World Economic Outloo

October 200

Financial Stress, Downturns, and Recoveries

International Monetary Fund

W o r l d E c o n o m i c a n d F i n a n c i a l S u r v e y s
©2008 International Monetary Fund

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World economic outlook : a survey by the staff of the International Monetary 

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iii

Contents

Assumptions and Conventions ix

Preface xi

Foreword xii

Executive Summary 

Chapter 1. Global Prospects and Policies

Global Economy under Stress 1

Financial System in Crisis 6

Deepening Housing Corrections 10

Overstretched Commodity Markets 15

Have Macroeconomic Policies Been Too Loose? 21

Prospects for a Turnaround 23

Policy Challenges for the Global Economy 35

Appendix 1.1. Assessing and Communicating Risks to the Global Outlook 41

References 46

Chapter 2. Country and Regional Perspectives

United States and Canada: Prognosis for the Downturn 49

Western Europe: Struggling with Multiple Shocks 51

Advanced Asia: Responding to External Shocks 59

Emerging Asia: Balancing Risks to Growth and Price Stability 63

Latin America and the Caribbean: Navigating a More Perilous Environment 66

Emerging Europe: Prospects for a Soft Landing 68

Commonwealth of Independent States: Managing the Commodity Price Boom 71

Sub-Saharan Africa: A Test of Policy Frameworks 74

Middle East: Overheating Still a Concern 77

References 80

Chapter 3. Is Inflation Back? Commodity Prices and Inflation

Surging Commodity Prices: Origins and Prospects 84

Commodity Price Shocks and Inflation 99

Monetary Policy Responses to Commodity Price Shocks 109

Summary and Conclusions 116

Appendix 3.1. Recent Commodity Market Developments 118

Appendix 3.2. Accounting for Food Price Increases, 2006–08 122

Appendix 3.3 Estimating Inflationary Effects of Commodity Price Shocks 124

References 126
contents

iv

Chapter 4. Financial Stress and Economic Downtur

Identifying Episodes of Financial Stress 131

Financial Stress, Economic Slowdown, and Recession 136

Has Financial Innovation Affected the Interplay between Financial Stress and Economic Cycles 141

The Current Financial Crisis in Historical Context 145

Conclusions 148

Appendix 4.1. Data and Methodology 154

References 156

Chapter 5. Fiscal Policy as a Countercyclical Too

Understanding the Fiscal Policy Debate 160

How Has Discretionary Fiscal Policy Typically Responded? 166

Are Fiscal Policy Reactions Different in Emerging and Advanced Economies? 170

The Macroeconomic Effects of Discretionary Fiscal Policy 173

A Simulation-Based Perspective on Fiscal Stimulus 180

Conclusions and Policy Considerations 183

Appendix 5.1. Data and Empirical Methods 187

References 195

Chapter 6. Divergence of Current Account Balances across Emerging Economi

Recent Current Account Patterns in Emerging Economies 199

What Factors Have Contributed to Recent Current Account Patterns 210

Sustainability of Current Account Imbalances 221

Conclusions and Policy Implications 228

Appendix 6.1. Variable Definitions and Data Source 229

Appendix 6.2. Econometric Approach 231

References 237

Annex: IMF Executive Board Discussion of the Outlook, September 2008 

Statistical Appendix 24

Assumptions 247

What’s New 250

Data and Conventions 250

Classification of Countries 252

General Features and Composition of Groups in the World Economic 

Outlook Classification 254

List of Tables

Output (Tables A1–A4) 259

Inflation (Tables A5–A7) 267

Financial Policies (Table A8) 273

Foreign Trade (Table A9) 274

Current Account Transactions (Tables A10–A12) 276

Balance of Payments and External Financing (Tables A13–A15) 282

Flow of Funds (Table A16) 286

Medium-Term Baseline Scenario (Table A17) 290
contents

World Economic Outlook and Staff Studies for the World Economic Outlook, Selected Topics 2

Boxes

1.1 The Latest Bout of Financial Distress: How Does It Change the Global Outlook? 11

1.2 House Prices: Corrections and Consequences 16

1.3 Measuring Output Gaps 26

2.1 EMU: 10 Years On 58

3.1 Does Financial Investment Affect Commodity Price Behavior? 88

3.2 Fiscal Responses to Recent Commodity Price Increases: An Assessment 103

3.3 Monetary Policy Regimes and Commodity Prices 112

4.1 Policies to Resolve Financial System Stress and Restore Sound Financial Intermediation 151

5.1 Differences in the Extent of Automatic Stabilizers and Their Relationship 

with Discretionary Fiscal Policy 161

5.2 Why Is It So Hard to Determine the Effects of Fiscal Stimulus? 164

5.3 Have U.S. Tax Cuts Been “TTT”? 172

6.1 Current Account Determinants for Oil-Exporting Countries 200 

6.2 Sovereign Wealth Funds: Implications for Global Financial Markets 204

6.3 Historical Perspective on Growth and the Current Account 214

A1 Economic Policy Assumptions Underlying the Projections for Selected Economies 248

Table

1.1 Overview of the World Economic Outlook Projections 2

2.1 Advanced Economies: Real GDP, Consumer Prices, and Unemployment 52

2.2 Advanced Economies: Current Account Positions 54

2.3 Selected Asian Economies: Real GDP, Consumer Prices, and Current Account Balance 65

2.4 Selected Western Hemisphere Economies: Real GDP, Consumer Prices, and 

Current Account Balance 67

2.5 Selected Emerging European Economies: Real GDP, Consumer Prices, and 

Current Account Balance 70

2.6 Commonwealth of Independent States (CIS): Real GDP, Consumer Prices, and 

Current Account Balance 73

2.7 Selected African Economies: Real GDP, Consumer Prices, and 

Current Account Balance 76

2.8 Selected Middle Eastern Economies: Real GDP, Consumer Prices, and 

Current Account Balance 78

3.1 Contributions of Common Factors to Commodity Price Fluctuations 94

3.2 Selected Indicators of Spillovers across Major Food Commodity Prices 98

3.3 Global Oil Demand and Production by Region 120

3.4 Elasticity Estimates Used for Price Calculations 123

4.1 Descriptive Statistics on Financial Stress Episodes 134

4.2 Descriptive Statistics on Financial Stress, Slowdowns, and Recessions 137

4.3 Cross-Section Regressions 144

4.4 Six Major Periods of Financial Stress and Economic Contractions 149

4.5 Data 154

4.6 Average Yearly Share of Total Bank Assets of Banks in Sample 156
contents

vi

5.1 Macroeconomic Indicators around Downturns, with and without a Fiscal Impulse: All 

Economies 176

5.2 Real GDP Growth and Fiscal Impulse under Various Initial Conditions: All Economies 178

5.3 Real GDP Growth and Fiscal Impulse by Composition: All Economies 180

5.4 Responses of Real GDP to Discretionary Fiscal Policy Changes 181

5.5 List of Countries and Downturn Episodes 189

5.6 Discretionary Fiscal Policy and Growth: Regression Results with 

Arellano-Bond Dynamic Panel Estimator Using Elasticity-Based Fiscal Impulse Measure 191

5.7 Discretionary Fiscal Policy and Growth: Regression Results with 

Arellano-Bond Dynamic Panel Estimator Using Regression-Based Fiscal Impulse Measure193

6.1 Determinants of the Current Account Balance 217

6.2 Duration Regressions of Persistent and Large Current Account Deficits 226

6.3 Explaining Differentiated Effects in Emerging Europe 233

6.4 List of Persistently Large Current Account Imbalance Episodes 235

6.5 Duration Analysis and Domestic Financial Sector Liberalization 236

6.6 Duration Analysis and Risk of Abrupt and Non-Abrupt Endings 236

Figures

1.1 Global Indicators 3

1.2 Current and Forward-Looking Indicators 4

1.3 Global Inflation 5

1.4 External Developments in Selected Advanced Economies 6

1.5 External Developments in Emerging and Developing Economies 7

1.6 Developments in Mature Credit Markets 8

1.7 Mature Financial and Housing Market Indicators 9

1.8 Emerging Market Conditions 10

1.9 Measures of Monetary Policy and Liquidity in Selected Advanced Economies 22

1.10 Measures of the Output Gap and Capacity Pressures 24

1.11 Global Outlook 25

1.12 Risks to the Global Outlook 30

1.13 Impact of Financial Shock on the Global Economy 32

1.14 Current Account Balances and Net Foreign Assets 34

1.15 Median Forecast Errors during Global Recessions and at Other Times, 1991–2007 42

1.16 Histograms of Forecast Errors, 1991–2007 43

1.17 Probability of Global Recessions 44

1.18 Illustrative GPM-Based 90 Percent Confidence Intervals 46

2.1 United States: Strains on Households 50

2.2 Western Europe: Slowing Demand and High Inflation 53

2.3 Japan: How Well Would the Economy Weather a Terms-of-Trade Shock? 57

2.4 Emerging Asia: Remaining Inflation Concerns 63

2.5 Latin America: Inflation Returns 68

2.6 Emerging Europe: Are Credit Booms Cooling Off? 69

2.7 Commonwealth of Independent States (CIS): Managing the Commodity Price Boom 72

2.8 Sub-Saharan Africa: The Mixed Blessing of High Commodity Prices 75

2.9 Middle East: Managing Inflation Pressures 79

3.1 Commodity Prices in Historical Context 85
contents

vii

3.2 Marginal Change in Energy Intensity, Commodity Inventories, and OPEC Spare Capacity 86

3.3 Grain and Oil Demand, Production, and Inventories in Comparison 87

3.4 Oil Supply Developments 95

3.5 Price Trends of Major Foods 97

3.6 Duration and Amplitude of Food and Crude Oil Price Cycles 99

3.7 Inflation around the World 100

3.8 Changes in International and Domestic Commodity Prices and Headline Inflation 101

3.9 The Relative Importance of Food and Energy 105

3.10 Monetary and Exchange Rate Policies 106

3.11 Commodity Price Pass-Through 107

3.12 Changes in Expected Inflation in Response to Changes in Actual Inflation 108

3.13 Activity, Interest Rates, and Inflation 110

3.14 Stylized Advanced Economy with Adverse and Favorable Supply Shocks 111

3.15 Stylized More-Vulnerable Emerging Market Economy with Adverse and 

Favorable Supply Shocks 115

3.16 Potential Costs of Delaying Interest Rate Hikes 116

3.17 Commodity and Petroleum Prices 118

3.18 World Oil Market Balances and Oil Futures Price 119

3.19 Developments in Food and Metal Markets 122

4.1 Financial Stress and Output Loss 130

4.2 Financial Stress Index 134

4.3 Financial Stress and Shocks 135

4.4 Contribution of Banking, Securities, and Foreign Exchange to Current 

Financial Stress Episode 136

4.5 Lag between Financial Stress and Downturns 138

4.6 Selected Macrovariables around Economic Downturns with and without Financial Stress 139

4.7 Banking-Related Financial Stress, Slowdowns, and Recessions 140

4.8 Cost of Capital and Bank Asset Growth around Banking Financial Stress Episodes 141

4.9 Selected Macrovariables around Financial Stress Episodes 142

4.10 Initial Conditions of Financial Stress Episodes 143

4.11 Financial Stress and Economic Downturns: Controlling for Four Main Shocks 145

4.12 The Procyclicality of Leverage in Investment and Commercial Banks 146

4.13 Procyclical Leverage and Arm’s-Length Financial Systems 147

4.14 Arm’s-Length Financial Systems, GDP Growth, and Bank Leverage 148

4.15 The Current Financial Stress Episode in the United States and Euro Area in 

Historical Context 150

5.1 How Often and Quickly Has Fiscal Stimulus Been Used in G7 Economies? 167

5.2 How Strong Was the Fiscal Policy Response in G7 Economies? 168

5.3 How Have Fiscal Policy Responses Varied across Advanced Economies? 169

5.4 Is There a Bias toward Easing during Downturns in G7 Economies? 170

5.5 Did G7 Economies Respond to Erroneously Perceived Downturns? 171

5.6 Composition of Fiscal Stimulus during Downturns for Advanced and 

Emerging Economies 174

5.7 Fiscal Policy Responses in Downturns and Upturns 175

5.8 Macroeconomic Indicators after Downturns, with and without a Fiscal Stimulus 177

5.9 Changes in Real GDP Growth and Fiscal Policies under Various Initial Conditions 179

5.10 Effect of Fiscal Expansion in a Large Economy 182
contents

viii

5.11 Fiscal Expansion in a Large Economy Compared with a Small Open Economy 

with Monetary Accommodation 184

5.12 Effect of Fiscal Expansion in a Small Economy with Market-Risk-Premium Reaction 185

6.1 Patterns of Divergence in Current Account Balance 199

6.2 External Balances by Component 203

6.3 Current Account Balance, Saving, and Investment 208

6.4 Saving and Investment by Components 209

6.5 Growth Takeoffs 210

6.6 Current Account Reversals around Crises 211

6.7 Current Account Balance and Real GDP per Capita Growth 212

6.8 Patterns of Financial Development 213

6.9 Explaining the Current Account Balances of Emerging Asia and Emerging Europe 218

6.10 Explaining Current Account Balances: Results by Subregion 219

6.11 Deviation from Predicted Real Effective Exchange Rates 221

6.12 Residual Current Account Balance, Deviation of Real Effective Exchange Rate from 

Predicted Level and Stock of Reserves 222

6.13 Persistently Large Current Account Deficit and Surplus Episodes, 1960–2007 223

6.14 Duration of Large, Persistent Current Account Deficits, 1960–2007 224

6.15 Survival Functions of Deficit Episodes 225

6.16 Predicted Duration and Actual Length of Ongoing Deficit Episodes 227

6.17 Corporate Profitability and Productivity Growth 228
A number of assumptions have been adopted for the projections presented in the World Economic 

Outlook. It has been assumed that real effective exchange rates will remain constant at their average 

levels during August 18–September 15, 2008, except for the currencies participating in the European 

exchange rate mechanism II (ERM II), which are assumed to remain constant in nominal terms 

relative to the euro; that established policies of national authorities will be maintained (for specific 

assumptions about fiscal and monetary policies in industrial countries, see Box A1); that the average 

price of oil will be $107.25 a barrel in 2008 and $100.50 a barrel in 2009, and remain unchanged in 

real terms over the medium term; that the six-month London interbank offered rate (LIBOR) on U.S. 

dollar deposits will average 3.2 percent in 2008 and 3.1 percent in 2009; that the three-month euro 

deposits rate will average 4.8 percent in 2008 and 4.2 percent in 2009; and that the six-month Japanese 

yen deposit rate will yield an average of 1.0 percent in 2008 and 1.2 percent in 2009. These are, of 

course, working hypotheses rather than forecasts, and the uncertainties surrounding them add to the 

margin of error that would in any event be involved in the projections. The estimates and projections 

are based on statistical information available through early October 2008.

The following conventions have been used throughout the World Economic Outlook:

. . . to indicate that data are not available or not applicable;

— to indicate that the figure is zero or negligible;

– between years or months (for example, 2006–07 or January–June) to indicate the years or 

months covered, including the beginning and ending years or months;

 / between years or months (for example, 2006/07) to indicate a fiscal or financial year.

“Billion” means a thousand million; “trillion” means a thousand billion.

“Basis points” refer to hundredths of 1 percentage point (for example, 25 basis points are equivalent 

to 1⁄4 of 1 percent point).

In figures and tables, shaded areas indicate IMF staff projections.

Minor discrepancies between sums of constituent figures and totals shown are due to rounding.

As used in this report, the term “country” does not in all cases refer to a territorial entity that is a 

state as understood by international law and practice. As used here, the term also covers some territorial entities that are not states but for which statistical data are maintained on a separate and independent basis.

Assumptions and Conve

ix
This report on the World Economic Outlook is available in full on the IMF’s website, www.imf.org. 

Accompanying it on the website is a larger compilation of data from the WEO database than in the 

report itself, consisting of files containing the series most frequently requested by readers. These files 

may be downloaded for use in a variety of software packages

Inquiries about the content of the World Economic Outlook and the WEO database should be sent by 

mail, electronic mail, or telefax (telephone inquiries cannot be accepted) to:

World Economic Studies Division

Research Department

International Monetary Fund

700 19th Street, N.W

Washington, D.C. 20431, U.S.A

E-mail: weo@imf.org Telefax: (202) 623-6343

Furrtherr Ini fform
The analysis and projections contained in the World Economic Outlook are integral elements of the 

IMF’s surveillance of economic developments and policies in its member countries, of developments 

in international financial markets, and of the global economic system. The survey of prospects and 

policies is the product of a comprehensive interdepartmental review of world economic developments, 

which draws primarily on information the IMF staff gathers through its consultations with member 

countries. These consultations are carried out in particular by the IMF’s area departments together 

with the Strategy, Policy, and Review Department (formerly Policy Development and Review Department), the Monetary and Capital Markets Department, and the Fiscal Affairs Department.

The analysis in this report has been coordinated in the Research Department under the general 

direction of Olivier Blanchard, Economic Counsellor and Director of Research. The project has been 

directed by Charles Collyns, Deputy Director of the Research Department, and Jörg Decressin, Division 

Chief, Research Department. The analysis has benefited from input during the early stages by Simon 

Johnson, the former Economic Counsellor and Director of Research. 

The primary contributors to this report are Roberto Cardarelli, Kevin Cheng, Stephan Danninger, 

Mark De Broeck, Selim Elekdag, Thomas Helbling, Anna Ivanova, Florence Jaumotte, Daehaeng Kim, 

Michael Kumhof, Subir Lall, Tim Lane, Douglas Laxton, Daniel Leigh, Valerie Mercer-Blackman, 

Jonathan Ostry, Alasdair Scott, Sven Jari Stehn, Steven Symansky, Natalia Tamirisa, and Irina Tytell. 

Toh Kuan, Gavin Asdorian, Ioan Carabenciov, Huigang Chen, To-Nhu Dao, Stephanie Denis, Nese Erbil, Angela Espiritu, Elaine Hensle, Patrick Hettinger, Annette Kyobe, Susana Mursula, Jair Rodriguez, 

Bennett Sutton, and Ercument Tulun provided research assistance. Saurabh Gupta, Mahnaz Hemmati, 

Laurent Meister, and Emory Oakes managed the database and the computer systems. Jemille Colon, 

Tita Gunio, Shanti Karunaratne, Laura Leon, Patricia Medina, and Sheila Tomilloso Igcasenza were 

responsible for word processing. Other contributors include Steven Barnett, Rudolf Bems, Irineu de 

Carvalho Filho, Stijn Claessens, Kevin Clinton, David Coady, Gianni de Nicolò, Ondrej Kamenik, Julie 

Kozack, Luc Laeven, Prakash Loungani, Dirk Muir, Krishna Srinivasan, Emil Stavrev, Stephen Tokarick. 

External consultants include Joshua Aizenman, Antonio Fatás, Christopher Meissner, and Hyun Song 

Shin. Linda Griffin Kean of the External Relations Department edited the manuscript and coordinated 

the production of the publication. Lucy Scott Morales provided editorial assistance.

The analysis has benefited from comments and suggestions by staff from other IMF departments, as 

well as by Executive Directors following their discussions of the report on September 17 and 19, 2008. 

However, both projections and policy considerations are those of the IMF staff and should not be 

attributed to Executive Directors or to their national authorities.

Pre

xi
Forew

xii

Forew

Having just joined the IMF, I can take very 

little credit for this edition of the World Economic 

Outlook. I regret it: Like its predecessors, this is 

a remarkable document which gives the reader 

a clear sense of what is happening in the world 

economy. I thank Simon Johnson, Charles Collyns, Jörg Decressin, and their team for their 

work. 

Chapters 1 and 2 assess the state and the 

evolution of the world economy, an exercise that 

has rarely been so difficult. The world economy 

is decelerating quickly—buffeted by an extraordinary financial shock and by still-high energy 

and commodity prices—and many advanced 

economies are close to or moving into recession. 

Developments in financial markets have dominated the news in recent weeks. The subprime 

crisis that unfolded in 2007 has now morphed 

into a credit crisis that has caused major disruption to financial institutions in the United States 

and Europe. Intensifying solvency concerns 

about a number of the largest U.S.-based and 

European financial institutions have pushed the 

global financial system to the brink of systemic 

meltdown. The effects on the real economy have 

been limited so far. In part, this may be because 

tax rebates in the United States supported consumption, while strong nonfinancial corporate 

balance sheets and profitability have allowed 

firms to use their own funds rather than borrow. 

But neither of these factors can be expected 

to last for very long. Credit conditions have 

become significantly tighter in recent weeks, 

threatening the ability of nonfinancial firms 

and a number of emerging economies to raise 

capital. The U.S. and European authorities have 

taken extraordinary measures, including massive liquidity provision, intervention to restore 

weak institutions, extension of guarantees, and 

recent U.S. legislation to use public funds to buy 

troubled assets from banks. But it is not yet clear 

that these measures will be sufficient to stabilize 

markets and bolster confidence, and the situation remains highly uncertain.

This is not the only shock buffeting the world 

economy. Prices of oil and basic commodities 

have reached historically high levels in recent 

months. In advanced economies, a combination of real wage flexibility, well-anchored 

inflation expectations, and prospects of sharply 

reduced activity have helped to limit rises in 

core inflation. But in emerging and developing 

economies, the impact has been much more 

damaging. Real wages have fallen substantially. 

Oil exporters have found it difficult to dampen 

overheating economies. 

Looking to the future, it is necessary to assess 

how these shocks will likely work their way 

through the world economy. Our forecasts are 

based on three major assumptions. The first 

is that commodity and oil prices are likely to 

stabilize, relieving pressure on inflation and 

giving more room, if needed, for expansionary 

policies. The second is that U.S. housing prices 

and activity will hit bottom within the next year, 

leading to a recovery of residential investment. 

The third is that, although credit will remain 

tight, the elements of a systemic solution to the 

financial crisis are now being put in place and 

will prevent a further worsening of financial 

intermediation. It is this combination that leads 

us to forecast that world growth will begin to 

recover at the end of 2009, albeit at a very slow 

pace. There is, however, more than the usual 

amount of uncertainty, and the downside risks 

are far from negligible. 

As usual, this World Economic Outlook also 

tackles a number of topically important issues 

in greater depth. Chapter 3 examines the threat 

that the recent boom in commodity prices could 

unwind the past two decades’ progress against 

inflation. To be sure, the fall in some prices—

notably for oil—since mid-July has eased some 

of the pressure, but it is too early to relax. Com-
Foreword

xiii

modity prices are likely to remain much higher 

in real terms than in recent decades, and this 

shift in relative prices will need to be absorbed 

without triggering second-round effects on price 

and wage formation. This task is likely to be 

easier in the advanced economies, where widening output gaps are helping to restrain inflation 

pressures. Moreover, these economies are much 

less commodity-intensive than they were in the 

1970s and have more flexible labor markets 

and well-established monetary policy frameworks that have largely succeeded in anchoring 

inflation expectations. However, emerging and 

developing economies are more vulnerable to 

inflation spillovers—because of their greater 

resource intensity, less-well-established policy 

frameworks, and more rapid rates of growth. In 

many of these economies, second-round effects 

are already increasingly visible, and although 

slowing global growth and softening commodity 

prices should help rein inflation back in, risks 

remain that continued inflationary excesses will 

degrade hard-earned inflation-fighting credentials, requiring even tougher action in the future 

to put the cork back in the bottle.

Chapter 4 addresses what is clearly a central 

concern for the global economy: What will be 

the impact of the current financial crisis on 

economic activity? It is now all too clear that we 

are seeing the deepest shock to the global financial system since the Great Depression, at least 

for the United States. Are we then doomed to 

a slump in output as occurred in the 1930s? As 

Chapter 4 shows, the historical record is mixed. 

Periods of financial stress have not always been 

followed by recessions or even by economic 

slowdowns. However, the analysis also shows that 

when the financial stress does major damage to 

the banking system—as in the current episode—the likelihood increases of a severe and 

protracted downturn in activity. This is clearly 

demonstrated by the experiences of many economies that have struggled with virulent financial 

crises over the past decades, for example, the 

Nordic countries and Japan. Moreover, economies with more-arm’s length or market-based 

financial systems seem to be particularly vulnerable to sharp contractions in activity in the 

face of financial stress. This is because leverage 

tends to be more procyclical in these economies—the risks of a credit crunch are greater. 

Does this mean that the United States—with a 

market-based financial system par excellence—is 

heading for a deep recession? Not necessarily, 

because, as the chapter shows, other factors 

also matter. Two sources of support for the U.S. 

economy are the quick and strong reaction of 

the Federal Reserve to lower policy rates and the 

robust state of the U.S. nonfinancial corporate 

sector. Low indebtedness and high profits have 

helped U.S. businesses ride the financial storm. 

However, the longer the financial crisis continues, the less likely it is that nonfinancial firms 

will be able to support strong growth.

Chapter 5 takes a fresh look at an old 

debate—about the value of fiscal policy as a 

countercyclical tool—which has taken on new 

relevance as the global economy slows and 

as turbulence in financial markets has raised 

questions about the effectiveness of monetary 

policy. The findings are not very encouraging 

for proponents of fiscal activism: fiscal multipliers—the impact of discretionary fiscal stimulus 

on output—are generally found to be quite low, 

and sometimes even to operate in the wrong 

direction, especially in economies with high 

debt levels where a turn to expansionary fiscal 

policy may raise doubts about long-term debt 

sustainability. This does not necessarily mean 

that policymakers should abandon fiscal policy 

as a countercyclical tool, but it does underline 

that fiscal initiatives, when needed, must be well 

targeted to have the maximum short-run impact 

without undermining long-run fiscal rectitude. 

It is also worthwhile to consider whether 

the role of fiscal policy as a macroeconomic 

stabilizer could be enhanced by strengthening 

the broader fiscal framework. Two options are 

worth considering. First, there is the possibility 

that automatic stabilizers could be boosted by 

making regular tax and transfer programs more 

cyclically responsive. For example, the generosity of unemployment insurance systems could be 

automatically increased when the economy is in 
Foreword

xiv

a downturn and jobs are harder to find. Second, 

steps could be taken to strengthen the overall 

governance structure for fiscal policy—thereby 

reducing the risk of “debt bias” by ensuring that 

fiscal easing during a downturn is balanced by 

tightening during expansions. Improved governance could bolster the credibility and thus 

the effectiveness of fiscal stimulus. Recognizing 

the pros and cons of these approaches, I do feel 

they are worthy of consideration.

Finally, Chapter 6 tries to solve an important 

puzzle: Why have the current account balances 

of emerging economies been so divergent in 

recent years, with some economies in emerging Asia registering large surpluses and others, 

particularly in emerging Europe, sustaining very 

large and long-lasting deficits? There is no single 

answer, but the chapter suggests that important 

contributors have been emerging Europe’s rapid 

financial liberalization and capital account opening, particularly in those economies integrating rapidly into the European Union, and the 

focus in emerging Asia on building large stocks 

of international reserves as self-insurance in 

the wake of the Asian crisis of 1997–98. This 

leaves open the question of whether the recent 

patterns will be sustained. Certainly the turbulent global environment is putting a strain on 

economies with large current account deficits 

and commensurately large external financing 

requirements.

Olivier Blanchard

Economic Counsellor and Director, Research Department
The world economy is entering a major downturn 

in the face of the most dangerous financial shock in 

mature financial markets since the 1930s. Global 

growth is projected to slow substantially in 2008, and 

a modest recovery would only begin later in 2009. 

Inflation is high, driven by a surge in commodity 

prices, but is expected to moderate. The situation is 

exceptionally uncertain and subject to considerable 

downside risks. The immediate policy challenge is to 

stabilize financial conditions, while nursing economies through a period of slow activity and keeping 

inflation under control.

Global Economy under Stre

After years of strong growth, the world 

economy is decelerating quickly (Chapters 1 

and 2). Global activity is being buffeted by an 

extraordinary financial shock and by still-high 

energy and other commodity prices. Many 

advanced economies are close to or moving into 

recession, while growth in emerging economies 

is also weakening. 

The financial crisis that first erupted with the 

U.S. subprime mortgage collapse in August 2007 

has deepened further in the past six months 

and entered a tumultuous new phase in September. The impact has been felt across the global 

financial system, including in emerging markets 

to an increasing extent. Intensifying solvency 

concerns have led to emergency resolutions of 

major U.S. and European financial institutions 

and have badly shaken confidence. In response, 

the U.S. and European authorities have taken 

extraordinary measures aimed at stabilizing 

markets, including massive liquidity provision, 

prompt intervention to resolve weak institutions, extension of deposit insurance, and recent 

U.S. legislation to use public funds to purchase 

troubled assets from banks. However, the situation remains highly uncertain as this report goes 

to press.

At the same time, the combination of the 

surge in food and fuel prices under way since 

2004 and tightening capacity constraints has 

propelled inflation to rates not seen in a decade. 

As analyzed in Chapter 3, consumer price rises 

have been particularly strong in emerging and 

developing economies. This acceleration reflects 

the high weight of food in consumption baskets, 

still-quite-rapid growth, and less-well-anchored 

inflation expectations. Notably, countries that 

have adopted inflation-targeting regimes have 

generally fared better. In the advanced economies, oil price increases have pushed up headline inflation, but underlying inflation pressures 

seem contained.

The recent deterioration of global economic 

performance follows sustained expansion built 

on the increasing integration of emerging and 

developing economies into the global economy. 

In hindsight, however, lax macroeconomic and 

regulatory policies may have allowed the global 

economy to exceed its “speed limit” and may 

have contributed to a buildup in imbalances 

across financial, housing, and commodity markets. At the same time, market flaws, together 

with policy shortcomings, have prevented equilibrating mechanisms from operating effectively 

and allowed market stresses to build.

Recovery Not Yet in Sight and Likely t

Be Gradual When It Come

Looking ahead, financial conditions are 

likely to remain very difficult, restraining global 

growth prospects. The baseline projections 

assume that actions by the U.S. and European 

authorities will succeed in stabilizing financial 

conditions and avoiding further systemic events. 

Nonetheless, even with successful implementation of the U.S. plan to remove troubled assets 

from bank balance sheets, counterparty risk is 

likely to remain at exceptionally high levels for 

xv

Executiv
Executive Summa

xvi

some time, slowing down the return to more 

liquid conditions in key financial markets. 

Furthermore, additional credit losses are very 

likely as the global economy decelerates. In this 

setting, financial institutions’ ability to raise new 

capital will remain very challenged. Accordingly, 

as discussed in the October 2008 Global Financial 

Stability Report, the required deleveraging will 

continue to be a protracted process, implying 

that limits on the pace of credit creation—and 

on activity—will be present at least through 

2009. 

Nonetheless, several factors are expected to 

lay the groundwork for a gradual recovery to 

emerge later in 2009: 

•	 Commodity prices are projected to stabilize, 

albeit at 20-year highs. The adverse termsof-trade effects of the more than 50 percent 

increase in oil prices during 2008 should 

begin to unwind in 2009, boosting consumption in oil-importing countries. 

•	 The U.S. housing sector is expected to finally 

reach bottom in the coming year, ending the 

intense drag on growth that has been present since 2006. The eventual stabilization of 

house prices should help restrain the financial 

sector’s mortgage-related losses, and the recent 

intervention in the two government-sponsored 

enterprises, Fannie Mae and Freddie Mac, 

should help support the availability of credit to 

the housing sector. Although the housing cycle 

and related adjustment might lag in other 

advanced economies, the overall impact of the 

financial crisis will be severely felt.

•	 Notwithstanding cooling of their momentum, 

emerging economies are still expected to 

provide a source of resilience, benefiting from 

strong productivity growth and improved 

policy frameworks. Of course, the longer the 

financial crisis lasts, the more they are likely 

to be affected.

Against this backdrop, the baseline growth 

projections have been marked down significantly relative to the July 2008 World Economic 

Outlook Update. On an average annual basis, 

global growth is expected to moderate from 

5.0 percent in 2007 to 3.9 percent in 2008 and 

3.0 percent in 2009, its slowest pace since 2002. 

The advanced economies would be in or close 

to recession in the second half of 2008 and early 

2009, and the anticipated recovery later in 2009 

will be exceptionally gradual by past standards. 

Growth in most emerging and developing 

economies would decelerate below trend. On 

the inflation front, the combination of rising slack and stabilizing commodity prices is 

expected to contain the pace of price increases, 

bringing inflation back below 2 percent in 2009 

in advanced economies. In emerging and developing economies, inflation would ebb more 

gradually, as recent commodity price increases 

continue to feed through to consumers.

There are substantial downside risks to this 

baseline forecast. The principal risk revolves 

around two related financial concerns: that 

financial stress could remain very high and that 

credit constraints from deleveraging could be 

deeper and more protracted than envisaged 

in the baseline. In addition, the U.S. housing market deterioration could be deeper and 

more prolonged than forecast, while European 

housing markets could weaken more broadly. 

Inflation risks to growth are now more balanced 

because commodity prices have retreated as the 

global economy slows. At the same time, potential disruptions to capital flows and the risks of 

rising protectionism represent additional risks to 

the recovery.

The connections between financial stress and 

economic downturns are explored in Chapter 4, 

which compares recent experience to earlier 

episodes. The analysis indicates that financial 

stress that is rooted in the banking sector typically has more adverse economic effects than 

stress in stock markets or exchange rates and 

that the shift toward more-arm’s-length financial 

intermediation may have increased the impact. 

Initial conditions appear to affect the outcomes. Thus, the relatively healthy nonfinancial 

corporate balance sheets in the United States 

and western Europe at the beginning of the 

current downturn provide a source of resilience, 

but would be at risk from a sustained period of 

financial stress. 
xvii

Chapter 6 raises concerns about countries 

with sustained large current account deficits. 

These concerns may be particularly relevant 

as global deleveraging reduces the availability 

of external financing for emerging economies. 

The analysis seeks to explain large divergences in current account behavior across the 

emerging world and relates the large deficits 

in emerging Europe to capital account liberalization, financial reform, and opportunities 

created by European economic convergence. 

However, sustained large deficits can end 

abruptly, and rigid exchange regimes heighten 

such risks. In fact, many economies with large 

current account deficits have already experienced a much greater impact from the financial 

market turmoil than those with small current 

account deficits or surpluses.

Policymakers between a Rock and a 

Hard Plac

Policymakers around the world today face the 

daunting task of stabilizing financial conditions 

while simultaneously nursing their economies 

through a period of slower growth and containing inflation. Multilateral efforts take on particular importance in current circumstances, 

including policy initiatives to remedy the financial turmoil, alleviate the tightness in commodity markets, and support low-income economies 

burdened by high food import bills. 

Country authorities are actively pursuing 

policies intended to stabilize financial conditions. Achieving this daunting task will require 

comprehensive responses that address the 

systemic problems––dealing with troubled 

assets, fostering the rebuilding of bank capital, 

and restoring liquid conditions in funding 

markets—while being mindful of taxpayer 

interests and moral hazard considerations. 

Approaches at the national level should be 

internationally coordinated to deal with joint 

problems and to avoid creating adverse, crossborder incentives. 

