participating in the 
forecasting competition. 

33    A similar result is found in Yesin (2016), op. cit. The analysis shows that, despite the usefulness of the 
MB model in the context of the global imbalances debate, the BEER model has stronger forecasting 
properties. 

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Table 2 
RMSFE for real and nominal exchange rates across different models relative to the 
RW benchmark; estimated adjustment path 

PPP 

BEER 

MB 

PPP 

BEER 

MB 

(estimated) 

(estimated) 

(estimated) 

(estimated) 

(estimated) 

(estimated) 

Real exchange rate 

4-quarter horizon 

20-quarter horizon 

AU 

CA 

CH 

EA 

UK 

JP 

NO 

NZ 

SE 

US 

1.01* 

0.99* 

1.02 

0.96** 

0.91** 

1.00* 

1.11 

1.00 

0.95*** 

1.05* 

0.94** 

0.95** 

1.02 

1.14 

0.96** 

0.88*** 

1.30 

0.98* 

1.09 

0.99* 

1.05 

1.01 

1.06 

1.07 

1.00 

1.03 

1.06** 

0.55*** 

0.84*** 

0.73*** 

1.14 

1.26 

0.81*** 

0.80*** 

0.80*** 

1.14 

0.90*** 

0.66*** 

1.24 

1.11 

1.02 

1.12 

1.05 

1.10 

0.99* 

0.86*** 

2.59 

0.95** 

1.00 

1.01 

0.98** 

0.84*** 

0.97* 

1.61 

1.19 

0.87*** 

0.99* 

0.75*** 

0.77*** 

0.97 

Nominal exchange rate 

4-quarter horizon 

20-quarter horizon 

AU 

CA 

CH 

EA 

UK 

JP 

NO 

NZ 

SE 

US 

1.04 

0.99* 

0.97** 

0.97* 

0.99* 

1.00 

0.96** 

1.14 

1.03 

0.92** 

0.98* 

1.06 

0.99 

1.05 

0.95** 

1.21 

1.10 

1.07 

1.08 

1.01 

1.08 

1.12 

1.02 

1.05 

0.97** 

1.02 

1.01 

1.23** 

0.66*** 

0.81*** 

0.77*** 

1.43 

1.11 

0.83*** 

0.95* 

0.88*** 

0.83*** 

0.86*** 

0.87*** 

1.11* 

1.13** 

0.87** 

1.56 

1.08 

0.85*** 

1.95 

1.43 

1.19* 

1.14 

1.00 

1.20 

0.84** 

1.22 

1.16* 

0.97** 

0.98* 

0.99* 

0.77*** 

0.79*** 

0.94** 

Source: Authors’ calculations.   
Notes: The table shows the ratios of the root mean squared forecast error from the models in comparison with the RW benchmark, 
where values below unity indicate that forecasts from the respective model are more accurate than those from the benchmark. 
Asterisks 
 denote respectively the 1%, 5% and 10% significance levels of the Clark-West test, where the long-run 
variance is calculated with the Newey-West method. 

 and 

 , 

∗∗∗

∗∗

∗

The analysis highlights that the RW model still remains a strong contender, 
which is most likely due to the role of estimation error. Indeed, the effort made 
to estimate the pace of the adjustment to equilibrium with the local projection 
regressions (5) and (6) has not been very helpful in terms of forecasting. To show 
this, the previous exercise can be repeated with the only differences being that (i) 
this time the nominal exchange rate moves to ensure a movement of the real 
exchange rate toward its equilibrium value and that (ii) such adjustment is calibrated 
to be rather slow in line with the real exchange rate literature. Table 3 reports the 
results for a half-life adjustment of three years, but the results are valid for the wider 
range of between two and a half years and five years, while for half-lives longer than 
five years the forecasts are almost the same as those given by the RW model. 

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The predictive power of equilibrium exchange rate models 

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Table 3 
RMSFE for real and nominal exchange rates across different models relative to the 
RW benchmark; adjustment calibrated with a half-life of three years 

PPP 

BEER 

MB 

PPP 

BEER 

MB 

(calibrated)   

(calibrated) 

(calibrated) 

(calibrated) 

(calibrated) 

(calibrated) 

Real exchange rate 

4-quarter horizon 

20-quarter horizon 

AU 

CA 

CH 

EA 

UK 

JP 

NO 

NZ 

SE 

US 

0.98** 

0.91** 

1.05 

0.92*** 

0.63*** 

1.27 

0.99* 

1.04 

0.95** 

0.98* 

1.07 

0.96* 

0.95*** 

0.99** 

0.91** 

0.96** 

1.02 

1.19 

0.89*** 

1.11 

0.97* 

1.08 

0.96** 

0.96** 

0.98** 

0.80*** 

0.78*** 

0.85*** 

1.08 

1.13 

0.98 

1.03 

1.07 

1.12 

1.13 

0.77*** 

0.78*** 

1.17 

1.26 

0.78*** 

0.97** 

0.93** 

0.79*** 

0.67*** 

1.02* 

0.86*** 

1.81 

1.51** 

0.97** 

0.98** 

0.86*** 

0.99* 

1.19 

1.11 

1.74 

1.30 

1.80 

0.77*** 

0.79*** 

1.19* 

Nominal exchange rate 

4-quarter horizon 

20-quarter horizon 

AU 

CA 

CH 

EA 

UK 

JP 

NO 

NZ 

SE 

US 

0.99*** 

0.95** 

1.04 

0.95*** 

0.79*** 

1.28 

0.99*** 

1.11 

0.96** 

0.97*** 

0.98* 

1.13 

0.97* 

1.02 

0.92** 

0.93** 

0.98* 

1.01 

1.17 

1.14 

0.97* 

1.08 

0.95** 

0.96** 

0.98** 

0.79*** 

0.78*** 

0.85*** 

1.07 

1.13 

0.97* 

1.02 

1.06* 

1.19 

1.17 

0.77*** 

0.79*** 

1.09 

1.32 

0.85*** 

1.04 

0.87*** 

0.86*** 

0.85*** 

0.95*** 

0.94* 

1.68 

1.37* 

0.97** 

0.96*** 

0.84*** 

0.97** 

1.15 

1.05 

1.65 

1.24 

1.72 

0.77*** 

0.78*** 

0.99** 

Source: Authors’ calculations.   
Notes: The table shows the ratios of the root mean squared forecast error from the models in comparison with the RW benchmark, 
where values below unity indicate that forecasts from the respective model are more accurate than those from the benchmark. 
Asterisks 
 denote respectively the 1%, 5% and 10% significance levels of the Clark-West test, where the long-run 
variance is calculated with the Newey-West method. 

 and 

 , 

∗∗∗

∗∗

∗

The forecasting power of the PPP equilibrium exchange rate (i.e. the sample 
mean) becomes fully evident in this setting. The PPP model beats the RW 
benchmark seven times out of ten in real exchange rate forecasting and eight times 
out of ten in nominal exchange rate forecasting. The performance of the BEER 
model is almost as good as that of the PPP model, but not better as it is also affected 
by other sources of estimation error. The predictive power of the MB model is 
generally considerably worse than that of the other two models. 

An additional insight gained from this result is that most of the forecasting 
power that the models possess relative to the RW benchmark arises directly 
from the mean-reverting properties of the real exchange rate. 34  It is enough to 

34   A similar conclusion is reached by Engel and Wu (2021), op. cit., by showing that the mean-reverting 

property of the bilateral US dollar exchange rate can be exploited in nominal exchange rate forecasting, 
while additional explanatory variables, like the risk premium, do not improve the predictability of the 
nominal exchange rate out-of-sample. 

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The predictive power of equilibrium exchange rate models 

89 

 
 
set up a simple “rule of thumb”, whereby the nominal exchange rate gradually moves 
to restore the real exchange rate to its mean, in order to outperform the RW model 
and most other models. In summary, PPP is both a competitive model in terms of 
equilibrium exchange rate estimation and an accurate benchmark for real and 
nominal exchange rate forecasting. The BEER model has good in-sample properties 
and a similar out-of-sample performance. Conversely, the MB model is highly 
unreliable in predicting future exchange rate changes. 

5 

Concluding remarks 

Exchange rates are not a policy target for major central banks but are an 
important variable in their information set. They are important because 
anticipating their future movements could help to gauge the transmission mechanism 
of monetary policy. And they are important because sharp, large deviations from fair 
values could presage a large adjustment with potentially adverse repercussions for 
the outlook for inflation and for output. This article has presented an analysis 
bringing together the literature on equilibrium exchange rates and exchange rate 
forecasting. Several important, unexpected lessons can be drawn from an analysis 
of the predictive power of equilibrium exchange rate models. 

The first is that in-sample, on average, real exchange rates tend to slowly 
converge to their equilibria if the latter are defined by the PPP or BEER 
models. This is not the case for equilibrium exchange rate models based on the 
current account. The equilibrium exchange rate estimates based on such models 
tend to be unstable and unreliable in explaining real exchange rate movements. 

The second is that this convergence property can be exploited to forecast not 
only real exchange rates but also nominal exchange rates. The reason is 
intuitive as most of the adjustment toward equilibrium is achieved by a currency 
movement and not through relative price changes across countries. While the RW 
model remains, in some cases, a formidable competitor, the PPP and BEER models 
can help to forecast future exchange rate movements. The MB model estimates 
conversely are not indicative of future exchange rate movements. 

The third is that most of the forecasting power comes from the mean-reverting 
properties of real exchange rates rather than from exploiting the relationship 
between exchange rates and economic fundamentals. While the notion of PPP is 
much more compatible with economic theory and hence offers reassurance as to the 
usefulness of macroeconomic theory and the role of the exchange rate in the 
transmission of monetary policy, the original insight of Meese and Rogoff (1983) – 
that the future path of exchange rates cannot easily be better extrapolated from 
economic models than by using a “rule of thumb” – remains largely true. In this 
article we have qualified this result by suggesting that the most competitive “rule of 
thumb” is often one which foresees a gradual adjustment of the exchange rate to 
restore the real exchange rate to its mean value rather than one that assumes “no 
change”. The key fundamentals that need to be known for out-of-sample forecasting 
are the real and nominal exchange rates, while there is relatively little information to 

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The predictive power of equilibrium exchange rate models 

90 

 
be extracted from other economic fundamentals in normal times. Large current 
account surpluses or deficits may influence the path of adjustment of the exchange 
rate but, in line with theory, they have a limited influence on the long-term value of 
the exchange rate. 

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2 

Key factors behind productivity trends in euro area 
countries 

Prepared by Paloma Lopez-Garcia and Bela Szörfi 

1 

Introduction 

Productivity, defined broadly as efficiency in production, plays a key role in 
the economic resilience and social welfare of countries.1  Productivity growth 
influences the economy in important ways, affecting key variables such as output, 
employment and wages. Productivity is also relevant for monetary policy as it is a 
fundamental determinant of potential output growth and the natural rate of interest 
and, therefore, of the monetary policy space needed to deliver price stability over the 
medium term. As such, changes in productivity can influence the transmission 
mechanism of monetary policy and should be closely monitored. 

The primary responsibility for enhancing productivity growth lies with national 
policies. National fiscal and structural policies can strengthen productivity growth by 
fostering greater efficiency in product, labour and financial markets, thereby 
providing the means and incentives for productive firms to thrive. High-quality 
education and public administration and the rule of law are also important 
institutional prerequisites for a competitive business environment, which in turn 
facilitates technological progress and increases incentives to invent and innovate. 
National governments therefore have ample scope to set the right framework 
conditions and incentives for productive investment and innovation decisions that 
determine long-term productivity growth. Cyclical polices, including monetary policy, 
may also support productivity growth under certain circumstances by increasing 
demand and stimulating investment. 

Global trends, such as population ageing and the slowdown in the pace of 
globalisation, have a bearing on productivity developments. Globalisation can 
increase productivity growth through a variety of channels, namely: i) enhancing 
export opportunities and market competition;2  ii) promoting “learning by exporting”;3 

1    Productivity can be defined in several ways. From a single-factor perspective, labour productivity is 
defined as units of output (real GDP or value added) produced per unit of labour input, where labour 
input can be the number of employed persons or the total hours they work. However, the productivity of 
any single input of production, such as labour input, depends on the quantity of the other inputs. To 
capture the efficiency with which all inputs are used, economists use a broader concept of productivity, 
namely, total factor productivity (TFP), unobservable and computed as a residual. In a production 
function framework, labour productivity growth is determined by TFP growth and the growth of capital 
per labour input (capital deepening). 

2    See Bustos, P., “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of 
MERCOSUR on Argentinian Firms”, American Economic Review, Vol. 101, No 1, February 2011, pp. 
304-340. 

3    That is, a firm’s efficiency may benefit from knowledge gained through its presence in foreign markets. 
See De Loecker, J., “Detecting Learning by Exporting”, American Economic Journal: Microeconomics, 
Vol. 5, No 3, August 2013, pp. 1-21. 

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Key factors behind productivity trends in euro area countries 

92 

 
 
iii) increasing the variety and quality of production inputs;4  and iv) improving the 
allocation of resources across firms.5  Regarding euro area population ageing, 
empirical evidence shows that workers’ physical abilities and innovativeness decline 
with age, as does the speed with which they adapt to new technologies.6  As 
population ageing causes the share of older workers in the labour force to rise, the 
above might have negative effects on overall productivity. There are, however, some 
counterbalancing factors, including increased longevity, more healthy years of life 
and higher education levels among the older population. 

This article looks at the key factors behind productivity developments over 
recent decades in the euro area. The article summarises the new insights provided 
by a report drafted for the ECB’s strategy review that documents productivity trends 
and drivers over the past few decades.7  For a more complete picture, we refer the 
reader to the full report, which draws from the extensive literature on productivity 
drivers, existing work within the European System of Central Banks (ESCB), and 
new analysis. The article is complemented by Box 4 in this issue of the Economic 
Bulletin, which presents preliminary evidence on the impact of the (coronavirus) 
COVID-19 pandemic, and of policy responses to it, on productivity. 

The article has five sections. Section 2 introduces the most important productivity-
related stylised facts, using macroeconomic and sector-level data to set the context. 
Section 3 uses firm-level data to discuss the key drivers behind productivity growth, 
distinguishing between those affecting within-firm productivity growth and those 
affecting the allocation of production factors across firms. Section 4 focuses on the 
interplay between monetary policy and those drivers of productivity growth. Section 5 
offers a few concluding remarks. 

2 

Key productivity developments in the euro area 

This section presents three stylised facts related to productivity growth in the 
euro area. 

First, aggregate labour productivity growth has been trending downwards for 
decades, both in the euro area and in other major economies. Average annual 
growth in labour productivity – measured as real GDP per hour worked – in those 
euro area countries that have sufficiently long time series has continuously declined 
from about 7% in the 1960s to just 1% since the early 2000s (Chart 1). This decline 
results from a variety of interacting factors, including global, country-specific, sector-
specific, structural and temporary factors, as well as events with potential scarring 

4    See Halpern, L., Koren, M. and Szeidl, A., “Imported Inputs and Productivity”, American Economic 

Review, Vol. 105, No 12, December 2015, pp. 3660-3703. 

5    See Melitz, M.J., “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry 

Productivity”, Econometrica, Vol. 71, No 6, 2003, pp. 1695-1725. 

6    See for example Aiyar, S., Ebeke, C. and Shao, X., “The Impact of Workforce Aging on European 

Productivity”, IMF Working Papers, WP/16/238, International Monetary Fund, 2016. 

7    The report was drafted by experts from 15 national central banks and the ECB. See “Key factors 

behind productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021.” 

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Key factors behind productivity trends in euro area countries 

93 

 
 
effects on productivity and potential output growth, such as the global financial crisis 
(GFC). 

Chart 1 
Trends in labour productivity (GDP per hour worked) growth in selected euro area 
countries and the United States 

(smoothed annual percentage change) 

Germany
France
Spain
Italy
United States

8

7

6

5

4

3

2

1

0

-1

-2

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

2020

Sources: Own calculations based on Bergeaud, A., Cette, G. and Lecat, R., “Productivity Trends in Advanced Countries between 1890 
and 2012”, Review of Income and Wealth, Vol. 62, No 3, 2016, pp. 420-444. 
Note: The trend is calculated using a Hodrick-Prescott filter with a smoothing parameter lambda of 10. 

Second, on average across years and countries, total factor productivity (TFP) 
growth accounts for about 60% of labour productivity growth. By decomposing 
growth in GDP per hour worked in the 12 countries that adopted the euro in 1999 
(EA-12)8  into capital deepening, capital capacity utilisation and TFP growth, we find 
that, from 1995 to 2019, TFP growth accounted for about 60% of labour productivity 
growth on average (Chart 2 – left panel).9  The contribution of TFP growth, however, 
has declined over time (from 68% in the period 1995-2001 to 55% in 2014-19). 
Capital deepening, defined as the change in capital per hour worked, accounted on 
average for about 40% of labour productivity growth. That average masks a very 
high contribution during the GFC as a result of the large drop in total hours worked, 
and a very small negative contribution during the post-GFC period owing to weak 
investment and employment recovery. Although the average contribution of capacity 
utilisation over the whole period is rather small, it plays an important role during 
specific periods. The picture is similar for the United States (Chart 2 – right panel). 

8    This selection is data-driven as information on late euro adopters typically starts in 2000. “EA-12” refers 

to Belgium, Germany, Ireland, Greece, Spain, France, Italy, Luxembourg, Netherlands, Austria, 
Portugal and Finland. 

9    Note that in this standard accounting decomposition, the portion of output that cannot be explained by 
the amount of inputs used in the production process, also called the “Solow residual”, measures TFP 
growth accurately only if the production function is of the Cobb-Douglas-type, if there is perfect 
competition on product and factor markets, and if the underlying data adequately capture the required 
information on quantities and prices. Regarding the latter, quality improvements of capital and labour 
not adequately captured by the data will be captured by the TFP growth measure. 

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Key factors behind productivity trends in euro area countries 

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Chart 2 
Contributions to growth in GDP per hour worked in EA-12 and the United States, 
different periods 

(percentage points) 

GDP per hour worked
Capital per hour worked
Capacity utilisation
TFP

a) EA-12

b) United States

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

1995-2001

2002-2007

2008-2013

2014-2019

1995-2001

2002-2007

2008-2013

2014-2019

Sources: Own calculations based on data from AMECO, Eurostat and (for the United States) the Board of Governors of the Federal 
Reserve System. 
Note: “EA-12” refers to Belgium, Germany, Ireland, Greece, Spain, France, Italy, Luxembourg, Netherlands, Austria, Portugal and 
Finland. The periods analysed in this article, if possible given data availability, are the pre-euro years (1995-2001), the pre-GFC period 
(2002-2007), the GFC and sovereign debt crisis (2008-2013) and the post-GFC period (2014 onwards). 

Third, intra-sector dynamics, rather than resource reallocation across sectors, 
explain a large part of aggregate labour productivity growth. Over time the 
relative importance – be it in terms of employment or value added share – of the 
different sectors of activity changes as a result of structural change or sector-
asymmetric shocks. To the extent that productivity growth differs across sectors10, 
this change in their relative importance can drive aggregate productivity growth. To 
gauge the importance of cross-sector reallocation of resources for aggregate 
productivity growth, we use a shift-share analysis and decompose aggregate 
productivity developments in three parts: first, the “intra-industry effect”, which 
describes the part of productivity growth that results from sector productivity growth, 
assuming no change in sector weights; second, the structural “shift effect”, which 
describes the impact of changes in sector weights, measured by sectoral 
employment shares, on aggregate productivity growth, keeping the productivity of 
each sector constant; and third, the structural “interaction effect”, which captures the 
interrelation between sectoral productivity growth and changes in sectoral 
employment shares. The sum of the “shift effect” and the “interaction effect’ 
approximate the impact of structural change on productivity growth. We find that, on 
average over the period 1995-2017, the contribution of structural change to annual 

10    Regarding productivity levels, the utilities sector (electricity, gas and water supply) as well as financial 

and insurance activities display the highest labour productivity – €77/hour worked and €71/hour worked 
respectively on average across EU countries in 2017 (due to their high capital intensity). At the other 
end of the spectrum, construction and accommodation and food services show the lowest productivity 
level – €27/hour worked and €20/hour worked respectively. Regarding growth rates, cumulative 
productivity growth between 1997 and 2017 in the information and communication technologies (ICT) 
sector was about 85% compared to -16% in the accommodation and food service sector. 

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labour productivity growth was negative and very small (Chart 3 – left panel). 11  This 
implies that aggregate labour productivity growth has mainly been driven by intra-
sector dynamics. This is in line with results from the United States (Chart 3 – right 
panel) and in related literature.12  Nevertheless, the impact of sectoral reallocation on 
productivity might be substantial in certain periods in certain countries, as was the 
case in the 20th century when agriculture lost weight to manufacturing, or during the 
GFC given the disproportionate effect that the crisis had on some low-productivity 
sectors, such as construction.13 

Chart 3 
Shift-share analysis of labour productivity growth, euro area vs. the United States 

(percentage points) 

Intra-sector effect
Shift effect 
Interaction effect
Productivity growth

a) Euro area

b) United States

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

1996-2001 2002-2007 2008-2013 2014-2017 1996-2017

2002-2007

2008-2013

2014-2017

2002-2017

Sources: Own calculations based on Eurostat and EU-KLEMS, using NACE two-digits data. 
Notes: The euro area aggregate includes 14 countries owing to data availability (BE, DE, GR, ES, FR, IT, CY, LT, NL, AT, PT, SI, SK 
and FI). Agriculture, forestry and fishing (NACE A), mining and quarrying (NACE B), real estate (NACE L), public sector (NACE O-Q) 
and extraterritorial organisations and bodies (U) are not covered. 

11   The analysis relies on sector-level data at the two-digit industry of the NACE Rev. 2 sector 

classification. 

12  

In almost all studies covering developed countries the shift effect is much weaker than the intra-sector 
effect and its size decreases over time. In many cases the shift effect is negative or mixed (see, for 
example, European Economy, European Commission, 2003, and OECD Compendium of Productivity 
Indicators 2018, OECD Publishing, Paris, 2018. 

13   Studies covering developing countries find a larger contribution of inter-sector reallocation to 

productivity growth as a result of the loss of weight of agriculture in favour of manufacturing. However, 
even in this set of countries, inter-sector reallocation is less important than intra-sector dynamics. For 
example, a very recent report of the World Bank finds that inter-sector reallocation has historically 
accounted for two-fifths of overall productivity growth in emerging economies (see Dieppe, A. (ed.), 
Global Productivity: Trends, Drivers, and Policies, World Bank Group, Washington, 2020). Regarding 
the euro area, the impact of the change in the weight in employment in the agricultural sector on 
productivity over the period 1960-1979 is estimated to be an annual 0.4% in Germany and 0.5% in 
France on average. The slowdown in sectoral reallocation after the shift from agriculture was 
completed is also estimated to have contributed to the overall productivity slowdown of these countries 
since the 1980s. See Card, D. and Freeman, R., “What Have Two Decades of British Economic Reform 
Delivered in Terms of Productivity Growth?” International Productivity Monitor, Vol. 5, 2002, pp. 41–52. 

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Key factors behind productivity trends in euro area countries 

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3 

Microeconomic drivers of sector productivity growth 

The previous section showed that the dynamics of euro area aggregate 
productivity are driven by intra-sector developments. This section explores the 
microeconomic drivers of sector productivity growth. 

Sector productivity growth depends on within-firm productivity growth and on 
the evolution of the market share of each firm. In organising the analysis of 
productivity growth drivers, it is useful to think of sector productivity as the weighted 
average productivity of all firms in the sector. Sector productivity could therefore be 
written as the sum of two components: the unweighted average of firm productivity, 
that is, average productivity without taking into account the market share of each 
firm; and the allocation of economic weight – measured in terms of employment or 
value added share – across firms with differing productivity levels. In this framework, 
sector productivity will be higher if firms become more efficient – that is, their TFP 
increases – by investing in productivity-enhancing activities; this increases the 
unweighted average productivity of firms and is referred to as “within-firm productivity 
growth”. Note that if investment in knowledge is complemented by investment in 
fixed capital – construction of new innovation facilities, for example – the capital 
intensity of the firm would also increase. Sector productivity will also be higher if 
resources are reallocated to relatively more productive firms; this increases their 
market share and is referred to as “dynamic allocation efficiency”. Note that 
resources are reallocated across firms as a result of the contraction and expansion 
in the size of incumbent firms (between-firm resource reallocation), but also as a 
result of firm entry and exit (firm demography). 

The remainder of this section will discuss the euro area drivers and trends related to 
within-firm productivity growth, between-firm resource reallocation and firm 
demography. 

Within-firm productivity growth 

Within-firm productivity growth depends on the quality of production inputs 
and on investment in productivity-enhancing activities. Managerial ability, which 
has been found to be a critical factor behind cross-country and cross-firm 
productivity differences14, could be considered a production input, alongside labour 
and capital. Therefore, increasing managerial ability, investing in workers’ training 
and substituting obsolete capital for new vintages would result in greater firm 
efficiency, i.e. TFP growth. Firm efficiency would also benefit from higher investment 
in R&D and innovation (e.g. technology creation) and from investment in existing 
technologies (technology adoption). Firms’ decisions on these different drivers can 
be affected by structural and fiscal policies shaping market regulation and framework 
conditions that set incentives for innovative investment. Among all these drivers, the 
role of technology – creation and adoption – features particularly prominently in the 
literature, for two main reasons. First, some authors find that the greater ability of the 

14   See Bloom, N., Sadun, R. and Van Reenen, J., “Americans Do IT Better: US Multinationals and the 

Productivity Miracle”, The American Economic Review, Vol. 102, No 1, 2012, pp. 167-201. 

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United States to create technology, and also to use it in the production process, is 
one of the main drivers of the US-Europe productivity gap.15  Second, despite the 
apparent rapid recent advance of new technologies, labour productivity growth in 
most developed economies has been slowing down since before the GFC, as shown 
in Section 1. 

The broad deceleration in productivity at a time of intense technology 
acceleration has been widely described as a puzzle or even as a paradox. 
There is extensive research and ongoing debate on what lies behind this paradox. 
The “techno-pessimists” argue that new technological innovations are simply less 
revolutionary than in the past, notably compared with those induced by the second 
industrial revolution.16  In contrast, “techno-optimists” are of the view that the 
potential of information and communication technologies (ICT) and other new 
technologies will unfold in the coming decades, with a profound impact on 
productivity growth. This strand of literature argues that we might not yet have seen 
the full benefits of new technologies because they are still in development and 
because it takes time for new technologies to diffuse, for companies and workers to 
adapt, and for complementary investments to take place.17  There is also a strand of 
thought arguing that the increasing prevalence of new technologies might have 
resulted in mismeasurement rather than in a productivity paradox.18 

The speed of technology creation and adoption in the euro area can be 
approximated by analysing separately the TFP dynamics of frontier firms and 
laggards. 19  Frontier firms are the most productive firms in a narrowly defined sector 
across all analysed countries.20  They are the ones expected to innovate and bring 
new technologies to the market. For this reason their TFP growth will be highly 
correlated with innovation and technology creation in the euro area. Laggards, in 
turn, are here defined as the median or representative firm in the sector. Their 
productivity growth hinges on the adoption of the technologies introduced by frontier 
firms in their sector of activity. Technology adoption (or speed of technology diffusion) 
can therefore be proxied by the TFP growth gap between frontier firms and laggard 
firms. It is understood that if new technology diffuses fast from frontier firms to the 
rest, the TFP growth developments of frontier and laggard firms should be similar, 
although starting at very different levels. 

