SLOVENIA
SLOVAK REP.
ROMANIA
POLAND
LITHUANIA
LATVIA
HUNGARY
ESTONIA
CZECH REP.
CROATIA
BULGARIA
EU11
REGULAR ECONOMIC
REPORT
ISSUE # 27
June 2013

Report No. 78341-ECA
EU11 Regular Economic Report
Macroeconomic Report: Economic Recovery On Hold
Special Topic: Determinants of Job Creation in EU11:
Evidence from Firm Level Data
June 2013
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No. 78341-ECA.
Contents
Macroeconomic Report: Economic Recovery on Hold 1
Summary 2
EU11 Recent Economic Developments 4
Lackluster Domestic Demand 4
Further Job Losses 10
Weak Foreign Trade and Narrowing External Imbalances 14
Subdued Inflation and Further Monetary Easing 21
A New Banking Model Emerging 24
Continued Fiscal Consolidation in a Challenging Environment 30
Some Progress on Structural Reforms 33
EU11 Near-Term Outlook 35
Tepid Economic Recovery 35
A Structural Reform Agenda to Restore Growth in EU11 40
Spotlight 1: Employment Expectations as Indicators of Labor Demand in EU11 43
Spotlight 2: Toward Inclusive Growth in Europe 49
Special Topic: Determinants of Job Creation in EU11:
Evidence from Firm Level Data 55
Introduction 57
Some Stylized Facts 58
Job Creation in EU11 prior and after the 2008 crisis 60
The high-growth firms in the EU11 economies 67
Conclusions 70
References 76
MACROECONOMIC REPORT: economic RECOVERY on hold
Contents | iii
List of Figures
Figure 1. GDP rate of change in EU15 and EU11 Countries 4
Figure 2. Credit Default Swap Rates 5
Figure 3. Industrial Production 5
Figure 4. Contributors to GVA annual rate of change, EU11 and EU15 6
Figure 5. Contributors to GDP rate of change, EU11 and EU15 7
Figure 6. Contributors of Domestic Demand to GDP Growth, EU11 Countries,
by quarters in 2012 7
Figure 7. High-Frequency Indicators, working day adjusted, EU11 and EU15 9
Figure 8. Selected Economic Sentiment Indicators, EU11 and EU15 9
Figure 9. GDP and Employment Growth in EU11 and EU15, Year-on-Year 10
Figure 10. Sectoral contribution to employment growth in 2012 in EU11 11
Figure 11. Contribution to employment growth by professional status in 2012
in EU11 and EU15 11
Figure 12. Unemployment rates, EU11 and EU15 11
Figure 13. Unemployment rates in EU11 countries, March 2012–March 2013 11
Figure 14. Maximum and minimum unemployment rates in EU11 and EU15 12
Figure 15. Share of long-term unemployed in total unemployed 12
Figure 16. Real Labor Productivity Per Person Employed and Real Unit Labor Costs,
EU11 and EU15 13
Figure 17. Growth in Real Labor Productivity and Real Unit Labor Costs
in 4th Quarter of 2012 13
Figure 18. Exports volume growth in 2007–2012 14
Figure 19. Imports volume growth in 2007–2012 14
Figure 20. Exports and Imports Growth in EU11 and EU15 15
Figure 21. Exports and Imports Growth in EU11 countries in 2012 15
Figure 22. Contribution to Exports Growth, 2012 16
Figure 23. EU11 export contribution by product in 2011–12 16
Figure 24. Current Account Balance 2011–12 17
Figure 25. Change in Current Account Balance 2011–12 by components 17
Figure 26. Current Account Financing 17
Figure 27. Gross external debt developments 2011–2012 18
Figure 28. Structure of the gross external debt in December 2012 18
Figure 29. Stock of inward FDI as share of GDP, end-2011 19
Figure 30. Top ten sources of EU11 Inward FDI 19
Figure 31. Top ten sources of EU15 Inward FDI 19
EU11 REGULAR ECONOMIC REPORT | june 2013
iv | Contents
Figure 32. Share of incoming FDI in EU11 countries by regional origin, end-2011 20
Figure 33. Policy Interest Rates in Selected EU Countries 21
Figure 34. Interest rates on new lending to enterprises and CDS spreads
in selected EU11 countries 22
Figure 35. Harmonized Consumer Price Index (HICP), Overall and Core, EU15 and EU11 23
Figure 36. Average HICP, EU15 and EU11 23
Figure 37. Real Effective Exchange Rates, CPI Deflated 23
Figure 38. 5Y CDS, EU11 Countries 25
Figure 39. Sovereign credit ratings and 5Y CDS spreads 25
Figure 40. Cumulative Gross Capital Inflows, EU11 25
Figure 41. Cumulative Other Investments, EU11 25
Figure 42. Total International Claims by Sectors 26
Figure 43. Change in Total International Claims by Country and Sector, 4th Quarter of 2011
to 4th Quarter of 2012 26
Figure 44. Change in Foreign Claims, 4th Quarter of 2011 to 4th Quarter of 2012 27
Figure 45. Annual growth in deposits and loans, 2012 27
Figure 46. Annual growth in deposits and foreign liabilities, 2012 27
Figure 47. Real Credit Growth, EU11 and EU15 28
Figure 48. Contribution to Real Private Sector Credit Growth 28
Figure 49. Emerging Europe Bank Lending Conditions Index, by Categories 28
Figure 50. Funding Conditions in Local Markets, by Region 28
Figure 51. Nonperforming Loans and Provisioning to Total Loans, EU11 countries,
4th Quarter 2012 29
Figure 52. General government fiscal deficit, 2011–2012 in EU11 and EU15 31
Figure 53. General government fiscal deficit, 2011–2012 in EU11 31
Figure 54. General Government Fiscal Deficit Reduction in 2012, difference between
planned and actual 31
Figure 55. General Government Fiscal Deficit in 2012, planned vs. actual 31
Figure 56. Public debt in 2011–12 in EU11 and EU15 32
Figure 57. Contribution to changes in public debt in EU11 countries, 2011–12 32
Figure 58. Global Competitiveness Country Ranking, 2012–2013 34
Figure 59. Ease of Doing Business Country Ranking, 2013 34
Figure 60. GDP Forecasts for 2013 36
Figure 61. Carry-over effects in EU15 and EU11 Countries 36
Figure 62. Unemployment rates 2012–14 37
MACROECONOMIC REPORT: economic RECOVERY on hold
Contents | v
Figure 63. Public sector financing needs 37
Figure 64. Fiscal policy stance in 2012 and 2013 38
Figure 65. Actual Employment and Expected Future Tendency, 2000–2012
(in selected cases) 46
Figure 66. Net Employment Expectations: Selected Sectors and Countries 47
Figure 67. People at risk of poverty or social exclusion in the EU, 2008 49
Figure 68. Simulated at-risk-of-poverty rates by employment scenario in EU11 51
Figure 69. Low work intensity, by employment scenario, in EU11 52
Figure 70. Anchored poverty rates are expected to decrease significantly
in countries achieving their Europe 2020 national targets 53
Figure 71. Job creation from 2002 to 2008 and Job destruction in 2009 59
Figure 72. Average employment growth rate by sector and year, 2002–2009 59
Figure 73. Employment growth and perceived business barriers, 2002–2009 60
Figure 74. Average TFP growth versus average employment growth, 2002–2008 65
Figure 75. Share and net job creation of high-growth firms, 2006–2008 67
List of Figures (continued)
EU11 REGULAR ECONOMIC REPORT | june 2013
vi | Contents
List of Tables
GDP 3
Table 1. EU11 Growth Prospects 35
Table 2. EU11 – Country Specific Recommendations for 2013–14 41
Table 3. Estimation results for annual average firm growth, 2002–2008 72
Table 4. Estimation results for annual average firm growth in 2009 73
Table 5. Estimation results for high-growth probability, 2006–2008 74
Table 6. Estimation results for high-growth probability, 2009 75
List of Boxes
Box 1. External Developments in 2012 5
Box 2. EU11 Goods Export Growth: The Changing Role of Traditional EU15 Markets 15
Box 3. Where does FDI come from? 19
Box 4. Near-Term Global Outlook and Risks 39
Box 5. Testing the predictive content of employment expectations data 45
Box 6. Estimating Empirical Employment Growth Equations at the Firm Level 61
Box 7. A probability model for high-growth firms 68
MACROECONOMIC REPORT: economic RECOVERY on hold
Contents | vii
Acknowledgments
This Regular Economic Report (RER) is a semiannual publication of the Europe and Central Asia
Region, Poverty Reduction and Economic Management Department (ECA PREM), The World
Bank. It covers economic developments, prospects, and policies in 10 European Union (EU) member
states—Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the
Slovak Republic, and Slovenia—and one forthcoming member, Croatia. Throughout the RER, for
simplicity, we refer to this group of eleven countries as EU11.
The RER comprises two parts: a Macroeconomic Report, and a Special Topic on an issue of
economic policy interest in EU11. The Macroeconomic Report is co-authored by Ewa Korczyc,
Matija Laco, Sanja Madzarevic-Sujster, and Gallina A Vincelette (team lead), with inputs from:
Stella Ilieva, Catalin Pauna, Emilia Skrok, Dilek Aykut, Raquel Letelier, Deborah Winkler, Thomas
Farole, and Hernan Winkler. The Spotlight on “Employment Expectations as Indicators of Labor
Demand in EU11” is co-authored by Erwin Tiongson and Ferhat Bilgin. The Spotlight on “Assessing
Progress toward Achieving the Europe 2020 Poverty and Social Exclusion Targets in EU11” is coauthored
by Ihsan Ajwad, Kenneth Simler, Mehtabul Azam, and Basab Dasgupta. The Special Topic
“Determinants of Job Creation in EU11: Evidence from Firm Level Data” is co-authored by Harald
Oberhofer and Gallina A Vincelette. Maria Andreina Clower, Christopher Pala, Fidel Prokopovich,
and Budy Wirasmo provided invaluable assistance in editing and designing this report.
The team is grateful to Yvonne M. Tsikata (Director, Europe and Central Asia Region, Poverty
Reduction and Economic Management), Mamta Murthi (Country Director, Central Europe and the
Baltic States), Satu Kahkonen (Sector Manager, Europe and Central Asia Region, Poverty Reduction
and Economic Management), Roumeen Islam (Economic Adviser, Europe and Central Asia Region,
Poverty Reduction and Economic Management), and Ismail Radwan (Country Program Coordinator,
Central Europe and the Baltic States) for their guidance in the preparation of this report. The team
is thankful for comments on earlier drafts of this report received from Central Banks and Ministries
of Finance in the EU11 countries.
EU11 REGULAR ECONOMIC REPORT | june 2013
viii | Acknowledgements
Macroeconomic Report:
Economic Recovery on Hold
The economic recovery of the EU11 countries
was put on hold in 2012 as the external
environment weakened and domestic demand
subsided.1 All EU11 countries, with the
exception of Latvia, grew slower than in 2011.
The overall GDP growth of 0.8 percent in
2012 was just a quarter of the pace recorded
the year before. Domestic demand, in particular
investment, abated, leaving net exports as the
sole driver of growth. In addition, the number
of EU11 countries in recession doubled to
four, after the Czech Republic and Hungary
joined Slovenia and Croatia.
Weak economic activity resulted in further
job losses. Prolonged uncertainty, corporatesector
restructuring and recession in some
EU11 countries led to increases in the
unemployment rates across the region. After
stabilizing at around 10 percent for most
of 2010–11, the unemployment rate passed
11 percent in early 2013. Employment growth
was negative throughout 2012, affecting
especially construction, industry and public
administration.
Even though net exports supported economic
growth in 2012, the EU11 trade performance
was disappointing. Both exports and imports
decelerated as global trade was put on hold
because of slow economic growth in the
high-income countries and recurring bouts
1 EU11 refers to the 10 European Union (EU) member states—
Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, the Slovak Republic, and Slovenia—and one
forthcoming member, Croatia. Throughout this Regular Economic
Report (RER), for simplicity, this group of eleven countries is
referred to as EU11.
of uncertainty over the future of the euro.
In early 2013, the EU11 region’s imports and
exports started declining as intra-EU trade
flows decelerated. While the non-EU markets
expansion continued to help generate favorable
trade results for EU11, it was not able to
compensate for the weakened export demand
from the EU.
Net FDI flows to the EU11 countries remained
stable. Gross external debt increased only
modestly due to sovereign borrowing. Public
debt-to-GDP ratios also increased slightly in
2012 on the back of slower-than-planned fiscal
consolidations.
While the pace of fiscal tightening slowed in
2012, the EU11 governments largely delivered
on their fiscal commitments. In addition, they
continued to pursue medium and long-term
fiscal structural reforms aiming at strengthening
public finances.
EU11 central banks stepped up their
expansionary monetary policies from an
already accommodative stance in 2012. Despite
substantial improvements in international
financial markets since the summer of 2012,
EU11 bank lending conditions to the real
economy remained tight. The share of EU11
non-performing loans was still elevated,
suppressing real credit growth. Inflation
subsided due to weak domestic demand and a
significant decline of energy prices.
There are indications that growth will return in
2013. The second half of the year is expected
Summary
EU11 REGULAR ECONOMIC REPORT | june 2013
2 | Summary
to bring the beginning of a firm economic
recovery in Europe. Provided that the Euro
area economy gains strength, the near-term
outlook for the EU11 countries is also expected
to gradually improve. While in 2013, the EU11
growth will still be below its potential—
hampered by weak domestic demand, ongoing
fiscal tightening, muted credit growth, and
challenging external environment—it is
projected to pick up in 2014. The labor market
is likely to recover only in the medium term.
Maintaining prudent fiscal policy and
supporting sustainable economic growth in
EU11 remain a priority to mitigate the adverse
impact of potential shocks from the Euro area.
Fiscal policy is set to remain contractionary,
but the speed of fiscal consolidation is
expected to further slow and hence its effect
on economic growth will be less negative as
compared to 2012. Monetary policy will stay
supportive of economic growth, with further
cuts in policy rates in some EU11 countries
and unconventional measures in others, even
though the scope for further monetary easing
is already tight.
Given the limitations of fiscal and monetary
policy to boost growth, the EU11 governments
should focus their attention on the unfinished
structural agenda aimed at increasing their
countries’ economic potential. Reforms in the
areas of labor markets and education, public
administration, the business environment,
public finance management and investment
planning are some of the priority areas for the
EU11 countries going forward.
GDP
Percent
2012 2013 2014
EU11 0.8 0.8 2.0
Bulgaria 0.8 1.2 2.1
Croatia -2.0 -0.4 1.5
Czech Republic -1.3 -0.4 1.6
Estonia 3.2 3.0 4.0
Latvia 5.6 3.6 4.1
Lithuania 3.6 3.0 3.5
Hungary -1.7 0.3 1.5
Poland 1.9 1.0 2.0
Romania 0.7 1.7 2.2
Slovenia -2.3 -2.3 -0.1
Slovak Republic 2.0 0.7 2.0
MACROECONOMIC REPORT: economic RECOVERY on hold
Summary | 3
A slowing global economy, especially the
recession in the Euro area, decelerated
economic growth in the EU11 countries
(Box 1).2 Overall, EU11 year-on-year growth
rate dropped from 3.1 percent in 2011 to 0.8
percent in 2012 (Figure 1). In EU15, economic
2 EU11 refers to the 10 European Union (EU) member states—
Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, the Slovak Republic, and Slovenia—and one
forthcoming member, Croatia. Throughout this Regular Economic
Report (RER), for simplicity, this group of eleven countries is
referred to as EU11. The group of EU15 countries comprises:
Austria, Belgium, Denmark, Finland, France, Germany, Greece,
Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain,
Sweden, and the United Kingdom.
activity contracted 0.4 percent last year. During
the year, economic growth slowed in all EU11
countries, except in Estonia and Lithuania,
which in the second half of 2012 recorded
fast GDP growth. Even though Latvia’s GDP
growth slowed down in the course of 2012, it
was the only EU11 country to grow marginally
faster in 2012 than in 2011. Hungary and the
Czech Republic went into recession in 2012,
joining Slovenia and Croatia.
EU11 Recent Economic Developments
Lackluster Domestic Demand
The EU11 economic growth continued to slow down in 2012 to reach a mere
0.8 percent, a quarter of what it was the previous year. Domestic demand, in
particular investment and lately household consumption, abated, leaving net
exports as the sole driver of growth. Sectorally, what little growth there was
stemmed from market services, with a marginal contribution of industry.
Figure 1. GDP rate of change in EU15 and EU11 Countries
Percent, year-on-year
-4
10
8
6
4
2
0
-2
EU15 EU11 LV LT EE RO PL SK BG HU CZ HR SI
-4
8
6
4
2
0
-2
EU15 EU11 LV LT EE RO PL SK BG HU CZ HR SI
2011 2012 H1 2012 H2 2012
Source: Eurostat; World Bank staff calculations.
EU11 REGULAR ECONOMIC REPORT | june 2013
4 | EU11 Recent Economic Developments
Box 1. External Developments in 20123
The economic policy efforts to restore financial stability in the high-income countries have
led to improved financial market conditions, especially in Europe. Financial conditions have
eased and remained stable after the European Central Bank’s (ECB) President commitment in
the summer of 2012 to do the necessary to save the Euro. Yields on Euro area- and sovereigndebt
holdings have fallen from their mid-2012 peaks (Figure 2). The uncertainty generated by
the Cyprus rescue effort, worries about Slovenia, and the elections in Italy have had only limited
impacts on financial conditions in the rest of the Euro area.
However, the economic policy measures are unevenly translating into real-sector growth
(Figure 3). The progress in the Euro area towards assuring fiscal sustainability and the bold
monetary policy steps taken by the Federal Reserve Bank in the United States (US), the Bank of
England, the Bank of Japan and the ECB have started to have an impact in some high-income
countries. The US economy grew by 2.5 percent in the first quarter of 2013 on the back of an
improved housing market and net job creation. In Japan, loose monetary and fiscal policies have
supported a rebound, with industrial production expanding by 8 percent in the first quarter of
2013 after contracting 7.2 percent in the last quarter of 2012.
However, while macroeconomic imbalances have narrowed, the policies are yet to deliver
economic growth in the Euro area. The area remains mired in recession. But the pace of
contraction has somewhat eased: the economy shrank by 0.2 percent in the first quarter of
2013, compared with 0.6 percent in the last quarter of 2012. Euro area trade imbalances have
improved.4 Fiscal imbalances are also on the mend, with Greece expected to balance its primary
budget by the end of 2013 after showing a deficit of 10.5 percent four years ago.
3 Based on World Bank. (2013). Global Economic Prospects - June 2013: Less volatile, but slower growth. Washington, DC: World Bank.
4 Southern European EU members have narrowed their current-account deficits and Germany has decreased its surplus For example Portugal’s
current account deficit shrunk to 1.5 percent in GDP in 2012 from 12.6 percent of GDP in 2008. Over the same period, Greece’s current
account deficit fell to 3 percent of GDP from 15 percent four years earlier.
Figure 2. Credit Default Swap Rates Figure 3. Industrial Production
Basis points Percent, 3m/3m seasonally adjusted annualized
0
1,800
1,600
1,400
1,200
1,000
800
600
400
200
Jan-10 Oct-10 Jul-11 Apr-12 Jan-13
-20
15
10
5
0
-5
-10
-15
Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13
Spain Ireland Italy Portugal Middle- and Low-Income Countries High-Income Euro Area Countries
United States Japan
Source: Bloomberg; World Bank staff calculations. Source: Datastream; World Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 5
The pace of economic growth in EU steadily
slowed over the past two years. The EU11
economic growth rates hit a three-year low as
they declined from 3.4 percent in the third
quarter of 2011 to mere 0.2 percent the end
of 2012. The EU15 real GDP growth rate
contracted for the last three consecutive
quarters at roughly the same pace.
The decline in EU11 economic activity
was driven by slowed industrial and
construction sectors. In both EU11 and
EU15, manufacturing and construction
contributed most to the slowdown. The
strongest fall in construction was recorded in
Slovenia, Croatia, Lithuania, and Poland. The
deterioration of sectoral activities mirrored the
high deleveraging needs of the corporate sector
in Slovenia and Croatia as well as the private
sectors’ delayed investment decisions and the
inventory cuts in Lithuania and Poland. Unlike
in EU15, a strong upward push in economic
activity came from value added in certain
services, in particular trade, transport and
tourism-related activities in Bulgaria, Lithuania,
and Poland. Information and communication
activities supported growth in Latvia, Hungary,
and Slovakia, while financial intermediation in
Poland.
Middle- and low-income countries continued to grow in 2012, though unevenly.5 The
acceleration in output by 7.5 percent in 2012 in Asia led the rebound of global economic
activity. China, Indonesia, and Thailand grew at a pace of 7.8, 6.2, and 6.4 percent respectively
in 2012. Driven by strong consumption and high oil prices, the economic expansion in Russia
(at a rate of 3.4 percent) in 2012 was faster than in Argentina (1.9 percent), Brazil (0.9 percent),
and Turkey (2.2 percent).
5 Middle– and low-income countries include countries with income of less than $12,276 GNI per capita in 2010.
Figure 4. Contributors to GVA annual rate of change, EU11 and EU15
EU11 EU15
Percent Percent
-1
5
4
3
2
1
0
Q1-11 Q2-11 Q3-11 Q4-11 Q1-12 Q2-12 Q3-12 Q4-12
-1
5
4
3
2
1
0
Q1-11 Q2-11 Q3-11 Q4-11 Q1-12 Q2-12 Q3-12 Q4-12
Agriculture, forestry, and fishing Industry Construction Agriculture, forestry, and fishing Industry Construction
Market services Non-market services Total Market services Non-market services Total
Source: Eurostat; World Bank staff calculations.
Note: In EU11 aggregate, Romania is excluded as there is no information for not seasonally adjusted data (reference year 2005).
EU11 REGULAR ECONOMIC REPORT | june 2013
6 | EU11 Recent Economic Developments
Net exports were the sole engine of growth
in both EU11 and EU15 in the second
half of 2012. The contribution of exports to
growth was positive in the EU11 region, but
it concealed differences at the country level.