The U.S. initiative to purchase real-estaterelated assets should help over time to reduce 

the pressure on banks from distressed assets, 

and thus support a return of stable funding sources and confidence. However, public 

funds are also likely to be needed to help 

banks rebuild their capital bases. In western 

Europe, restoring confidence now requires a 

decisive commitment to concerted and coordinated action to facilitate timely recognition of 

troubled assets and bank recapitalization. A key 

task will be to develop cooperative agreements, 

adapted to a broad range of circumstances, 

including for resolving stress in large crossborder institutions and ensuring consistency in 

approaches to expanding deposit insurance.

Macroeconomic policies in the advanced 

economies should aim at supporting activity, 

thus helping to break the negative feedback 

loop between real and financial conditions, 

while not losing sight of inflation risks.

•	 Rapidly slowing activity and rising output 

gaps should help contain inflation. Moderating inflation pressure and the deteriorating 

economic outlook already provide scope for 

monetary easing in some cases, notably in the 

euro area and the United Kingdom, where 

short-term interest rates are quite high. 

•	 Regarding fiscal policy, automatic stabilizers play a useful role in buffering shocks to 

activity and should be left to operate freely, 

provided that adjustment paths are consistent 

with long-term sustainability. Discretionary 

fiscal stimulus can provide support to growth 

in the event that downside risks materialize, 

provided the stimulus is delivered in a timely 

manner, is well targeted, and does not undermine fiscal sustainability. In the current 

circumstances, available fiscal room should 

be focused on supporting stabilization of 

the financial and housing sectors as needed, 

rather than for more broad-brush stimulus. 

In due course, offsetting adjustments to fiscal 

policies will be needed to safeguard mediumterm consolidation objectives. 

Macroeconomic policy priorities vary considerably across emerging and developing 

economies, as policymakers balance growth and 

inflation risks.

Executive Summa
xviii

•	 In an increasing number of economies, 

the balance of risks has now shifted toward 

concern about slowing activity as external 

conditions deteriorate and headline inflation 

starts to moderate. This shift would justify a 

halt to the monetary policy tightening cycle, 

particularly where second-round effects on 

inflation from commodity prices have been 

limited, and a turn to easing would be called 

for if the outlook continues to deteriorate. 

In the face of sharp capital outflows, countries will need to respond quickly to ensure 

adequate liquidity, while using the exchange 

rate to absorb some of the pressure. Furthermore, they should step up efforts to improve 

capabilities to prevent, manage, and resolve 

financial stress, including through contingency planning.

•	 However, in a number of other countries, 

inflation pressures are still a concern because 

of sharp food price increases, continued 

strong growth, tightening supply constraints, 

and accelerating wages, notably in the public 

sector. Although the recent moderation in 

international commodity prices may ease 

some of the pressure, the gains in reducing 

inflation in recent years are being jeopardized; once credibility is eroded, rebuilding it 

will be a costly and lengthy process. In these 

countries, additional monetary policy tightening may still be called for.

•	 Countries with heavily managed exchange rate 

regimes are facing significant challenges. More 

flexible exchange rates would help contain 

inflation pressures by providing greater scope 

for monetary adjustment and provide more 

room for maneuver in the face of capital outflows. Of course, other considerations feed into 

choices of exchange rate regimes, including, 

for example, the degree of financial development and the diversity of the export base. 

•	 Fiscal policy can play a supportive role in macroeconomic management. Greater restraint 

in public spending would help ease inflation pressures in a number of countries still 

facing overheating concerns. This is particularly important for current account deficit 

countries with pegged exchange rates. In 

the oil-exporting economies with currencies 

pegged to the U.S. dollar, spending can be 

focused on relieving supply bottlenecks. While 

emerging economies have greater scope than 

in the past to use countercyclical fiscal policy 

should their economic outlook deteriorate, 

the analysis in Chapter 5 cautions that this is 

unlikely to be effective unless confidence in 

sustainability has been firmly established and 

measures are timely and well targeted. More 

broadly, general food and fuel subsidies have 

become increasingly costly and are inherently 

inefficient. Targeted programs that help poor 

families meet rising living expenses are a preferred option.

Policy Frameworks in Need of Refor

The deteriorating performance of the global 

economy has raised concerns about the choice 

of macroeconomic policy frameworks and the 

appropriateness of policies affecting financial 

and commodity markets. 

Operationalizing “Leaning against the Win

The current exceptional environment has 

heightened interest in developing policies that 

would be better geared toward avoiding asset 

price booms and busts, including through stronger policy responses in boom times. A promising 

approach would be to introduce a macroprudential element into the regulatory framework 

to weigh against the inherent procyclicality of 

credit creation. Consideration could also be 

given to extending monetary policy frameworks 

to provide for “leaning against the wind” of asset 

price movements, especially when these are 

rapid or seem to be moving prices seriously out 

of line with fundamentals, although this raises 

complex issues. 

Moreover, interest has increased in making 

fiscal policy frameworks more credible and thus 

making fiscal policy more effective as a countercyclical tool. The Achilles heel of an active fiscal 

policy remains political economy settings that 

Executive Summa
xix

foster short-term decision-making. As a result, 

many countries fail during good times to build 

room for effective discretionary stimulus during downturns, or are struggling with addressing long-term fiscal sustainability challenges. 

Chapter 5 suggests that the shift toward more 

rules-based policy frameworks—analogous to 

constrained discretion in monetary policy—and 

the stronger fiscal governance mechanisms that 

can be observed in a growing number of countries could boost the effectiveness of fiscal policy 

in combating downturns.

Plugging Gaps in Regulatory and Supervisory

Infrastructures

As well as dealing with the immediate systemic 

threats, determined efforts are being marshaled 

to address the manifold weaknesses revealed by 

the current financial turbulence. As laid out in 

the October 2008 Global Financial Stability Report, 

a central objective is to ensure more effective 

and resilient risk management by individual 

institutions, including by setting more robust 

regulatory capital requirements and insisting 

on stronger liquidity management practices and 

improved disclosure of on- and off-balance-sheet 

risk. Another important task is to strengthen 

crisis resolution frameworks.

Moreover, the financial turmoil has revealed 

that national financial stability frameworks have 

failed to keep up with financial market innovation and globalization, at the price of deleterious cross-border spillovers. Greater cross-border 

coordination and collaboration among national 

prudential authorities are needed, particularly 

for the purposes of preventing, managing, and 

resolving financial stress both in markets and in 

major financial institutions. 

Fostering Energy Conservation and Greater Oi

and Food Suppl

The recent decline in commodity prices 

should not detract from efforts to relieve strains 

in commodity markets. There is little concrete 

evidence that rising investor interest in commodities as an alternative asset—or outright 

speculation—had a systematic or lasting impact 

on prices. However, the combination of unusual 

swings in market sentiment and greater financial 

market liquidity may have contributed to shortterm price dynamics in some circumstances. 

Accordingly, the focus should be on policies 

to encourage better balance between supply 

and demand in the longer term and to avoid 

measures that could exacerbate market tightness 

in the short term. This could include greater 

pass-through of international price changes to 

domestic markets and greater energy conservation. Lower biofuel subsidies in the advanced 

economies could also relieve short-term pressures on food prices. In general, priority should 

be given to strengthening the supply response 

to higher prices. For now, greater donor support 

for the poorest economies will be crucial to 

address the humanitarian challenges raised by 

the surge in food prices.

Unwinding Global Imbalance

The surge in commodity prices has led to 

a further widening in global imbalances, with 

wider current account surpluses in oil exporters 

and larger deficits in oil importers. Of course, 

exporters’ intent to save some of the additional 

revenues is sensible: to date, the associated recycling of funding from surplus to deficit countries is working well. At the same time, the U.S. 

non-oil deficit has fallen substantially, in part 

reflecting the depreciation of the U.S. currency 

back toward a real effective rate that is broadly 

consistent with medium-term equilibrium. 

However, U.S. dollar depreciation has occurred 

mainly against the euro and some other flexibly 

managed currencies. 

The multilateral strategy endorsed by the 

International Monetary and Financial Committee in 2005 and elaborated by the Multilateral 

Consultation on Global Imbalances in 2006 and 

2007 remains relevant but needs to be applied 

flexibly. U.S. fiscal consolidation remains a key 

medium-term objective, but recent countercyclical fiscal stimulus and public support to stabiExecutive Summa
Executive Summa

xx

lize financial institutions have been warranted. 

Further effective appreciation of the renminbi 

would contribute to China’s broader strategy 

to shift the sources of growth toward internal demand and to increase the effectiveness 

of monetary policy. A slowdown in spending 

growth in Middle Eastern oil exporters would 

help reduce overheating in their economies, as 

would a heightened focus on relieving supply 

bottlenecks. At the same time, product and 

labor market reforms in the euro area and 

Japan would raise potential growth. 

Finally, rising protectionist pressures on 

both trade and capital flows reflect a worrisome risk to the prospective recovery. Breaking the current Doha Round deadlock would 

help strengthen the open multilateral trading 

system, an important underpinning of strong 

global growth in recent years. At the same time, 

sovereign wealth funds (SWFs) continue to grow 

as investment vehicles for surplus countries. The 

set of principles and practices recently agreed 

by SWFs for their governance, investment, and 

risk management (the “Santiago Principles”) 

will contribute to reducing concerns about these 

types of funds that could lead to counterproductive restrictions on such inflows. Moreover, 

guidelines for recipient countries, which are 

under development at the Organization for Economic Cooperation and Development, would 

help reassure the SWFs of fair, transparent, and 

open access to markets.


c h a pte r 1

The world economy is now entering a major downturn 

in the face of the most dangerous shock in mature 

financial markets since the 1930s. Against an exceptionally uncertain background, global growth projections for 2009 have been marked down to 3 percent, 

the slowest pace since 2002, and the outlook is subject 

to considerable downside risks. The major advanced 

economies are already in or close to recession, and, 

although a recovery is projected to take hold progressively in 2009, the pickup is likely to be unusually 

gradual, held back by continued financial market 

deleveraging. In this context, elevated rates of headline 

inflation should recede quickly, provided oil prices stay 

at or below current levels. The emerging and developing 

economies are also slowing, in many cases to rates well 

below trend, although some still face significant inflation pressure even with more stable commodity prices. 

The immediate policy challenge is to stabilize global 

financial markets, while nursing economies through a 

global downturn and keeping inflation under control. 

Over a longer horizon, policymakers will be looking to 

rebuild firm underpinnings for financial intermediation and will be considering how to reduce procyclical 

tendencies in the global economy and strengthen supplydemand responses in commodity markets.

This chapter opens with an overview of 

a global economy under stress. It then 

examines the expanding financial crisis 

and its macroeconomic implications in 

more detail, as well as the imbalances in housing 

and commodity markets. This analysis sets the 

stage for the discussion of the outlook and risks. 

The final part of the chapter discusses the policy 

challenges. Chapter 2 looks in more detail at 

developments and policy issues in each of the 

world’s main regions.

Global Economy under Stre

For four years through the summer of 2007, 

the global economy boomed. Global GDP rose 

at an average of about 5 percent a year, its highest sustained rate since the early 1970s. About 

three-fourths of this growth (measured on a purchasing-power-parity basis) was attributable to a 

broad-based surge in the emerging and developing economies (Table 1.1 and Figure 1.1). Inflation remained generally contained, albeit with 

some upward drift.

Over the past year, the global economy has 

been buffeted by the deepening crisis in financial markets, by major corrections in housing 

markets in a number of advanced economies, 

and by surges in commodity prices. Indeed, the 

financial crisis that erupted in August 2007 after 

the collapse of the U.S. subprime mortgage 

market entered a tumultuous new phase in September 2008 that has badly shaken confidence 

in global financial institutions and markets. Most 

dramatically, intensifying solvency concerns have 

triggered a cascading series of bankruptcies, 

forced mergers, and public interventions in the 

United States and western Europe, which has 

resulted in a drastic reshaping of the financial 

landscape. Moreover, interbank markets have 

virtually locked up as trust in counterparties 

has evaporated. Responding rapidly, the U.S. 

and European authorities have announced 

far-reaching measures aimed at supporting key 

institutions, stabilizing markets, and bolstering 

confidence, but markets remains highly unsettled and volatile as this report goes to press.

Faced by increasingly difficult conditions, 

the global economy has slowed markedly. The 

advanced economies grew at a collective annualized rate of only 1 percent during the period 

from the fourth quarter of 2007 through the 

second quarter of 2008, down from 21⁄2 percent 

during the first three quarters of 2007. The 

U.S. economy has suffered most from the direct 

effects of the financial crisis that originated in 

its own subprime mortgage market, which has 

tightened credit conditions and amplified the 

Global Prospects
Chapter1  Global Prospects and Poli



Table 1.1. Overview of the World Economic Outlook Projectio

(Percent change, unless otherwise noted)

Year over Year

Difference from July 

2008 WEO Projections

Q4 over Q4

Projections Estimates Projections

2006 2007 2008 2009 2008 2009 2007 2008 2009

World output1 5.1 5.0 3.9 3.0 –0.2 –0.9 4.8 2.8 3.

Advanced economies 3.0 2.6 1.5 0.5 –0.2 –0.9 2.6 0.7 1.0

United States 2.8 2.0 1.6 0.1 0.3 –0.7 2.3 0.8 0.4

Euro area 2.8 2.6 1.3 0.2 –0.4 –1.0 2.1 0.4 0.6

Germany 3.0 2.5 1.8 — –0.2 –1.0 1.7 0.7 0.6

France 2.2 2.2 0.8 0.2 –0.8 –1.2 2.2 –0.1 0.8

Italy 1.8 1.5 –0.1 –0.2 –0.6 –0.7 0.1 –0.1 0.2

Spain 3.9 3.7 1.4 –0.2 –0.4 –1.4 3.2 0.1 0.1

Japan 2.4 2.1 0.7 0.5 –0.8 –1.0 1.4 0.2 0.9

United Kingdom 2.8 3.0 1.0 –0.1 –0.8 –1.8 2.9 –0.3 0.7

Canada 3.1 2.7 0.7 1.2 –0.3 –0.7 2.8 0.3 1.7

Other advanced economies 4.5 4.7 3.1 2.5 –0.2 –0.8 5.0 2.0 3.7

Newly industrialized Asian economies 5.6 5.6 4.0 3.2 –0.2 –1.1 6.1 2.6 5.4

Emerging and developing economies2 7.9 8.0 6.9 6.1 — –0.6 8.5 6.1 6.5

Africa 6.1 6.3 5.9 6.0 –0.5 –0.4 . . . . . . . . .

Sub-Sahara 6.6 6.9 6.1 6.3 –0.5 –0.5 . . . . . . . . .

Central and eastern Europe 6.7 5.7 4.5 3.4 –0.1 –1.1 . . . . . . . . .

Commonwealth of Independent States 8.2 8.6 7.2 5.7 –0.6 –1.5 . . . . . . . . .

Russia 7.4 8.1 7.0 5.5 –0.7 –1.8 9.5 5.9 5.8

Excluding Russia 10.2 9.8 7.6 6.2 –0.2 –0.8 . . . . . . . . .

Developing Asia 9.9 10.0 8.4 7.7 — –0.7 . . . . . . . . .

China 11.6 11.9 9.7 9.3 — –0.5 11.3 9.2 9.4

India 9.8 9.3 7.9 6.9 –0.1 –1.1 8.9 7.2 6.9

ASEAN–5 5.7 6.3 5.5 4.9 –0.1 –1.0 6.6 4.7 5.7

Middle East 5.7 5.9 6.4 5.9 0.2 –0.1 . . . . . . . . .

Western Hemisphere 5.5 5.6 4.6 3.2 0.1 –0.4 . . . . . . . . .

Brazil 3.8 5.4 5.2 3.5 0.3 –0.5 6.2 3.9 3.7

Mexico 4.9 3.2 2.1 1.8 –0.3 –0.6 4.2 0.9 2.4

Memorandum

European Union 3.3 3.1 1.7 0.6 –0.4 –1.1 . . . . . . . . .

World growth based on market exchange rates 3.9 3.7 2.7 1.9 –0.2 –0.8 . . . . . . . . .

World trade volume (goods and services) 9.3 7.2 4.9 4.1 –1.2 –1.9 . . . . . . . . 

Imports

Advanced economies 7.5 4.5 1.9 1.1 –1.6 –2.3 . . . . . . . . .

Emerging and developing economies 14.7 14.2 11.7 10.5 –0.7 –1.1 . . . . . . . . .

Exports

Advanced economies 8.4 5.9 4.3 2.5 –0.7 –1.8 . . . . . . . . .

Emerging and developing economies 11.0 9.5 6.3 7.4 –2.0 –1.7 . . . . . . . . .

Commodity prices (U.S. dollar

Oil3 20.5 10.7 50.8 –6.3 –13.0 –13.6 . . . . . . . . .

Nonfuel (average based on world 

commodity export weights) 23.2 14.1 13.3 –6.2 –1.3 –1.0 . . . . . . . . .

Consumer prices

Advanced economies 2.4 2.2 3.6 2.0 0.2 –0.3 3.0 3.3 1.7

Emerging and developing economies2 5.4 6.4 9.4 7.8 0.3 0.4 6.7 7.9 6.2

London interbank offered rate (percent)4

On U.S. dollar deposits 5.3 5.3 3.2 3.1 0.4 –0.5 . . . . . . . . .

On euro deposits 3.1 4.3 4.8 4.2 –0.2 –1.1 . . . . . . . . .

On Japanese yen deposits 0.4 0.9 1.0 1.2 –0.1 –0.3 . . . . . . . . .

Note: Real effective exchange rates are assumed to remain constant at the levels prevailing during August 18–September 15, 2008. 

1The quarterly estimates and projections account for 90 percent of the world purchasing-power-parity weights.

2The quarterly estimates and projections account for approximately 76 percent of the emerging and developing economies.

3Simple average of prices of U.K. Brent, Dubai, and West Texas Intermediate crude oil. The average price of oil in U.S. dollars a barrel was 

$71.13 in 2007; the assumed price based on future markets is $107.25 in 2008 and $100.50 in 2009. 4Six-month rate for the United States and Japan. Three-month rate for the euro area. 


housing correction that has been under way 

since 2006. Aggressive policy easing by the Federal Reserve, a timely fiscal stimulus package, 

and strong export performance on the back of 

a weakening U.S. dollar have helped cushion 

these blows, but the economy has still managed 

to grow by only 11⁄4 percent on average since 

the fourth quarter of 2007. Activity in western 

Europe has also slowed appreciably, dampened 

by high oil prices, tightening credit conditions, 

housing downturns in several economies, the 

U.S. slowdown, and the appreciating euro. 

Japan’s economy initially showed more resilience but has recently been affected by slowing 

exports and the impact of deteriorating terms of 

trade on domestic demand.

Available data for the third quarter and forward-looking indicators suggest that the downturn in the advanced economies is continuing 

to deepen (Figure 1.2). Indeed, business and 

consumer confidence indicators for the United 

States and the euro area are now close to lows 

experienced during the 2001–02 recession.

The emerging and developing economies 

have not decoupled from this downturn. Growth 

in these countries eased from 8 percent in the 

first three quarters of 2007 to 71⁄2 percent in the 

subsequent three quarters, as domestic demand 

(particularly business investment) and net 

exports have moderated. Moreover, recent trade 

and business activity indicators are signaling continuing deceleration. Growth has been most resilient in commodity-exporting countries, which 

are benefiting from still-high export prices. By 

contrast, countries with the strongest trade links 

with the United States and Europe are slowing 

markedly, while some countries that relied on 

bank-related or portfolio inflows to finance large 

current account deficits have been hit hard by an 

abrupt tightening of external financing. Nevertheless, as a group, emerging economies have so 

far sustained market access better than in earlier 

episodes of financial turbulence, reflecting 

improvements in policy frameworks and stronger 

public sector balance sheets.

Despite the deceleration of global growth, 

headline inflation has risen around the world 

0

25

50

75

100

-4

0

4

8

12

16

0

2

4

6

8

10

0

2

4

6

8

Trend,

1970–2007

World Trade Volume

(goods and services)

World Real GDP Growth

Figure 1.1. Global Indicators

(Annual percent change unless otherwise noted)

1

After four years of strong growth, the global economy is heading into a major 

downturn, led by the advanced economies. At the same time, inflation has risen 

to its highest rates in a decade, pushed up by a surge in commodity prices .

Trend,

1970–2007

 Source: IMF staff estimates.

 Shaded areas indicate IMF staff projections. Aggregates are computed on the basis of

purchasing-power-parity (PPP) weights unless otherwise noted.

 Average growth rates for individual countries, aggregated using PPP weights; the 

aggregates shift over time in favor of faster-growing economies, giving the line an upward

trend.

 Simple average of spot prices of U.K. Brent, Dubai Fateh, and West Texas Intermediate

crude oil.

2

1

2

2

0

5

10

15

20

25 Consumer Prices

Advanced

economies

Emerging and 

developing economies

Real GDP Growth

Advanced

economies

1970 80 90 2000 10 1970 80 90 2000 10

1970 80 90 2000 10

1970 80 90 2000 10

Emerging and 

developing economies 

(median)

Real Commodity Prices

(1995 = 100)

Food

Oil prices3

Metals

1980 85 90 95 2000 05 10 0

100

200

300

400

500

3

Contribution to Global GDP

Growth, PPP Basis

(percent, five-year averages)

1970–74 80–84

China Other advanced economies United States

Rest of the world

90–94 2000–04

75–79 85–89 95–99 05–09

Global Economy under Stres
Chapter1  Global Prospects and Poli



to the highest rates since the late 1990s, pushed 

up by the surge in fuel and food prices. In 

the advanced economies, 12-month headline 

inflation registered 41⁄4 percent in August 2008, 

down modestly from a peak in July in the wake 

of some commodity price easing (Figure 1.3). 

Measures of underlying inflation—price indices 

excluding food and fuel prices, inflation expectations, and labor costs—have been broadly 

contained, although there has been upward 

drift in some measures. Reflecting heightened 

inflation concerns, the Federal Reserve has held 

the federal funds rate at 2 percent since April, 

after six months of steep cuts, and the European 

Central Bank increased its policy rate one notch 

to 41⁄4 percent in early July.

The resurgence in inflation has been more 

marked in the emerging and developing economies, with headline inflation reaching 81⁄4 percent in the aggregate in August and with a wide 

swath of countries now experiencing doubledigit inflation. To some extent, the difference 

reflects the considerably greater weight of food 

prices in consumption baskets in these economies—typically in the range of 30–45 percent 

as opposed to 10–15 percent in the advanced 

economies. However, inflation excluding food 

and fuel has also accelerated markedly, and 

there are signs of rising inflation expectations 

and wage increases, although such data are not 

as systematically available as in the advanced 

economies. Chapter 3 looks at the relationship between commodity prices and inflation 

and finds that emerging economies have been 

more vulnerable to second-round effects. This 

is because the greater weight of food prices 

has put more pressure on real wages, because 

inflation expectations are less well anchored by 

central bank credibility, and because fast growth 

has eroded margins of spare capacity.

Policymakers in emerging and developing 

economies have responded to rising inflation 

with an eclectic mix of measures. Many central 

banks have raised interest rates, but others have 

relied more on increasing reserve requirements 

and tightening credit, particularly where interest rate policy has been constrained by inflex20

40

60

80

100

120

140

160

180

-24

-20

-16

-12

-8

-4

0

4

8

40

45

50

55

60 Manufacturing Purchasing 

Managers Index

(index)

Consumer Confidence

(index)

United States

(left scale)

Euro area

(right scale)

-10

-5

0

5

10

15 Industrial Production

World

Figure 1.2. Current and Forward-Looking Indicators

(Percent change from a year earlier unless otherwise noted)

Domestic demand has slowed considerably in the advanced economies, and 

indicators of business sentiment and consumer confidence suggest that the 

deceleration is likely to intensify. Emerging economies have not decoupled, as

slowing world trade has dampened manufacturing activity.

 Sources: CPB Netherlands Bureau for Economic Policy Analysis for CPB trade volume

index; for all others, NTC Economics and Haver Analytics.

 Australia, Canada, Denmark, euro area, Japan, New Zealand, Norway, Sweden,

Switzerland, United Kingdom, and United States.

 Argentina, Brazil, Bulgaria, Chile, China, Colombia, Czech Republic, Estonia, Hong Kong

SAR, Hungary, India, Indonesia, Israel, Korea, Latvia, Lithuania, Malaysia, Mexico, 

Pakistan, Peru, Philippines, Poland, Romania, Russia, Singapore, Slovak Republic, South 

Africa, Taiwan Province of China, Thailand, Turkey, Ukraine, and Rep. Bolivariana de

Venezuela. 

 Data for China and Pakistan are interpolated.

 Percent change from a year earlier in SDR terms.

 Japan’s consumer confidence data are based on a diffusion index, where values greater

than 50 indicate improving confidence.

1

2

3

Emerging

economies2

Japan

(left scale)

5

4

2000 02 04 06 Aug.

 08

Sep.

 08

Sep.

 08

Advanced

economies1

2000 02 04 06 2000 02 04 06

0

2

4

6

8

10

Advanced

economies1

World

Domestic Demand Growth

(percent)

Emerging

economies2,3

08:

Q2

2000 02 04 06 -8

-4

0

4

8

12

16

20

24

Advanced

economies1

World

Real Exports

(percent)

08:

Q2

2000 02 04 06

Emerging

economies2

Emerging 

economies2

Advanced

economies1

-15

-10

-5

0

5

10

15

20

25

World Trade 30

2000 02 04 06 Jul.

 08

CPB trade 

volume index

5

Trade value4


ible exchange rate management. However, as 

discussed below, some of these steps have been 

reversed recently in the face of intense liquidity 

strains related to recent financial turmoil. Some 

countries have also tightened fiscal policies to 

help restrain the growth of aggregate demand. 

Going beyond macroeconomic policies, a number of countries have sought to limit the impact 

of rising international commodity prices on 

domestic prices by delaying or limiting the passthrough of oil prices—with a potentially heavy 

fiscal cost—by lowering tariffs on imported food, 

and in some cases by prohibiting or imposing 

taxes on food exports.

The weakening of U.S. growth relative to its 

trading partners and the sustained depreciation 

of the U.S. dollar since 2002 helped lower the 

U.S. current account deficit to 5 percent of GDP 

in the first half of 2008, from 61⁄2 percent in late 

2005 (Figure 1.4). The decrease is even larger 

if net oil imports are excluded. Despite some 

strengthening since early 2008, the real effective 

exchange rate of the U.S. dollar is at its lowest 

level in decades, and the dollar is now assessed 

to be broadly in line with medium-term fundamentals. The adjustment in the dollar in recent 

years has largely come against other advanced 

economy currencies, notably the euro (which is 

now judged to be on the strong side of fundamentals) and the yen (which is still assessed to 

be undervalued relative to fundamentals), as 

well as other floating rate currencies.

Among emerging economies, China’s 

exchange rate has continued to appreciate at a 

moderate pace, with a somewhat faster rise in 

real effective terms owing to the pickup in inflation (Figure 1.5). Nevertheless, China’s current 

account surplus has remained above 10 percent 

of GDP, and with strong capital inflows despite a 

tightening of controls, reserves have continued 

to mount. In the IMF staff’s view, the renminbi 

remains substantially undervalued relative to 

medium-term fundamentals. Many oil exporters in the Middle East have continued to peg 

against the U.S. dollar. As a result, their nominal 

effective exchange rates have tended to depreciate, although exchange rates have appreciated 

-12

-6

0

6

12

18

24

0

1

2

3

4

5

6

7

8

9

 Sources: Bloomberg Financial Markets; Haver Analytics; and IMF staff calculations.

 Personal consumption expenditure deflator.

 Ten-year breakeven rates.

1

2

Global Aggregates

0

1

2

3

4

5

6

7

8

9 Headline Inflation

World

Advanced 

economies

Emerging 

economies

Core Inflation

World

Advanced

economies

-2

-1

0

1

2

3

4

5 Advanced Economies: Headline 

Inflation

Euro area

Japan

-2

-1

0

1

2

3

4

Advanced Economies: Core 5

Inflation

Japan

Euro area

Figure 1.3. Global Inflation

(Twelve-month change in the consumer price index unless otherwise 

noted)

Headline inflation has surged, particularly in emerging and developing economies,

reflecting both a jump in food and fuel prices and a more general tightening of

capacity constraints. The advanced economies have also experienced a marked

acceleration of headline inflation, driven mainly by the pass-through of high

international oil prices, but indicators of underlying inflation have risen only

modestly.

Emerging 

economies

2002 03 04 05 06 Aug.

 08

2002 03 04 05 06 Aug.

 08

2002 03 04 05 06 Aug.

 08

2002 03 04 05 06 Aug.

 08

United States1

-5

0

5

10

15

20

Emerging Economies: Headline 25

Inflation

India

China

2002 03 04 05 06 Aug.

 08

Brazil

Russia

07

07 07

07 07

Country Indicators

-4

0

4

8

12

16 Food Price Inflation

World

Advanced

economies

Fuel Price Inflation

Emerging 

economies

World

Advanced

economies

Emerging

economies

United States1

2002 03 04 05 06 Jul.

 08

2002 03 04 05 06 Jul.

 08

07 07

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5 Advanced Economies: Inflation 

Expectations

2002 03 04 05 06 Sep.

 08

United States

Euro area

Japan

2

07

Global Economy under Stre
Chapter1  Global Prospects and Poli



moderately in real terms because of rising inflation. Elsewhere, experiences are quite diverse. 

Currencies in emerging Europe and Latin 

America have generally appreciated, as monetary policy has been tightened and commodity 

exporters have benefited from terms-of-trade 

gains, although some currencies have come 

under pressure recently as commodity prices 

softened and risk aversion increased. A number 

of currencies in Africa and south and east Asia 

(for example, India, Korea, Pakistan, and South 

Africa) have depreciated over a longer period, 

in part owing to rising costs of commodity 

imports and widening current account deficits.

Financial System in Crisis

The April 2008 World Economic Outlook was 

finalized just after the Federal Reserve engineered the emergency sale of a major U.S. 

investment bank (Bear Stearns) and increased 

broker-dealer access to emergency liquidity. 

Banks also made progress in recognizing their 

losses on subprime-mortgage-related exposures, 

rebuilding their capital, and reducing their 

leverage.2

Despite these efforts, financial market strains 

intensified again over the summer as solvency 

concerns resurfaced and as it became clear that 

the process of balance-sheet repair would be 

protracted. Bank funding came under particular 

stress (Figure 1.6). One source of pressure was 

the increasing concern that credit losses were 

mounting in the grip of a negative feedback 

loop between the economy and the financial 

system. At the same time, bank adjustment 

was hampered by high funding costs, reduced 

revenue streams from fee-based securitization 

1Financial sector developments are discussed in detail 

in the October 2008 Global Financial Stability Report (IMF, 

2008b). 2As of September 2008, banks reported $518 billion in 

losses on U.S. subprime mortgages and related exposure, 

the lion’s share by U.S. and European banks. Banks also 

raised $364 billion in new capital. These amounts compare to losses on U.S.-based loans and related securities 

now estimated at $1.4 trillion, of which $640 billion–

$735 billion would correspond to banks (IMF, 2008b).

-9

-6

-3

0

3

6

60

80

100

120

140

70

80

90

100

110

120

130

140

150

 Sources: Haver Analytics; and IMF staff calculations.

Nominal Effective Exchange Rate

(index, 2000 = 100)

United

States

Figure 1.4. External Developments in Selected

Advanced Economies

Depreciation of the real effective value of the U.S. dollar combined with slowing 

domestic demand have contributed to some moderation in the U.S. current account 

deficit. The current account positions of the euro area and Japan have weakened over

the past year, reflecting exchange rate appreciation and higher oil prices.

Euro

area

Japan

Real Effective Exchange Rate

(index, 2000 = 100)

United States

Euro area

Japan

Current Account Positions

(percent of GDP)

United States

Euro

area

Japan

Sep.

 08

Sep.

 08

2000 02 04 06

08:

Q2

2000 01 02 03 04 05 06

01 03 05

2000 01 02 03 04 05 06 07

07

07


business, and forced accumulation of assets from 

off-balance-sheet entities and prior loan commitments. Falling equity prices made raising 

new capital increasingly expensive, often prohibitively so, while at the same time, markets as 

well as regulators were looking for a significant 

increase in capital-to-asset ratios to levels well 

above those prevailing before the crisis.

Once more, the greatest strains have been 

experienced by institutions heavily exposed to 

the still-weakening U.S. housing market. Starting in August, Fannie Mae and Freddie Mac, 

the two giant government-sponsored enterprises (GSEs),3 came under heavy pressure over 

concerns about the adequacy of their capital 

bases in the face of rising losses, which were not 

relieved by assurances from the U.S. authorities 

that these two institutions would have access to 

federal funding to meet their liquidity and capital needs. In light of the crucial current role of 

these agencies in the U.S. housing market and 

the global financial system, the two institutions 

were placed under the conservatorship of the 

U.S. Federal Housing Finance Agency, with the 

U.S. government pledging additional financial 

support as needed to maintain adequate capital 

and funding.

Notwithstanding these efforts, global financial 

markets were plunged into turmoil in mid-September following the bankruptcy of a second 

major U.S. investment bank (Lehman Brothers), involving significant losses to creditors and 

counterparties. In the next few days, market 

pressure drove the merger of another (Merrill 

Lynch & Co.) with a large commercial bank and 

the effective acquisition by the Federal Reserve 

of the world’s largest insurance company 

(American International Group, A.I.G.) to avoid 

a disorderly bankruptcy. All of these institutions 

3Formally, the Federal National Mortgage Association 

and Federal Home Loan Mortgage Corporation, respectively. The GSEs hold or guarantee about 50 percent of 

U.S. mortgages and have supported 80 percent of new 

mortgage lending in recent months. Moreover, their securities are held widely across the global financial system 

and have provided a major conduit for external financing 

of the U.S. current account deficit.

60

80

100

120

140

160

-8

-4

0

4

8

12

16

20

24

28

70

80

90

100

110

120

130

140

60

70

80

90

100

110

120

 Sources: IMF, International Financial Statistics; and IMF staff calculations.

 Newly industrialized Asian economies (NIEs) comprise Hong Kong SAR, Korea,

Singapore, and Taiwan Province of China.

 Indonesia, Malaysia, Philippines, and Thailand.

 Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania,

Slovak Republic, and Turkey.

 Botswana, Burkina Faso, Cameroon, Chad, Republic of Congo, Côte d'Ivoire, Djibouti, 

Equatorial Guinea, Ethiopia, Gabon, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritius,

Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, 

Uganda, and Zambia.

 Bahrain, Egypt, I.R. of Iran, Jordan, Kuwait, Lebanon, Libya, Oman, Qatar, Saudi Arabia, 

Syrian Arab Republic, United Arab Emirates, and Republic of Yemen.

 Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Rep. Bolivariana de Venezuela.

1

2

Real Effective Exchange Rate

80

90

100

110

120 Nominal Effective Exchange 

Rate

China

India

Real Effective Exchange Rate

Figure 1.5. External Developments in Emerging

and Developing Economies

(Index, 2000 = 100, unless otherwise noted)

Exchange rate movements have recently been quite diverse across emerging and 

developing economies. A number of oil-importing countries in Asia, especially those 

with close trade ties to the United States, have experienced currency depreciation,

while China's currency has continued to appreciate. Currencies in Latin America and 

emerging Europe have also generally remained buoyant, although weakening recently.