15   See Van Ark, B., O'Mahoney, M. and Timmer, M., “The Productivity Gap between Europe and the 

United States: Trends and Causes”, Journal of Economic Perspectives, Vol. 22, No 1, 2008, pp. 25-44. 

16   See, for example, Gordon, R., “Secular Stagnation: A Supply-Side View”, American Economic 

Association Papers & Proceedings, Vol. 105, No 5, 2015, pp. 54-59. 

17   See, for example, Brynjolfsson, E. and McAfee, A., “The Second Machine Age: Work, Progress, and 

Prosperity in a Time of Brilliant Technologies”, WW Norton & Company, 2014. 

18   See, for example, Syverson, C., “Challenges to mismeasurement explanations for the US productivity 

slowdown”, Journal of Economic Perspectives, Vol. 31, No 2, 2017, pp. 165-186. 

19   See Andrews, D., Criscuolo, C. and Gal, P.N., “Frontier Firms, Technology Diffusion and Public Policy”, 

OECD Productivity Working Papers, No 2, OECD Publishing, 2015. 

20  

Ideally we should consider a global frontier, given that technology has no boundaries. However, due to 
data restrictions this sections analyses frontier firms in six large euro area countries. 

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Key factors behind productivity trends in euro area countries 

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Data on six euro area countries21  show that technology creation slowed down 
in manufacturing over the period 2005-2017. 22  The analysis shows that TFP 
growth of frontier manufacturing firms in the pooled sample decreased from an 
annual average rate of 4.8% in the 2005-2007 period to 2.6% in the 2013-2017 
period (Chart 4 – top panel). When manufacturing sectors are split according to their 
technology intensity, it becomes clear that the slowdown took place entirely in high-
technology manufacturing sectors (Chart 5 – left panel). The ultimate reasons for the 
slowdown in technology creation in manufacturing are not yet clear. One possible 
explanation could be the high (and increasing over time) average age of 
manufacturing frontier firms, particularly in high-technology sectors (Chart 4 – bottom 
panel). A higher average age of firms within an industry can be a sign of an 
advanced technology lifecycle and/or of reduced firm dynamics (i.e. reduced entry 
and exit rates), which are often associated with a lower level of innovation activity.23 
Another possible reason could be related to the slowdown in trade integration, which 
may have contributed to the slowdown in technology creation of European 
manufacturing firms through decreased incentives to engage in technology 
upgrading and innovation and muted learning-by-exporting. 

In contrast, the technology creation of frontier firms has accelerated in the 
services sectors. Over the post-GFC period, the TFP of frontier firms in services 
has grown at a higher annual rate than that of manufacturing frontier firms, showing 
an acceleration in innovation relative to previous periods (Chart 4 – top panel).24 
Chart 5 (right panel) shows that this transformation is taking place across 
traditionally low-tech service sectors such as retail and accommodation as much as 
across high-tech service sectors like financial or professional services. The 
acceleration in technology creation in services compensates for the observed 
slowdown in manufacturing, so the aggregate picture of euro area technology 
creation remains relatively stable over time. However, given that manufacturing 
industries have so far been a key driver of aggregate productivity growth in the euro 
area, the observed slowdown in manufacturing innovation might have consequences 
for the productivity outlook going forward. 

21   The countries covered are Belgium, Germany, Spain, France, Italy and Portugal, over the period 2005-
2017. Data are sourced from ORBIS (BvD) and iBACH (firm-level data underlying the Bank for the 
Accounts of Companies Harmonized), refer to corporations with employees operating in the non-
agricultural business sector, excluding the financial sector, and are treated with inverse population 
weights to improve sample representativeness. 

22   Note that the distinction between manufacturing and services is becoming blurrier over time. The 

reason is that manufacturing firms increasingly provide services related to their products. To mitigate 
that problem, the analysis in this article uses unconsolidated accounts and classifies firms by their main 
activity. 

23   See for example Huergo, E. and Jaumandreu, J., “How does probability of innovation change with firm 

age?”, Small Business Economics, Vol. 22, No 3-4, 2004, pp. 193-207. 

24   This increase reflects partly the changing nature of services sectors. Take the example of retail sales, 
which are increasingly online and use sophisticated algorithms to detect costumer’s preferences and 
needs in real time. 

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Chart 4 
Technology creation in the euro area 

a) TFP growth of frontier firms (5% most productive firms), average across countries 

(average TFP growth, annual percentage change) 

2005-2007
2008-2012
2013-2017

3.1

3.4

1.9

6.0

5.0

4.0

3.0

2.0

1.0

0.0

4.8

3.9

2.4

2.6

2.0

1.4

All sectors

Manufacturing

Services

b) Age of frontier firms by sector and technology-intensity/knowledge-intensity, average 
across countries, 2006-2017 

(years of activity) 

Manufacturing (lower-tech)
Manufacturing (higher-tech)
Services (less knowledge intensive)
Services (more knowledge intensive)

24

20

16

12

8

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Sources: ECB iBACH-Orbis database and own calculations. 
Notes: The top chart shows average annual TFP growth rates of the 5% most productive firms in the pooled sample of countries in a 
given year within a NACE four-digit industry. Industry value added weights are used to aggregate up to the corresponding broad 
sector. In the bottom chart manufacturing industries are classified according to their R&D intensity (R&D by value added of the 
industry) into higher-tech (high-technology and medium high-technology) on the one hand, and lower-tech (medium low-technology 
and low-technology) on the other hand, following the Eurostat classification. Service industries are classified into knowledge-intensive 
services and less knowledge-intensive services based on the share of tertiary educated persons at NACE two-digit level, also following 
Eurostat standards. 

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Chart 5 
TFP growth of frontier firms according to sector and technology-intensity/knowledge-
intensity in the pre- and post-GFC periods, average across countries 

(average annual TFP growth rate) 

2005-2007
2013-2017

a) Manufacturing

b) Services

8

7

6

5

4

3

2

1

0

8

7

6

5

4

3

2

1

0

Low

High

Low

High

Sources: ECB iBACH-Orbis database and own calculations. 
Notes: The chart shows average annual TFP growth rates of the 5% most productive firms in the pooled sample of countries in a given 
year within a NACE four-digit industry. Industry value added weights are used to aggregate up to the corresponding broad sector. 
Manufacturing industries are classified according to their R&D intensity (R&D by value added of the industry) into high-technology and 
medium high-technology on the one hand, and medium low-technology and low-technology on the other hand following the Eurostat 
classification. Service industries are classified into knowledge-intensive services and less knowledge-intensive services based on the 
share of tertiary educated persons at NACE two-digit level, also following Eurostat standards. 

The TFP growth gap between frontier firms and laggards in services is 
widening over time, reflecting a slowdown in technology diffusion (Chart 6 – 
right panel).25  The reasons for the slowdown are manifold. It might be that tacit 
knowledge and the number of complex technologies have increased, thereby 
creating barriers to the catching-up of laggard firms. Also, the use of both new 
technologies and intangible capital is often characterised by high fixed costs and by 
network effects, possibly implying non-replicable increasing returns to scale.26  This 
can also lead to “superstar” and “winner-takes-all” effects which might discourage 
laggard firms from investing in technology creation and adoption.27  Finally, even if 
incentives to innovate and adopt technologies exist, necessary complementary 
inputs might be missing in laggard firms. One of the most important complementary 
investments is human capital, both of workers and of managers. It has been shown 
that employees need complementary information technology skills to exploit the full 
potential of new technologies; firms might also need to reorganise to adopt and 
benefit from new technologies. Other potentially lacking input factors are investment 
in necessary infrastructure (e.g. broadband) or complementary intangible inputs (e.g. 

25   This is a widely documented fact across a variety of countries. See, for example, Andrews, D., 
Criscuolo, C. and Gal, P.N., “Frontier Firms, Technology Diffusion and Public Policy”, OECD 
Productivity Working Papers, No 2, OECD Publishing, 2015, for OECD evidence. 

26   Network effects refer to a situation where a good or service becomes more valuable when more people 

use it. See Calvino, F. and Criscuolo, C. “Business dynamics and digitalisation”, OECD Science, 
Technology and Industry Policy Papers, No 62, OECD Publishing, Paris, 2019. 

27   “Winner-takes all” refers to market dynamics whereby globalisation and technological changes push 

sales towards the most productive firms in each industry, which results in product market concentration 
and the rise of very large dominant firms, also called “superstar” firms. See Autor, D., Dorn, D., Katz, L., 
Patterson, C. and Van Reenen, J., ”The Fall of the Labor Share and the Rise of Superstar Firms”, The 
Quarterly Journal of Economics, Vol. 135, Issue 2, May 2020, pp. 645-709. 

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organisational capital), while more advanced digital tools and applications have 
diffused to very few firms in most advanced countries.28 

Chart 6 
TFP growth gap between frontier and laggard firms as a proxy for technology 
diffusion in the euro area 

(Index, 2006=1) 

Frontier firms
Non-frontier firms

a) Manufacturing

1.2

1.1

1.0

0.9

b) Services

1.2

1.1

1.0

0.9

2006

2008

2010

2012

2014

2016

2006

2008

2010

2012

2014

2016

Sources: ECB iBACH-Orbis database and own calculations. 

Resource reallocation: between-firm resource reallocation and firm 
demography 

At any moment in time, in every sector and country, there is significant 
reallocation of capital and labour across firms. Seminal work using US 
longitudinal firm-level data in the 1990s showed that about one in ten jobs were 
created and one in ten destroyed every year within narrowly defined sectors.29  This 
high pace of job reallocation – measured as the sum of gross job creation and 
destruction – is a common feature across countries, sectors and years and is closely 
linked to worker reallocation given that most annual job creation and destruction 
reflects persistent firm-level employment changes. 

If resources flow from low-productivity firms to firms with high productivity, 
reallocation will be productivity-enhancing even if average firm productivity 
does not change. If resources flowed instead to low-productivity firms, resources 
become misallocated. Several studies report increasing misallocation of resources 
since before the GFC. 30  This finding suggests that there might be structural factors, 

28   For a literature overview, see Akcigit, U. and Ates, S.T., “What Happened to US Business Dynamism?”, 
NBER Working Paper, No 25756, National Bureau of Economic Research, 2019; and “Key factors 
behind productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021. 
29   Davis, S.J, and Haltiwanger, J., “Job Creation, Gross Job Destruction, and Employment Reallocation”, 

The Quarterly Journal of Economics, Vol. 107, No 3, 1992, pp. 819-863. 

30   See Gopinath, G., Kalemli-Özcan, S., Karabarbounis, L. and Villegas-Sanchez, C., “Capital allocation 

and productivity in South Europe”, The Quarterly Journal of Economics, Vol. 132, No 4, 2017, pp. 1915-
1967; and Gamberoni, E., Giordano, C. and Lopez-Garcia, P., “Capital and labour (mis)allocation in the 
euro area: some stylized facts and determinants”, Working Paper Series, No 1981, ECB, November 
2016. 

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besides cyclical ones, behind this trend. Distortions impairing the efficient allocation 
of production factors (capital and labour) across heterogeneous producers can stem 
from the design of taxes and tariffs, the regulation of input and output markets, 
financial frictions, or imperfect information.31 

The impact of increased misallocation on TFP growth in the euro area could be 
substantial. Estimates from a meta-analysis of empirical papers quantifying TFP 
losses due to resource misallocation show that increasing resource misallocation 
could cost up to 0.2 percentage points of annual TFP growth in the euro area, which 
represents about half the average annual TFP growth over the period 2014-2019 
(Chart 2). 32 

The process of creative destruction whereby new firms replace obsolete ones 
contributes to the reallocation of resources across firms, albeit to a small 
extent in the short term. The limited short-term contribution of firm demography is 
the result of two counterbalancing effects. Data on four euro area countries show 
that, on the one hand, the very low productivity of exiting firms relative to other firms 
in their market increases the productivity contribution of firm exit. On the other hand, 
new firms start up with relatively low productivity levels, which acts as a drag on 
productivity growth (Chart 7 – top panel).33  The net contribution of entry and exit is, 
in consequence, positive overall but relatively small over the short term across all 
countries. 

Post-entry selection of new firms increases the productivity contribution of 
young firms over the medium term. After entry, firms learn about their relative 
productivity and, if well below the average in the sector, exit after few years of 
operation. This is called the “selection effect”.34  In the sample analysed, about one-
third of firms exit before completing three years of activity (Chart 7 – bottom panel). 35 
Which firms survive the first years of operations depends on their productivity: young 
surviving firms are up to two and a half times more productive than young exiting 
firms in the same age bracket.36 

31   See “Key factors behind productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, 

September 2021 for a review of the literature. 

32   This estimate comes from a meta-analysis based on 21 primary studies, with about 200 observations in 

total, for euro area countries. For further details on the meta-analysis, see “Key factors behind 
productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021. 
33   The data used for the analysis of the post-entry development of firms are sourced from ORBIS and 
iBACH and cover four euro area countries: Belgium, France, Italy and Spain, over the period 2006-
2018. For more details on the data, please refer to See “Key factors behind productivity trends in EU 
countries”, Occasional Paper Series, No 268, ECB, September 2021. 

34   See, for example, Jovanovic, B., “Selection and the Evolution of Industry”, Econometrica, Vol. 50, No 3, 

1982, pp. 649-670. 

35   The results show selection of firms before completing three years of activity taking into account two 

cohorts of firms: those entering in 2006-2008 and those entering in 2013-20,14. 

36   See “Key factors behind productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, 

September 2021. 

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Chart 7 
Productivity at entry and exit, and selection effect 

a) Productivity of new and exiting firms relative to incumbent firms, different periods 

(index, 1=productivity of incumbent firms in the same country, sector and year) 

Entry: 2007-2010
Exit: 2007-2010
Entry: 2010-2013
Exit: 2010-2013
Entry: 2013-2017
Exit: 2013-2017

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Belgium

Spain

France

Italy

b) Share of new firms exiting the market before completing three years of activity 

(percentage share of new firms in each cohort) 

Cohort 2006-2008
Cohort 2013-2014

100

90

80

70

60

50

40

30

20

10

0

Belgium

Spain

France

Italy

Source: Own calculations based on ECB iBACH-Orbis data. 

If they survive, new firms converge to the average scale of efficiency in the 
sector. This implies that these firms grow faster in terms of productivity than 
incumbents in the same sector, particularly during their first five years of activity. This 
is known as the “learning effect”. After controlling for the country and sector of 
activity, sector demand conditions and entry year, it is found that young surviving 
firms in Belgium and France converge in about ten years to the productivity level of 
mature incumbent firms with more than 20 years of activity in the same sector. In 
Italy and Spain, where new firms’ productivity level is well below that of incumbents, 
the catch-up process takes longer (Chart 8 – top panel). 

The strong productivity performance of young surviving firms is driven by a 
few high-growth firms. Focusing on young firms entering after the GFC to negate 
the possible negative impact of the crisis, it is found that the distribution of average 

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annual productivity growth during their first six years of activity is extremely skewed 
(Chart 8 – bottom panel). Annual productivity growth of firms in the top 10% of the 
growth distribution is more than 80% on average, compared with the annual 
productivity growth of the median firm of up to 5%. Indeed, young firms have been 
shown to introduce radical innovations more frequently than mature firms,37  although 
according to the findings of this analysis, few succeed. Moreover, recent analysis for 
the United States has found that firm entry and the prevalence of high-growth firms 
has declined over time.38  Although the dataset used in this section does not cover a 
long enough period to analyse these facts in the European context, there is some 
evidence that business dynamism is also declining in Europe.39 

37   See Acemoglu, D., Akcigit, U., Alp, H., Bloom, N. and Kerr, W.R., “Innovation, Reallocation and 

Growth”, American Economic Review, Vol. 108, No 11, 2018, pp. 3450-3491. 

38   Decker, R., Haltiwanger, J., Jarmin, R. and Miranda, J., “Declining Business Dynamism: What We 

Know and the Way Forward”, American Economic Association Papers & Proceedings, Vol. 106, No 5, 
2016, pp. 203-207.   

39   Calvino, F., Criscuolo, C. and Menon, C., “Cross-country evidence on start-up dynamics”, OECD 

Science, Technology and Industry Working Papers, No 6, OECD Publishing, Paris, 2015. 

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Chart 8 
Learning effects 

a) Productivity convergence of new firms to incumbents in the same sector, conditional on 
survival 

(percentage deviation from productivity of reference category – firms with 20 or more years of activity) 

Belgium
Spain
France
Italy

0.05

0.00

-0.05

-0.10

-0.15

-0.20

-0.25

-0.30

-0.35

-0.40

-0.45

At entry

Between entry
and 5 years

Between 5
and 10 years

Between 10
and 20 years

b) Average annual productivity growth during the first six years of activity of firms born in 
2012-13 

(average annual productivity growth) 

Bottom 10th percentile
Median
Top 10th percentile – superstars
Mean

100

80

60

40

20

0

-20

-40

-60

Belgium

Spain

France

Italy

Source: Own calculations based on ECB iBACH-Orbis data. 
Notes: The top panel shows the coefficient of each age bracket in a regression of labour productivity on age conditional on the survival 
of the firm and controlling for sector of activity, sector demand conditions and entry year of the firm. The bottom panel shows the 
distribution of annual productivity growth of firms during their first six years of activity. 

New firms also contribute to aggregate productivity growth by increasing 
market competition. New innovative firms can stimulate innovative efforts of 
incumbents through the pressure of competition, leading to a positive impact on their 
within-firm productivity growth.40 

40   Anderton, R., Di Lupidio, B. and Jarmulska, B., “The impact of product market regulation on productivity 
through firm churning: Evidence from European countries”, Economic Modelling, Vol. 91, September 
2020, pp. 487-501. 

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The productivity implications of financially distressed firms 

The delayed exit of financially distressed firms with low productivity could be 
a drag on productivity growth. As shown in Chart 7 (top panel), exiting firms tend 
to be significantly less productive than incumbents operating in the same sector. 
Hence, if these firms become financially distressed and do not exit the market, they 
could have an important negative impact on aggregate productivity growth. This 
impact can be either direct, given their relative low productivity, or indirect, because 
of possible crowding out of resources from healthy firms (congestion effects). 
Delayed exit may reflect stagnant productivity growth of incumbent firms, 
misallocation of resources or disrupted entry and exit of firms. 

The literature has categorised firms as financially distressed according to 
different criteria. In their seminal papers focusing on firm-bank relationships in 
Japan in the 1990s, Hoshi41  and Caballero et al.42  identify “zombies” as firms with 
extremely low interest payments and high levels of debt that are likely to receive 
financial aid from lenders. In a second approach, recent studies, including this 
article, use various measures of sustained weak financial performance to identify 
financially distressed firms. These measures flag firms with persistently low profits – 
at least for three consecutive years – relative to interest paid and financial charges.43 

The dynamics of firms in distress – those with an interest coverage ratio below 
one for three consecutive years – is highly procyclical. Using recent firm-level 
data from five euro area countries (Belgium, Italy, Netherlands, Portugal and 
Finland)44, it can be shown that the share of financially distressed firms increased 
from 2006 to 2014 and declined thereafter (up to 2017, the year of the latest 
available data) across all analysed countries (Chart 9 – top panel). To identify the 
driver of this decline, the stock of financially distressed firms at any given moment is 
decomposed into inflows into distress, that is, firms entering financially distressed 
status, and outflows from distress, or firms exiting financially distressed status, either 
because they recover or because they exit the market. The exercise reveals that the 
cyclical dynamics of financially distressed firms are driven entirely by the entry of 
firms into distress, which declined sharply with the post-crisis economic recovery. On 
the other hand, outflows from distress have remained relatively unchanged over time 
(Chart 9 – bottom panel). This means that the average duration of firms’ financially 
distressed status has been stable over time. 

41   Hoshi, T., “Economics of the Living Dead”, Japanese Economic Review, Vol. 57, Issue 1, pp. 30-49, 

March 2006. 

42   Caballero, R.J., Hoshi, T. and Kashyap, A.K., “Zombie Lending and Depressed Restructuring in Japan”, 

American Economic Review, Vol. 98, No 5, pp. 1943-1977, December 2008. 

43   See Adalet McGowan, M., Andrews, D. and Millot, V., “The Walking Dead?: Zombie Firms and 

Productivity Performance in OECD Countries”, Economic Policy, Vol. 33, No 96, 2018, pp. 685-736. 
For a discussion of the pros and cons of the different zombie definitions, see “Key factors behind 
productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021. 

44   Further details regarding the data and methodology can be found in See “Key factors behind 

productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021. 

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107 

 
 
Chart 9 
The evolution of financially distressed firms 

a) Share of financially distressed firms over time, weighted average across countries 

(percentage of firms, employment and value added) 

Firms
Employment
Value added

13

12

11

10

9

8

7

6
2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

b) Inflows into and outflows from distress, weighted average across countries 

(left-hand scale: percentage of healthy firms; right-hand scale: percentage of zombies) 

Inflows into distress (left-hand scale)
Outflows from distress (right-hand scale)

6.5

6.0

5.5

5.0

4.5

4.0

3.5

3.0

2.5

65

60

55

50

45

40

35

30

25

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Sources: Central Balance Sheet Database, Cerved Centrale dei Bilanci, Istituto Nazionale Previdenza Sociale, National Bank of 
Belgium Central Balance Sheet Office, Statistics Finland, Statistics Netherlands and authors’ calculations. 
Notes: Charts show the weighted average of developments in five euro area countries: BE, IT, NL, PT and FI. Financially distressed 
firms are defined as firms with a ratio of earnings before interest and taxes (ebit) and interest paid+financial charges of less than one 
(ebit/(interest+financial charges)<1) for three consecutive years. Manufacturing includes NACE Rev. 2 sectors 10-33 and private 
services includes sectors 45-63 and 69-82. 

Not all firms in financial distress are alike. On average across years and 
countries, it is found that about half of firms in distress exit this status after three 
years: between 40% and 70% of firms exiting financially distressed status recover 
financial health and between 60% and 30% of them exit the market (Chart 10 – top 
panel). The other half of financially distressed firms, amounting to about 5% of total 
firms, stay in financially distressed status and therefore could be labelled as 
“zombies” (Chart 10 – bottom panel). This heterogeneity within financially distressed 
firms also becomes evident when looking at their labour productivity relative to 
healthy firms in the same country and sector. Whereas financially distressed firms as 
defined by the interest coverage ratio criterion are, on average, 60% as productive 
as healthy firms in the same country and sector, financially distressed firms that 

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finally exit the market are significantly less productive than firms that recover 
financial health after a period in distress (Chart 10 – bottom panel). 

Chart 10 
Different types of financially distressed firms 

a) Exits from financially distressed status: recoveries or out of market 

(percentage share of exits from financially distressed status) 

Recoveries
Out of market

100

90

80

70

60

50

40

30

20

10

0

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

b) Relative productivity and population share of different types of financially distressed firms, 
average across countries and years   

(left-hand scale: index, 100=productivity of healthy firms in the same country and sector; right-hand scale: percentage share of 
population of active firms) 

Population share of zombie firms (right-hand scale)
Productivity of zombie firms (left-hand scale) 

80

60

40

20

0

20

15

10

5

0

All zombies

Firms that exit zombie status
into recovery

Firms that exit zombie status
out of market

Firms that continue in zombie
status (true zombies)

Sources: Central Balance Sheet Database, Cerved Centrale dei Bilanci, Istituto Nazionale Previdenza Sociale, National Bank of 
Belgium Central Balance Sheet Office, Statistics Finland, Statistics Netherlands and authors’ calculations. 
Notes: Financially distressed firms are defined as firms with a ratio of earnings before interest and taxes (ebit) and interest 
paid+financial charges of less than one (ebit/(interest+financial charges)<1) for three consecutive years. Manufacturing includes NACE 
Rev. 2 sectors 10-33 and private services includes sectors 45-63 and 69-82.   

The literature has pointed to different factors behind the emergence of the 
zombie phenomenon, i.e. financially distressed and non-viable firms. 45  They 
primarily relate to institutional and structural factors such as the efficiency of 
insolvency frameworks, banking sector health and, as discussed in greater detail in 
the next section, also possibly a prolonged period of accommodative monetary 
policy. Empirical evidence suggests that inefficient insolvency regimes have been 
preventing non-viable firms from exiting the market. These firms’ ability to exit is 

45    Schivardi, F., Sette, E. and Tabellini, G., “Credit misallocation during the financial crisis”, BIS Working 

Papers, No 669, 2017. 

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109 

 
 
 
 
particularly important after an adverse aggregate shock to enable fast restructuring 
and to free up resources for other, more productive uses.46  A weak banking sector 
also seems to be associated with the prevalence of zombie firms. The reason is that 
under-capitalised banks are less likely to foreclose zombie firms compared with 
stronger banks, which hinders the reallocation process. A typical mechanism is that 
weak banks seek to postpone registering losses in their accounts in an attempt to 
avoid recapitalisation and gamble on recovery. Finally, as discussed in the next 
section, there have been studies showing that a prolonged period of accommodative 
monetary policy could reduce incentives for firms to repair their balance sheet and 
delay the exit of unproductive or unviable firms. Overall, these findings highlight the 
importance of policies that aim to facilitate the allocation of resources towards more 
innovative and productive firms. 

4 

How does monetary policy interrelate with productivity 
growth? 

Cyclical polices, including monetary policy, may under certain circumstances 
support productivity growth by affecting demand and financial conditions and 
thus also capital deepening and TFP growth. This section first discusses the 
theoretical channels through which monetary policy can affect productivity. It then 
presents novel empirical evidence of the effects of monetary policy on productivity 
and credit allocation in the euro area. It is important to emphasise that this novel 
analysis does not address all channels jointly, in a general equilibrium context, and 
covers only countries for which data were available. 

An accommodative monetary policy stance may stimulate demand and 
investment in productivity-improving technologies. Beyond supporting demand 
and investment, an accommodative monetary policy stance might result in 
favourable financing conditions, with an impact on corporate profitability and on the 
productivity threshold for market survival, which also stimulates firm entry and delays 
firm exit. In the presence of tight financing conditions, an accommodative monetary 
policy prevents the death of highly productive firms that become financially 
constrained and facilitates their investment, thereby favouring aggregate productivity 
growth. At the same time, an accommodative monetary policy may also create some 
negative productivity effects under certain circumstances. First, low interest rates 
stimulate risk-taking which can worsen resource allocation, especially in the absence 
of appropriate bank supervision or macroprudential policies. Second, while an 
accommodative monetary policy stance can facilitate the flow of resources from firms 
with low productivity to firms with high productivity, particularly if the latter has been 
financially constrained, the opposite can also occur. For example, if firms with low 
productivity are less financially constrained because of high collateral (e.g. in the 
construction sector), resources could also flow to this type of firm and away from 
highly productive firms with more financial frictions (low net worth, information 
asymmetries owing to age or intangible assets, etc.). Third, easier financing 

46   See Andrews, D. and Petroulakis, F., “Breaking the shackles: zombie firms, weak banks and depressed 

restructuring in Europe”, OECD Working Papers, No 1433, OECD Publishing, 2017. 

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conditions may reduce the incentives for firms and banks to carry out necessary 
restructuring and balance sheet repair, with adverse effects on resource allocation.47 
This implies the need for monetary policy to be complemented by appropriate micro-
prudential and macroprudential policies to prevent such distortions to build up and 
result in boom-bust episodes. 