In 2012, Bulgaria, Estonia, and Romania
were the only EU11 countries where declining
net exports reduced growth. In Bulgaria and
Romania, this was due to the negative export
growth, while in Estonia, to the strong import
growth. In both EU11 and EU15, domestic
demand registered a negative contribution to
economic growth in 2012, strongly influenced
by the inventory cycle on the back of ongoing
Figure 5. Contributors to GDP rate of change, EU11 and EU15
EU11 EU15
Percent Percent
-4
5
4
3
2
1
0
-1
-2
-3
Q1-11 Q2-11 Q3-11 Q4-11 Q1-12 Q2-12 Q3-12 Q4-12
-3
3
2
1
0
-1
-2
Q1-11 Q2-11 Q3-11 Q4-11 Q1-12 Q2-12 Q3-12 Q4-12
Government consumption Investment Changes in inventory Government consumption Investment Changes in inventory
Net exports Household consumption GDP Net exports Household consumption GDP
Source: Eurostat; World Bank staff calculations.
Note: The sum of contributions may not add to overall GDP due to statistical discrepancy.
Figure 6. Contributors of Domestic Demand to GDP Growth, EU11 Countries,
by quarters in 2012
Percent
-8
12
10
8
6
4
2
0
-2
-4
-6
BG
Q3-12
Q4-12
Q1-12
Q2-12
CZ EE LV LT HU PL RO SI SK HR
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q3-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q4-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q1-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Q2-12
Government consumption Investment Household consumption
Source: Eurostat; World Bank staff calculations.
Note: In the case of Bulgaria data refers to gross fixed capital formation, hence changes in inventories are not included.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 7
destocking. While throughout the year
investment in the EU11 countries fuelled by
projects co-financed by the EU fostered growth,
by the end of the year its contribution turned
negative. Investment activity remained negative
in the EU15 countries amid sluggish near-term
growth prospects and tight credit conditions.
Even though household consumption had been
subdued since mid-2011 in the EU11, it was
still an important driver of growth up to the
second half of 2012, when it turned negative.
The EU11 economy lacked strong sources of
demand growth in 2012. Most of the EU11
countries saw a reduction of their domestic
demand, driven by decreases in private and
public investment. Slovenia’s economic growth
in 2012 was the weakest among all EU11
countries, as its economy shrank by 2.3 percent
on the back of steadily declining domestic
demand. The Hungarian economy entered a
second recession, with GDP contracting by
1.7 percent, which was led by a 3.7-percent fall
in domestic demand, in particular destocking.
Similarly, due to a strong downturn in consumer
confidence, a drop in public investment and a
weaker external environment, GDP declined
by 1.3 percent in the Czech Republic. However,
domestic demand did support the economic
activity in some countries (Estonia and Latvia)
where growth stemmed primarily from robust
private consumption and in the case of Estonia
very strong investment growth.
The EU11 economic activity remained
weak in the first quarter of 2013. GDP
estimates for the first quarter of 2013 point
to a slight increase in the EU11 output. The
GDP is expected to have increased at a rate of
0.6 percent, up from 0.2 percent in the fourth
quarter 2012. This was due to the rebound of
the Hungarian economy and to the higherthan-
expected economic growth in Romania.
In contrast, Poland’s economy grew at a record
low 0.5 percent. The pace of expansion also
declined in Estonia and Lithuania, albeit from
much higher levels.
Short-term, high-frequency indicators
improved toward the end of the first quarter
of 2013 in EU11. In 2012, the aggregate
EU11 industrial production expanded by
1.4 percent, driven by manufacturing (Figure
7). However, the expansion was not uniform
across the EU11 countries. While the industrial
production surged by 8.1 percent in Slovakia
and 6.8 percent in Latvia, it fell on average
in 2012 in Bulgaria and the four countries in
recession. Throughout the year, the pace of
industrial growth weakened, but gained some
strength in the beginning of 2013. Retail sales
showed similar dynamics, slowing towards
the end of 2012 and increasing somewhat in
early 2013, driven by strengthened consumer
confidence (Figure 7).
Business and consumer surveys paint a mixed
picture. Confidence in industry remains below
the long-term average, but the negative trend
from late 2012 was put on hold in early 2013.
Similarly, construction confidence decreased
in 2012 based on weaker employment
expectations and assessments of future orders,
but has leveled-off recently. Survey indicators
suggest subdued prospects for both private
consumption and investment activity in the
near future. Although consumer confidence
recently improved, its current level remains
below its long-term average. Weak confidence
indicators in the retail sector persisted in the
first quarter of 2013.
EU11 REGULAR ECONOMIC REPORT | june 2013
8 | EU11 Recent Economic Developments
Figure 7. High-Frequency Indicators, working day adjusted, EU11 and EU15
Industrial Production Retail Sales
Percent, year-on-year Percent, year-on-year
-4
10
8
6
4
2
0
-2
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
Jan-13
Mar-13
May-12
Jul-12
Sep-12
Nov-12
-4
10
8
6
4
2
0
-2
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
Jan-13
Mar-13
May-12
Jul-12
Sep-12
Nov-12
EU15 EU11 EU15 EU11
Source: Eurostat; World Bank staff calculations.
Figure 8. Selected Economic Sentiment Indicators, EU11 and EU15
Industry Confidence Index Construction Confidence Index
Balances, in percent Balances, in percent
-40
10
0
-10
-20
-30
EU11 long-term average
EU15 long-term average
Jan-11
May-11
Sep-11
Jan-12
Jan-13
May-12
Sep-12
May-13
-50
0
-10
-20
-30
-40
EU11 long-term average
EU15 long-term average
Jan-11
May-11
Sep-11
Jan-12
Jan-13
May-12
Sep-12
May-13
Services Confidence Index Consumer Confidence Index
Balances, in percent Balances, in percent
-50
0
-10
-20
-30
-40
EU11 long-term average
EU15 long-term average
Jan-11
May-11
Sep-11
Jan-12
Jan-13
May-12
Sep-12
May-13
-40
30
20
10
0
-10
-20
-30
EU11 long-term average
EU15 long-term average
Jan-11
May-11
Sep-11
Jan-12
Jan-13
May-12
Sep-12
May-13
EU15 EU11 EU15 EU11
Source: European Commission services, World Bank staff calculations.
Note: Long-term average refers to the period from Dec-2002 to May 2013.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 9
Economic growth in the EU11 countries
remained too sluggish to create jobs. The last
quarter of 2012 was the seventh consecutive
one of negative employment growth for EU11
(Figure 9). In 2012 alone, the region shed
around 340,000 jobs, for a total of 840,000
since mid-2011. The losses in EU11 were
higher than in EU15. Employment growth
was either negative or close to zero in EU11
countries, except in Estonia, Latvia, and
Romania. In these three countries, employment
grew supported by relatively strong economic
performance in the two Baltic countries and
changes to the labor code in Romania. Despite
recessionary pressures, employment growth was
also positive in the Czech Republic.6
Most sectors recorded job losses in 2012.
Apart from the growing sector of market
services, employment growth declined
in all sectors (Figure 10). Jobs were lost
in industry and construction, where the
corporate restructuring was most pronounced.
Employment in public administration also
suffered as governments continued fiscal
consolidation efforts, also through the public
sector wage bill. In contrast, employment
increased slightly in information technology
6 In the case of the Czech Republic, the outcomes of the 2011
Census resulted in a break in the LFS series in 1Q 2012. According
to the adjusted series, employment growth was positive.
and communications, in professional, scientific
and technical activities, in administrative
and support service activities and in finance
and insurance. But these increases were not
sufficient to compensate for the layoffs in
industry and construction. Both employees and
the self-employed contributed to the decline in
employment growth. In EU15, the number of
self-employed rose slightly (Figure 11).
The EU11 regional unemployment rate
increased further in the second half of
2012. Then it reached 10.8 percent in March
2013, from 10.4 percent a year ago. While it
Further Job Losses
Economic activity in the EU11 was too weak to generate new jobs in 2012.
Employment fell for a seventh consecutive quarter, affecting especially
construction, industry, and public administration. The unemployment rate
increased in the second half of 2012, driven by the economic slowdown and the
continuing restructuring of the corporate sector.
Figure 9. GDP and Employment Growth in
EU11 and EU15, Year-on-Year
Percent
-2
4
3
2
0
-1
1
Q1-11 Q3-11 Q1-12 Q3-12 Q1-11 Q3-11 Q1-12 Q3-12
EU15 GDP growth EU11 GDP growth Employment growth
Source: Eurostat; World Bank staff.
Note: Employment according to Eurostat, LFS.
EU11 REGULAR ECONOMIC REPORT | june 2013
10 | EU11 Recent Economic Developments
remained lower than in EU15, there were over
five million unemployed in EU11. The increase
in unemployment over the last year was more
pronounced in the EU15 (+0.8 percentage
points) than in the EU11 (+0.4 percentage
points).
The regional aggregate concealed differences
across the EU11 countries. Unemployment
rates continued to fall substantially in the Baltic
countries (albeit from very high levels) as these
economies kept growing. Unemployment rate
in Latvia went down by more than 3 percentage
point since March 2012 and reached
12.4 percent in March 2013. In Romania, it
Figure 10. Sectoral contribution to
employment growth in 2012 in EU11
Figure 11. Contribution to employment
growth by professional status in 2012 in
EU11 and EU15
Percentage points Percentage points
Industry
Construction
Wholesale and retail trade
Public administration, etc.
Agriculture, forestry and shing
Real estate activities
Arts, entertainment and recreation
Financial and insurance activities
Professional, scient. and tech. activities
Information and communication
-0.2 -0.1 0 0.1 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1
Employees
Self-employed
Contributing family workers
EU11 EU15
Source: Eurostat; World Bank staff.
Note: Employment according to Eurostat, domestic concept.
Source: Eurostat; World Bank staff.
Note: Employment according to Eurostat, domestic concept.
Figure 12. Unemployment rates, EU11
and EU15
Figure 13. Unemployment rates in EU11
countries, March 2012–March 2013
Percent Percent
9
12
11
10
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
Jan-13
Mar-13
May-12
Jul-12
Sep-12
Nov-12
-5
20
15
10
5
0
EU15 EU11 HR SK LV LT BG HU PL SI EE CZ RO
EU15 EU11 Mar-12 Mar-13
Source: Eurostat; Monthly unemployment based on EU LFS, seasonally
adjusted; own aggregation.
Source: Eurostat; Monthly unemployment based on EU LFS, seasonally
adjusted; own aggregation.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 11
fell slightly on the back of employment gains
in agriculture and market services. In parallel,
unemployment rates increased substantially
in Croatia and Slovenia due to the prolonged
recession and the ongoing restructuring of
the corporate sector. The economic slowdown
in Poland and Slovakia led to increases in the
unemployment rate of around 1 percentage
point over the last year. In Bulgaria and the
Czech Republic unemployment rates remained
fairly stable.
The differences in unemployment rates across
the region reflect divergent macroeconomic
performance of EU11 countries. With some
EU11 countries growing strongly and some
in midst of recession, the gap between the
highest and the lowest unemployment rate in
the region widened. While in March 2012, the
unemployment gap in EU11 was 8 percentage
points, it reached 12 percentage points in
March 2013. In EU15, this gap was even larger
at 20 percentage points reflecting growing
divergence between the North and the South
of the Euro Area (Figure 14).
Long-term unemployment in the EU11
continued to rise in 2012. Long-term
unemployment (i.e. the share of people
unemployed for longer than 12 months in total
unemployment) in the EU11 has been on the
rise since the end of 2009 (Figure 15). By the
end of 2012, it climbed to nearly 50 percent
in the EU11from 44 percent before the crisis.
The recent hikes stemmed from weakening
economic activity in 2012 and a negative
employment creation. By the end of 2012,
the share of long-term unemployed among the
unemployed remained very high: the largest
rate, close to 70 percent, was recorded in
Slovakia, followed by Croatia with 64 percent.
The lowest one was about 41 percent in Poland.
Overall, EU11 long-term unemployment
remained consistently higher than in EU15.
Growth in labor productivity continued
to outpace growth in labor costs, thereby
strengthening the EU11 region’s competitive
position in 2012, albeit with notable countrylevel
differences (Figure 17). At the end of
2012, Lithuania, Bulgaria, and Latvia recorded
Figure 14. Maximum and minimum
unemployment rates in EU11 and EU15
Figure 15. Share of long-term unemployed
in total unemployed
Percent, year-on-year Percent
0
30
25
20
10
15
5
Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13
30
50
45
35
40
Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11 Q1-12 Q3-12
Min EU11 Max EU11 Min EU15 Max EU15 EU15 EU11
Source: Eurostat; World Bank staff. Source: Eurostat; World Bank staff.
EU11 REGULAR ECONOMIC REPORT | june 2013
12 | EU11 Recent Economic Developments
significant gains in productivity coupled
with a fall in real compensation. However,
in a number of other EU11 countries, real
wages grew at a faster pace than productivity.
In Hungary, Romania, and the Czech
Republic, real wages continued to grow while
productivity declined. The loss of productivity
in these three countries was ascribed to an
increase in the net employment creation. In
Hungary, the rise in real wages did not translate
in recovery of overall domestic demand, while
in Romania its effect was marginally positive on
household consumption. In the case of Czech
Republic, the positive growth of real wages
is to be attributed to one-off effect related to
tax avoidance—a higher tax burden on highincome
workers effective from 2013 resulted in
exceptional bonuses paid out already at the end
of 2012.
Figure 16. Real Labor Productivity Per
Person Employed and Real Unit Labor
Costs, EU11 and EU15
Figure 17. Growth in Real Labor
Productivity and Real Unit Labor Costs in
4th Quarter of 2012
Percent, year-on-year, not seasonally adjusted Percent, year-on-year, not seasonally adjusted
-3
4
3
2
0
1
-2
-1
Q1-10 Q3-10 Q1-11 Q3-11 Q1-12 Q3-12
-10
15
10
5
0
-5
EU15 EU11 LT BG LV SK SI EE CZ RO HU
RLPP EU15 RLPP EU11 RULC EU15 RULC EU11 RLPP RULC
Source: Eurostat; World Bank staff.
Note: EU11 without Romania and Croatia; EU15 after 1st Quarter 2011,
without Greece.
Source: Eurostat; World Bank staff.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 13
Despite global trade sinking to record
low levels in 2012, it remained crucial for
both EU11 and EU15. Global trade lost
momentum in the second half of 2011 on the
back of the slowdown in advanced economies,
especially in the Euro area. The negative trends
continued into 2012, with the volume of trade
recording its slowest rate of growth since early
2000s (excluding the decline in 2009). Both
exports and imports in EU11 and EU15 grew
slower than the average global rate (Figure 18
and Figure 19). Nevertheless, even this modest
growth in trade was a major contributor
to economic growth in most of the EU11
countries.
While overall EU11 trade performance
weakened steadily in 2012, there was
significant variation across countries. Exports
from Latvia, Lithuania, and Slovakia grew by
double digits due to record-high harvests in
the two Baltic countries and an increase in
car exports from Slovakia. But exports were
either stagnant or decreased slightly in Croatia,
Slovenia, and Romania. On the import side,
the Baltic countries and Bulgaria recorded the
highest growth rates among the EU11 countries
(Figure 21), driven by household consumption
coupled with investment in Estonia. The
declining trend in trade performance continued
Weak Foreign Trade and Narrowing External Imbalances
While overall exports and imports in EU11 declined markedly towards the
end of 2012, stronger linkages to non-European markets helped to generate
favorable trade results and compensated partly for the weak import demand
from the Euro area. Current account balances improved in the EU11 as trade
balances continued to narrow. Net FDI flows to the EU11 remained stable and
gross external debt increased modestly due to higher sovereign borrowing.
Figure 18. Exports volume growth in
2007–2012
Figure 19. Imports volume growth in
2007–2012
Percent Percent
-20
20
15
10
5
0
-10
-5
-15
2007 2008 2009 2010 2011 2012
-20
20
15
10
5
0
-10
-5
-15
2007 2008 2009 2010 2011 2012
World EU15 EU11 World EU15 EU11
Source: Eurostat, World Bank staff estimates and calculations. Source: Eurostat, World Bank staff estimates and calculations.
EU11 REGULAR ECONOMIC REPORT | june 2013
14 | EU11 Recent Economic Developments
into 2013, when both exports and imports
declined.
Trade was fueled by strengthened links with
non-EU markets. Export growth started to
decelerate in mid-2012, falling to zero in EU15
and 2 percent in EU11 by the end of 2012
because of the contracting Euro area economy
(Figure 20). The positive export dynamics in
EU11 were to a large extent driven by gains
of new market shares in non-EU countries.
The non-EU trade contributed over one half
of the growth in exports in EU11 (Figure 21).
Bulgarian exports of energy and base-metal
products to non-EU countries contributed
strongly.
Figure 20. Exports and Imports Growth in
EU11 and EU15
Figure 21. Exports and Imports Growth in
EU11 countries in 2012
Year-on-year, 3-month moving average Year-on-year
-10
8
6
4
2
0
-2
-6
-4
-8
Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13
-5
20
15
10
5
0
LV LT SK PL EE CZ BG HR SI HU RO
EU15 exports EU15 imports EU11 exports EU11 imports Exports Imports
Source: Eurostat, World Bank staff estimates and calculations. Source: Eurostat, World Bank staff estimates and calculations.
Box 2. EU11 Goods Export Growth: The Changing Role of Traditional EU15 Markets
In 2012, non-EU15 countries accounted for more than 87 percent of the EU11 export
growth. (Figure 22). Exports of EU11 goods grew by 4.5 percent on average in 2012. However,
this rate masked strong differences between traditional EU15 and non-EU15 export destinations.
Geographically, while EU11 intra-regional exports alone contributed a quarter to the overall
EU11 export growth between 2011 and 2012, the biggest contributors outside the region were
Russia (22.7 percent), Germany (11.9 percent), the United Kingdom (10.7 percent), Ukraine
(6.4 percent), and the US (4.7 percent).
Sectorally, the groups of food and machinery accounted for almost one half of total EU11
export growth. Food and live animals contributed more than a quarter, followed by machinery
and transport equipment, which accounted for more than 20 percent of the EU11 export
growth (Figure 23).
Food and live animals:
Both EU15 and non-EU15 destinations contributed a similar share to EU11 export growth
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 15
(45 percent and 55 percent, respectively). Food export growth to non-EU15 markets was
driven by cereals, sugars, vegetables and fruits. The most important destinations for food
export growth were Iran, Germany, Russia, Libya, and Italy, confirming the importance of
both non-EU15 and traditional EU15 markets in this sector.
Machinery and transport equipment:
In this category, export growth was entirely driven by demand from the non-EU15 countries
(32 percent), mainly Russia. This more than compensated for the 11-percent decline in
demand from the EU15 markets. At the product level, growth of machinery and transport
equipment exports to non-EU15 countries was mixed. The largest contributor was road
vehicles, accounting for almost a third of export growth, followed by electrical machinery,
general industrial machinery and equipment, and office equipment. Telecommunications,
by contrast, fell 15.4 percent.
The EU11’s exports to Russia alone, dominated by machinery and transport equipment,
accounted for more than half of its export growth. While road vehicles accounted for
almost 37 percent of the growth in this category, specialized and general industrial machinery
contributed 13.4 and 12.6 percent, respectively. Electrical machinery contributed 10.2 percent
and office equipment 9.3 percent.
Russia’s WTO accession was a key factor driving the recent diversification of EU11 exports.
Its strong contribution to EU11 machinery and transport equipment export growth is likely to
have been influenced by the lower import tariffs in this sector, after the country’s WTO accession
in mid-2012. Russian tariffs (weighted average) on machinery and transport equipment in 2011
were generally lower than for other imports, ranging from 2.9 to 12.7 percent in Poland. But
they varied strongly across products and exporters, so it remains unclear if tariffs alone explain
Russia’s growing importance as an importer of EU11 goods.7
7 2011 is the latest year for which tariff data are available.
Figure 22. Contribution to Exports
Growth, 2012
Figure 23. EU11 export contribution by
product in 2011–12
Percentage points Percent
-5
20
15
10
5
0
EU15 EU11 LV LT SK PL EE CZ BG SI HU RO
Crude materials, inedible, except fuels
Animal and vegetable oils, fats
and waxes
Commodities and transactions NCE
Beverages and tobacco
Chemicals and related products, N.E.S.
Mineral fuels, lubricants and
related materials
Manufactured goods classied
chiey by material
Miscellaneous manufactured articles
Machinery and transport equipment
Food and live animals
0 10 20 30
25.4
20.1
12.7
11.8
10.4
10.2
3.2
2.9
1.9
1.4
Extra-EU Intra-EU
Source: Eurostat, World Bank staff estimates and calculations. Source: Eurostat, World Bank staff estimates.
EU11 REGULAR ECONOMIC REPORT | june 2013
16 | EU11 Recent Economic Developments
The EU11 current-account deficit narrowed in
2012 thanks to a positive trade performance.
The overall current-account deficit shrank from
3 percent of GDP in 2011 to around 2 percent
of GDP in 2012. All countries but Estonia
and Bulgaria recorded improvements in their
current-account positions, which were almost
entirely attributed to changes in their trade
balances (Figure 24, Figure 25). Changes in
other components—notably improvement in
the services account—further supported the
narrowing of current account deficits in the
EU11 countries, except in the Czech Republic,
and Estonia. In the case of Romania and
Bulgaria, the reduction in the income deficit
(reflecting lower dividend payments abroad)
helped cut the current-account deficit, though
in Bulgaria, it was not enough to compensate
for the widening trade deficit.
Figure 24. Current Account Balance
2011–12
Figure 25. Change in Current Account
Balance 2011–12 by components
Percent of GDP Percentage point
-5
3
2
1
0
-1
-3
-2
-4
EU11 SI SK HU HR LT EE BG LV CZ PL RO
-4
5
2
3
1
0
-1
-3
-2
EU11 SK LT SI PL HR HU RO LV CZ BG EE
4
2011 2012 Change in trade of goods Change in other comp. Change in current account
Source: Eurostat, World Bank staff estimates and calculations. Source: Eurostat, World Bank staff estimates and calculations.
Figure 26. Current Account Financing
Percent of GDP
-15
15
10
5
0
-5
-10
EU11 BG CZ EE LV LT HU PL RO SI SK HR
2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012 2011 2012
FDI Portfolio Other investments and changes in reserves CAB inverted
Source: Eurostat, World Bank staff estimates and calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 17
Current-account deficits remained
adequately funded through FDI investments
and capital transfers from the EU. Net FDI
inflows to the region were stable in 2012
because of the relatively good economic
performances of major investor countries (see
Box 3) and fully covered the deficits in most
EU11 countries. In Poland and Romania,
large portfolio inflows in 2012, stemming
from acquisitions of government bonds by
non-residents, supported the financing of the
current account deficits. Overall, portfolio
investment increased in 2012, primarily driven
by bond issuances by EU11 sovereigns (Figure
26).