ASEAN-42

Emerging

Europe3

NIEs1

Middle

East

5

3

4

5

6

Nominal Effective Exchange 

Rate

China

India

ASEAN-42

NIEs1

Middle

East5

Emerging

Europe3

Africa4

Current Account Positions

(percent of GDP)

Latin 

America

Emerging Europe

Latin

America6 Latin

America6

Africa4

Sep.

 08

2000 02 04 06

Sep.

 08

2000 02 04 06

Sep.

 08

2000 02 04 06

Sep.

 08

2000 02 04 06

2000 02 04 06

Asia

100

200

300

400

500

600

700

800

International Reserves 900

Latin America

Asia

Aug.

 08

08 2000 02 04 06

Middle

East

5

Middle

East5

Emerging

Europe3

3

Financial System in Cris
Chapter1  Global Prospects and Poli



were heavily exposed to mortgage-related losses. 

As confidence in counterparties all but vanished, 

interbank markets effectively seized up, despite 

coordinated injections of massive liquidity by 

major central banks and agreement on foreign 

exchange swaps of unprecedented magnitude. 

Subsequently, a number of other U.S. and 

European banks needed to be resolved through 

closure, nationalization, or merger with public 

support.

The authorities in the United States and 

Europe responded to this firestorm with a series 

of new initiatives. Notably, in early October, legislation was passed in the United States to set up 

a $700 billion fund to purchase troubled mortgage-related securities from banks in order to 

contain risks of further losses from this source, 

encourage the development of more transparent 

pricing of these assets, and reduce illiquidity on 

bank balance sheets. At the same time, depositguarantee schemes were extended in the United 

States and a number of European countries, 

including a temporary guarantee for U.S. money 

market funds and a guarantee for creditors as 

well as depositors in Ireland. Restrictions also 

were imposed on short-selling of financial stocks 

to alleviate speculative pressure.

As this report goes to press, financial conditions continue to be under extraordinary stress. 

Interbank markets remain highly disrupted 

beyond overnight maturities, equity prices have 

fallen sharply, and market volatility continues 

to be at a high pitch (Figure 1.7). Moreover, 

market sectors that had been less affected by the 

turmoil have come under substantial increased 

pressure, including the nonfinancial corporate 

sector and emerging markets, as outlined in 

Box 1.1. Amid this turbulence, government 

securities have been viewed as a safe haven; U.S. 

Treasury bill yields were driven to close to zero.

Intensifying financial strains are beginning 

to take an increasingly heavy toll on economic 

activity. One of the main channels for such 

macrofinancial linkage is through tightening 

bank lending standards in both the United 

States and western Europe (see Figure 1.6). This 

has occurred in response to banks’ efforts to 

0

50

100

150

200

250

300

350

Figure 1.6. Developments in Mature Credit Markets

Credit market stresses intensified again in September, reflected in soaring spreads 

in the interbank market. Risk spreads have widened sharply across a broad range of 

financial assets. At the same time, bank lending standards have been tightened 

sharply in the United States and euro area, and credit growth is now starting to

moderate.

Bank CDS Spreads

(ten-year; median; in basis 

points)

 Sources: Bank of Japan; Bloomberg Financial Markets; Board of Governors of the Federal 

Reserve System; European Central Bank; Merrill Lynch; and IMF staff calculations.

 Three-month London interbank offered rate minus three-month government bill rate.

 CDS = credit default swap.

 Percent of respondents describing lending standards as tightening “considerably” or

“somewhat” minus those indicating standards as easing “considerably” or “somewhat” over

the previous three months. Survey of changes to credit standards for loans or lines of credit

to enterprises for the euro area; average of surveys on changes in credit standards for 

commercial/industrial and commercial real estate lending for the United States; average of

changes in credit standards for small, medium-size, and large firms for Japan. 

1

United

States

Euro

area

2003 04 Sep.

 08

06

High-Yield Corporate Spreads

(basis points)

0

200

400

600

800

1000

1200

1400

1600

United States

BB

Europe

BB

Sep.

 08

2000 02 04 06

2

3

-7

0

7

14

Private Credit Growth 21

(twelve-month percent change)

Japan

Euro 

area

United

States

2000 02 04 Aug.

 08

06 -40

-20

0

20

40

60

80 Business Lending Conditions

02 08:

Q3

2000 04 06

United

States

Euro 

area

Japan

3

05 07

2

-100

0

100

200

300

400 TED Spreads

(basis points)

2000 02 04 06

U.S.

dollar 

Yen

Sep.

 08

1

Euro

High-Grade Corporate Spreads

(basis points)

0

50

100

150

200

250

300

350

United States

AAA

Europe

AAA

Sep.

 08

2000 02 04 06


decrease their leverage in the face of reduced 

market tolerance for balance-sheet risk, increasingly expensive bank capital, and reduced access 

to wholesale funding. Actual credit growth was 

sustained for a while by the reintermediation 

of off-balance-sheet exposure and prior lending 

commitments, but credit growth is now slowing 

visibly both to the nonfinancial corporate sector 

as well as to households, and this winding back 

of credit is likely to continue until bank capitalization is raised substantially. It is also clear that 

financing through securities markets is likely to 

remain highly constrained for higher-risk borrowers as spreads have widened and securitization has fallen dramatically.

The financial crisis is increasingly affecting 

emerging markets too, reflecting rising risk aversion among investors, the reduced availability 

of funding for leveraged investors like hedge 

funds, and a weakening of growth prospects 

in emerging economies. Local money markets 

have experienced particular pressures, prompting central banks in a number of countries to 

ease reserve requirements and to take other 

actions to reduce strains on liquidity. Moreover, 

equity prices have fallen sharply, and spreads 

on both sovereign and corporate paper have 

widened markedly (Figure 1.8). Countries with 

large external financing needs and commodity 

exporters facing the prospect of lower prices 

have faced particular pressure from the reversal 

of capital flows. Nevertheless, looking back over 

the past year, overall capital flows to emerging 

economies have been quite resilient, certainly by 

past standards. Against this background, private 

credit growth has continued to be rapid in many 

of these economies, and domestic interest rates 

have declined in real terms as rising inflation 

has outstripped increases in policy rates.

The concerns expressed in the April 2008 

World Economic Outlook about the impact of 

sustained tight credit conditions on economic 

activity remain highly relevant. These concerns have been reinforced by the analysis in 

Chapter 4, which outlines how past episodes of 

financial stress involving shocks to the banking 

sector have typically been followed by deeper-

-20

-10

0

10

20

30

-30

-15

0

15

30

45

3

6

9

12

15

18

Adjusted Price-Earnings

Ratios

Figure 1.7. Mature Financial and Housing 

Market Indicators

Financial strains are being reflected in a sharp correction in equity prices and 

sustained high volatility in equity and currency markets. Property price dynamics

have continued to weaken, most notably in the United States, but also in France, 

Italy, Spain, and the United Kingdom.

0

10

20

30

40

50 Equity Market Volatility

(three-month moving average)

S&P 500

daily volatility

Euro/dollar

(implied)

 Sources: Bloomberg Financial Markets; Datastream; CEIC Data Company Limited; Haver

Analytics; IMF, International Financial Statistics; OECD, Economic Outlook; and IMF staff 

calculations. 

 Adjusted price-earnings ratio is the ratio of stock prices to the moving average of the 

previous 10 years’ earnings, adjusted for nominal trend growth. Adjusted price-earnings

ratios are measured as the three-month moving average of deviations from the 1990–2008 

(August) average.

 VIX is the Chicago Board Options Exchange volatility index. This index is calculated by

taking a weighted average of implied volatility for the eight S&P 500 calls and puts.

1

Currency Volatility

(three-month moving average)

VIX2

Yen/dollar

(implied)

Germany

(left scale)

United States

(left scale)

Japan

(right scale)

1

2

Aug.

 08

Sep.

 08

2000 02 06

2000 02 04 06

Sep. 04

 08

2000 02 04 06

30

40

50

60

70

80

90

100

110

120

DJ Euro 

Stoxx

Wilshire

5000

Equity Markets

(March 2000 = 100; national

currency)

Topix

2000 02 04 06 Sep.

 08

-10

0

10

20

30 Residential Property Prices 

(12-month percent change)

United 

States

Japan

-10

0

10

20

30

Canada

United 

Kingdom

08:

Q2

1995 97 99 2001 03 05

Residential Property Prices

(12-month percent change)

08:

Q2

1995 97 99 2001 03 05

Germany

Italy

France Spain

Financial System in Cris
Chapter1  Global Prospects and Poli

1

than-usual business-cycle downturns and more 

protracted recoveries. The main transmission 

channel from financial sector shocks to downturns in activity seems to be a contraction in net 

lending to the business and household sectors. 

Chapter 4 points out that the growing role of 

securities markets and of arm’s-length financing 

has not in fact reduced the vulnerability of the 

economy in the face of banking stress and presents evidence suggesting that the impact could 

be even larger because of procyclical swings in 

leverage.

One important lesson from Chapter 4 is that 

the extent of damage to the economy depends 

on the initial strength of corporate and household financial positions and housing price developments. The U.S. economy seems particularly 

vulnerable because household balance sheets 

are stretched and the housing sector is undergoing a major correction. The relatively strong 

initial position of the U.S. corporate sector and 

the rapid shift toward monetary easing are identified as mitigating factors. Western European 

economies should gain some protection from 

the strong position of households, but would 

nevertheless also be at considerable risk from a 

sustained period of financial stress.

Deepening Housing Correction

Financial factors have interacted in important ways with housing cycles to amplify the 

extent of housing booms and busts and procyclical swings in leverage. The historic housing 

booms experienced in the United States and 

many western European economies since the 

early years of this decade had their origin in 

falling real interest rates, strong growth, and 

in some cases rapid immigration. However, the 

expansion was also fueled by new financing 

techniques based on securitization and weakening lending standards, particularly in the 

United States.4 By 2006, more than 40 percent 

4Dell’Ariccia, Igan, and Laeven (2008) document how 

the weakening of lending standards contributed to the 

deterioration of credit quality in the U.S. subprime sector.

 Sources: Bloomberg Financial Markets; Capital Data; IMF, International Financial 

Statistics; and IMF staff calculations.

 JPMorgan EMBI Global Index spread.

 JPMorgan CEMBI Broad Index spread.

 Total of equity, syndicated loans, and international bond issuances.

 Relative to headline inflation.

1

Figure 1.8. Emerging Market Conditions

Emerging market conditions have been affected increasingly by financial strains in 

mature markets. Equity prices have dropped sharply in recent months, spreads have

widened, and new issues have moderated from last year’s highs. At the same time, 

domestic interest rates have been increased in response to rising inflation, but real

rates have declined. Although private credit growth has moderated some, it remains 

high.

0

200

400

600

800

1000

1200

1400

New Issues

(billions of U.S. dollars)

High-yield

Interest Rate Spreads 

(basis points)

0

100

200

300

400

500

600 Equity Markets

(2001 = 100;

national currency)

-8

0

8

16

24

32

40 Private Credit Growth

(12-month percent change)

Latin

America

Asia

Eastern

Europe

0

5

10

15

20

25 Nominal Short-Term Interest

Rates

(percent)

AAA

Asia

Latin

America Eastern

Europe

Latin

America

Eastern

Europe

Asia

2002 03 04 05 06 08:

 Q3

Sep.

 08

2002 03 04 05 06 Sep. 2002 03 04 05

08

2002 03 04 05 06

2002 03 04 05 06 Aug.

 08

06

Sovereign1

2

07 07

07

07 07

-4

-2

0

2

4

6

8

10

12 Real Short-Term Interest Rates

(percent)

Latin

America

Eastern

Europe

Asia

2002 03 04 05 06 Aug.

 08

07

3

0

25

50

75

100

125

150

175

200

Europe

Asia

Africa

Western Hemisphere

Middle East

Corporate2

3

Jul.

 08

4

4
Since the beginning of the financial crisis in 

mid-2007, the World Economic Outlook baseline 

forecast has envisaged that financial strains 

would be protracted and would take a significant toll on economic activity. However, the 

resilience of the nonfinancial corporate sector 

in advanced economies and the momentum of 

growth in emerging economies were expected 

to cushion the impact on global growth. Data 

through mid-September 2008 were broadly 

consistent with this assessment. With the financial crisis entering a new, more severe stage 

in September 2008, the question arises as to 

whether the likely course of the global economy 

has changed. This box specifically explores how 

the nonfinancial corporate sector in advanced 

economies and emerging markets have been 

affected by the latest financial events, highlighting mounting risks to these segments of the 

global economy.

The latest stage of the financial crisis started 

in September 2008, when several systemically 

important U.S. financial institutions abruptly 

exited the market. Lehman Brothers’ decision 

to file for bankruptcy, in particular, reverberated across global financial markets, exacerbating the severe contraction in market liquidity 

and heightening concerns about counterparty 

risks. The cost of U.S. dollar funding surged 

globally, and other money markets also came 

under severe strain. As investors’ appetite for 

risk declined, pressures extended to emerging 

markets, particularly to Russia, which faced 

a confluence of shocks. The global financial 

turmoil has been met with a far-reaching public 

response. However, financial markets remain 

under strain, and confidence is still fragile. 

Major structural shifts in the U.S. financial sector, which took place during this latest stage of 

the crisis, have intensified and broadened the 

deleveraging process, laying the groundwork for 

a further downsizing of the financial sector.1

Note: The authors of this box are Andreas Jobst 

and Natalia Tamirisa.

A worrying aspect of this latest bout of turbulence is that there are now increasing signs that 

market strains are starting to fall more heavily 

on the nonfinancial corporate sector and on 

emerging markets. If sustained, such strains 

could well foreshadow a more severe macroeconomic impact of the financial crisis than 

previously anticipated. 

The nonfinancial sector in advanced economies is now more broadly affected than during 

the earlier stages of the crisis. Spreads on highgrade nonfinancial corporate bonds, which have 

risen gradually since the beginning of the crisis, 

rose further during the latest round of turbulence (first figure). They now stand at almost 

double the 2002 peaks and indicate a default 

risk comparable to that of emerging market 

sovereign debt. Low-grade corporate spreads 

also surged, but they remain below the historical highs of 2002. Access to short-term financing 

has tightened and equity prices have declined 

(upper panel of second figure), although equity 

prices still remain above previous troughs. 

The recent surge in borrowing costs for 

nonfinancial firms has taken place against 

the backdrop of a gradual worsening of their 

risk profiles over the course of the financial 

crisis. The market-based measures of default 

risk and leverage ratios2 have risen across the 

credit spectrum in both the United States 

and Europe—not only for low-grade bonds, as 

would be expected during a slowdown,3 but for 

high-grade bonds too (middle panel of second 

figure). For high-grade corporate bonds in the 

1For more details, see the main text of Chapter 

1 and Box 1.1 of the October 2008 Global Financial 

Stability Report (IMF, 2008b). 2The default probabilities are calculated for individual companies from market data using the modified 

Black-Scholes-Merton option pricing formula and 

balance-sheet data over a one-year risk horizon before 

they are aggregated to the country and regional levels. 

The market value, based on equity prices, approximates 

the company’s asset value. Market leverage is defined as 

the ratio of debt to equity, valued at market prices. 3See Box 1.1 in the April 2008 World Economic Outlook.

Box 1.1. The Latest Bout of Financial Distress: How Does It Change the Global Outlo

Deepening Housing Correc
Chapter1  Global Prospects and Poli

United States, for example, the probability of 

default has doubled since June 2007, although 

it remains below the levels experienced in 

2004,4 in part owing to strong corporate balance 

sheets, particularly, ample internal funds. 

Why are high-grade nonfinancial firms being 

affected more severely during the current crisis 

than during the previous major decline in 

financial markets in 2000–02, following the collapse of the dot-com bubble? A possible general 

explanation relates to differences between the 

shocks that triggered the respective downturns. 

4Earlier data are not available.

The current downturn has its roots in the 

financial sector, where the originate-to-distribute 

model largely ceased to function. The financial 

shock is being transmitted to the nonfinancial 

sector via tighter financing conditions and, 

more recently, a drying up of market liquidity. 

The ubiquity of these channels leaves little room 

for differentiation across the credit spectrum. In 

contrast, the dot-com bubble originated in the 

nonfinancial sector, notably high-yield corporate 

credit, and was transmitted mainly through the 

solvency channel, affecting low-grade nonfinancial corporate bonds to an appreciably larger 

extent than high-grade ones. A more specific 

reason for increased pressures on high-grade 

Box 1.1 (continued)

 Sources: Bloomberg Financial Markets; Datastream; JP Morgan; Moody’s KMV; Thomson Reuters; and IMF staff calculations.

 The corporate bond spreads are derived as the difference between the asset swap spread and the commensurate London interbank

offered rate. The sovereign bond spread series for advanced markets is a composite of the five-year U.S. Treasury rate over the effective 

federal funds rate and the five-year German Bund over the EONIA rate (i.e., the effective European Central Bank policy rate).

Advanced and Emerging Markets: Sovereign and Corporate Bond Spreads, 2000–08

(In basis points)

1

1

-200

0

200

400

600

800

1000

1200

U. 1400 S. high-yield 

crisis

Sep.

 08

2000 01 02 03 04 05 06 07

Enron

U.S. high-yield

crisis

Start of global

liquidity glut

and U.S.

yield-curve 

flattening

Northern Rock,

Countrywide

Bear Stearns

Lehman

Brothers, AIG,

Merrill Lynch

Advanced market sovereign bonds Emerging market sovereign bonds

Advanced market corporate bonds (high-grade)

Advanced market corporate bonds (low-grade)

Emerging market corporate bonds
nonfinancial firms relates to a growing concern 

about their rollover risk during the current 

crisis, because refinancing plans have led to a 

bunching of maturing bond obligations over the 

coming years, while bank financing has tightened. Moreover, declines in equity prices have 

increased the cost of raising capital.

The cost of borrowing for emerging markets 

has also increased further in recent weeks, 

although it remais below the peaks during 

2001–02 and the Asian crisis of 1997–98. There 

has been a sharp and broad-based retrenchment from emerging market assets as a result 

of investors’ reduced appetite for risk and their 

need to sell assets to raise cash in response 

to margin calls. Idiosyncratic risks are rising. Emerging Europe and Latin America are 

experiencing the largest declines in sovereign 

and corporate bond returns, while the effect 

on emerging Asian assets has been more 

muted (see upper panel of second figure). The 

increased differentiation in credit markets 

according to countries’ financing needs points 

to a heightened risk of sudden stops in capital 

0

200

400

600

800

1000

1200

1400

1600

Emerging Economies: Credit Default Swap

Spreads, 2004–08

(In basis points)

 Sources: Bloomberg Financial Markets; and IMF staff 

calculations.

 Equally weighted composites of five-year sovereign credit

default swap contracts.

Countries with current account

surpluses (or small deficits)

Countries with current

account deficits > 5%

Sep.

 08

2004 05 06 07

1

1

0.0

0.2

0.4

0.6

0.8

1.0

0.00

0.04

0.08

0.12

0.16

0.20

Europe LG

20

30

40

50

-20

-15

-10

-5

0

 Sources: Bloomberg Financial Markets; Datastream; 

Moody’s KMV; Thomson Reuters; and IMF staff calculations.

 LG = low-grade; HG = high-grade.

 The change in returns of sovereign bonds in the United

States and Europe is based on prices of the one-month 

futures contract on the effective Fed Funds rate and the total 

return index on German Bunds, respectively.

Selected Financial Indicators

(Percent)

Returns, August 31–September 22, 2008

LG nonfinancial corporate bonds

1

United States Asia Eastern 

Europe

Latin 

America

Median Market Leverage of Nonfinancial 

Corporates, 2004–08

HG nonfinancial corporate bonds

Sovereign bonds

1

Europe

Sep.

 08

2004 05 06 07 10

20

30

40

Sep.

 08

2004 05 06 07

U.S. HG

Emerging

HG

Nonfinancial equity

Corporate bonds

-40

-30

-20

-10

0

Europe HG

U.S. LG Emerging 

LG

Europe LG

(left scale)

Median Market-Based Default Probability of Nonfinancial

Corporates, 2004–08

Sep.

 08

2004 06 0.0

0.2

0.4

0.6

0.8

1.0

0.00

0.04

0.08

0.12

0.16

0.20

Sep.

 08

2004 06

U.S. HG

(right scale) Emerging HG

(right scale) Europe HG

(right scale)

U.S. LG

(left scale)

Emerging LG

(left scale)

2

2

Deepening Housing Correc
Chapter1  Global Prospects and Poli

of new U.S. mortgages were nonprime mortgages, often with very high loan-to-value ratios 

and minimal documentation. In European 

countries, there is less evidence of declining 

lending standards, but, as in the United States, 

in several countries the availability of housing 

finance was sustained through the increased 

availability of wholesale financing, involving 

serious liquidity mismatches in some cases.

The subsequent downswing in the U.S. 

housing market has been the largest of the 

postwar period, as housing activity and prices 

have both fallen steeply. The downswing has 

been exacerbated by the virtual disappearance 

of the subprime market, a general tightening 

of lending standards, increasing spreads on 

conventional mortgages despite monetary easing (due to the deteriorating financial situation 

of the GSEs), and sharply rising foreclosures. 

In western Europe, housing cycles have also 

turned down recently, in some cases because 

lending standards have been tightened and 

flows and currency crises in vulnerable emerging economies (third figure).

Since the beginning of the crisis, corporate 

spreads for emerging economies have risen 

above sovereign spreads (see first figure), suggesting that investors consider the emerging 

market nonfinancial sector to be more vulnerable than the public sector, possibly owing to 

their more limited domestic finance opportunities, higher leverage, and greater rollover risks 

compared with sovereigns. The latter are perceived to be more protected, including by high 

official international reserves and improved 

public sector balance sheets. 

As in advanced economies, the recent 

increase in emerging market corporate spreads 

comes on the heels of an earlier weakening in 

the risk profiles of nonfinancial firms, in part 

owing to slower growth (see lower panels of 

second figure). Market-based default probabilities have nearly tripled since the beginning of the crisis for both high- and low-grade 

bonds, although they remain below recent 

peaks. High-grade nonfinancial corporates 

from emerging Asia currently have the highest 

default probabilities, reflecting the fact that 

they have the highest market-based leverage 

ratios in the respective subgroup. This is partly 

due to increased external corporate borrowing 

on the back of appreciating currencies in the 

past two years. In the low-grade segment, Latin 

American corporate bonds have the highest 

leverage ratios.

Nonetheless, corporate spreads and emerging 

sovereign spreads remain well below the levels 

experienced after the Asian crisis, the Argentine default, and the dot-com collapse (see first 

figure). One reason is that emerging economies 

have become more resilient to external financing 

shocks because of larger international reserves, 

higher revenues from commodities, and more 

robust domestic demand. Another reason is 

that emerging economies are facing a lessdirect shock: the collapse of the dot-com bubble 

revolved around a technological innovation that 

was shared more broadly across the world than 

the originate-to-distribute banking model, and the 

Asian crisis originated in emerging economies.

In sum, the latest stage of the financial crisis 

has seen a further steady weakening in corporate and emerging economies’ positions. 

Whether this deterioration will be sustained is 

unclear at the moment. Markets remain exceptionally volatile, and it is difficult to predict how 

long this volatility will persist. The longer the 

turmoil lasts, the more entrenched the feedback 

loop between the financial and real sectors 

will become and the more broadly real sectors 

across the world will suffer. This, together with 

intensified and broadened deleveraging, would 

delay the recovery and increase the likelihood 

of a global recession. Accordingly, recent developments suggest that the outlook for global 

growth has weakened considerably as a result of 

recent events and that the downside risks to the 

baseline forecast have increased.

Box 1.1 (concluded)
credit has become more expensive. The most 

severe downswings have been concentrated in 

a few national markets—Ireland, Spain, and 

the United Kingdom—which had experienced 

the most rapid house price appreciation or the 

greatest building booms, but house prices are 

slowing more broadly (see Figure 1.7, lower panels). IMF staff analysis of house price valuations 

provided in Box 1.2 suggests that, after allowing 

for the impact of key fundamentals, houses continue to appear overvalued across a broad range 

of advanced economies, although prices in the 

United States are now moving closer in line with 

past relationships.

As discussed in Box 1.2, housing downturns 

can have a strong negative impact on growth 

through a range of channels. Most directly, 

the contraction of residential investment has 

subtracted 3⁄4 percentage point a year from U.S. 

growth over the past two years, and similar 

retrenchments are having an even larger impact 

in Ireland and Spain. In addition, the heavy and 

continuing losses from mortgage-related assets—

both direct losses through rising loan delinquencies and indirect losses on mortgage-backed 

assets being marked to market—have been a 

central driver of the financial crisis and the 

related tightening of credit conditions. Finally, 

there is the negative impact of declining house 

prices on opportunities for borrowing using 

housing collateral, as well as possible wealth 

effects. While consumption has been quite resilient in the United States, in part because of tax 

rebates, it is now slowing fast.

Overstretched Commodity Market

Commodity prices remain at much higher 

levels in real terms than at any time in the past 

20 years, despite some correction since mid-July 

amid the slowdown of the global economy (see 

Figure 1.1). Chapter 3 lays out evidence that 

the driving force behind the sustained run-up 

in commodity prices has been the tightness of 

demand-supply balances for many key products 

and realization that markets are likely to remain 

tight for the foreseeable future, after many years 

of ample spare capacity. Commodity demand 

growth has essentially been driven by the continuing integration of large pools of low-income 

labor, especially in Asia, into the global economy—groups with low per capita consumption 

but high income elasticity of demand. Moreover, 

the supply response to rising relative prices has 

been sluggish, in part because of geological and 

technological constraints, particularly in the oil 

sector, in part because of lingering concerns 

that oil prices may yet revert to the much lower 

levels observed in the second half of the 1980s 

and the 1990s, and in part because of policy 

shortcomings that have discouraged investment 

in new supply, for both energy and food. With 

inventories low and spare capacity limited, and 

with very low short-term supply-and-demand 

price elasticities, commodity prices have become 

highly sensitive to news about possible supply 

disruptions or changing perceptions of cyclical 

prospects. Thus, the recent softening in prices 

seems to have been driven largely by perceptions that global growth is slowing and emerging 

evidence of a demand response to high prices 

(notably in the United States), as well as by 

some favorable supply developments.

Some observers have suggested that recent 

large commodity price swings are related to 

speculation or increasing investment in commodities as assets, rather than to shifts in fundamentals affecting supply and demand. IMF staff 

has found some evidence that the depreciation 

of the U.S. dollar and declining U.S. interest rates have had an effect on prices through 

their impact on supply and demand. However, 

as discussed in Box 3.1, while limitations on 

data availability make it hard to reach definitive 

judgments, there is little concrete evidence that 

rising speculation or increased investor interest 

in commodities as alternative assets has had a 

systematic or lasting impact on prices, although 

swings in market sentiment may well have contributed to short-term price dynamics in some 

circumstances.

The most immediate and direct macroeconomic impact of the boom in commodity prices 

has been on inflation. As already mentioned and 

Overstretched Commodity Mar
Chapter1  Global Prospects and Poli

Housing prices have begun falling this year 

in several advanced economies, a sharp contrast 

from the increase in prices seen during 2007 in 

almost all countries except the United States, 

where a housing correction has been under 

way since 2006. In real terms, and on a seasonally adjusted basis, house prices fell in the first 

half of 2008 at an annual rate of 5 percent to 

12 percent in Canada, Denmark, Spain, New 

Zealand, and the United Kingdom (first figure).1

How much more are house prices likely to come 

down? And what are the consequences of the 

declines in house prices for the macroeconomy?

Corrections in house prices. As a basis for assessing the potential for house price declines, a first 

step is to try to account for the increase in house 

prices that has taken place over the past decade 

in terms of important driving forces. To this end, 

real house price growth is modeled as a function 

of the following variables: growth in per capita 

disposable income, working-age population, 

credit and equity prices, and the level of shortterm and long-term interest rates. The dynamic 

effects of these variables are captured through 

the inclusion of lagged real house price growth 

and an affordability ratio (the lagged ratio of 

house prices to disposable incomes). This model 

is estimated for each country using quarterly 

data for the time period 1970 to 2007.2

The increase in house prices not explained 

by these fundamental factors—referred to as 

The main author of this box is Prakash Loungani. 

Ercument Tulun and Jair Rodriguez provided research 

assistance. This box updates analysis presented in 

the October 2007 and April 2008 issues of the World 

Economic Outlook. 1These data are provided by the Organization for 

Economic Cooperation and Development (OECD) 

and are based on commonly used national sources, 

as shown here: www.olis.oecd.org/olis/2006doc.nsf/

linkto/ECO-WKP(2006)3 (p. 34). The data are seasonally adjusted by the OECD if the national authority 

does not provide a seasonally adjusted series. The use 

of seasonally adjusted data leads to some difficulty in 

comparability with headline figures on house prices 

but may be a better indication of developments in 

house prices over the coming months. 2The data start in 1971 for Spain and in 1986 for Korea.

the house price gap—is taken as an estimate of 

the potential for correction in house prices. Of 

course, the gap estimates could partly reflect 

omitted fundamental factors, such as changes in 

supply-side factors in the housing market.3 Nevertheless, the estimates provide an indication 

of how large those omitted factors would have 

3The models estimated here focus on explaining 

short- to medium-run changes in house prices rather 

than the long-run level of house prices, which could 

differ considerably across countries, reflecting national 

supply constraints and long-term institutional factors, 

such as the extent of taxation of housing (Poterba, 

1984). A study of European housing markets by Hilbers 

and others (2008) provides a good exposition of the 

role such factors can play in house price movements.

Box 1.2. House Prices: Corrections and Consequence

Denmark

New Zealand

United Kingdom

Spain

Sweden

Canada

Norway

United States

Australia

France

Finland

Japan

Ireland

Italy

Netherlands

Switzerland

Korea

-16

-12

-8

-4

0

4

8

12

Changes in Real House Prices

(Percent)

1

Change in 2008:H1, annualized

Change during 2007

 Sources: Organization for Economic Cooperation and 

Development; and IMF staff calculations.

 Change in 2008:H1, annualized, for Canada, Denmark, France, 

Ireland, Italy, Japan, New Zealand, and United States.

1

Box1_2_1
to be for the rise in house prices over the past 

years to be considered an equilibrium outcome. 

The second figure shows the house price 

gaps—the percent increase in house prices 

during the period 1997 to end-2007 that is not 

accounted for by fundamentals. Also shown, as 

an indicator of the robustness of these results, 

is the range of gap estimates generated by small 

perturbations of the estimated models. These 

changes include using the average value of 

housing prices over 1994 to 1997, instead of the 

1997 value, as the starting point for computing 

the gap estimates; estimating a parsimonious 

version of the model with only incomes and 

interest rates as the driving forces; and changing 

the dynamic specification by estimating a vector 

autoregressive model for house prices instead of 

a single-equation model. 

The countries that have experienced the 

largest unexplained increases in house prices 

over the past decade are Australia, Ireland, and 

the United Kingdom;4 house prices in these 

countries were 20 percent to 30 percent higher 

in 2007 than can be attributed to fundamentals. 

A group of other countries—including France, 

Italy, the Netherlands, and Spain5—have house 

price gaps of between 10 percent and 20 percent.6 The gap estimate for the United States—

4As noted in the 2008 IMF staff report for Australia, if some country­-specific factors, particularly th

impact of long-term migration on housing demand, 

are taken into account, the results do not produce evidence of a significant overvalulation of house prices. 5The 2008 IMF Article IV staff report for the Netherlands notes that the estimated house price gap—

estimated here as ranging from 9 to 15 percent—is 

likely to be much smaller if the rise in single-person 

households, which is very important in boosting housing demand in the Netherlands, is taken into account 

along with institutional factors (for example, strict 

zoning regulations and generous mortgage interest 

deductibility). 6Hilbers and others (2008) group European countries into “fast,” “average,” or “slow movers,” depending on the extent to which their house prices in recent 

years have risen above long-term averages. The gap 

estimates presented here turn out to be consonant 

with this classification: the average estimated gap for 

the three groups is 19 percent, 11 percent, and –3 perabout 7 percent—is smaller than for most other 

countries and has been narrowing compared 

with earlier estimates, partly reflecting the 

decline in U.S. house prices over the past 18 

months.7 The range of estimates for each councent, respectively. Recent IMF Article IV staff reports 

that point to either a cooling of housing markets or 

the onset of a correction include reports for Canada, 

Korea, New Zealand, Norway, Spain, and the United 

Kingdom. For Germany, some studies have found 

higher undervaluation than the estimate of 5 percent 

reported here, perhaps reflecting supply-side impacts 

from social housing in post-reunification Germany. 7Klyuev (2008) estimates that single-family homes in 

the United States “remained 8 to 20 percent overvalued as of the first quarter of 2008.” The U.S. house 

price gap was estimated at about 12 percent in 2007 

(Box 3.1 in April 2008 World Economic Outlook) and 

about 20 percent in 2006 (Box 2.1 in October 2007 

World Economic Outlook). Ireland United Kingdom Australia Norway France Sweden Spain Belgium Japan Denmark

Netherlands

Greece

Italy

New Zealand

United States

Portugal

Canada

Korea

Finland

Germany

Austria

-15

-10

-5

0

5

10

15

20

25

30

35

House Price Gaps 

(Percent)

 Source: IMF staff calculations. 

Range

Median

Overstretched Commodity Mar
Chapter1  Global Prospects and Poli

try is about 31⁄2 percent on average, though for 

the Netherlands, Norway, and Sweden the range 

is considerably higher. 

To put these gap estimates in perspective, it is 

useful to compare them with house price cycles 

in the advanced economies over the past several 

decades (OECD, 2006). Between 1970 and 

2005, the average house price cycle lasted about 

10 years, with an expansion phase of 6 years during which real house prices increased by about 

45 percent. During the subsequent four-year 

contraction phase, real house prices declined 

about 25 percent, with the range of declines 

across countries varying from about 10 percent 

in the United States to more than 30 percent in 

Japan and several European countries. 

Thus, if house price corrections were to occur 

in line with the gaps shown in the second figure, 

they would be well within the range of previous 

experience. Moreover, the evidence indicates 

that corrections typically occur over several 

years. Evidence from countries with regional 

(that is, subnational) data suggests that for some 

regions, price-level corrections could be much 

more pronounced and last longer than the 

national cycle (Calomiris, Longhofer, and Miles, 

2008; Estevão and Loungani, forthcoming).

Macroeconomic consequences. Experience during past housing market cycles can also be a 

guide to the macroeconomic consequences of 

these price corrections (Claessens, Kose, and 

Terrones, forthcoming; World Economic Outlook, 

April 2008 and April 2004). The evidence suggests, not surprisingly, that the consequences 

are more adverse if they occur in the context of 

a weakening economy and tight credit conditions, which is likely to be the situation facing 

many countries at present. Over the period 

1960 to the present, recessions in advanced 

economies that are associated with house price 

busts and credit crunches are slightly longer 

and deeper than other recessions. The duration 

of a recession is more than one quarter longer 

in the case of a housing bust, total output loss 

during the recession is somewhat higher, and 

the unemployment rate increases notably more 

and for longer in recessions with housing busts 

(third figure, top panel). Over the 12 quarters 

following the onset of a recession, the unemployment rate has increased on average by 

1.5 percentage points. But in recessions associated with house price busts, the increase in 

unemployment is 3 percentage points. 