New empirical work suggests that accommodative monetary policy shocks 
have a positive impact on TFP growth in the long term.48  In the short run there 
are only hints of anticipation of technology-enhancing investments for the euro area. 
In the longer term, however, TFP reacts positively to a monetary policy shock in both 
the United States and the euro area (Chart 11). 

Chart 11 
The impact of monetary accommodation on TFP over time 

Dynamic response of cyclically adjusted TFP to a 100-basis point decrease in nominal 
interest rates 

(x-axis in quarters; y-axis in cumulative percentage change relative to pre-shock TFP growth) 

Local projections (smooth)

a) Euro area

b) United States

6

5

4

3

2

1

0

-1

-2

-3

-4

6

5

4

3

2

1

0

-1

-2

-3

-4

1

3

5

7

9

11 13 15 17 19 21 23

1

3

5

7

9

11 13 15 17 19 21 23

Sources: Euro area TFP computed using growth accounting at the industry level. Euro area cyclically-adjusted TFP stems from the 
European Commission Spring 2020 forecast while US cyclically adjusted TFP is taken from the database by Fernald. 
Notes: The figures depict cumulative TFP growth at different time horizons (quarters, x-axis) after an expansionary monetary policy 
shock at t=0. Monetary policy shocks are identified using high-frequency surprises around monetary policy announcements as in 
Jarociński, M. and Karadi, P., “Deconstructing Monetary Policy Surprises – The Role of Information Shocks”, American Economic 
Journal: Macroeconomics, Vol. 12, No 2, 2020, pp. 1-43l. Solid lines denote smooth local projections estimates; dashed lines denote 
68% confidence intervals. 

Empirical analysis shows little evidence of a deterioration in resource 
allocation at the time of accommodative monetary policy decisions. In a low 
interest rate environment, banks could relax lending standards (heightened risk-
taking, credit booms) and might increasingly lend also to non-viable firms, which 
could put pressure on aggregate productivity given the relatively lower productivity of 
such firms, as shown in the previous section (Chart 10 – bottom panel). To explore 
this possible channel, three new pieces of analysis are conducted. The first exploits 

47   See “Key factors behind productivity trends in EU countries”, Occasional Paper Series, No 268, ECB, 

September 2021, for a review of the literature exploring the channels through which monetary policy 
affects productivity. 

48   For more information on the exercise on US data, see also Hartwig, B. and Lieberknecht, P., “Monetary 

Policy, Firm Exit and Productivity”, Bundesbank Discussion Paper, No 61, 2020. 

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111 

 
 
 
the ECB survey on the access to finance of enterprises (SAFE)49, to find out whether 
the accommodative monetary policy of recent years facilitated access to finance, and 
therefore survival, of weak firms in the euro area.50  Results show that the easing of 
financing conditions following expansive monetary policy decisions facilitated access 
to finance for most firms, but significantly less for weak or vulnerable firms (Chart 
12). The only exception was large firms with an interest coverage ratio temporarily 
below 1 (that is, with profits below interest payments), which actually gained access 
to finance when financing conditions were eased. This result could be driven by the 
fact that bank balance sheets are more sensitive to large firms in distress and large 
firms have more bargaining power.51  The second piece of analysis focuses on the 
pricing behaviour of banks in France and uses credit registry data to analyse 
whether the share of low-solvency firms benefiting from exceptionally low interest 
rates52  has increased during the low interest rate period. The results show that this 
share remained subdued and stable in France over the past decade, suggesting that 
credit misallocation is not pervasive in France. The third piece of analysis focuses on 
Spain, Italy and Portugal – countries with a relatively large share of financially 
constrained firms – and finds that capital allocation improved after a positive 
monetary policy shock. The reason is that the decrease in the interest rate increased 
investment relatively more in firms with high marginal revenue productivity of capital 
that were initially financially constrained (younger, more dependent on external 
finance and featuring low mark-ups).53 

49   The analysis covers the period 2009-2020 and therefore monetary policy decisions taken by the ECB 
since the GFC. For more details on data and methodology, refer to “Key factors behind productivity 
trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021; and Survey on the 
access to finance of enterprises: Methodological information on the survey and user guide for the 
anonymised micro dataset, ECB, Frankfurt am Main, 2020. 

50   Weak firms are defined in different ways: i) according to a strict SAFE indicator of vulnerable firms, i.e. 
firms that have reported simultaneously lower turnover, decreasing profits, higher interest expenses 
and higher or unchanged debt to total assets in the last six months; ii) according to the interest 
coverage ratio, i.e. they are firms with interest expenses/operating profits below one for three 
consecutive years, as discussed in the previous section; iii) according to the Altman Z-score; and iv) 
according to the relative productivity of the firm, where productivity is defined as real value added or 
turnover per employee. 

51   For further results, see Acharya, V., Eisert, T., Eufinger, C. and Hirsch, C., “Whatever It Takes: The Real 
Effects of Unconventional Monetary Policy”, The Review of Financial Studies, Vol. 32 ,No 9, 2019, 
pp. 3366-3411; and “Key factors behind productivity trends in EU countries”, Occasional Paper Series, 
No 268, ECB, September 2021. 

52   That is, credit misallocation is identified as the incidence of low-solvency firms receiving an interest rate 
on their new loans below a “prime” rate reserved only for the highest quality firms. See Caballero, R.J., 
Hoshi, T. and Kashyap, A.K., “Zombie lending and depressed restructuring in Japan”, The American 
Economic Review, Vol. 98, No 5, 2008, pp. 1943-1977; and “Key factors behind productivity trends in 
EU countries”, Occasional Paper Series, No 268, ECB, September 2021. 

53   For more details on data and methodology, see “Key factors behind productivity trends in EU 

countries”, Occasional Paper Series, No 268, ECB, September 2021; and Albrizio, S. and González, 
B., “Monetary policy and capital misallocation in Europe”, forthcoming working paper, Banco de 
España, Madrid. 

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112 

 
 
Chart 12 
Improvements in access to bank finance by firm type after selected monetary policy 
decisions 

(net share of firms responding that access to finance has improved over the preceding six months; weighted percentages of 
respondents) 

Vulnerable
OMT
TLTRO1
TLTRO2-CSPP
All sample

0.4

0.3

0.2

0.1

0.0

H1

H2

H1

H2

H1

H2

H1

H2

H1

H2

H1

H2

H1

H2

H1

H2

H1

H2

H1

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: ECB/EC SAFE. 
Notes: Enterprises that had applied for bank loans. Vulnerable firms are firms that have reported simultaneously lower turnover, 
decreasing profits, higher interest expenses and higher or unchanged debt to total assets in the last 6 months OMT: outright monetary 
transactions programme; TLTRO: targeted longer-term refinancing operations; CSPP: corporate sector purchase programme The first 
vertical line denotes the announcement of the OMT; the second vertical line denotes the start of TLTRO I and the negative rate policy; 
and the third vertical line denotes the start of TLTRO II and the CSPP. Figures refer to rounds 3 (March-September 2010) to 22 
(October 2019-March 2020) of the survey. 

5 

Conclusion 

Productivity growth in the euro area has been muted for decades, owing to a 
variety of interacting factors. First, capital deepening has been weak in the euro 
area since the GFC as a result of low investment and employment recovery. Second, 
within-firm productivity growth has slowed down since the pre-GFC period, reflecting 
slower technology creation in manufacturing firms with a negative impact on euro 
area TFP growth. Although this development has been partly compensated for by an 
acceleration of innovation in services, new technologies resulting in higher services 
TFP growth seem to benefit largely frontier firms; most firms in services, in contrast, 
are lagging behind in technical adoption, which is slowing their TFP growth. Third, 
the contribution of resource reallocation across incumbents to productivity growth 
has been declining since before the GFC. Fourth, firm demographics make only a 
small positive contribution to productivity growth, because new firms have below-
average productivity and few surviving firms record high productivity growth. Delayed 
exit of low-productivity financially distressed firms has probably played a role, albeit a 
minor one, in dragging down productivity growth over past decades. 

These factors hinge on firms’ internal and external levers, suggesting a strong 
role for national policies in spurring productivity growth. There is ample 
evidence that euro area countries could achieve significantly stronger productivity 
growth by following global best practices in terms of structural policies and regulation 

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113 

 
 
of various markets.54  Those include policies that enhance labour mobility across and 
also within firms, sectors and regions; that support the diffusion of technology and 
the growth of more innovative and productive firms; that create a more competitive 
environment in product markets; that strengthen the contribution of finance to a more 
efficient allocation of savings and discourage the excessive accumulation of 
corporate debt; and that strengthen insolvency frameworks to facilitate the exit of 
less productive firms. 

At the same time, the article finds tentative evidence of monetary policy having 
a positive effect on productivity growth, at least under a partial equilibrium 
approach. The preliminary new evidence presented in this article suggests that by 
supporting demand and investment of financially constrained firms with high 
marginal revenue productivity of capital, the accommodative monetary policy stance 
in the euro area may have improved capital allocation. It is also found that the 
accommodative stance has not, overall, adversely affected credit allocation, although 
there may be some exceptions relating to large firms with profits below interest 
payments. 

Looking ahead, productivity developments will also depend on the interaction 
of key drivers of productivity growth with the effects of the COVID-19 
pandemic. Despite recent encouraging signs, there remain some threats, owing to 
the possible restructuring of global value chains after the pandemic and a further rise 
in trade barriers. The massive policy support for the corporate sector in response to 
the pandemic crisis has been crucial in mitigating the initial impact of the shock. 
However, once the economic recovery takes hold on a sustainable basis, policy 
support needs to be lifted gradually, also to avoid impairing the efficient reallocation 
of resources by setting wrong incentives. In addition, such withdrawal should avoid 
an undue tightening of financial conditions that would increase the financing cost and 
reduce the expected benefits of new productivity-enhancing projects and delay 
investment, with impacts on productivity growth. 

On the upside, accelerated digital uptake as a result of the COVID-19 shock 
might yield to higher productivity growth going forward. Available evidence on 
the productivity impact of the COVID-19 shock seems to support this possibility, as 
discussed in the accompanying Box 4 in this issue of the Economic Bulletin. 
However, the pace and distributional impacts of accelerated digital uptake are still 
uncertain and depend on the development of institutions, infrastructure, skills, 
methods of production and management competencies. Also, large investment in 
green technologies could significantly push the technological frontier outwards. 
However, in order to facilitate the structural change required to put green production 
practices in place, new investment should be complemented with favourable 
framework conditions. 

54   For an overview of the impact of structural policies on productivity, see “Key factors behind productivity 
trends in EU countries”, Occasional Paper Series, No 268, ECB, September 2021, and Masuch, K., 
Anderton, R., Setzer, R. and Benalal, N., “Structural policies in the euro area”, Occasional Paper 
Series, No 210, ECB, 2018. 

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3 

The euro area housing market during the COVID-19 
pandemic 

Prepared by Niccolò Battistini, Matteo Falagiarda, Johannes Gareis, 
Angelina Hackmann and Moreno Roma 

1 

Introduction 

The euro area housing market was in a relatively long expansionary cycle 
before it entered the coronavirus (COVID-19) crisis. 1  On the eve of the COVID-
19 crisis, the euro area housing market was on solid ground. In the last quarter of 
2019, house prices, housing investment and housing loans were on an upward 
trend, supported by robust income developments and bank lending rates for house 
purchases at historical lows (Charts 1 and 2).2  Given the phase in which the housing 
cycle stood, an economic shock like the COVID-19 crisis might have been expected 
to turn the cycle. 

Chart 1 
House prices, housing investment and housing loans in the euro area 

(annual percentage changes) 

House prices
Housing investment
Housing loans
Recessions

15

10

5

0

-5

-10

-15

First wave

Second/
third 
waves

2000

2004

2008

2012

2016

01/20

07/20

01/21

Sources: Eurostat and ECB. 
Note: Grey areas delimit recessions, as identified by the Centre for Economic Policy Research (CEPR) Euro Area Business Cycle 
Dating Committee. 

However, the reaction of the euro area housing market to the COVID-19 crisis 
differed from that in previous crises owing to the different nature of the 

1    For an assessment of the state of the euro area housing market before the COVID-19 pandemic, see 

the article entitled “The state of the housing market in the euro area”, Economic Bulletin, Issue 7, ECB, 
2018. 

2    Throughout this article, unless otherwise indicated, house prices refer to the nominal house price index, 

housing investment to real investment in residential construction and housing loans to loans to 
households for house purchases in nominal terms. 

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115 

 
 
 
underlying shock. 3  The global financial crisis of 2008 originated in the US housing 
market and the sovereign debt crisis that started in 2010 stemmed primarily from 
financial shocks. Initially, the shock caused by the COVID-19 pandemic was 
unrelated to economic fundamentals and – especially in its early phases – afflicted 
the economy mainly through mandatory and voluntary restrictions on mobility aimed 
at containing the spread of the virus. These restrictions induced peculiar features 
compared with the global financial crisis and the sovereign debt crisis, notably as a 
result of their diverse impact on real and nominal housing dynamics and differing 
housing developments across countries. The particular nature of the COVID-19 
pandemic triggered vigorous monetary, fiscal and macroprudential policy responses. 

This article explores the developments in the euro area housing market during 
the pandemic and compares them with those in previous crises, paying 
particular attention to the role of policy support measures. Throughout, the 
article takes a holistic approach that covers developments in and prospects for euro 
area housing investment, house prices and loans for house purchase. Section 2 
focuses on the diverse impacts on the euro area housing market of the first wave of 
the COVID-19 pandemic from the first to the third quarter of 2020, when strict 
containment measures had the greatest effect on activity. Section 3 elaborates on 
the subsequent resilience of the housing sector through the second and the third 
waves of the pandemic up to the second quarter of 2021, amid more targeted 
containment measures and significant policy support measures. Section 4 provides a 
forward-looking perspective on the prospects and risks for the euro area housing 
market. 

2 

The first wave of the COVID-19 pandemic – the diverse 
impacts of containment measures on the euro area housing 
market 

The containment measures in response to the first wave of the COVID-19 
pandemic led to a divergence between real and nominal housing dynamics. 
The severe decline in mobility induced by containment measures and voluntary 
social distancing during the first wave of the pandemic had a negative impact on 
euro area housing investment, pushing it to 3.1% below its end-2019 level in the 
third quarter of 2020, broadly in line with the levels seen during the global financial 
crisis and the sovereign debt crisis (Chart 2). However, while in the previous crises 
deteriorating economic fundamentals hampered growth in house prices and housing 
loans, the COVID-19 shock did not weigh on the upward trajectory of prices and 
loans, which surpassed their levels in the fourth quarter of 2019 by 4.3% and 2.6% 

3    Throughout the article, in line with the chronology of euro area business cycles established by the 

CEPR Euro Area Business Cycle Dating Committee, unless otherwise stated, we refer to the COVID-
19 crisis as the period between the fourth quarter of 2019 (pre-crisis peak) and the latest available 
quarter (since no end date has yet been set). The global financial crisis refers to the period between the 
first quarter of 2008 and the second quarter of 2009, while the sovereign debt crisis refers to the period 
between the third quarter of 2011 and the first quarter of 2013. 

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respectively in the third quarter of 2020, supported by resilient housing demand amid 
policy support measures (Box 1).4 

Chart 2 
Euro area housing market developments in the global financial crisis, sovereign debt 
crisis and the first wave of the COVID-19 crisis 

(percentage changes; lending rate and indebtedness as percentages) 

Global financial crisis
Sovereign debt crisis
First wave of COVID-19 crisis

2

0

-2

-4

6

5

4

3

2

1

0

100

99

98

97

96

95

94

93

92

Housing
investment

House
prices

Housing
loans

Lending
rate

Indebtedness

Sources: Eurostat, ECB and authors’ calculations. 
Notes: Lending rate refers to the composite lending rate on housing loans. Indebtedness refers to the ratio of housing loans to annual 
gross disposable income. All variables are computed as average percentage changes over the reference periods, except the lending 
rate and indebtedness, where the bars refer to their levels in the quarter before the reference periods and the diamonds refer to the 
level in the final quarter of the reference periods. The reference periods are defined in Section 1. 

The different nature of the COVID-19 pandemic compared with previous crises 
is also visible in the larger degree of diversity in housing investment dynamics 
across countries. Losses in housing investment during the first three quarters of 
2020 varied widely, with nine countries recording gains and two countries (Spain and 
Malta) incurring larger losses than during the global financial crisis (Chart 3). These 
heterogeneous dynamics can partly be explained by the timing and relative degree 
of restrictiveness of containment measures,5  with construction activity being 
temporarily halted in some countries.6  Other factors included the initial fiscal support 
measures, which varied considerably across countries in terms of scale and timing,7 
as well as the different demographic structures of the national housing markets. 

4    For an analysis of developments in euro area house prices and their relation to macroeconomic 

conditions along different dimensions, see the box entitled “Euro area house price developments during 
the coronavirus pandemic”, Economic Bulletin, Issue 4, ECB, 2021. 

5    The stringency of containment measures, proxied by the Oxford stringency index, explains around 25% 
of the total cross-country variation in housing investment levels over the first three quarters of 2020. 
For the Oxford stringency index, see Hale, T., Angrist, N., Cameron-Blake, E., Hallas, L., Kira, B., 
Majumdar, S., Petherick, A., Phillips, T., Tatlow, H. and Webster, S., “Oxford COVID-19 Government 
Response Tracker,” Blavatnik School of Government, 2020. 

6    See the study entitled “Impacts of the COVID-19 pandemic on EU industries”, European Parliament, 
March 2021; the box entitled “The impact of containment measures across sectors and countries 
during the COVID-19 pandemic”, Economic Bulletin, Issue 2, ECB, 2021; the box entitled “The 
heterogeneous economic impact of the pandemic across euro area countries”, Economic Bulletin, 
Issue 5, ECB, 2021, and the references therein. 

7    See, for example, “The initial fiscal policy responses of euro area countries to the COVID-19 crisis”, 

Economic Bulletin, Issue 1, ECB, 2021. 

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117 

 
 
 
Chart 3 
Housing investment across euro area countries during the global financial crisis and 
the first wave of the COVID-19 crisis 

(average percentage changes) 

x ≤ -5%

-5% < x ≤ -1% 

-1% < x ≤ 0% 

0% < x ≤ 1% 

1% < x ≤ 5% 

5% < x 

a) Global financial crisis

b) First wave of COVID-19 crisis

Sources: Eurostat and authors’ calculations. 
Note: In the legend, variable “x” refers to the average percentage change in housing investment during the respective reference 
periods in each panel, as defined in Section 1. 

Demographic structures may also have induced diverse housing investment 
dynamics across countries, reflecting the differing impact of the first wave 
along the income distribution. Countries where a larger share of income is earned 
by poorer households experienced stronger declines in housing investment during 
the first wave of the pandemic.8  Euro area survey data corroborates this, as lower-
income households remained significantly less willing to purchase a house 
compared with pre-crisis levels by the end of the first wave of the pandemic in 
contrast to developments during the global financial crisis and the sovereign debt 
crisis (Chart 4). Instead, the intentions of medium and higher-income households to 
purchase property increased. This most likely resulted from these income groups’ 
high levels of accumulated savings induced by restrictions on the consumption of 
high-contact services.9 

8    This is obtained controlling for time- (quarterly-) fixed effects and the stringency of containment 

measures, as proxied by the Oxford stringency index based on the first three quarters of 2020 for euro 
area countries. 

9   

Indeed, the strong rebound of intentions to purchase property for medium and higher-income 
households (in contrast to lower-income households) in response to the easing of containment 
measures at the end of the first wave of the pandemic followed the large increase in savings in the 
early phases of the pandemic, in line with evidence from the Consumer Expectations Survey (CES). 
See the box entitled “COVID-19 and the increase in household savings: an update”, Economic Bulletin, 
Issue 5, ECB, 2021. According to CES data, higher-income homeowners reported less pressing 
financial concerns and more buoyant house purchases in the third quarter of 2020. See Christelis, D., 
Georgarakos, D., Jappelli, T. and Kenny, G., “The COVID-19 crisis and consumption: survey evidence 
from six EU countries”, Working Paper Series, No 2507, ECB, December 2020. 

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118 

 
 
 
Chart 4 
Households’ intentions to purchase property across income quartiles in the global 
financial crisis, the sovereign debt crisis and the first wave of the COVID-19 crisis 

(average changes; net balances) 

Global financial crisis
Sovereign debt crisis
First wave of COVID-19 crisis

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

First

Second

Third

Fourth

Sources: European Commission and authors’ calculations. 
Note: The reference periods are defined in Section 1. 

Box 1 
The impact of restrictions on mobility on the housing market – a structural approach 

Prepared by Niccolò Battistini and Johannes Gareis 

This box examines empirically the impact of mandatory and voluntary restrictions on economic 
agents’ mobility following the outbreak of the coronavirus (COVID-19) pandemic on housing 
investment and house prices, accounting for several transmission mechanisms that are relevant for 
the housing market. On the basis of euro area aggregate data between the first quarter of 2000 and 
the first quarter of 2021, a Bayesian vector autoregression (BVAR) model exploits information from 
the dynamic interactions among seven endogenous variables: housing investment; real house 
prices; the composite lending rate on loans for house purchases; the stock of loans for house 
purchases; real GDP; consumer prices (HICP); and the shadow rate.10  Following a large body of 
empirical literature,11  the model identifies the main drivers of the housing market, imposing zero 
and sign restrictions on the co-movements among the endogenous variables upon the impact of 

10   All the variables are quarter-on-quarter percentage changes, except for the lending and the shadow 

rates, which are quarter-on-quarter changes. The shadow rate is taken from Lemke, W. and Vladu, A. 
L., “Below the zero lower bound: a shadow-rate term structure model for the euro area”, Working Paper 
Series, No 1991, ECB, January 2017. 

11   Among studies that include the euro area, see Smets, F. and Jarociński, M., "House prices and the 

stance of monetary policy", Working Paper Series, No 891, ECB, April 2008; Bijsterbosch, M. and 
Falagiarda, M., “Credit supply dynamics and economic activity in euro area countries: A time-varying 
parameter VAR analysis” , Working Paper Series, No 1714, ECB, August 2014; Gambetti, L. and 
Musso, A., “Loan Supply Shocks and the Business Cycle”, Journal of Applied Econometrics, Vol. 32, 
Issue 4, 2017, pp. 764-782; Nocera, A. and Roma, M., “House prices and monetary policy in the euro 
area: evidence from structural VARs”, Working Paper Series, No 2073, ECB, June 2017; and Altavilla, 
C., Darracq Pariès, M. and Nicoletti, G., “Loan supply, credit markets and the euro area financial crisis”, 
Journal of Banking & Finance, Vol. 109, December 2019. 

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119 

 
 
 
 
various fundamental shocks.12  To account for the specific characteristics of the COVID-19 crisis, 
the model includes – as an exogenous variable – a measure of the effective stringency of 
containment measures, namely the effective lockdown index. Conceptually, this index aims to 
isolate the economic impact of restrictions on mobility – accounting for both containment measures 
and voluntary social distancing – during the different waves of the pandemic.13  In practice, the 
index acts as an augmented dummy variable, limiting the estimation problems induced by the 
abrupt and large fluctuations in economic developments observed since the start of the COVID-19 
crisis.14 

The historical decomposition of the drivers of housing investment and house prices highlights the 
peculiar effects of the COVID-19 crisis on the housing market (Chart A). During the global financial 
crisis and the sovereign debt crisis, economy-wide shocks and housing market-related forces, such 
as housing demand and supply, as well as credit supply shocks, were behind the protracted decline 
in both housing investment and house prices. In contrast to the two preceding crises, economic 
fundamentals mostly supported both housing investment and house prices, on average, over the 
COVID-19 crisis. However, containment measures induced a dichotomy between real and nominal 
housing dynamics. In fact, effective restrictions on mobility significantly weighed on activity, while 
they left house prices relatively unaffected throughout the COVID-19 crisis. Over the COVID-19 
crisis, the identified shocks can explain housing investment relatively well, but less so for house 
prices. The gap between actual and explained house prices is the result of unidentified factors, 
such as risk aversion and possible changes in preferences, and a positive average growth rate. 

12   The housing demand and supply shocks are identified based on the contemporaneous reaction by 

housing investment and house prices, which are assumed to move in the same direction for housing 
demand shocks and in opposite directions for housing supply shocks. Housing demand and credit 
supply shocks differ, as they imply opposite reactions in the lending rate, while housing investment and 
house prices co-move positively in response to both shocks. Expansionary aggregate demand shocks 
induce an increase in real GDP, consumer prices and the shadow rate, while expansionary aggregate 
supply shocks lead to an increase in real GDP and a decline in consumer prices. A monetary policy 
loosening induces a decline in the lending and the shadow rates and puts upward pressure on housing 
investment, house prices, real GDP and consumer prices. Housing-related shocks are assumed to 
have no contemporaneous impact on aggregate variables, i.e. real GDP, consumer prices and the 
shadow rate. The model includes one unidentified shock to capture the effects of any further remaining 
disturbances. For technical details on the implementation, see Arias, J. E., Rubio-Ramírez, J.F. and 
Waggoner, D. F., “Inference Based on Structural Vector Autoregressions Identified With Sign and Zero 
Restrictions: Theory and Applications”, Econometrica, Vol. 86, Issue 2, 2018, pp. 685-720. 

13   The effective lockdown index is constructed multiplying the Oxford stringency index (Hale et al., op. cit.) 
by the Google residential mobility index, which closely reflects the dynamics in footfall related to work-
from-home arrangements, identified as being among the main drivers of the process of learning by 
economic agents, and explaining the cross-sectoral heterogeneity in the impact of containment 
measures over the different phases of the COVID-19 pandemic. See the box entitled “The impact of 
containment measures across sectors and countries during the COVID-19 pandemic”, op. cit. Results 
are qualitatively robust to the use of alternative Google mobility measures. 

14   See Lenza, M. and Primiceri, G., “How to estimate a VAR after March 2020”, Working Paper Series, No 

2461, ECB, August 2020. 

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The euro area housing market during the COVID-19 pandemic 

120 

 
 
Chart A 
Drivers of housing investment and real house prices during the global financial crisis, the sovereign 
debt crisis and the COVID-19 crisis 

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

Housing demand
Housing supply
Credit supply
Aggregate demand
Aggregate supply
Monetary policy
Effective lockdown index
Total

a) Housing investment

b) Real house prices

3

2

1

0

-1

-2

-3

-4

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

Global financial
crisis

Sovereign debt
crisis

COVID-19
crisis

Global financial
crisis

Sovereign debt
crisis

COVID-19
crisis

Sources: Eurostat, Hale et al., op. cit., Lemke and Vladu, op. cit., Google residential mobility index, ECB, and authors’ calculations. 
Notes: For comparability, the figures are reported as average quarter-on-quarter percentage changes and contributions over the reference periods, as defined 
in Section 1. The contribution of the constant term and other unidentified residuals (related for instance to risk aversion and possible changes in preferences) 
is not shown. 