Gross external debt-to-GDP ratios increased
slightly over the last year, largely due to
public-sector borrowing. In EU11, external
debt increased marginally from 78.6 percent of
GDP at the end of 2011 to 80.4 percent of GDP
at the end of 2012 (Figure 27). Government
external debt went up in all EU11 countries,
most steeply in Slovenia and Poland. Banks
continued to deleverage throughout 2012 in
all EU11 countries. For the EU11 region, the
increase in government external debt in 2012
contributed to a shift in the structure of total
external debt. In Hungary, Lithuania, and
Slovakia, public external borrowing at the end
of 2012 comprised around one half of total
external debt (Figure 28). In contrast, public
external debt remained particularly low in
Bulgaria and Estonia.
Figure 27. Gross external debt
developments 2011–2012
Figure 28. Structure of the gross external
debt in December 2012
Percent of GDP Percent of GDP
0
160
140
120
100
80
40
60
20
EU11 LV HU SI HR EE BG LT SK RO PL CZ
0
160
140
120
100
80
40
60
20
EU11 LV HU SI HR EE BG LT SK RO PL CZ
2011 2012 Government Banks Other sectors Intercompany lending
Source: Central Banks, World Bank staff estimates and calculations.
Note: For Hungary data excluding SPEs.
Source: Central Banks, World Bank staff estimates and calculations.
Note: External debt of monetary authorities is included in the government
sector.
EU11 REGULAR ECONOMIC REPORT | june 2013
18 | EU11 Recent Economic Developments
Box 3. Where does FDI come from?
Geographical breakdown of FDI flows helps to assess the patterns and volumes of foreign
direct investment as well as the increasing interconnectedness of economies.
In recent years, the EU11 countries have
been successful in attracting FDI. By the
end of 2011, the stock of inward FDI in
EU11 was 56 percent of aggregate GDP, with
large differences across countries. Hungary’s
share of incoming FDI was well above
150 percent of GDP, while in the case of
Slovenia it was mere 22 percent (Figure 29).8
The Netherlands, followed by Germany
and Austria, was the top country of origin
for the EU11 FDI inflows.9 Among the top
ten sources of FDI, which constituted over
70 percent of total FDI inflows to the EU11,
the only non-EU country was the US (Figure
30). Both Netherlands and Luxembourg
were considered “transit” sources as both have
special legislations that provide incentives for
multi-nationals to use them as pass-through.
8 Data includes SPEs.
9 See IMF’s Coordinated Direct Investment Survey (2011).
Figure 29. Stock of inward FDI as share
of GDP, end-2011
0
180
160
140
120
100
80
40
60
20
EU15 EU11 HU BG EE CZ SK HR LV PL RO LT SI
Source: IMF Coordinated Direct Investment Survey, World Bank staff
estimates.
Figure 30. Top ten sources of EU11
Inward FDI
Figure 31. Top ten sources of EU15
Inward FDI
in EU11 in EU15
US$ millions US$ millions
0 20 40 60 80 100 120 140
Netherlands
Germany
Austria
Luxembourg
France
United States
Spain
Sweden
Cyprus
Ireland
0 200 400 600 800 1,000 1,200 1,400 1,600
United States
Luxembourg
United Kingdom
Netherlands
France
Switzerland
Germany
Belgium
Italy
Bermuda
Source: IMF Coordinated Direct Investment Survey 2011; World Bank
staff calculations.
Note: Top ten investors represent 70 percent of total FDI inflow worth
793 million US$ at the end-2011.
Source: IMF Coordinated Direct Investment Survey 2011; World Bank
staff calculations.
Note: Top ten investors represent 65 percent of total FDI inflow worth
11.4 billion US$ at the end-2011.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 19
The high degree of EU concentration can be attributed to the integration of the European
economies and geographic proximity. At the end of 2011, the EU11 region had a close to
USD 800 million in inward investment positions and had invested USD 260 million abroad. In
the case of the EU15 region, the top source of inward FDI was the US, followed by Luxembourg
and the UK (Figure 31). The degree of FDI concentration in EU15 was somewhat lower than
in EU11, with some 65 percent of FDI stemming from top ten source countries. At the end of
2011, the EU15 attracted FDI of close to USD 11.5 billion and it invested over USD 14.4 billion
abroad.
The EU15 region was a major source for FDI for the EU11 countries, with wide crosscountry
differences. Overall, in EU11, over 70 percent of total inward FDI came from EU15
countries, while 4 percent represented intraregional FDI inflows and 25 percent came from
other countries (Figure 32). In the case of EU15, intraregional FDI inflows represented over a
half of total inflows, 46 percent came from other countries, while the share of EU11 inflows into
EU15 countries was negligible (0.4 percent).
In the EU11, Croatia had the highest share
of European FDI—over 90 percent of FDI
in Croatia came from the EU (with Austria
having the largest share), but also with a hefty
contribution from other EU11 countries
(mainly due to Hungarian investment in
oil industry). By contrast, in Hungary the
large share of FDI (over 45 percent) came
from non-EU countries, primarily the US,
Canada and China, and also large resources
originating from offshore financial centers
such as Bermuda and Cayman Islands10. In
the Baltic countries, the largest single source
of FDI was Sweden. Germany was the top
investor in Hungary, while Austria was in
Slovenia and Croatia. In the rest of the
EU11 countries, the prime investor was the
Netherlands.
10 In the case of Hungary, FDI flow without SPE, suggest that over 70% of FDI stock originated in Germany.
Figure 32. Share of incoming FDI in
EU11 countries by regional origin,
end-2011
Percent
0
100
80
70
60
50
40
20
30
10
90
EU15 EU11 HR SK PL CZ RO SI EE BG LT LV HU
EU11 EU15 Other
Source: IMF Coordinated Direct Investment Survey 2011; World Bank
staff calculations.
EU11 REGULAR ECONOMIC REPORT | june 2013
20 | EU11 Recent Economic Developments
Macroeconomic weaknesses coupled with
subdued bank lending warranted monetary
easing in the Euro area. Against weak economic
sentiment, the ECB’s monetary policy stance
remained accommodative. By May 2013,
the ECB cut the interest rate on its main
refinancing operations to 0.5 percent (Figure
33). This brought the policy rate to a historic
low after eight months in which the rate was
kept unchanged at 0.75 percent. In addition,
the ECB committed to continue conducting
its refinancing operations as fixed-rate tender
procedures with full allotment, which aim to
ensure ample liquidity for the banks.
Monetary policy in EU11 was also
accommodative. Policy rates declined in an
environment of ample liquidity, low inflation,
and below-potential output. The central banks
of Hungary and Poland were most active in
reducing policy interest rates, attempting to
support a recovery in output growth in each
country (Figure 33). After the beginning of
2013, the Central Bank of Hungary decreased
its policy rates five times, to 4.5 percent.
Similarly, the Central Bank of Poland eased its
monetary policy stance, in five steps in 2013
by reducing the policy rate from 4 percent at
end of 2012 to 2.75 percent in June 2013.
The Romanian Central Bank did not change
its policy rate in the past months in order to
anchor inflation expectations, capital flows
and exchange rate volatility, but it pursued
non-conventional measures to ease monetary
policy.11
The monetary-policy easing worked with
mixed results in EU11. In the Czech Republic,
Hungary and Poland, lending rates declined as
banks passed on their funding savings to their
corporate clients while sovereign risks followed
11 To temper interest rates volatility on the money and banking
markets, the NBR narrowed the symmetrical corridor by
1 percentage point around the monetary policy rate, thus reducing
the NBR’s lending facility to an annual 8.25 percent and increasing
its deposit facility to 2.25 percent.
Subdued Inflation and Further Monetary Easing
Monetary policy remained accommodative in EU11 throughout 2012, with
policy interest rates further declining in 2013. Cuts in policy rates and declining
sovereign risk delivered reductions in lending rates in some EU11 countries,
while in others the results were muted because of the heightened risk aversion
of lenders on the local market. Inflation subsided due to weak domestic demand
and a significant decline of energy prices.
Figure 33. Policy Interest Rates in
Selected EU Countries
Percent
0
8
7
6
5
4
2
3
1
Jan-11
Feb-11
Mar-11
Apr-11
May-11
Jun-11
Jul-11
Aug-11
Sep-11
Oct-11
Nov-11
Dec-11
Jan-12
Feb-12
Mar-12
Apr-12
May-12
Jun-12
Jul-12
Aug-12
Sep-12
Oct-12
Nov-12
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
CZ HU PL RO EURO zone
Source: Central banks; World Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 21
the policy rates’ downward path since mid-
2012 (Figure 34). In Romania, in contrast,
the lending rate remained high (between 9 and
10 percent) while sovereign risk declined (Figure
34). Overall supply-side constraints may have
impeded new lending in EU11 countries where
local market lenders’ risk aversion was high
because of the inability to restore the quality of
their balance sheets.
Inflationary pressures eased in the second
half of 2012 and early 2013 in most EU11
economies as domestic demand weakened.
The inflationary pressures of the first half of
2012, stemming from administratively set
prices and food price hikes in global markets,
gradually declined at the beginning of 2013
(Figure 35). The overall inflation rate in EU11
in the first four months of 2013 stood at
1.4 percent, around half the mid-2012 level,
reflecting a significant fall in energy prices
(Figure 36). In fact, Latvia registered annual
deflation in April 2013, joining Greece—the
only two countries in EU to record deflation.
Figure 34. Interest rates on new lending to enterprises and CDS spreads in selected EU11
countries
Czech Republic Poland
Percent Basis points Percent Basis points
1
6
5
4
2
3
0
180
160
140
120
100
80
40
60
20
2010/1 2010/7 2011/1 2011/7 2012/1 2012/7 2013/1
1
8
7
5
6
4
2
3
0
300
250
200
150
100
50
2010/1 2010/7 2011/1 2011/7 2012/1 2012/7 2013/1
Enterprises CDS (rhs) Enterprises CDS (rhs)
Hungary Romania
Percent Basis points Percent Basis points
6
14
13
11
12
10
7
9
8
0
700
600
500
400
300
200
100
2010/1 2010/7 2011/1 2011/7 2012/1 2012/7 2013/1
6
14
13
11
12
10
7
9
8
0
500
400
300
200
100
2010/1 2010/7 2011/1 2011/7 2012/1 2012/7 2013/1
Enterprises CDS (rhs) Enterprises CDS (rhs)
Source: EU11 Central banks; Bloomberg; World Bank staff calculations.
EU11 REGULAR ECONOMIC REPORT | june 2013
22 | EU11 Recent Economic Developments
The EU11 core inflation remained on a
downward trend as well, reaching 1.6 percent
in April 2013.
The real effective exchange rate (REER)
in the EU11 countries followed divergent
dynamics in 2013 (Figure 37). While price
competitiveness improved in the first four
months of 2013 in some countries, in others
it deteriorated due to appreciation pressures.
Compared to the end of 2012, the depreciation
was most pronounced in Hungary (3.3 percent
in April 2013). Export price competitiveness
also improved in Bulgaria and the Czech
Republic. Signs of real effective appreciation
were evident in Romania in particular, where
Figure 35. Harmonized Consumer Price
Index (HICP), Overall and Core, EU15 and
EU11
Figure 36. Average HICP, EU15 and
EU11
Percent, year-on-year Percent, year-on-year
0
5
4
3
2
1
Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13
-1
6
5
4
2
3
0
1
EU15 EU11 BG CZ EE LV LT HU PL RO SI SK HR
EU11 overall EU11 core EU15 overall EU15 core 2012 April 2013
Source: Eurostat; World Bank staff calculations.
Note: Core inflation is defined as overall index excluding energy and
unprocessed food.
Source: Eurostat; World Bank staff calculations.
Note: Core inflation is defined as overall index excluding energy and
unprocessed food.
Figure 37. Real Effective Exchange Rates, CPI Deflated
Index: August 2008=100 Index: August 2008=100
85
110
105
100
95
90
Dec-09
Apr-10
Aug-10
Dec-10
Dec-11
Dec-12
Apr-11
Apr-12
Apr-13
Aug-11
Aug-12
85
110
105
100
95
90
Dec-09
Apr-10
Aug-10
Dec-10
Dec-11
Dec-12
Apr-11
Apr-12
Apr-13
Aug-11
Aug-12
BG EE LV LT SK SI HR CZ HU PL RO
Source: BIS (broad indices comprising 61 economies); World Bank staff calculations.
Note: REER are deflated by CPI. Movement upward denotes real effective appreciation.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 23
REER appreciated by 4.2 percent in April
2013 since the beginning of the year. In the
same period, the REER appreciated by 0.7 in
Slovenia, 0.8 in Slovakia, 1.6 percent in Estonia.
The Euro area’s REER inched up 0.5 percent.
A New Banking Model Emerging
Despite improved financial markets conditions and accommodative monetary
policy, progress on the lending side was muted, with private-sector credit growth
still subdued. On the funding side, the risk of disorderly deleveraging receded
and banks continued to gradually shift their funding toward local sources. But
a decline in credit coupled with rising levels of non-performing loans impaired
the private sector’s contribution to growth in EU11.
EU11 financial markets remained relatively
calm and conditions stayed near the levels
registered at the end of last year (Figure 38).
In 2013, the financial markets continued to
differentiate among the EU11 countries on
the basis of their economic fundamentals and/
or perceived risks. As such, Credit Default
Swap (CDS) spreads for a number of EU11
countries remained around the 100bp level.
In others, such as Slovenia, Hungary, Croatia,
and Romania, they stayed at higher levels. In
the beginning of 2013, CDSs registered spikes
in these four countries due to their particular
economic and political circumstances, although
they have now returned to levels below 300bp
(Figure 38). Currently, Slovenia has the highest
CDS spreads in the region, reflecting the
perceived risk in its economic outlook. For
the rest of the EU11, CDSs spreads were by
and large in line with sovereign credit ratings
(Figure 39).
The ample availability of global liquidity did
not translate into improved capital inflows
to EU11 in 2012. Gross capital flows to the
EU11 countries amounted to €12.5 billion
in 2012marking a contraction of more than
two-thirds relative to the same period of
2011 (Figure 40). While FDI remained stable
throughout 2012, cumulative bank-related
debt flows continued falling, but at a slower
pace towards the end of the year (Figure 41).
Portfolio investments remained robust, as
capital market remained supportive of sovereign
bond issuances.
European foreign banks continued to
deleverage in the EU11 countries. Compared
to end-2011, the international exposure by
foreign banks dropped in EU11 banking and
corporate sector as parent banks continued to
reduce their presence in the region especially
EU11 REGULAR ECONOMIC REPORT | june 2013
24 | EU11 Recent Economic Developments
in the last two quarters of 2012 (Figure 42).12
Throughout 2012, claims to the corporate
sector declined by 6 percent to €242 billion,
while those to banks decreased by close to
12 As reported by BIS, cross-border institutions such as EBRD and
EIB as well as institutional investors are not factored in these
cross-border flows, and they are significant creditors of the EU11
countries. For more detailed information on banks deleveraging
see the latest CESEE Deleveraging Monitor published by the
Vienna Initiative – www.vienna-initiative.com
10 percent to €37 billion. In contrast, claims to
the public sector increased by almost 10 percent
to €79 billion. The picture varied substantially
among the EU11 countries (Figure 43).
Slovakia managed to attract sizeable inflows,
especially to the public sector; while Poland
and Hungary faced significant outflows from
Figure 40. Cumulative Gross Capital
Inflows, EU11
Figure 41. Cumulative Other Investments,
EU11
€ billions € billions
-60
100
60
40
20
0
-40
-20
80
Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11 Q1-12 Q3-12
-50
70
50
40
20
30
0
10
-40
-30
-20
-10
60
Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11 Q1-12 Q3-12
Direct investment Portfolio investment Other investment Liabilities Assets Net
Source: Eurostat; World Bank staff calculations. Source: Eurostat; World Bank staff calculations.
Figure 38. 5Y CDS, EU11 Countries Figure 39. Sovereign credit ratings and 5Y
CDS spreads
Basis points 5Y CDS, most recent value
-50
400
300
250
150
200
0
50
100
350
CZ EE PL SK BU LT LV RO HR HU SI
0
300
250
200
150
100
50
0 2 4 6 8 10 12 14
EE CZ
SK
PL
LT LV
BU
RO
HR
SI HU
2013 highest value 2012 last value 2013 most recent value Sovereign rating (S&P)
Source: Reuters; Bloomberg; World Bank staff calculations.
Note: Data as of May 7, 2013. Sovereign ratings were converted into points, with a lower value indicating a higher sovereign rating. The ratings range from
4=AA- to 12=BB.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 25
the corporate sector.13 In Slovenia, almost the
entire 2012 capital outflow came from the
banking sector, which decreased by about a
quarter compared to 2011.
Non-European countries increased their
presence in EU11, while West European
banks deleveraged selectively. During
2012, claims from non-European countries
increased by 23 percent (Figure 44, A) to
€20 billion, although they still represented less
than 3 percent of total claims in the region.
Claims from European countries decreased by
1.4 percent to €661 billion, but with variations
depending on the country of origin. While Italy
increased its exposure to the EU11 countries by
5 percent and The Netherlands by 16 percent,
France and Greece reduced their positions by
more than 20 percent (Figure 44, B). Austrian
banks maintained their overall posture, while
13 In the case of Poland, the decline in claims to banks is due to the
sale of stakes at Bank Pekao SA (owned by UniCredit) and BZ
WBK (owned by KBC and Santander).
increasing their stake in Poland and reducing
it in Hungary, Slovenia, the Czech Republic
and Romania. Belgian banks did the opposite,
reducing their exposure to Poland and
increasing it in the Czech Republic.
Despite improved global financial market
conditions, the continued deleveraging of
European banks led to an increased reliance
on local funding in EU11. Banks increasingly
financed their loan books with core domestic
deposits as deposits grew faster than loans in
most EU11 countries (Figure 45). This trend
is expected to persist, given that parent banks
remain under market and regulatory pressures
that prevent them from expanding their
balance sheets at the group level and encourage
their subsidiaries to keep diversifying their
sources of funding. In fact, foreign liabilities,
which include both parent and wholesale
funding, decreased in the past 12 months and
were partly compensated by the growth in local
deposits (Figure 46). Notable exceptions were
Figure 42. Total International Claims by
Sectors
Figure 43. Change in Total International
Claims by Country and Sector, 4th Quarter
of 2011 to 4th Quarter of 2012
4th Quarter of 2009=100 € billions
80
85
110
105
100
95
90
Q4-09 Q2-10 Q4-10 Q2-11 Q4-11 Q2-12 Q4-12
-10
10
6
4
2
0
-2
-4
-8
-6
8
SK RO EE HR SI LT LV CZ BG PL HU
EU11 vis-à-vis the non-bank private sector EU11 vis-à-vis banks Non-bank private sector Public Sector Banks Total claims
EU11 vis-à-vis the public sector
Source: Bank for International Settlements; World Bank staff calculations.
Note: International claims include cross-border and foreign currency claims on local residents.
EU11 REGULAR ECONOMIC REPORT | june 2013
26 | EU11 Recent Economic Developments
Hungary and Slovenia, which showed negative
deposit and loan growth. Slovenia increased
its foreign liabilities in 2012 as foreign-owned
banks recapitalized. However, the Slovenian
system as a whole remained undercapitalized,
particularly its dominant state-owned banks.
Despite notable cuts in policy rates
throughout 2012 and 2013, real credit
growth in the EU11 did not recover. Overall
bank lending remained weak. After some signs
of a pick-up in private real credit growth in 2012,
the trend lost momentum in the beginning of
2013 and contracted by 2.7 percent year-on-
Figure 44. Change in Foreign Claims, 4th Quarter of 2011 to 4th Quarter of 2012
A. EU11 B. By Country
€ Billions € billions
-15
20
10
5
0
-5
-10
15
ITA NLD SWE ESP DEU AUT GRC BEL FRA Other
-15
25
20
10
5
0
-5
-10
15
PL SK CZ EE BG LV RO LT HR SI HU
Other European Other non-European SPA SWE NLD AUT GRC
DEU ITA BEL FRA Other
Source: Bank for International Settlements; World Bank staff calculations.
Note: Foreign claims include cross-border and local claims.
Figure 45. Annual growth in deposits and
loans, 2012
Figure 46. Annual growth in deposits and
foreign liabilities, 2012
Loans, percent Foreign liabilities, percent
-14
4
2
-2
-4
-6
-8
-10
-12
0
-4 -2 0 2 4 6 8 10
EE
CZ
PL SK
LT
LV
BG
RO
HR
HU
SI
-30
10
5
-5
-10
-15
-20
-25
0
-4 -2 0 2 4 6 8 10
EE
CZ
SK
PL
LT
LV
BG
RO
HR
HU
SI
Deposits Deposits
Source: IMF International Financial Statistics; World Bank staff calculations.
Note: Foreign liabilities of Euro area countries are liabilities to non-Euro area residents. In Bulgaria, the positive foreign liabilities growth was due in full to an
one-off operation made by a single bank in November 2012.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 27
year. At the country level in 2013, real credit
grew only in Slovakia on the back of credit to
households. It accelerated at a rate of 8 percent
in Poland in end-2012 but slowed to less
than 1 percent in 2013. Croatia and Hungary
experienced declines, and Slovenia and Latvia
saw their credit contract (Figure 48). While
in Croatia and Slovenia, the reduction was
mostly due to a fall in lending to enterprises, in
Hungary and Latvia it was blamed on curtailed
access to finance of households. Real credit
to non-government borrowers in Hungary
continued a decline that began in 2009.14
14 For Croatia, the negative credit growth to enterprises reflects
the assumed shipyards’ loan liabilities, and the transfer of bad
placements of one bank to a connected company. Thus, the
nominal contraction excluding those effects would amount to
-2.1 percent.
Figure 47. Real Credit Growth, EU11 and
EU15
Figure 48. Contribution to Real Private
Sector Credit Growth
Index: Oct 2008=100 Percentage points
90
92
102
100
98
96
94
Oct-08
Oct-09
Oct-10
Oct-11
Feb-09
Feb-10
Feb-11
Feb-12
Oct-12
Feb-13
Jun-09
Jun-10
Jun-11
Jun-12
-20
-15
10
5
0
-5
-10
EU15 EU11 SK PL CZ HR BG SI RO EE LV HU
2012
LT
Feb-13
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
Feb-13
EU15 EU11 Credit to HHS Credit to enterprises Credit growth
Source: European Central Bank; World Bank staff calculations.