There is some evidence that this pattern 

holds up at both the national and regional 

levels. As shown in the lower panel of the third 

figure, during regional recessions in the United 

States that are associated with a house price 

bust the peak impact on unemployment is an 

increase of 4 percentage points, compared 

with an increase of 2 percentage points for all 

regional recessions (Estevão and Loungani, 

forthcoming).

What about the impact of house price 

declines on the components of output? There 

is a growing literature on the possible impact 

of changes in housing wealth on consumption. Buiter (2008) demonstrates that changes 

in house prices are redistributions of wealth 

and hence do not have much impact on net 

wealth in the aggregate; however, they can 

affect individual consumption by relaxing collateral constraints. Consistent with this point, 

Muellbauer (2008) finds that with careful 

modeling of the effect of credit market development and deregulation, which raises access to 

housing collateral, changes in house prices have 

a medium-run liquidity effect on U.S. and U.K. 

consumption. 

The impact on investment is more readily apparent. Claessens, Kose, and Terrones 

(forthcoming) find that investment—residential 

investment in particular—tends to fall more 

sharply in recessions associated with housing 

busts and with credit crunches than in other 

recessions.8 There are also significant cross8Benito (2007) finds, using household-level data 

for the United Kingdom, that it is much more common for withdrawal from home equity to flow into 

residential investment than consumer spending, which 

suggests that the collateral channel stressed by Buiter 

(2008) and Muellbauer (2008) could be stronger for 

investment than consumption.

Box 1.2 (continued)
country differences in the extent of the residential investment declines, which in principle 

can depend on a wide range of characteristics 

of national financial and legal systems. One 

important dimension is the ease with which 

households can access mortgage credit. This can 

be measured either by the depth of mortgage 

markets or by an index that summarizes the 

institutional features of mortgage markets. The 

mortgage market index incorporates features 

such as the typical ratio of mortgage loans to 

property values, the standard length of mortgage loans, the capacity to borrow against 

accumulated home equity, and the degree of 

development of secondary markets for mortgage loans. As shown in the top two panels of 

the fourth figure, declines in residential investment have tended to be higher in countries 

where households have had more access to 

mortgage credit.9

Other factors can play a role in explaining 

the amplitude of the economic cycle following house price corrections. In addition to the 

characteristics of mortgage markets already 

discussed, a key feature at the current juncture 

is the prevalence of mortgages with variable (as 

opposed to fixed) interest rates. There are differences within Europe in this respect, where 

Finland, Ireland, and Spain have mostly variable rate mortgages. Higher interest payments 

(relative to household disposable income) have 

also been historically associated with bigger declines in residential investment during 

housing busts—see the bottom panel in the 

fourth figure.10 Countries also differ in terms 

of legal provisions, such as those that govern 

9Data on the depth of mortgage markets—the 

ratio of outstanding mortgage debt to income—are 

reported in Warnock and Warnock (2007) and OECD 

(2006). The mortgage market index is described in 

Chapter 3 of the April 2008 World Economic Outlook. 

The debt measure used here is the ratio of mortgage 

debt to household disposable income for the 1990s 

(from OECD, 2006), but the use of other measures 

of debt—for other years or expressed as a ratio to 

GDP—gives similar results. Controlling for the magnitude of the house price corrections makes the correlation between residential investment declines and 

the mortgage-debt-to-GDP ratio stronger. Cardarelli 

and others (forthcoming) take this analysis a step 

further by using sign restrictions to identify housing 

demand shocks and tracing the impact of these shocks 

on house prices, residential investment, and output. 

They conclude that housing finance innovation has 

amplified the spillovers from housing to the rest of 

the economy by strengthening the role of housing as 

collateral. 10See Tsatsaronis and Zhu (2004). Warnock and 

Warnock (2007) add Greece, Portugal, Sweden, and 

the United Kingdom to the list of European countries with mostly variable rate mortgages; outside of 

Europe, Canada, Japan, and the United States are classified as countries with mostly fixed rate mortgages.

Unemployment Rate

(Percent)

1

Recessions without 

housing bust

Recessions with

housing bust

 Sources: Claessens, Kose, and Terrones (forthcoming); Estevão

and Loungani (forthcoming); and IMF staff estimates.

 OECD = Organization for Economic Cooperation and 

Development.

t – 4 t = 0 t + 4 t + 8 t + 12

10

5

6

7

8

9

4

Other recessions

Recessions with

housing bust

t = 0 t + 4 t + 8

-1

0

1

2

3

4

t – 4 t + 12

OECD Countries

U.S. Regions

Quarters

Quarters

1

box1_2_3

Overstretched Commodity Mar
Chapter1  Global Prospects and Poli

2

residential mortgage lenders’ recourse regarding defaulted residential mortgages, which can 

influence foreclosure rates.11 In many of the 

countries that are the focus of study in this 

box—France, Germany, Ireland, the Netherlands, Spain, and the United Kingdom—debtors are personally liable for the full amount of 

mortgaged debt, thus reducing incentives for 

foreclosure. In the United States, mortgage 

foreclosure is regulated at the state level. In 

six states, lenders have recourse only to the 

mortgaged property, which they may repossess 

and sell. In the other states, debtors are also 

personally liable for the full amount of the 

debt, but there are differences in the extent 

to which lenders can recover the difference 

between the mortgage debt and the foreclosure 

sale price. In practice, lenders may choose not 

to seek deficiency judgments mainly because of 

the time and cost involved.

Another factor that can play a role in explaining the amplitude of the economic cycle following house price corrections is banking sector 

exposure to the housing sector, which varies 

across countries as well as across lending institutions within countries. The value of mortgage 

loans held by banks, expressed as a multiple 

of their overall market capitalization, gives 

an indication of their ability to withstand the 

deterioration of their real estate loan portfolios. 

This indicator varies from about 4 in Denmark 

and Germany, less than 3 in Spain, about 1.5 in 

Canada, Japan, and the United Kingdom, and 

less than 1 in the United States.12 Cross-country 

declines in residential investment during housing cycles have been higher in countries with 

greater banking sector exposure to mortgage 

lending, but the effect has not been as strong 

as that shown earlier with the mortgage-debt-to11See Klyuev (2008) and Deutsche Bank (2008) 

for a discussion of the impact of foreclosure rates on 

house prices. 12Estimates for countries other than the United 

States are from Ahearne and others (2005) and are 

based on bank-level data on mortgage loans and 

market capitalization from Bloomberg L.P. and Worldscope; the U.S. estimate is based on total real estate 

loans by the banking sector and total banking sector 

market capitalization.

Box.1.2 (concluded)

Residential Investment Impact

 Sources: Claessens, Kose, and Terrones (forthcoming);

OECD (2006); and IMF staff calculations.

Residential Investment Impact and 

Mortgage Debt

-25 -20 -15 -10 -5 0 50

2

4

6

8

10

Residential Investment Impact and 12

Interest Payments

-25 -20 -15 -10 -5 0 50.0

0.2

0.4

0.6

0.8

1.0

Residential Investment Impact and Mortgage 1.2

Market Index

Residential investment impact

2

-25 -20 -15 -10 -5 0 50

20

40

60

80

100

120

140

Mortgage-debt-to-GDP ratio Mortgage index Interest payments

Residential investment impact

Residential investment impact

R = 0.4244

R = 0.194

R = 0.4397

2

2

box1_2_4
as examined in detail in Chapter 3, rising food 

prices have been a key factor behind surging 

inflation in emerging economies. By contrast, oil 

price increases have played the lead role in spurring inflation in the advanced economies.

How far will these direct contributions feed 

into second-round effects? Three structural 

trends should mitigate such risks: (1) increasing 

real wage flexibility, in contrast to the real wage 

resistance seen particularly in western Europe 

during the 1970s; (2) more secure anchoring of 

inflation expectations by vigilant central bankers; and (3) declining energy intensity.5 Slowing 

economic activity is also mitigating inflation risks, 

particularly in the advanced economies. However, 

there remain concerns in some emerging economies, particularly those where capacity constraints 

are still tightening, where public wages have been 

increased rapidly, and where inflexible exchange 

rates may constrain the monetary response.

Rising commodity prices also have important potential effects on the terms of trade and 

purchasing power and hence on growth. At 

the global level, the key factor is oil, not food, 

because the production of food is more evenly 

distributed around the globe: on average, oil 

5Blanchard and Galí (2007) provide a careful analysis 

of why the macroeconomic impact of the recent oil price 

boom is likely to be smaller than in the 1970s.

imports are two-and-a-half times greater than 

food imports.

Overall, rising oil prices have had a net dampening impact on global demand, because oil 

exporters save a high proportion of additional 

oil revenues, particularly since their economies 

are already running into absorptive capacity 

limits. The size of the redistributional effect also 

depends on the source of the commodity price 

shock; there is a greater effect when the price 

surge reflects a pure supply shock instead of 

a combination of supply and demand factors, 

as seems to be the case in the current episode. 

The redistributional effects are sizable, although 

substantially smaller than in the 1970s, when the 

intensity of oil output was about twice its current 

level in advanced economies and 25 percent 

higher in emerging markets (see Figure 3.9). 

At the country level, low-income countries are 

particularly vulnerable to strains from rising 

food and fuel importation costs. Some countries 

in sub-Saharan Africa have experienced termsof-trade losses of more than 5 percent of GDP 

(IMF, 2008a).

Have Macroeconomic Policies Been To

Loose?

The recent deterioration of performance in 

the global economy comes on the heels of four 

GDP ratio. Nevertheless, at the current juncture, with bank balance sheets under renewed 

stress and bank equity prices low, the potential 

for an adverse impact on the real economy from 

banking system exposure to mortgage lending is 

perhaps greater than in the past.

Conclusions. Many advanced economies experienced a house price run-up in recent years that 

is difficult to account for fully in terms of fundamental driving forces such as income growth 

and interest rates. The correction in house 

prices appears to have now begun in most of 

these economies. If past is prologue, these corrections could average about 25 percent and be 

spread out over a period of two to four years. 

Past evidence also suggests that cross-country 

differences in the impact of these corrections 

on the macroeconomy are likely to depend 

on the characteristics of the housing finance 

systems, particularly the ease with which households have been able to access mortgage credit 

in recent years. This feature is likely to be correlated with the extent of investment declines 

that occur during the house price corrections 

and could also have a dampening impact on 

consumption.

Have Macroeconomic Policies Been Too Lo
Chapter1  Global Prospects and Poli

years of exceptionally strong expansion, during 

which healthy gains from the increasing integration of emerging and developing economies 

into the world economy contributed to the 

strongest period of global growth since the early 

1970s. With the benefit of hindsight, however, 

it is clear that major imbalances built up across 

crucial financial, housing, and commodity markets, reflecting serious flaws in the operations 

of these markets and inadequate regulatory 

responses, with an inevitable payback.

Some observers argue that these imbalances 

in financial, housing, and commodity markets 

were exacerbated by excessively loose macroeconomic policy settings during the strong 

expansion over 2003–07. In particular, the 

pronounced success in bringing down inflation 

during the 1990s and the global productivity gains from the integration of China and 

other labor-intensive economies into the world 

trading system allowed for excessively easy 

monetary policy in the advanced economies, 

which generated a series of market bubbles. 

Following the collapse of the hi-tech dot-com 

bubble early this decade, monetary policy settings were kept very loose to counter deflation 

concerns. Indeed, in the United States and 

to a lesser extent in the euro area and Japan, 

policy rates were set well below what would 

be implied by the Taylor rule (Figure 1.9). 

Moreover, even though monetary policy was 

tightened starting in 2003, it has been suggested that the tightening did not do enough 

to “lean against the wind” as credit flowed into 

the housing sector and house prices rose to 

levels that were far out of line with underlying 

fundamentals.

In addition, inflexible exchange rate 

regimes have recently limited the capacity of 

some key emerging economies to carry out 

independent monetary policies, a constraint 

that became increasingly relevant after August 

2007 as the U.S. dollar depreciated and the 

Federal Reserve aggressively cut interest rates. 

Thus, these economies effectively imported 

an increasingly easy monetary stance from the 

United States, just as inflation pressures were 

-6

-4

-2

0

2

-2

0

2

4

6

8

10

12

Figure 1.9. Measures of Monetary Policy and Liquidity

in Selected Advanced Economies

(Interest rates in percent unless otherwise noted)

Following a period of easy monetary conditions during 2001–05, monetary policy

was tightened across the advanced economies. Since the onset of financial stress in

August 2007, the Federal Reserve has eased its policy stance aggressively. By

contrast, monetary policy settings in the euro area and Japan have been kept broadly 

unchanged.

0

1

2

3

4

5

6

7

Euro 

area

Nominal Short-Term Interest

Rates

Japan

United

States

 Sources: Bloomberg Financial Markets; Eurostat; Haver Analytics; Merrill Lynch; OECD

Economic Outlook; and IMF staff calculations.

 Three-month treasury bills.

 Relative to core inflation.

 Ten-year government bonds.

 Ten-year inflation-linked government bonds.

 The Taylor rate depends on (1) the neutral real rate of interest, which in turn is a 

function of potential output growth, (2) the deviation of expected consumer price inflation 

from the inflation target, and (3) the output gap. Expected inflation is derived from

breakeven rates of inflation-indexed bonds.

 Change over three years for euro area, Japan, and United States (G3), denominated in

U.S. dollars.

1

Deviation from Taylor Rule

2000 02 04 06 Sep.

 08

Real Short-Term Interest Rates2

Aug.

 08

2000 02 04 06

Quantitative Liquidity Measures

(percent of G3 GDP)

08:

Q1

2

3

4

1

5

Base money

plus reserves

Base

money 

Reserves

5 6

6

08:

Q2

Japan

United

States

Euro area

2000 06

-2

-1

0

1

2

3

4

United States

Euro area

Japan

02 04 2000 02 04 06

Nominal Long-Term Interest

Rates

Japan

United

States

Euro area

2002 04 06 Sep.

 08

0

1

2

3

4

United

States

Real Long-Term Interest Rates

Euro area Japan

4

Sep.

 08

2002 04 06

3

0

1

2

3

4

5

6
rising.6 At the same time, the sustained surge 

in commodity prices was accentuated by strong 

growth in emerging economies, a weakening 

U.S. dollar, lower U.S. interest rates, and—in 

the view of some observers although not 

IMF staff—financial flows into commodity 

futures markets. Central banking orthodoxy 

is to accommodate a temporary rise in inflation from a relative price shock, provided 

underlying inflation remains consistent with 

forward-looking objectives. However, repeated 

shocks in the same direction have increased 

the risks of second-round effects from the sustained shift in relative prices.

Measures of global liquidity shown in Figure 1.9 provide only inconclusive support for 

these concerns. The monetary base of the 

largest advanced economies certainly grew 

rapidly through 2005, and although the rate of 

base expansion has moderated since then, the 

emerging economies’ continued strong buildup 

of international reserves implies rapid monetary growth in these economies. However, the 

relationship between monetary aggregates and 

prices is tenuous at best in advanced economies 

and is not well understood in emerging economies. Long-term interest rates have been low 

by historical standards throughout this decade, 

although such rates are arguably determined 

more by fundamental forces affecting the supply of and demand for savings—including the 

high rates of saving in emerging economies, 

increased public saving in advanced economies, 

and low rates of investment globally (outside 

China)—than by monetary policy settings.

Measures of the output gap provide more 

direct evidence of excess demand at the global 

level. To be sure, such measures are imprecise 

and need to be interpreted cautiously, as highlighted in Box 1.3, which discusses the approach 

used in the World Economic Outlook for assessing 

potential growth and output gaps. That said, 

on balance the data suggest that the global 

economy has been operating well above a cycli6Such concerns are illustrated in model simulations 

provided in Box 3.3.

cally neutral level—comparable to the late 1990s 

(Figure 1.10).7 The advanced economies seem 

to be operating at somewhat below a cyclically 

neutral level—and their output gaps are likely 

to widen, given that current rates of growth are 

well below estimated potential. By contrast, the 

emerging economies seem to have been growing 

faster than trend until recently, and pressures 

on capacity are still high. Even though estimates 

of output gaps are particularly subject to error 

for this group of countries, these assessments 

are broadly consistent with the observed recent 

acceleration in inflation.

Thus, while there is indeed some evidence 

that monetary policy may have been too easy 

at the global level and that the global economy 

may have exceeded its collective speed limit, 

excessive demand pressures seem to be concentrated in emerging economies and do not 

appear egregious at the global level by the standards of other recent cycles. It is hard to explain 

the intensity of the recent stress in financial, 

housing, and commodity markets purely 

through these macroeconomic factors, although 

they have played some role.

Prospects for a Turnaroun

Prospects for the global economy are exceptionally uncertain as this report goes to press. A 

key assumption underlying the baseline projections is that comprehensive actions by the U.S. 

and European governments succeed in stabilizing financial market conditions and avoiding 

further systemic events. Nonetheless, markets 

are likely to remain under heavy strain throughout 2008 and 2009. Even with successful implementation of the plan to remove troubled assets 

from U.S. bank balance sheets, it will take time 

to rebuild confidence in asset valuations and 

alleviate counterparty concerns. Moreover, banks 

are going to remain under pressure from the 

need for more capital combined with growing 

7It is worth noting, however, that estimates of potential output are frequently marked down after a cyclical 

downturn.

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

credit losses coming from the broader economy. 

Detailed projections laid out in the October 

2008 Global Financial Stability Report (IMF, 2008b) 

indicate that sustained deleveraging will reduce 

credit growth to very low levels in the advanced 

economies during 2009 and even beyond, 

while spreads on riskier asset classes will remain 

wide. Emerging and developing economies will 

continue to face difficult external financing 

conditions, and those with large current account 

deficits or other vulnerabilities will remain 

under the most pressure.

In commodity markets, in the absence of 

further supply shocks or a major downgrading of growth prospects, prices are projected 

to stay around current high levels, in line with 

pricing in forward markets. Thus, the price of 

petroleum would average about $100 a barrel in 

2009. But markets are likely to remain volatile, 

responding quickly to shifting perceptions of 

demand and supply trends.

Against this backdrop, the baseline projections show the global economy undergoing 

a major downturn, with growth falling to its 

slowest pace since the 2001–02 recession. A 

gradual recovery is projected to get under way 

later in 2009, but global growth is not expected 

to return to trend until 2010. Important supports for the eventual recovery will be the 

unwinding of adverse terms-of-trade effects as 

commodity prices stabilize, a turnaround in 

the U.S. housing market, and rising confidence 

that the liquidity and solvency problems in core 

financial institutions are being resolved. On an 

annual basis, global growth is expected to moderate from 5.0 percent in 2007 to 3.9 percent 

in 2008 and 3.0 percent in 2009 (see Table 1.1 

and Figure 1.11). These projections are well 

below those provided in the July 2008 World 

Economic Outlook Update, reflecting increasing 

evidence in recent months of slowing activity, 

the further burgeoning of the financial crisis, 

and a heightened appreciation of the degree to 

which financial deleveraging is likely to be an 

extended constraint on growth.

The advanced economies are expected to be 

particularly weak for the remainder of 2008 and 

1980 85 90 95 2000 05 -12

-8

-4

0

4

8

12

16

20

1980 85 90 95 2000 05 -16

-12

-8

-4

0

4

8

12

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

1980 85 90 95 2000 05 -8

-6

-4

-2

0

2

4

6

1980 85 90 95 2000 05 -8

-6

-4

-2

0

2

4

6

8

1980 85 90 95 2000 05 -1

0

1

2

3

4

5

6

7

8

9

Euro Area

Japan

Output gap

Real and Potential GDP

Growth

World

United States

Figure 1.10. Measures of the Output Gap and

Capacity Pressures

After a period of above-trend growth, global activity is now slowing well below 

potental. In the advanced economies, output gaps are expected to widen to the 

range of 1–2 percent of GDP in 2009. In the emerging economies, output would 

remain somewhat above cyclically-neutral levels, although capacity pressures 

would ease some. The methodology used to estimate potential GDP growth and 

output gaps is explained in Box 1.3.

Asia

 Sources: OECD, Economic Outlook; and IMF staff estimates.

 Estimates of the nonaccelerating inflation rate of unemployment (NAIRU) come from the 

OECD. Estimates of the output gap, in percent of potential GDP, are based on IMF staff 

calculations. Capacity utilization measured as deviations from 1980–2007 averages for the United

States (percent of total capacity) and Japan (operation rate index for manufacturing sector), and 

deviations during 1985–2007 for the euro area (percent of industry capacity).

 GDP growth rates of actual (solid line) versus potential (dashed line) for advanced economies. 

For emerging economies, a Hodrick-Prescott filter is applied for potential GDP.

1

Output Gap: Emerging

Economies

Latin 

America

Emerging

economies

Advanced

economies

Output gap

(left scale)

Output gap

1

Capacity

utilization

Capacity utilization

(left scale)

Capacity

utilization

Emerging

Europe

2

NAIRU minus 

unemployment rate NAIRU minus

unemployment rate

09

09

09

09

09

1980 85 90 95 2000 05 -6

-4

-2

0

2

4

World Economy 6

(output gap only)

World

Emerging

economies

Advanced

economies

09

NAIRU minus 

unemployment rate

(right scale)

2
the first half of 2009. The U.S. economy faces 

flat to negative growth during this period, as 

support from the fiscal stimulus ebbs, export 

momentum moderates, and tight financial 

conditions take an increasing toll. An eventual 

turnaround in the housing sector and more 

stable oil prices should help lay the basis for 

incipient recovery in the second half of 2009, 

but the revival is expected to be much more 

gradual than in previous business cycles, as tight 

credit conditions continue to weigh heavily on 

domestic demand.8 Most other advanced economies are also expected to go through a period 

of extremely sluggish growth or contraction in 

2008 and the first half of 2009, and to experience only a modest upturn in the latter part of 

the year. In fact, all the G7 countries but Canada 

are now projected to grow by less than 1 percent 

on a fourth-quarter-over-fourth-quarter basis 

during both 2008 and 2009.

Growth in emerging and developing economies is also projected to continue to decelerate, 

falling somewhat below trend during the second 

half of 2008 and early 2009 before picking up 

during the course of the year. Over this period, 

overall growth is projected to remain well above 

rates experienced in the 2001–02 global downturn. Export growth will continue to slow and 

domestic demand will also moderate, although 

demand will continue to be supported by the 

strong productivity gains made in recent years. 

Commodity-exporting countries—particularly 

oil exporters—are expected to maintain their 

momentum, but growth in countries dependent 

on food and fuel imports or external financing will slow quite sharply. Net external capital 

inflows are projected to fall by half in the aggregate, and some countries could face substantial 

pressure on reserve positions.

On the inflation front, the combination of 

rising slack and stabilizing commodity prices is 

8By itself, however, slow credit growth need not prevent 

a recovery. Evidence from past business cycles shows that 

activity typically recovers in advance of a turnaround 

in the credit cycle (Claessens, Kose, and Terrones, 

forthcoming).

2000 02 04 06 08 0

1

2

3

4

5

6

7

8

0

2

4

6

8

10

-3

-2

-1

0

1

2

3

4

5

6

-4

-2

0

2

4

6

8

10

-2

0

2

4

6

8

10

12

14

NIEs2

 Sources: Haver Analytics; and IMF staff estimates.

 Australia, Canada, Denmark, euro area, Japan, New Zealand, Norway, Sweden, 

Switzerland, United Kingdom, and United States.

 Newly industrialized Asian economies (NIEs) comprise Hong Kong SAR, Korea,

Singapore, and Taiwan Province of China.

 Indonesia, Malaysia, Philippines, and Thailand.

 Czech Republic, Estonia, Hungary, Latvia, Lithuania, and Poland.

 Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Rep. Bolivariana de Venezuela.

 Commonwealth of Independent States.

1

2

3

4

China

Latin America5

Emerging Europe4

Figure 1.11. Global Outlook

(Real GDP; percent change from a year earlier)

Emerging economies

Euro 

area Japan

Advanced 

economies1

United States

The global economy is projected to slow further in the second half of 2008 and early 

2009, and then to start a gradual recovery. The advanced economies will be most

affected by the downturn and will be in or close to recession. Growth will also

moderate in the emerging economies, particularly those in Asia, emerging Europe, 

and Latin America with close trade links.

Brazil

India

0

2

4

6

8

10

12

14

Russia

Sub-Saharan 

Africa

ASEAN-43

5

2000 03 06 09

CIS6

6

Middle East

2000 03 06 09

2000 03 06 09 2000 03 06 09

2000 03 06 09

World

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

Rising inflation concerns have brought 

increasing attention to the issue of whether 

economies are overheating and how to measure an economy’s productive capacity. All else 

being equal, an economy operating beyond its 

capacity—with a positive gap between actual and 

potential output—is likely to face rising inflation 

pressures, whereas an economy well within its 

capacity—with a negative output gap—will tend 

to experience declining inflation. Measurements 

of capacity are also important for other purposes, including assessment of the fiscal stance 

over the cycle, as discussed in Chapter 5. Overall, understanding the current and future cyclical position of the economy is crucial to making 

sound monetary and fiscal policy decisions.

Measuring output gaps is, however, a highly 

inexact science, because productive capacity 

for a whole economy is not directly observable (although some measures of capacity are 

typically available for some sectors, such as 

the industrial sector). Accordingly, a mix of 

approaches has been used, with varying degrees 

of sophistication, adjusting to data limitations. 

This box reviews methods used in estimates 

of output gaps in the World Economic Outlook 

(WEO) projections and discusses a new modelbased approach that is now being developed.

Measurement of output gaps. For most 

advanced economies, estimates of output gaps 

used in the WEO are derived from an assessment of potential GDP based on a production 

function approach. Under such an approach, a 

production function is estimated for the economy, relating output to measured inputs of labor 

and capital. The residual is a measure of total 

factor productivity (TFP) in the economy, which 

can then be related to explanatory variables 

such as competition, structural reforms, and 

import penetration.1 Considerable attention has 

been paid in the literature to devising increasThe main authors of this box are Charles Collyns, 

Douglas Laxton, and Natalia Tamirisa, with input from 

Gianni de Nicolò and assistance from Ercument Tulun. 1Box 3.1 of the September 2006 World Economic 

Outlook provides an example of this approach.

ingly careful measures of inputs—for example, 

by adjusting labor inputs for the impact of education and training on the quality of labor and 

by introducing a measure of the flow of capital 

services—and trying to explain the TFP residual. 

This approach has the advantage that once 

the basic relationship is estimated, an assessment can be made of the impact of shifting factors that affect potential growth—for example, 

the impact of demographics on the growth of 

labor services and the impact of investment 

rates on capital services. 

Turning to the emerging economies, data on 

labor and capital inputs are typically inadequate 

for the production function approach. Moreover, the possibility of rapid change following 

major reforms reduces continuity and would 

make the approach more difficult to apply. 

Estimates of output gaps in the economies 

presented in this issue of the WEO therefore 

rely on time-series techniques to estimate trend 

GDP based on observed and projected GDP 

series. Specifically, the output data presented 

used standard Hodrick-Prescott (HP) filters, 

which disentangle a time series into a trend 

component and a cyclical component (Hodrick 

and Prescott, 1997), using a λ coefficient of 100 

on annual data.2

Despite their simplicity and widespread use, 

one difficulty with the HP filters (and timeseries techniques more generally) is the sensitivity of the estimates to the choice of end point. 

As a rough-and-ready approximation, the HP 

filter is applied to data (in log form) over the 

period 1980–2008 (which can essentially be considered historical data) and again to data and 

projections over the period 1980–2013. Using 

the latter estimates takes advantage of the IMF 

desk economists’ best judgment on mediumterm growth prospects. Potential output and 

output gaps were are then derived as the average of these two estimates. 

2Filtering results depend heavily on the value for 

the smoothing parameter λ. The value of 100 captures 

the properties of the U.S. business cycle well, but it 

has been less useful for other countries.

Box 1.3. Measuring Output Gap
Applying this technique to 1980–2008 data 

suggests a significant acceleration in potential 

growth over the past decade across emerging 

economies (first figure, left-hand column). The 

extent of acceleration is estimated to be even 

larger using data that include medium-term projections. Using either series, emerging economies 

are seen as operating significantly above capacity, 

especially in emerging Europe and Latin America, with the excess approaching 4 percent of 

GDP in each region in 2008 using the more 

conservative potential growth estimates.3

Quantifying the impact of oil-price shocks on 

potential output. One issue of current relevance 

is how much the recent increase in oil prices, 

if sustained, could affect the level and the rate 

of growth potential output. Oil is a key input 

for the production of many goods and services, 

in part because it is used in transportation. If 

the relative price of oil rises, other inputs into 

production (capital and labor), which are available in limited supply and with limited substitution possibilities in the short run will need to be 

used more intensively, implying a fall in productive potential. The impact of the growth rate of 

potential output would depend on how quickly 

output converges to its long-run level.4

Using a production function approach, 

Organization for Economic Cooperation and 

Development (OECD) estimates suggest that 

an increase in oil price by 240 percent from 

its 20-year average in the United States and by 

170 percent above that in the euro area (to 

$120 a barrel) would reduce potential output by 

4 percent in the United States and 2 percent in 

the euro area (OECD, 2008). The impact on 

3Vamvakidis (2008) compares estimates of potential 

growth across emerging Europe using an HP filter, a 

production function approach, and a growth equation 

similar to a specification used by Barro and Sala-íMartin (2004). The production function approach 

provides the highest estimates for potential growth, 

assuming continued strong TFP growth. 4For example, press reports suggest that the automotive industry in the United States is moving quickly 

to retool car manufacturing plants to produce smaller, 

more energy efficient vehicles. 

Potential Growth and Output Gaps in Emerging

Economies

 Source: IMF staff estimates. 

 HP = Hodrick-Prescott.

 1980 90 2000 10 2

4

6

8

10

12

1980 90 2000 10 -4

-2

0

2

4

6

Asia

 1980 90 2000 10 -4

-2

0

2

4

6

8

 1980 90 2000 10 -6

-4

-2

0

2

4

Latin America

 1980 90 2000 10 -10

-8

-6

-4

-2

0

2

4

6

8

 1980 90 2000 10 -8

-4

0

4

8

12

16

Emerging Europe

With HP filter

(1980–2008)

With HP filter

(1980–2013)

Potential Growth Output Gaps

Actual GDP

growth

Actual GDP 

growth

Actual GDP 

growth

Although potential growth probably rose in emerging

economies over the past decade, time-series techniques

suggest that recent growth has been above potential, implying

the opening of significant output gaps in the past year or so. 

The estimated size of the gap is sensitive to the choice of end 

point and to the IMF staff’s judgment of the extent to which 

potential growth has risen.

1 1

1

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

U.S. potential output is higher because of 

a larger share of oil in production and the 

declining value of the dollar. Potential growth 

is estimated to decline by 0.2 percentage point 

a year in the United States and 0.1 percentage 

point in the euro area in the first year of adjustment, based on the average rate at which existing capital is typically scrapped and replaced. 

However, the adjustment could well occur more 

rapidly in the face of a large relative price shock 

because the renewal rate is likely to accelerate—

although energy-intensive capital tends to have 

an above-average service life.

Model-based estimates of output gaps. Recent 

work for the Global Projection Model (GPM) 

has developed model-consistent measures 

of potential output—and thus of the output 

gap—that exploit information on observable 

variables, such as GDP, unemployment, and 

inflation. Like any macroeconomic model, the 

GPM contains a system of equations, an array 

of key observable variables, and a few unobservable but crucial variables, notably potential 

output. Estimates for the latent variables may be 

based on predictive power. Using this criterion, 

of all the economically plausible paths that 

potential output might take, the procedure 

selects the one that best predicts the observable 

variables in the model. In other words, the procedure “backs out” values of the latent variables 

implied by the structure of the model and the 

behavior of the observable variables.

The model contains two critical equations 

in this regard. The first links inflation to the 

output gap. The second is a dynamic Okun’s 

law, which links unemployment gaps (actualminus-equilibrium levels) to past movements in 

the output gap. 

The model-based technique is less mechanical, 

with much more economic content, than the HP 

and other univariate filters. It offers a potentially 

substantial improvement, especially in gauging 

the current level of potential output in real time, 

although it requires more advanced modeling 

than simple filters like the HP filter.

The second figure provides some illustrative 

GPM estimates for the United States and contrasts them with WEO estimates based on the 

production function approach.5 The top panel 

5In current versions of the GPM, the observable 

variables for the U.S. economy include oil prices, 

headline CPI inflation, real GDP, unemployment, 

exchange rates, the federal funds rate, and a measure 

of bank lending tightness. The last variable is calculated from the Federal Reserve Board’s Senior Loan 

Officer Opinion Survey on Bank Lending Practices.

Box 1.3 (concluded)

0

1

2

3

4

5

6

Alternative Measures of U.S. Potential Growth

 Source: IMF staff calculations.

Actual and Potential Growth

(year-over-year percent change)

World Economic 

Outlook (WEO)

potential growth

1996 98 2000 02 04 06 08

3.5

4.0

4.5

5.0

5.5

6.0

Unemployment Rate 6.5

(percent)

1996 98 2000 02 04 06 08

-3

-2

-1

0

1

2

3

4

5

Output Gaps, Unemployment Gaps, and Inflation 6

(percent)

1996 98 2000 02 04 06 08

Actual growth

Global Projection 

Model (GPM)

potential growth

Actual unemployment

GPM equilibrium 

unemployment

Inflation

GPM output gap

WEO gap GPM unemployment 

gap

Box1_3_2
expected to contain the pace of price increases 

in the advanced economies and bring inflation 

back below 2 percent by the end of 2009. In 

emerging and developing economies, inflation 

is projected to remain at about 8 percent at 

end-2008 as recent commodity price increases 

continue to feed through the pipeline. Inflation 

is expected to ease to 61⁄4 percent during 2009 

but to remain well above central bank objectives 

in a number of countries.

There are substantial downside risks to the 

baseline forecast, as illustrated in the global 

growth fan chart (Figure 1.12).9 The princi9Appendix 1.1 reviews these and other approaches 

used here to assess and communicate risk, including the 

compares the estimates of potential growth 

from the GPM and from the WEO. The GPM 

estimates display considerably more variation 

than the WEO estimates. This is to be expected, 

given that the former vary in line with the outcomes for inflation and unemployment. 

The figure shows a marked discrepancy 

between the two estimates in the second half 

of the 1990s, a period with strongly increasing 

output and declining unemployment, yet stable 

inflation. The model interprets these facts to be 

consistent with a more marked increase in the 

growth of potential output during this period 

(and hence a permanent increase in the trend 

level of output) and a decline in the equilibrium (or natural) unemployment rate (middle 

panel). By the end of the decade, inflation 

pressure, as gauged by the output gap, or by the 

deviation of unemployment from equilibrium, 

was present under either estimate, but much 

less under the model estimate. By the same 

token, the GPM estimate of the negative output 

gap in the 2001–02 recession is significantly 

larger than that in the WEO estimate.