3 

The second and the third waves – housing market resilience 
amid policy support measures 

The housing market proved resilient during the second and third waves of the 
COVID-19 pandemic. In spite of the deterioration in the epidemiological situation 
that led to a tightening of restrictions in the fourth quarter of 2020, the euro area 
housing market gained further momentum. House prices remained on an upward 
trend, increasing in annual terms by around 6% in both the fourth quarter of 2020 
and the first quarter of 2021, a pace not seen since mid-2007. Housing investment 
recovered further in the same reference period, settling close to its pre-crisis levels. 
These signs of significant resilience stemmed from both the supply side, as indicated 
by the momentum in value added and employment in construction and real estate, 
and the demand side, as suggested by the return of the number of transactions to 
pre-crisis levels in many euro area countries and the increased demand for 
mortgage loans. The milder impact of restrictions compared with the first wave and 
the significant stepping-up of fiscal and monetary policy measures, continuous 
favourable financing conditions, the increased attractiveness of housing for 
investment purposes – in view of forced savings – helped to strengthen housing 
investment and exert upward pressure on house prices.15 

15   For more details, see “The impact of containment measures across sectors and countries during the 

COVID-19 pandemic”, op. cit. 

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Fiscal policy measures were key in mitigating the negative effects of the 
second and the third waves of the pandemic on the housing market. These 
measures included short-time work schemes, targeted transfers to more vulnerable 
segments, cuts to personal income taxes, social contributions and indirect taxes. 
Policy interventions to support firms also contributed to mitigating the impact of the 
crisis on employment and income, and helped construction firms maintain housing 
supply.16  These measures included direct support schemes for firms and the self-
employed, partial compensation of losses, subsidies, tax deferrals and public 
guarantees on bank loans.17  Other important policy tools were moratoria schemes, 
which provided households and firms with short-term relief through the suspension of 
principal and/or interest payments on loans, and very generous fiscal incentives for 
house renovation in some countries. 

Monetary policy also provided key support to the euro area housing market by 
preserving favourable financing conditions for households and firms. First, the 
Pandemic Emergency Purchase Programme (PEPP) announced in March 2020, by 
impacting yields especially at the long end of the maturity spectrum, exerted 
significant downward pressure on lending rates. This was particularly pronounced for 
mortgage rates, as they are typically linked to longer-term interest rates. Second, the 
negative interest rate policy continued to contribute to historically low lending rates, 
thereby supporting bank lending. Third, the targeted longer-term refinancing 
operations (TLTRO III) offered attractive bank funding conditions, which banks 
passed on to firms and households, even for the non-targeted segment of the facility 
(i.e. housing loans).18  Overall, according to the ECB bank lending survey (BLS), the 
ECB’s monetary policy measures contributed to an increase in housing lending 
volumes and an easing of bank lending conditions on new mortgages during the 
COVID-19 period. 19  As regards existing mortgages, households at the lower end of 
the income distribution seem to have benefited the most from the reduced interest 
rates via the so-called cash-flow effect of monetary policy, which increased their 
available resources for spending (Box 2). 

Financing conditions remained favourable, especially for less risky 
households, supporting the robust demand for housing. Apart from the first two 
months of 2020, flows of housing loans remained robust, with the annual growth rate 
of the loan stock reaching 4.2% in the first quarter of 2021, a rate not observed since 
2008, significantly moving in tandem with house prices. Households’ demand for 
mortgages was met by historically low bank lending rates, which remained immune 
to the tightening in credit standards reported by banks in 2020 and to the increase in 
market rates in the first months of 2021. This muted response reflected favourable 
bank funding costs (buttressed by the policy support), but concealed a widening of 
lending margins for riskier borrowers and higher collateral requirements in the 

16   Under short-time work schemes, firms experiencing economic difficulties could temporarily reduce the 
hours worked while providing their employees with income support from the government for the hours 
not worked. 

17   For more details on the fiscal policy measures implemented during the COVID-19 pandemic, see “The 
initial fiscal policy responses of euro area countries to the COVID-19 crisis”, op. cit. and “Public loan 
guarantees and bank lending in the COVID-19 period”, Economic Bulletin, Issue 6, ECB, 2020. 
18   Moreover, from a microprudential policy perspective, ECB Banking Supervision provided important 

capital relief for banks, which created further space for bank balance sheet expansion. 

19   See Section 3 of the April 2021 ECB bank lending survey (BLS). 

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The euro area housing market during the COVID-19 pandemic 

122 

 
 
context of deteriorated perceptions of households’ creditworthiness. In the first half of 
2021, tightening pressures on bank lending policies for housing loans vanished, 
primarily reflecting lower risk perceptions related to the improved economic outlook 
(Chart 5). The favourable developments observed during the second and third waves 
of the pandemic allowed households to experience a significantly smaller tightening 
of bank lending conditions during the COVID-19 period as a whole compared with 
previous crisis episodes (Chart 6). 20  The contribution of housing market prospects to 
loan demand was also strikingly different to that in previous crises in that it held up 
particularly well throughout the pandemic. Bank lending conditions for firms in the 
construction and the real estate sectors over the COVID-19 period were more 
favourable compared with those for firms in sectors that were hit harder by the 
containment measures.21 

Chart 5 
Bank lending conditions and loan demand for households and firms 

(net percentages of banks) 

Loans to households for house purchase
Loans to firms in the construction sector
Loans to firms in the residential real estate sector
Loans to firms in other sectors

30

25

20

15

10

5

0

-5

-10

45

30

15

0

-15

-30

-45

-60

-75

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

19

20

21

19

20

21

19

20

21

Credit standards

Terms and conditions

Loan demand (right-hand scale)

Sources: ECB (BLS) and authors’ calculations. 
Notes: The net percentages are defined as the difference between the sum of the percentages for “tightened/increased considerably” 
and “tightened/increased somewhat” and the sum of the percentages for “eased/decreased somewhat” and “eased/decreased 
considerably”. “Loans to firms in other sectors” is the unweighted average of loans to firms in manufacturing, services, wholesale and 
retail trade and commercial real estate. For loans to firms by sector, the questions have a biannual frequency, hence banks report on 
two quarters at once instead of one. Q3 21 and Q4 21 denote expectations indicated by banks in the July 2021 BLS. 

20   Previous crises had a more direct impact on the banking sector, which went through a process of 

significant balance sheet adjustment. This process constrained banks’ intermediation capacity, resulting 
in tighter lending policies. 

21   As with other sectors, firms in the construction and real estate sectors reduced their recourse to bank 

financing in the second half of 2020 on account of abated emergency liquidity needs and the significant 
precautionary buffers built up in the early stages of the pandemic (see Falagiarda, M. and Köhler-
Ulbrich, P., “Bank lending to euro area firms – What have been the main drivers during the COVID-19 
pandemic?”, European Economy: Banks, Regulation, and the Real Sector, Vol. 1, 2021, pp. 119-143). 
Moreover, during this period, loan demand continued to be dampened by high uncertainty, especially 
for the financing of fixed investment. In the first half of 2021, firms in the construction and real estate 
sectors started to significantly increase their demand for bank borrowing (Chart 6) owing to the 
improved economic outlook and robust demand for housing. 

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123 

 
 
 
Chart 6 
Bank lending conditions for housing loans, loan demand and loan volumes 

(for BLS indicators: net percentages of banks, quarterly average over crisis episodes; for loan volumes: flows in EUR billions, quarterly 
averages over crisis episodes) 

Global financial crisis
Sovereign debt crisis
COVID-19 crisis

t
i
d
e
r
C

s
d
r
a
d
n
a
t
s

d
n
a

s
m
r
e
T

s
n
o
i
t
i
d
n
o
c

n
a
o
L

d
n
a
m
e
d

Total

Risk perceptions

Cost of funds and BS constr.

Margins on average loans

Margins on riskier loans

Collateral requirements

Maturity

Loan-to-value ratio

Non-interest rate charges

Total

Housing market prospects

Consumer confidence

Loan volumes

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

Sources: ECB (BLS), ECB (BSI) and authors’ calculations. 
Notes: The net percentages are defined as the difference between the sum of the percentages for “tightened/increased considerably” 
and “tightened/increased somewhat” and the sum of the percentages for “eased/decreased somewhat” and “eased/decreased 
considerably”. “Risk perceptions” is the unweighted average of “general economic situation and outlook” and “housing market 
prospects, including expected house price developments”. “Cost of funds and BS constr.” stands for “Cost of funds and balance sheet 
constraints”. 

In a context of low interest rates, high uncertainty and large savings, housing 
demand has also been supported by investment motives. The demand for 
housing for investment purposes has been a distinctive feature of the recovery in 
housing markets that started in 2013.22  This factor seems to have strengthened 
during the COVID-19 period, reflecting a further increase in the relative 
attractiveness of housing as an investment class and a further expansion of the 
availability of savings amid considerable economic uncertainty.23  24  Moreover, flows 
into real estate funds, albeit declining slightly in 2020, remained at relatively high 
levels, also as a share of residential investment. Although some of these funds could 
also be directed to commercial real estate or outside the euro area, this evidence 
suggests that private and institutional investors searching for yield and safety may 
have contributed to additional housing demand during the COVID-19 period. 

Supply-side constraints have also exerted upward pressures on house prices. 
Constraints on housing supply have been an important factor behind housing market 
dynamics over the 2013-19 period. Following the significant decline in building 
permits in the aftermath of the pandemic outbreak, supply bottlenecks were further 
aggravated during the different waves of the pandemic (Chart 7). While in the first 
wave financing conditions and other factors (notably, containment measures) 
particularly constrained production, in the second and third waves supply bottlenecks 

22   See the article entitled “The state of the housing market in the euro area”, op. cit. 
23   The impact of the forced accumulation of savings associated with the pandemic is discussed in the box 
entitled “The recovery of housing demand through the lens of the Consumer Expectations Survey” of 
this issue of the Economic Bulletin. 

24   Estimates of the return on housing-related investment point to an increase in the relative attractiveness 
of investment in residential property vis-à-vis government bonds and deposits during the COVID-19 
period. Increased housing returns may have in turn fuelled higher house price expectations, thereby 
further boosting the demand for housing. 

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124 

 
 
 
 
were mainly due to labour and material shortages. The lack of (especially high-
skilled) workers was also a major factor limiting production before the COVID-19 
crisis,25  but the shortage of materials reflected global supply-chain disruptions and a 
reallocation of resources induced by the outbreak of the pandemic, leading to 
increases in supplier delivery times and input costs. Overall, survey data suggest 
that, for construction firms, supply-side constraints increased relative to demand 
constraints during the COVID-19 period. 

Box 2 
Monetary policy and the cash-flow effect on households via mortgages 

Prepared by Lucía Kazarian Avakian, Giulio Nicoletti and Christophe Van Nieuwenhuyze 

This box assesses the benefit households received from lower interest payments on their existing 
mortgage debt since the beginning of the ECB’s unconventional monetary policy in 2015. This so-
called cash-flow effect of monetary policy contributed to the decrease in interest payments of the 
aggregate euro area household sector, which overall reached a record low of 2.2% of disposable 
income at the end of 2020 (Chart A, panel a). This positive effect helped households deal with the 
COVID-19 shock. 

Chart A 
Household interest payments and fixed rate mortgages 

(panel a): percentages of gross disposable income; panel b): percentages) 

Euro area
Germany
Spain
France
Italy

a) Household interest payments

b) Share of newly issued fixed rate mortgages

8

6

4

2

100

75

50

25

0
2010

2012

2014

2016

2018

2020

0
2010

2012

2014

2016

2018

2020

Sources: Eurostat and ECB quarterly sectoral accounts (QSA) and ECB monthly data on euro area interest rates on loans and deposits (MIR). 
Notes: Panel a): actual gross interest payments, i.e. including FISIM (financial intermediation services indirectly measured). Panel b): 12-month moving 
average of the share of new loans for house purchase with initial fixation period above ten years in total new loans for house purchase. 

To investigate the distributional impact of this monetary policy transmission channel, we calculate 
the size of the advantage across the income distribution of households with a mortgage. 
Furthermore, we differentiate between the impact via adjustable rate mortgages (ARMs), which are 
relinked periodically to the change in short-term rates, and fixed rate mortgages (FRMs), which are 
affected by the change in long-term rates if households engage in refinancing their mortgage. Given 

25   See, for instance, the KfW-ifo Skilled Labour Barometer for German construction in June 2021. 

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the relatively large decrease in long-term rates since 2015 and the rising share of FRMs (Chart A, 
panel b), the advantage obtained via this channel is likely to have increased. 

We calculate the benefit across the joint income distribution of households with a mortgage in the 
five largest euro area economies, combining household-level information on mortgages and income 
with country-level information on interest rates. Considering all households with a mortgage in 2014 
(based on the second wave of the Household Finance and Consumption Survey (HFCS)), we 
calculate the income gain they booked at the end of 2020 compared with their initial situation in 
2014 as a result of lower interest payments on their mortgages. For ARMs, calculations are based 
on developments in the short-term rate (EURIBOR 3-month), while for FRMs, 26  they take into 
account developments in long-term interest rates and refinancing volumes. In general, the latter 
increase with the size of the interest advantage, i.e. with the gap between long-term rates on new 
mortgages and the rate on outstanding ones. 

Chart B 
Income gain through lower interest payments on mortgages by income percentile 

(percentages of household gross income) 

Fixed rate mortgages
Adjustable rate mortgages

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Average

0-20

20-40

40-60

60-80

80-100

Sources: ECB (HFCS, MIR) and authors’ calculations. 
Notes: The chart shows, for the households with a mortgage in 2014 in Germany, Spain, France, Italy and the Netherlands and grouped according to the 
aggregate income quintiles over those countries (x-axis), the average income gain booked at the end of 2020 compared with the initial situation in 2014 as a 
result of lower interest charges on adjustable (ARMs) and fixed rate mortgages (FRMs). For ARMs, this is calculated by the change in the short-term rate 
(EURIBOR 3-month) times the value of the outstanding ARMs in 2014. The gain due to lower interest rates on FRMs is calculated by the product of three 
components: the outstanding amount of FRMs in 2014, the average share of households that renegotiated their loans over the period 2015-20, and the 
average interest advantage they booked computed as the average difference between renegotiated rates and the rates on new mortgages five years earlier. 

The cash-flow effect benefited all households with a mortgage and was on average around 0.9% of 
gross income via FRMs and 2% via ARMs (Chart B), supporting – all else being equal – household 
balance sheets, including during the COVID-19 period. 27  In the latter period, the income gain might 

26   We consider all housing loans with an initial rate fixation period over ten years as FRMs, although 

these may allow for rate changes after this period which resemble ARM properties (e.g. rate changes 
every five years after the first ten years).   

27   Note that the part of these gains related to unconventional monetary policy would require the 

identification of the monetary policy shock. Such exercises confirm the larger effect via ARMs than via 
FRMs. See, for example, Pietrunti, M. and Signoretti, F. M., “Unconventional monetary policy and 
household debt: The role of cash-flow effects”, Journal of Macroeconomics, Vol. 64, No 103201, June 
2020. Similar to the methodology used here, Ehrmann, M. and Ziegelmeyer, M., “Mortgage choice in 
the euro area: Macroeconomic Determinants and the effect of monetary policy on debt burdens”, 
Journal of Money, Credit and Banking, Vol. 49, March-April 2017, pp. 469-494 find that the largest 
impact is via ARMs, although these results date back to the period before the existence of 
unconventional monetary policy. 

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126 

 
 
 
have taken the form of extra savings, contributing to the resilience of the housing market.28 
Furthermore, the cash-flow effect benefited low income households in particular, which were most 
exposed to labour income losses during the COVID-19 period.29  As such, lower interest payments 
might have mitigated the overall negative impact on the income of debtors, thereby also dampening 
inequality forces,30  the likelihood of payment arrears and the need to make extensive and 
prolonged recourse to moratoria.31  Finally, lower long-term rates encouraged both refinancing and 
a higher share of FRMs, so that households have been able to lock in the low interest rates, 
enhancing their debt sustainability and reducing their interest sensitivity in the event that monetary 
policy tightens. 

4 

Prospects and risks for the euro area housing market 

Several factors are likely to support housing market prospects in the near 
term. The expected recovery in economic activity – sustained by a successful 
vaccination campaign in the euro area – should hold up households’ income and 
employment prospects, including when fiscal support gradually recedes. Financing 
conditions are likely to remain favourable, reflecting the policy support and the 
expected improvements in borrowers’ creditworthiness. Recent lending dynamics 
and indications from the BLS, which tend to display good leading properties in 
around two to three quarters in terms of house prices and housing investment, point 
to continued dynamism in the housing market in the coming quarters. Housing 
investment is likely to continue its positive trend observed since the third quarter of 
2020, reinforced by resilient house prices relative to construction costs, improving 
real disposable incomes and buoyant intentions to buy and renovate properties 
(Chart 7). In addition, a part of the savings accumulated during the pandemic could 
be redirected into the housing market amid a low-yield environment and the 
increased relative attractiveness of housing for investment purposes. The share of 
residential property in real estate portfolios is likely to increase since it is perceived 
as a safer asset in times of uncertainty (housing is a primary need) entailing stable 
income streams (rents). 

Nevertheless, the outlook for the housing market remains highly dependent on 
uncertainties related to the pandemic. Adverse developments related to the 
COVID-19 pandemic, such as the possible spread of new variants, might weigh on 

28   Given forced savings, marginal propensities to consume have been relatively low during the COVID-19 
pandemic. See the box entitled “COVID-19 and the increase in household savings: precautionary or 
forced?”, Economic Bulletin, Issue 6, ECB, 2020; and Christelis, D., Georgarakos, D., Jappelli, T. and 
Kenny, G., “The COVID-19 crisis and consumption: survey evidence from six EU countries”, op. cit. 
29   See Schnabel, I., “Unequal scars – distributional consequences of the pandemic”, speech at the panel 
discussion “Verteilung der Lasten der Pandemie” (“Sharing the burden of the pandemic”), Deutscher 
Juristentag 2020, Frankfurt am Main, 18 September 2020 and the references therein. 

30   See the article entitled “Monetary policy and inequality”, Economic Bulletin, Issue 2, ECB, 2021. 
31   The positive impact of the cash-flow effect via mortgages on households goes hand-in-hand with a 

negative effect on banks’ balance sheets. However, positive effects on banks’ balance sheets also arise 
as a result of the improved creditworthiness of households. For a study on the overall impact of 
monetary policy on banks’ profitability, see Altavilla, C., Bouchina, M. and Peydró, J.L., “Monetary 
policy and bank profitability in a low interest rate environment”, Economic Policy, Vol. 33, Issue 96, 
October 2018, pp. 531-586. 

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The euro area housing market during the COVID-19 pandemic 

127 

 
 
 
housing market prospects, and particularly on housing demand, as observed at the 
beginning of the pandemic. Amid high uncertainty, the withdrawal of policy support 
measures is an additional factor that could possibly limit prospects for the housing 
market if such policies were to be phased out before the recovery is on track. In 
addition, the overall increase in risk-free interest rates observed since the beginning 
of 2021 may exert upward pressure on mortgage rates. Moreover, the developments 
in shortages of raw materials and the associated increases in supplier delivery times 
and input costs could negatively affect construction activity and exert strong upward 
pressure on prices in the near term (Chart 7). This would contribute to keeping 
house prices in the euro area at an elevated level,32  thus possibly increasing the 
importance of and need for macroprudential measures (Box 3). 

Chart 7 
Euro area supply constraints, construction input prices, supplier delivery times and 
intentions to buy and renovate 

(input prices and supplier delivery times: deviation from baseline (50); intentions to buy and renovate: standardised levels; supply 
constraints: levels) 

Input prices
Supplier delivery times
Intentions to buy (right-hand scale)
Intentions to renovate (right-hand scale)
Supply constraints

50

40

30

20

10

0

4

3

2

1

0

-1

/

9
1
1
0

/

9
1
2
0

/

9
1
3
0

/

9
1
4
0

/

9
1
5
0

/

9
1
6
0

/

9
1
7
0

/

9
1
8
0

/

9
1
9
0

/

9
1
0
1

/

9
1
1
1

/

9
1
2
1

/

0
2
1
0

/

0
2
2
0

/

0
2
3
0

/

0
2
4
0

/

0
2
5
0

/

0
2
6
0

/

0
2
7
0

/

0
2
8
0

/

0
2
9
0

/

0
2
0
1

/

0
2
1
1

/

0
2
2
1

/

1
2
1
0

/

1
2
2
0

/

1
2
3
0

/

1
2
4
0

/

1
2
5
0

/

1
2
6
0

/

1
2
7
0

Sources: European Commission, IHS Markit and own calculations. 

Changes in housing preferences may also affect the housing market going 
forward. The COVID-19 pandemic may bring changes in preferences and behaviour 
that could influence housing demand over the medium-to-long term. Should work-
from-home arrangements become more prevalent, housing demand may partly shift 
away from city centres towards suburban and rural areas, as the opportunity costs 
associated with peripheral working places would decrease in tandem with 
commuting time.33  This could contribute to limiting upward pressure on rent and 
house prices in large cities characterised by limited housing supply. As observed in 

32   For further details, see the ECB’s Financial Stability Review May 2021. 
33   According to a survey by the German ifo Institute “Wie beeinflusst die Corona-Pandemie die 

Wohnortpräferenzen?”, ifo Schnelldienst, Vol. 74, No 08, 2021, around 20% of households living in 
urban areas plan to move within the next two years in contrast to a share of 12% of households living in 
rural areas planning to move (Chart 8). For almost half of these respondents (46%), their decision was 
influenced by the COVID-19 pandemic. 

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other advanced economies,34  preliminary evidence for some euro area countries 
tends to corroborate this narrative, hinting, for example, at buoyant house prices in 
rural areas in Germany and softening house price increases in capital cities in the 
euro area compared with the pre-pandemic period (Chart 8). 35  36  The accelerated 
introduction of remote working arrangements slowed down the demand for 
commercial office and retail spaces, potentially opening up the possibility to convert 
some of these properties into residential housing in areas where supply is limited.37 
Both climate change and climate policies could also affect the housing market going 
forward. Investment in the energy efficiency of buildings could boost housing 
investment – also helped by fiscal incentives in some countries – thus lowering 
household spending on energy. In addition, properties meeting enhanced energy 
credentials could command higher prices, thus posing additional challenges for 
housing affordability. 

Chart 8 
Households’ plans to move and price developments in urban areas vis-à-vis rural 
areas 

(average growth rates; percentages) 

Pre-COVID-19 crisis
COVID-19 crisis
Plan to move within the next two years (right-hand scale)
Willingness to change living situation fundamentally (right-hand scale)
No willingness to move (right-hand scale)

16

14

12

10

8

6

4

2

0

80

70

60

50

40

30

20

10

0

Flats

Houses

All dwellings

Flats

Houses

All dwellings All dwellings All dwellings

Urban areas (Germany)

Rural areas (Germany)

Capital cities
(euro area)

Aggregate
(euro area)

Sources: ECB, DESTATIS, ifo Institute, Eurostat, national sources and own calculations. 
Notes: Residential property prices for urban areas in Germany are calculated as average growth rates of metropolis, cities not 
attached to a district and urban districts, and for rural areas, as average growth rates of densely and sparsely populated rural districts. 
The pre-COVID-19 period spans from the fourth quarter of 2016 to the fourth quarter of 2019, and the COVID-19 crisis period runs 
from the first quarter of 2020 to the first quarter of 2021. The “willingness to move” of households living in urban areas is calculated as 
the average of households’ responses from urban areas, suburban areas and small cities expressed as a percentage to a survey by 
the German ifo Institute. The “plan to move within the next two years” is calculated as the sum of percentages of households planning 
to move within the next six months, six to twelve months and within the next two years. The euro area aggregate series is a weighted 
average based on GDP weights, which includes Belgium, Germany, Estonia, Ireland, Spain, France, Italy, the Netherlands, Austria, 
Slovenia and Finland. 

34   For further details, see “How Covid Has Reshaped Real Estate From New York to Singapore”, 

Bloomberg, May 2021 and “The Impact of the COVID-19 Pandemic on the Demand for Density: 
Evidence from the U.S. Housing Market”, Federal Reserve Bank of Dallas, August 2020,, which show that in the United 
States the pandemic has led to a shift in housing demand away from neighbourhoods with high 
population density. 

35   For further details, see the box entitled, “Euro area house price developments during the coronavirus 

pandemic”, op. cit. 

36  

In March 2021 Ireland launched a plan to create a network of more than 400 remote working hubs, 
introducing tax breaks for individuals and companies which support work-from-home arrangements and 
launching a Rural Development Policy for the period 2021-25. 

37   For further details, see “Brick by Brick”, OECD, May 2021. 

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Box 3 
Macroprudential policy for residential real estate before, during and after the COVID-19 
pandemic 

Prepared by Jan Hannes Lang, Marek Rusnák, Marco Lo Duca, Barbara Jarmulska 

Prior to the COVID-19 pandemic, many euro area countries had activated macroprudential 
measures to address the build-up of residential real estate (RRE) vulnerabilities or to act as a 
prudent backstop. At the beginning of 2020, 14 euro area countries had in place borrower-based 
macroprudential measures (BBMs), such as loan-to-value (LTV), debt-service-to-income (DSTI), 
debt-to-income (DTI) or maturity limits.38  In addition, seven euro area countries had activated 
macroprudential risk weight policies to increase the amount of capital banks need to hold against 
mortgage loans.39  BBMs were put in place by many countries to act as a prudent backstop for 
lending standards, affecting only a small fraction of mortgage loan origination at the time of 
implementation, but providing an automatic limit to a potential widespread loosening of lending 
standards in the future. However, in some countries RRE vulnerabilities had been building up over 
preceding years, leading the European Systemic Risk Board (ESRB) to issue warnings and 
recommendations to six euro area countries in September 2019.40  In some countries, 
macroprudential measures were therefore put in place to more actively contain the build-up of RRE 
vulnerabilities and increase bank resilience against associated systemic risks. 

Following the outbreak of the pandemic, in line with the countercyclical nature of macroprudential 
policy, some national authorities eased macroprudential measures for RRE in order to limit the 
possible amplification effects of a tight macroprudential stance. In Malta, Portugal, Slovenia and 
Finland, national authorities adjusted existing BBMs at the height of the pandemic in spring 2020, 
fearing that the pandemic shock could constrain market access for solvent borrowers facing 
temporary income and liquidity shocks.41  Two countries provided some capital headroom to absorb 
losses and meet credit demand. In the Netherlands, the planned implementation of an LTV-
dependent risk weight floor for mortgage loans was postponed, and, in Finland, the existing risk 
weight floor for IRB mortgage loans was not extended beyond 2020. All of the above measures 

38   The only euro area countries without BBMs in place at the beginning of 2020 were Germany, Greece, 
Spain, Italy and Luxembourg. Details on the BBMs implemented across countries can be found in 
Section 4 of Lang, J.H, Pirovano, M., Rusnák, M. and Schwarz, C., “Trends in residential real estate 
lending standards and implications for financial stability”, Special Feature A, Financial Stability Review, 
European Central Bank, May 2020. 

39   Risk weight policies affect capital ratios by increasing risk weights on banks’ exposures to residential 
real estate. This generally results in higher risk-weighted exposures and implies that additional capital 
is needed to meet capital requirements. Belgium, Estonia and Finland had activated risk weight 
multipliers, add-ons and floors under Article 458 of the Capital Requirements Regulation (CRR) for 
domestic residential mortgage loans of banks using the internal ratings-based approach (IRB) to 
determine risk weights. Ireland, Malta and Slovenia had activated risk weight floors under Article 124 of 
the CRR for mortgage loans of banks using the standardised approach (STA) to determine risk weights. 
Luxembourg had recommendations in place regarding risk weight floors for mortgages under both the 
STA and IRB approaches.   

40   Germany and France were subject to a warning by the ESRB, while Belgium, Luxembourg, the 

Netherlands and Finland received ESRB recommendations. See ESRB Press Release, “ESRB issues 
five warnings and six recommendations on medium-term residential real estate sector vulnerabilities”, 
23 September 2019. 