Note: In countries with floating exchange rates, exchange rate effects are excluded. Data on credit growth is CPI deflated.
Figure 49. Emerging Europe Bank
Lending Conditions Index, by Categories
Figure 50. Funding Conditions in Local
Markets, by Region
Index: Oct 2008=100 Percentage points
30
70
65
55
50
45
40
35
60
Q4-09 Q2-10 Q4-10 Q2-11 Q4-11 Q2-12 Q4-12
20
90
80
60
50
40
30
70
Q4-09 Q2-10 Q4-10 Q2-11 Q4-11 Q2-12 Q4-12
Credit standards Nonperforming loans Demand for loans Emerging Europe Global emerging markets
Source: IIF, World Bank staff calculations.
Note: 50=neutral
EU11 REGULAR ECONOMIC REPORT | june 2013
28 | EU11 Recent Economic Developments
Overall lending conditions in EU11
improved in the first quarter of 2013, but
not yet sufficiently to jump-start credit
growth. The recovery in lending conditions
in EU11 was mainly due to improvements in
overall funding conditions, credit standards,
and demand for loans (Figure 49). Domestic
funding conditions eased substantially in
comparison with other emerging regions
(Figure 50). Additionally, banks relaxed
credit standards on residential mortgages and
stopped tightening their standards on business
and consumer loans in the first quarter of
2013. The latest Emerging Markets Bank
Lending Conditions Survey by the Institute
for International Finance (IIF) suggested that
the credit-tightening cycle might be coming
to an end. Finally, demand for loans in EU11
was once again above the “neutral” threshold,
partly due to the reduction of policy rates and
the consequent decline in lending rates.
Non-performing loans (NPLs) remained
elevated in 2012, further dampening credit
growth. Reflecting challenging macroeconomic
situation, NPLs reached worrying levels of
above 15 percent of total loans in Romania,
Bulgaria, Hungary and Slovenia by the end
of 2012 (Figure 51). Their rise was driven by
a mix of weak economic activity and borrower
vulnerabilities such as foreign currency loans to
unhedged borrowers and loans to construction
and retail sectors. In contrast, NPLs decreased
in the Baltics due to bad-loan resolution
efforts. The loan-loss provisioning covered half
of NPLs on average in the EU11 countries
in 2012, with notable differences among the
countries ranging from 33 percent in Estonia
to 87 percent in Romania at the end of 2012.
Figure 51. Nonperforming Loans and
Provisioning to Total Loans, EU11
countries, 4th Quarter 2012
Provisions to NPLs, percent
0
100
80
70
60
50
40
20
30
10
90
0 5 10 15 20
EE
CZ
SK
PL
LT
LV
BG
RO
HR
HU
SI
EU11
NPLs to Total Loans
Source: IMF Global Financial Stability Report April 2013; EU11 central
banks; World Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 29
Despite faltering growth, EU11 governments
proceeded with fiscal consolidation.
The EU11 fiscal deficit was reduced by
0.4 percentage points of GDP to reach
3.4 percent of GDP in 2012. An adjustment of
the same magnitude occurred in EU15, albeit
from a higher initial level. With the exception
of Poland and Lithuania, other EU11 countries
required to correct their excessive deficits
(Hungary, Latvia and Romania) managed to
keep them under the threshold of 3 percent of
GDP by the end of 2012.15 Lithuania’s fiscal
deficit of 3.2 percent of GDP in 2012 proved
to be in line with the EDP target. Lithuania
implemented systemic pension reform allowing
it to deduct the cost of the pension reform from
the fiscal deficit. The Czech Republic, Slovenia,
and Slovakia (another set of EU11 countries
under the Excessive Deficit Procedure) recorded
fiscal deficits of around 4 percent of GDP in
2012, which they planned to correct by the end
of 2013.
While the continuing fiscal consolidation
in 2012 further strengthened the fiscal
balances in the EU11 region, the speed of the
15 If a Member State exceeds the deficit ceiling, the excessive deficit
procedure is triggered. This entails several steps—including
the possibility of sanctions—to encourage the Member State
concerned to take measures to rectify the situation by the deadline
set by the European Council.
adjustment was lower than had been planned
in April 2012. When governments drafted their
Stability and Convergence Programs in April
2012, both EU11 and EU15 had embarked
on ambitious fiscal consolidation programs.
But macroeconomic conditions deteriorated
throughout the year and the fiscal adjustments
turned out smaller than envisioned. In the case
of the EU11 countries, the fiscal adjustment
was 0.5 percentage points of GDP lower than
originally planned compared to 0.9 percentage
points for the EU15.
The slower-than-planned fiscal consolidation
reflected revenue underperformance rather
than an expansion in spending. In EU11,
diminished revenue collection was the sole
reason for a weaker fiscal balance (Figure 54).
The decline in domestic consumption lowered
receipts of indirect taxes and took a toll on
revenue collection in 2012. Lower-thanplanned
expenditures did not compensate
for the drop in revenues. By contrast, higher
spending was the primary factor contributing
to a higher than expected fiscal deficit in EU15.
Even though the EU11 fiscal deficit was
wider than expected in 2012, five countries
recorded better fiscal outcomes than they
had planned. In Bulgaria, lower investment
Continued Fiscal Consolidation in a Challenging
Environment
Against the backdrop of faltering growth across the region, the EU11 governments
continued their fiscal retrenchment in 2012, but failed to meet their targets.
Revenue underperformance, in particular from indirect taxes, delayed the fiscal
adjustment. Public debt-to-GDP ratios increased further in 2012 as fiscal efforts
proved insufficient to stabilize debt levels.
EU11 REGULAR ECONOMIC REPORT | june 2013
30 | EU11 Recent Economic Developments
spending coupled with exceptionally strong
VAT revenues helped reduce the fiscal deficit.
Similarly, strong tax revenue collection and
smaller-than-expected expenditures brought
considerably better fiscal outcomes in Estonia.
In Latvia, it was strong economic growth and
a robust labor market recovery that pulled in
higher-than-expected revenues, despite a midyear
cut in the VAT standard rate by 1 percentage
point. In Hungary higher than expected savings
on expenditure side overcompensated the loss
of revenue, while in Slovakia, better-thanplanned
outcomes was due to a combination of
revenue and expenditure measures.
Figure 52. General government fiscal
deficit, 2011–2012 in EU11 and EU15
Figure 53. General government fiscal
deficit, 2011–2012 in EU11
Percent of GDP Percent of GDP
-5
0
-1
-3
-4
-2
EU15 EU11
-4.6
-4.2
-3.8
-3.4
-8
6
4
-2
-6
-4
2
0
BG CZ EE LV LT HU PL RO SI SK HR
EU15 EU11 2011 2012
Source: Eurostat; Convergence/Stability Programs 2013, World Bank staff
calculations.
Source: Eurostat; Convergence/Stability Programs 2013, World Bank staff
calculations.
Figure 54. General Government Fiscal
Deficit Reduction in 2012, difference
between planned and actual
Figure 55. General Government Fiscal
Deficit in 2012, planned vs. actual
Percent of GDP Percent of GDP
-1.0
0.4
0.2
-0.4
-0.8
-0.6
0
-0.2
EU15 EU11
-5
3
2
-1
-2
-4
-3
1
0
EU15 EU11 CZ SK SI PL HR LT RO HU LV BG EE
Revenue Expenditure Total Planned Change Actual 2012
Source: Eurostat; Convergence/Stability Programs 2012 and 2013, World
Bank staff calculations.
Source: Eurostat; Convergence/Stability Programs 2012 and 2013, World
Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 31
In contrast, the fiscal outcomes in the Czech
Republic, Poland, Slovenia, and Romania
were worse than projected. In Poland, this was
due to an unexpected decline in indirect tax
revenues. In the Czech Republic, it stemmed
from the adoption of the one-off financial
compensation to churches (amounting to
1.5 percent of GDP) and the corrections related
to the EU funds investment (0.3 percent of
GDP)16. Similarly in Romania the corrections
related to the EU funds resulted in wider fiscal
deficit. For Slovenia, which undertook a large
fiscal effort in 2012, the smaller slippage (as
compared to its April 2012 plan) was due
to higher expenditures on public wages and
intermediate consumption.
In spite of the fiscal consolidation efforts in
2012, public debt-to-GDP ratios in EU11
and EU15 increased. The EU11 indebtedness
rose of around 1.8 percentage points of GDP
compared to 4.7 percent for the EU15. In
16 In the case of the Czech Republic, the fiscal deficit corrected by
one-off expenditures would amount to 2.5 percent of GDP in
2012 and not 4.4 percent of GDP.
addition, the public debt relative to the size of
the overall EU11 economy remained far lower
than in EU15. While Hungary, Poland, and
Latvia recorded public debt-to-GDP ratios
declines in 2012, public indebtedness increased
in the rest of the EU11 countries. Except for
Hungary, public borrowing increased and
in most cases was not offset by the growth in
nominal GDP. In the case of the Euro area
countries, the contributions to the European
Financial Stability Facility and to the European
Stability Mechanism increased their debt.17
17 Contributions to the ESM and the EFSF are based on Euro area
member state shares in the paid up capital of the ECB.
Figure 56. Public debt in 2011–12 in
EU11 and EU15
Figure 57. Contribution to changes in
public debt in EU11 countries, 2011–12
Percent of GDP Percent of GDP
0
100
80
70
60
50
40
20
30
10
90
EU15 EU11
86.5
91.2
48.7
50.5
-10
0
90
80
70
60
50
40
20
30
10
HU HR PL SI SK CZ LV LT RO BG EE
2011 2012 2011 Debt contribution GDP contribution 2012
Source: Eurostat; World Bank staff calculations. Source: Eurostat; World Bank staff calculations.
EU11 REGULAR ECONOMIC REPORT | june 2013
32 | EU11 Recent Economic Developments
Removing structural barriers in EU11
remains crucial to economic growth and job
creation. Growth and income convergence
with the rest of Europe are the ultimate goals
of the structural reforms agenda in the EU11
countries. In 2012, the EU11 governments
took steps towards achieving the goals
recommended by the Council, in the following
six priority areas.18
First, the EU11 countries undertook
reforms to ensure sustainability of their
public finances over the medium- and longterm.
Six out of eleven countries worked
on containing the cost of pension systems,
including through the revision of disability
pensions. Seven introduced measures to control
the overall public-sector wages. Given the rise in
unemployment, social benefit and employment
policy reforms were focused on protecting the
vulnerable and facilitating employment of the
youth and elderly. Three countries raised the
minimum wage in 2012 and two followed suit
in 2013 to protect the low income cohorts.
Second, in the area of public service delivery
and administration, the EU11 countries made
progress in improving public procurement
and removing bottlenecks to the absorption
18 The reform progress in each priority area below is presented as
reported in the EU11 governments’ National Reform Programs
(2013).
of EU funds. Countries made efforts to
optimize hospital costs, improve prevention,
develop online health care, and step up training
in human resources. Meanwhile, the analytical
capacities of key ministries continued to be
reinforced and regional offices were reorganized
to make the management of public funds more
transparent.
Third, EU11 countries continued pursuing
reforms to make it easier to do business
by simplifying business regulation,
addressing lingering governance issues, and
strengthening the enforcement of contract
and property rights.19 In the last year, five
EU11 countries streamlined construction
permits issuance and business registration,
simplified trade-license issuance or improved
access to financing for SMEs and startups.
In the judiciary, real-time centralized and
authorized access to judicial services showed
signs of improvement and efforts are made
to shorten the duration of court proceedings.
Programs were launched to provide extra
support to green firms for the development
of innovative products and to introduce tax
incentives for job-creation. Finally, several
countries made efforts to raise the efficiency of
their energy markets.
19 On the importance of business-friendly institutional environment
in EU11, see the Special Topic Paper in this issue. Also, World
Bank. (2013). From Jobless Growth to Growing Jobs. forthcoming.
Some Progress on Structural Reforms
Despite the ongoing macroeconomic challenges, the EU11 countries made
progress in introducing structural reforms in 2012 aimed at increasing their
growth potential. The structural agenda remains unfinished, however, to give
EU11 a competitive edge in the world market.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Recent Economic Developments | 33
Looking ahead, the EU11 countries need
to continue their reforms in order to gain a
competitive edge in the world market. Today,
with some exceptions, the EU11 economies are
still considerably less competitive than the rest
of the EU20, despite the continuous pursuit of
economic reforms. They lag behind the EU15
average across all competitiveness components.
Estonia has the highest overall competitiveness
score among the EU11 countries, followed
by the Czech Republic and Poland, while
Slovakia, Romania and Croatia have the lowest
rankings (Figure 58). In terms of the ease of
doing business, Estonia, Latvia, and Lithuania
perform better than the EU15 average while
Romania and Croatia again rank last (Figure
59).
20 World Economic Forum (WEF) Competitiveness Survey 2012–
2013 and Doing Business 2013.
Figure 58. Global Competitiveness
Country Ranking, 2012–2013
Figure 59. Ease of Doing Business
Country Ranking, 2013
0 20 40 60 80 100
EU 15
EU 11
Croatia
Romania
Slovakia
Bulgaria
Hungary
Slovenia
Latvia
Lithuania
Poland
Czech R.
Estonia
0 20 40 60 80 100
EU 15
EU 11
Croatia
Romania
Slovakia
Bulgaria
Hungary
Slovenia
Latvia
Lithuania
Poland
Czech R.
Estonia
Source: World Bank. Source: World Bank.
EU11 REGULAR ECONOMIC REPORT | june 2013
34 | EU11 Recent Economic Developments
Overall, the EU11 economic performance
is projected to gradually stabilize in 2013,
before it picks up steam in 2014. The EU11
economies are expected to grow at 0.8 percent
in 2013 (Table 1). The EU11 countries
will retain their relatively stronger growth
performance over EU15, which is, in the
aggregate, contracting.
Table 1. EU11 Growth Prospects
Percent
2012 2013 2014
EU15 -0.4 -0.2 1.4
EU11 0.8 0.8 2.0
Bulgaria 0.8 1.2 2.1
Croatia -2.0 -0.4 1.5
Czech Republic -1.3 -0.4 1.6
Estonia 3.2 3.0 4.0
Latvia 5.6 3.6 4.1
Lithuania 3.6 3.0 3.5
Hungary -1.7 0.3 1.5
Poland 1.9 1.0 2.0
Romania 0.7 1.7 2.2
Slovenia -2.3 -2.3 -0.1
Slovak Republic 2.0 0.7 2.0
Source: World Bank staff.
Note: Forecasts for the EU15 and for the Czech Republic are from the
Spring 2013 European Economic Forecast.
In mid-2013, prospects for EU11 look
weaker than they did at the end of 2012.
Weak domestic demand and the continuing
recession in the Euro area have led to a less
optimistic forecast compared with the one
issued last January (Figure 60). With the
exception of Latvia, Lithuania, and Romania,
the 2013 outlook for all EU11 countries has
been downgraded.
Economic outcomes in 2012 will influence
the EU11’s 2013 growth prospects. The weak
outcome in 2012 suggests there will be a negative
carry-over effect on 2013 economic growth
for the Czech Republic, Hungary, Croatia,
and Slovenia (Figure 61).21 In comparison to
the EU15 countries, where the carry-over is
expected to subtract 0.4 percentage points from
output in 2013, its overall effect will be neutral
in EU11. However, the aggregate neutrality
hides significant differences among the EU11
countries. While in the Czech Republic,
21 Carry-over measures the contribution to annual growth in the
current year, of the quarterly expansion during the previous year. It
is defined as the rate of growth that would be observed if quarterly
GDP in the current quarter remained unchanged from the level of
the fourth quarter of the previous year.
EU11 Near-Term Outlook
Tepid Economic Recovery
Overall, the EU11 economic growth is expected to increase in 2013 by 0.8 percent
before accelerating in 2014. Economic activity will remain constrained by
weak domestic demand and unfavorable labor-market conditions. Net exports
will likely remain the driver of economic growth in the near term. Fiscal
consolidation will continue to dampen growth, but to a lesser extent than in
2012. Monetary policy is expected to continue to support growth, with different
effects on the individual EU11 countries.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Near-Term Outloo k | 35
Hungary, Croatia, and Slovenia, 2013
economic growth will be highly influenced by a
negative carry-over effect, a positive carry-over
effect will result in higher economic growth in
the rest of the EU11 countries.
Weak domestic demand in the EU11 and the
ongoing recession in the Euro area are likely
to hamstring the overall EU11 economic
growth in 2013. In most EU11 countries,
domestic demand will remain weak due to the
ongoing fiscal tightening and muted credit
growth. In many countries, the 2007–2013
EU Financial Framework will still be driving
up public investment, although insufficiently
to compensate for the lackluster private-sector
investment growth. Without a pronounced
and lasting recovery in the Euro area, private
investment is not expected to contribute
strongly to economic growth in EU11. Despite
low inflationary pressures and higher disposable
income of households, private consumption
will remain low until the labor market
recovers. Faced with the ongoing economic
uncertainly, households will continue to build
up precautionary saving rather than spend.
The Baltic countries, Bulgaria, and Romania
are likely to be the exceptions to this trend;
private consumption will remain a significant
contributor to growth there.
In the near term, the EU11 net exports
will continue to support economic growth,
but not as strongly as in 2012. EU11 trade
flows are expected to remain depressed in the
near term. Overall, net exports will continue
to support growth in the near term, even
though this is likely to be a result of the higher
contraction in imports rather than an increase
in exports. Weak domestic demand is expected
to reduce import growth. And with the ongoing
recession in the Euro area, EU11 export growth
is not expected to increase to those countries.
However, non-European trade diversification
is likely to continue to prop up EU11 exports,
suggesting further gains in market share for
those countries.
Figure 60. GDP Forecasts for 2013 Figure 61. Carry-over effects in EU15 and
EU11 Countries
Percent Percentage points
-3
4
3
0
-2
-1
2
1
EU11 LV EE LT RO PL BG SK HU HR CZ SI
-1.5
2.5
2.0
0.5
0
-1.0
-0.5
1.5
1.0
EU15 EU11 LV EE LT SK RO PL BG CZ HU HR SI
-0.4
0.0
2.2
1.7
1.4
0.4 0.4
0.2 0.2
-0.5
-1.0 -1.0
-1.3
Jun-13 Jan-13
Source: World Bank staff estimates.
Note: Jun-2013 presents the current estimate of economic growth; Jan-
2013 refers to the 2013 estimate at the end-2012, as presented in the
World Bank. 2013. EU11 Regular Economic Report (Issue 26).
Source: Eurostat, World Bank staff estimates and calculations.
Note: Carry-over effects are calculated using Eurostat seasonally and
working day adjusted GDP data.
EU11 REGULAR ECONOMIC REPORT | june 2013
36 | EU11 Near-Term Outloo k
Net job creation prospects for the near term
remain bleak in EU11. In 2013, employment
growth will exceed 1 percent only in Lithuania
and Latvia. For the majority of the EU11
countries it will be close to zero (Slovakia,
Romania, Hungary22, and Estonia) or shrink
(Croatia, Slovenia, Poland and Bulgaria).
Overall, EU11 employment is projected to
decline by 0.3 percent in 2013, against a
contraction of 0.5 percent in EU15. For 2014,
some employment growth is foreseen. However,
due to a lag, the anticipated acceleration in
economic growth is unlikely to sizably boost job
creation in the near term. Most recent Business
Expectation Surveys indicate that employment
prospects for the EU11 are generally not back to
their pre-crisis levels. Most recent expectations
data suggest stronger expected hiring activities
in the retail and other services sectors than in
construction and manufacturing in the next
couple of quarters (see Spotlight 1). The EU11
average unemployment rate will rise to about
11 percent in 2013 and stay elevated during
2014 as well (Figure 62). In the EU15, it will
rise marginally to just over 11.5 percent in
2013 and stay at a similar level in 2014.
Capital flows to the EU11 region are
projected to accelerate in the near future.
Given the favorable financial market
conditions, ample liquidity, and stepped-up
foreign investors’ risk appetite, capital inflows
to the EU11 countries are likely to increase in
the near term. These countries still face large
borrowing needs in 2013 and beyond, which
will drive up debt-portfolio flows (Figure 63).
At the same time, equity portfolio investment
will also increase as foreign investors search
22 In Hungary, the Government expects positive employment growth
in 2013 on the back of the Job Action Plan.
for returns. In addition, privatization deals in
the services sector are expected to materialize
in several EU11 countries, which will further
support FDI. External debt levels are projected
to remain at current levels.
Although the fiscal policy stance is set to
remain contractionary, the EU11 public debt
will continue to increase in the near term.
Most EU11 countries are pursuing pro-cyclical
fiscal tightening, even though the output
Figure 62. Unemployment rates 2012–14
Percent
0
25
20
5
10
15
EU15 EU11 HR SK BG LV HU PL LT SI EE CZ RO
2012 2013 2014
Source: Eurostat; World Bank staff.
Figure 63. Public sector financing needs
Percent of GDP
0
25
20
5
10
15
EU11 LV BG LT HR SK CZ PL RO SI HU
Total financing need 2013 Total financing need 2014
Source: IMF Fiscal Monitor April 2013, Stability/Convergence Programs
2013; World Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Near-Term Outloo k | 37
gaps continue to be large and the economic
cycle is making consolidation efforts more
difficult (Figure 64). Since the speed of fiscal
adjustment in 2013 in the EU11 countries will
slow compared to 2012, its effect on economic
growth will be less negative than in 2012.
Therefore, in the countries with low market
pressures, there is some scope for a neutral
fiscal stance, but it should remain in line with
the medium-term reform plans anchored in
adjusting structural imbalances. The fiscal
consolidation efforts currently envisaged will
not be sufficient to reduce the public debt ratio
before 2015.
Overall in the EU11 region, monetary policy
will continue to shelter the economy against
external shocks, but its impact on growth
will remain muted. Slow economic growth
and weak price pressures suggest that there is
further space for cuts in policy rates in 2013 in
EU11. However, the scope for loose monetary
policy varies among the individual countries:
for example, further conventional easing in the
Czech Republic is unlikely given the near-zero
policy rate levels. However, in Poland, where
financial market volatility is limited, a further
reduction in policy rates is plausible. Reduction
in policy rates will increase the disposable
income of households holding mortgages with
variable interest rates. Importantly, policy rate
cuts may not deliver in full the desired effect
on domestic demand. Given falling inflation
and credit constraints on both the demandand
the supply side, private credit growth is not
expected to contribute to private sector growth
in EU11. As all stakeholders (households,
corporations, financial intermediaries, and the
public sector) continue to repair their balance
sheets in the near term, bank lending is likely to
remain low, weighing depressing on economic
activity.