A widening discrepancy is again evident in 

2008, with potential growth in GPM dropping 

from 3 percent to 2 percent, whereas the WEO 

measure continues on a smoother path. A major 

factor at play is the sharp increase in the price of 

energy, which causes productivity growth in the 

GPM to drop for a while below its long-run rate. 

This implies a smaller negative output gap in the 

GPM for 2008, and hence less downward pressure on the core inflation rate, than in the WEO.

The third figure provides estimates of the output gap based on applying the GPM approach 

to a group of five Latin American countries. 

The output gap series tracks quite closely estimates derived from the HP filter approach, providing some support for using the HP filter as 

a credible first attempt at estimating the output 

gap across groups of countries.

All in all, it is unlikely that a methodological 

silver bullet for measuring potential output and 

output gaps will be found anytime soon. In the 

meantime, policymakers will need to continue 

to rely on an eclectic approach, drawing on various measures of slack in the economy (output 

gaps and unemployment gaps) as well as surveybased measures of capacity utilization and highfrequency indicators, while continually testing 

available estimates against reality.

Output Gap in Selected Latin American

Countries

(Percent)

 Source: IMF staff calculations.

 For the aggregate of Brazil, Chile, Colombia, Mexico, and Peru. 

-8

-4

0

4

8

12

-4

-2

0

2

4

6

2002 04 06 08:

Q2

Global Projection

Model output gap

(right scale)

Hodrick-Prescott

filter gap

(right scale)

1

1

Inflation

(left scale)

Box1.3.3

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

3

pal downside risk revolves around two related 

financial concerns: that financial stress could 

continue at very high levels and that credit 

constraints from deleveraging could be deeper 

and more protracted than envisaged in the 

baseline. In addition, the U.S. housing market 

could deteriorate for longer than envisaged, 

and European housing markets could weaken 

more broadly. Inflation risks to growth are now 

more balanced, in light of the retreat in commodity prices and the slower trajectory of the 

global economy. Global imbalances remain an 

issue, but with some shift in focus away from the 

potential problems of financing the U.S. current 

account deficit toward risks created by the need 

to recycle large surpluses from oil exporters and 

toward risks of protectionism now that the Doha 

Round has again stalled.

Financial Market Risks

Financial market risks remain acute, even 

more of a concern than at the time of the 

April 2008 World Economic Outlook. Despite 

unprecedented actions by financial authorities 

to prevent systemic events and a major new 

initiative to help banks in the United States deal 

with illiquid assets, markets remain under heavy 

stress, and the threat of disorderly deleveraging 

remains a serious risk to the outlook.

After the events of recent weeks, concerns 

remain high about the solvency of financial 

institutions in mature markets faced with rising 

losses, tight funding conditions, and dwindling 

capital bases. Successful implementation of the 

U.S. government’s plan to purchase troubled 

assets would over time reduce such risks by limiting the downside to U.S. real estate exposure 

in U.S.-based institutions, but low bank capital 

could remain a serious issue, especially because 

asset sales could imply greater loss recognition and because weakening activity is likely to 

push up losses on a broad range of assets in the 

methodology used to develop the growth fan chart and 

associated risk factors, and discusses work now under way 

to enhance such assessments.

1

2

3

4

5

6

Figure 1.12. Risks to the Global Outlook

There are substantial downside risks to the growth outlook. The greatest concern 

relates to the risk that financial strains will be more intense and more protracted 

than already envisaged in the baseline projections. Negative risks also relate to 

concerns about domestic demand and global imbalances, while risks related to

inflation and the oil market are now seen as balanced.

Upside risk to

global growth

Global Risk Factors

April 2008 WEO

Downside risk to

global growth

Prospects for World GDP Growth

(percent change)

1

Domestic

demand in

the United

States

Domestic

demand in

emerging

economies

Oil

market 

Domestic

demand in 

Europe

and Japan

Inflation 

risks

Global 

imbalances

Financial

conditions

2

 Source: IMF staff estimates.

 The fan chart shows the uncertainty around the World Economic Outlook (WEO) central 

forecast with 50, 70, and 90 percent probability intervals. As shown, the 70 percent 

confidence interval includes the 50 percent interval, and the 90 percent confidence interval

includes the 50 and 70 percent intervals. See Box 1.3 in the April 2006 WEO for details.

 The chart shows the contributions of each risk factor to the overall balance of risks to global

growth, as reflected by the extent of asymmetry in the probability density for global GDP growth

shown in the fan chart. The balance of risks is tilted to the downside if the expected probability of

outcomes below the central or modal forecast (the total “downside probability”) exceeds 50

percent (Box 1.3 in the April 2006 WEO). The extent of asymmetry in the probability density in the 

fan chart depends on the various sources of risk and their potential impact as well as the 

standard deviation of past forecast errors—which, among other factors, varies with the length of

the forecasting horizon. To make the risk factors comparable across forecast vintages, their 

contributions are rescaled to correct for differences in the standard deviations.

1

2

Current WEO 

Baseline forecast

50 percent confidence interval

70 percent confidence interval

90 percent confidence interval

2005 06 07 08 09

October 2007 WEO
United States and Europe. Moreover, funding pressures are likely to remain intense until 

counterparty confidence is restored.

A related concern is that the process of 

deleveraging and balance-sheet repair could 

be deeper and more extended than projected, 

implying that credit constraints on growth could 

be greater than built into the baseline. At this 

point it is hard to gauge how much bank capital 

levels will need to rise to be considered adequate by markets and by regulators. Indeed, the 

events of recent weeks seem likely to increase 

pressure on banks to accelerate deleveraging 

efforts and to be extremely cautious in extending new credit as long as financial conditions 

remain highly volatile. Moreover, prospects for 

raising capital are highly uncertain, particularly 

in light of the large losses suffered by equity 

holders in recent resolutions and continuing 

uncertainty over valuation. In the baseline, 

credit continues to grow moderately in the 

advanced economies, in line with projections 

presented in the October 2008 Global Financial 

Stability Report (IMF, 2008b), but credit supply 

would contract under a “stress scenario” that factors in more aggressive deleveraging efforts.

Recent events have underlined the vulnerability of emerging economies to turbulence 

in advanced financial markets. Intensified or 

extended deleveraging in U.S. or European 

banks or growing risk aversion among investors 

could prompt a further scaling back of bank and 

portfolio flows to emerging economies, putting 

particular pressure on those economies considered vulnerable, including those with large 

current account deficits, such as in emerging 

Europe, or countries that have experienced rapid 

credit growth based on heavy capital inflows, such 

as in Russia and other countries in the Commonwealth of Independent States. Further cutbacks in 

financing flows would put increasing pressure on 

domestic credit conditions at a time when activity 

is slowing, leading to rising stress on financial 

intermediaries and borrowers.

The global repercussions of an intensification 

of financial strains are illustrated in Figure 1.13, 

based on simulations of a global general equilibrium model (BoC-GEM).10 The shock is modeled as an additional 100-basis-point widening 

of credit spreads in the United States and lesser 

increases elsewhere, combined with a loss of 

confidence that knocks equity prices down by 

a further 10 percent. As a result, U.S. domestic 

demand would slow relative to baseline, lowering real GDP growth by a further 1 percentage 

point over the next year, with lingering negative 

effects over a three-year period. The implication 

would be a considerably deeper U.S. recession 

and only a gradual recovery thereafter, with 

similar if less-intense effects elsewhere. Slower 

global growth would tend to depress commodity 

prices and raise output gaps, moderating pressure on inflation and providing greater room to 

ease policy interest rates.

Risks to Domestic Demand in Advanced 

Economie

Downside risks to domestic demand in 

advanced economies remain clearly evident. 

Related to the financial risks just discussed is the 

threat of deeper and more prolonged housing 

corrections than built into the forecast. The 

intervention in the GSEs and the troubled-asset 

purchase plan should alleviate risks in the U.S. 

market to some degree by providing assurances 

of the availability of housing finance and reducing risks of fire sales of distressed real estate in a 

declining market. Moreover, U.S. housing valuations are moving closer in line with fundamentals; residential construction is already near a 

40-year low; and inventories are falling. However, the real possibility remains that U.S. housing prices and activity will not find the projected 

bottom in 2009, and instead will overshoot, 

in the context of still-depressed sentiment. In 

western Europe, housing market prospects are 

uncertain, and dynamics could be affected by 

financial deleveraging that restricts the supply of 

10BoC-GEM is a version of the IMF’s global economy 

model (GEM), developed jointly with the Bank of Canada, which includes explicit modeling of oil and other 

commodity sectors.

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

mortgage financing. Thus, existing downturns 

could intensify, and a broader range of countries 

could experience house price declines, a sharp 

reduction in residential investment, and greater 

strains on household balance sheets.

The possibility of greater-than-projected 

resilience of domestic demand in the face 

of credit strains provides some upside to the 

forecast. Nonfinancial corporate balance sheets 

are generally sound—much improved since the 

early years of this decade—and profitability is 

high, although corporate bond spreads have 

widened sharply in recent weeks. In the euro 

area, consumption could be stronger than projected, as oil prices stabilize, particularly because 

unemployment rates remain exceptionally low 

and household balance sheets are stronger than 

in the United States.

Risks to Domestic Demand in Emerging Economi

Risks to domestic demand in the emerging 

economies are now distinctly to the downside. 

The principal concerns for these economies 

are external—exposure to slower global trade, 

tighter external financing conditions, and 

adverse terms-of-trade shocks—but domestic 

demand also could be adversely affected by deteriorating conditions in financial markets and by 

corrections in housing markets. Countries that 

have allowed easy access to external financing 

and buoyant commodity-related revenues to 

drive rapid growth in domestic credit and strong 

growth in government spending are at particular risk of a “sudden stop” in capital inflows that 

could have a damaging impact on domestic 

financial conditions and apply a sharp knock 

to domestic demand. Conversely, there remains 

potential for domestic demand to surprise on 

the upside, for example in China, where the 

government has moved quickly to introduce 

measures to support growth.

Inflation Risks

Inflation risks have receded relative to the 

April 2008 World Economic Outlook, as commod0 2 4 6 8 10 12

-2.0

-1.5

-1.0

-0.5

0.0

0.5

0 2 4 6 8 10 12 -15

-10

-5

0

5

0 2 4 6 8 10 12 -3

-2

-1

0

1

 Source: IMF staff calculations, based on BoC-GEM simulations.

 CX = commodity exporters.

 AS = emerging Asia.

 OA = other advanced economies.

World United States CX

GDP Growth (year over year; in percent)

Figure 1.13. Impact of Financial Shock on the Global 

Economy

(Deviation from control; quarters on x-axis)

Headline Inflation (year over year; in percentage points)

Core Inflation (year over year; in percentage points)

Oil and Nonenergy Commodity Prices (U.S. dollars; in percent)

Federal Funds Rate (in percentage points)

0 2 4 6 8 10 12 -3

-2

-1

0

1

Oil

Nonenergy 

commodities 

1 AS2 OA3

1

2

3

0 2 4 6 8 10 12 -1.5

-1.0

-0.5

0.0

0.5
ity prices have retreated and slowing growth has 

reduced pressure on capacity. In the advanced 

economies, headline inflation could drop even 

faster than projected, back into line with central 

bank objectives, which would provide more 

scope to ease monetary policy in response to 

slowing activity. The concern remains, however, 

that wages could accelerate in response to the 

loss in purchasing power from higher food and 

fuel prices if activity does not slow as projected, 

particularly in western Europe, where unemployment remains low by recent standards.

Inflation risks are still manifest in a number 

of emerging and developing economies, amid 

signs that higher commodity prices and increasing pressure on local supply conditions are 

already spilling into wage demands and inflation 

expectations. The moderation in commodity 

prices since July is helping to relieve some of 

the upward momentum, but pressures from 

this source are likely to remain for some time 

because past price increases have only partially 

passed through the supply chain, particularly for 

oil, given that many countries have held prices 

well below international levels. The concern 

is that once inflation expectations become 

unanchored, central banks may be forced to 

tighten abruptly to generate a “hard landing”—a 

period of subtrend growth—in order to bring 

inflation back in line. As discussed in Chapter 3, 

the output costs of regaining control over inflation could be sizable, particularly in economies 

where initial policy credibility is low and the 

monetary response is delayed (see Figures 3.15 

and 3.16). To be sure, as emphasized in Box 1.3, 

“speed limits” are hard to estimate for economies that have been able to achieve rapid rates 

of growth through trade and financial integration. Although continued pools of underutilized 

labor may suggest a capacity for sustained strong 

growth, bottlenecks in the infrastructure and 

availability of skilled labor may start to bind.

Risks from Oil Price

Given the likely continued volatility, oil prices 

are an important source of two-way risks to the 

projections. Option market data suggest that 

market participants are operating with an unusually wide band of uncertainty about the future 

price, with outcomes from $60 a barrel to $165 

a barrel falling within the 90 percent confidence 

band over the period through end-2008 (see 

Appendix 3.1). On the upside, oil prices could 

continue to decline, providing some stabilizing 

benefit to the global economy, although such 

an occurrence would most likely be associated 

with weakening global demand rather than a 

positive supply shock, with a correspondingly 

lower multiplier (see discussion in Box 1.1 of the 

April 2007 World Economic Outlook). Against this, 

further supply shocks could again push oil prices 

up, in the context of continued limited spare 

capacity, keeping pressure on consumer purchasing power, particularly in oil-importing countries, 

and limiting the relief to headline inflation from 

stabilizing oil prices built into the baseline.

Risks from Global Imbalances

Global imbalances remain an issue, even as 

the sources of risk are shifting. In the past, the 

central concern was the possibility of a disorderly unwinding of the imbalances driven by a 

discontinuous shift in foreign investors’ willingness to continue financing the large U.S. current 

account deficit and add to the share of U.S. assets 

in their wealth portfolios. Such risks have moderated somewhat as the U.S. dollar’s depreciation 

has brought it closer in line with medium-term 

fundamentals and the U.S. current account 

deficit has moved onto a more sustainable trajectory (Figure 1.14, top panel). Still, rising oil 

prices have slowed the adjustment process as the 

U.S. oil deficit has jumped, and U.S. net foreign 

liabilities are still projected as a rising share 

of global GDP (Figure 1.14, middle panel).11

Moreover, reduced confidence in the liquidity 

11Projections are constructed assuming unchanged 

exchange rates and asset prices. In fact, U.S. dollar depreciation and the relative decline of U.S. equity prices have 

generated net valuation gains in recent years that have 

served to offset the flow accumulation of new liabilities. 

See Box 1.2 in the April 2008 World Economic Outlook.

Prospects for a Turnaro
Chapter1  Global Prospects and Poli

and risk-return characteristics of U.S. assets in the 

wake of the financial crisis mean that the risk of 

disorderly unwinding cannot be discounted. The 

recent difficulties of the GSEs—whose securities 

have been purchased heavily by foreign investors, thereby providing a significant share of the 

financing for the U.S. current account deficit 

in recent years—are a reminder of continuing 

vulnerabilities on this front.

At the same time, three other types of concern have become salient. The first is that the 

adjustment of the dollar has been concentrated 

in a number of flexibly managed currencies 

while certain major currencies continue to be 

tightly managed or pegged to the dollar. This 

situation could create new imbalances over time, 

for example in the euro area, whose currency is 

now somewhat overvalued.

Second, the sustained rise in international 

oil prices has increased the need to ensure the 

stable recycling of exporters’ large surpluses. 

Allowing current account surpluses to increase is 

a reasonable response by oil exporters, reflecting their desire to save some of the additional 

revenues. The annual aggregate surplus of oilexporting countries projected over 2008–09 has 

jumped to 11⁄2 percent of global GDP, notwithstanding the rapid increase in domestic demand 

in these countries. At the same time, emerging Asia continues to run surpluses of about 

1 percent of global GDP. To date, the recycling 

of these funds has been relatively smooth, and 

indeed investment by sovereign wealth funds 

(SWFs) has played a valuable stabilizing role in 

providing capital to banks during the financial 

crisis. However, there is a concern that continued investment of large surpluses could lead to 

protectionist resistance to rising foreign ownership. A related concern is that large investment 

flows into other emerging economies, akin to 

the recycling of petrodollars in the 1970s, could 

contribute to excessive growth of liquidity and 

increase the vulnerability of these economies.12

12See Box 2.2 in the April 2008 World Economic Outlook

on the recycling of commodity surpluses and Box 6.1 in 

this World Economic Outlook on the role of SWFs.

1997 99 2001 03 05 07 09 11 13

-10

-5

0

5

10

1997 99 2001 03 05 07 09 11 13

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Figure 1.14. Current Account Balances

and Net Foreign Assets

(Percent of global GDP)

The U.S. current account deficit has moderated in recent years and is projected to

continue to narrow over the medium term, although net foreign liabilities would 

continue to build. Oil exporters’ surpluses have been boosted by rising international 

oil prices, and although these surpluses are expected to come down, oil exporters are 

projected to accumulate rising net foreign assets. Emerging Asia would sustain large 

current account surpluses and continue to build net holdings of foreign assets.

United States

Japan

Euro area

Oil exporters

Emerging Asia

1

2

Current Account Balance

 Sources: Lane and Milesi-Ferretti (2006); and IMF staff estimates.

 Algeria, Angola, Azerbaijan, Bahrain, Republic of Congo, Ecuador, Equatorial Guinea,

Gabon, I.R. of Iran, Kuwait, Libya, Nigeria, Norway, Oman, Qatar, Russia, Saudi Arabia, 

Syrian Arab Republic, Turkmenistan, United Arab Emirates, Rep. Bolivariana de Venezuela,

and Republic of Yemen.

 China, Hong Kong SAR, Indonesia, Korea, Malaysia, Philippines, Singapore, Taiwan

Province of China, and Thailand.

 

1

2

Net Foreign Assets

1970 75 80 85 90 95 2000 05 10

0

2

4

6

Global Imbalances 8

(absolute sum of current account balances in percent of

world GDP)

13
The third concern is that continuing large 

trade deficits combined with weakening employment prospects in some advanced economies 

could prompt rising trade protectionism. Such 

concerns are heightened by the recent deadlock in the Doha Round of multilateral trade 

negotiations.

Policy Challenges for the Global 

Econom

Policymakers around the world today face 

the imperative of stabilizing global financial 

markets, while nursing their economies through 

a global downturn and tight credit and ensuring that the recent rise in inflation is reversed. 

While these are the immediate priorities, work 

must also progress on tackling the market and 

regulatory flaws that have contributed to recent 

stresses. Financial markets and institutions 

must be placed on a healthier footing and supply-demand responses in commodity markets 

strengthened. Continued commitment to trade 

and financial integration of the global economy 

remains essential to underpin longer-term 

growth prospects.

Stabilizing Global Financial Market

Policymakers face the enormous challenge of 

dealing with the immediate threat to financial 

stability, while also paving the way for rebuilding 

a firm underpinning for financial intermediation. Achieving this daunting task will require 

comprehensive solutions that address the 

systemic problems—the proliferation of illiquid, 

problem assets; the shortage of capital; and the 

collapse of counterparty confidence—while 

dealing rapidly and effectively with emerging 

problems in individual institutions. Approaches 

at the national level must be internationally 

coordinated in order to address joint problems 

and avoid creating adverse cross-border incentives. At the same time, while recognizing the 

urgent need to restore stability to the system, it 

is important to protect taxpayers’ interests, to 

ensure that government intervention is temporary, and to avoid exacerbating moral hazard as 

much as possible.

The plan by the U.S. government to purchase 

troubled real estate assets from banks is a bold 

initiative aimed at restoring liquidity to balance 

sheets, achieving more transparent pricing of 

problem assets, and reducing fears about further 

losses from fire-sale liquidations. Although the 

implementation details of this plan are uncertain as this report goes to press, the principal 

challenge will be to balance the need for quick 

and effective implementation against the 

longer-term objective of containing the overall 

fiscal costs, including by creating mechanisms 

to ensure that the government will share in 

any gains as banks return to health. It will also 

be critical to ensure that bank capital is rebuilt 

quickly, especially because sales of problem 

assets may spur recognition of additional losses. 

Public money may be needed to help sound and 

viable institutions meet their capital needs.

Comprehensive solutions will be important 

in western Europe too, where cross-border 

issues are particularly relevant. Appropriate 

policy actions would be mutually reinforcing 

with those taken in the United States. Cooperative approaches within Europe should aim 

at rebuilding confidence through timely bank 

recapitalization, dealing with problem assets, 

and protecting depositors in a consistent manner. As recent events have shown, cooperative 

agreements are essential for resolution of large 

cross-border institutions, requiring that weaknesses in the cross-border crisis management 

framework be remedied, including through 

much greater sharing of supervisory data. It 

will also be important to ensure consistency of 

approaches when providing temporary extensions of deposit insurance coverage.

Beyond these immediate tasks, determined 

efforts will be required to address the manifold 

underlying weaknesses in financial markets 

revealed by the current period of financial turbulence. As laid out in the October 2008 Global 

Financial Stability Report (IMF, 2008b), a central 

objective is to ensure more effective and resilient risk management by individual institutions, 

Policy Challenges for the Global 
Chapter1  Global Prospects and Poli

including by setting more robust regulatory capital requirements, insisting on stronger liquidity 

management practices, and improving disclosure of risk, on and off balance sheets. Another 

important task will be to strengthen approaches 

to crisis resolution frameworks, including by 

clarifying the roles of various official agencies, 

bolstering deposit insurance systems, and ensuring adequate intervention instruments.

The emergency actions taken to deal with 

the collapse of major nonbank financial intermediaries (NBFIs) over the past six months 

have underlined the need for more effective 

regulation and more secure capitalization of 

systemically important intermediaries outside 

the traditional banking system. A clear and 

permanent solution will be needed for the GSEs 

that addresses the long-known systemic vulnerabilities resulting from their size, the nature 

of their risks, and their hybrid public-private 

governance structure, while dealing with their 

current shortage of capital. There is also a need 

to rethink the regulatory structure for and capital adequacy of other NBFIs that play a systemic 

role in securities and derivatives markets. 

Emerging economies should also learn lessons 

from recent strains. While less directly exposed 

to the problems created by the proliferation of 

structured credits, financial systems in a number of emerging economies have been seriously 

disrupted by shifts in capital flows in the wake of 

the financial crisis. Basic lessons concerning the 

importance of strong risk management, transparency, contingency planning, and effective 

crisis management are thus highly relevant to 

these countries as well.

Recent events have demonstrated that greater 

coordination of approaches across national 

boundaries will be crucial in many of these areas, 

given the growing international integration of 

institutions and markets. First, differences in 

national legal and regulatory frameworks open 

up room for regulatory arbitrage. Although 

some differences can foster healthy competition and innovation, this process has gone too 

far. Second, regulatory and supervisory failures, 

particularly in major financial centers, have 

large cross-border spillover effects. And third, 

cooperative approaches to resolving difficulties in the financial sector are likely to be more 

effective than individual approaches because of 

the interconnectedness of financial institutions 

and markets. In general, policymakers have 

found it challenging to stay abreast of a financial 

system that, on the one hand, is globalizing but, 

on the other hand, is governed by a multitude 

of national legal and regulatory frameworks. 

Although international bodies such as the Financial Stability Forum and the Bank for International Settlements, as well as the IMF, are playing 

a crucial role in alleviating the tensions between 

global and national forces, more political will 

to drive collaboration forward is essential. The 

latest steps in this direction, including proposals 

for colleges of supervisors for the world’s largest 

financial institutions, are welcome in this regard.

Nursing Economies through a Global Downtu

Macroeconomic policymakers are seeking to 

find a balance between supporting activity in 

the face of a global downturn and extremely 

difficult financial conditions and ensuring that 

the sustained shift in relative prices implied by 

the surge in commodity prices does not drive 

a ratcheting up of inflation, as occurred in the 

1970s. The appropriate policy stance will vary 

across countries. A turn to more supportive 

stances is justified in some economies now 

facing recession as a result of financial strains, 

housing downturns, and terms-of-trade losses. 

Nevertheless, policy tightening is still called for 

in a number of countries that are still growing 

well above their speed limits.

Turning first to the major advanced economies, 

although macroeconomic policies alone can have 

a limited impact as long as financial markets are 

under extreme degrees of stress, steps to provide 

support to economies in or near recession should 

supplement efforts to stabilize financial conditions, 

thus helping to break the negative feedback loops 

between real and financial conditions.

• In the United States, monetary policy settings 

are already highly accommodative, providing 
needed support to the economy in the face 

of extreme financial stress and the continuing housing correction. Underlying price 

pressures should be contained as economic 

slack rises, providing room for further policy 

easing if the downturn seems likely to deepen, 

even though its effectiveness may be limited 

if financial strains persist. On the fiscal front, 

the stimulus package provided well-timed support to the economy, and recent initiatives to 

stabilize the housing market and the financial 

system are justified by the need to avert a systemic crisis. Given the potential costs of these 

measures and the need for medium-term consolidation, however, adjustment measures will 

be required elsewhere in the fiscal accounts as 

conditions normalize, to offset the additional 

spending over time.

• In the euro area, monetary conditions are 

now quite tight, especially after considering 

the widening in risk spreads. Rapidly slowing 

activity, rising output gaps, and the recent 

softening in commodity prices should contribute to lowering inflation to below 2 percent by 

end-2009, providing scope to ease monetary 

policy. Fiscal policy is already providing support to the euro area economy through automatic stabilizers and discretionary measures 

in some countries. The limited further scope 

for fiscal easing available under the revised 

Stability and Growth Pact should be used to 

focus public resources on stabilizing financial 

conditions, as needed.

• In Japan, the monetary policy stance remains 

accommodative and should remain so, given 

that the economy is weakening and that 

underlying price pressures are well contained, 

with inflation excluding food and fuel still 

close to zero. The priority for fiscal policy 

continues to be medium-term consolidation, 

which suggests that the currently planned fiscal package should be limited in size.

Macroeconomic policy priorities vary 

considerably across emerging and developing economies. In an increasing number of 

these countries, the balance of risks has now 

shifted toward concerns with slowing activity as 

external conditions deteriorate and headline 

inflation starts to moderate. This shift would 

justify a halt to the monetary policy tightening 

cycle, particularly in countries where secondround effects on inflation from commodity 

prices have been limited, and a turn to easing 

would be called for if the outlook continues 

to deteriorate. Moreover, in the face of sharp 

capital outflows, countries will need to respond 

quickly to ensure adequate liquidity and deal 

with emerging problems in weaker institutions. 

The exchange rate should be allowed to absorb 

some of the pressure, but stockpiles of reserves 

provide room for intervention to avoid disorderly market conditions.

However, in some other countries, notably but 

not exclusively in the Middle East and Commonwealth of Independent States, inflation 

pressures are still a concern in the context of 

sharp increases in food prices, continued strong 

growth, and tightening supply constraints. 

Although the recent moderation in international commodity prices may ease some of the 

pressure, the gains made over the past years 

on the inflation front are already being jeopardized, and once credibility is eroded, rebuilding 

it will be a costly and lengthy process. Thus, 

policymakers in a number of countries may still 

need to tighten policy settings further.

In most cases, monetary policy should play 

the lead role in macroeconomic policy management, but it should be supported by prudent 

fiscal policy and, in some cases, by flexible 

exchange rate management. Inflation-targeting regimes have generally served well as a 

framework that has encouraged early responses 

to rising inflation pressures, while also providing scope to respond to deteriorating external 

conditions. However, countries with tightly 

managed exchange rate regimes have faced 

greater difficulties. Efforts to tighten the monetary stance in the face of rising inflation are 

undermined by capital inflows attracted by the 

increase in interest rate differential, boosting 

money and credit growth, and many of these 

countries, particularly in emerging Asia and 

the Middle East, have faced sharp increases in 

Policy Challenges for the Global 
Chapter1  Global Prospects and Poli

inflation. In China, the authorities have used 

administrative and prudential measures in 

an effort to limit credit growth, but allowing 

greater exchange rate flexibility would increase 

the room for a more independent monetary 

policy and support efforts to rebalance from 

external to domestic sources of growth.

Fiscal policy should play a supportive role in 

macroeconomic management. Fiscal deficits 

have generally been reduced in recent years 

across emerging and developing economies as 

rapid growth has boosted revenues, but government spending has increased rapidly in many 

countries, adding to demand pressures. Greater 

restraint on spending growth, including public sector wage increases, would complement 

tighter monetary policy, in the face of rising 

inflation, which is particularly important in 

economies with inflexible exchange regimes. 

Within a given spending envelope, giving 

greater priority to infrastructure spending may 

help relieve supply bottlenecks, a particular concern in Middle Eastern oil-exporting countries, 

which have clearly been overheating and whose 

dollar pegs leave little scope for monetary tightening. Some countries with limited exchange 

rate flexibility have also been more exposed to 

sharply deteriorating capital inflows, and here 

again fiscal tightening may be required to help 

stabilize conditions.

In the face of deteriorating economic prospects, a number of emerging economies have 

greater scope than in the past to use fiscal policy 

as a countercyclical tool, in particular by letting 

automatic stabilizers operate. However, the results 

of Chapter 5 caution that fiscal stimulus packages 

are unlikely to be effective—and could be counterproductive—unless confidence in medium-term 

fiscal sustainability has been firmly established and 

measures are timely and well targeted.

Strengthening Macroeconomic Policy

Frameworks

Beyond such immediate cyclical considerations, a more difficult global environment 

has raised questions about monetary and fiscal 

policy frameworks more broadly. Are modifications to these frameworks warranted to improve 

their stabilization properties?

The inflation-targeting approach has been 

challenged by the need to deal with a series of 

large and one-sided commodity price shocks. 

Clearly, there would be risks in focusing singlemindedly on measures of inflation excluding 

food and fuel prices because such an approach 

could accommodate years of high headline 

inflation that could eventually spill over into 

expectations and wage formation. At the same 

time, however, allowing some deviation of headline inflation from inflation targets does seem 

justified to help accommodate a relative price 

shift without undue output volatility, although 

sustained large deviations could undermine 

policy credibility, as discussed in Chapter 3. This 

underlines the need for clear communications 

and a forward-looking approach, prepared to 

tolerate temporary deviations from inflation targets, provided that expectations are sufficiently 

well anchored.

Is there now a global inflation bias inherent 

to the way monetary policy is set, implying a 

need for more coordinated approaches to policy 

setting? Policymakers tend to treat international 

commodity prices as exogenously determined 

and thus do not account for the impact of the 

country’s demand on global commodity markets, exacerbating the global supply constraint. 

However, the size of the externality seems likely 

to be of second-order magnitude even for major 

oil consumers, and it is not clear how such an 

externality could be effectively internalized. 

Practically, it seems sensible for monetary policymakers to continue to focus on minimizing 

volatility in domestic inflation and output while 

relying on more direct action to relieve commodity market pressures, as discussed below. If 

they do so successfully, they will also contribute to minimizing volatility in global markets, 

including those for commodities.

A second concern is that countries that manage their currencies tightly against another 

country’s currency find themselves importing 

the other country’s monetary conditions, which 
may not be appropriate to their circumstances. 

The tension is particularly great where countries 

face large shocks of opposite sign. Thus, the 

United States has been easing at a time when 

many countries with dollar pegs are running 

current account surpluses and operating at or 

beyond capacity. These latter countries would 

benefit from tighter monetary conditions and 

exchange rate appreciation. However, absent a 

formal currency union arrangement, it is not 

reasonable to expect the central bank with the 

reserve currency to adjust its policy to reflect 

monetary conditions in other countries that 

choose to peg against that reserve currency. 

Moreover, such tightening would be likely to 

contribute to dollar appreciation and thus not 

be helpful in terms of the desired rebalancing 

of current accounts. Although there are many 

considerations that feed into the choice of an 

exchange rate regime, there would be stabilization benefits for countries with adequately developed financial institutions to move over time 

to more flexible rate regimes that provide for 

greater control over domestic monetary conditions. This issue is explored further in Box 3.3.

Recent events in housing and financial markets have again brought attention to the extent 

to which monetary policy should respond to 

asset price movements. Inflation-targeting central banks do take asset price movements into 

account to the extent that they have an impact 

on short-term output and price prospects and 

risks. There is a concern, however, that this may 

lead to asymmetrical responses, because sharp 

declines in asset prices may lead to quick policy 

easing, whereas a longer period of asset price 

buildup may not generate much resistance, 

provided near-term prospects remain fair. This 

has led to proposals for leaning against the wind 

of asset price movements, especially when these 

are rapid or seem to be moving prices seriously 

out of line with fundamentals (Chapter 3 of 

the April 2008 World Economic Outlook and BIS, 

2008). The usual counterarguments are that 

such a policy would be hard to calibrate and 

that it is not clear how successful monetary 

policy by itself can be in dampening asset price 

cycles. However, recent research has emphasized 

that short-term interest-rate settings have played 

an increasingly important role in the monetary 

transmission mechanism as the shift toward market-based financing has increased the procyclicality of leverage (Adrian and Shin, 2008).

A complementary approach would be to 

introduce a systemwide element to the regulatory framework to weigh against the inherent 

procyclicality of credit creation. Such a “macroprudential” approach could involve increasing 

regulatory attention to the way financial incentives and constraints affect risk-taking behavior 

throughout the credit cycle (Bernanke, 2008). 

Moreover, capital and provisioning requirements 

could be tightened during the upswing of the 

economic cycle to reduce the risk of destabilizing 

credit booms and could be aligned with reforms 

to strengthen risk management within individual 

institutions. Such reforms would need to be 

developed in the broader context of an overhaul 

of regulatory approaches discussed further below.

Increasing attention is also being paid to fiscal 

policy frameworks. As discussed in Chapter 5, 

fiscal policy can play a useful countercyclical 

role, provided its support is timely, does not 

undermine medium-term sustainability, and is 

well structured to maximize impact. Automatic 

stabilizers provide support that generally satisfies at least the first two of these criteria, and 

reforms could be considered, for example to 

safety net programs, that would increase their 

countercyclical impact without distorting the 

basic purpose of government tax or spending 

policies. Discretionary policy can also play a 

countercyclical role, but timeliness and, especially, reversibility can be more problematic. A 

“deficit bias” can contribute to undermining 

policy credibility and therefore effectiveness, 

as shown in Chapter 5, by the limited impact 

of fiscal stimulus in high-debt countries. To 

remedy this, a rules-based countercyclical policy 

response could be considered, supported by 

stronger fiscal governance mechanisms to give 

greater emphasis to ensuring consistency with 

long-term fiscal sustainability. Such an approach 

could reinforce the overall stabilization properPolicy Challenges for the Global 
Chapter1  Global Prospects and Poli

4

ties of macroeconomic policies and reduce some 

of the burden on monetary policy.