41  

In Portugal, in April 2020, it was decided that personal credit with maturities of up to two years will not 
need to comply with DSTI limits and is exempted from the recommendation of regular principal and 
interest payments until September 2020. In Slovenia, an amendment of the macroprudential 
restrictions on household lending to provide temporary flexibility when calculating income was 
implemented in May 2020. In Finland, the LTV/C limit for other than first-time buyers was restored from 
85% to 90% in June 2020. In Malta, an extension of the phasing-in period for the LTV limit and the 
adoption of a temporary relaxation of the stressed DSTI limit of 40% was implemented in June 2020. 

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provided relief to new borrowers and banks alike and complemented other support measures, such 
as loan repayment moratoria or short-term working schemes (Section 3). 

In most euro area countries, authorities did not adjust the BBMs that were already in place, as they 
were considered to be prudent back-stops for which adjustment had not been foreseen throughout 
the cycle. In addition, depending on the legal basis, there was a possibility that adjusting BBMs 
might involve lengthy processes compared with capital measures. However, Belgium and Estonia 
extended the application of risk weight measures on mortgages (under Article 458 of the CRR) in 
2021 to retain bank resilience for accumulated RRE risks. Since existing risk weight measures 
affect minimum requirements or sectoral buffers, they might need to be released in the event that 
risks and losses in the RRE market materialise. 

Chart A 
Macroprudential measures should be considered in countries where vulnerabilities continue to build 
up as short-term downside risks recede 

a) Annual RRE price growth in the fourth quarter of 2019 and the first quarter of 2021 

(panel a): percentages; panel b): current policy considerations for RRE macroprudential measures) 

ESRB recommendation 2019
ESRB warning 2019
Other countries
Euro area

Overvaluation in Q4 2019 < 0

Overvaluation in Q4 2019 > 0

18

16

14

12

10

8

6

4

2

0

18

16

14

12

10

8

6

4

2

0

CY

IT

LV

IE

FI

GR MT

SI

EE

SK

ES

PT

FR

EA

BE

DE

NL

LT

AT

LU

b) Current policy considerations for RRE macroprudential measures 

Further increases in RRE
vulnerabilities in selected countries

Policies continue to support households, 
but policy withdrawal entails risks

Short-term downside risks to RRE 
markets receding in most countries

FURTHER POLICY MEASURES TO BE CONSIDERED IF COUNTRY-SPECIFIC RRE RISK IS ELEVATED AND 
THE LIKELIHOOD OF PROCYCLICAL AMPLIFICATION IS LOW

Sources: ECB and authors’ calculations. 
Notes: Panel a): Hollow dots refer to the values in the fourth quarter of 2019; coloured dots refer to the values in the first quarter of 2021 (the fourth quarter of 
2020 for Cyprus and Finland). Average overvaluation denotes the average of the price-to-income ratio and the results of an econometric model in the fourth 
quarter of 2019. 

Going forward, as pandemic risks recede, further macroprudential measures for RRE should be 
considered in countries where RRE vulnerabilities continue to build up. Robust house price and 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The euro area housing market during the COVID-19 pandemic 

131 

 
 
 
mortgage loan growth continued throughout the pandemic, particularly in countries with pre-existing 
RRE vulnerabilities (Chart A, panel a). Nevertheless, the divergence between the RRE and 
economic cycles during the COVID-19 pandemic can imply downside risks in adverse growth 
scenarios, especially if government support is scaled back too early. In this context, 
macroprudential measures should be used in countries where vulnerabilities continue to build up as 
short-term downside risks recede (Chart A, panel b). In this regard, Luxembourg activated BBMs at 
the end of 2020, while in spring 2021 the Netherlands confirmed its intention to activate the LTV-
dependent risk weight floor for mortgage loans.42  These actions notwithstanding, further 
macroprudential measures could be needed in some euro area countries if current trends in RRE 
markets continue. 

5 

Conclusion 

This article discussed developments in the euro area housing market since the 
outbreak of the COVID-19 pandemic. The mandatory and voluntary restrictions on 
economic agents’ mobility in response to the first wave of the COVID-19 pandemic 
had a strong impact on activity without significantly impairing the upward trend in 
prices and loans, in contrast to the global financial crisis and the sovereign debt 
crisis. Moreover, the first wave of the pandemic induced greater diversity in housing 
investment across countries compared with previous crises, which is partly explained 
by the varying impact of restrictions along the income distribution. 

Several factors supported the housing sector throughout the pandemic. The 
resilience of the housing market originated in part from the declining impact of 
restrictions after the first wave. Other factors included fiscal, monetary and 
macroprudential policy measures, continuously favourable financing conditions, the 
increased attractiveness of housing for investment purposes, as well as supply-side 
bottlenecks exerting upward pressure on house prices without significantly weighing 
on activity. 

Overall, pandemic-related uncertainties and associated structural changes will 
continue to influence the prospects for the housing market. The broad-based 
economic recovery and the use of the large stock of accumulated savings are likely 
to support housing market prospects going forward. However, the outlook remains 
uncertain and depends on how the pandemic develops and the timing of the 
withdrawal of policy support. Changes in housing preferences may also lead to a 
reallocation within the housing market, away from commercial and urban residential 
properties and towards suburban and rural residential real estate. Heterogeneous 
developments across households are likely to persist and possibly intensify. 

42   The measure is expected to enter into effect on 1 January 2022, provided that the economic recovery 

continues in line with current expectations (DNB Financial Stability Report Spring 2021). 

ECB Economic Bulletin, Issue 7 / 2021 – Articles 
The euro area housing market during the COVID-19 pandemic 

132 

 
 
 
 
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

 -

.

...

(p)

s.a.

data do not exist/data are not applicable 

data are not yet available

nil or negligible

provisional

seasonally adjusted

n.s.a.

non-seasonally adjusted

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 1

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)

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

1

3.7
2.8
-3.3

7.8
1.9

0.8
. 

- 
- 
- 
- 
- 
- 

2

2.9
2.3
-3.4

7.5
1.1

1.5
1.6

- 
- 
- 
- 
- 
- 

3

4

1.7
1.7
-9.7

17.4
1.1

-1.4
5.5

- 
- 
- 
- 
- 
- 

0.6
0.0
-4.6

5.4
2.8

-1.1
0.5

- 
- 
- 
- 
- 
- 

5

6.7
6.0
2.3

3.0
2.6

0.6
1.3

- 
- 
- 
- 
- 
- 

6

7

1.8
1.6
-6.4

12.6
-0.4

-0.3
2.1

- 
- 
- 
- 
- 
- 

2.6
2.1
1.4

1.2
1.2

1.9
3.7

3.3
3.8
4.0
4.2
4.3
4.6

Sources: Eurostat (col. 6, 13); BIS (col. 9, 10, 11, 12); OECD (col. 1, 2, 3, 4, 5, 7, 8).
1) Quarterly data seasonally adjusted; annual data unadjusted.
2) Data refer to the changing composition of the euro area.

8

2.1
2.2
1.8

1.6
1.6

1.7
2.8

2.4
2.8
3.1
3.1
3.1
3.2

9

2.4
1.8
1.2

1.2
1.2

1.9
4.8

4.2
5.0
5.4
5.4
5.3
5.4

10

2.5
1.8
0.9

0.6
0.5

0.6
2.0

1.5
2.1
2.5
2.0
3.2
3.1

11

1.0
0.5
0.0

0.2
-0.8

-0.5
-0.8

-1.1
-0.8
-0.5
-0.3
-0.4
. 

12

2.1
2.9
2.5

2.3
0.1

0.0
1.1

0.9
1.3
1.1
1.0
0.8
. 

13

1.8
1.2
0.3

0.0
-0.3

1.1
1.8

1.6
2.0
1.9
2.2
3.0
3.4

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

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 May   
         June   
         July   
         Aug.   
         Sep.   
         Oct.   

1

2

3

4

5

53.4
51.7
47.5

54.2

54.3
57.5
52.9

59.0
56.1
54.9
51.3
52.4
. 

55.0
52.5
48.8

56.8

59.3
65.3
56.8

68.7
63.7
59.9
55.4
55.0
57.3

53.3
50.2
46.5

50.5

49.1
61.9
56.3

62.9
62.2
59.2
54.8
54.9
. 

52.1
50.5
42.4

48.2

48.4
49.6
47.4

48.8
48.9
48.8
45.5
47.9
. 

52.3
51.8
51.4

56.3

52.3
53.0
50.6

53.8
50.6
53.1
47.2
51.4
. 

6

54.6
51.3
44.0

48.1

49.9
56.8
58.4

57.1
59.5
60.2
59.0
56.2
54.3

7

53.1
50.3
48.5

54.6

53.8
53.9
51.7

54.4
52.9
53.2
50.6
51.4
. 

8

53.8
52.2
46.3

54.0

54.5
58.8
53.2

60.5
57.2
55.5
51.5
52.8
. 

New export
orders

Global

Advanced
economies

Emerging
market
economies

9

50.8
48.8
45.3

50.8

50.3
52.9
50.3

53.6
51.7
51.4
49.5
50.1
. 

10

4.3
-0.4
-4.3

4.8

4.5
1.6
. 

3.9
1.6
-0.4
-0.9
. 
. 

11

3.2
-0.3
-4.5

5.2

1.8
1.4
. 

2.2
1.4
0.5
-0.2
. 
. 

12

5.6
-0.4
-4.2

4.4

7.5
1.9
. 

5.7
1.9
-1.3
-1.7
. 
. 

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 7 / 2021 - Statistics

S 2

 
   
   
   
 
 
 
   
   
   
   
   
 
 
2 Financial developments

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

Euro short-term
rate
(€STR)  2)

Overnight
deposits
(EONIA)

1-month
deposits
(EURIBOR)

3-month
deposits
(EURIBOR)

6-month
deposits
(EURIBOR)

12-month
deposits
(EURIBOR)

3-month
deposits
(LIBOR)

3-month
deposits
(LIBOR)

   Euro area 1) 

United States

Japan

2018   
2019   
2020   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

1

-0.45
-0.48
-0.55

-0.56
-0.57
-0.56
-0.56
-0.57
-0.57
-0.57

2

-0.36
-0.39
-0.46

-0.48
-0.48
-0.48
-0.48
-0.48
-0.48
-0.49

3

-0.37
-0.40
-0.50

-0.55
-0.56
-0.56
-0.55
-0.56
-0.56
-0.56

4

-0.32
-0.36
-0.43

-0.54
-0.54
-0.54
-0.54
-0.54
-0.55
-0.55

5

-0.27
-0.30
-0.37

-0.52
-0.52
-0.51
-0.51
-0.52
-0.53
-0.52

6

-0.17
-0.22
-0.31

-0.49
-0.48
-0.48
-0.48
-0.49
-0.50
-0.49

7

2.31
2.33
0.64

0.19
0.19
0.15
0.13
0.13
0.12
0.12

8

-0.05
-0.08
-0.07

-0.08
-0.07
-0.09
-0.09
-0.08
-0.10
-0.08

Source: Refinitiv and ECB calculations.
1) Data refer to the changing composition of the euro area, see the General Notes.
2) The ECB published the euro short-term rate (€STR) for the first time on 2 October 2019, reflecting trading activity on 1 October 2019. Data on previous periods refer to the

pre-€STR, which was published for information purposes only and not intended for use as a benchmark or reference rate in any market transactions.

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

5

2018   
2019   
2020   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

-0.80
-0.68
-0.75

-0.64
-0.63
-0.63
-0.65
-0.66
-0.68
-0.71

-0.75
-0.66
-0.76

-0.69
-0.68
-0.68
-0.69
-0.75
-0.73
-0.73

-0.66
-0.62
-0.77

-0.72
-0.70
-0.69
-0.70
-0.80
-0.77
-0.72

-0.26
-0.45
-0.72

-0.62
-0.57
-0.54
-0.56
-0.75
-0.68
-0.54

0.32
-0.14
-0.57

-0.28
-0.18
-0.15
-0.20
-0.44
-0.39
-0.17

6

1.07
0.52
0.19

0.41
0.50
0.53
0.49
0.31
0.34
0.56

7

0.08
0.34
0.80

1.68
1.57
1.54
1.40
1.16
1.24
1.41

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

8

0.51
0.24
0.32

0.82
0.80
0.75
0.68
0.52
0.56
0.78

9

10

11

-0.67
-0.62
-0.77

-0.75
-0.73
-0.72
-0.72
-0.83
-0.79
-0.74

-0.45
-0.52
-0.77

-0.73
-0.70
-0.67
-0.68
-0.86
-0.79
-0.66

0.44
-0.13
-0.60

-0.32
-0.21
-0.16
-0.22
-0.50
-0.43
-0.16

12

1.17
0.41
-0.24

0.37
0.53
0.57
0.45
0.16
0.16
0.46

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

2018   
2019   
2020   

1

2

375.5 3,386.6
373.6 3,435.2
360.0 3,274.3

2021 Mar.   422.4 3,813.3
         Apr.    440.1 3,987.3
         May    443.8 4,003.6
         June   455.3 4,105.8
         July    453.8 4,062.6
         Aug.   468.5 4,177.0
         Sep.   465.5 4,158.3

Source: Refinitiv.

3

766.3
731.7
758.9

911.1
952.7
959.5
958.5
979.0
1,014.5
993.9

4

5

6

7

8

9

10

11

12

13

14

264.9
270.8
226.8

271.6
286.0
290.0
305.3
300.5
303.3
295.0

172.6
183.7
163.2

168.4
177.2
183.0
188.6
190.2
191.9
188.1

115.8
111.9
83.1

97.0
93.2
94.8
97.4
91.2
91.6
93.9

173.1
155.8
128.6

159.1
161.5
167.8
168.5
162.2
169.0
169.0

629.5
650.9
631.4

774.6
807.2
808.7
831.8
835.4
865.0
863.3

502.5
528.2
630.2

770.1
835.4
811.7
850.4
875.2
938.2
969.5

278.8
322.0
347.1

367.2
387.5
384.1
375.9
372.0
380.0
371.3

292.9
294.2
257.6

264.5
267.3
278.3
287.2
290.2
303.6
294.8

800.5
772.7
831.9

838.1
874.0
870.2
883.4
896.1
922.1
917.5

2,746.2 22,310.7
2,915.5 21,697.2
3,217.3 22,703.5

3,910.5 29,315.3
4,141.2 29,426.8
4,169.6 28,517.1
4,238.5 28,943.2
4,363.7 28,118.8
4,454.2 27,692.7
4,449.6 29,893.6

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 3

 
   
 
 
   
   
   
   
   
 
   
   
   
 
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

2020 Sep.    0.02
         Oct.    0.02
         Nov.    0.02
         Dec.    0.01

2021 Jan.    0.01
         Feb.    0.01
         Mar.    0.01
         Apr.    0.01
         May    0.01
         June    0.01
         July    0.01
         Aug. (p)  0.01

0.35
0.35
0.35
0.35

0.35
0.35
0.35
0.35
0.34
0.34
0.34
0.34

0.19
0.20
0.20
0.17

0.22
0.23
0.20
0.21
0.18
0.16
0.19
0.17

0.70
0.69
0.71
0.72

0.68
0.66
0.61
0.62
0.57
0.59
0.58
0.59

5.23
5.18
5.11
4.99

5.00
5.01
4.98
4.89
4.88
4.88
4.77
4.83

15.86
15.82
15.78
15.78

15.81
15.74
15.77
15.75
15.75
15.70
15.57
15.70

5.08
5.14
5.01
4.93

4.84
5.05
4.88
5.16
5.16
5.16
5.31
5.76

5.25
5.26
5.25
5.08

5.32
5.25
5.12
5.17
5.31
5.15
5.24
5.30

5.75
5.80
5.90
5.71

5.87
5.86
5.72
5.78
5.93
5.77
5.85
5.91

1.94
2.03
2.04
1.93

1.91
1.98
1.94
1.98
2.04
1.94
1.98
2.04

1.39
1.37
1.37
1.35

1.35
1.30
1.32
1.32
1.32
1.31
1.34
1.33

1.61
1.56
1.54
1.52

1.49
1.48
1.43
1.49
1.43
1.43
1.45
1.47

1.31
1.27
1.29
1.27

1.29
1.27
1.24
1.27
1.26
1.26
1.27
1.24

1.37
1.36
1.35
1.33

1.35
1.32
1.32
1.31
1.31
1.30
1.30
1.28

1.66
1.64
1.63
1.62

1.60
1.58
1.58
1.59
1.61
1.60
1.61
1.59

16

1.38
1.36
1.35
1.32

1.33
1.31
1.31
1.31
1.32
1.32
1.32
1.32

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

2020 Sep.   
         Oct.   
         Nov.   
         Dec.   

0.00
0.00
-0.01
-0.01

2021 Jan.   
-0.01
         Feb.   
-0.01
         Mar.   
-0.01
         Apr.   
-0.01
         May   
-0.01
         June   
-0.02
         July   
-0.02
         Aug. (p)   -0.03

-0.20
-0.21
-0.20
-0.18

-0.14
-0.21
-0.11
-0.18
-0.23
-0.31
-0.31
-0.35

0.26
0.26
0.42
0.25

0.39
0.25
0.22
0.25
0.19
0.27
0.13
0.17

4

1.88
1.82
1.83
1.83

1.84
1.84
1.82
1.80
1.79
1.84
1.72
1.76

5

1.91
1.91
1.97
2.01

2.14
1.96
1.91
2.04
1.87
1.89
1.82
1.79

6

7

2.10
2.20
2.00
1.94

2.00
2.00
1.97
1.96
1.95
1.97
2.14
1.90

1.94
1.96
1.98
1.94

1.92
1.95
2.02
1.98
2.04
2.02
2.00
2.02

8

1.54
1.55
1.57
1.61

1.61
1.58
1.56
1.57
1.57
1.55
1.59
1.56

9

10

11

12

13

1.43
1.46
1.41
1.42

1.44
1.44
1.45
1.44
1.45
1.43
1.43
1.46

1.49
1.50
1.47
1.44

1.41
1.43
1.40
1.40
1.42
1.54
1.37
1.37

1.22
1.22
1.22
1.34

1.17
1.15
1.09
1.32
1.16
1.20
1.28
1.23

1.31
1.42
1.29
1.23

1.18
1.22
0.71
1.33
1.17
1.13
1.32
1.12

1.31
1.40
1.30
1.27

1.29
1.23
1.23
1.38
1.27
1.24
1.16
1.14

14

1.51
1.53
1.51
1.51

1.50
1.48
1.39
1.56
1.46
1.46
1.48
1.44

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 7 / 2021 - Statistics

S 4

 
   
   
   
   
   
   
 
   
   
   
   
   
   
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

5

2018  
2019  
2020  

1,215
1,283
1,530

2021 Mar.   1,588
         Apr.   1,563
         May   1,537
         June   1,542
         July   1,540
         Aug.   1,538

2018  
2019  
2020  

15,748
16,315
17,213

2021 Mar.  17,704
         Apr.  17,704
         May  17,830
         June  18,005
         July  18,108
         Aug.  18,136

503
550
455

487
475
464
481
478
492

3,688
3,817
3,892

3,970
3,956
3,947
3,981
3,992
3,991

170
181
145

150
147
151
152
149
148

3,162
3,397
3,136

3,226
3,214
3,234
3,270
3,313
3,306

.
.
.

.
.
.
.
.
.

.
.
.

.
.
.
.
.
.

72
85
98

95
98
100
90
101
99

1,249
1,324
1,453

1,471
1,467
1,489
1,504
1,507
1,506

6

7
Short-term

424
406
714

726
706
692
694
688
678

7,022
7,152
8,006

8,274
8,308
8,393
8,473
8,517
8,556

47
61
118

130
136
130
126
124
121

Long-term

627
626
725

763
760
768
779
779
778

8

9

10

11

12

13

14

389
415
455

460
416
410
450
466
411

228
247
296

371
313
269
337
299
136

171
177
177

218
180
187
217
224
230

64
69
68

107
64
46
75
56
28

66
80
70

51
42
48
56
44
40

68
74
71

94
74
69
82
97
35

.
.
.

.
.
.
.
.
.

.
.
.

.
.
.
.
.
.

41
47
45

31
39
37
34
39
25

15
20
27

27
17
21
29
17
4

76
73
114

118
107
105
105
109
91

75
78
114

125
146
121
136
119
66

35
38
49

43
47
33
39
51
25

6
7
16

17
12
12
15
10
3

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

2018  
2019  
2020  

16,962.7
17,597.8
18,742.8

2021 Mar.   19,291.1
         Apr.  
19,266.5
         May  
19,366.9
         June   19,547.5
         July  
19,648.0
         Aug.   19,674.4

2018  
2019  
2020  

2021 Mar.  
         Apr.  
         May  
         June  
         July  
         Aug.  

Source: ECB.

1.9
3.1
7.4

8.5
7.0
5.6
4.4
4.5
4.1

4,190.4
4,367.2
4,347.3

4,457.1
4,430.6
4,410.2
4,462.0
4,470.2
4,483.1

1.7
3.8
1.2

2.2
0.9
0.1
-0.4
0.2
0.7

3,332.4
3,577.7
3,281.1

3,376.3
3,360.8
3,384.8
3,421.2
3,462.2
3,453.8

3.0
4.9
2.6

4.2
4.6
5.0
4.2
5.0
4.3

.
.
.

.
.
.
.
.
.

.
.
.

.
.
.
.
.
.

1,320.6
1,408.4
1,550.8

1,566.2
1,565.5
1,588.4
1,593.8
1,607.6
1,605.1

7,445.8
7,558.1
8,720.3

8,999.1
9,013.8
9,085.1
9,166.3
9,204.3
9,233.7

Growth rate

3.3
5.6
12.4

11.9
8.1
5.4
4.2
3.7
3.4

1.9
1.5
10.9

11.9
10.2
8.1
6.6
6.2
5.4

673.5
686.5
843.3

892.5
895.8
898.3
904.2
903.8
898.7

-4.3
1.8
24.3

24.5
19.5
12.2
9.6
10.0
9.1

7,023.4
8,586.6
8,448.2

9,237.3
9,456.9
9,664.8
9,791.5
9,911.0
10,178.8

0.7
0.0
1.1

1.7
2.0
2.3
2.5
2.4
2.5

465.0
538.4
469.3

542.9
554.3
575.7
564.9
559.2
587.9

0.3
0.5
0.0

1.4
1.4
1.4
1.8
1.8
1.8

1,099.2
1,410.6
1,321.5

1,467.6
1,467.6
1,508.7
1,521.5
1,526.8
1,610.3

2.4
0.0
3.1

5.0
5.3
6.1
6.4
6.5
7.6

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

5,459.2
6,637.6
6,657.4

7,226.8
7,434.9
7,580.5
7,705.2
7,825.0
7,980.6

0.4
0.0
0.8

1.1
1.5
1.6
1.7
1.7
1.5

S 5

 
 
   
   
   
   
   
   
   
   
 
 
 
 
   
   
   
   
   
 
 
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

Real ULCT

Nominal

Real CPI

   EER-19

   EER-42

1

99.9
98.1
99.6

101.2

100.7
100.5
99.5

100.6
100.8
100.2
99.7
99.3
99.4

0.1

-2.1

2

95.5
93.1
93.4

94.6

94.6
94.1
93.3

94.2
94.3
93.7
93.5
93.2
93.2

0.1

-1.7

3

94.1
92.9
94.1

95.4

95.2
94.9
94.4

94.9
95.1
94.8
94.5
94.2
94.4

4

90.6
88.9
89.3

90.3

89.9
88.8
.

- 
- 
- 
- 
- 
- 

Percentage change versus previous month

0.2

- 

Percentage change versus previous year

-1.3

- 

5

80.7
77.9
77.4

74.5

74.3
72.8
.

- 
- 
- 
- 
- 
- 

- 

- 

6

89.6
87.1
87.5

87.9

87.4
86.1
.

- 
- 
- 
- 
- 
- 

- 

- 

7

117.3
115.4
119.4

122.3

121.7
121.9
120.5

121.9
122.3
121.5
120.8
120.4
120.4

0.0

-1.7

8

94.9
92.3
93.8

95.5

95.3
94.9
94.0

95.1
95.2
94.5
94.2
93.9
93.8

-0.1

-2.0

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

2021 Sep.   

2021 Sep.   

Source: ECB.
1) For a definition of the trading partner groups and other information see the General Notes to the Statistics Bulletin.

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

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

7.808
7.735
7.875

7.901

7.808
7.784
7.626

7.805
7.811
7.739
7.654
7.624
7.601

7.418
7.418
7.538

7.559

7.572
7.528
7.497

7.568
7.523
7.498
7.503
7.496
7.492

25.647
25.670
26.455

26.667

26.070
25.638
25.500

25.924
25.558
25.454
25.636
25.470
25.392

Polish
zloty

7

4.261
4.298
4.443

4.505

4.546
4.529
4.566

7.453
7.466
7.454

7.443

7.437
7.436
7.437

318.890
325.297
351.249

130.396
122.006
121.846

360.472

124.607

361.206
354.553
353.871

127.806
131.930
129.763

360.583
353.647
349.937
357.257
351.843
352.514

7.437
7.436
7.436
7.437
7.437
7.436
Percentage change versus previous month

130.489
132.569
132.631
130.349
129.284
129.656

4.561
4.528
4.501
4.562
4.569
4.568

Pound Romanian Swedish
krona
sterling

leu

Swiss
franc

US
Dollar

8

9

10

11

12

0.885
0.878
0.890

0.903

0.874
0.862
0.855

0.865
0.863
0.859
0.856
0.853
0.857

4.6540
4.7453
4.8383

4.8718

4.8793
4.9240
4.9319

4.9231
4.9250
4.9238
4.9255
4.9232
4.9471

0.5

1.8

10.258
10.589
10.485

10.268

10.120
10.141
10.195

10.162
10.147
10.117
10.198
10.216
10.171

-0.4

-2.5

1.155
1.112
1.071

1.078

1.091
1.098
1.083

1.103
1.097
1.094
1.086
1.076
1.086

0.9

0.7

1.181
1.119
1.142

1.193

1.205
1.206
1.179

1.198
1.215
1.205
1.182
1.177
1.177

0.0

-0.2

2021 Sep.   

-0.3

0.0

-0.3

0.0

0.2

0.3

0.0

0.5

2021 Sep.   

Source: ECB.