Downside risks to the near-term EU11
macroeconomic outlook exist, stemming
from both external and domestic sources.
In addition to the risks surrounding the global
economic outlook (Box 4), the key external risks
affecting the EU11 prospects for growth relate
to the Euro area. They include a deeper–thanexpected
and protracted recession; inability
to overcome the impaired monetary policy
mechanism in the Euro area; and insufficient
progress in structural reforms implementation.
These external factors, if materialized, will
affect negatively the prospects for growth in
the EU11 countries by dampening confidence
and delaying the recovery. On the domestic
side, the key challenge for the EU11 countries
is in the lack of policy options, especially on
the fiscal side, to spur growth. Given subdued
domestic demand in a number of countries and
falling inflation, implementation of countercyclical
policies would be warranted. However,
there is limited scope for fiscal loosening, given
Figure 64. Fiscal policy stance in 2012
and 2013
Change in structural balance
-6
10
8
2
0
-4
-2
6
4
-1.0 -0.5 0 0.5 1.0 1.5
HU
LV
LT
EE
BG
SK
RO
SI
CZ
PL
BG
LT
LV
SI RO
PL
SK
BG
EE
HU
Pro-cyclical scal tightening Counter-cyclical scal tightening
Counter-cyclical scal relaxation Pro-cyclical scal relaxation
Change in cyclical component
2012 2013
Source: European Commission, Spring Forecast, May 2013, World Bank
Staff.
EU11 REGULAR ECONOMIC REPORT | june 2013
38 | EU11 Near-Term Outloo k
the rising levels of public debt. Accommodative
monetary policy could still play an effective
role. However, the transmission mechanism
operates with a considerable lag and has not
necessarily delivered the expected results in
recovery of credit growth, given the countereffect
of inflation, deleveraging, and tightened
standards.
Box 4. Near-Term Global Outlook and Risks23
Overall, global GDP is projected to expand 2.3 percent in 2013 and strengthen to 3.0 and
3.3 percent in 2014 and 2015. High-income countries’ growth will remain a weak 1.2 percent
in 2013, but should firm to 2.0 and 2.3 percent in 2014 and 2015, respectively. The fragile Euroarea
economy is expected to stabilize by the end of 2013 and to gain some traction in 2014.
Economic growth is likely to accelerate gradually in the middle- and low-income countries to
5.1 percent in 2013, and to 5.6 and 5.7 percent in 2014 and 2015—roughly in line with their
underlying potential. Growth in regions such as East Asia and the Pacific, Latin America and
the Caribbean and Sub-Saharan Africa, where economies are already operating at close to full
capacity, is not expected to strengthen significantly in the next couple of years. Although many
European countries outside the Euro area are expanding at rates close to their potential, they
are not growing strongly enough to quickly reduce post-crisis output gaps and unemployment.
The expected modest global recovery faces downside risks. The severity and likelihood of
downside risks have declined from a year ago, but not disappeared. They include:
• The risks of slowing the implementation of announced policy measures in the Euro
area remain. While important structural and fiscal consolidation reforms have been
undertaken over the past years, the pace of progress has slowed, as some of the fiscal targets
in the Euro area have been relaxed.24
• Accommodative monetary policy by the ECB has not spurred private sector credit
growth in in the southern part of Europe, leading to financial fragmentation within
the Euro area. The reduced bank lending and risk appetite there increases risks to the
economic recovery of the Euro area as a whole.
• Over the past year, energy and metals prices have been easing in response to supplyand
demand-side substitution induced by high prices. If prices decline further, it would
put a considerable pressure on the finances of exporting economies, heavily dependent on
commodity-related revenues, and push down their GDP growth.
• Quantitative easing has benefited low- and middle-income countries by stimulating
high-income-country GDP, lowering borrowing costs, and avoiding a financial-sector
meltdown. Once high-income countries begin to pursue quantitative easing less actively or
begin to unwind long-term positions, interest rates are likely to rise. Higher interest rates
will increase debt-servicing costs.
23 Based on World Bank. (2013). Global Economic Prospects - June 2013: Less volatile, but slower growth. Washington, DC: World Bank.
24 The IMF estimates that by the end of 2013, two thirds of the Euro area economies have already gone through enough fiscal adjustment
to achieve debt sustainability and debt reduction in the medium term. See IMF (2013). Fiscal Monitor: Fiscal Adjustment in an Uncertain
World. Washington, DC.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Near-Term Outloo k | 39
The EU Action for Stability, Growth and
Jobs calls for lasting, sustainable growth and
higher living standards that can only be built
on sound public finances, deep structural
reforms and well-targeted investments. The
Europe 2020 strategy emphasizes specific
measures in three priority areas: (i) smart
growth based on knowledge and innovation; (ii)
sustainable growth promoting competitiveness,
resource efficiency, and a low-carbon economy;
(iii) inclusive growth that fosters poverty
reduction and social inclusion through higher
employment rates and improved human
capital development (see Spotlight 2).25 The
EU national governments have submitted their
2013 national reform programs and mediumterm
convergence programs (2013–16), aligned
with the Europe 2020 strategy, where they
outline the priorities and structural measures to
boost growth and employment.26
A structural reform agenda is being planned
in each of the EU11 countries to restore
growth and competitiveness. The following
25 European Union, “Europe 2020: Commission proposes new
economic strategy in Europe,” Europa Press Release IP/10/225,
Brussels, March 3, 2010. Available online at: http://europa.eu/
rapid/press-release_IP-10-225_en.htm.
26 Spotlight 2 focuses on assessing the EU11 countries’ progress
toward achieving the Europe 2020 poverty and social exclusion
targets.
structural reform areas have gained priority
in EU11, as reported in their national
reform programs and the country specific
recommendations proposed by the European
Commission to the EU Council (Table 2):
?? High and rising unemployment and job
losses, especially in the low-skilled segment
of the labor market, suggest structural
challenges of the EU11 education system and
labor market.27 A comprehensive set of labormarket
measures may be necessary to address
the skills mismatch problem, while reforms
in life-long learning, vocational training
and higher education should be pursued in
parallel (in particular in Latvia, Slovakia and
Croatia).
?? Improving the EU11 business environment
would attract more investments in
productive sectors and would improve
future economic growth prospects.28 The
business climate could benefit from further
reduction in administrative costs and red
tape (Lithuania and Croatia) and from
improving the efficiency of the judiciary
(Romania, Slovakia, Hungary, Latvia,
Bulgaria and Croatia) to ensure a level
27 See Spotlight 2.
28 See the Special Topic Paper on jobs in the EU11 countries in this
EU11 RER issue.
A Structural Reform Agenda to Restore Growth in EU11
While combatting the ongoing macroeconomic challenges, the EU11 governments
plan to focus their reform agendas toward restoring economic growth and
increasing their growth potential. Structural reforms in the areas of labor
markets and education, the business environment, EU funds absorption, and
public administration are some of the priority areas for the EU11 in the medium
term.
EU11 REGULAR ECONOMIC REPORT | june 2013
40 | EU11 Near-Term Outloo k
playing field. Civil justice systems needs to
be improved in many countries, in particular
by reducing backlogs, speeding up judicial
proceedings and introducing alternative
forms of dispute resolution. Enhancement
in the insolvency framework could prove
useful for increasing business confidence
along with the improvement of the corporate
sector legislation and its application by the
judicial system. Opening up the network
industries such as energy, railways, postal
services and telecoms to competition to
deliver better services at better prices for
business and citizens is planned along with
the greater independence for the regulators
(Bulgaria, Estonia, Croatia, Hungary,
Poland, Romania, Slovakia, Lithuania and
Latvia). Removing unjustified restrictions
in provision of non-tradable services should
improve competition, increase investments
and make services more accessible for lower
income groups.
?? Better EU funds absorption by the EU11
would help reduce the need for external
payments and indebtedness through the
capital account and would have a positive
effect on future growth (this is particularly
important for Bulgaria, Croatia, Hungary,
Romania and Slovenia).
?? Public administrations remain under fiscal
pressure across all of the EU11. Apart
from introducing e-government services
and simplifying administrative processes,
governments will have to embark on
organizational innovations to enhance
public service delivery. Sharing best practices
Table 2. EU11 – Country Specific Recommendations for 2013–14
Country
Public finances Financial
sector Structural reforms Employment and social policies
Sound public
finances
Pension and
healthcare
systems
Fiscal framework
Taxation
Banking and
access to finance
Housing market
Network
industries
Competition in
service sector
Public admin.
and smart
regulation
R&D and
innovation
Resource
efficiency
Labor market
participation
Active labor
market policy
Wage setting
mechanisms
Labor market
segmentation
Education
Poverty and
social inclusion
BG v v v v v v v v v v v v v
CZ v v v v v v v v v
EE v v v v v v v v v v v
HR* v v v v v v v v v v v v v v v v
HU v v v v v v v v v v v v
LT v v v v v v v v v v v v
LV v v v v v v v v v v v
PL v v v v v v v v v v v v v v
RO v v v v v v v v v v v v
SI v v v v v v v v v v v v
SK v v v v v v v v v v
Source: European Commission - http://ec.europa.eu/europe2020/index_ent.htm, World Bank staff.
Note: EC’s recommendations presented on May 29, 2013 for 2013–2014.
* For Croatia, World Bank staff estimate based on the EC’s separate assessment since Croatia participated in the European Semester only voluntarily.
MACROECONOMIC REPORT: economic RECOVERY on hold
EU11 Near-Term Outloo k | 41
and opening public procurement markets by
actively seeking cross-border tenders would
also stimulate new opportunities, processes
and innovation in the delivery of public
services.
While restoring financial and macroeconomic
balances is a top policy priority for all EU11
countries, the structural-reform agenda will
have to be pursued vigorously. For most
EU11 countries, the priority is now to correct
their excessive deficits and ensure long-term
sustainability of public finances. The EC
has put a proposal to abrogate the EDP for
Hungary, Latvia, Lithuania and Romania in
front of the Council, which would bring the
number of countries under the EDP within
the EU11 down to 4, with Croatia likely to
enter at end-2013. However, the mediumterm
economic growth potential of the EU11
countries can only be realized if structural
barriers to economic activity are removed.
EU11 REGULAR ECONOMIC REPORT | june 2013
42 | EU11 Near-Term Outloo k
Typically, there is no real-time information on
job creation and labor demand. As the EU11
region confronts the challenge of sluggish job
creation, identifying the sectors that can lead
employment growth and job creation in the
fragile post-crisis period is important. However,
real-time data on employment growth in the
enterprise sector are typically not available and
labor demand is difficult to predict ex ante.
To assess employment growth prospects
in the enterprise sector, one option is to
use employment expectations information
from high-frequency business-expectations
surveys. Rather than allocate large amounts of
resources to collect new information, the idea
is to make better use of firm-level data already
collected regularly by the authorities. Data
from Business Expectations Surveys (BES)—
sometimes referred to as Business Confidence or
Business Tendency Surveys—include detailed
information about employment expectations of
respondent enterprises and represent an underutilized
source of information on employment
growth prospects.
29 Based on Bilgin, Dorofeev and Tiongson (2013), prepared as
a background paper for From Jobless Growth to Growing Jobs
(forthcoming, World Bank 2013)
Expectations data as possible
leading indicators of labor demand
Central Banks routinely conduct expectations
surveys using a broad cross-sectional sample
of enterprises. These companies often number
a thousand or more, are from different sectors,
and together are representative of the entire
enterprise sector. The surveys are conducted
on a quarterly or monthly basis, usually by the
Central Banks. The results of the surveys are
usually consolidated into an aggregate index of
business confidence, the movements of which
are reported alongside real-time information
on financial and economic variables.
This confidence index is underpinned by
information provided by enterprises about their
economic and financial outlook, including their
expected output, planned purchases, scheduled
investment activities and expected hiring and
firing activities over the coming quarter or over
the coming year.
In the literature on business expectations,
there is compelling evidence that aggregate
business confidence indicators correlate well
with aggregate economic activity. Over the
past decade, a number of empirical studies of
business confidence indicators using timeseries
techniques have been conducted in
Spotlight 1:
Employment Expectations as Indicators
of Labor Demand in EU1129
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 1 | 43
high-income economies.30 The results suggest
that business confidence indicators are closely
correlated with contemporary indicators of
aggregate economic activity or can serve as
leading indicators of domestic demand and are
shown to have significant predictive content. A
small but growing literature has emerged the
last few years that provide evidence, though
weaker, that business confidence indicators
may have predictive content.31
Together, these two strands of the literature
imply that employment expectations data
drawn from BES may have predictive
value as well. To date, however, there are
only three such studies, all using data from
high-income economies.32 Of the three, the
most compelling is a cross-country study of
30 See, for example, Nilsson, R. and E. Guidetti. (2008), “Predicting
the Business Cycle: How good are early estimates of OECD
Composite Leading Indicators?” Statistics Brief, February, No. 14
(Paris: OECD); Nilsson, R. (2006), “Composite Leading Indicators
and Growth Cycles in Major OECD Non-Member Economies
and Recently New OECD Members Countries”, OECD Statistics
Working Papers No. 2006/05 (Paris: OECD). http://dx.doi.
org/10.1787/118143571177; Park, A. (2011), “Business Surveys
and Economic Activity”, Reserve Bank of Australia Bulletin;
Taylor, K. and McNabb, R. (2007), “Business Cycles and the
Role of Confidence: Evidence from Europe,” Oxford Bulletin of
Economics and Statistics, 69(2): pp. 185–208. There is strong
evidence that household confidence indicators also help predict
macroeconomic variables. See for example Kuzmanovic, M. and
P. Sanfey (2013) “Can Consumer Confidence Data Predict Real
Variables? Evidence from Croatia,” Croatian Economic Survey,
Vol. 15, No. 1(April):pp. 5–24.) on consumer confidence and
retail turnover in Croatia.
31 See for example Soric, P. and M. Markovic (2010), “Predicting
Downturn: Are tendency Surveys a Good Estimator of Retail
Activity in Croatia?” Ekonomski Pregled, 61 (9–10): pp. 559–579
and Cizmesija, M., Erjavec, N., Bahovec, V. (2010), “EU Business
and Consumer Survey Indicators and Croatian Economy”, Zagreb
International Review of Economics and Business, Vol. 13, No. 2,
pp. 15–25. They provide contrasting evidence of the usefulness
of business expectations indicators in Croatia. In Hungary, the
industrial sentiment index may have been useful to anticipate an
economic slowdown but gave no indications of its depth.
32 Pashourtidou and Tsiaklis (2011); Jonsson, A. (2007), “Managers’
Employment Expectations,” EU Workshop on Recent
Developments in Business and Consumer Surveys, European
Commission, Directorate General for Economic and Financial
Affairs, November 2007; Claveria, O., E. Pons and R. Ramos
(2007) “Business and Consumer Expectations and Macroeconomic
Forecasts,” International Journal of Forecasting, Vol. 23: pp. 47–
69.
managers’ employment expectations. The
research finds that such expectations are
significantly correlated with employment
growth, for the economy as a whole and for the
service and industry sectors. The correlation
with construction sector employment is much
weaker and the data suggest that employment
expectations are coincident indicators, rather
than leading indicators, of employment.
The results of the econometric analysis of
employment expectations in selected EU
countries provide preliminary evidence that
employment expectations can help predict
employment growth. Box 5 describes the
steps taken to conduct an assessment of the
predictive value of employment expectations
using monthly or quarterly sector-level
manufacturing and construction data from
the OECD Business Tendency Survey over
the period 2000 to 2011. The data are from
the responses to a specific question about
next-quarter employment. They measure the
net employment balance of expanding and
contracting firms.33 They are then merged
with data on actual employment growth, by
sector and by quarter, over the entire period.
However, the strength of this relationship varies
by country and by sector, as reported in Box
5. In general, some sectors (manufacturing
and construction) yield stronger statistical
relationships between employment expectations
and actual employment than other sectors
(retail). The lag structure may differ as well;
in some cases expectations help predict next
quarter employment growth, while in others,
they help predict employment growth a couple
of quarters later.
33 The net balance is calculated by subtracting the percentage of firms
that expect an employment increase from the percentage of firms
that expect an employment decrease for each period.
EU11 REGULAR ECONOMIC REPORT | june 2013
44 | Spotlight 1
Box 5. Testing the predictive content of employment expectations data
This empirical exercise conducts a preliminary assessment of the predictive value of
employment expectations using monthly or quarterly sector-level manufacturing and
construction data from the OECD Business Tendency Survey over the period 2000 to
2011.34 The data are from the responses to a specific question about next-quarter employment
(“How do you expect your firm’s total employment to change over the next 3 months?” “It will
... ‘+’ increase, ‘=’ remain unchanged, ‘-’ decrease”), calculated as the net balance (subtracting
the percentage of firms that expect an employment increase from the percentage of firms that
expect an employment decrease) for each period. They are then merged with data on actual
employment growth, by sector and by quarter, over the entire period (2000 to 2011). In order
to match the data, the quarterly BES data are calculated by taking the arithmetic average of the
monthly data. The tests, however, could only be conducted for a sample of EU11 countries, for
which all the required and comparable data for assessment exist.35
To investigate the predictive content of the survey data on the actual values of the
corresponding variables, co-integration analysis and Granger causality tests are conducted.
As a first step of the analysis, the stationarity of the variables is examined using Augmented
Dickey Fuller (ADF) test and Phillips Perron (PP) test. The results show the following timeseries
characteristics of the data. In the construction sector, the employment and the future
tendency of employment (the survey data) are non-stationary in levels, but stationary in the
first difference in the sample period in the cases of Poland, Hungary, Slovenia, Slovakia, Czech
Republic and Estonia. In the case of Bulgaria, the employment is the first difference stationary,
while the future tendency of employment is stationary in level. In the manufacturing sector, the
employment and the corresponding survey data are stationary in the first difference in the cases
of Poland, Slovenia, Czech Republic and Estonia. And in the cases of Slovakia, Slovenia and
Hungary, the employment series are stationary in the first difference and the future tendency of
employment is stationary in levels.
In a second step, the Johansen test for co-integration is used to investigate the long-term
relationship between the actual employment and the survey on employment for all the
cases in which both variables are integrated or order one, or I(1). The test results indicate
that there exist a long-run equilibrium relationship between the actual employment and the
future tendency of employment in the construction sectors of Poland, Hungary, Slovenia, the
Czech Republic and in the manufacturing sector of Poland and Estonia (Figure 65).
In a third step, the Granger Causality test is employed to examine the short-term “causal”
relationship between the actual employment and the corresponding survey data. In
conducting this test, the survey data is recorded for the period in which the survey is taken.
For example, if the survey is taken in the first quarter of 2000 and it is about the employment
prospects in the second quarter of 2000, the data is recorded for the first quarter of 2000. Such
data entry practice allows the Granger Causality procedure to be used in testing the predictive
34 Employment expectations data from the other sectors in the OECD database (retail and services) are yet to be tested, pending the availability
of more complete information.
35 The employment data are from Eurostat for all countries except for Bulgaria, for which the data are taken from the Bulgarian national
statistical service.
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 1 | 45
power of survey data over the corresponding actual employment. In all the cases, apart from
the construction sector of Bulgaria, for which all the required and comparable data exist, the
employment survey data Granger “cause” the actual employment. In other words, the past values
of expected future tendency of employment provide information that is useful for forecasting
actual employment, beyond that contained in the past values of employment. The lag structure,
however, varies across countries. In the case of Bulgaria, it is found that there is mutual “feedback”
between the two variables.
In sum, the results of the time-series tests, which are described above, suggest that business
expectations—in particular, the survey component related to expected hiring activities in
a coming quarter—do have predictive value. The results suggest that not only there exists
a long-run equilibrium relationship between the employment expectation survey and the
corresponding actual employment, but also there are short-run “causal” relationships between
the expectations data and real employment data.
Figure 65. Actual Employment and Expected Future Tendency, 2000–2012
(in selected cases)
Poland (manufacturing sector) Estonia (manufacturing sector)
Employment Future tendency Employment Future tendency
2,400
Q1-00
-40
10
0
-10
-20
-30
Q1-03 Q1-06 Q1-09 Q1-12
3,200
3,000
2,800
2,600
40
Q3-03 Q3-05 Q3-07 Q3-09 Q3-11
90
80
70
60
50
-60
20
0
-20
-40
Hungary (construction sector) Slovenia (construction sector)
Employment Future tendency Employment Future tendency
20
Q1-00
-40
20
0
-20
Q1-03 Q1-06 Q1-09 Q1-12
100
80
60
40
50
Q3-02 Q3-05 Q3-08 Q3-11
70
65
60
55
-60
40
0
-20
-40
20
Czech Republic (construction sector) Poland (construction sector)
Employment Future tendency Employment Future tendency
420
Q1-00
-40
10
0
-10
-20
-30
Q1-03 Q1-06 Q1-09 Q1-12
500
480
460
440
600
1,400
1,200
1,000
800
Q1-00 Q1-03 Q1-06 Q1-09 Q1-12
-60
20
0
-20
-40
Employment (lhs) Future tendency (rhs) Employment (lhs) Future tendency (rhs)
Source: World Bank staff estimates using consolidated OECD data and employment data from national authorities.
EU11 REGULAR ECONOMIC REPORT | june 2013
46 | Spotlight 1
Implications for Near-Term
Employment Growth Prospects in
EU11
Expectations data suggest that aggregate
employment prospects remain dampened in
EU11. On average, the BES data indicate that
employment prospects are generally not yet
back to their pre-crisis levels. Summary data for
the period 2006 to 2013 are presented in Figure
1. Summary data from alternative sources (for
example, from a private firm, Manpower Group,
that conducts employment outlook surveys
across a broad sample of countries worldwide)
suggest that net employment outlook for
selected EU11 countries is indeed down, well
below its pre-crisis peak. These results are also
consistent with the OECD Business Tendency
data.