Strengthening Supply and Demand Responses i

Commodity Markets

The recent decline in commodity prices in 

the face of a global slowdown should not be 

allowed to undercut policy efforts to relieve 

strains in commodity markets. The focus should 

be on policies to improve supply and demand 

responsiveness, while avoiding measures that 

could exacerbate market tightness in the short 

term. It will be important to pass through 

changes in international prices to domestic 

markets, while developing well-targeted safety 

nets to cushion the impact on low-income 

groups. Policies that discourage exports in 

favor of domestic markets should continue to 

be rolled back. Advanced economies generally 

allow commodity price changes to feed through 

but should take steps to moderate their use of 

energy and food—far higher per capita than in 

the emerging and developing economies—by 

encouraging greater energy conservation (for 

example, through fuel-efficiency standards as 

well as price-based measures) and reducing 

biofuel subsidies.

Priority should also be given to policies that 

strengthen the supply response to higher prices. 

Agricultural production in emerging markets 

could be fostered by steps to build up the infrastructure for irrigation and transportation and 

to ensure more effective transfer of new technologies to improve yields in developing economies 

so they are more in line with those in advanced 

economies. In energy markets, improved provision of information about resources, inventories, and investment plans, and clear and 

stable investment frameworks, would provide a 

better basis for the needed long-term buildup of 

investment in this sector. Finally, liberalization 

of access for agricultural products to advanced 

economy markets, through a successful conclusion of the Doha Round, would play an important part in establishing a stronger long-term 

framework for agricultural development.

Managing Global Imbalances

As emphasized above, the issue of global 

imbalances has multiple dimensions. Some progress has been made toward unwinding the large 

U.S. current account deficit, and more adjustment is in train, even though rising oil prices 

have slowed the process and financial vulnerabilities have added to concerns. The multilateral 

strategy endorsed by the International Monetary 

and Financial Committee in 2005 and elaborated by the Multilateral Consultation on Global 

Imbalances in 2006–07 remains broadly relevant, 

but needs to be applied flexibly (Box 1.3 in the 

April 2008 World Economic Outlook provides a 

comprehensive overview of progress since the 

Multilateral Consultation). U.S. fiscal consolidation remains a key medium-term objective, but 

recent countercyclical fiscal stimulus and public 

support for the housing and financial sectors 

have been justified to alleviate the current slowdown and to stabilize markets. Progress needs to 

continue toward appreciation of the renminbi 

as part of China’s broader strategy to shift the 

sources of growth toward internal demand 

and to increase the effectiveness of monetary 

policy. Middle Eastern oil exporters will need 

to adjust plans to build up spending out of oil 

revenues in order to reduce overheating in their 

economies, including less-ambitious spending 

increases and a tighter focus on relieving supply 

bottlenecks. For their part, the euro area and 

Japan should press ahead with product and 

labor market reforms to raise potential growth 

in their economies.

Even with implementation of such a strategy, 

global current account imbalances are likely to 

be sustained at high levels for a considerable 

period, particularly given the impact of rising oil 

prices and increasingly binding capacity constraints on oil exporters’ current account surpluses. It will be important to ensure that such 

imbalances do not undermine continued commitment to open trade and capital flows, which 

has underpinned global growth over the past 

decades. One challenge is to ensure the investment of these resources in a secure fashion that 
does not lead to the buildup of vulnerabilities in 

capital-importing countries. Recently, a number 

of emerging economies—notably in emerging Europe but elsewhere too—have had large 

current account deficits for sustained periods 

that stand out by historical standards on both 

dimensions. As discussed in Chapter 6, to some 

degree this experience can be understood in 

terms of the opportunities created by financial 

development, capital account liberalization, and 

European integration. However, the experience 

of the Latin American debt crisis in the early 

1980s after years of strong oil-related inflows 

provides a salutary lesson that such episodes 

can end with a painful bump. Countries receiving capital inflows must therefore be careful to 

ensure that the flows do not lead to a buildup 

of vulnerabilities or balance-sheet mismatches, 

including by strengthening financial supervision 

and domestic financial institutions and ensuring 

an overall macroeconomic context conducive to 

sustainable growth.

Finally, it will be important to ensure that 

large imbalances in trade flows do not lead to a 

buildup in protectionist measures on either the 

current or capital account. Breaking the current deadlock on the Doha Round would help 

strengthen the open multilateral trading system. 

On the capital account side, the growing role of 

SWFs as an investment vehicle is an important 

development. The set of principles and practices 

recently agreed by SWFs (the Santiago Principles) to guide their governance, investment, 

and risk management will help make such flows 

more transparent and thus should help reduce 

concerns about governance of such funds that 

could lead to counterproductive restrictions on 

such inflows (see Box 6.1).13 Moreover, the new 

guidelines that are under development at the 

Organization for Economic Cooperation and 

Development for recipient countries will help 

reassure SWFs of fair, transparent, and open 

access to markets.

13The Generally Agreed Principles and Practices of Sovereign 

Wealth Funds (forthcoming, October 2008).

Appendix 1.1. Assessing and 

Communicating Risks to the 

Global Outlo

The main authors of this appendix are Kevin Clinton, 

Thomas Helbling, Douglas Laxton, and Natalia 

Tamirisa, with assistance from Juigang Chen, Ioan 

Carabenciov, and Ondra Kamenik.

Like all forecasts, the World Economic Outlook (WEO) central, or baseline, projections 

are subject to considerable uncertainty. This 

appenddix discusses approaches that have been 

used in the WEO to assess and communicate 

risks to the WEO forecasts and reports on ongoing work to strengthen macroeconomic risk 

analysis.

As background, it is important to understand 

how the global projections are prepared by IMF 

staff. The process underlying the preparation 

of the WEO forecast is not based on a single 

formal model. It is driven by the judgment of 

specialists who prepare individual country projections combined, through a multistage interactive process based on a consistent set of basic 

assumptions, with assessments from the teams 

covering global economic and financial developments. This process is supported by a suite 

of country-specific, regional, and multicountry 

macroeconomic models. It also draws on discussions with country authorities in the course 

of bilateral surveillance as well as with market 

participants and academics during multilateral 

surveillance missions.

The Fan Char

In recent years, following the recommendations of Timmermann (2006), the IMF staff has 

presented risks to the WEO projections using 

a fan chart (see, for example, Figure 1.12). 

The chart shows the estimated confidence 

intervals around the baseline world growth 

forecast, which widen as the forecast horizon 

stretches into the future. The methodology for 

constructing the fan chart is similar to that 

originally developed by the Bank of England 

Appendix 1.1. Assessing and Communicating Risks to the Gl
Chapter1  Global Prospects and Poli

(see Britton, Fisher, and Whitley, 1998). Outcomes for world growth are assumed to follow 

a “two-piece-­normal” distribution. The centra

forecast is represented as the mode, or the 

most likely outcome, and the width of the fan is 

determined by the distribution of past forecast 

errors. The skewness of the distribution, or the 

relative size of the two pieces of the normal 

distribution, represents the balance of risks to 

the central forecast.

The preparation of the fan chart incorporates 

an array of empirical judgments about the most 

likely sources of risk and about the way they 

may affect macroeconomic developments. The 

contributions of each risk factor to the overall 

balance of risks to global growth are shown in 

the risk factor chart, which complements the 

fan chart. The impact of individual risk factors 

is quantified using the IMF’s suite of macroeconomic models and the IMF staff’s judgment.

The assessed risks are usually not symmetric—

but weighted more to one side. The sum of the 

risk factors provides a measure of the balance 

of risks, or the skew of the probability distribution around the mode, defined as the distance 

between the mean (the average outcome) and 

the mode (the most likely outcome). When the 

risks are symmetric, the average of all possibilities is the most likely outcome. However, when 

the risks are unbalanced, for example to the 

downside, the left-hand tail of the distribution 

is longer, the mean forecast is below the mode, 

and the skew is negative. The median (or the 

point that splits the forecast distribution in half, 

with 50 percent probability on either side) falls 

between the mode and the mean.

Skewed distributions reflect the IMF staff’s 

views on the risks to the forecasts. The staff 

might see a higher risk of deviation from the 

forecast in one direction than the other for 

a number of reasons. First, asymmetric risk 

assessment may result from an acknowledgment of nonlinearities in the global economy. 

For example, capacity constraints in the goods 

market and labor market would limit the room 

for upside potential when the economy is 

operating close to full capacity. The zero bound 

Figure 1.15. Median Forecast Errors during Global

Recessions and at Other Times, 1991–2007

Recession

No recession

1

 Sources: World Economic Outlook (WEO) database; and IMF staff estimates.

 Forecast errors are defined as the difference between actual world growth and the WEO 

forecast of world growth. The errors are calculated for the current-year and following-year

forecasts in the April and October issues of the WEO for the period 1991–2007. A negative 

(positive) forecast error indicates that the actual value is below (above) the forecast, that is, 

the forecast is biased upward (downward).

1

April current year

October current year

April following year

October following ye

-1.5

-1.0

-0.5

0.0

0.5

1.0

ar
on nominal interest rates, financial accelerator 

mechanisms that amplify shocks throughout the 

system, and herd behavior in financial markets 

could all generate complex and asymmetric 

feedback effects.

The second reason for asymmetries is to 

reflect incoming new information, after the forecast is “frozen.” For example, oil prices could 

move substantially out of line with the assumed 

path, or there could be rapid financial developments whose impact is hard to assess but that 

clearly could have a significant and asymmetric 

impact, as was the case last year.

The third reason for asymmetry stems from 

possible internal inconsistency of the WEO forecasts. 

These are not based on an internally consistent 

macroeconomic model and assume interest rates 

and oil prices broadly consistent with market 

expectations and constant real exchange rates, 

which may be at odds with the IMF staff’s assessment of the outlook.

The fourth reason relates to the possibility 

of a systematic behavioral bias in the WEO baseline 

forecasts. An analysis of past forecast errors suggests that during 1991–2007 the World Economic 

Outlook had a general tendency to underpredict 

world growth somewhat while overpredicting it 

substantially in the years immediately preceding global recessions—defined as annual world 

growth (based on purchasing-power-parity 

weights) falling below 3 percent (see Figures 

1.15 and 1.16). This may reflect the well-known 

difficulty of predicting “tail events” (defined as 

adverse outcomes that have up to a 10 percent 

probability of occurring), for example, systemic 

financial events or hard-landing outcomes.

Even though the fan chart provides a useful illustrative device for communicating risks 

underlying the WEO baseline forecasts, and the 

heuristic approach underlying its construction is 

sufficiently flexible to incorporate a wide range 

of complex considerations, the methodology has 

some drawbacks. The sources of uncertainty are 

somewhat ad hoc, because they are not derived 

from a formal model of the economy, and the 

actual distribution of likelihood of different 

outcomes may not be normal. In addition, the 

Appendix 1.1. Assessing and Communicating Risks to the Gl

Density

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.0

0.1

0.2

0.3

0.4

0.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.0

0.1

0.2

0.3

0.4

0.5

Figure 1.16. Histograms of Forecast Errors, 1991–2007

 Source: IMF staff calculations.

 Forecast errors are defined as the difference between actual world growth and the WEO 

forecast of world growth. The errors are calculated for the current-year and following-year 

forecasts in the April and October issues of the WEO for the period 1991–2007. A negative 

(positive) forecast error indicates that the actual value is below (above) the forecast, that is, 

the forecast is biased upward (downward).

1

April Current Year

April Following Year

October Current Year

October Following Year

Density Density Density

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.0

0.1

0.2

0.3

0.4

Illustration of a Normal Distribution 0.5

Density 1

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.0

0.2

0.4

0.6

0.8
Chapter1  Global Prospects and Poli

standard deviation of the distribution used to 

construct the fan chart is fixed and does not 

vary with the state of the world. The risk factors 

used to determine the extent of asymmetry in 

the distribution are typically ad hoc and, in reality, jointly distributed rather than independent. 

Thus, inflation risks are greater in the presence 

of an oil price spike, whereas risks to domestic 

demand depend on the evolution of financial 

conditions. As discussed above, a fan chart based 

on a two-piece-normal distribution may underestimate the risks of tail events such as global 

recessions.

Leading Indicator Approach

One way to complement the fan chart 

approach is to gauge the risk of a global recession using a leading indicator approach.

Leading indicators are variables that help 

predict the probability of global downturns 

(recessions) some three to nine months 

ahead.14 A suitable indicator has a turning 

point that precedes a change in global activity 

in a systematic and consistent manner. Leading indicators have long been used in business 

cycle analysis (for example, Zarnowitz, 1992), 

although finding reliable indicators remains 

surprisingly difficult.15

IMF staff analysis suggests that an index 

constructed as a combination of U.S. financial 

and real variables and cyclical commodity prices 

has promising leading indicator properties. The 

financial variables include the slope of the term 

structure (proxied by the spread between 10-

year and three-month Treasury rates) and stock 

returns (S&P 500). The other variables are U.S. 

industrial production and the IMF’s metals price 

index. Based on September 2008 data, this indicator points to a probability of global recession 

within the next three months of approaching 

60 percent, up from almost 50 percent late last 

14The dating of the cycle in global activity is based on a 

monthly series of global industrial production. 15Another difficulty is the lack of sufficiently long time 

series for many relevant high-frequency indicators.

0.0

0.2

0.4

0.6

0.8

1.0

Figure 1.17. Probability of Global Recessions

 Source: IMF staff estimates.

 Shaded area represents periods of global recession.

1980 85 90 95 2000 05 Nov.

08

1

1
year.16 As Figure 1.17 shows, together these variables have predicted past global recessions with 

a probability of more than 50 percent without 

providing false signals during 1980–2007.17

This approach should also be used cautiously. 

The strength and timing of the signal varied 

across recessions, which is consistent with the 

general experience with leading indicators 

(for example, Stock and Watson, 1989, 2003). 

Moreover, the leading indicator approach is 

essentially statistical and does not provide much 

insight into the processes generating adverse 

outcomes or how they might change over time. 

Thus, a leading indicator approach at the global 

level, although simple and intuitive, is not a 

panacea when it comes to assessing risks to 

global growth.

Scenario Analysi

An alternative way to address the above 

issues is to complement the judgment-based 

risk assessment, as embodied in fan charts, with 

analyses using a fully articulated model to assess 

the impact of shocks to key variables. Thus, 

Figure 1.15 in the April 2008 World Economic 

Outlook illustrated the impact of a deeper financial sector shock, and Box 1.1 in the April 2007 

World Economic Outlook illustrated the effects of 

oil-price shocks stemming, respectively, from 

demand and supply factors. Model simulations 

are particularly useful for tracing the complex 

dynamic interactions that occur when a shock 

moves the economy away from its previously 

expected path. However the simulated scenarios, 

in themselves, do not provide a guide to the 

distribution of risks. This requires inclusion 

within the model of a probabilistic framework 

that contains estimates of the distributions of 

relevant shocks.

16For comparison, the fan chart now suggests that the 

risk of global recession is almost 20 percent. 17A false signal would be a prediction of more than 

50 percent probability of a global recession at a time the 

global economy was expanding.

Macroeconomic Model-Based 

Confidence Intervals

Work is now under way at the IMF on an 

estimated multicountry model capable of producing baseline forecasts and fan charts, with 

all numerical assumptions—including distribution of shocks—clearly spelled out.18 This new 

Global Projection Model (GPM) builds on the 

significant progress that has been made, at 

various central banks, in estimating a complete 

system of equations that link demand and supply shocks in different markets to macroeconomic variables.19 Such a model is not capable 

of producing forecasts with the full country 

detail provided by the WEO forecasts, but has 

the advantage of greater consistency and clarity 

between assumptions and outcomes. It can also 

be used to produce conditional forecasts to 

indicate the impact of shocks on one or more 

variables.

Almost all this research has so far been based 

on symmetric shock distributions and linear 

models, which will result in symmetric confidence bands, but the IMF staff has been working to introduce three sources of asymmetry: 

(1) the zero-interest-rate floor; (2) a nonlinear 

output-inflation process, in which positive 

shocks to aggregate demand have larger inflation implications when the economy is already 

overheating than when there is significant slack 

in the economy; and (3) a credit-tightness 

effect on the real economy, whereby an easing 

of financial conditions may not increase lending much beyond a certain threshold (once 

there is sufficient collateral to satisfy lenders of 

18See Carabenciov and others (forthcoming) for a 

description of a preliminary three-region version of the 

GPM that includes models for the United States, the euro 

area, and Japan. In the near term, the GPM will be used 

to run scenarios and check the macro consistency in the 

IMF desk economists’ baseline forecasts much as the 

Federal Reserve Board of Governors uses macro models 

to check the consistency of their own forecasts. 19This has been made possible by the development of 

user-friendly Bayesian-estimation routines, which are now 

being used extensively in policymaking institutions and 

academia to estimate macro models—see Laxton, Rose, 

and Scott (forthcoming). 

Appendix 1.1. Assessing and Communicating Risks to the Gl
Chapter1  Global Prospects and Poli

the safety of their loans, a further increase in 

the value of the collateral may not affect their 

behavior very much).

Figure 1.18 provides some illustrative confidence intervals from this extended version of 

the GPM.20 The central path lines within the 

fans represent the baseline solutions of the 

model for the expected path of the economy. 

Unlike conventional forecasts, this is an unconditional forecast, which assumes that all shocks 

are set to zero, with none of the judgmentbased input that usually proves to be very useful 

when producing near-term forecasts.21 The 

boundaries of the fans represent 90 percent 

confidence intervals, which are derived from 

estimated historical distributions of shocks. 

The wider confidence intervals depicted in the 

fourth panel are based on building into the 

GPM an assumption that shocks to credit conditions become larger when credit conditions are 

exceptionally tight. The wider bands suggest 

that the increased uncertainty would all be on 

the downside for the output gap, inflation, and 

short-term interest rates.

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construct the GPM fan charts. 21Efficient model-based projections developed in policymaking institutions typically rely very heavily on judgment for setting the first two quarters of the projection. 

This judgment is based on considerably more information than can be summarized by pure forecasts generated from a macro model—see Laxton, Rose, and Scott 

(forthcoming).

0 5 10 15

0

2

4

6

8

Figure 1.18. Illustrative GPM-Based 90 Percent 

Confidence Intervals1

 Source: IMF staff calculations.

 GPM = Global Projection Model.

Short-Term Interest Rate

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-6

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-2

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Output Gap 2

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CPI Inflation 6

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Calomiris, Charles, Stanley D. Longhofer, and William 

Miles, 2008, “The Foreclosure-House Price Nexus: 

Lessons from the 2007–2008 Housing Turmoil,” 

NBER Working Paper No. 14294 (Cambridge, Massachusetts: National Bureau of Economic Research).

Carabenciov, I., I. Ermolaev, C. Freedman, M. Juillard, O. Kamenik, D. Korsunmov, and D. Laxton, 

forthcoming, “A Small Quarterly Multi-Country 

Projection Model with Financial-Real Linkages and 

Oil Prices,” IMF Working Paper (Washington: International Monetary Fund).

Cardarelli, Roberto, Tommaso Monacelli, Alessandro 

Rebucci, and Luca Sala, forthcoming, “Housing 

Finance, Housing Shocks, and the Business Cycle: 

Evidence from OECD Countries,” IMF Working Paper (Washington: International Monetary 

Fund).

Chen, H., K. Clinton, Ondrej Kamenik, and Douglas Laxton, forthcoming, “Constructing Forecast 

Confidence Bands with GPM,” IMF Working Paper 

(Washington: International Monetary Fund).

Claessens, Stijn, M. Ayhan Kose, and Marco Terrones, 

forthcoming, “What Happens During Recessions, 

Crunches, and Busts?” IMF Working Paper (Washington: International Monetary Fund).

Dell’Ariccia, Giovanni, Deniz Igan, and Luc Laeven, 

2008, “Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market,” IMF 

Working Paper 08/106 (Washington: International 

Monetary Fund).

Deutsche Bank, 2008, “Housing Correction: U.S. 

Ahead of Europe,” Global Economic Perspectives.

Estevão, Marcello, and Natalia Barrera, forthcoming, 

“House Prices and Regional Cycles in the United 

States,” IMF Working Paper (Washington: International Monetary Fund). (A summary of this work is 

available as Chapter V in United States: Selected Issues, 

IMF Country Report No. 08/256 (Washington: 

International Monetary Fund). Available at www.

imf.org/external/pubs/ft/scr/2008/cr08256.pdf.

Estevão, Marcello, and Prakash Loungani, forthcoming, “Housing Markets and Labor Market 

Dynamics,” IMF Working Paper (Washington: International Monetary Fund).

Hilbers, Paul, Alexander W. Hoffmaister, Angana 

Banerji, and Haiyan Shi, 2008, “House Price Developments in Europe: A Comparison,” IMF Working 

Paper 08/211 (Washington: International Monetary 

Fund).

Hodrick, Robert J., and Edward C. Prescott, 1997, 

“Postwar U.S. Business Cycles: An Empirical Investigation,” Journal of Money, Credit and Banking, Vol. 29 

(February), pp. 1–16.

International Monetary Fund (IMF), 2008a, “Food 

and Fuel Prices—Recent Developments, Macroeconomic Impact, and Policy Responses” (Washington, 

June and September). Available at www.imf.org/

external/np/pp/eng/2008/063008.pdf and www.

imf.org/external/np/pp/eng/2008/091908.pdf.

———, 2008b, Global Financial Stability Report: Financial Stress and Deleveraging—Macrofinancial Implications and Policy (Washington, October).

Klyuev, Vladimir, 2008, “What Goes Up Must Come 

Down? House Price Dynamics in the United States,” 

IMF Working Paper 08/187 (Washington: International Monetary Fund).

Lane, Philip, and Gian Maria Milesi-Ferretti, 2006, 

“The External Wealth of Nations Mark II: Revised 

and Extended Estimates of Foreign Assets and 

Liabilities, 1970–2004,” IMF Working Paper 06/69 

(Washington: International Monetary Fund).

Laxton, Douglas, D. Rose, and Alasdair Scott, forthcoming, “Developing a Structured Forecasting and 

Referenc
Chapter1  Global Prospects and Poli

Policy Analysis System,” IMF Working Paper (Washington: International Monetary Fund).

Muellbauer, John, 2008, “Housing, Credit and Consumer 

Expenditure,” CEPR Discussion Paper No. 6782 (London: Centre for Economic Policy Research).

Organization for Economic Cooperation and 

Development (OECD), 2006, “Recent House 

Price Developments: The Role of Fundamentals,” 

OECD Economic Outlook, No. 78 (December), 

pp. 123–54.

———, 2008, OECD Economic Outlook, No. 83 (June).

Poterba, James, 1984, “Tax Subsidies to OwnerOccupied Housing: An Asset-Market Approach,” 

Quarterly Journal of Economics, Vol. 99 (November), 

pp. 729–52.

Stock, James, and Mark Watson, 1989, “New Indexes 

of Coincident and Leading Economic Indicators,” 

in NBER Macroeconomic Annual: 1989 (Cambridge, 

Massachusetts: MIT Press), pp. 351–94.

———, 2003, “How Did Leading Indicator Forecasts 

Perform During the 2001 Recession?” Federal Reserve 

Bank of Richmond Economic Quarterly, Vol. 89 (Summer), pp. 71–90.

Timmermann, Allan, 2006, “An Evaluation of the 

World Economic Outlook Forecasts,” IMF Working 

Paper 06/59 (Washington: International Monetary 

Fund).

Tsatsaronis, Kostas, and Haibin Zhu, 2004, “What 

Drives Housing Price Dynamics: Cross-Country Evidence,” BIS Quarterly Review (March), 

pp. 65–78.

Vamvakidis, Athansios, 2008, “Convergence in Emerging Europe: Sustainability and Vulnerabilities,” IMF 

Working Paper 08/81 (Washington: International 

Monetary Fund).

Warnock, Veronica Cacdac, and Francis E. Warnock, 

2007, “Markets and Housing Finance,” NBER Working Paper No. 13081 (Cambridge, Massachusetts: 

National Bureau of Economic Research).

Zarnowitz, Victor, 1992, Business Cycles: Theory, History, 

Indicators, and Forecasting (Chicago: University of 

Chicago Press).
c h a pte r 2 Country and Regional 

Against the background of the global outlook outlined 

in Chapter 1, this chapter discusses how the main 

countries and regions that make up the world economy 

are coping with intense financial strains, high commodity prices, and the global downturn. It then draws 

policy lessons, with a view to finding an appropriate 

balance between responding to deteriorating short-term 

growth prospects, containing inflation, and fostering 

longer-term growth potential.1

United States and Canada: Prognosis fo

the Downturn

The United States has been at the center of the 

intensifying global financial storm discussed in 

Chapter 1, and the economy is now slowing fast. 

The latest data suggest that GDP rose 2.8 percent 

at an annualized rate in the second quarter as 

surging net exports and tax rebate checks buoyed 

consumption and outweighed the drag from 

financial turmoil, a continuing housing correction, and high commodity prices. However, taking 

the most recent three quarters together, the 

pace of growth averaged only 11⁄4 percent, well 

below potential. More important, available data 

for the third quarter suggest a further slowdown 

and forward-looking indicators—such as consumer and business confidence and accumulating evidence of the negative impact on credit of 

recent financial market disruptions—suggest that 

the economy is likely to contract in the current 

quarter and into early 2009.

Since the summer of 2007, declining residential investment has been a major drag on output 

(subtracting about 3⁄4 percentage point off 

growth), inventories have been compressed, and 

consumption has slowed. By contrast, there have 

been two sources of resilience. First, net exports 

have continued to boom—adding 11⁄2 percent1Further analysis of regional and country developments 

is provided in the IMF Regional Economic Outlooks.

age points to growth over the period—although 

surging oil prices kept the current account deficit at about 5 percent of GDP. Second, despite 

the slowing economy and tighter credit conditions, U.S. firms have remained healthy, benefiting from relatively low leverage, high profits, 

and strong export demand, which explain why 

cuts in business spending and employment were 

moderate until recently.

With a recession now looking increasingly 

likely, the key questions are, how deep will the 

downturn be, when will a recovery get under 

way, and how strong will it be? The key determinants of the short-term outlook will be the 

effectiveness of recent government initiatives to 

stabilize financial market conditions, as well as 

the behavior of U.S. households in the face of 

rising stress, the depth of the housing cycle, and 

the extent to which inflation concerns constrain 

monetary policy.

Faced by a rapidly expanding and increasingly 

dangerous financial crisis, the authorities have 

acted to deal with immediate threats to systemic 

stability and have passed a major initiative to 

purchase illiquid assets from banks—although 

it will take time to organize these operations. 

Once fully in place, these measures should 

help to stabilize market conditions, but even 

so the process of balance-sheet repair will be 

long and arduous. It will take considerable time 

before losses are fully recognized, banks are 

recapitalized, leverage is reduced, and market 

confidence is regained. Tight bank lending 

conditions are now having a visible impact on 

the growth of new loans, and credit availability 

is likely to remain tight throughout 2009. The 

impact is likely to be greatest on households, 

given their deteriorating balance sheets, but 

under current dislocated financial market 

conditions, firms are also likely to be affected 

adversely, notwithstanding stronger balance 

sheets and still-healthy profit margins.
CHAPTER2  Country and Regional Persp

5

While consumption has continued to increase 

in recent quarters, U.S. households face rising 

cash-flow and balance-sheet strains. Income 

growth has slowed as employment has dropped 

since January, the average workweek has shrunk, 

unemployment has risen by a full percentage 

point, wages have stagnated, and gas prices 

remain high (Figure 2.1). Falling house prices 

and the weakening equity market have contributed to a 10 percent drop in household 

net wealth relative to GDP. Moreover, access to 

credit has tightened markedly, most notably for 

mortgages but also for other sources of consumer finance.

A key element of the baseline forecast is that 

consumption will now show more obvious signs 

of weakness. In the projections, consumption 

declines in the second half of 2008, as the stimulus from the the tax rebate checks wears off, and 

in the early part of 2009. It would then recover 

gradually during the remainder of the year, held 

back by cash-flow strains and the need to rebuild 

savings.

Strains on households are in part a reflection of the massive continuing downturn in the 

housing market. The drop in house prices—in 

the range of 5 to 17 percent over the past year, 

depending on the index used—is unprecedented since the Great Depression. As a result, 

more than 10 million households owe more on 

their mortgages than the market value of their 

homes. Housing-related activity has also plummeted—housing starts have fallen 60 percent 

from their peak. This was a needed correction 

after a period of excess, and there are now some 

tentative signs of stabilization, for example in 

recent home sales data, although up to onethird of sales now involve foreclosure, an indicator of weakness rather than strength.

The baseline projections anticipate that the 

housing cycle will eventually find a floor in 2009 

after four years of correction. The projections 

build in a further 10 percent decline in house 

prices by the end of the year, in line with market 

expectations (although these forward markets 

are very thin), which will bring prices closer in 

line with fundamentals. Moreover, residential 

1.0

1.1

1.2

1.3

1.4

1.5

4.5

5.0

5.5

6.0

6.5

-600

-300

0

300

600

900

2

3

4

5

6

7

Figure 2.1. United States: Strains on Households

 Sources: Davis, Lehnert, and Martin (2007); Haver Analytics; and IMF staff estimates.

 Quarterly change in total nonfarm payrolls, thousands.

 National Association of Realtors; three-month moving average of 12-month percent 

change.

 Office of Federal Housing Enterprise Oversight (OFHEO).

 Ratio of OFHEO house price index to personal disposable income per capita.

 Inverse of the rent-price ratio for the aggregate stock of owner-occupied housing.

 Index equal to 100; median family income qualifies for an 80 percent mortgage on a 

median-priced existing single-family home, reindexed to 1995 = 100.

U.S. households are coming under increasing strain. So far they have maintained

moderate spending growth, but current drags on growth include falling employment, 

tightening credit, and declining net worth, as well as rising fuel and food prices. 

However, housing affordability has improved and there are signs that housing 

valuations are moving closer in line with historical trends.

Labor Market

1

Change in employment

(left scale)

Unemployment rate

(right scale)

1

2

-3

0

3

6

9

12

-1

0

1

2

3

Real Personal Consumption 4

and Saving Rate

(percent)

09:

Q4

Real consumption 

growth

(left scale)

Saving rate

(right scale)

2002 04 06

0

200

400

600

800

1000

1200

1400 Credit to Households

(billions of U.S. dollars)

Other credit to

households

Residential 

mortgages

08:

Q2

2002 04 06

Household Net Worth and 

Indebtedness

(ratio to disposable income)

Household 

indebtedness

(left scale)

Household 

net worth

(right scale)

08:

Q2

2002 04 06

08:

Q3

2002 04 06

3

4

08

50

75

100

125

150

175

200

225

250 Real House Prices

(index, 1995 = 100)

NAR

OFHEO

2 Case-Shiller

3

Aug.

08

1970 80 90 2000 25

50

75

100

125

150

Housing Affordability 175

(index, 1995 = 100)

Aug.

08

1970 80 90 2000

4

Price-to-income

ratio Price-to-rent

ratio5

Affordability

index6

5

6

OFHEO trend

1975–2000

NAR trend

1970–2000
investment relative to GDP is around a 40-year 

low, and the drop in housing starts is bringing 

down inventories of unsold new homes. Support 

should also be provided by recent legislation 

to facilitate the refinancing of “underwater” 

mortgages with federal guarantees and to provide assurances that Fannie Mae and Freddie 

Mac—the government-sponsored enterprises 

(GSEs) that are behind about 80 percent of 

new mortgage lending in recent quarters—will 

continue to provide housing finance.

On the inflation front, still-high energy prices 

boosted headline personal consumption expenditure (PCE) inflation to 4.6 percent (12-month 

rate) in August 2008, while core PCE inflation ticked up to 2.6 percent. Given the recent 

retreat in international oil prices, headline 

figures are likely to start coming down, and the 

widening output gap, moderate wage increases, 

and a pickup in productivity should all help 

contain underlying inflation.

Putting together the pieces of the puzzle, 

activity is projected to decline in the final 

quarter of 2008 and the first quarter of 2009, 

stabilize in the second quarter, and then embark 

on a gradual recovery. On a year-over-year basis, 

growth moderates from 2.0 percent in 2007 to 

1.6 percent in 2008 and 0.1 percent in 2009 

(Table 2.1). The economy only returns to potential growth in 2010. Risks around this forecast 

are to the downside. Particular concerns are that 

the credit crunch could impose an ever-greater 

constraint on activity, that the house price 

correction could extend into 2010, and that 

inflation pressures may prove more persistent, 

limiting the Federal Reserve’s room for maneuver. The principal upside potential is that U.S. 

firms may be able to maintain their spending 

patterns better than expected, despite financial 

strains, which would provide greater-than-anticipated support for household incomes.

While the immediate task is to stabilize 

financial conditions, policymakers must ensure 

appropriately supportive macroeconomic policy 

settings, including efforts to contain negative 

macrofinancial feedback loops. The present 

highly accommodative monetary policy stance 

could be eased still further if the downturn 

seems likely to deepen, even though its effectiveness may be limited if financial strains 

persist, and the Federal Reserve will continue to 

be watchful on the inflation front. Eventually, 

policy will need to move toward a more neutral 

stance as an economic recovery gathers steam 

and financial conditions improve. On the fiscal 

front, the stimulus package provided well-timed 

support, but the fiscal deficit is rising sharply 

and is now projected at 41⁄2 percent of GDP in 

2009, the highest among the G7 countries, without allowing for the potentially sizable impact 

of recent measures to stabilize the financial and 

housing sectors. The need for fiscal consolidation in the face of medium-term spending 

pressures and long-term challenges posed by 

demographics and rising medical costs point 

to the need for adjustment measures elsewhere 

once the situation has stabilized in order to 

offset the costs of financial system support.

In Canada, economic activity has slowed 

sharply since mid-2007, and growth is projected 

to come down from 2.7 percent in 2007 to 

0.7 percent in 2008 before picking up to 1.2 percent in 2009. Although the resource-intensive 

sectors have benefited from high commodity 

prices, the lagged effect of past real appreciation of the Canadian dollar, together with the 

U.S. slowdown, has hit manufacturing hard. 

The Bank of Canada eased interest rates by 

150 basis points between December 2007 and 

April 2008 and has held rates steady since then. 

Despite its recent acceleration to 31⁄2 percent, 

inflation has generally remained well anchored, 

in part owing to the rising currency. Banks have 

generally weathered the financial strains well 

so far, reflecting conservative regulation and 

low exposure to structured products, but risks 

remain given the strong economic and financial 

linkages with the United States.

Western Europe: Struggling with Multip

Shock

Western Europe is being hit by major shocks 

that are weakening economic activity, notably 

Western Europe: Struggling with Mul
CHAPTER2  Country and Regional Persp

extraordinary financial stress. Real GDP growth 

has stalled in the euro, following a first-quarter 

rebound. Growth was already noticeably weaker 

elsewhere during the first quarter, including in 

the United Kingdom and most Nordic countries, 

and conjunctural indicators now suggest that 

many countries are moving close to or into recession. At the same time, high oil and food prices 

are still keeping inflation at elevated levels.

Economic growth is being slowed by a number of factors, initially mainly by rising oil prices 

but now increasingly by tightening financial 

conditions.2 Relative to 2007, oil prices are some 

40 percent higher in euro terms and, together 

with surging food prices, have squeezed alreadysluggish consumption growth. All other things 

equal, standard rules of thumb would imply 

output losses from such a shock in a broad 

range up to about @/3 percent of GDP for the 

2See also the forthcoming Regional Economic Outlook: 

Europe, where the implications for Europe of commodity 

price shocks and ongoing financial turmoil are discussed 

in more detail. 