-5.4

-0.7

-5.0

-0.1

-2.2

4.1

2.1

-5.8

Percentage change versus previous year

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 6

 
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
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

5

6

7

8

9

10

11

12

2020 Q3   
         Q4   

2021 Q1   
         Q2   

28,055.9
28,374.9

29,652.8
30,116.9

28,650.3
28,979.5

30,301.7
30,693.1

-594.4
-604.6

-649.0
-576.2

11,129.6
11,045.2

11,379.0
11,342.2

9,381.6
9,365.9

9,479.4
9,459.7

9,997.8
10,695.1

11,437.8
11,950.1

12,579.7
12,875.6

13,678.6
14,081.7

-91.7
-82.2

-122.1
-114.2

6,110.7
5,836.9

6,108.6
6,069.7

6,689.0
6,738.0

7,143.7
7,151.7

909.5
879.7

849.4
869.0

15,163.1
14,854.7

15,485.7
15,388.9

Outstanding amounts as a percentage of GDP

2021 Q2   

255.2

260.1

-4.9

96.1

80.2

101.3

119.3

-1.0

51.4

60.6

7.4

130.4

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   

195.4
80.6

523.0
194.3

78.0
200.6
14.0
-20.4
143.3
185.5

75.0
-48.2

425.0
93.6

74.0
193.9
-26.3
-74.1
102.5
183.4

120.4
128.8

97.9
100.7

4.0
6.7
40.3
53.7
40.8
2.2

33.6
-59.1

100.7
-37.2

12.8
30.1
-51.3
-16.0
23.1
5.9

Transactions

-7.4
45.4

-7.1
-38.0

-6.5
5.4
-65.1
21.6
-10.7
-6.9

94.7
348.7

266.3
227.6

82.4
56.4
75.2
96.0
22.6
45.2

85.5
-225.1

178.8
87.0

89.3
39.7
4.7
42.6
40.9
19.7

-31.3
-14.5

6.4
8.5

-5.3
4.5
-2.1
6.2
8.9
-5.9

95.1
-196.6

152.6
-12.1

-11.4
109.0
-9.1
-111.9
89.0
18.2

-3.1
131.5

253.4
44.6

-8.9
148.8
34.1
-138.2
72.2
170.6

3.3
2.1

-3.1
7.5

-0.5
0.7
1.4
5.3
-0.3
122.1

12-month cumulated transactions

2021 Aug.   

1,105.2

684.7

420.5

-9.1

-37.3

941.6

129.3

-6.3

48.3

592.7

130.8

12-month cumulated transactions as a percentage of GDP

2021 Aug.   

9.4

5.8

3.6

-0.1

-0.3

8.0

1.1

-0.1

0.4

5.0

1.1

-
-

-
-

-
-
-
-
-
-

-

-

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

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 7

 
 
   
   
   
   
   
   
 
 
 
 
 
 
 
 
 
3 Economic activity

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

Total

   Domestic demand

   External balance 1) 

Total

Private Government
consumption consumption

   Gross fixed capital formation

Changes in
inventories 2)

Total Exports 1)

Imports 1)

   GDP

Total

Total
construction machinery

Intellectual
property
products

1

2

3

4

5

7
Current prices (EUR billions)

6

2018   
2019   
2020   

11,600.2 11,132.8
11,982.7 11,577.1
11,400.4 10,976.4

2020 Q3    2,917.8
         Q4    2,927.3

2021 Q1    2,939.1
         Q2    3,003.1

2,778.0
2,787.7

2,807.9
2,879.3

6,223.0
6,378.5
5,902.0

1,529.1
1,485.8

1,470.4
1,527.3

2,369.5 2,430.6
2,456.6 2,652.5
2,573.5 2,494.9

622.3
641.4

648.8
661.2

663.0
669.8

1,177.5
1,253.6
1,216.3

311.8
318.1

746.1
770.3
681.9

179.8
182.9

645.8
659.1

324.7
334.9
as a percentage of GDP

185.4
187.7

8

9

10

11

12

500.5
621.7
589.7

128.9
138.6

134.0
134.7

109.6
89.4
6.1

-22.1
-0.8

28.8
23.0

467.5
405.6
423.9

139.7
139.5

131.2
123.8

5,571.6
5,765.4
5,173.4

1,300.0
1,364.2

1,399.6
1,463.5

5,104.2
5,359.7
4,749.4

1,160.3
1,224.6

1,268.5
1,339.7

2020   

100.0

96.3

51.8

22.6

21.9

10.7

6.0

5.2

0.1

3.7

- 

- 

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

12.6
-0.4

-0.3
2.1

1.8
1.6
-6.4

-4.0
-4.4

-1.2
14.2

12.6
-0.4

-0.3
2.1

1.8
1.6
-6.4

-4.0
-4.4

-1.2
14.2

10.5
-0.3

-0.4
2.1

1.8
2.5
-6.2

-4.2
-6.5

-3.8
12.0

10.2
-0.3

-0.4
2.0

1.7
2.4
-6.0

-4.0
-6.3

-3.7
11.7

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

5.5
0.8

-0.5
1.2

1.1
1.8
1.3

2.7
3.3

2.9
7.1

13.9
2.7

14.7
1.5

24.8
1.8

1.8
0.0
1.1
0.5
annual percentage changes

0.6
1.4

3.1
6.7
-7.0

-4.0
-10.1

-6.1
18.2

3.9
3.3
-4.6

-2.7
-0.5

2.7
18.8

3.7
1.8
-12.0

-7.9
-4.8

6.8
30.1

0.0
6.6

-3.9
1.2

0.4
22.0
-5.9

-1.5
-30.6

-31.6
3.6

14.5
-3.2

-2.3
3.4

1.5
1.3
-7.9

-4.5
-7.6

-5.6
12.1

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

7.5
-1.7

-1.2
1.7

1.3
0.2

-0.1
0.3

2.9
0.6

0.0
0.2

1.5
0.2

0.1
0.2

1.4
0.1

0.1
0.0

0.0
0.3

-0.2
0.1

contributions to annual percentage changes in GDP; percentage points

0.8
0.7
-4.2

-2.4
-4.1

-2.9
6.2

0.2
0.4
0.3

0.6
0.7

0.6
1.7

0.6
1.4
-1.5

-0.9
-2.4

-1.4
3.8

0.4
0.3
-0.5

-0.3
-0.1

0.3
2.0

0.2
0.1
-0.8

-0.5
-0.3

0.4
1.7

0.0
0.9
-0.3

-0.1
-2.0

-2.1
0.2

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

-1.6
0.6

0.9
-0.2

0.1
-0.1
-0.5

-1.3
-0.6

0.1
-0.1

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

2.4
-0.1

0.1
0.1

0.1
-0.8
-0.4

0.0
2.0

2.4
2.5

16.4
4.2

1.1
2.7

3.6
2.7
-9.1

-8.8
-4.9

-0.2
26.0

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

11.7
4.8

1.0
2.8

3.8
4.7
-9.1

-9.6
-9.3

-5.7
21.6

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

Sources: Eurostat and ECB calculations.
1) Exports and imports cover goods and services and include cross-border intra-euro area trade.
2) Including acquisitions less disposals of valuables.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 8

 
 
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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-

forestry and
fishing

Infor-
energy and ruction transport, mation

Finance
and
accom- and com- insurance

utilities

Trade,

modation munica-
and food
tion
services

Real Professional,

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

Public ad- Arts, enter-
tainment
and other
services

Taxes less
subsidies
on
products

1

2

3

4

5

6

7

8

9

10

11

12

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

10,395.4
10,741.0
10,268.6

2,626.9
2,634.4

2,647.5
2,696.0

175.4
178.5
176.6

44.2
43.7

44.0
44.9

Current prices (EUR billions)

2,055.6 525.8
2,099.1 561.4
1,969.5 552.6

1,963.1
2,041.8
1,800.2

505.5 142.3
521.3 146.7

532.3 145.7
534.4 149.1

474.2
458.8

455.8
478.3

499.9
531.4
545.0

139.9
139.5

141.5
145.0

477.2 1,170.0
478.9 1,204.9
469.0 1,211.2

116.8
117.2

118.6
118.4

305.4
305.7

306.3
309.3

as a percentage of value added

1,210.2
1,250.1
1,168.3

295.1
302.0

303.0
306.1

1,960.3
2,025.7
2,054.3

519.5
522.2

523.3
529.9

358.0
369.3
321.9

84.0
77.5

77.1
80.6

1,204.8
1,241.7
1,131.7

290.9
292.8

291.5
307.1

2020   

100.0

1.7

19.2

5.4

17.5

5.3

4.6

11.8

11.4

20.0

3.1

- 

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

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

12.5
-0.4

0.0
1.8

1.8
1.6
-6.3

-4.1
-4.5

-1.3
14.2

12.5
-0.4

0.0
1.8

1.8
1.6
-6.3

-4.1
-4.5

-1.3
14.2

0.7
0.6

-3.0
0.8

-0.7
1.7
-0.7

-0.2
-0.8

16.1
3.3

1.0
0.3

1.9
0.5
-6.8

-5.0
-1.3

15.0
2.2

-1.1
1.2

2.2
2.0
-5.3

-3.4
-0.7

23.3
-3.6

7.5
-0.7

2.7
-0.4

-1.0
4.5

2.0
2.3
annual percentage changes

1.3
0.7

1.5
2.4
-13.7

-9.8
-12.8

6.4
5.6
1.2

3.0
1.2

0.2
0.4
-1.0

-0.3
-0.8

3.2
-0.3

-0.3
1.0

1.4
1.4
-0.8

0.0
-0.8

12.0
1.8

0.3
0.9

4.0
1.7
-7.9

-7.2
-5.6

-1.0
-0.8

3.8
11.5
contributions to quarter-on-quarter percentage changes in value added; percentage points

3.1
21.6

0.5
17.5

-8.2
22.8

-2.5
15.4

-0.1
3.6

1.5
4.4

0.0
0.0

-0.1
0.0

0.0
0.0
0.0

0.0
0.0

0.0
0.0

3.0
0.6

0.8
0.1

3.8
-0.6

0.4
0.0

0.1
0.0

0.4
0.0

1.4
0.2

0.2
0.1

0.1
0.1
contributions to annual percentage changes in value added; percentage points

-0.1
0.1

-0.2
0.8

0.1
0.0

0.0
0.1

0.0
0.1

0.4
0.1
-1.3

-1.0
-0.2

0.6
4.0

0.1
0.1
-0.3

-0.2
0.0

0.0
0.9

0.3
0.4
-2.6

-1.9
-2.4

-1.5
3.7

0.3
0.3
0.1

0.1
0.1

0.2
0.6

0.0
0.0
0.0

0.0
0.0

0.1
0.2

0.2
0.2
-0.1

0.0
-0.1

0.0
0.4

0.5
0.2
-0.9

-0.8
-0.7

-0.3
1.7

9.4
-1.3

0.0
1.7

0.7
1.1
-2.9

0.1
-1.5

0.8
9.9

1.9
-0.3

0.0
0.3

0.1
0.2
-0.5

0.0
-0.3

0.2
2.0

24.1
-11.4

-0.7
6.0

1.3
1.6
-17.1

-11.6
-21.8

-16.5
15.7

0.7
-0.4

0.0
0.2

0.0
0.1
-0.6

-0.4
-0.7

-0.6
0.5

13.0
-0.3

-3.3
4.8

1.7
1.6
-6.5

-3.5
-3.7

-0.4
14.3

- 
- 

- 
- 

- 
- 
- 

- 
- 

- 
- 

Sources: Eurostat and ECB calculations.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 9

 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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

2018   
2019   
2020   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

100.0
100.0
100.0

1.6
1.3
-1.5

-2.0
-1.8

-1.8
1.9

100.0
100.0
100.0

1.7
1.0
-7.4

-4.4
-6.0

-2.6
15.6

0.1
-0.3
-6.0

-2.4
-4.3

-0.8
13.4

85.9
86.0
86.0

1.9
1.5
-1.5

-2.0
-1.8

-1.8
2.3

81.1
81.3
82.0

2.2
1.3
-6.7

-4.2
-5.4

-2.8
14.4

0.3
-0.2
-5.3

-2.3
-3.7

-1.0
11.8

14.1
14.0
14.0

0.0
0.2
-1.7

-1.9
-1.5

-1.3
-0.1

18.9
18.7
18.0

0.0
-0.2
-10.6

-5.1
-8.3

-1.5
21.3

0.0
-0.4
-9.1

-3.2
-6.9

-0.2
21.4

3.1
3.0
3.0

-0.4
-2.4
-2.4

-1.8
-1.6

0.4
3.6

4.3
4.1
4.3

-0.2
-3.3
-3.0

-1.3
-1.6

1.9
6.9

0.3
-1.0
-0.6

0.5
0.0

1.5
3.1

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

as a percentage of total persons employed

14.6
14.6
14.5

1.5
1.1
-1.8

-2.7
-2.3

-2.2
-0.4

15.0
14.9
14.9

1.4
0.5
-7.3

-5.5
-5.3

-1.3
14.7

-0.1
-0.6
-5.6

-2.9
-3.0

0.9
15.2

25.0
6.0
25.0
6.1
6.2
24.5
annual percentage changes

2.9
2.9
3.0

2.6
2.5
0.7

0.8
0.8

1.4
4.7

1.6
1.5
-3.7

-4.3
-4.7

-5.5
0.4

3.8
3.3
1.5

1.1
1.6

2.2
4.5

2.4
2.4
2.4

-1.0
0.0
-0.5

-0.7
-0.4

-0.4
0.9

Hours worked
as a percentage of total hours worked

25.9
6.7
25.9
6.8
6.9
24.4
annual percentage changes

3.0
3.1
3.3

3.2
2.3
-6.1

-0.6
-2.6

5.1
24.2

1.8
1.1
-12.9

-8.1
-12.6

-10.3
21.9

3.9
3.3
-1.6

-1.9
-0.4

1.9
11.7

2.5
2.4
2.6

-1.0
0.3
-2.4

-1.6
-1.3

0.9
6.1

Hours worked per person employed
annual percentage changes

0.5
-0.2
-6.8

-1.4
-3.4

3.7
18.6

0.2
-0.4
-9.6

-3.9
-8.2

-5.1
21.4

0.1
0.0
-3.0

-3.0
-2.0

-0.4
6.8

0.1
0.3
-1.9

-0.9
-0.9

1.3
5.1

1.0
1.0
1.0

2.4
1.6
-0.2

0.8
1.7

1.4
2.0

1.0
1.0
1.1

3.1
1.9
-6.4

-2.2
-2.6

3.1
18.6

0.7
0.3
-6.3

-3.0
-4.2

1.7
16.2

14.0
14.0
13.9

2.8
1.4
-2.4

-3.5
-2.1

-1.7
4.4

13.8
13.9
13.8

3.1
1.3
-7.9

-6.7
-5.5

-1.7
18.1

0.3
-0.1
-5.6

-3.3
-3.4

-0.1
13.1

24.3
24.3
24.9

1.4
1.4
0.9

0.8
1.1

1.3
2.5

21.7
21.7
23.1

1.2
1.3
-1.8

0.1
-0.6

2.0
8.0

-0.2
-0.1
-2.6

-0.6
-1.7

0.7
5.4

6.8
6.7
6.6

0.3
0.4
-3.3

-3.3
-4.1

-4.8
1.5

6.1
6.1
5.7

0.7
0.2
-12.8

-5.5
-12.0

-8.6
22.8

0.4
-0.2
-9.8

-2.2
-8.3

-4.0
21.0

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 10

 
 
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3 Economic activity

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

Labour

Under-
force, employ-
ment,
% of

millions

   Total

labour Millions % of
labour
force

force

   Unemployment 1) 

   By age

   By gender

Job
vacancy
rate 3)

   Adult

   Youth

   Male

   Female

Long-term
unemploy-
ment,
% of

labour Millions
force 2)

1

2

3

4

5

% of total 
in 2020 

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

163.438
164.210
162.523

162.962
163.265

162.380
163.499

100.0

13.380
12.405
12.742

13.840
13.089

13.542
13.154

3.9
3.6
3.6

3.7
3.6

3.7
3.5

8.2
7.6
7.8

8.5
8.0

8.3
8.0

3.8
3.3
3.0

3.1
3.2

3.2
3.3

6

80.6

10.913
10.101
10.265

11.131
10.615

10.895
10.520

% of Millions

% of Millions

% of Millions

labour
force

7

7.3
6.7
6.9

7.5
7.1

7.4
7.1

8

19.4

2.467
2.304
2.477

2.709
2.474

2.648
2.633

labour
force

9

17.2
16.0
17.7

19.2
17.8

18.6
18.0

10

51.4

6.879
6.352
6.555

7.105
6.736

6.875
6.648

labour
force

11

7.9
7.2
7.6

8.2
7.7

7.9
7.6

12

48.6

6.501
6.053
6.188

6.735
6.353

6.667
6.505

% of % of total
posts

labour
force

13

14

8.6
7.9
8.2

8.9
8.3

8.8
8.5

2.1
2.2
1.8

1.7
1.9

2.1
2.3

- 
- 
- 
- 
- 
- 

13.101
13.350
13.156
12.712
12.423
12.162

2021 Mar.   
- 
         Apr.   
- 
         May   
- 
         June   
- 
         July   
- 
         Aug.   
- 
Sources: Eurostat and ECB calculations.
1) Where annual and quarterly Labour Force Survey data have not yet been published, they are estimated as simple averages of the monthly data. There is a break in series from
the first quarter of 2021 due to the implementation of the Integrated European Social Statistics Regulation. Owing to technical issues with the introduction of the new German
system of integrated household surveys, including the Labour Force Survey, the figures for the euro area include data from Germany, starting in the first quarter of 2020,
which are not direct estimates from Labour Force Survey microdata, but based on a larger sample including data from other integrated household surveys.

10.503
10.663
10.562
10.207
10.043
9.845

2.598
2.687
2.594
2.505
2.380
2.317

6.608
6.706
6.638
6.375
6.205
6.138

6.493
6.643
6.518
6.337
6.218
6.024

18.4
18.8
18.0
17.3
16.7
16.4

7.6
7.7
7.6
7.3
7.1
7.1

8.6
8.7
8.5
8.3
8.1
7.9

8.1
8.2
8.0
7.8
7.6
7.5

7.1
7.2
7.1
6.8
6.7
6.6

- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 

2) Not seasonally adjusted.
3) 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.

Data are non-seasonally adjusted and cover industry, construction and services (excluding households as employers and extra-territorial organisations and bodies).

3.5 Short-term business statistics

   Industrial production

   Total
   (excluding construction)

   Main Industrial Groupings

Manu-

Inter- Capital Consumer Energy

facturing mediate
goods

goods

goods

Con- ECB indicator
on industrial
new orders Total

struction
produc-
tion

   Retail sales

Food, Non-food

Fuel

beverages,
tobacco

New
passenger
car regis-
trations

1

2

3

4

5

6

7

8

9

100.0

88.7

32.1

34.5

21.8

11.6

100.0

100.0 100.0

10

40.4

11

52.5

12

7.1

13

100.0

% of total 
in 2015 

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

0.8
-1.3
-8.6

-1.6

3.5
22.5
. 

39.8
20.5
10.6
8.0
5.1
. 

1.1
-1.3
-9.1

-1.6

3.6
24.1
. 

43.0
22.2
11.2
8.6
6.1
. 

0.6
-2.4
-7.4

1.4

4.9
25.4
. 

38.6
24.2
15.8
11.3
6.6
. 

1.2
-1.8
-13.2

-3.2

5.1
29.0
. 

64.2
27.0
6.8
5.9
2.8
. 

annual percentage changes

1.5
1.4
-4.7

-2.6

0.4
17.6
. 

26.7
14.5
12.9
10.5
10.0
. 

-1.5
-2.1
-5.3

-1.8

0.9
7.7
. 

13.5
6.4
3.3
1.5
-0.6
. 

1.7
2.1
-5.7

-1.2

2.9
17.8
. 

45.5
12.2
4.1
3.5
-1.6
. 

3.6
-3.1
-9.7

-1.0

7.5
45.5
. 

68.4
47.9
26.6
28.3
18.8
. 

1.6
2.4
-0.8

1.6

2.4
11.9
. 

23.7
8.7
5.6
3.1
0.0
. 

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

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   
Sources: Eurostat, ECB calculations, ECB experimental statistics (col. 8) and European Automobile Manufacturers Association (col. 13).

-0.3
-0.4
-0.7
0.1
-1.3
. 

1.9
-2.4
-0.5
-0.3
0.5
. 

-3.6
4.2
1.9
-2.6
0.3
. 

0.7
-2.6
-1.3
2.6
-3.9
. 

3.3
-1.4
3.6
3.8
-3.8
. 

2.2
-1.6
2.6
2.1
-1.8
. 

0.6
-0.7
0.2
1.6
-2.0
. 

0.8
-0.1
0.1
0.6
-1.5
. 

0.7
-1.1
0.2
1.4
-1.6
. 

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

1.4
1.0
3.6

4.5

2.7
2.1
. 

4.1
0.4
2.0
1.2
-1.9
. 

-1.6
-0.6
-1.2
-0.5
-1.7
. 

0.7
2.0
3.7
0.8
-2.4 -14.4

1.3 -13.8

3.1
18.7
. 

42.8
14.2
7.4
4.5
1.3
. 

-5.9
8.7
3.6
-4.2
1.8
. 

-5.1
29.6
. 

62.3
28.6
11.7
3.8
1.2
. 

-0.8
8.1
2.2
0.6
-0.1
. 

0.9
1.8
-25.0

-9.2

3.4
53.8
-23.5

262.5
49.5
5.4
-22.0
-24.8
-24.2

-1.5
1.8
-0.4
-5.9
-3.8
3.4

S 11

 
   
   
   
   
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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)

1999-15   

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 May   
         June   
         July   
         Aug.   
         Sep.   
         Oct.   

1

99.3

111.8
103.7
88.2

91.4

95.3
114.3
118.1

114.5
117.9
119.0
117.6
117.8
. 

2

-5.2

6.7
-5.2
-14.4

-8.8

-2.4
11.7
14.2

11.5
12.8
14.5
13.8
14.1
. 

3

80.6

83.7
82.0
74.0

76.9

80.0
82.7
. 

- 
- 
82.9
- 
- 
- 

4

-11.6

-4.8
-6.9
-14.3

-15.6

-13.7
-5.5
-4.6

-5.1
-3.3
-4.4
-5.3
-4.0
-4.8

5

-15.4

7.2
6.7
-7.4

-8.3

-5.9
4.4
5.7

4.9
5.2
4.0
5.5
7.5
. 

6

-8.6

1.3
-0.5
-12.9

-10.9

-16.6
0.7
3.4

0.5
4.7
4.4
4.6
1.3
. 

7

7.3

15.2
10.8
-16.5

-15.4

-14.8
10.5
16.9

11.3
17.9
18.9
16.8
15.1
. 

8

- 

90.4
90.5
86.3

85.7

85.8
87.2
. 

- 
- 
88.0
- 
- 
- 

9

10

11

51.2

54.9
47.4
48.6

54.6

58.4
63.1
60.9

63.1
63.4
62.8
61.4
58.6
58.5

52.5

54.7
47.8
48.0

56.7

58.5
62.7
58.6

62.2
62.6
61.1
59.0
55.6
53.2

53.0

54.5
52.7
42.5

45.0

46.9
54.7
58.4

55.2
58.3
59.8
59.0
56.4
54.7

12

52.8

54.6
51.3
44.0

48.1

49.9
56.8
58.4

57.1
59.5
60.2
59.0
56.2
54.3

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
ratio
(gross)

Debt Real gross
ratio disposable investment

Financial Non-financial

investment worth

Net Hous-
ing
 2) wealth

income

(gross)

   Annual percentage changes

share 3)

Profit Saving
ratio
(net)

Debt
ratio 4)

investment

Financial Non-financial
investment
(gross)

Finan-
cing

   Percentage of net
   value added

Percent-
age of
GDP

   Annual percentage changes

3

1.9
1.8
-0.5

0.7
0.3

-0.2
3.4

4

2.0
2.6
4.1

3.6
4.1

4.8
4.2

5

6

6.3
3.8
-3.5

-1.6
2.6

10.3
29.8

2.5
5.7
5.1

3.6
5.1

7.4
6.5

7

4.6
3.9
4.7

4.2
4.7

4.6
4.8

8

35.5
35.3
31.3

31.5
31.3

32.3
34.2

9

5.6
6.3
4.5

4.4
4.5

5.8
7.9

10

76.5
75.9
83.1

82.8
83.1

84.5
81.7

11

2.0
2.1
3.3

2.7
3.3

3.9
4.3

12

7.6
7.9
-14.8

-15.2
-21.3

-10.7
20.6

13

1.6
1.7
2.0

1.9
2.0

2.2
2.2

Sources: ECB and Eurostat.
1) Based on four-quarter cumulated sums of saving, debt and gross disposable income (adjusted for the change in pension entitlements).
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) Defined as consolidated loans and debt securities liabilities.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 12

   Percentage of gross
   disposable income
   (adjusted) 1) 

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

1

12.6
13.1
19.5

17.8
19.5

20.6
19.1

2

93.3
93.6
96.4

95.6
96.4

96.7
96.7

 
   
   
   
   
 
 
 
 
   
   
   
                                   
   
                 
   
   
   
   
   
 
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

Balance Credit

Debit

Credit

Debit

Credit

Debit

Credit

Debit

Credit

Debit

1

2

3

4

5

6

7

8

9

958.7
1,021.2

1,050.8
1,073.5

348.0
354.3
360.3
358.9
357.2
359.2

884.9
930.4

945.3
1,007.8

324.1
336.7
342.1
329.0
334.6
345.8

73.8
90.8

105.5
65.6

23.9
17.6
18.2
29.9
22.6
13.4

548.0
581.6

602.3
617.7

201.7
203.2
208.5
206.1
208.9
204.9

455.5
479.6

496.9
536.1

175.3
176.6
180.6
178.9
183.1
187.4

198.2
225.5

226.5
232.5

75.2
77.1
77.0
78.4
79.1
79.9

188.6
197.6

203.5
212.4

70.4
71.5
72.0
68.8
72.4
80.6

184.0
183.3

191.2
193.8

60.7
63.4
65.7
64.7
59.1
57.8

12-month cumulated transactions

177.9
173.9

169.0
188.1

54.8
64.9
65.6
57.7
55.2
53.1

10

28.5
30.7

30.8
29.4

10.4
10.7
9.0
9.7
10.1
16.7

11

62.9
79.3

75.8
71.2

23.6
23.7
23.9
23.6
23.8
24.7

12

12.2
23.8

15.4
17.2

7.4
4.6
5.3
7.3
6.6
7.1

13

10.4
24.6

12.4
11.6

4.7
3.6
3.0
4.9
4.8
2.7

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   

2021 Aug.   

4,190.2

3,862.1

328.1 2,402.9
12-month cumulated transactions as a percentage of GDP

2,038.0

913.9

830.4

746.4

697.2

127.1

296.6

74.0

60.0

2021 Aug.   

35.5

32.8

2.8

20.4

17.3

7.8

7.0

6.3

5.9

1.1

2.5

0.6

0.5

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

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Feb.   
         Mar.   
         Apr.   
         May   
         June   
         July   

-8.6
-2.7

0.6
34.3

12.5
46.8
35.0
23.7
11.5
18.2

-7.2
-1.4

0.7
29.2

-2.0
11.4
41.7
29.4
19.0
4.3

-11.4
-5.7

0.3
33.6

19.2
38.1
35.0
28.4
17.2
26.5

-6.8
-0.9

0.2
20.3

-3.5
15.4
25.2
20.5
15.7
2.4

531.9
568.7

582.0
595.3

197.8
198.1
199.3
197.9
200.0
200.6

98.6
104.3

104.5
104.5

104.1
105.7
104.6
104.7
104.1
103.4

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

248.2
265.5

280.5
290.6

95.7
95.2
97.5
98.0
98.5
. 

108.4
114.2

114.9
116.8

38.4
39.6
39.0
38.3
40.3
. 

166.0
178.4

174.6
176.2

59.4
60.0
58.8
57.4
56.9
. 

448.4
480.2

487.5
492.2

161.9
164.6
164.6
163.1
165.7
164.0

469.6
493.1

512.7
556.8

179.7
184.9
185.8
186.1
186.5
189.4

242.5
261.9

285.1
322.2

101.6
106.3
107.7
108.3
110.4
. 

85.0
86.8

91.3
91.7

32.0
30.6
30.7
30.4
30.0
. 