Across sectors, however, employment
expectations in EU11 are diverse (Figure
66). For example, employment prospects in
the manufacturing sector across countries in
the sample have moved almost in lock-step
since the boom years—uniformly plunging
during the global financial crisis, recovering
Figure 66. Net Employment Expectations: Selected Sectors and Countries
Construction Employment Tendency Manufacturing Employment Tendency
Net balances by period and by sector Net balances by period and by sector
-100
60
40
-20
-40
-80
-60
20
0
Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12
-70
30
20
-20
-30
-40
-60
-50
10
0
-10
Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12
Retail Employment Tendency Service Employment Tendency
Net balances by period and by sector Net balances by period and by sector
-60
50
40
0
-10
-20
-30
-50
-40
30
20
10
Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12
-40
30
20
-10
-30
-20
10
0
CZE EST HUN POL SVK SVN CZE EST HUN POL SVK SVN
Source: OECD Business Tendency data and World Bank staff calculations.
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 1 | 47
slowly after, diminishing again in late 2011
and showing some signs of recovery since late
2012. In contrast, service sector employment
has been more volatile and heterogeneous,
though falling during the crisis as well. The
retail and construction sectors are somewhere
in-between. The interpretation of this diversity
requires some caution: the diversity may reflect
both differences in employment prospects over
time across countries as well differences in the
degree of difficulty of anticipating employment
changes across sectors.
Recent EU11 expectations data suggest that
some sectors may be approaching their precrisis
employment expectations levels. In the
retail and other services sector in EU11, the net
employment balances among countries in the
sample are on average hovering around zero.
However, in the case of the Slovak Republic’s
retail sector, expectations are approaching precrisis
levels through the first quarter of 2013.
Data on the Slovak Republic’s aggregate service
sector employment from the ILO through
the end of 2012 indeed suggest stronger
employment numbers in this sector, compared
to the construction and manufacturing
sectors. Meanwhile, net balances in the
construction sector, a key driver of job creation
in the boom years in most countries, are still
hovering around zero through the first part of
2013, far below their pre-crisis peak of about
16 percent. Similar patterns can be seen in the
manufacturing sector, though employment
expectations in Estonia seem slightly more
positive than those of other countries.36
36 Pre-crisis employment growth indicators in the EU11 suggest the
general decline in manufacturing employment, except in a couple
of countries including Estonia. See Raiser and Gill (2012).
In sum, the available BESs for the
EU11 countries suggest that business
expectations—in particular, the survey
component related to expected hiring
activities in a coming quarter—do have
predictive value. While aggregate employment
prospects remain dampened, some sectors
(such as retail and other services) are showing
signs of recovery in the near term. However, the
BES data suggest that more traditional sectors,
such as manufacturing and construction, are
expected to remain below their pre-crisis peaks.
EU11 REGULAR ECONOMIC REPORT | june 2013
48 | Spotlight 1
In an effort to strengthen the European
Union’s growth model and confront the
harsh effects of the global economic crisis on
the member countries of the EU, a ten-year
economic strategy known as Europe 2020 was
launched in early 2010 with specific measures
in three mutually reinforcing priority areas:
(i) smart growth based on knowledge and
innovation; (ii) sustainable growth promoting
competitiveness, resource efficiency, and a
low-carbon economy; (iii) inclusive growth
that fosters poverty reduction and social
inclusion through higher employment rates
and improved human capital development.38
When the Europe 2020 strategy was approved
in June 2010, the European Council chose three
measures of poverty and social exclusion: (i) the
at-risk-of-poverty rate, which is a measure of
relative poverty defined as the percentage of the
population with disposable incomes lower than
60 percent of the national median income after
social transfers; (ii) the index of severe material
deprivation, which is the percentage of people
who cannot afford a number of necessities that
37 Based on Ajwad, Simler, Azam, and Dasgupta (2013) “Poverty
Prospects in Europe: Assessing Progress towards the Europe 2020
Poverty and Social Exclusion Targets in the New European Union
Member States.” Croatia is not included in this study because it did
not collect EU-SILC data in the relevant years.
38 European Union (2010) “Europe 2020: Commission proposes new
economic strategy in Europe,” Europa Press Release IP/10/225,
Brussels, March 3, 2010. Available online at: http://europa.eu/
rapid/press-release_IP-10-225_en.htm.
are considered essential in order to live decent
lives in Europe;39 and (ii) low work intensity,
which is the percentage of people living in
households in which adults worked less than
20 percent of their potential.
As of 2008, 120 million people were at
risk of poverty or social exclusion in the
EU.40 According to a recent study on income
and living conditions in Europe, as of 2008
39 Severely materially deprived households are those that state
they cannot afford at least four of the following nine items: (i)
mortgage payments, rent, or utility bills; (ii) adequate heating;
(iii) unexpected expenses; (iv) meat, fish, or a protein equivalent
every second day; (v) a week-long holiday away from home; (vi) a
car; (vii) a washing machine; (viii) a color television set; or (ix) a
telephone.
40 Eurostat Press Office, “Income and living conditions in Europe,”
News Release 190/2010, 13 December 2010. http://epp.eurostat.
ec.europa.eu/.
Spotlight 2: Toward Inclusive Growth in
Europe
Assessing Progress toward Achieving the Europe 2020
Poverty and Social Exclusion Targets in EU1137
Figure 67. People at risk of poverty or
social exclusion in the EU, 2008
At risk of
poverty
49.6
Low
work
intensity
17
Severe
material
depriviation
19
Source: Eurostat and Atkinson and Marlier (2010).
Note: Diagram is not to scale.
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 2 | 49
81.4 million people (nearly 17 percent of the
population) were at risk of poverty after social
transfers, 40.4 million were severely materially
deprived, and 40.3 million lived in low work
intensity households (Figure 67).41 Several
million people were at risk of poverty and
social exclusion by multiple criteria, including
almost 7 million people suffering from all three
dimensions of poverty and social exclusion
used for the Europe 2020 targets.42
To gauge progress toward meeting the
objectives of poverty reduction and social
inclusion, EU-wide headline targets were set
for 2020, which countries then translated
into national targets reflecting their starting
points and aspirations.43 Although the
protracted economic crisis has hampered
progress toward meeting the Europe 2020
goals and the recovery process continues
on an uncertain path, the countries remain
committed to meeting their national targets.
Only a few of them, however, have assessed the
realism of their national targets under various
scenarios. Whether the strategy’s targets can be
met by the stated deadline is an open question.
The rest of this Spotlight focuses on this task
for the EU11 countries.
41 Anthony B. Atkinson and Eric Marlier, eds., Income and living
conditions in Europe (Eurostat Statistical Books), Luxembourg, LU:
Publications Office of the European Union, 2010.
42 It is important to note that the indicators of poverty and social
exclusion are not mutually exclusive, and hence, there may be
instances in which some families find themselves at risk of poverty
or social exclusion by multiple indicators.
43 The five EU-wide headline targets set for 2020 include: (1)
Employment: at least 75 percent of the population between the
ages of 20 and 64 should be employed. (2) R&D: a minimum of
3 percent of the EU’s gross domestic product (GDP) should be
invested in research and development (R&D). (3) Climate change
and energy sustainability: the EU’s 20-20-20 climate and energy
targets should be met. (4) Education: the rates of early school
withdrawal should be below 10 percent and at least 40 percent
of 30- to 34-year-olds should have a tertiary education degree. (5)
Combating poverty and social exclusion: at least 20 million fewer
people in the EU should be at risk of poverty and social exclusion.
To analyze the links between employment
and education and between poverty and
social exclusion, a simple partial equilibrium
model is developed, flexible enough to
be implemented in a number of different
settings using widely available household
survey data.44 The simulation analysis is a useful
tool to help policymakers understand how
progress towards poverty reduction and social
exclusion goals is likely to be achieved with the
desired speed under different scenarios. The key
underlying assumption of this model is that
the structural relationships between poverty
and social exclusion outcomes and education
and employment indicators remain constant
over time. The primary data source employed
is EU-SILC household survey data from 2005
through 2009.
Applied to the EU11, the simulation model
indicates that the Europe 2020 national
employment targets are ambitious, given
historical employment patterns in these
countries.45 Especially in light of the slow and
uncertain recovery, labor markets remain weak
and employment rates in 2020 could fall short
of rates targeted by national policy makers.
Under this scenario, the poverty and social
exclusion goals may not be reached in many
EU11 without additional policy measures.
44 The analysis presented in this Spotlight complements and builds
upon existing analytical studies, including those produced by
Anthony B. Atkinson and Eric Marlier (2010); Pascal Wolff
(2010); Ive Marx, Pieter Vandenbroucke, and Gerlinde Verbist
(2010); and the European Commission (2009).
45 The results relate to the at-risk-of-poverty and the anchored poverty
indicators. The material deprivation indicator is not analyzed here.
Numerous limitations of the severe material deprivation indicator
itself have been documented, and it has been singled out as an
area for revision and improvement during the 2015 mid-term
review of the Europe 2020 initiative (Anne-Catherine Guio, David
Gordon and Eric Marlier, 2012, “Measuring Material Deprivation
in the EU: Indicators for the Whole Population and Child-Specific
Indicators,” Eurostat Methodology and Working Papers Series).
EU11 REGULAR ECONOMIC REPORT | june 2013
50 | Spotlight 2
At-risk-of-poverty rate
The EU11 countries have set ambitious
goals with respect to the at-risk-of-poverty
indicator relative to historic trends. The
employment goals set in their National Reform
Programs imply a reduction of the number
of people at risk of poverty by 3.7 million by
2020.
Under the scenario of employment growth at
the pre-crisis (2000–08) rate, the reduction
of people at risk of poverty is projected to
be 3.5 million people, slightly less than the
target. Should the more modest employment
growth patterns seen in each of the EU11
countries during 2000–11 prevail in the run up
to 2020, the number of people at risk of poverty
could fall by about 2.4 million relative to the
population in 2008. In the most pessimistic
scenario, if the crisis period’s (2008–11)
employment patterns remain constant, but
employment rates do not decline below rates in
2008, the number of people at risk of poverty
could decline by only 1.5 million.
In most EU11 countries, the employment
scenario in 2020 plays a big role in
determining the at-risk-of-poverty rates in
the country. Interestingly, the simulations
indicate that employment patterns seen during
the 2000–11 period are unlikely to reduce
the at-risk-of-poverty population in Bulgaria,
the Czech Republic, Estonia, Hungary, and
Slovenia below the rates seen in 2008 (Figure
68). The Czech Republic, Hungary, Poland,
Romania, and Slovenia are expected to achieve
more favorable at-risk-of-poverty rates if they
reach their Europe 2020 national targets for
employment than if they reach the high growth
years’ (2000–08) employment scenario through
to 2020, meaning that they have set ambitious
employment assumptions relative to historical
performance. In Slovakia, Estonia, Lithuania,
Bulgaria, and Latvia, the simulated at-riskof-
poverty rate is more favorable under the
high-growth (2000–08) employment scenario,
which is also an ambitious assumption, than
if their Europe 2020 national employment
targets are reached.
Low work intensity rate
Compared to the 2008 population, the low
work intensity population in the EU11
could fall by as much as 3.0 million people
if the Europe 2020 national targets for
employment and education are reached or
by as little as 277,000 people if the crisis
period’s (2008–11) employment patterns
persist. In other words, employment rates in
the run-up to 2020 matter a great deal when
low work intensity is considered. If the 2000–
Figure 68. Simulated at-risk-of-poverty
rates by employment scenario in EU11
0
30
25
10
5
20
15
CZE SVN SVK HUN POL EST LTU BGR ROU LVA
2008 2020 (assuming national goals are met)
2020 (assuming 2000–11 growth rate) 2020 (assuming 2000–08 growth rate)
Source: World Bank staff calculations using EU-SILC data.
Note: We assume no deterioration in employment and education
indicators by 2020 relative to their rates in 2008.
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 2 | 51
11 employment patterns are assumed to persist
through to 2020, then 1.4 million people are
expected to drop out of the low work intensity
category. If the more optimistic employment
patterns of the high growth period (2000–08)
are assumed, the population living in low work
intensity households could be reduced by
2.7 million people.
In almost all EU11, low work intensity
rates could decrease relative to their rates
in 2008 as a result of achieving the Europe
2020 national employment and education
targets, but historic patterns reveal that the
magnitude of the change could be small in
many countries. Employment patterns seen
during the 2000–11 period are unlikely to
appreciably reduce the low work intensity
population in the Czech Republic, Estonia,
Hungary, Latvia, Lithuania, Romania, Slovakia,
and Slovenia (Figure 69). In parallel, the low
work intensity population could decrease
significantly if other employment scenarios
take hold. For example, in Hungary, low work
intensity rates could decrease from 16.2 to
8.8 percent if the Europe 2020 national targets
for employment and education are achieved.
In Slovenia, low work intensity rates could fall
from 7.6 to 2.3 percent if the Europe 2020
national targets for employment and education
are met, reflecting the ambitious targets set
by the two countries. Bulgaria, a country that
saw one of the fastest increases in employment
rates during the 2000–08 boom years, would
almost eliminate low work intensity if those
employment patterns persisted through
to 2020. Latvia, Lithuania, and Slovakia,
however, show less promising results: Latvia’s
and Lithuania’s low work intensity rates could
increase marginally by 2020 if national targets
for employment and education are achieved,
and Slovakia’s low work intensity rate could
remain constant. This result is not surprising
given that all three countries enjoy very high
employment rates, and hence, achieving the
national targets will not have a significant
impact on low work intensity rates.
Anchored poverty rate46
Anchored poverty in EU11 could fall by
about 9 million people by 2020 relative to
anchored poverty in 2008 if either the 2000–
08 employment patterns hold through to
2020 or if the Europe 2020 national targets
for employment and education are met. If the
2000–11 employment patterns persist through
to 2020, then anchored poverty could fall by
8.5 million people. If employment patterns of
46 Anchored poverty is defined as the percentage of people whose
disposable income is less than the inflation-adjusted 60 percent of
the median income in 2008. Although anchored poverty is not an
explicit Europe 2020 target, it is an important indicator of progress
in improving poor people’s quality of life.
Figure 69. Low work intensity, by
employment scenario, in EU11
0
30
25
10
5
20
15
LVA SVK EST LTU CZE SVN BGR ROU POL HUN
2008 2020 (assuming national goals are met)
2020 (assuming 2000–11 growth rate) 2020 (assuming 2000–08 growth rate)
Source: World Bank staff calculations using EU-SILC data.
Note: We assume no deterioration in employment and education
indicators by 2020 relative to their rates in 2008.
EU11 REGULAR ECONOMIC REPORT | june 2013
52 | Spotlight 2
the crisis period (2008–11) are repeated, then
anchored poverty could still fall by as much as
7.4 million people by 2020.
Across EU11, anchored poverty rates are
more stable across employment scenarios
than other poverty and social exclusion
indicators. The key exceptions are Bulgaria,
Romania, and Slovenia. In Romania and
Slovenia, anchored poverty rates in 2020 are
expected to be considerably lower under the
Europe 2020 national target scenario than
under any other scenario; while in Bulgaria,
anchored poverty rates in 2020 are considerably
lower under the 2000–08 employment growth
rate scenario than under any other employment
scenario (Figure 70). In the Czech Republic,
Hungary, Romania, and Slovenia, achieving
the Europe 2020 national targets is expected
to lead to lower anchored poverty rates than
in any of the other employment scenarios. In
Bulgaria, Estonia, Latvia, Lithuania, Poland,
and Slovakia, the lowest anchored poverty rates
are achieved when the 2000–08 employment
growth rates are applied.
In most EU11 countries, anchored poverty
rates can be expected to decrease significantly
if countries achieve their national targets
for employment and education. Poland can
expect a sharp reduction in anchored poverty,
from 16.9 to 3.5 percent; Bulgaria from 20.6 to
9.0 percent; Latvia from 25.8 to 12.5 percent;
and Romania from 23.4 to 13.6 percent. The
smallest reductions are expected in the countries
with the lowest initial anchored poverty rates,
namely, the Czech Republic, Slovakia, and
Slovenia.
In sum, increases in the employment rate
and educational levels have the potential
to contribute significantly to achieving the
Europe 2020 targets for reducing poverty
and social exclusion. However, the results
from the micro-simulation analysis indicates
that untargeted employment and education
measures alone will not be sufficient to meet
the Europe 2020 poverty and social exclusion
targets, even under optimistic employment
growth scenarios. Additional measures
specifically targeted to raising the incomes
of those at risk of poverty and increasing
employment of those living in low-work
intensity households will be needed.
Figure 70. Anchored poverty rates are
expected to decrease significantly in
countries achieving their Europe 2020
national targets
0
30
25
10
5
20
15
CZE SVN SVK HUN POL EST LTU BGR ROU LVA
2008 2020 (assuming national goals are met)
2020 (assuming 2000–11 growth rate) 2020 (assuming 2000–08 growth rate)
Source: World Bank staff calculations using EU-SILC data.
Note: We assume no deterioration in employment and education
indicators by 2020 relative to their rates in 2008.
MACROECONOMIC REPORT: economic RECOVERY on hold
Spotlight 2 | 53

Special Topic:
Determinants of Job Creation in EU11:
Evidence from Firm Level Data
Abstract: This paper builds on the analysis of job creation developed in World Bank (2013) to
provide an empirical investigation of the industry- and firm-specific determinants of the job creation
process in the EU11 economies during 2002–2009. It relies on the Amadeus dataset of firms. The
main results indicate that during the years prior to the global financial crisis, traditional industries
were crucial for the net creation of jobs in EU11. However, traditional industries were the ones most
severely affected by the financial crisis. In contrast, services firms were less vulnerable to the economic
downturn. At the firm level, small and young firms registered the highest employment growth
rates. The empirical results also indicate that more productive firms tended to be less vulnerable to
economic downturns. Moreover, the results demonstrate that the perceived quality of the business
climate by the EU11 enterprises correlated with not only the firms’ employment growth, but also
with their productivity. In the post-crisis period, poor business restrictions were also negatively
associated with the creation of jobs. All these findings also hold for the group of high-growth firms
that disproportionately accounted for the creation of new jobs in the EU11 economies.
* The team is grateful for comments from Omar Arias, Jesus Crespo Cuaresma, Xavier Devictor,
Doerte Doemeland, Hongjoo J. Hahm, Satu Kahkonen, Ismail Radwan, Carolina Sanchez-Paramo,
Erwin H. R. Tiongson, and Charles Udomsaph.
EU11 REGULAR ECONOMIC REPORT | june 2013
56 | Special Topic
The EU11 countries made notable gains
in economic and productivity growth in
the years prior to the global financial crisis
(2000–2007), with some of this growth
translating into job creation. However, the
increase in jobs that took place was reversed
by the crisis. The question which firms created
the most jobs prior to the crisis and how these
jobs were affected by the crisis is highly topical
among economists and policy makers, trying to
mitigate the effect of the crisis.47
In the EU11 countries, the structural
change in economic activities over the
last two decades involved two different
developments. First, after the break-up of the
former communist bloc, economic activities
were re-organized into market-based economic
systems. Second, as in other middle income
economies, economic activity shifted away
from agriculture and manufacturing to services,
where the average firm size was relatively small
but the number of firms large (see, e.g., Pilat
et al. 2009). These structural changes had an
impact on how, where and what type of jobs
were created.
After the vast majority of the EU11 countries
successfully reorganized their centrally
planned economies, they experienced
varying degrees of success in creating
productive jobs. Different levels of market
regulations and entry barriers were crucial
47 The issue of job creation and the broader constraints that affect
the Europe and Central Asia region are analyzed in a forthcoming
World Bank (2013) report on jobs. The analysis presented here
relates solely to the EU11 countries.
determinates explaining the differences in the
economic structures across the EU11 countries.
In general, countries that most successfully
liberalized their economies, maintained
macroeconomic stability, and improved the
quality of their business environment and
institutions were able to create the conditions
for firms to flourish and to attract the largest
amounts of FDI. This, in turn, contributed
not only to the structural changes in these
economies, but also to the job-creation process
(see, e.g., Bevan and Estrin 2004, Pournarakis
and Varsakelis 2004, Fabry and Zeghni 2006
Harding and Javorcik 2011, Jimenez et al.
2011, Crespo Cuaresma et al. 2012 and Tintin
2013).
The economic downturn induced by the
financial crisis may have triggered yet
another structural change in EU11 and,
therefore, a clear understanding of the key
industry- and firm-specific determinants of
job creation is important for both policy
makers and entrepreneurs. A focus on the
institutional framework that supports the
creation of new jobs should be especially
informative for informing tailored and effective
policy measures to counterbalance the process
of job destruction.
Accordingly, this paper builds on the work of
the forthcoming World Bank report on jobs
and zooms into the patterns of job creation
across surviving firms in the EU11 economies.
The analysis utilizes the Amadeus database
(provided by Bureau van Dijk) which contains
comparable and comprehensive balance sheet
Introduction
MACROECONOMIC REPORT: economic RECOVERY on hold
Special Topic | 57
and profit and loss account data for the EU11
countries for a time period spanning from 2002
to 2009.48 This data set is augmented with data
from the World Bank’s Business Environment
and Enterprise Performance Survey (BEEPS)
which collects information on the business
environment in which the firms operate. In
the EU11 economies, these surveys have been
conducted in the years 2002, 2005, 2007 and
2009, allowing the analysis of dynamics of job
creation at the firm level in the boom years prior
to the global financial crisis as well as during
the “bust”. The next section presents stylized
facts on job creation in the EU11 economies,
48 A detailed data description for the Amadeus database is provided
in Udomsaph (2013). One should note the large variation in the
coverage and representativeness across the EU11 countries in the
Amadeus data base, and hence the quality of data across the EU11
countries. The results presented here pertain solely to the surveyed
firms in the data base.
while Section 3 offers a structural analysis
of job creation patterns across and withinindustries.
The comparison of employment
growth dynamics across industries allows for
examining whether the EU11 economies
have been successful in shifting employment
to more productive sectors, while the withinindustry
analysis provides evidence on which
firms have been most crucial for overall job
creation. Section 4 re-examines this latter issue
by investigating the key-industry and firmspecific
characteristics of high-growth firms.
Section 5 offers policy conclusions.
Some Stylized Facts
Many jobs created in EU11 during 2002–
2008 were lost during the 2008 global
financial crisis. Among the surveyed firms, the
number of employees in firms that were active
in 2002 increased by approximately 76 percent
(Figure 71). For all countries the minimum
increase in the number of workers amounted to
approximately 50 percent. Lithuania, Poland,
Bulgaria, Slovenia and Slovakia were able to
more than double the number of employed
workers. However, the global financial crisis
induced an overall job loss in 2009, nearly
halving the cumulative gains of the previous
period (2002–2008) among the surveyed firms.