Table 2.1 Advanced Economies: Real GDP, Consumer Prices, and Unemploym

(Annual percent change and percent of labor force)

Real GDP Consumer Prices Unemployment

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Advanced economies 3.0 2.6 1.5 0.5 2.4 2.2 3.6 2.0 5.7 5.4 5.7 6.5

United States 2.8 2.0 1.6 0.1 3.2 2.9 4.2 1.8 4.6 4.6 5.6 6.9

Euro area1 2.8 2.6 1.3 0.2 2.2 2.1 3.5 1.9 8.7 7.4 7.6 8.3

Germany 3.0 2.5 1.8 — 1.8 2.3 2.9 1.4 9.8 8.4 7.4 8.0

France 2.2 2.2 0.8 0.2 1.9 1.6 3.4 1.6 9.2 8.3 7.7 8.3

Italy 1.8 1.5 –0.1 –0.2 2.2 2.0 3.4 1.9 6.8 6.2 6.7 6.6

Spain 3.9 3.7 1.4 –0.2 3.6 2.8 4.5 2.6 8.5 8.3 11.2 14.7

Netherlands 3.4 3.5 2.3 1.0 1.7 1.6 2.9 2.6 3.9 3.2 2.8 2.9

Belgium 2.9 2.8 1.4 0.2 2.3 1.8 4.6 2.8 8.3 7.5 7.1 8.6

Austria 3.4 3.1 2.0 0.8 1.7 2.2 3.5 2.3 4.8 4.4 4.2 4.4

Finland 4.9 4.5 2.5 1.6 1.3 1.6 3.9 2.5 7.7 6.8 6.2 6.2

Greece 4.2 4.0 3.2 2.0 3.3 3.0 4.4 3.1 8.9 8.3 7.7 8.3

Portugal 1.4 1.9 0.6 0.1 3.0 2.4 3.2 2.0 7.7 8.0 7.6 7.8

Ireland 5.7 6.0 –1.8 –0.6 2.7 2.9 3.5 2.4 4.4 4.5 5.7 7.0

Luxembourg 6.1 4.5 2.3 1.8 2.7 2.3 3.7 1.8 4.4 4.4 4.4 4.8

Slovenia 5.7 6.1 4.3 3.7 2.5 3.6 5.9 3.3 5.9 4.8 4.8 5.0

Cyprus 4.0 4.4 3.4 2.8 2.2 2.2 4.6 3.5 4.6 3.9 3.9 3.9

Malta 3.1 3.7 2.8 2.3 2.6 0.7 3.7 2.2 7.3 6.4 6.5 7.0

Japan 2.4 2.1 0.7 0.5 0.3 — 1.6 0.9 4.1 3.8 4.1 4.5

United Kingdom1 2.8 3.0 1.0 –0.1 2.3 2.3 3.8 2.9 5.4 5.4 5.4 6.0

Canada 3.1 2.7 0.7 1.2 2.0 2.1 2.5 2.1 6.3 6.0 6.2 6.3

Korea 5.1 5.0 4.1 3.5 2.2 2.5 4.8 4.0 3.5 3.3 3.1 3.0

Australia 2.7 4.2 2.5 2.2 3.5 2.3 4.6 3.6 4.8 4.4 4.3 4.8

Taiwan Province of China 4.9 5.7 3.8 2.5 0.6 1.8 4.2 2.5 3.9 3.9 3.9 4.1

Sweden 4.1 2.7 1.2 1.4 1.5 1.7 3.4 2.8 7.0 6.1 6.6 7.1

Switzerland 3.4 3.3 1.7 0.7 1.0 0.7 2.6 1.5 3.0 2.5 2.6 2.8

Hong Kong SAR 7.0 6.4 4.1 3.5 2.0 2.0 4.8 4.3 4.8 4.1 3.5 3.6

Denmark 3.9 1.7 1.0 0.5 1.9 1.7 3.4 2.8 3.9 2.8 1.8 2.6

Norway 2.5 3.7 2.5 1.2 2.3 0.8 3.2 2.7 3.4 2.5 2.5 3.0

Israel 5.2 5.4 4.3 2.8 2.1 0.5 4.8 3.3 8.4 7.3 6.0 6.2

Singapore 8.2 7.7 3.6 3.5 1.0 2.1 6.5 3.3 2.7 2.1 2.1 2.2

New Zealand2 1.9 3.2 0.7 1.5 3.4 2.4 4.2 3.8 3.8 3.6 4.0 4.3

Iceland 4.4 4.9 0.3 –3.1 6.8 5.0 12.1 11.2 1.3 1.0 2.2 3.9

Memorandum

Major advanced economies 2.7 2.2 1.2 0.1 2.4 2.2 3.5 1.7 5.8 5.5 5.8 6.6

Newly industrialized Asian economies 5.6 5.6 4.0 3.2 1.6 2.2 4.8 3.5 3.7 3.4 3.3 3.3

1Based on Eurostat’s harmonized index of consumer prices.

2Consumer prices excluding interest rate components.
euro area—less for oil producers such as the 

United Kingdom and Norway. Although oil 

prices increased sevenfold over 1999–2008, the 

response of wages has remained generally subdued, unlike during the 1970s, reflecting structural reforms and improved policy frameworks. 

Together with rapidly cooling activity and rising 

unemployment fears, these factors should help 

contain wages over the coming year (Figure 2.2). 

Thus, while headline inflation has recently 

been running in the 3–4 percent range in many 

countries, core inflation (excluding all food 

and energy) has generally been below 2 percent 

in the euro area and the United Kingdom.3

Inflation expectations have generally remained 

well anchored, although somewhat less so in the 

United Kingdom than in the euro area.

While oil and food price hikes are undercutting real disposable incomes, financial conditions are tightening quickly. European banks are 

struggling with a confluence of adverse shocks. 

They have been exposed to losses on their 

holdings of U.S.-mortgage-related assets and 

deteriorating overall credit quality since 2007. 

Concerns that initially focused on liquidity are 

also affecting solvency. Confidence in the sector 

has weakened and highly leveraged banks are 

struggling to maintain funding in the face of rising creditor concerns about balance-sheet risk. 

Equity-to-asset ratios will need to be boosted, 

which, to a large extent, will have to be achieved 

by cutting back on lending because bank stock 

prices have declined. The process of deleveraging, including market exit by some institutions, 

will likely be long and arduous and banks have 

already tightened lending standards to far above 

pre-turmoil levels.

Households and firms operating in real estate 

are struggling under growing debt burdens, 

3Consumer price index (CPI) food price inflation in 

the euro area increased from about 2 percent in mid2007 to 6.2 percent in August 2008, contributing 0.8 percentage point to the 3.8 percent August headline CPI 

inflation rate. Energy contributed 1.1 percentage points. 

Higher food prices mainly redistribute income within 

western Europe and thus have much smaller direct effects 

on economic growth than high oil prices (see Chapter 3).

0

1

2

3

4

5

6

7

-20

-10

0

10

20

30

40

50

PRT

DEU

AUT

NLD

ITA

FRA

BEL

UK

FIN

US

GRC

ESP

IRL

-2

-1

0

1

2

3

-4

-2

0

2

4

6

GRC

UK

FRA

PRT

ITA

AUT

DEU

IRL

BEL

NLD

ESP

FIN

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7 8 9 10 11 0

1

2

3

4

5

6

Figure 2.2. Western Europe: Slowing Demand and

High Inflation

 Sources: European Central Bank; European Commission; Eurostat; Haver Analytics;

Thomson Datastream; and IMF staff estimates.

 Data refer to the euro area unless otherwise noted.

 Deviation from 1993–2002 average.

 AUT: Austria; BEL: Belgium; FIN: Finland; FRA: France; DEU: Germany; GRC: Greece; IRL:

Ireland; ITA: Italy; NLD: Netherlands; PRT: Portugal; ESP: Spain; UK: United Kingdom; US: 

United States.

 The formula gives equal weight (0.5) to the deviation of breakeven inflation from the 

inflation target and to the output gap; the lower band is based on a natural rate equal to 3.5 

percent; the upper band is based on a natural rate equal to 4.5 percent.

 Euro interbank offered rate.

Although headline inflation is high, wages have generally remained subdued, and 

slowing activity and rising unemployment fears should restrain demand for pay 

hikes. In the euro area, monetary conditions are on the tight side by the standards of

recent history, and countries have little room for discretionary fiscal stimulus, lest

they breach the Maastricht limit of 3 percent of GDP deficit.

Compensation per Employee 

and Unemployment

Residential Investment, 

2003–07

(percent of GDP)

1

Taylor Rule, Money Market

Rates, and Unemployment

Expecations (percent)

2007 Structural Balance

(percent of potential GDP)

2

1999

1993:Q1

2008:Q2

Overall euro area

Unemployment rate (percent)

Compensation (percent change)

1995:Q1

2000:Q1

2005:Q1

Right

scale

1

4 Taylor rule

3-month 

EURIBOR

Unemployment

expectations

(right scale)

2,3

3

2001 03 05 07 09

4

-1.5 -1 -0.5 0 0.5 1 1.5-3

-2

-1

0

1

2 Unit Labor Costs (ULC) and 

Output Gap

Real ULC (percent)

Output gap (percent of potential GDP)

Inflation Expectations and 

the Price of Oil

1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

-40

-20

0

20

40

60

Inflation expectations (percent)

Oil price (percent change from a year earlier)

2008:Q3

3

5

5

Western Europe: Slowing Demand and High
CHAPTER2  Country and Regional Persp

particularly in countries such as Ireland, Spain, 

and the United Kingdom, where floating-rate 

mortages indexed to short-term interest rates 

are common. In real terms, residential property prices are falling in these and some other 

countries, while slowing quickly elsewhere (see 

Box 1.2). While there is a risk of an outright 

housing-related credit crunch, some factors 

would mitigate pernicious feedback loops 

between the financial and real sectors (Chapter 4). Although residential real estate generally accounts for a larger share of activity in 

western Europe than the United States, the 

recent expansion of residential investment was 

generally less pronounced, except in Finland, 

Greece, Ireland, Spain, and the United Kingdom. Furthermore, these countries are less 

likely to suffer from the financial vulnerabilities 

exposed in the United States: household savings 

are generally higher and debt lower, non-prime 

lending is much less widespread, loan-to-value 

ratios are more conservative, and opportunities 

for equity withdrawal are much more limited. 

However, even in the absence of an outright 

credit crunch, the downturn in residential real 

estate will have an appreciable short-run impact 

in some countries (for example, Ireland, Spain, 

and the United Kingdom) and, with the exception of a few countries (for example, Austria, 

Germany, and Switzerland), produce noticeable 

medium-term headwinds.

Turning to nonfinancial firms, balance sheets 

are stronger than at the onset of the 2001–02 

cyclical downturn, and this should limit the 

effect of financial sector strains on investment. 

On the other hand, the longer the financial 

stresses last, the more corporate profits, balance 

sheets, and investment will suffer.

The euro area’s external current account was 

close to balance in 2007 (Table 2.2). Exports 

are expected to slow in line with world demand, 

however, and the current account is expected to 

deteriorate, mainly on account of worsened terms 

of trade. Also, the real effective exchange rate is 

on the strong side of medium-term fundamentals, 

despite some recent weakening. By contrast, the 

United Kingdom’s current account is in noticeable deficit, and the pound sterling has depreciated by more than 10 percent in real effective 

terms since the onset of the market turmoil.

In this challenging environment, central 

banks have tried to ease liquidity pressures, 

including through provision of U.S. dollar 

liquidity, and a number of governments (for 

example, Belgium, France, Germany, Luxembourg, the Netherlands, and the United 

Kingdom) have stepped in to provide solvency 

support for resolutions of a number of major 

Table 2.2. Advanced Economies

Current Account Positions

(Percent of GDP)

2006 2007 2008 2009

Advanced economies –1.3 –0.9 –1.0 –0.6

United States –6.0 –5.3 –4.6 –3.3

Euro area1 0.3 0.2 –0.5 –0.4

Germany 6.1 7.6 7.3 6.8

France –0.7 –1.2 –2.8 –2.7

Italy –2.6 –2.5 –2.8 –2.4

Spain –8.9 –10.1 –10.1 –7.7

Netherlands 8.2 6.8 5.6 5.1

Belgium 2.7 2.1 — –1.1

Austria 2.4 3.2 2.8 2.4

Finland 4.6 4.6 3.4 2.9

Greece –11.1 –14.1 –14.0 –14.1

Portugal –10.1 –9.8 –12.0 –12.7

Ireland –3.6 –5.4 –5.0 –4.4

Luxembourg 10.5 9.9 8.6 8.2

Slovenia –2.8 –4.9 –4.7 –4.7

Cyprus –5.9 –9.7 –9.7 –7.8

Malta –8.2 –5.4 –7.7 –6.4

Japan 3.9 4.8 4.0 3.7

United Kingdom –3.4 –3.8 –3.6 –3.4

Canada 1.4 0.9 0.9 —

Korea 0.6 0.6 –1.3 –0.7

Australia –5.3 –6.2 –4.9 –4.3

Taiwan Province of China 7.2 8.6 7.8 6.5

Sweden 8.5 8.5 6.4 5.8

Switzerland 14.7 16.6 9.3 8.7

Hong Kong SAR 12.1 13.5 11.7 10.3

Denmark 2.9 1.1 1.3 1.8

Norway 17.3 15.4 19.1 18.0

Israel 5.9 3.2 0.4 0.5

Singapore 21.8 24.3 19.2 17.0

New Zealand –8.7 –8.2 –9.3 –8.1

Iceland –25.4 –14.6 –18.2 –13.7

Memorandum

Major advanced economies –2.0 –1.5 –1.3 –0.9

Euro area2 — 0.3 –0.5 –0.5

Newly industrialized Asian 

economies 5.3 6.2 4.7 4.3

1Calculated as the sum of the balances of individual euro area 

countries. 2Corrected for reporting discrepancies in intra-area transactions.
financial institutions (including Dexia and 

Fortis) or by raising deposit guarantee limits and 

extending guarantees to creditors (for example, 

Ireland), or by providing explicit assurances 

to depositors (for example, Germany and the 

United Kingdom). However, even if a comprehensive approach to address the growing concerns is put in place rapidly, it will take time to 

return perceptions of counterparty risk to more 

normal levels. Accordingly, banks’ deleveraging 

will weigh noticeably on economic growth over 

the coming quarters.

The baseline projections thus envisage a 

significant slowdown in activity across western 

Europe followed by a very gradual recovery 

beginning in the second half of 2009. Euro area 

growth is expected to moderate from 2.6 percent in 2007 to 1.3 percent and 0.2 percent, 

respectively, in 2008–09, before returning to 

1.4 percent in 2010. In the United Kingdom, real 

GDP growth would fall from 3.0 percent in 2007 

to 1.0 percent in 2008; activity would contract 

by 0.1 percent in 2009 and then accelerate to 

2.2 percent in 2010. The risks around these 

growth projections are to the downside. They 

include accelerated deleveraging in the financial 

sector set off by broader asset price deflation and 

a global credit crunch, an abrupt unwinding of 

global imbalances, and sharp appreciation of the 

euro. On the upside, risks relate to still-buoyant 

employment and therefore higher-than-projected 

consumption, but these are small. Volatile energy 

and food prices are a source of two-way risk.

The fiscal positions of western European 

countries differ widely, but many have made significant progress toward consolidation since the 

previous downturn. Even with some widening 

in 2008–09 related to both cyclical factors and 

policy support, the general government deficit for euro area countries would still average 

about 13⁄4 percent of GDP, 11⁄4 percentage points 

less than in 2003–04. However, fiscal deficits of 

some countries (France, Greece, Ireland, Italy, 

and Portugal) are still far from their mediumterm objectives and, in some cases, are likely to 

exceed the Maastricht deficit limit of 3 percent 

of GDP in the near term. The United Kingdom’s 

fiscal position—a deficit of 31⁄2 percent of GDP is 

projected for 2008—is considerably weaker than 

before the previous downturn.

The rules of the revised Stability and Growth 

Pact provide room for fiscal objectives to be 

adjusted in response to changing economic 

conditions. With the effectiveness of discretionary easing diminished in the context of a loss 

of confidence in financial markets, discretionary use of fiscal resources should be primarily 

focused on measures to stabilize the financial 

sector, as needed. More generally, given the 

challenges associated with an aging population, 

fiscal policy should be consistent with achieving 

medium-term objectives. Automatic stabilizers 

can be allowed to operate freely around the 

adjustment path in response to weakening activity, except when this might breach fiscal rules. 

Similarly, the United Kingdom should set policy 

consistent with meeting its medium-term fiscal 

rules. For 2009 and 2010, the agreed mediumterm expenditure plans should remain in place, 

with automatic stabilizers allowed to operate 

fully around the adjustment path.

Over the past six months, changes in policy 

interest rates have been limited, and central 

banks have focused on providing liquidity to 

stressed markets. The European Central Bank 

increased policy rates by 25 basis points to 

41⁄4 percent in July 2008, the first move since June 

2007, whereas the Bank of England has kept 

rates unchanged at 5 percent since April 2008. 

The projections see headline inflation falling 

below 2 percent in the euro area and the United 

Kingdom by end-2009. The immediate priority for 

central banks is to maintain calm in financial markets by continuing to provide liquidity as needed. 

However, the deteriorating outlook, moderating 

inflation pressure, and tightening financial conditions provide scope for monetary easing in both 

the euro area and the United Kingdom.

The continuing financial turmoil presents 

important policy challenges on various fronts, 

including because of complex cross-border 

financial linkages and spillovers. The latter is a 

particular concern for EU countries, given their 

quest to build a single market in financial serWestern Europe: Slowing Demand and High
CHAPTER2  Country and Regional Persp

vices. The specific challenges are already evident 

as actions taken in specific countries to alleviate 

financial strains are having adverse effects on 

financial institutions in other countries. Addressing the concerns raised by spillovers will require 

movement toward more joint responsibility and 

accountability for financial stability, notably for 

crisis prevention, management, and resolution, 

in line with the commitments of the ECOFIN 

Council of Finance Ministers in May 2008.4

Restoring confidence now requires a decisive 

commitment to concerted and coordinated 

action to alleviate financial stresses and avoid 

the serious risk of backtracking on European 

financial integration.

The emphasis on policies to limit the damage from the financial turmoil should, however, 

not distract from structural policy challenges. 

Ten years following the introduction of the 

euro, the main medium-term policy challenge 

facing euro area member countries is to make 

economic union as successful as monetary union 

(Box 2.1). In this regard, productivity growth 

has lagged that in other advanced economies, 

and large and persistent intra-euro-area current 

account divergences are raising concerns about 

adjustment mechanisms in the monetary union. 

Accordingly, the structural reform momentum needs to be kept up and reoriented in a 

coordinated manner to improve adjustment in 

response to intra-area disparities. The ongoing 

reforms are bearing fruit, contributing to the 

marked growth in employment and to improved 

productivity in liberalized sectors. However, 

large parts of the services sector remain unaffected, forfeiting important income, resilience, 

and inflation benefits. Thus, the specific reform 

recommendations under the Lisbon Agenda 

that concern the euro area as a whole appropriately emphasize accelerating services market 

reform and financial integration. Enhanced 

4For further details see May 15, 2008, “ECOFIN Council of Finance Ministers adopt conclusions on financial 

supervision and provision of financial stability in the EU,” 

available at www.eu2008.si/en/News_and_Documents/

Press_Releases/May/0514MF_Svet_ECOFIN.html.

policy coordination is needed to ensure greater 

consistency of national reform programs with 

these euro area recommendations.

Advanced Asia: Responding to External

Shock

Although growth in Japan held up well 

through the first quarter, rising commodity 

prices and weakening external demand have 

started to weigh on economic activity. In the 

second quarter of 2008, the economy contracted 

at a 3 percent quarter-over-quarter annualized 

rate, and growth over the past four quarters was 

below 1 percent. The recent decline was led by 

private consumption and fixed investment, while 

the contribution from net exports fell to zero 

(Figure 2.3).

Recent indicators point to growing weakness ahead. Slowing external demand from 

the United States and western Europe, rising 

input costs, and diminishing profit expectations are weighing on corporate sentiment and 

companies’ investment plans. At the same time, 

high food and fuel prices and weakening wage 

prospects have pushed consumer confidence 

to low levels. Although financial conditions 

have tightened to a lesser extent than in other 

major economies, in part owing to Japanese 

banks’ lower exposure to securitized products, 

the stock market has fallen sharply, driven by 

concerns about the weaker growth outlook. 

Bank credit default swap (CDS) spreads have 

risen on concerns about global financial strains 

and the weaker outlook.5 Following the collapse 

of Lehman Brothers, concerns about Japanese banks’ exposures to the failed institution 

rose, contributing to a sharp widening of CDS 

spreads for some entities and a broad-based fall 

5Although liquidity pressures have been less acute in 

Japan than in other G3 economies, the Bank of Japan’s 

significant liquidity provision also contributed to stabilizing money markets. See the Selected Issues paper accompanying the 2008 report on Article IV consultations with 

Japan (available at www.imf.org) for more details on the 

impact of the global financial turmoil on the Japanese 

economy (IMF, 2008c).
in equity prices. Strains in the financial system 

are expected to persist, even if to a lesser extent 

than in the United States or western Europe.

Headline inflation has risen well above the 

2007 level, to above 2 percent, on the back of 

higher food and fuel prices, but core inflation, 

excluding food and fuel, remains around zero.6

Firms have started to pass through cost increases 

to consumers, but they are also granting smaller 

wage increases as activity is slowing and unit 

labor costs continue to fall on a year-over-year 

basis. Measures of inflation expectations suggest 

that short-term expectations have edged up, 

while long-term expectations remain contained.

Although the trade surplus deteriorated 

because of a higher bill for commodity imports, 

rising investment income helped keep the 

current account balance near historical highs. 

Through March 2008, the yen appreciated 

against other major currencies, particularly the 

U.S. dollar, reflecting an unwinding of carry 

trades in volatile foreign exchange markets 

(see Figure 2.3). Then the currency started to 

weaken again, as expectations of monetary tightening dissipated amid slowing growth, before 

resuming an appreciating trend in September owing to declining commodity prices and 

increased risk aversion on the part of investors. 

Given the medium-term prospects for continued 

large external surpluses, the yen is still assessed 

to be undervalued relative to medium-term 

fundamentals.

As shown in Figure 2.3, Japan has experienced a much larger decline in its terms of 

trade in past years than other major advanced 

economies. Several factors have so far mitigated 

the impact on activity. First, robust exports to 

emerging and developing economies, many 

of which are exporters of commodities, have 

partly offset the effect of the deterioration in 

Japan’s terms of trade. Second, although Japan 

is dependent on imported oil for almost all its 

6Core inflation on the authorities’ definition (excluding only fresh food) has been running at about 2 percent, 

reflecting recent increases in fuel and other commodity 

prices.

Advanced Asia: Responding to Externa

-4

-2

0

2

4

6

20

30

40

50

60

70

-2

-1

0

1

2

3

60

70

80

90

100

110

120

130

-1

0

1

2

3

4

Figure 2.3. Japan: How Well Would the Economy 

Weather a Terms-of-Trade Shock?

Inflation

(percent)

Private Consumption

(three-month moving average)

GDP

growth

 Sources: Haver Analytics; IMF, Direction of Trade Statistics; and IMF staff calculations.

 Percent of total exports, 2006–08 averages.

 Year-over-year percent changes of real private consumption and real earnings; current 

economic conditions, Econ Watch Survey diffusion index; and consumer confidence, all 

households diffusion index.

 CPI = consumer price index.

1

The Japanese economy has been particularly exposed to deterioration in the terms of

trade. Robust exports to emerging and developing economies should provide 

support for growth, but the outlook for consumption is weakening.

Terms of Trade

(index, 2000 = 100)

01 02 03 04 05 06 07

Contributions to Real GDP Growth

(percent, two-quarter moving 

average)

2

2000

Share of Exports to Emerging and 

Developing Economies

(percent)

2005 06 08:

Q2

07

Other domestic 

demand Net exports

0 10 20 30 40 50 60 70 

2000 02 04 06 08 2000 02 04 06 Aug.

08

Private consumption

CPI

CPI excluding

food and energy

Japan

United

States

Emerging and 

developing economies

Euro area

Consumer confidence

(right scale)

Private consumption 

(left scale)

Current economic conditions

(right scale)

Real wages 

(left scale)

Recession

1

European Union

Emerging and 

developing 

economies

United States

Japan

Aug.

08

2

3

3
CHAPTER2  Country and Regional Persp

Ten years ago, EU heads of state gave the goahead for the third stage of European Economic 

and Monetary Union (EMU), approving the introduction of the euro in 11 EU member countries 

on January 1, 1999. Since then, four more EU 

members have adopted the euro, and the Slovak 

Republic is set to follow at the beginning of 2009. 

Monetary union is a distinct success, and the 

euro area is a zone of stability in the international 

economy. Thanks to the high credibility of the 

European Central Bank, inflation has declined 

over the past decade, and inflation expectations 

are now less variable in the euro area than in other 

advanced economies (Beechey, Johannsen, and 

Levin, 2008).1 The key remaining challenge on 

the monetary front is the integration into the euro 

area of all EU members that are committed to 

adopting the euro (which does not include Denmark or the United Kingdom).2 Economic union, 

however, remains a challenge even among the current euro area members, and there are concerns 

about the area’s growth performance and large 

intra-area current account divergences.

The record on growth. The widespread perception among observers that EMU has delivered economic stability but not growth is not 

well founded. The EMU record with respect 

to employment has been strong, which helps 

explain why in per capita terms euro area real 

GDP growth has not lagged behind U.S. growth 

during EMU (first table). The euro area’s employment performance is in part related to EMU, 

which likely has contributed to greater monetary 

policy credibility, as well as labor market reforms, 

including well ahead of the introduction of 

the single currency in 1999. Employment rates 

remain lower than in the United States, and per 

capita income is still about 30 percent below U.S. 

levels. However, full convergence is not likely: 

1The main authors of this box are Jörg Decressin and 

Emil Stavrev, based on the findings in Decressin and 

Stavrev (forthcoming). 2Adoption of the euro depends on certain economic 

convergence criteria. For further details, go to http://

ec.europa.eu/economy_finance/the_euro/­joinin

euro9413_en.htm.

Europeans have used the growth in productivity since World War II to increase leisure more 

than in the United States (Blanchard, 2004), and 

insofar as this greater leisure is sustainable, this 

development is not undesirable.

The euro area’s poor productivity performance 

under EMU has attracted much attention (for 

example, Pisani-Ferry and others, 2008), but, as in 

the case of economic growth, the reality may well 

be more complex than the raw numbers suggest. 

Labor productivity growth, for example, has averaged about 1 percent a year in the past 15 years, 

down considerably compared with previous years. 

Total factor productivity (TFP) growth has almost 

ground to a halt during the current decade. 

However, the low productivity growth may well be 

related to the boom in employment, as argued by 

Dew-Becker and Gordon (2008). In fact, there is 

Box 2.1. EMU: 10 Years

Euro Area and United States: K

Macroeconomic Variable

(Seven-year trailing average, in percent)

1992 1999 2007

Per capita real 

GDP growth

Euro area 2.5 1.7 1.3

United States 1.7 2.6 1.3

Real GDP 

growth

Euro area 2.9 2.0 1.8

United States 2.8 3.7 2.4

Employment 

growth

Euro area 0.5 0.7 0.9

United States 1.8 1.8 1.4

Employmentpopulation 

ratio, end of 

period

Euro area 40.8 41.7 44.4

United States 45.3 47.4 47.8

General 

government 

fiscal balance

Euro area –5.0 –3.7 –2.1

United States –4.5 –1.7 –3.1

Current 

account

Euro area –0.7 0.3 0.3

United States –1.7 –2.0 –5.1

Inflation Euro area 3.4 2.1 2.2

United States 3.9 2.5 2.7

Per capita 

GDP1 Euro area 75.1 70.8 70.9

Memorandum 

item

TFP2 1980–95 1995–2004

Euro area 0.8 0.2

United States 0.7 1.6

Sources: Eurostat; IMF International Financial Statistics; and 

IMF staff calculations. 1Percent of U.S. per capita GDP, at purchasing power parity.

2TFP = total factor productivity.
Advanced Asia: Responding to Externa

strong evidence for a negative relationship between 

TFP growth and labor input in a cross section of EU 

KLEMS data (van Ark, O’Mahony, and Ypma, 2007) 

for 12 advanced economies and six sectors for each 

country (see first figure). There could be many 

reasons for such a trade-off. One obvious reason is 

capital-labor substitution in response to reforms that 

raised labor supply and, possibly, demand (via cuts 

in payroll taxes). Accordingly, as the labor market 

completes its adjustment to reforms and demographic changes, TFP growth may well revert to the 

higher levels recorded in the 1980s. 

There is still concern, however, that even with 

adjustment for differences in labor input, TFP 

growth on average has been lower in the euro 

area than in the United States, particularly in the 

service sectors. This points to a need to open up 

the more sheltered service sectors to competition, 

that is, to make services tradable.

The dispersion of real GDP growth rates has also 

raised concern; however, business cycles among 

EMU members have in fact become more synchronized as the importance of common shocks has 

increased over time. These shocks now explain 

some 60 percent of output developments, as 

opposed to 30 percent before the introduction of 

the euro (Stavrev, 2007). Furthermore, countryspecific developments in output reflect, to a considerable extent, per capita income convergence 

to higher levels. Overall, the dispersion of growth 

rates in the euro area is now similar to that among 

U.S. states, although it is more persistent. The slow 

speed of adjustment means that prolonged periods 

of strong growth may be followed by prolonged 

periods of sluggish activity, as experienced recently, 

for example, by Portugal (Blanchard, 2007). 

The record on the current account. The external 

position of the euro area as a whole has remained 

in balance since EMU inception and thus has not 

raised concern. This external balance has been 

preserved despite the significant appreciation of 

the real effective exchange rate of the euro over 

the past several years (see Chapter 1). In addition, 

the euro has firmly established itself as the world’s 

number two international currency, accounting for 

more than one-quarter of international reserves 

and more than one-half of trade invoicing. 

The European Commission and others have 

raised concerns about large intra-area current 

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3 4

-6

-4

-2

0

2

4

6

8

10

Productivity Performance

(Percentage points unless otherwise noted)

Euro Area: Labor-Input-Adjusted Productivity

 Sources: EU Klems database; and IMF staff calculations.

 Based on a cross-country, cross-sector pooled regression of

total factor productivity (TFP) on labor input.

 For average TFP growth rates. Negative numbers indicate a 

deterioration of TFP performance.

Euro Area: Developments in TFP by Sector

(1995–2004 minus 1980–95)

1

1

U.S., 1995–2004

U.S., 1980–95

Euro area, 1995–2004

Euro area, 1980–95

Labor input

TFP growth (percent)

2

2 Electrical machinery,

post, and 

communication

Manufacturing,

excluding electrical

Other goodsproducing industries

Distribution services

Finance and business

services

Personal and social

services

Unadjusted TFP

TFP adjusted for the impact of labor input1

y = –0.79x + 1.36

R = 0.25 2
CHAPTER2  Country and Regional Persp

6

account and competitiveness imbalances (see, for 

example, European Commission, 2008). Current 

account divergence among euro area members 

appears large, ranging from deficits close to or 

exceeding 10 percent of GDP (for example, in 

Greece, Portugal, and Spain) to surpluses greater 

than 5 percent of GDP (for example, in Germany 

and the Netherlands—second table). In fact, this 

divergence—measured by the standard deviation 

across countries in each year—has risen substantially over the past two decades (second figure). 

But is it unusually large for today’s world? As 

Chapter 6 shows, greater divergence of current 

account behavior is a widespread phenomenon. 

A simple approach to determining whether the 

divergence is unusually large is to divide the standard deviation of current accounts for euro area 

countries by the same measure for the current 

accounts of a group of 13 other advanced economies.3 The results show that this ratio has not 

3Specifically, the two samples include 11 EMU 

members (Austria, Belgium, Finland, France, Germany, 

Greece, Ireland, Italy, Netherlands, Portugal, and 

Spain) and 13 other advanced economies (Australia, 

Canada, Denmark, Iceland, Israel, Japan, Korea, New 

Zealand, Norway, Sweden, Switzerland, United Kingdom, and United States). Data are from the April 2008 

World Economic Outlook.

grown systematically. This is because the divergence among the other 13 advanced economies 

has also been on the rise.

A related question is whether current account 

imbalances are a reflection of equilibrium or 

disequilibrium forces and how these have evolved 

over time. To answer this question, a model of 

equilibrium current account balances, based 

closely on the IMF CGER methodology (Lee and 

others, 2008), is used to compute the developments in deviations from equilibrium over time. 

Specifically, the equilibrium current account for 

each euro area country is obtained as a function 

of real GDP growth, relative per capita income, 

population growth, the net foreign-assets-to-GDP 

ratio, the fiscal balance in percent of GDP, the 

old-age dependency ratio, and the oil balance 

in percent of GDP. The next step is to compute 

for each year the sum across countries of the 

absolute values of the deviation from equilibrium 

current accounts. This shows that divergence 

from equilibrium has not increased over time.

Although the size of divergence might not be 

of major concern, slow adjustment to equilibrium 

current account balances might be. Sustained deficits add to countries’ external debt and, unlike 

in a currency union, such as the United States, 

people cannot expect help from a federal authorCurrent Account Dispersions and Implications for Net Foreign Asset (NFA) Positi

Current Account 

Balance1

Estimated Equilibrium 

Current Account NFA Position NFA Position When the Current 

Account Balance Reaches Estimated 

Equilibrium2 (2007, in percent of GDP)

Austria 2.7 1.1 –22 –10

Belgium 3.2 2.5 34 40

Finland 4.6 –0.3 –28 10

France –1.3 0.6 5 –9

Germany 5.6 2.5 28 52

Greece –13.9 –4.4 –100 –174

Ireland –4.5 1.1 –1 –45

Italy –2.2 –0.1 –6 –22

Netherlands 6.6 2.2 0 35

Portugal –9.4 –5.8 –80 –107

Spain –10.1 –5.7 –74 –109

Sources: IMF International Financial Statistics; and IMF staff estimates. 1Data are based on the April 2008 World Economic Outlook estimates. Please see Table A11 in the Statistical Appendix for the latest 

figures. 2The estimated speed of convergence implies that 70 percent of the deviation of the current account from the steady state is closed in 

about 10 years.

Box 2.1 (concluded)
Advanced Asia: Living with Terms-of-Tr

ity or easily move to better-off areas to improve 

their repayment capacity. Moreover, in a currency union of countries, adverse economic 

developments in parts of the union are more 

likely to affect the strength of the union as a 

whole. The persistence of the current account 

imbalances in the EMU over time can be 

gauged with a pooled univariate regression of 

the current account balances on their own lags, 

allowing for country-specific steady-state current 

account balances (fixed effects). The results 

suggest that since 1992 it takes roughly 10 years 

to close about 70 percent of a gap between 

actual and steady-state current accounts within 

the euro area (see second figure). This speed 

of adjustment is significantly lower than during 

the pre-1992 period, when exchange rates were 

more flexible, but has not slowed significantly 

further during EMU. Moreover, it is significantly 

lower than in the group of 13 other advanced 

economies. However, the typical country-specific 

current account shock in the euro area is only 

about half as large as in the other countries 

during 1992–2007, which is a mitigating factor. 