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

100.0
105.9

108.5
108.8

107.6
110.5
107.6
109.2
109.6
108.1

95.6
99.6

100.7
101.7

103.4
100.2
103.9
101.0
100.1
103.8

100.1
106.5

101.8
101.1

100.6
102.4
102.8
101.3
99.3
96.4

97.9
103.8

103.9
103.1

104.8
103.0
103.6
103.2
102.5
102.3

101.8
105.6

104.9
109.4

103.7
108.4
109.9
109.7
108.7
105.7

96.9
102.7

103.4
109.8

101.8
107.8
110.6
110.5
108.5
106.9

106.2
107.8

112.3
113.1

110.7
118.5
112.7
113.4
113.2
108.0

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.

134.3
135.2

129.8
134.9

45.2
45.7
44.6
44.6
43.1
. 

110.8
111.1

105.4
107.6

104.1
109.1
109.0
106.9
106.9
101.7

360.7
380.1

383.0
403.4

132.4
133.5
135.2
134.7
133.3
136.4

104.5
109.3

108.2
111.6

107.5
111.5
111.0
112.5
111.4
107.7

34.2
35.4

47.1
53.5

17.1
17.1
18.3
18.2
19.4
. 

81.2
84.9

85.6
84.9

85.2
84.5
83.9
87.2
83.5
85.1

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 13

 
   
   
   
   
   
   
   
   
 
 
 
 
 
 
   
   
   
   
   
   
 
 
 
 
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) 2) 

Index:
2015
= 100

   Total

Goods Services

Total
excluding
food and
energy

Total Processed Unpro- Non-energy
industrial
goods

cessed
food

food

Energy
(n.s.a.)

Services

   Administered prices

Total HICP Admini-
stered
prices

excluding
administered
prices

% of total 
in 2021 

2018  
2019  
2020  

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

1

2

3

4

5

6

100.0 100.0

68.7

58.2

41.8

100.0

103.6
104.8
105.1

1.8
1.2
0.3

105.0

-0.3

105.8
107.4
108.0

107.1
107.4
107.7
107.6
108.0
108.5

1.1
1.8
2.8

1.6
2.0
1.9
2.2
3.0
3.4

1.0
1.0
0.7

0.2

1.2
0.9
1.4

0.7
1.0
0.9
0.7
1.6
1.9

2.0
1.0
-0.4

-0.9

0.8
2.5
4.1

2.1
2.6
2.8
3.3
4.5
4.6

1.5
1.5
1.0

0.5

1.3
0.9
1.2

0.9
1.1
0.7
0.9
1.1
1.7

-
-
-

0.0

1.3
0.5
1.1

0.2
0.2
0.3
0.6
0.3
0.2

7

16.7

-
-
-

0.1

0.6
0.4
0.6

0.2
0.1
0.2
0.3
0.2
0.2

8

5.1

-
-
-

0.8

-0.3
1.4
0.6

1.4
-0.2
-0.2
0.3
0.6
0.2

9

26.9

-
-
-

-0.7

1.5
-0.3
1.3

0.0
0.1
0.3
1.1
0.2
-0.5

10

9.5

-
-
-

0.5

6.5
3.7
4.3

0.7
0.8
1.3
2.0
1.0
1.4

11

41.8

12

86.7

13

13.3

-
-
-

0.3

0.6
0.3
0.5

0.0
0.1
0.2
0.0
0.2
0.4

1.7
1.1
0.2

-0.4

1.0
1.8
2.7

1.5
1.9
1.8
2.0
2.9
3.3

2.1
1.9
0.6

0.5

1.4
2.4
3.5

2.2
2.4
2.5
3.5
3.5
3.6

   Goods

   Services

   Food (including alcoholic
   beverages and tobacco)

Total Processed
food

Unpro-
cessed
food

14

21.8

15

16.7

2.2
1.8
2.3

1.7

1.3
0.6
1.9

0.6
0.5
0.5
1.6
2.0
2.0

2.1
1.9
1.8

1.2

1.2
0.8
1.7

0.9
0.7
0.8
1.5
1.7
1.9

16

5.1

2.3
1.4
4.0

3.5

1.7
-0.2
2.5

-0.3
0.0
-0.3
1.9
3.0
2.6

   Industrial goods

   Housing

Transport Communi- Recreation Miscel-
and laneous

cation

Total

17

36.4

1.9
0.5
-1.8

-2.4

0.5
3.6
5.4

3.0
3.8
4.1
4.3
6.0
6.1

Non-energy
industrial
goods

Energy

Rents

18

26.9

0.3
0.3
0.2

-0.3

0.9
0.8
1.8

0.4
0.7
1.2
0.7
2.6
2.1

19

9.5

20

12.2

6.4
1.1
-6.8

-7.8

-0.6
12.0
15.8

10.4
13.1
12.6
14.3
15.4
17.6

1.2
1.4
1.4

1.2

1.3
1.4
1.4

1.4
1.4
1.4
1.4
1.4
1.5

21

7.5

1.2
1.3
1.3

1.2

1.2
1.3
1.1

1.3
1.3
1.3
1.1
1.1
1.2

22

6.5

1.5
2.0
0.5

-0.6

1.1
0.8
2.4

0.5
1.2
0.7
1.7
2.3
3.3

personal
care

24

11.4

2.0
1.7
1.0

0.6

1.4
0.5
1.1

0.6
0.8
0.1
0.3
1.0
1.9

23

2.7

-0.1
-0.7
-0.6

-1.5

-0.4
-0.1
0.7

0.1
-0.1
-0.1
0.7
0.7
0.6

25

9.0

1.4
1.5
1.4

1.3

1.5
1.6
1.6

1.4
1.6
1.7
1.7
1.6
1.5

% of total 
in 2021 

2018  
2019  
2020  

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

Sources: Eurostat and ECB calculations.
1) Data refer to the changing composition of the euro area.
2) In May 2016 the ECB started publishing enhanced seasonally adjusted HICP series for the euro area, following a review of the seasonal adjustment approach as described

in Box 1, Economic Bulletin, Issue 3, ECB, 2016 (https://www.ecb.europa.eu/pub/pdf/ecbu/eb201603.en.pdf).

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 14

 
   
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
   
   
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 Prices and costs

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

Total
(index:
2015 = 100)

   Industrial producer prices excluding construction 1)

   Total

   Industry excluding construction and energy

Energy

Manu- Total

facturing

Intermediate Capital
goods

goods

   Consumer goods

Con-
struction
 2)

Residential
property
prices 3)

Experimental
indicator of
commercial
property
prices 3)

Total

Food, Non-
beverages food

and tobacco

% of total 
in 2015 

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2021 Mar.   
         Apr.   
         May   
         June   
         July   
         Aug.   

1

2

3

4

5

6

7

8

9

100.0 100.0

77.3

72.1

28.9

20.7

22.5

16.5

5.9

104.1
3.3
0.6
104.7
102.0 -2.6

101.4 -2.7
102.6 -1.7

105.9
109.4

2.1
9.2

4.4
106.9
7.6
107.9
109.3
9.6
110.9 10.3
113.7 12.4
114.9 13.4

2.4
0.6
-1.7

-2.0
-1.7

1.3
6.8

3.5
5.8
7.2
7.4
8.4
9.2

1.5
0.8
-0.1

-0.3
0.0

1.4
4.7

2.4
3.6
4.9
5.6
6.8
7.4

2.7
0.1
-1.6

-1.8
-0.6

2.7
9.0

4.5
7.0
9.3
10.7
12.7
14.2

1.0
1.5
0.9

0.8
0.8

1.0
1.7

1.2
1.4
1.8
2.0
2.5
3.1

0.4
1.0
1.0

0.5
0.0

0.0
1.8

0.5
1.0
2.0
2.4
2.7
2.9

0.1
1.1
1.1

0.3
-0.5

-0.7
1.8

-0.1
0.8
1.9
2.6
2.8
2.9

0.6
0.9
0.6

0.6
0.7

0.7
1.2

0.9
1.1
1.3
1.4
2.0
2.2

10

27.9

8.4
-0.1
-9.7

-9.3
-6.7

3.8
23.7

10.3
20.6
25.1
25.5
30.1
32.0

11

12

13

2.5
1.9
1.3

1.0
1.6

2.7
4.5

- 
- 
- 
- 
- 
- 

4.9
4.2
5.4

5.3
6.0

6.1
7.2

- 
- 
- 
- 
- 
- 

4.1
4.5
1.7

1.1
-0.9

. 
. 

- 
- 
- 
- 
- 
- 

Sources: Eurostat, ECB calculations, and ECB calculations based on MSCI data and national sources (col. 13).
1) Domestic sales only.
2) Input prices for residential buildings.
3) Experimental data based on non-harmonised sources (see https://www.ecb.europa.eu/stats/ecb_statistics/governance_and_quality_framework/html/experimental-data.en.html

for further details).

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

Total Total
(s.a.;
index:
2015
= 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 

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

103.6
105.3
107.1

107.4

108.2
108.2
. 

- 
- 
- 
- 
- 
- 

1.5
1.7
1.6

1.2

1.6
0.5
. 

- 
- 
- 
- 
- 
- 

1.8
1.5
1.1

0.9

1.6
1.5
. 

- 
- 
- 
- 
- 
- 

1.5
1.1
0.5

0.0

1.0
1.5
. 

- 
- 
- 
- 
- 
- 

1.9
1.8
3.5

2.8

2.4
-1.4
. 

- 
- 
- 
- 
- 
- 

1.9
2.3
1.2

0.4

1.1
2.2
. 

- 
- 
- 
- 
- 
- 

1.5
0.8
-1.3

-1.4

0.9
4.2
. 

- 
- 
- 
- 
- 
- 

2.2
0.3
-2.6

-2.6

0.8
7.0
. 

- 
- 
- 
- 
- 
- 

  100.0

45.4

54.6 100.0

50.4

60.4
57.2
37.0

37.4

50.4
57.0
61.9

54.1
56.0
60.7
62.9
59.5
63.4

-0.9
2.0
1.5

4.1

18.3
38.3
31.1

35.4
41.1
38.2
36.9
30.0
26.7

-6.3
4.4
3.4

0.1

9.1
20.1
26.3

17.5
20.7
22.2
26.8
29.1
23.1

4.3
-0.1
-0.3

7.9

27.3
56.4
35.4

54.0
61.9
53.4
46.0
30.7
29.9

-0.6
3.0
-0.9

-0.5

14.0
35.6
32.4

33.8
37.2
35.9
36.1
31.7
29.3

-6.2
8.3
-0.1

-6.1

5.1
20.2
28.3

19.4
19.2
21.9
27.4
31.6
25.9

15

49.6

5.7
-2.3
-1.8

6.2

24.6
54.4
36.7

51.4
59.5
52.1
45.5
31.8
33.0

Sources: Eurostat, ECB calculations and Bloomberg (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 2009-11 average import structure; use-weighted: weighted according to 2009-11 average domestic demand structure.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 15

 
   
   
   
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
   
   
   
   
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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

1999-15   

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3   

2021 May   
         June   
         July   
         Aug.   
         Sep.   
         Oct.   

1

4.3

11.5
4.2
-1.3

1.6

10.7
30.0
36.9

29.9
36.0
35.5
37.2
38.2
. 

2

5.6

7.5
7.3
1.6

2.6

5.0
18.2
28.0

17.5
23.1
26.1
27.3
30.5
. 

3

- 

9.6
9.1
-0.8

-2.7

-1.8
8.5
12.3

9.4
10.9
12.2
11.7
13.1
. 

4

-4.5

12.6
7.5
-5.8

-7.8

-3.8
15.7
26.1

16.7
21.9
25.7
27.8
24.8
. 

5

32.3

20.6
18.2
10.9

7.0

8.1
20.4
35.0

19.2
24.7
31.2
34.4
39.3
. 

6

56.7

65.4
48.8
49.0

56.7

74.0
85.9
87.7

87.1
88.5
89.2
87.0
86.9
90.2

7

56.3

57.9
57.1
52.1

52.6

54.0
60.1
63.8

59.6
63.2
63.1
63.3
65.2
67.0

8

- 

56.1
50.4
48.7

51.6

56.5
68.2
70.3

69.1
71.1
71.9
68.6
70.4
72.3

9

49.7

52.7
52.4
47.2

48.3

48.6
53.1
55.1

52.6
55.6
55.4
54.7
55.1
56.1

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:
2016 = 100)

1

100.0

104.4
106.9
110.2

105.0
116.6

104.6
115.8

Total

   By component

   For selected economic activities

Wages and
salaries

Employers’ social
contributions

Business economy

Mainly non-business
economy

2

100.0

2.5
2.4
3.1

1.5
2.9

1.3
-0.2

3

75.3

2.3
2.6
3.7

2.2
3.4

2.0
-0.4

4

24.7

3.2
2.1
1.2

-0.4
0.7

-1.0
0.8

5

69.0

2.6
2.4
2.7

1.3
2.2

1.2
-0.9

6

31.0

2.3
2.5
3.8

2.1
4.2

1.9
1.6

Memo item:
Indicator of
negotiated
wages 1)

7

2.0
2.2
1.8

1.7
2.0

1.4
1.7

% of total 
in 2018 

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

Sources: Eurostat and ECB calculations.
1) Experimental data based on non-harmonised sources (see https://www.ecb.europa.eu/stats/ecb_statistics/governance_and_quality_framework/html/experimental-data.en.html

for further details).

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 16

 
   
   
   
   
   
   
   
 
 
 
 
   
   
 
 
 
 
 
 
 
 
 
 
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:
2015
=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

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

103.4
105.3
110.1

108.6
109.9

110.3
108.9

105.2
107.4
106.7

108.6
109.1

109.3
109.5

101.7
102.0
97.0

100.0
99.3

99.1
100.5

104.9
107.4
112.6

110.9
113.3

114.0
112.5

101.9
102.5
103.7

102.9
104.3

104.3
104.1

1.9
1.9
4.5

2.9
3.7

1.5
-4.2

2.1
2.1
-0.6

0.7
1.0

2.1
7.3

0.2
0.3
-4.9

-2.1
-2.7

0.5
12.0

1.9
2.3
4.9

3.0
4.8

3.1
-4.0

0.1
0.6
1.1

0.3
1.7

1.4
-1.2

Sources: Eurostat and ECB calculations.

1.3
-1.0
-1.2

-1.3
-0.5

3.2
6.7

1.0
3.1
0.5

0.3
0.2

1.9
2.1

-0.3
4.2
1.7

1.7
0.8

-1.3
-4.3

1.4
3.7
2.3

-0.1
1.4

0.7
-2.4

-0.5
5.2
2.4

1.1
0.8

-2.8
-7.2

1.5
2.0
2.8

1.0
-1.0

-3.1
-10.5

1.8
1.4
-2.3

-1.4
0.0

2.2
9.3

0.3
-0.6
-5.0

-2.4
1.1

5.4
22.1

1.8
1.9
3.1

1.5
2.9

1.2
-4.3

0.4
0.0
0.6

0.6
4.2

4.5
6.0

2.6
1.9
4.7

6.1
3.1

5.0
-1.3

2.2
1.4
-1.6

1.6
1.6

4.1
10.7

2.1
0.8
6.5

4.7
5.6

2.0
-7.4

0.1
0.9
0.7

-1.5
1.8

0.5
0.6

Compensation per employee 

2.0
1.6
-4.6

-1.3
-3.3

-0.9
13.2

2.6
3.3
0.4

0.4
1.4

2.0
7.3

Labour productivity per person employed

-0.4
-0.5
-6.0

-4.2
-1.5

-0.9
12.2

-0.2
0.8
-10.5

-5.7
-8.5

-2.8
22.3

2.5
2.3
-0.3

1.9
-0.4

1.5
6.7

Compensation per hour worked 

1.5
1.7
4.0

2.8
3.9

0.8
-5.6

-0.9
-0.3
0.8

-2.8
2.0

-4.4
-5.4

1.4
2.0
5.0

2.9
5.1

5.0
-5.3

2.4
3.1
2.6

2.9
2.4

2.5
1.2

Hourly labour productivity

-0.4
1.2
-0.9

-1.9
-0.3

2.4
0.7

2.4
2.3
2.8

5.0
1.6

1.9
-0.2

1.1
1.8
0.3

-0.7
1.2

1.2
-2.7

2.3
2.2
-0.2

-0.3
0.7

3.1
0.7

1.2
0.3
-0.5

0.4
-0.4

1.9
3.5

2.3
1.8
1.1

0.2
1.2

1.8
-3.6

1.1
0.1
1.4

1.3
0.5

4.0
2.6
1.6

3.7
5.0

4.5
8.6

2.9
2.4
0.9

2.8
2.5

3.0
10.2

-1.0
-0.2
-0.7

-0.9
-2.4

-1.5
1.5

2.0
2.1
5.4

5.3
6.3

3.4
0.6

-1.7
-0.5
6.0

2.2
1.9

0.6
-1.5

-3.1
-12.7

1.6
2.5
5.6

5.1
4.8

2.5
-1.9

2.7
2.8
-0.3

1.1
1.1

1.6
8.4

1.1
0.2
-5.6

-3.8
-3.6

-0.9
10.5

2.2
2.8
4.5

4.2
3.7

2.1
-2.6

0.8
0.3
0.0

-0.5
-0.2

-0.8
-2.3

2.6
2.6
6.3

3.1
6.1

2.8
-4.7

1.9
2.3
2.4

2.4
3.4

2.3
2.2

-0.7
-0.3
-3.7

-0.7
-2.6

-0.5
7.2

2.2
2.4
4.7

2.8
4.8

1.7
-2.4

-0.5
-0.2
-1.1

-0.1
-0.9

-1.2
1.7

2.0
2.1
13.6

10.0
22.1

15.4
-2.5

3.0
3.3
-2.6

0.5
-0.5

1.2
11.1

1.0
1.2
-14.3

-8.6
-18.5

-12.4
14.0

2.4
3.7
6.1

2.8
6.5

4.6
-4.6

0.6
1.4
-5.0

-6.5
-11.2

-8.7
-5.7

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 17

 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
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

Outstanding amounts

3,427.2
3,435.5
3,487.2

11,706.1
12,383.9
13,744.9

3,487.2

13,744.9

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

1,164.2
1,221.5
1,359.2

1,359.2

1,391.8
1,419.5
1,443.7

2021 Apr.   
1,402.4
         May   
1,411.7
         June   
1,419.5
         July   
1,426.8
         Aug.   
1,435.6
         Sep. (p)   1,443.7

7,114.7
7,726.9
8,898.4

8,278.9
8,948.4
10,257.6

8,898.4

10,257.6

9,147.2
9,360.8
9,606.2

9,182.3
9,241.0
9,360.8
9,418.4
9,509.1
9,606.2

10,539.0
10,780.3
11,049.9

10,584.7
10,652.6
10,780.3
10,845.3
10,944.7
11,049.9

1,128.3
1,073.1
1,039.9

1,039.9

985.6
932.4
916.3

965.6
964.3
932.4
934.9
918.0
916.3

2,298.9
2,362.4
2,447.3

2,447.3

2,483.8
2,489.8
2,490.0

2,486.4
2,486.3
2,489.8
2,487.3
2,484.1
2,490.0

3,469.5
3,422.3
3,406.3

3,452.0
3,450.5
3,422.3
3,422.2
3,402.1
3,406.3

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

50.6
57.3
137.6

468.0
605.8
1,255.9

518.6
663.2
1,393.5

28.6

32.6
27.7
24.5

10.6
9.3
7.8
7.6
8.8
8.1

4.5
4.9
11.3

11.3

10.0
9.0
8.5

9.8
9.1
9.0
8.9
8.6
8.5

296.1

236.2
218.0
229.5

44.4
60.9
112.7
56.8
81.2
91.5

7.0
8.5
16.3

16.3

14.2
12.2
11.4

12.8
12.0
12.2
11.3
11.4
11.4

324.7

268.9
245.7
254.0

54.9
70.2
120.5
64.4
90.0
99.6

6.7
8.0
15.6

15.6

13.7
11.8
11.0

12.4
11.6
11.8
11.0
11.0
11.0

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

-73.2
-59.7
-27.2

-35.0

-57.9
-52.1
-17.5

-17.6
-0.7
-33.8
2.7
-17.1
-3.0

-6.1
-5.3
-2.5

-2.5

-8.0
-13.0
-15.1

-9.2
-11.5
-13.0
-13.5
-12.5
-15.1

Transactions

44.8
61.5
85.7

24.0

38.0
6.1
8.1

2.6
-0.1
3.6
-2.5
4.7
5.8

-28.5
1.8
58.5

-10.9

-19.9
-46.0
-9.4

-15.0
-0.8
-30.2
0.2
-12.5
2.8

Growth rates

2.0
2.7
3.6

3.6

5.0
3.8
3.1

4.6
4.1
3.8
3.4
3.3
3.1

-0.8
0.1
1.7

1.7

0.9
-1.4
-2.5

0.3
-0.8
-1.4
-1.8
-1.5
-2.5

14,008.5
14,202.6
14,456.2

14,036.7
14,103.2
14,202.6
14,267.5
14,346.8
14,456.2

490.1
664.9
1,452.0

313.8

249.0
199.7
244.6

40.0
69.4
90.3
64.6
77.5
102.4

4.4
5.7
11.7

11.7

10.2
8.3
7.5

9.1
8.3
8.3
7.6
7.8
7.5

74.4
78.7
100.6

100.6

109.6
112.0
121.1

109.3
107.2
112.0
116.8
110.7
121.1

-0.9
4.1
19.2

-3.5

8.0
2.7
6.1

0.4
-1.9
4.2
4.8
-6.2
7.4

-1.3
5.4
24.2

24.2

-3.6
13.5
12.6

13.6
8.9
13.5
5.1
15.1
12.6

521.8
529.1
647.0

647.0

612.4
608.6
600.8

608.7
609.7
608.6
612.6
615.7
600.8

12.6
-2.1
124.0

41.3

-34.6
-3.1
-7.8

-3.7
1.6
-1.1
4.0
3.1
-14.9

2.5
-0.4
23.5

23.5

14.9
5.7
-0.8

11.7
10.1
5.7
4.0
5.0
-0.8

82.0
19.4
28.4

28.4

12.6
22.1
32.3

20.3
28.4
22.1
30.4
29.5
32.3

-0.9
-56.6
8.8

27.6

-14.2
9.4
9.1

7.7
8.5
-6.8
8.4
-1.3
2.1

- 
- 
- 

- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

678.2 12,384.3
627.1 13,011.0
776.0 14,520.9

776.0 14,520.9

734.6 14,743.0
742.7 14,945.2
754.3 15,210.5

738.3 14,774.9
745.4 14,848.5
742.7 14,945.2
759.8 15,027.3
755.9 15,102.7
754.3 15,210.5

10.8
-54.6
152.0

65.4

-40.8
9.0
7.4

500.9
610.3
1,604.0

379.2

208.1
208.7
251.9

4.4
8.3
-3.7
17.2
-4.4
-5.5

1.6
-8.0
24.2

24.2

6.3
8.3
5.7

10.1
11.0
8.3
7.9
10.0
5.7

44.4
77.7
86.6
81.8
73.2
96.9

4.2
4.9
12.3

12.3

10.0
8.3
7.4

9.2
8.5
8.3
7.6
7.9
7.4

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 18

 
 
   
   
   
   
   
   
   
 
 
 
 
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

2018   
2019   
2020   

2,334.0
2,482.3
2,985.3

1,901.2
2,068.7
2,528.6

2020 Q4   

2,985.3

2,528.6

2021 Q1   
         Q2   
         Q3 (p)  

3,071.4
3,105.4
3,164.1

2021 Apr.   
3,051.4
         May   
3,059.6
         June    3,105.4
         July   
3,109.3
         Aug.    3,133.4
         Sep. (p)   3,164.1

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

94.6
149.6
514.0

32.1

81.1
36.3
59.5

-15.4
9.4
42.3
11.5
22.6
25.4

4.2
6.4
20.7

20.7

18.0
8.5
7.1

12.8
8.9
8.5
6.8
6.9
7.1

2,618.4
2,667.0
2,739.0

2,606.1
2,615.9
2,667.0
2,681.5
2,712.7
2,739.0

106.8
167.1
468.0

51.9

85.0
50.5
65.6

-8.7
10.8
48.5
18.8
25.0
21.8

5.9
8.8
22.6

22.6

19.8
11.5
10.2

14.8
11.6
11.5
10.4
10.2
10.2

277.3
256.9
310.3

310.3

301.3
290.0
284.5

294.9
295.2
290.0
284.3
281.7
284.5

-9.7
-18.9
55.8

-12.5

-9.0
-11.1
-6.0

-5.7
0.7
-6.1
-5.6
-2.8
2.4

-3.4
-6.8
21.6

21.6

15.2
-8.3
-12.0

4.7
-5.8
-8.3
-14.2
-13.0
-12.0

147.9
150.2
143.1

143.1

143.8
139.7
131.1

143.0
141.7
139.7
135.8
130.6
131.1

-1.0
1.7
-6.9

-3.7

0.6
-4.0
-0.8

-0.7
-1.4
-1.8
-0.7
-0.4
0.4

-0.7
1.2
-4.6

-4.6

-2.7
-5.7
-5.3

-2.7
-3.9
-5.7
-5.5
-5.6
-5.3

7.6
6.5
3.3

3.3

7.8
8.7
9.5

7.4
6.8
8.7
7.7
8.5
9.5

-1.4
-0.4
-3.0

-3.5

4.4
0.9
0.7

-0.3
-0.6
1.8
-1.0
0.8
0.9

-16.2
-6.8
-46.9

-46.9

9.4
47.9
38.5

26.2
47.4
47.9
46.9
96.7
38.5

6,645.3
7,041.2
7,647.5

4,035.6
4,397.1
4,954.5

7,647.5

4,954.5

7,825.3
7,908.1
8,015.7

7,844.1
7,874.6
7,908.1
7,939.3
7,989.5
8,015.7

5,109.5
5,199.4
5,311.6

5,129.7
5,165.7
5,199.4
5,234.4
5,286.6
5,311.6

Transactions

326.6
394.5
611.6

158.4

176.4
83.6
100.1

20.3
30.9
32.3
22.9
50.9
26.4

325.4
360.2
561.1

139.1

152.6
90.5
106.1

21.3
36.3
32.9
29.9
50.9
25.2

Growth rates

5.2
5.9
8.7

8.7

9.2
7.7
6.9

8.3
7.9
7.7
7.3
7.3
6.9

8.8
8.9
12.8

12.8

12.7
11.0
10.1

11.6
11.3
11.0
10.6
10.7
10.1

517.8
492.3
437.5

437.5

422.2
407.5
389.8

417.6
411.6
407.5
398.9
394.2
389.8

-45.0
-26.2
-53.8

-8.5

-16.0
-14.5
-17.8

-4.3
-5.9
-4.4
-8.7
-4.7
-4.5

-8.0
-5.1
-10.9

-10.9

-10.4
-11.8
-12.7

-10.4
-11.3
-11.8
-12.6
-12.6
-12.7

2,090.6
2,151.0
2,254.7

2,254.7

2,292.9
2,300.4
2,313.6

2,295.9
2,296.5
2,300.4
2,305.2
2,307.9
2,313.6

45.6
61.0
104.4

27.9

39.7
7.6
12.0

3.1
0.6
3.9
1.5
4.7
5.7

2.2
2.9
4.9

4.9

6.0
4.6
3.9

5.4
4.9
4.6
4.2
4.1
3.9

1.3
0.8
0.8

0.8

0.8
0.7
0.7

0.9
0.8
0.7
0.8
0.8
0.7

0.5
-0.5
-0.1

-0.2

0.0
-0.1
-0.1

0.1
-0.2
0.0
0.1
-0.1
-0.1

66.7
-36.8
-6.5

-6.5

39.5
-20.0
-31.8

4.0
-13.7
-20.0
-27.9
-27.7
-31.8

996.1
1,032.6
1,106.7

1,106.7

1,127.9
1,171.3
1,218.5

1,128.7
1,144.4
1,171.3
1,183.1
1,174.9
1,218.5

1.7
26.9
144.6

53.9

12.5
46.0
44.6

6.6
17.2
22.2
12.0
-9.2
41.7

0.2
2.7
14.5

14.5

4.1
15.5
14.9

8.8
11.5
15.5
14.9
16.4
14.9

204.8
217.1
237.9

237.9

209.4
219.4
227.5

225.5
229.4
219.4
232.0
229.9
227.5

-3.6
11.0
22.3

-1.9

-29.1
10.1
5.3

16.5
4.0
-10.4
12.5
-2.1
-5.1

-1.7
5.3
10.3

10.3

-6.2
-2.8
-6.5

-0.5
-0.7
-2.8
-3.9
-1.7
-6.5

436.2
468.0
508.9

508.9

492.3
490.9
507.9

493.8
490.7
490.9
493.9
494.2
507.9

19.2
29.7
41.1

39.2

-16.5
-1.3
16.6

1.7
-3.2
0.2
2.9
0.4
13.3

4.6
6.8
8.8

8.8

4.2
5.4
8.1

6.0
6.5
5.4
4.1
5.7
8.1

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 7 / 2021 - Statistics

S 19

 
 