In the EU11 countries, half the jobs created
during 2002–2008 were lost in 2009.
While jobs were lost throughout the EU11
region, the magnitude of job destruction
varied among the countries (Figure 71). In
Lithuania and Poland, the number of workers
employed in firms that already existed in
2002 was about threefold in 2008. The crisis,
however, reversed these dynamics in 2009 to
174 percent in Lithuania and 131 in Poland
of the respective 2002 level. In Slovakia and
Latvia, despite notable gains in employment
prior to 2008, the overall number of employees
in established firms was at or below the 2002
level among the surveyed firms.
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58 | Special Topic
The average job creation rates among
surviving firms also substantially differed
across industries (Figure 72).49 Prior to the
financial crisis, the EU11 average employment
growth among the surveyed firms was smallest
in agricultural and fishing and largest in
construction and in the transport, storage
and communications industries. Interestingly,
in 2009 only two sectors registered positive
employment growth rates –agricultural and
fishing and other services industries. The average
job destruction rate in 2009 amounted to
4.4 percent in the construction industry alone.
49 The industry categorization is based on the NACE Rev. 2
classification. Accordingly, the farming and fishing industry is
based on sector A while mining and utilities aggregate the sectors
B, D and E. Construction and manufacturing firms operate in the
sectors F and C, respectively. The production sector, including
wholesale, retail trade, restaurants and hotels, is made of sectors
G and I, while transport, storage and communications includes
firms from the sectors H and J. The remaining sectors from K to
U are subsumed in a sector labeled other, which primarily includes
services firms operating in financial and real estate industries,
professional and public services. Further details on the NACE Rev
2 classification can be obtained from Eurostat (2008).
While the manufacturing sector experienced
decent job creation rates from 2002 to 2008,
it was most severely affected by the 2008 global
financial crisis, with an average job destruction
rate of around 6 percent in 2009.
One potential reason for the differing job
creation performance among the EU11
economies prior and during the global
financial crises relates to the quality of the
business environment in which firms operate.
Among the surveyed firms, Romania was
perceived to be the most unfriendly to business
country (Figure 73). Romania, the Czech
Republic, and Poland were all perceived to have
had institutional obstacles to doing business
and experienced below average employment
rates in the observed period. Latvia, on the
other side, was perceived as a relatively business
friendly economy, that showed the highest
average employment growth rates among
the EU11 economies. Interestingly, however,
Figure 71. Job creation from 2002 to
2008 and Job destruction in 2009
Figure 72. Average employment growth
rate by sector and year, 2002–2009
Percent Percent
0
400
300
200
LT PL BG SI SK RO CZ LV HR EE EU11
2002
level
-7.5
13.0
12.5
10.0
-2.5
-5.0
7.5
5.0
2.5
0
2002
2004
2006
2008
2002
2004
2006
2008
2002
2004
2006
2008
2002
2004
2006
2008
2002
2004
2006
2008
2002
2004
2006
2008
2002
2004
2006
2008
Employment in 2008 relative to 2002 Agriculture and fishing Mining and utilities Construction
Relative employment in 2009 relative to 2002 Manufacturing Wholesale, retail trade, restaurants and hotels
Transport, storage, communications Other
Source: World Bank Staff calculations, based on Bureau van Dijk data.
Note: Covers all EU11 countries, except for Hungary, where the number of
active firms in 2002 is too low to make reasonable comparisons. The gray
100 percent-line indicates the 2002 employment benchmark.
Source: World Bank Staff calculations, based on Bureau van Dijk data.
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jobs in Latvia were strongly affected by the
financial crisis, while employment in Romania
remained relatively stable. Yet, Estonia—the
most business friendly country between 2002
and 2009—saw its employment growth lag
behind the other EU11 economies. Some
countries such as Lithuania and Bulgaria were
perceived to have relatively unfriendly business
environment, but the average job creation rates
of firms located in these economies were above
the EU11 average.
Overall, looking at the simple relationship
between the average employment growth
and the perceived barriers to business at
the country level does not reveal strong
regularities. The question of whether firm-level
employment performance differs across EU11
countries with different quality of the business
institutions becomes warranted. However,
the impact on employment growth of the
key elements underpinning the environment
for doing business, while important, may
not be sufficient for sustained job creation.
With this hypothesis in hand, the rest of the
paper investigates econometrically the effect of
industry- and firm-specific determinants of the
job creation process in the EU11 countries.
Figure 73. Employment growth and
perceived business barriers, 2002–2009
Percent
0
2.0
1.5
1.0
0.5
RO CZ PL LT BG SK HR HU LV SI EE
Average employment growth Average business barriers (scale:0-4)
Source: World Bank Staff calculations, based on Bureau van Dijk data and
Business Environment and Enterprise Performance Survey (BEEPS).
Note: Employment growth is measured in percent. The average business
barriers index is based on questions regarding perceived barriers for doing
business in the categories institutional regulations, access to finance,
crime, corruption, taxation, and labor regulations. The answers to each
question range from 0 (no obstacle) to 4 (very severe obstacle).
Job Creation in EU11 prior and after the 2008 crisis
Firm and industry characteristics as well
as the perceived business environment
features are likely to account for some of the
differences in the job creation rates across
the EU11 region. A structural analysis of the
relationship between the business environment
and job creation is warranted. A sample of
180,986 firms of different size, vintage, sector
across the EU11 countries is used. The sample
captures only surviving firms, making it
impossible to examine exit dynamics. Hence,
the results presented here are attributable solely
to the sample of surviving firms in EU11. The
data is made available by Bureau van Dijk
and have been standardized for the analysis
forthcoming in World Bank (2013). To unveil
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60 | Special Topic
the firm and industry level characteristics as
well as the business environment determinants
of employment creation in the EU11, empirical
firm growth equations are estimated for the
annual average employment growth rate for
surviving firms (Box 1). The main advantage of
the regression framework presented in Box 1 is
that it allows controlling for differences in firmcharacteristics
such as size when examining
the impacts of industry and institutional
characteristics on job creation. Given the large
effect of the global financial crisis on countries,
the period under investigation is divided into
two: the “boom” years capturing the strong
economic growth and job creation in the EU11
countries between 2002 and 2008; and a “bust”
year representing 2009, the first year following
the global financial crisis.
Box 6. Estimating Empirical Employment Growth Equations at the Firm Level
The regression analysis is based on empirical firm growth equations (for surviving firms)
in the spirit of the Gibrat’s law (see, e.g., Hart 2000 and Coad 2009, for surveys). A crosssectional
Gibrat-law type of regression can be written as (see, e.g., Geroski 2005 and Oberhofer
and Pfaffermayr 2013):
gij= a+ pij S0ij+ xij ?+ zj d + eij, where
pij = ß0 + ß1 Aij
This equation states that the average employment growth rate gij of firm i in industry j is a
function of (log) initial firm size S0i 50 (i.e, the number of employees in the first observed period)
and other firm- and industry-specific control variables collected in xij and zj, respectively. ?
and d are column vectors to be estimated. pij captures the (conditional) speed of convergence/
divergence and is modeled to depend on firm age Aij.
One standard result in the empirical firm growth literature states that the observed speed of
convergence declines with age. This finding would be confirmed in this application if the
estimated parameters ß0 < 0 and ß1 > 0. Economically, such a result would imply that younger
and smaller firms grow faster, while old small firms would not exhibit increased employment
growth dynamics.51
The estimation covers the time period 2002 to 2008. xij and zj contain firm- and industry-specific
variables that may affect job creation in firms located in the EU11 economies. Among the latter,
industry dummy variables (based on the classification mentioned in footnote 4) and business
environment indicators are included. Based on the BEEPS data, an indicator that measures the
average institutional barriers within 2-digit industries and countries is constructed. The overall
industry-country specific measure for institutional barriers is based on several questions on
50 The firm size is a particularly interesting (and debated) characteristic of job creation in the literature. Davidsson et al. 1998 and Neumark et
al. 2011 demonstrate that small and medium-sized enterprises (SMEs) are the most important contributors to net job creation. By contrast,
Haltiwanger et al. (2013) highlight the important role of business startups and young firms for job creation in the USA. Huber et al. (2012)
document that in Austria large firms irrespective of their firm age positively contribute to (net) job creation, while in small firms more jobs
are destroyed than created.
51 However, as stressed by Haltiwanger et al (2013), disentangling the role of firm size vis-à-vis age requires more comprehensive data than
available for this paper. In particular, census data would allow for the proper estimation of employment shares and hazard rates of nonsurviving
firms.
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Most countries in the EU11 region enjoyed
both positive economic and employment
growth in the pre-2008 crisis period. 53 The
“boom” years in EU11 were characterized by
the transformation of domestic productive
structures, accompanied by deepening of
international trade and financial development.54
To a large extent, economic growth did
translate into job creation in EU11 at the
macroeconomic level.55
52 The presented results here are based on the above described
econometric model which allows to (at least) identify partial
correlations between employment growth and the included
covariates. This implies that the findings might not necessarily
reflect a causal relationship, but are suggestive for the modeled
relationships.
53 The link between economic and employment growth for the preand
post-crisis period is explored in detail in World Bank (2013,
forthcoming). One should note, however, that employment
creation rates vary significantly across countries—even countries
with similar growth experiences.
54 See Raiser and Gill (2012).
55 See World Bank (2013, forthcoming).
At the firm level, traditional industries were
the key creators of new jobs prior to the
crisis. Employment growth was greatest in the
construction and manufacturing industries
and lowest in the service industries among
the surveyed firms. When controlling for
differences in firm-characteristics, employment
growth was lowest in the group of other
industries, which mainly consisted of services
firms.56 Across different specifications of the
firm growth model, an average construction
firm was estimated to grow by 3.8 (column
1 of Table 2) to 6.1 (column 6) percentage
points more annually in comparison to a firm
of the same size, age, and productivity in the
“other sector.” The comparable numbers for
manufacturing firms varied between 3.2 and
4.1 percentage points. In half of specifications,
56 The reported industry effects from the regression analysis have
to be interpreted relative to the omitted group of firms, which
consists of service firms.
perceived obstacles for conducting business that are included in the BEEPS data. In particular,
the constructed indicator comprises information on the degree of institutional regulations,
access to finance, crime, corruption, taxation and labor regulations. The country averages of this
measure are reported as red bars in Figure 2. In the second step, the job creation effects of these
specific business barriers are separately investigated.
With regard to additional firm-specific controls, (log) firm age and (log) firm’s total factor
productivity (TFP) are included. TFP is estimated via the approach suggested by Levinsohn
and Petrin (2003), which uses a firm’s demand for intermediate inputs (such as materials) in
order to overcome the problem of simultaneity when estimating firm level production functions.
For more details on the calculation of TFP and corresponding data restrictions see footnote 60.
The interaction effect of firm size with firm age is already incorporated in p.
For the crisis year 2009, the model is re-estimated. Job creation is defined as the employment
growth rate from 2008 to 2009. The firm- and industry-specific characteristics are, consequently,
taken from 2008. The only exception refers to the BEEPS data which are not available for 2008.
The institutional barriers variable, therefore, is based on the within-industry survey averages
from 2009. Tables 2 and 3 report the corresponding estimation results for both the pre-crisis
period and the year 2009.
Employment Growth at the Firm Level: The Boom Years (2002–2008)52
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62 | Special Topic
agricultural and fishing firms were estimated
to grow by approximately 3 percentage points
faster than other service firms.
In contrast, while firms operating in service
were important in terms of overall value
added, they did not contribute much to job
creation.57 The number of firms that operate in
the EU11 services sector was very large (around
72 percent of all sampled firms were service
providers), but these firms were very small and
not willing to grow in terms of employment.
In more traditional sectors, the average firm
size was larger and, therefore, they strongly
contributed to overall job creation in 2002–
2008. Controlling for differences in firmspecific
productivity, service industries were
estimated to create jobs at a slower pace than
firms in the rest of the economy.
The smallest surviving firms exhibited the
highest rates of job creation prior to the
crisis. In line with typical estimation results
from empirical firm growth equations á la
Gibrat’s law (see, e.g., Coad 2009), the average
employment growth rate was largest in the
initially smallest firms. More precisely, the
empirical results showed that a one percent
increase in the initial firm size (i.e., the firm
size at the first observed year) correlates with
a decrease in average annual job creation rate
of 5.3 to 7.8 percentage points. Hence, in the
EU11 economies, small surviving firms tended
to rapidly adjust their size to market conditions.
57 It is worth noting that this finding applies to strictly to the
surveyed surviving firms. If entry and exit dynamics systematically
differ between traditional industries and services providers, this
result might be reversed. For this reason, it would be crucial to
reexamine the job creation analysis using census data that allow for
accounting for firm entry and exit. The data used in the analysis
here do not allow for accounting for firm exit.
Start-ups and (very) young surviving firms
grew at the fastest pace. In line with the
literature (see, e.g., Haltiwanger et al. 2013),
the job creation in surviving start-ups and
young firms outperformed the employment
growth rates of older firms in the sampled EU11
firms.58 In quantitative terms, one percentage
point younger firms exhibited employment
growth rates that are 4.6 to 7.7 percentage
points higher than the rest of the surveyed
firms. Here, it is important to note that, this
finding holds true for both—initially small and
large firms.
The employment growth performance of
small-old surviving firms was substantially
worse. The positive parameter estimates for
the interaction effect of firm size and firm
age indicates that the speed of adjustment
was slower for small-old firms. Small firms
contributed to job creation when they were
young. In later periods, the number of their
employees stabilized, indicating that they were
less willing to increase their scale of production
in terms of hiring new workers.
While the EU11 business and regulatory
environment improved significantly prior to
the crisis, an inhibitive business environment
affected negatively the efficient allocation
of labor across industries. In industries with
heavy business restrictions and regulations,
employment growth prior to the crisis was lower
(Table 3). In quantitative terms, a one unit
decrease in the overall level of business barriers,
or put differently, a unit improvement in firms’
58 However, Haltiwanger et al. 2013 and Huber et al. 2012 also
document that young firms exhibit an increased exit hazard.
Accordingly, an overall assessment of young firms’ contribution to
overall job creation would require census data that also contain
information on market entry and exit. The data used in the analysis
here do not account for firm exit.
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perception of their business environment (for
example from major to moderate obstacle to
business) was associated positively with job
creation by approximately 1.1 percentage
points (Table 3, see column 2). Had the EU11
completely removed all perceived business
restrictions, these surveyed firms would have
increased their average annual employment
growth by as much as 4.4 percentage points.
Removing obstacles to access to financial
resources, simplifying tax systems and
fighting corruption positively correlates
with job creation by already established
firms. The econometric analysis reveals that, a
one unit decrease in the perceived difficulties to
obtain sufficient financial resources correlated
positively (by 2.5 percentage points, on average)
with job creation among the EU11 surveyed
firms (Table 3, see column 4).59 In a similar
vein, restrictive business taxation rules correlate
with substantial job creation. In industries
with complex tax systems, employment growth
rates were reduced by 1.9 percentage points
(Table 3, column 7). Corruption reduced job
creation among the surveyed firms (Table 3, see
column 6). Interestingly, however, institutional
regulations (such as customs and trade
regulations and business licensing proceedings)
were estimated to have increased firm’s average
job creation by 1.8 percentage points (Table 3,
see column 3). These results were probably be
driven by surveyed firms observing other firms
enjoying preferences and protection on the local
market from competition. While such practices
seemed to have positively affected job creation
in the short run, they have likely deferred the
59 This finding is in line with the previous work on the impact of
institutional barriers on firm growth which identified financial
constraints as the most crucial obstacle to growth (see, e.g.,
Ayyagari et al. 2008).
necessary structural changes for the long-run
competitiveness of the EU11 economies.
Productive firms contributed positively to
overall job creation.60 In quantitative terms, a
one percent increase in firm-specific total factor
productivity (TFP) increases firm’s average
employment growth rate by approximately
1.5 percentage points (Table 3).61 This finding is
robust across all different specifications, driven
by differences in the initial level of productivity.
A simple explanation might be that more
productive firms competed more successfully
than less productive firms on the domestic and
the world markets enabling them to expand
their level of production. This expansion may
have also increased the firms’ labor demand
and, therefore, accelerated job creation rates.
Prior to the crisis, labor resources seemed to
have efficiently reached firms with growing
productivity. The positive effect of productivity
on employment growth also indicates that
within-industries efficient firms were able to
grow more rapidly than the rest. Moreover,
the quantitative dimension of the effect points
to the usefulness of creating an economic
environment that stimulates productivity
growth. A firm that, for example, successfully
increased its level of total factor productivity
by 10 percentage points (through innovation,
learning-by-doing, technology adoption)
expanded its employment by 15 percentage
points between 2002 and 2008 (Table 3).
60 The results refereeing to productivity (TFP) are based on eight
out of the EU11 economies. TFP is calculated by applying the
Levinsohn and Petrin (2003) approach to data on value added (or
sales), inputs (i.e., labor and capital) and intermediate inputs (such
as material costs). Unfortunately, the Amadeus data at hand do not
contain such information for Croatia, Estonia and Lithuania.
61 In fact, replacing average firm-specific TFP by its initial value, the
corresponding marginal effect amounts to 1.4 percentage points.
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64 | Special Topic
In addition to directly reducing job growth
rate, an unfriendly business environment was
negatively associated with firm productivity.
In the period 2002–2008, the overall level of
within-industry productivity was negatively
correlated with perceived business restrictions
and regulations. The partial correlation from
a bivariate regression of TFP on the overall
measure of institutional barriers amounts to
-0.2, implying that each unit of reduction in
institutional barriers was associated with a
0.2 percentage points increase in productivity
levels. In terms of growth rates, the relationship
between the employment growth and the TFP
growth among the sampled firms was positive
prior to 2008, but statistically insignificant
(Figure 74).62 Nonetheless, the positive
association between the job growth rate and
the TFP growth rate implied that the EU11
exploited cost advantages and increased the
number of jobs without a substantial change in
overall productivity.
Employment growth during the
financial crisis
The global financial crisis affected
asymmetrically firms operating in different
industries, with the construction and
62 This result is in contrast to recent empirical studies that find
a negative relationship between TFP growth and employment
growth over time (see, e.g., De Michelis et al. 2013). There are
several reasons for this finding. First, results presented here are
based on firm-level econometric TFP estimates, whereas industry
and country studies typically rely on TFP measures based on
growth accounting (see, e.g., De Michelis et al. 2013). Accordingly,
one avenue for future research could include a systematic
comparison of micro- and macro-based TFP measures. Second, in
contrast to De Michelis et al. (2013), the sample in this analysis
covers only less developed countries. When comparing Figure 4
below with Figure 2 in De Michelis et al. (2013), it turns out that
(with the exception of New Zealand) all countries exhibit positive
TFP growth. By contrast, the majority of industries in the current
analysis are characterized by a decrease in TFP over time.
manufacturing industries showing the largest
job losses. The global economic crisis put a
transitory stop to the 2000s’ growth spurt in
the EU11 countries. Financial flows dried up,
commodity prices collapsed, external demand
plummeted, and unemployment rates rose.63
The effects of the crisis were felt not only at the
macroeconomic, but also at the firm level. When
controlling for other factors and in comparison
to firms operating in other industries, surveyed
construction and manufacturing firms reduced
their employment by 1.8 to 7.4 percentage
points more than the rest of the firms (see
Table 4). Accordingly, these two sectors drove
the large drop in employment in 2008–2009
(Figure 72).
In contrast, surveyed firms operating in the
services sector were least affected by the
global recession. Overall, surveyed firms that
63 Raiser and Gill (2012).
Figure 74. Average TFP growth versus
average employment growth, 2002–2008
TFP growth
-0.1
0.1
.05
0
-.05
-0.2 -0.1 0 0.1 0.2
Employment growth
TFP growth Fitted values
Source: Amedeus data base; own calculations.
Note: Average TFP growth and employment growth plotted along with the
fitted values from a bivariate regression for the seven industries and eight
countries where TFP measures were constructed for the period 2002-
2008. Approximately size of the sample 71,000 firms operating in eight
of the EU11 countries.
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operated in agriculture, fishing, mining and
utilities industries tended to be least affected
by the financial crisis. When controlling for
productivity, interestingly, farms became
severely affected by the financial crisis. A similar
result can be inferred for the transport and
storage industries as well as communications
providers.
During the global financial crisis, the firms’
size and vintage were negatively correlated
with job creation, while the more productive
firms exhibited higher employment growth
rates. More precisely, (one percent) larger and
older firms showed job creation rates that were
approximately 7.4 and 5.5 percentage points
lower than for the rest of the surveyed firms.
A one percent increase in TFP, by contrast,
enabled on average about 4 percentage
points faster employment growth. Moreover,
productivity differentials were more crucial for
job creation among the surveyed firms during
the economic crisis than during the “boom”
years.
Institutional barriers and regulations
negatively correlate with job creation in the
EU11 economies during the financial crisis.
In 2009, industries with more severe barriers
in terms of taxation, labor restrictions, financial
resources as well as institutional regulations
exhibited lower job creation rates. A one-unit
increase in institutional regulations decreased
employment growth by approximately
1.8 percentage points. This result highlights,
that (at least) during economic downturns
institutional regulations can act as severe
barriers to job creation. Moreover, the overall
level of corruption also decreased employment
growth by approximately 2.8 percentage
points, on average. These findings suggest that
EU11 economies, characterized by unfriendly
business environments, were highly vulnerable
to job losses in times of acute macroeconomic
shocks. Finally, firms operating in sectors with
perceived stringent labor regulations exhibited
negative employment growth rates (Figure 72,
column 8). Accordingly, in industries with a
one-unit larger labor regulation, job creation
was reduced by 2.7 percentage points.
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66 | Special Topic
As highlighted in World Bank (2013), the
fastest growing firms (the Gazelles) were
rare, but essential for providing new jobs
in the EU11 economies.64, 65, 66 The share of
high-growth firms relative to all surveyed firms
with positive employment growth was around
3.5 percent in the years prior to the global
financial crisis. It was by far largest in Bulgaria,
where approximately 7.5 percent of all net
job creating firms were high-growth ones.
High-growth firms were most important in
Romania and Bulgaria with corresponding net
job creation shares exceeding 12.5 percent. In
addition to Hungary and Slovakia, the role of
high-growth firms for overall job creation was
negligible in the Czech Republic and Poland.
To unveil the probability of being a Gazelle,
simple probit regressions are estimated. The
methodology is described in Box 7.
64 The analysis presented here partially relies on the OECD (2009)
definition of the so-called Gazelles. These are firms that are: (i)
younger than 5 years; (ii) initially employ more than 10 workers;
and (iii) experienced annual employment growth rates of (at least)
20 percent during 3 consecutive years. Given the focus of the
current analysis on the distribution of high growth firms across
different firm size and firm age cohorts, only the third part of
the definition is applied (i.e. related to the 20 percent tri-annual
growth performance). The measure of fast-growing firms, thus, is
a dummy variable that takes on the value of 1 if a firm exhibits
employment growth rates above 20 percent in each year from 2006
to 2008.
65 World Bank (2013) reveals evidence that net job creation in the
region has typically been led by a handful of firms, many of them
young firms. On average, in the Europe and Central Asia region,
about 10–15 percent of all firms accounted for over two-thirds of
net job creation in the years leading to the crisis. This pattern holds
regardless of whether the entire enterprise sector is experiencing net
job creation or net job destruction.
66 This finding is consistent with recent literature on the role of high
growth firms for job creation. Henrikson and Johansson (2010),
for example, provide a meta-study on the impact of Gazelles for
overall job creation and confirm the few that this group of firms
accounts for the vast majority of newly created firms.
The high-growth firms in the EU11 economies
Figure 75. Share and net job creation of
high-growth firms, 2006–2008
Percent
0
15
12.5
5
2.5
10
7.5
BG CZ EE HR HU LT LV PL RO SI SK
Share of high-growth firms relative to all net job creatiors
Share of all net jobs created relative to all net job creatiors
Source: Amedeus data base; own calculations.
Note: Shares of high-growth firms and their contribution to net job
creation from 2006 to 2008 reported. Both shares are calculated relative
to all net-job creating firms. Correspondingly, all firms that examine a
negative average employment growth rate from 2006 to 2008 are not
included. No firms from Hungary and Slovakia fulfill the criterion to be
considered as high-growth firms.
Gazelles prior to the crisis68
The probability to be a high-growth firm was
largest in the construction, manufacturing,
transport and communications industries
(Table 5). In comparison to the other services
sector, construction firms were between 2 and
8.2 percentage points more likely to grow with
more than 20 percent annually in each year
from 2006 to 2008. Moreover, and again in
comparison to the other services industries
sector, agricultural and fishing industries as well
68 It is worth noting again that the results presented in this section
might reflect correlations rather than causal effects.
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as firms operating in the wholesale trade and
retailing or providers of restaurant and hotel
services were also around 2 percentage points
more likely to be high-growth firms.
Prior to the crisis, smaller and younger
firms were more likely to be high-growth
firms. Among the surveyed firms, an increase
in firm size or firm age by 1 percent affected
negatively the average probability to grow by
more than 20 percent annually from 2006 to
2008 by 0.1 to 0.5 and 0.8 to 1.4 percentage
points, respectively (Table 5). The latter result
highlights the documented crucial role of
young firms for the overall job creation prior
to the crisis.
Institutional barriers and regulations
correlated with a lower probability of being
a high-growth firm prior to the crisis. A
one-unit increase in the perceived overall
business friendliness by the surveyed EU11
firms increased the probability for high-growth
by 2.4 percentage points. In this regard,
Box 7. A probability model for high-growth firms
For the econometric analysis of firm- and industry-specific determinants of a firm’s highgrowth
probability, a simple probit model is employed. Thereby, drawing on the analysis
from Section 3, the probability to be a high-growth firm is modeled as a function of the same
industry- and firm-specific characteristics. The only exception is that, in this exercise, an
interaction effect of firm size with firm age is not included.67 Formally, the model is given by:
Pr(HGij=1¦a, xij , zj) = F(a+xijß+ zj?), where
HGij= 1,if a firm i in industry j is a high-growth firm and 0, else.
xij includes all firm-specific characteristics; zj comprises industry-level information; ß are ? are row
vectors of parameters that are to be estimated; F denotes the cumulative distribution function
(CDF) of the normal distribution which allows to apply maximum-likelihood estimation of the
resulting model. This model is non-linar in the covariates and, therefore, the average marginal
effects are calculated as suggested by Bartus (2005).
The probability to be a high-growth firm is captured by the dummy variable HGij, which takes
on the value of 1 if firm growth exceeds 20 percent in each and every year from 2006 to 2008.
The covariates of interest (collected in xij and zj) include the initial firm size, firm age, TFP,
business barriers and the industry dummy variables. They are all measured in 2005. Given the
lack of observations of high-growth firms in Hungary and Slovakia in the EU11 sample, the
specification for the boom years from 2006 to 2008 does not include country-fixed effects.
The model is re-run to examine a firm’s probability of experiencing high-growth after the crisis,
using the same model specifications and covariates. Therefore, the probability to grow with
more than 20 percent in 2009 is explained by the same covariates as in the pre-crisis period, but
measured in 2008. The perceived institutional barriers for doing business are taken from the
2009 survey. This specification controls for country-fixed effects.
67 Given the non-linear functional form of the probit model, the marginal firm size and firm age effects are already firm-specific making an
interaction effect unnecessary.
EU11 REGULAR ECONOMIC REPORT | june 2013
68 | Special Topic
regulations related to the labor markets and
financial restrictions correlated most strongly
with the occurrence of being a high-growth
firm. In a similar vein, the tax system, crime
and institutional regulations, also seemed to be
crucial predictors of high-growth firms.69
Productivity among the surveyed EU11 firms
was associated with a higher probability
of being a high-growth firm prior to the
crisis. More precisely, an increase in a firm’s
TFP by 10 percent was associated with a
higher probability to be a Gazelle by about
1 to 2 percentage points. While productivity
remained one critical predictor of job creation
among the surveyed firms, in quantitative
terms, the firm size and the age were better
predictors. Nevertheless, engaging in any
productivity enhancing activities might still
have resulted in a higher likelihood to become
a high-growth firm. In line with the results
on firm growth, more productive firms grew
faster and, consequently, were more often highgrowth
firms.
High-growth firms in the post crisis
period
In the post-crisis period, firm-specific
determinants for high-growth firms were of
69 Interestingly, in industries where corruption was identified as major
obstacle for doing business the probability to observe high-growth
firms was slightly increased (Table 3, column 6). There might be at
least two potential reasons for this positive effect. First, corruption
might allow a few very influential firms to attract an overproportional
share of business contracts allowing them to grow
at a very fast pace. Second, since our measure reflects perceived
corruption it might be the case that firms which, for example, did
not succeed in public procurement procedures might attribute this
to corruption in their sector regardless of where this was the case
or not. This finding, however, was mainly driven by Romania and
Bulgaria. In fact, when excluding these two countries the average
marginal effect of corruption turns out to be significantly negative.
crucial importance. In 2009, firm size, age
and productivity were important restrictions
to become a high growth firms. A one percent
increase in size and age reduced a firm’s
probability of growing more than 20 percent
by approximately 2.3 to 4.3 and 2.9 to
3.6 percentage points, on average. A 10 percent
increase in TFP, by contrast, increased the
probability to be a Gazelle by 3 percentage
points. These findings, once more, highlight
the importance of small, young, productive
firms for the creation of new jobs in the EU 11
economies.
Surveyed firms in manufacturing were most
severely affected by the economic downturn
and were the least likely to be high-growth
firms in the years after the global financial
crisis (Table 6). In contrast, the probability of
becoming a Gazelle among the surveyed firms
in farming and fishing was not significantly
affected by the economic crisis. Fast-growing
firms in these sectors, however, comprised a very
small portion of the high-growth firms in the
EU11, given the small size of the agricultural
and fishing industries. Firms operating in the
construction sector also exhibited a relatively
high probability of being high-growing firms.
Coupled with the firm-growth results from
above (i.e. that on average, firms in this sector
performed relatively poor during the financial
crisis), the regression results suggest that only
the top-performing firms in the construction
sector were able to grow very fast. One driving
force for this result might be an increase in
public demand for construction activities
induced by governmental investment and EUsupported
programs that aimed to mitigate the
negative employment effects of the financial
crisis.
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After 2008, barriers for doing business
depressed the likelihood that a firm
would grow fast. A one unit decrease in the
overall institutional barriers perceived by the
surveyed EU11 firms was associated with a
0.5 percentage point increase in the probability
to be a high-growth firm (Table 6, column
2). Similar qualitative and quantitative effects
were obtained for institutional regulations,
access to finance, crime, corruption and labor
regulations.70
The analysis of the industry- and firm-specific
determinants of the job creation process in
the EU11 economies relied on a dataset of
surviving firms during 2002–2009.71 To
unveil the structural drivers of employment
growth in the sampled firms and to assess the
role of an adverse macroeconomic shock, it
examined the process in two periods–before
and after the start of the global financial crisis.
The main results indicate that during the
boom years prior to the global financial crisis,
traditional industries such as agriculture
70 The impact of tax related barriers for doing business on the highgrowth
probability was positive. Accordingly, more severe perceived
tax regulations were associated with an increase in the probability
to grow very fast. This effect, however, turns out to be negative and
significant, when excluding both Bulgaria and Romania.
71 It is important to note that the data used in the analysis presented
here relate only to surviving (small and young) firms. A proper
understanding of business entry and business exit in the EU11
economies would be essential in order to tailor policies that most
successfully contribute to overall job creation. The availability of
firm census data for the EU11 economies should, for the future,
allow for providing a comprehensive picture on job creation in
these countries.
and fishing, mining, construction and
manufacturing were crucial for the net
creation of jobs among surveyed firms. In
contrast, while the number of firms in the
services sector was large, their role in creating
jobs was negligible. At the firm level, small firms
as well as start-ups and young surviving firms
were the most important contributors to job
creation in the EU11. In addition, the results
demonstrated that firm productivity and the
creation of new jobs among the surveyed firms
went hand in hand. Moreover, a crucial correlate
of employment growth among the EU11
surveyed firms was the business environment.
The perceived quality of the business climate
was associated not only the firms’ employment
growth, but also with their productivity. All
these findings were also confirmed for the
share of high-growth surveyed firms, which
disproportionately accounted for the creation
of new jobs in the EU11 economies prior to
the crisis.
Construction and manufacturing industries
most severely suffered from the economic
downturn. The EU11 countries that still heavily
relied on these industries faced a hefty decline
in their overall number of jobs. The results
demonstrated that surveyed firms in the services
industries were less vulnerable to the economic
downturn. Accordingly, in small service firms,
a substantially smaller (proportionate) number
of employees was lost among the surveyed firms
Conclusions
EU11 REGULAR ECONOMIC REPORT | june 2013
70 | Special Topic
than in the rest of the economy, and especially,
in the traditional sectors. The empirical results
also suggest that (total factor) productivity
correlated positively with employment growth
among surveyed firms. Business restrictions
also adversely affected the creation of jobs in
the post-crisis period.
Overall, the empirical results confirm that,
in qualitative terms, the analyzed firm
characteristics (such as size, age, TFP, sectoral
affiliation) affect job creation both during
recessions and economic recoveries. They
indicate that the more productive firms tend
to be less vulnerable to economic downturns.
Accordingly, any type of activities that increase
productivity can be expected to reduce the
overall exposure of the EU11 economies to
recessions and, therefore, should allow firms to
compete more successfully with international
competitors.
In addition, the empirical results point to
the key role of improving the quality of the
overall business environment for job creation
in EU11. The empirical evidence suggests that
improving the business climate, strengthening
labor and regulatory practices, modernizing
institutions, and deepening access to financial
advances job creation. Put broadly, improving
the institutions for doing business will lead to
leveling the playing field for all firms, boost
overall productivity and, thus, contribute
to the creation of new jobs. Also, given the
importance of the business environment for
FDI inflows, reducing business restrictions
would also increase the EU11 economies’
medium- and long-run productivity and overall
competitiveness and indirectly contribute to
job creation. A sound business environment,
however, seems to be a necessary but not
sufficient condition for sustained job creation.72
72 See World Bank (2013) for specific policy recommendations.
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Table 3. Estimation results for annual average firm growth, 2002–2008
Variables
No
regulations
(1)
Overall
inst.
(2)
Inst.
Regulation
(3)
Access to
finance
(4)
Crime
(5)
Corruption
(6)
Tax
(7)
Labor
regulation
(8)
Firm characteristics
Initial size -0.053*** -0.077*** -0.078*** -0.077*** -0.077*** -0.077*** -0.077*** -0.077***
(0.001) (0.001) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Age -0.046*** -0.077*** -0.077*** -0.076*** -0.077*** -0.077*** -0.077*** -0.077***
(0.001) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Initial size × age 0.005*** 0.013*** 0.013*** 0.013*** 0.013*** 0.013*** 0.013*** 0.013***
(0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
TFP - 0.015*** 0.016*** 0.015*** 0.015*** 0.015*** 0.016*** 0.015***
- (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Industry characteristics
Agriculture 0.004 0.031** 0.021 0.015 0.032** 0.031* 0.025 0.032**
(0.003) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)
Mining 0.043*** - - - - - - -
(0.004) - - - - - - -
Construction 0.038*** 0.060*** 0.050*** 0.049*** 0.060*** 0.061*** 0.054*** 0.060***
(0.002) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Manufacturing 0.032*** 0.041*** 0.026** 0.036*** 0.039*** 0.040*** 0.034*** 0.039***
(0.001) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Wholesale 0.006*** 0.016 0.005 0.008 0.015 0.014 0.007 0.015
(0.001) (0.012) (0.012) (0.012) (0.012) (0.011) (0.012) (0.012)
Transport 0.017*** 0.019 0.010 0.015 0.018 0.017 0.016 0.019
(0.001) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Overall inst. - -0.011* - - - - - -
- (0.006) - - - - - -
Inst. Regulation - - 0.018*** - - - - -
- - (0.004) - - - - -
Access to finance - - - -0.025*** - - - -
- - - (0.004) - - - -
Crime - - - - -0.003 - - -
- - - - (0.004) - - -
Corruption - - - - - -0.007* - -
- - - - - (0.004) - -
Taxation - - - - - - -0.019*** -
- - - - - - (0.004)
Labor regulation - - - - - - - 0.002
- - - - - - - (0.004)
Fixed Effects
Country 871.87*** 136.99*** 140.55*** 130.21*** 138.21*** 127.37*** 139.61*** 137.50***
R2 0.097 0.138 0.139 0.139 0.138 0.138 0.138 0.138
Observations 180,986 34,068 34,093 34,071 34,099 34,099 34,099 34,099
Note: Constant not reported. Robust standard errors in parenthesis. *, ** and *** denote significance at 10 percent. 5 percent and 1 percent levels,
respectively.
EU11 REGULAR ECONOMIC REPORT | june 2013
72 | Special Topic
Table 4. Estimation results for annual average firm growth in 2009
Variables
No
regulations
(1)
Overall
inst.
(2)
Inst.
Regulation
(3)
Access to
finance
(4)
Crime
(5)
Corruption
(6)
Tax
(7)
Labor
regulation
(8)
Firm characteristics
Initial size -0.073*** -0.074*** -0.074*** -0.074*** -0.074*** -0.074*** -0.074*** -0.074***
(0.001) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Age -0.038*** -0.055*** -0.054*** -0.054*** -0.054*** -0.054*** -0.054*** -0.055***
(0.001) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Initial size × age 0.016*** 0.016*** 0.016*** 0.016*** 0.016*** 0.016*** 0.016*** 0.016***
(0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
TFP 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.038*** 0.039***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Industry characteristics
Agriculture 0.026*** -0.066*** -0.053*** -0.055*** -0.066*** -0.042** -0.052*** -0.050***
(0.004) (0.018) (0.018) (0.018) (0.019) (0.018) (0.018) (0.018)
Mining 0.032*** - - - - - - -
(0.006) - - - - - - -
Construction -0.035*** -0.050*** -0.038*** -0.041*** -0.047*** -0.024* -0.039*** -0.032**
(0.002) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)
Manufacturing -0.018*** -0.073*** -0.062*** -0.065*** -0.074*** -0.062*** -0.059*** -0.060***
(0.002) (0.014) (0.013) (0.013) (0.014) (0.013) (0.014) (0.013)
Wholesale -0.004*** -0.016 -0.003 -0.008 -0.013 -0.001 -0.006 -0.006
(0.002) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)
Transport -0.002 -0.042*** -0.030** -0.034*** -0.038*** -0.029** -0.033** -0.036***
(0.002) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)
Overall instit. - -0.010** - - - - - -
- (0.004) - - - - - -
Inst. regulation - - -0.018*** - - - - -
- - (0.005) - - - - -
Access to finance - - - -0.012** - - - -
- - - (0.005) - - - -
Crime - - - - -0.009* - - -
- - - - (0.005) - - -
Corruption - - - - - -0.028*** - -
- - - - - (0.004) - -
Taxation - - - - - - -0.009* -
- - - - - - (0.005)
Labor regulations - - - - - - - -0.027***
- - - - - - - (0.005)
Fixed Effects
Country 477.95*** 79.96*** 67.51*** 100.25*** 131.48*** 71.85*** 97.22*** 61.56***
R2 0.040 0.049 0.049 0.049 0.049 0.049 0.049 0.049
Observations 300,347 71,227 71,297 71,297 71,297 71,297 71,297 71,278
Note: Constant not reported. Robust standard errors in parenthesis. *, ** and *** denote significance at 10 percent. 5 percent and 1 percent levels,
respectively.
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Table 5. Estimation results for high-growth probability, 2006–2008
Variables
No
regulations
(1)
Overall
(2)
Institutions
(3)
Finance
(4)
Crime
(5)
Corruption
(6)
Tax
(7)
Labor
(8)
Firm characteristics
Initial size -0.001*** -0.005*** -0.005*** -0.005*** -0.005*** -0.004*** -0.005*** -0.005***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Age -0.008*** -0.014*** -0.014*** -0.014*** -0.014*** -0.014*** -0.014*** -0.014***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
TFP - 0.001*** 0.001*** 0.001*** 0.001*** 0.002*** 0.002*** 0.002***
- (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Industry characteristics
Agriculture 0.001 0.020*** 0.033*** 0.005 0.022*** 0.024*** 0.012** 0.018***
(0.002) (0.006) (0.008) (0.004) (0.007) (0.007) (0.005) (0.006)
Mining 0.010** - - - - - - -
(0.004) - - - - - - -
Construction 0.020*** 0.063*** 0.082*** 0.041*** 0.065*** 0.067*** 0.049*** 0.062***
(0.002) (0.009) (0.010) (0.007) (0.009) (0.009) (0.008) (0.009)
Manufacturing 0.006*** 0.038*** 0.046*** 0.026*** 0.030*** 0.035*** 0.023*** 0.029***
(0.001) (0.007) (0.007) (0.006) (0.006) (0.006) (0.005) (0.006)
Wholesale 0.005*** 0.026*** 0.030*** 0.018*** 0.026*** 0.026*** 0.016*** 0.021***
(0.001) (0.005) (0.005) (0.004) (0.005) (0.005) (0.004) (0.004)
Transport 0.012*** 0.053*** 0.061*** 0.042*** 0.044*** 0.052*** 0.043*** 0.045***
(0.002) (0.008) (0.008) (0.007) (0.007) (0.008) (0.007) (0.007)
Overall institutions - -0.024*** - - - - - -
- (0.001) - - - - - -
Inst. regulation - - -0.006*** - - - - -
- - (0.001) - - - - -
Access to finance - - - -0.015*** - - - -
- - - (0.001) - - - -
Crime - - - - -0.011*** - - -
- - - - (0.001) - - -
Corruption - - - - - 0.004*** - -
- - - - - (0.001) - -
Taxation - - - - - - -0.012*** -
- - - - - - (0.001)
Labor regulations - - - - - - - -0.016***
- - - - - - - (0.001)
Pseudo R2 0.034 0.052 0.047 0.050 0.048 0.047 0.050 0.051
Observations 197,411 45,476 45,535 45,479 45,540 45,540 45,540 45,540
Note: Constant not reported. Robust standard errors in parenthesis. Average marginal effects reported (see, e.g., Bartus 2005) *, ** and *** denote
significance at 10 percent. 5 percent and 1 percent levels, respectively.
EU11 REGULAR ECONOMIC REPORT | june 2013
74 | Special Topic
Table 6. Estimation results for high-growth probability, 2009
Variables
No
regulations
(1)
Overall
(2)
Institutions
(3)
Finance
(4)
Crime
(5)
Corruption
(6)
Tax
(7)
Labor
(8)
Firm characteristics
Initial size -0.023*** -0.043*** -0.043*** -0.043*** -0.043*** -0.043*** -0.043*** -0.043***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Age -0.029*** -0.036*** -0.036*** -0.036*** -0.036*** -0.036*** -0.036*** -0.036***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
TFP - 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003***
- (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Industry characteristics
Agriculture 0.051*** 0.041*** 0.045*** 0.046*** 0.041*** 0.055*** 0.041*** 0.047***
(0.002) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Mining 0.025*** - - - - - - -
(0.003) - - - - - - -
Construction 0.018*** 0.009*** 0.012*** 0.012*** 0.010*** 0.022*** 0.010*** 0.014***
(0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Manufacturing -0.000 -0.012*** -0.009*** -0.009*** -0.012*** -0.008*** -0.011*** -0.008***
(0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Wholesale 0.012*** -0.004** -0.001 -0.001 -0.003** 0.003* -0.004** -0.001
(0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Transport 0.015*** 0.001 0.004** 0.003** 0.002 0.006*** 0.003* 0.003*
(0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Overall institutions - -0.005*** - - - - - -
- (0.001) - - - - - -
Inst. Regulation - - -0.002** - - - - -
- - (0.001) - - - - -
Access to finance - - - -0.004*** - - - -
- - - (0.001) - - - -
Crime - - - - -0.003*** - - -
- - - - (0.001) - - -
Corruption - - - - - -0.015*** - -
- - - - - (0.001) - -
Taxation - - - - - - 0.004*** -
- - - - - - (0.001) -
Labor regulations - - - - - - - -0.006***
- - - - - - - (0.001)
Pseudo R2 0.031 0.051 0.051 0.051 0.051 0.051 0.051 0.051
Observations 300,347 79,914 79,999 79,999 79,999 79,999 79,999 79,980
Note: Constant not reported. Robust standard errors in parenthesis. Average marginal effects reported (see, e.g., Bartus 2005) *, ** and *** denote
significance at 10 percent. 5 percent and 1 percent levels, respectively. Wald tests for country-fixed effects not reported.
MACROECONOMIC REPORT: economic RECOVERY on hold
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