   
   
 
 
 
 
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
loans 2)

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

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

4,684.1
4,660.7
5,925.4

5,925.4

6,092.3
6,185.9
6,365.7

2021 Apr.   
6,098.6
         May   
6,133.9
         June   
6,185.9
         July   
6,290.8
         Aug.   
6,341.8
         Sep. (p)   6,365.7

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

91.5
-87.2
1,050.4

177.0

162.2
109.8
182.6

25.3
37.6
47.0
79.3
60.0
43.3

2.0
-1.9
22.3

22.3

21.9
13.1
11.0

18.0
15.4
13.1
12.4
12.1
11.0

1,008.4
986.8
996.1

3,664.3
3,662.2
4,917.3

13,416.5 11,123.0
13,865.5 11,452.4
14,343.2 11,927.3

11,483.4
11,839.6
12,301.1

4,404.9
4,475.8
4,723.6

996.1

4,917.3

14,343.2 11,927.3

12,301.1

4,723.6

993.9
1,005.9
997.9

1,002.7
1,004.4
1,005.9
1,009.9
1,005.1
997.9

5,096.8
5,178.4
5,366.2

5,093.7
5,127.7
5,178.4
5,279.2
5,335.1
5,366.2

14,461.3 12,059.3
14,485.7 12,073.0
14,609.3 12,179.8

14,417.3 12,037.9
14,455.7 12,064.9
14,485.7 12,073.0
14,530.3 12,113.7
14,550.1 12,131.4
14,609.3 12,179.8

12,419.4
12,436.6
12,532.7

12,393.8
12,415.5
12,436.6
12,474.1
12,487.4
12,532.7

4,782.9
4,745.5
4,773.2

4,751.5
4,745.8
4,745.5
4,745.6
4,754.0
4,773.2

Transactions

-28.2
-23.3
13.3

119.7
-64.3
1,037.0

-1.9

-1.7
11.9
-8.2

8.5
1.8
1.6
4.0
-5.0
-7.2

-2.7
-2.3
1.3

1.3

-0.8
0.6
0.0

-0.5
-0.2
0.6
1.1
1.0
0.0

178.7

174.4
97.2
190.8

16.1
35.7
45.5
75.3
65.0
50.6

3.4
-1.8
27.9

27.9

28.1
16.2
13.6

22.9
19.4
16.2
15.1
14.8
13.6

375.0
452.1
735.1

156.7

144.4
47.2
132.1

-20.6
40.6
27.2
46.1
26.5
59.6

2.9
3.4
5.3

5.3

4.6
3.6
3.4

4.0
3.5
3.6
3.4
3.2
3.4

307.5
378.3
539.6

83.6

132.5
38.5
119.4

-0.7
30.6
8.6
46.2
25.7
47.6

2.8
3.4
4.7

4.7

3.6
3.1
3.2

3.3
2.8
3.1
3.1
3.0
3.2

382.6
424.9
560.6

119.9

114.8
38.4
121.5

-8.8
23.0
24.3
46.1
23.3
52.1

Growth rates

3.4
3.7
4.7

4.7

3.5
3.0
3.2

3.2
2.7
3.0
3.0
3.0
3.2

124.1
115.6
288.8

3.5

59.6
-25.8
39.4

-22.8
-3.9
1.0
13.5
7.2
18.7

2.9
2.6
6.5

6.5

4.6
1.4
1.6

2.6
1.5
1.4
1.3
1.0
1.6

5,741.9
5,931.1
6,119.9

6,119.9

6,173.4
6,240.3
6,310.1

6,191.4
6,213.7
6,240.3
6,274.6
6,291.2
6,310.1

166.1
200.4
209.2

60.8

56.6
78.9
64.1

27.6
23.0
28.3
26.0
17.2
20.9

3.0
3.5
3.5

3.5

3.8
4.5
4.3

4.3
4.3
4.5
4.5
4.5
4.3

849.8
893.5
916.1

916.1

947.8
937.6
952.1

944.6
948.3
937.6
945.5
939.1
952.1

-0.3
41.2
25.8

9.6

28.9
-9.2
23.8

-0.7
4.8
-13.2
8.4
2.3
13.1

0.0
4.8
2.9

2.9

-1.4
3.3
5.9

0.8
0.6
3.3
4.2
5.1
5.9

126.4
152.0
167.7

167.7

155.3
149.7
144.4

150.4
157.1
149.7
148.0
147.1
144.4

17.7
21.1
15.8

9.7

-12.6
-5.5
-7.8

-4.7
6.7
-7.4
-1.8
-0.9
-5.2

16.3
16.1
10.4

10.4

-3.5
-3.5
-10.3

-3.5
1.8
-3.5
-5.0
-6.0
-10.3

1,519.9
1,562.5
1,549.9

1,549.9

1,521.2
1,523.8
1,533.4

1,505.5
1,505.0
1,523.8
1,527.4
1,521.6
1,533.4

88.5
30.5
167.2

30.0

3.7
2.7
10.1

-12.6
-0.4
15.7
3.0
-4.9
12.0

6.1
2.0
11.2

11.2

10.0
5.1
3.1

6.5
5.1
5.1
4.4
2.6
3.1

773.6
850.6
866.0

866.0

880.8
888.9
896.1

873.8
885.8
888.9
889.2
897.0
896.1

-21.1
43.4
28.3

43.1

8.2
6.0
2.6

-7.3
10.4
3.0
-3.1
5.6
0.0

-2.6
5.5
3.4

3.4

8.4
7.6
7.3

9.0
9.6
7.6
6.9
7.2
7.3

Source: ECB.
1) Data refer to the changing composition of the euro area.
2) Adjusted for loan sales and securitisation (resulting in derecognition from the MFI statistical balance sheet) as well as for positions arising from notional cash pooling services

provided by MFIs.

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 7 / 2021 - Statistics

S 20

 
 
   
   
   
   
 
 
 
 
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
loans 4)

and up to
5 years

   Households 3) 

Loans for
consumption

Adjusted
loans 4)

Loans for Other loans

house
purchase

1

2

3

4

5

6

7

8

9

10

Outstanding amounts

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

4,404.9
4,475.8
4,723.6

4,723.6

4,782.9
4,745.5
4,773.2

4,751.5
4,745.8
4,745.5
4,745.6
4,754.0
4,773.2

124.1
115.6
288.8

3.5

59.6
-25.8
39.4

-22.8
-3.9
1.0
13.5
7.2
18.7

2.9
2.6
6.5

6.5

4.6
1.4
1.6

2.6
1.5
1.4
1.3
1.0
1.6

4,489.0
4,577.9
4,841.3

4,841.3

4,900.9
4,865.8
4,890.4

4,870.4
4,859.5
4,865.8
4,860.0
4,871.5
4,890.4

176.3
143.4
325.1

22.3

59.5
-26.4
45.5

-25.8
-11.7
11.1
10.2
12.4
22.9

4.1
3.2
7.1

7.1

5.3
1.9
2.1

3.2
1.9
1.9
1.7
1.5
2.1

991.0
966.7
898.2

898.2

895.1
833.7
836.3

874.3
873.3
833.7
828.9
828.6
836.3

17.8
-13.5
-54.1

-21.7

-3.1
-57.6
4.6

-17.4
-0.4
-39.8
-3.1
-0.2
7.8

1.8
-1.4
-5.7

-5.7

-9.1
-11.7
-8.5

-9.8
-7.7
-11.7
-11.3
-10.9
-8.5

844.2
878.0
1,012.0

1,012.0

1,017.5
971.9
971.2

996.2
972.9
971.9
966.6
968.5
971.2

32.8
43.6
138.9

-1.5

5.8
-42.6
0.6

-19.4
-22.8
-0.4
-3.2
1.6
2.2

4.0
5.2
15.9

15.9

11.0
-2.2
-3.7

3.6
-2.6
-2.2
-3.2
-3.9
-3.7

2,569.7
2,631.1
2,813.4

2,813.4

2,870.2
2,939.9
2,965.6

2,881.0
2,899.6
2,939.9
2,950.2
2,956.8
2,965.6

Transactions

73.5
85.6
204.0

26.6

56.9
74.4
34.2

13.9
19.2
41.2
19.7
5.8
8.7

Growth rates

2.9
3.3
7.8

7.8

7.6
7.3
6.9

6.8
6.2
7.3
7.2
6.7
6.9

5,741.9
5,931.1
6,119.9

6,119.9

6,173.4
6,240.3
6,310.1

6,191.4
6,213.7
6,240.3
6,274.6
6,291.2
6,310.1

166.1
200.4
209.2

60.8

56.6
78.9
64.1

27.6
23.0
28.3
26.0
17.2
20.9

3.0
3.5
3.5

3.5

3.8
4.5
4.3

4.3
4.3
4.5
4.5
4.5
4.3

6,024.9
6,224.0
6,390.1

6,390.1

6,435.8
6,496.9
6,568.8

6,451.6
6,472.0
6,496.9
6,532.3
6,552.2
6,568.8

188.4
217.2
195.0

68.0

50.9
72.7
69.0

25.6
21.2
25.9
27.6
20.4
21.1

3.2
3.6
3.1

3.1

3.3
4.0
4.1

3.8
3.9
4.0
4.2
4.2
4.1

682.6
720.1
700.2

700.2

695.4
693.4
697.1

690.6
691.7
693.4
696.1
695.8
697.1

41.2
41.0
-11.8

-1.7

-3.1
3.1
4.6

-0.7
1.5
2.3
2.4
0.3
1.9

6.3
6.0
-1.6

-1.6

-1.6
0.6
0.4

0.4
0.7
0.6
0.4
0.1
0.4

4,356.4
4,524.6
4,725.1

4,725.1

4,785.0
4,851.1
4,913.2

4,809.0
4,829.9
4,851.1
4,873.1
4,893.0
4,913.2

134.2
168.6
210.8

61.6

60.4
72.9
63.4

28.1
21.0
23.8
22.9
19.9
20.6

3.2
3.9
4.7

4.7

5.0
5.7
5.5

5.4
5.4
5.7
5.7
5.8
5.5

702.9
686.4
694.6

694.6

693.1
695.7
699.7

691.8
692.0
695.7
705.4
702.3
699.7

-9.3
-9.2
10.2

1.0

-0.7
2.9
-3.9

0.1
0.6
2.2
0.6
-3.0
-1.5

-1.3
-1.3
1.5

1.5

1.5
0.6
-0.1

1.3
0.7
0.6
0.6
0.1
-0.1

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 loan sales and securitisation (resulting in derecognition from the MFI statistical balance sheet) as well as for positions arising from notional cash pooling services

provided by MFIs.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 21

 
 
   
   
   
   
 
 
 
 
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

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

2018   
2019   
2020   

2020 Q4   

2021 Q1   
         Q2   
         Q3 (p)  

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep. (p)  

389.2
364.2
748.5

748.5

698.7
657.0
703.0

726.3
690.4
657.0
683.2
723.1
703.0

45.5
-24.3
321.2

-57.5

-49.7
-41.8
46.0

27.6
-36.0
-33.4
26.2
40.0
-20.2

13.0
-6.3
88.4

88.4

56.3
-10.6
-12.8

27.8
5.3
-10.6
-9.6
-11.8
-12.8

6,817.4
7,058.9
6,967.2

6,967.2

6,892.6
6,848.6
6,852.7

6,839.2
6,835.3
6,848.6
6,892.0
6,868.4
6,852.7

51.0
107.8
-32.8

2.1

-31.7
-19.3
-4.3

-18.2
-27.4
26.3
0.0
-16.7
12.4

0.8
1.6
-0.5

-0.5

-0.3
-0.6
-0.8

-0.3
-0.9
-0.6
-0.5
-0.8
-0.8

1,940.0
1,946.1
1,916.7

1,916.7

1,897.6
1,868.5
1,849.3

1,869.7
1,868.2
1,868.5
1,860.9
1,848.2
1,849.3

-37.8
-5.3
-14.6

-4.0

-22.5
-22.4
-19.9

-20.2
-1.0
-1.2
-7.4
-12.9
0.4

-1.9
-0.3
-0.8

-0.8

-1.6
-2.7
-3.6

-2.3
-2.7
-2.7
-3.0
-3.9
-3.6

56.1
50.1
42.1

42.1

41.2
40.4
38.7

41.2
40.8
40.4
39.4
39.0
38.7

-4.9
-3.3
-8.0

-0.9

-0.9
-0.7
-1.7

0.0
-0.3
-0.4
-1.0
-0.4
-0.3

-8.0
-5.9
-15.9

-15.9

-12.7
-8.2
-9.9

-10.8
-9.6
-8.2
-9.4
-9.4
-9.9

2,099.7
2,156.5
1,994.9

1,994.9

1,984.7
1,953.6
1,978.8

1,968.1
1,944.2
1,953.6
1,965.4
1,962.1
1,978.8

Transactions

16.1
27.3
-99.3

-43.9

-33.5
-26.1
14.7

-3.2
-20.5
-2.4
13.3
-5.4
6.9

Growth rates

0.8
1.3
-4.6

-4.6

-4.1
-4.8
-4.3

-4.2
-4.9
-4.8
-4.0
-3.8
-4.3

2,721.6
2,906.1
3,013.6

3,013.6

2,969.1
2,986.2
2,985.9

2,960.1
2,982.1
2,986.2
3,026.3
3,019.1
2,985.9

77.6
89.0
89.2

50.9

25.2
30.0
2.6

5.3
-5.5
30.3
-4.8
2.0
5.5

2.9
3.2
3.0

3.0

3.5
3.9
3.7

4.0
3.1
3.9
3.7
3.4
3.7

1,030.0
1,455.6
1,428.7

1,428.7

1,392.6
1,404.1
1,366.1

1,444.6
1,467.8
1,404.1
1,442.6
1,456.8
1,366.1

88.4
309.5
-49.5

-110.5

2.9
-7.3
-49.0

50.0
-5.4
-51.9
16.1
10.2
-75.2

- 
- 
- 

- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

460.2
452.3
539.3

539.3

388.1
375.2
425.0

379.9
316.9
375.2
338.9
345.6
425.0

42.6
19.3
156.4

100.7

-182.8
-2.1
27.8

-1.0
-58.4
57.3
-33.4
-0.2
61.4

- 
- 
- 

- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

187.0
178.9
130.1

130.1

127.2
123.7
139.0

132.8
133.4
123.7
133.4
125.3
139.0

16.2
-2.7
-48.8

-9.8

-2.9
-3.6
15.3

5.6
0.6
-9.7
9.7
-8.0
13.7

8.1
-1.5
-27.3

-27.3

-30.7
-22.3
-0.6

-29.2
-32.1
-22.3
-17.9
-26.5
-0.6

194.9
187.2
139.2

139.2

130.2
134.5
146.0

131.3
130.8
134.5
133.2
128.4
146.0

23.6
-2.5
-48.0

-8.1

-8.9
4.3
11.5

1.0
-0.5
3.7
-1.3
-4.8
17.6

7.7
-1.5
-25.7

-25.7

-33.7
-22.9
-0.9

-35.4
-38.1
-22.9
-23.5
-27.7
-0.9

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 7 / 2021 - Statistics

S 22

 
 
   
   
   
   
   
 
 
 
 
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

-0.9
-0.4
-0.6
-7.2

-5.2
-7.2

-8.4
-7.0

2

-1.4
-1.0
-1.0
-5.9

. 
. 

. 
. 

3

0.1
0.1
0.0
-0.4

. 
. 

. 
. 

4

0.2
0.2
0.0
0.0

. 
. 

. 
. 

Social
security
funds

5

0.2
0.3
0.3
-0.9

. 
. 

. 
. 

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

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

1.0
1.4
1.0
-5.7

-3.7
-5.7

-6.8
-5.6

Capital
expenditure

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

1

2

3

46.2
46.4
46.3
46.6

46.6
46.6

46.5
46.3

45.8
45.9
45.8
46.1

46.1
46.1

46.1
45.8

12.8
12.9
12.9
13.0

13.0
13.0

13.0
12.8

4

13.0
13.0
13.0
12.8

12.8
12.8

12.7
12.8

Sources: ECB for annual data; Eurostat for quarterly data.

5

15.2
15.2
15.0
15.6

15.5
15.6

15.7
15.5

6

0.4
0.5
0.5
0.5

0.4
0.5

0.5
0.5

7

8

47.1
46.9
46.9
53.8

51.8
53.8

54.9
53.3

43.3
43.2
43.2
49.2

47.6
49.2

50.2
48.7

9

9.9
9.9
9.9
10.7

10.5
10.7

10.8
10.5

10

5.3
5.3
5.3
6.0

5.8
6.0

6.1
5.9

11

1.9
1.8
1.6
1.5

1.6
1.5

1.5
1.5

12

22.4
22.3
22.4
25.5

24.8
25.5

25.8
25.0

13

3.8
3.7
3.7
4.6

4.2
4.6

4.6
4.6

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

Currency Loans

and
deposits

Debt   Resident creditors Non-resident
creditors

securities

Up to
1 year

Over
1 year

Over 1

Up to
Euro or
1 year and up to 5 years participating
currencies

5 years

Over

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

1

87.5
85.5
83.6
97.3

96.6
97.3

100.0
98.3

2

3.2
3.1
3.0
3.2

3.1
3.2

3.2
3.1

3

14.5
13.7
12.9
14.2

13.9
14.2

14.1
13.9

4

69.9
68.7
67.6
79.9

79.6
79.9

82.7
81.4

5

48.0
47.9
45.2
54.6

. 
. 

. 
. 

Sources: ECB for annual data; Eurostat for quarterly data.

MFIs

6

32.0
32.2
30.4
39.1

. 
. 

. 
. 

7

39.5
37.7
38.4
42.7

. 
. 

. 
. 

8

8.6
8.1
7.6
11.3

. 
. 

. 
. 

9

78.9
77.5
75.9
86.0

. 
. 

. 
. 

10

16.4
16.0
15.6
19.1

. 
. 

. 
. 

11

28.9
28.3
27.7
31.5

. 
. 

. 
. 

12

42.3
41.2
40.3
46.7

. 
. 

. 
. 

13

85.7
84.1
82.2
95.6

. 
. 

. 
. 

Other
curren-
cies

14

1.8
1.5
1.4
1.7

. 
. 

. 
. 

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 23

 
   
 
 
   
   
   
   
 
 
   
   
   
   
   
   
 
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

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

1

-2.5
-2.0
-2.0
13.8

11.3
13.8

14.3
3.9

2

-1.0
-1.4
-1.0
5.7

3.7
5.7

6.8
5.6

3

-0.1
0.4
0.1
2.3

3.0
2.3

1.9
-1.3

4

0.4
0.5
0.3
2.5

3.2
2.5

2.2
-0.4

5

0.5
0.4
0.1
2.0

2.9
2.0

1.6
-1.0

6

0.0
-0.1
0.0
0.4

0.3
0.4

0.5
0.4

7

-0.1
0.0
0.0
-0.1

-0.1
-0.1

0.0
0.0

8

0.1
0.2
0.2
0.1

0.1
0.1

0.2
0.2

9

10

-0.2
0.1
-0.2
-0.1

-0.3
-0.1

-0.1
0.0

-0.4
-0.1
0.0
-0.1

0.0
-0.2

-0.3
-0.9

11

-1.3
-1.0
-1.1
5.8

4.6
5.8

5.5
-0.3

12

1.0
0.8
0.9
9.6

8.5
9.6

10.3
5.8

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

2018   
2019   
2020   

2020 Q2   
         Q3   
         Q4   

2021 Q1   

2021 Apr.   
         May   
         June   
         July   
         Aug.   
         Sep.   

12.5
12.2
14.9

15.3
15.8
14.9

15.7

15.8
15.7
15.5
15.4
15.4
15.5

11.0
10.8
13.6

13.9
14.5
13.6

14.3

14.4
14.3
14.2
14.1
14.0
14.1

3.7
3.6
4.2

5.0
4.7
4.2

5.5

5.1
4.7
5.2
5.2
5.4
4.7

1.5
1.4
1.4

1.4
1.4
1.4

1.4

1.4
1.4
1.4
1.3
1.4
1.4

5

0.4
0.4
0.3

0.4
0.3
0.3

0.4

0.4
0.3
0.3
0.3
0.3
0.3

6

7

8

9

10

7.3
7.5
7.6

7.5
7.5
7.6

7.8

7.9
7.9
7.9
7.9
7.9
7.9

2.3
2.2
1.9

2.0
1.9
1.9

1.8

1.7
1.7
1.7
1.6
1.7
1.7

1.1
1.3
1.1

1.1
1.1
1.1

1.1

1.0
0.5
0.5
0.5
1.1
1.1

-0.1
-0.1
-0.2

-0.2
-0.2
-0.2

-0.2

-0.3
-0.3
-0.3
-0.3
-0.3
-0.3

2.7
2.5
2.2

2.3
2.3
2.2

2.1

2.1
2.1
2.0
2.0
2.0
2.0

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

2.5
2.1
2.3

2.0
2.2
2.3

2.1

2.1
2.1
2.1
1.9
1.9
1.9

12

0.4
0.3
0.0

0.1
0.1
0.0

0.0

-0.1
-0.1
-0.1
-0.1
-0.1
-0.1

13

0.9
1.1
0.8

0.9
0.8
0.8

0.5

0.6
0.6
0.5
0.5
0.5
0.5

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 24

 
   
   
 
 
   
   
   
   
   
   
   
 
 
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

-0.7
-0.8
-1.9
-9.1

-7.1
-9.1

-8.8
-6.4

102.0
99.9
97.7
112.8

112.0
112.8

116.9
113.7

2

1.3
1.9
1.5
-4.3

-2.8
-4.3

-5.8
-5.2

64.7
61.3
58.9
68.7

69.1
68.7

69.9
69.7

3

4

5

Government deficit (-)/surplus (+)

6

7

8

9

-0.5
-0.6
0.1
-5.6

-4.0
-5.6

-5.6
-4.3

9.1
8.2
8.6
19.0

19.1
19.0

19.6
19.6

-0.3
0.1
0.5
-4.9

-3.4
-4.9

-5.6
-4.4

Government debt

67.8
63.1
57.2
58.4

61.2
58.4

60.4
59.1

0.6
0.9
1.1
-10.1

-5.5
-10.1

-12.6
-11.2

179.5
186.4
180.7
206.3

199.8
205.7

209.0
207.2

-3.0
-2.5
-2.9
-11.0

-8.2
-11.0

-11.6
-8.7

98.6
97.5
95.5
120.0

114.1
120.0

125.3
122.8

-3.0
-2.3
-3.1
-9.1

-7.2
-9.1

-10.5
-9.2

98.1
97.8
97.5
115.0

115.4
115.0

117.9
114.6

-2.4
-2.2
-1.5
-9.6

-7.4
-9.6

-10.2
-8.9

134.2
134.4
134.3
155.6

154.2
155.6

159.6
156.3

1.9
-3.5
1.3
-5.7

-4.2
-5.7

-7.4
-6.2

92.9
98.4
91.1
115.3

116.0
115.3

121.4
112.0

Latvia

Lithuania Luxembourg

Malta Netherlands

Austria

Portugal

Slovenia

Slovakia

Finland

10

11

12

13

14

15

16

17

18

19

Government deficit (-)/surplus (+)

-0.8
-0.8
-0.6
-4.5

-3.5
-4.5

-6.6
-7.0

39.0
37.1
36.7
43.2

44.4
43.2

45.4
43.3

0.4
0.5
0.5
-7.2

-4.1
-7.2

-7.2
-5.4

39.1
33.7
35.9
46.6

45.4
46.6

45.1
44.6

1.4
3.0
2.3
-3.5

-2.6
-3.5

-2.5
-0.6

21.8
20.8
22.3
24.8

26.2
24.8

28.0
26.2

3.2
1.9
0.5
-9.7

-7.0
-9.7

-10.0
-8.6

47.7
43.6
40.7
53.4

51.5
53.4

57.5
59.5

1.3
1.4
1.7
-4.2

-2.5
-4.2

-5.8
-4.2

Government debt

56.9
52.4
48.5
54.3

55.1
54.3

54.9
54.2

-0.8
0.2
0.6
-8.3

-4.5
-8.3

-10.6
-8.5

78.5
74.0
70.6
83.2

78.5
83.2

87.0
86.2

-3.0
-0.3
0.1
-5.8

-4.2
-5.8

-7.0
-5.8

126.1
121.5
116.6
135.2

131.6
135.2

139.1
135.4

-0.1
0.7
0.4
-7.7

-4.9
-7.7

-8.2
-6.4

74.2
70.3
65.6
79.8

77.8
79.8

84.9
80.0

-1.0
-1.0
-1.3
-5.5

-4.0
-5.5

-6.4
-6.2

51.6
49.6
48.1
59.7

60.2
60.1

60.1
61.4

-0.7
-0.9
-0.9
-5.5

-4.1
-5.5

-6.0
-4.4

61.2
59.8
59.5
69.5

67.2
69.5

70.4
69.4

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

2017   
2018   
2019   
2020   

2020 Q3   
         Q4   

2021 Q1   
         Q2   

Source: Eurostat.

ECB Economic Bulletin, Issue 7 / 2021 - Statistics

S 25

 
 
 
 
 
 
 
 
 
 
 
 
© European Central Bank, 2021 

Postal address 
Telephone 
Website 

60640 Frankfurt am Main, Germany 
+49 69 1344 0 
www.ecb.europa.eu 

All rights reserved. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. 

This Bulletin was produced under the responsibility of the Executive Board of the ECB. Translations are prepared and published by the 
national central banks. 

The cut-off date for the statistics included in this issue was 27 October 2021. 

For specific terminology please refer to the ECB glossary (available in English only). 

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ISSN 2363-3417, QB-BP-21-007-EN-N 
ISSN 2363-3417, QB-BP-21-007-EN-Q