In addition, it is only about half as large as 

in 1970–91 within the euro area, which again 

underscores the growing integration of EMU 

members. 

These results can gauge the implications of 

today’s current account divergence for the net 

foreign asset positions of euro area countries 

(see second table). Clearly, some countries will 

be footing considerably higher external debt 

servicing bills that will limit consumption and 

slow growth. Because there is a risk that during 

periods of austerity the performance of EMU 

might be questioned, it is imperative for policymakers to ensure that goods and factor markets 

are flexible enough to deliver rapid adjustment: 

boosting productivity is one way to foster internal 

adjustment as are measures to foster more wage 

flexibility (Blanchard, 2007). At the same time, 

social safety nets need to provide sufficiently 

generous but temporary help to those who suffer 

from the dislocations that accompany adjustment. Moreover, internal adjustment processes 

(via their effects on the union) concern all countries, and so addressing this challenge is a matter 

of common interest. The European Union’s 

Lisbon Agenda offers the right vehicle for the 

design and implementation of the required structural reforms in a manner that leverages reform 

spillovers and complementarities.

0 1 2 3 4 5 6 7 8 9 10

-1

0

1

2

3

1980 83 86 89 92 95 98 2001 04 07

1

2

3

4

5

6

7

8

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Current Account Divergence

Euro Area: Current Account Divergences, 

Absolute and Relative

 Source: IMF staff calculations.

 Based on current account balances in percent of GDP.

Divergences are measured by the standard deviation across

European Economic and Monetary Union members and across the 

other advanced economies, respectively.

Speed of Adjustment to Country-Specfic

Current Account Shocks

1

1

Euro area countries,

1992–2007

Euro area countries

relative to other 

advanced economies

(right scale)

Euro area

countries

(left scale)

Euro area countries relative

to medium-term equilibrium 

(left scale)

Other advanced economies,

1992–2007

Other advanced economies,

1970–91

Euro area countries, 

1970–91

Years
CHAPTER2  Country and Regional Persp

domestic needs, the efficiency with which oil is 

used in the economy is much higher than in the 

United States. Last, with underlying price pressures subdued, monetary conditions are likely to 

remain accommodative.

The outlook envisages that growth in 2008–09 

will be well below 1 percent, significantly below 

potential. Although weakness in the United 

States and western Europe will weigh appreciably on activity, growth in emerging economies is 

expected to remain more resilient and should 

continue to support exports. Private consumption is expected to remain moderate because 

of weakening prospects for wage increases and 

high food and fuel prices, while the weakening demand and profit outlook will slow private 

investment.

The outlook is subject to considerable uncertainty surrounding the external environment, 

with the overall risks tilted to the downside. 

External risks relate mainly to a larger-thanexpected slowdown in emerging and developing economies and a renewed bout of global 

financial instability. On the domestic front, high 

commodity prices could weigh further on firms’ 

profits and households’ incomes.

Given the outlook for further weakening of 

domestic demand and subdued inflation pressures, the Bank of Japan (BoJ) is appropriately 

retaining an accommodative monetary policy 

stance and keeping interest rates at the current 

low levels until uncertainties over the outlook 

are resolved. Japan has been battling deflation 

for nearly a decade, and although an end to this 

problem appears in sight, it is not yet ensured. 

While core inflation on the authorities’ definition is close to the 2 percent upper bound of 

the “understanding” of price stability by the BoJ 

policy board members, wage growth is slowing, 

and inflation expectations have been contained. 

There are few indications of excessive risk taking 

in asset markets (the second perspective of the 

BoJ’s monetary policy framework) or of bubbles 

in the financial or real estate markets.7

7The BoJ’s monetary policy framework encompasses 

two perspectives: the short-term outlook for economic 

A sharper-than-expected slowdown of the 

economy may justify a further reduction of the 

policy interest rate, although with the current 

rate already at 50 basis points, room for easing 

is limited. In this context, the BoJ’s move to 

greater transparency, by expanding the discussion of the policy board members’ views on 

the outlook and the risks to it and by placing a 

greater emphasis on the 1 percent median of 

the “understanding of price stability” in its communications, should help guide expectations.

Looking beyond the near term, the Japanese 

economy continues to face a rapidly aging population and rising public debt. The pace of fiscal 

consolidation has understandably slowed in the 

environment of diminishing global growth, with 

the general government primary deficit excluding social security expected to widen in 2008 

and 2009 and discussions on raising the consumption tax rate postponed. In late August, the 

Japanese government put forward an economic 

stimulus package aimed at supporting faltering 

growth. However, the scope for fiscal stimulus is 

limited, and building fiscal space for projected 

increases in expenditures owing to demographic 

pressures remains a top priority for the medium 

term.8 The authorities’ current plans, which target a primary balance by fiscal year 2011, need 

to be strengthened further to prevent net public 

debt from trending up.

Australia and New Zealand are slowing down 

noticeably, after prolonged economic expansions driven by commodity and housing booms. 

The expansions have stretched productive 

capacity, pushing inflation to historical highs. 

The authorities have responded by tightening 

monetary policies, and domestic demand pressures have eased. Real GDP growth in Australia 

is projected to fall below potential, to about 

21⁄2 percent in 2008–09 from 41⁄4 percent in 2007. 

activity and prices and a longer-term outlook for risks to 

the outlook, including from asset price bubbles. 8The government’s proposal includes spending of 

around $17 billion (0.4 percent of GDP) and government 

guarantees for business loans. The government has also 

committed to an income tax cut by the end of fiscal year 

2008.
The recent moderation in domestic demand 

and the tightening of credit conditions have 

prompted the Reserve Bank of Australia (RBA) 

to cautiously ease monetary policy. The Reserve 

Bank of New Zealand has also moved to ease 

monetary policy. In both countries, sound fiscal 

positions provide scope for allowing automatic 

stabilizers to operate in full and for judicious 

use of discretionary stimulus if the outlook deteriorates further.

Emerging Asia: Balancing Risks to

Growth and Price Stabilit

The economic cycle in emerging Asia started 

to turn in early 2008, and more weakness is 

expected ahead in response to slowing demand 

from advanced economies and growing strains 

in regional financial markets. Growth in China 

eased to 101⁄2 percent (year over year) in the first 

half of 2008, from 12 percent in 2007, partly 

because of slowing exports. Activity continued to 

be supported by steady investment growth and 

accelerating consumption. In India, growth in 

the second quarter came down to about 8 percent, on the back of weakening investment, 

while private consumption and export growth 

have held up well. In the newly industrialized 

Asian economies (NIEs) and the Association of 

Southeast Asian Nations (ASEAN) economies, 

activity has also been decelerating. Domestic 

demand has softened, as rising food and fuel 

prices have started to weigh on consumption, 

while declining profit margins and weakening 

demand have prompted firms to scale back their 

investment plans. Vietnam is undergoing a sharp 

correction as the demand boom caused by large 

capital inflows unwinds.

Financial markets have weakened in recent 

months, driven by increasing concerns about 

the global outlook and declining investor risk 

appetite, particularly in the context of the 

September market turbulence. Equity markets 

that experienced the largest run-up in prices in 

recent years—during 2005–07, prices more than 

quadrupled in China and tripled in India—have 

declined the most (Figure 2.4). In some coun90

95

100

105

110

115

120

90

100

110

120

130

140

150

-5

0

5

10

15

20

25

30

-2

0

2

4

6

8

10

12

0

2

4

6

8

10

12

0

100

200

300

400

500

Figure 2.4. Emerging Asia: Remaining Inflation 

Concerns

 Sources: Haver Analytics; IMF, International Financial Statistics; and IMF staff 

calculations.

 The definition of core inflation varies across countries, though it generally excludes food

and energy prices from overall consumer price index (CPI).

 Indonesia, Malaysia, Philippines, and Thailand.

 Newly industrialized Asian economies (NIEs) comprise Hong Kong SAR, Korea,

Singapore, and Taiwan Province of China.

 Calculated as overall compensation divided by GDP, except for China, where wages are 

used instead of compensation, and Indonesia and Malaysia, where calculations cover only

the manufacturing sector. Data for China need to be treated with caution because of their 

partial coverage, biased toward large state-owned manufacturing enterprises.

 Bangladesh, India, Maldives, Nepal, Pakistan, and Sri Lanka.

 ASEAN-4 and Vietnam.

Underlying inflation pressures rose across emerging Asia in recent quarters. Wage

increases, despite productivity improvements, have contributed to a buildup in

inflation in some cases. In part owing to a rapid expansion of bank loans, house

prices have continued to trend upward. In contrast, an extended run-up in equity

prices ended with a sharp correction in early 2008, triggered by global financial 

turmoil. Exchange rates have failed to provide much respite for inflation, because 

currencies have either appreciated too little or weakened. 

Unit Labor Costs

(percent change from a year

earlier)

House Prices

(2005:Q1 = 100)

Real Credit Growth

(percent change from a year 

earlier)

Core Inflation

(percent change from a year

earlier)

1

Equity Prices

(2005:Q1 = 100)

Real Effective Exchange Rate

(CPI based; 2005 = 100)

2005

2

06 07 Jul.

08

2005 06 07 08:

Q2

2005 06 07 08:

Q2

2005 06 07 08:

Q2

2005 06 07 08:

Q3

2005 06 07 08:

Q3

South Asia

ASEAN-5

China

Thailand

Korea

Hong Kong SAR

China

China

China

China

China

South Asia

ASEAN-5

ASEAN-4

ASEAN-4

NIEs

NIEs

India

India India

2

3

3

NIEs3

NIEs3

NIEs 2 3

6

6

3

5

4

5

ASEAN-4

2

4

5

1

6

Emerging Asia: Balancing Risks to Growth and Price 
CHAPTER2  Country and Regional Persp

tries, borrowing spreads have risen markedly for 

banks relying on wholesale funding.

Current account balances have generally 

come under pressure from rising import bills for 

commodities and slowing export growth, while 

capital account and exchange rate developments have varied. Capital inflows to China have 

remained strong, as evidenced by a continuing 

surge of foreign reserves in excess of the trade 

surplus; capital flows to some other countries 

have become more volatile, particularly to those 

running sizable external deficits. Their currencies have come under pressure, prompting 

central banks to intervene in support (India, 

Pakistan, and Vietnam). The Korean won has 

also weakened, partly owing to a turnaround in 

the current account balance to a small deficit, 

on the back of worsening terms of trade. Differing nominal exchange rate developments, in 

turn, have driven divergent trends in real effective exchange rates, with the Chinese renminbi 

and the ASEAN currencies continuing to appreciate, and the South Asian and NIEs’ currencies 

weakening (see Figure 2.4).

Growth in the region is projected to moderate to 73⁄4 percent in 2008 and 7 percent in 2009 

from 91⁄4 percent in 2007 (Table 2.3). Weakening 

external demand is likely to weigh on exports, 

but, in some cases, the impact may be mitigated 

by still-loose macroeconomic policies and currency depreciation. Investment will also moderate, mainly because of deteriorating export 

prospects. Consumption will ease because of 

still-high fuel and food prices, although subsidies, which are common in the region, may 

cushion the impact on purchasing power. The 

risks to the outlook are firmly to the downside. 

The main concern is that a buildup of stress in 

the global financial system and a sharper-thananticipated global slowdown could further weigh 

on activity. On the upside, domestic demand 

may prove more resilient, with falling commodity prices providing a boost to real incomes.

Headline CPI inflation has soared in a number of countries on the back of increases in food 

prices and administered fuel prices in the first 

half of the year. Indicators of underlying inflation pressure have risen to a lesser extent, but 

some countries still face risks of further secondround effects. In China, headline CPI inflation 

has steadily declined since its 81⁄2 percent peak 

in April, as food supply conditions have normalized. Core inflation has been rising gradually, 

but remains contained. In India, CPI inflation 

jumped to 9 percent in August. Underlying inflation pressures have increased, as high resource 

utilization and robust credit growth have created 

fertile ground for second-round effects (see 

Figure 2.4). Insufficient policy tightening has 

also contributed.

Although increases in food and fuel prices 

may continue to subside in the coming months 

and growth will moderate, inflation is expected 

to remain at elevated rates over the near term. 

Headline inflation is projected to rise to about 

71⁄4 percent (year over year) in 2008 for the 

region as a whole from 5 percent in 2007, before 

declining to near 6 percent in 2009. Underlying 

inflation pressures are also likely to remain high 

in the environment of tight resource utilization 

and still-loose macroeconomic policies.

Responses to rising inflation have varied 

across the region. Some economies tightened 

monetary policy—by hiking interest rates (India, 

Indonesia, Korea, Philippines, Thailand, Taiwan 

Province of China, and Vietnam), tightening 

reserve requirements (Cambodia, India, Vietnam), and creating more scope for appreciation 

in the exchange rate band (Singapore). More 

recently, the People’s Bank of China lowered 

the benchmark lending rates and reduced the 

reserve requirement ratio, on concerns about 

slowing growth. India and Korea intervened in 

the foreign exchange market to support their 

currencies. Countries that have continued to 

accumulate foreign reserves have partially sterilized them through the issuance of bonds and 

increases in reserve requirements to contain 

the buildup of liquidity. Offsetting the effects 

of monetary tightening, the fiscal policy stance 

has been eased in many countries, however, 

reflecting significant increases in fuel subsidies. 

Although several countries have raised administered fuel prices (for example, Bangladesh, 
China, India, Malaysia, and Vietnam), the 

increase was small (except in Vietnam) compared with the increase in world fuel prices, and 

in some cases resources raised have been used to 

increase other expenditures, for example, food 

subsidies. Several major rice exporters in the 

region (Cambodia, China, India, and Vietnam) 

introduced export bans, quotas, or taxes to 

raise domestic food supplies and lower domestic 

prices, adding to pressure on world prices.

Against this background, a major policy 

dilemma for the region is how to respond to 

the weakening growth outlook and global 

financial turbulence, without losing sight of 

inflation risks. Although there is considerable 

divergence in country circumstances, downside 

risks to growth in emerging Asia have risen in 

recent months, while inflation risks have moderated as food and oil prices came down from 

the peaks observed earlier in the year. Policy 

priorities have shifted accordingly, although 

there remain notable differences in country 

circumstances.

• In most countries domestic demand is weakening rapidly and some policy tightening has 

already taken place. Although authorities 

need to remain alert to inflation risks, policy 

easing could be justified if downside risks 

to growth are significant. In countries with 

strong fiscal positions, automatic stabilizers 

can be allowed to operate in full. However, 

the use of discretionary fiscal policy needs to 

be approached with caution, as past experience suggests that such actions are difficult 

to time well and have not been very effective 

(see Chapter 5). But it could be justified in 

the face of deteriorating growth prospects 

in countries with strong underlying fiscal 

positions.

• By contrast, in a few countries where growth 

is expected to remain relatively strong, 

where risks for further second-round inflation effects are higher, and where monetary 

policy credibility has not yet been firmly 

established—macroeconomic policies still 

need to lean toward tightening (for example, 

Table 2.3. Selected Asian Economies: Real GDP, Consumer Prices, and Current Account Bala

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Emerging Asia3 9.2 9.3 7.7 7.1 3.8 4.9 7.3 5.8 5.8 6.8 5.2 5.

China 11.6 11.9 9.7 9.3 1.5 4.8 6.4 4.3 9.4 11.3 9.5 9.2

South Asia4 9.2 8.7 7.6 6.4 6.5 6.9 8.8 8.8 –1.4 –1.7 –3.3 –3.

India 9.8 9.3 7.9 6.9 6.2 6.4 7.9 6.7 –1.1 –1.4 –2.8 –3.1

Pakistan 6.9 6.4 5.8 3.5 7.9 7.8 12.0 23.0 –3.9 –4.8 –8.7 –6.4

Bangladesh 6.5 6.3 7.0 5.6 7.1 9.1 10.1 10.0 1.2 1.1 1.0 0.9

ASEAN–5 5.7 6.3 5.5 4.9 8.1 4.4 9.6 7.2 4.8 5.1 2.7 

Indonesia 5.5 6.3 6.1 5.5 13.1 6.2 9.8 8.8 3.0 2.5 0.1 –0.1

Thailand 5.1 4.8 4.7 4.5 4.6 2.2 5.7 3.2 1.1 6.4 3.1 2.0

Philippines 5.4 7.2 4.4 3.8 6.2 2.8 10.1 7.0 4.5 4.4 2.4 2.2

Malaysia 5.8 6.3 5.7 4.8 3.6 2.0 6.0 4.7 16.1 15.6 14.8 13.2

Vietnam 8.2 8.5 6.3 5.5 7.5 8.3 24.0 15.0 –0.3 –9.9 –11.7 –10.4

Newly industrialized Asian economies 5.6 5.6 4.0 3.2 1.6 2.2 4.8 3.5 5.3 6.2 4.7 4.

Korea 5.1 5.0 4.1 3.5 2.2 2.5 4.8 4.0 0.6 0.6 –1.3 –0.7

Taiwan Province of China 4.9 5.7 3.8 2.5 0.6 1.8 4.2 2.5 7.2 8.6 7.8 6.5

Hong Kong SAR 7.0 6.4 4.1 3.5 2.0 2.0 4.8 4.3 12.1 13.5 11.7 10.3

Singapore 8.2 7.7 3.6 3.5 1.0 2.1 6.5 3.3 21.8 24.3 19.1 17.0

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP.

3Consists of developing Asia, the newly industrialized Asian economies, and Mongolia.

4Includes Maldives, Nepal, and Sri Lanka.

Emerging Asia: Balancing Risks to Growth and Price 
CHAPTER2  Country and Regional Persp

in Indonesia and Vietnam). Monetary policy 

tightening should be the first line of defense 

against rising inflation but may need to 

be complemented in some cases by greater 

exchange rate flexibility or fiscal action. 

Fiscal restraint could help reduce inflation 

pressures, especially in countries where rising 

food and fuel subsidies, as well as public wage 

increases, have weakened fiscal positions and 

contributed to price pressures.

Latin America and the Caribbean: 

Navigating a More Perilous Environme

As in other parts of the world, Latin American economies are facing an awkward combination of slowing activity, more difficult external 

conditions, and still-high inflation. After four 

years of strong output growth, the pace eased 

in most economies of the region during the 

first half of 2008, largely because of moderating 

exports. Domestic demand has remained quite 

robust so far this year, sustained by terms-oftrade gains for commodity exporters, but is 

expected to be dampened as the global economy slows and by the shift toward monetary 

policy tightening to contain inflation. Countries 

in the region have also been facing more difficult external conditions in recent months. Latin 

America has been increasingly affected by turbulent conditions in mature financial markets, 

with equity prices falling sharply, spreads widening markedly, access to dollar funding tightening appreciably, and exchange rates coming 

under pressure, especially in commodity-exporting countries facing lower export prices.

Overall, GDP growth is projected to come 

down from 51⁄2 percent in 2007 to 41⁄2 percent 

in 2008 and 31⁄4 percent in 2009 (Table 2.4). 

The somewhat sharper deceleration in 2009 

than envisaged in the July 2008 World Economic Outlook Update reflects the weaker global 

outlook, softer commodity prices, and more 

difficult external financial conditions. Growth 

in Brazil would come down below trend, and 

activity would remain sluggish in Mexico as 

exports and remittances are dampened by the 

U.S. slowdown. Growth in Central America and 

the Caribbean is also expected to ease, reflecting the impact of slow U.S. growth on remittances, trade, and tourism, as well as high fuel 

costs.

Headline inflation for the region as a whole 

rose to 8 percent in August, the highest rate 

in five years, although it is expected to moderate in the latter part of 2008 and 2009, 

helped by softening international commodity 

prices, tighter monetary policies, and slowing 

demand growth. Still, inflation will remain at 

double-digit levels in a number of countries 

in the region, including Bolivia, Paraguay, the 

República Bolivariana de Venezuela, and several Central American countries, and analysts 

believe that actual inflation in Argentina is considerably higher than the official rate of 9.0 percent in August (Figure 2.5).9 Although nominal wage growth has remained under control 

in most countries, high inflation expectations 

are feeding into wage negotiations in countries 

such as Argentina and the República Bolivariana de Venezuela, where capacity constraints 

are tight. In countries with inflation-targeting 

central banks—Brazil, Chile, Colombia, Mexico, 

and Peru—inflation has also risen, in some 

cases above target ranges, but increases have 

generally been more contained than elsewhere 

in the region, and there are signs of stabilizing or even declining inflation expectations for 

some countries.

In response, central banks have raised policy 

interest rates, most actively in the inflation-targeting countries, where exchange rate appreciation has also helped contain inflation pressures. 

In Brazil, monetary policy tightening has been 

supported by an increase in the primary fiscal 

surplus target for 2008 by 1⁄2 percentage point of 

GDP. However, fiscal policy has not in general 

been restrictive across the region, in part because 

of the budgetary impact of delayed pass-through 

9Data for CPI inflation for several provincial capitals for 

August 2008 are generally well above this rate, although it 

should be noted that provincial data do not reflect price 

changes on the same basket of goods.
of international oil price increases and increased 

explicit subsidies. Although inflation should now 

gradually recede, monetary tightening is still 

warranted in some countries where real interest rates have become significantly negative and 

there is a sense that policy credibility is being 

eroded. Central banks with inflation-targeting 

regimes have earned some limited scope to tolerate temporary deviations of headline inflation 

from objectives, but, depending on evolving 

risks to activity, some may still need to raise rates 

further. At the same time, tighter control over 

the growth of government spending would help 

restrain domestic demand growth and reduce 

exposure to adverse shifts in market sentiment.

External positions are generally robust, 

although the turbulence in the global economy 

may erode the cushions that have been built 

up over the past few years. The region’s current account balance is expected to move to 

deficit in 2008 and 2009, after being in surplus 

since 2003, but the deficit will remain quite low. 

Moreover, reserve levels are high, and flexible 

exchange rates provide room to maneuver in 

a number of countries. Overall, public sector 

balance sheet vulnerabilities have been reduced 

and credit ratings raised—Brazil and Peru both 

achieved “investment grade” ratings in recent 

months. However, conditions for U.S. dollar 

funding have tightened in several countries over 

the past month, which—together with a sustained drop in commodity prices—could stretch 

macroeconomic policy frameworks.

Risks to this outlook are to the downside, 

largely related to external developments. A 

deeper downturn in global growth could trigger 

a sharp drop in commodity prices, while external 

financing conditions facing Latin America could 

continue to tighten. Such a scenario would slow 

growth in the region even more, and although 

inflation would moderate considerably, external 

positions could come under serious stress. In this 

event, policymakers would need to stand ready to 

adapt policies as needed to preserve macroeconomic stability and the prospects for long-term 

growth. Those few countries with very strong 

fiscal positions may have some scope for a countercyclical fiscal response. Flexible exchange rate 

management would provide resilience in the face 

of potentially volatile foreign exchange flows.

Latin America and the Caribbean: Navigating a More Perilous En

Table 2.4. Selected Western Hemisphere Economies: Real GDP, Consumer Pri

and Current Account Balance

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Western Hemisphere 5.5 5.6 4.6 3.2 5.3 5.4 7.9 7.3 1.5 0.4 –0.8 –1

South America and Mexico3 5.4 5.6 4.6 3.1 5.2 5.3 7.6 7.1 1.8 0.8 –0.5 –1.

Argentina 8.5 8.7 6.5 3.6 10.9 8.8 9.1 9.1 2.6 1.7 0.8 –0.6

Brazil 3.8 5.4 5.2 3.5 4.2 3.6 5.7 5.1 1.3 0.1 –1.8 –2.0

Chile 4.3 5.1 4.5 3.8 3.4 4.4 8.9 6.5 4.7 4.4 –1.1 –0.9

Colombia 6.8 7.7 4.0 3.5 4.3 5.5 7.3 5.5 –1.8 –2.9 –2.2 –1.9

Ecuador 3.9 2.5 3.0 3.0 3.3 2.3 8.5 5.1 3.9 2.3 5.6 1.5

Mexico 4.9 3.2 2.1 1.8 3.6 4.0 4.9 4.2 –0.2 –0.6 –1.4 –2.2

Peru 7.7 8.9 9.2 7.0 2.0 1.8 5.6 4.4 3.0 1.4 –2.0 –1.8

Uruguay 7.0 7.4 6.5 5.5 6.4 8.1 6.8 6.2 –2.4 –0.8 –2.6 –1.9

Venezuela, Rep. Boliv. de 10.3 8.4 6.0 2.0 13.7 18.7 27.2 33.5 14.7 8.8 8.5 3.4

Central America4 6.2 6.6 4.6 4.2 6.5 6.7 10.9 8.5 –4.8 –6.9 –9.1 –8.7

The Caribbean4 7.8 5.6 3.7 2.9 7.8 6.7 12.1 10.1 –0.7 –1.7 –5.3 –4.

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP.

3Includes Bolivia and Paraguay.

4The country composition of these regional groups is set out in Table F in the Statistical Appendix.
CHAPTER2  Country and Regional Persp

Emerging Europe: Prospects for a So

Landing

Following a prolonged economic expansion, activity in emerging Europe has started to 

moderate and a significant slowdown appears in 

the offing. Weaker external demand, especially 

owing to the cooling of demand in western 

Europe, and tighter external financing conditions are weighing on investment and exports, 

while private consumption has slowed in the 

face of soaring food and energy prices. Nonetheless, ongoing expansion of productive capacity 

and rapid lending to the private sector by mostly 

foreign banks (particularly in Bulgaria and 

Romania) have continued to support domestic demand, although prospects for continued 

strong capital inflows have weakened noticeably. 

The Baltics, notably Estonia and Latvia, are 

already undergoing sharp corrections as large 

domestic and external imbalances that had 

accumulated during drawn-out consumptionand-investment booms are starting to unwind 

(Figure 2.6). With real incomes eroded by high 

debt service and inflation and with foreign 

banks pulling back loan expansion on increasing 

concerns about a buildup of imbalances, private 

consumption and, to a lesser extent, investment 

have plummeted and current account deficits 

have started to decline.

Amid still-buoyant domestic demand and 

still-tight labor markets, inflation has been 

further boosted by increases in food and energy 

prices. By June 2008, headline inflation in most 

countries was double what it was a year earlier, 

reaching double digits in the Baltics (especially 

Latvia), Bulgaria, and Turkey. Underlying inflation has also climbed because of rising wages 

(for example, in Poland and the Slovak Republic) and strong demand pressures (particularly 

in Bulgaria, Romania, and Turkey). There is also 

evidence of second-round effects from surging 

food and energy prices, and inflation expectations have edged up in many countries. With 

inflation targets exceeded by wide margins, 

monetary policy has been tightened in central and eastern Europe (CEE), Romania, and 

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Venezuela

Others

0

2

4

6

8

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Venezuela

Others

0

4

8

12

16

20

24

28

32

36

2002 03 04 05 06 07 08 -6

-4

-2

0

2

4

6

8

10

Figure 2.5. Latin America: Inflation Returns

 Sources: Haver Analytics; Inter-American Development Bank; and IMF staff estimates.

 Bolivia, Costa Rica, Guatemala, Honduras, Paraguay, and Uruguay.

 Estimates of the output gap, expressed as ratio to potential GDP, are based on IMF staff 

calculations.

 Since December 2006.

 Relative to one-year inflation expectations.

Inflation has risen across the region, driven by rising food prices and tightening 

capacity constraints. Inflation-targeting central banks have generally been more

active in raising interest rates, supported by more flexible exchange rate

management.

GDP Growth and Output Gap

(percent)

1

Output gap

GDP growth

1

2

4

Headline Inflation by Country

(percent)

Latest

Dec. 2006

Inflation target

0

4

8

12

16

20 Policy Interest Rate

(percent)

Brazil

Chile Mexico

Argentina

2006 07 Sep.

 08

2

Colombia

Peru

Brazil

Chile 

Colombia

Mexico

Peru 0

2

4

6

8

10

Real Policy Interest Rate 12

(percent)

September 2008

December 2006

1

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Venezuela

Others

-5

0

5

10

15

20

25

Exchange Rate Appreciation 30

against U.S. Dollar

(percent)

3

3

4

1

Government Spending

(average annual change in ratio

of government spending to trend 

GDP, 2005–08) September 2008
Turkey in the context of floating exchange rate 

regimes. In the CEE economies, strengthening 

currencies and good harvests have helped combat inflation pressures. However, countries with 

fixed exchange rates (the Baltics and Bulgaria) 

have had less room to tighten the monetary 

stance; in Romania and Turkey, currency depreciation contributed to inflation despite interest 

rate increases. Nonetheless, in Estonia, Hungary, 

and Latvia, inflation pressures have started to 

ebb in the course of economic corrections (see 

Figure 2.6).

Growth is expected to continue to decelerate 

markedly, including on account of diminishing 

capital inflows and tighter financial constraints. 

In the CEE economies, growth is projected 

to ease from about 6 percent in 2007 to near 

41⁄2 percent in 2008 and 31⁄2 percent in 2009 

(Table 2.5). Weakening demand from western 

Europe will slow exports and investment, and 

high inflation will take a toll on real incomes 

and consumption. Growth in southern and 

southeastern Europe (SEE) and Turkey will also 

weaken, as a worsening external outlook and 

still-high commodity prices weigh on consumption and investment. Corrections in the Baltics 

are expected to continue, with growth projected 

to drop from an average of near 9 percent in 

2007 to 11⁄4 percent in 2008 and to –1⁄4 percent 

in 2009. The economies are projected to start 

to recover gradually during the second half of 

2009. Inflation pressures are expected to ease 

in 2009 as increases in commodity prices and 

domestic cost and demand pressures subside.

The risks to the outlook are tilted to the 

downside. They relate to deterioration in external financing conditions, larger than expected 

weakening of external demand, and renewed 

increases in commodity prices. Countries with 

large current account deficits financed in part 

by non-foreign-direct-investment capital inflows 

are particularly vulnerable to a sharp reversal 

of capital inflows. In some countries in emerging Europe, large nonresident holdings of local 

currency bonds, extensive domestic borrowing in foreign currencies, and dependence of 

some financial institutions on wholesale fundReal Private Sector Credit, 2003–07

(percent change from a year earlier)

-5

0

5

10

15

20

25

30

35

-4

-2

0

2

4

6

8

10

-5

0

5

10

15

20

Bulgaria/Romania

Baltics

Turkey

Central Europe 

Latin America

Developing Asia

0 10 20 30 40 50

0

2

4

6

8

10

12

Figure 2.6. Emerging Europe: Are Credit Booms 

Cooling Off?

 Sources: Haver Analytics; IMF, International Financial Statistics; and IMF staff estimates.

 Includes Czech Republic, Hungary, Poland, and Slovak Republic.

 Two-quarter moving average.

 Overall consumer price index (CPI) excluding energy, food, alcohol, and tobacco.

 Calculated as overall compensation divided by GDP, except for Romania, where wages 

are used instead of compensation.

Credit to the private sector has expanded at a much faster rate in the Baltics, Bulgaria, 

and Romania than in other countries in the region. Credit booms have been

accompanied by a buildup of significant external imbalances. Estonia and Latvia are 

now undergoing a sharp correction, while consumption and GDP continue to grow

briskly in Bulgaria and Romania and, to a lesser extent, in Lithuania. Core inflation 

and growth of unit labor costs have started to stabilize in Estonia and Latvia, while in

Bulgaria, Lithuania, and Romania inflation pressures remain strong.

Real GDP

(Q4-over-Q4 percent change)

Real Consumption Growth

(percent change from a year

earlier)

1

Core Inflation

(percent change from a year

earlier)

Unit Labor Costs

(percent change from a year

earlier)

2005 06 07 08:

Q2

2006 07 08:

Q2

2005 06 07 08:

Q2

Latvia

Lithuania

Estonia

Bulgaria

Romania

Latvia

Lithuania

Estonia

Bulgaria

Romania

Latvia

Lithuania

Estonia

Bulgaria

Romania

Latvia

Estonia Lithuania

Bulgaria

Romania

1

2

2

2008

2009

Current Account, 2003–07

(percent of GDP)

Baltics

Bulgaria/Romania

Turkey

Central Europe 

Latin America

Developing Asia

-15 -10 -5 0 5

1

3

3

4

4

Emerging Europe: Prospects for a S
CHAPTER2  Country and Regional Persp

7

ing from abroad accentuate vulnerabilities to 

further changes in market sentiment. Beyond 

these risks, there is considerable uncertainty 

about the pace of adjustment in the Baltics. An 

even-sharper-than-projected slowdown cannot be ruled out, should external or domestic 

confidence plummet. The downturn may also 

be more prolonged than expected, if the labor 

markets, which must adjust to restore competitiveness, proves to be less flexible than expected.

Financial risks have risen appreciably with the 

onset of the turbulence in advanced financial 

markets. Although banks operating in emerging 

Europe have little if any direct exposure to the 

U.S. subprime market, they (and their foreign 

parents) have been affected by the widening 

turmoil in western Europe. A decline in global 

risk appetite has raised liquidity risks by increasing banks’ external funding costs and shortening maturities. Credit risks have also risen: the 

credit boom was accompanied by lengthening 

maturities, rising loan-to-value ratios, and more 

exposure to riskier products (for example, 

yen-denominated loans in Hungary and Swissfranc-denominated loans in Poland, including 

at variable rates). A recent deceleration in real 

estate prices, at least in some countries, makes 

banks more vulnerable to credit risk. Foreign 

currency lending is widespread, in both fixedexchange-rate countries (for example, most 

lending is done in euros in the Baltics) and in 

floating-exchange-rate countries (for example, 

in Hungary and Romania, foreign currency 

loans accounted for about 60 percent of total 

household loans in 2007). Lower equity prices 

and rising bond spreads have raised funding 

costs.

The policy challenge is how to engineer a soft 

landing, while continuing to lay the groundwork 

for sustainable convergence to western European living standards.

• In the CEE economies, the risks to inflation 

and growth appear balanced, justifying keeping monetary policy on hold for the moment, 

although the balance of risks hinges on 

unpredictable capital flows and exchange rate 

developments. Fiscal positions have recently 

been strengthened by buoyant revenues and 

Table 2.5. Selected Emerging European Economies: Real GDP, Consumer Pri

and Current Account Balance

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Emerging Europe 6.7 5.7 4.5 3.4 5.4 5.7 7.8 5.8 –6.0 –6.6 –7.1 –7

Turkey 6.9 4.6 3.5 3.0 9.6 8.8 10.5 8.4 –6.0 –5.7 –6.5 –6.7

Excluding Turkey 6.6 6.3 5.0 3.5 3.2 4.1 6.4 4.5 –6.0 –7.1 –7.4 –7.4

Baltics 9.8 8.8 1.2 –0.3 4.8 7.3 12.5 7.3 –15.8 –18.1 –14.0 –8.6

Estonia 10.4 6.3 –1.5 0.5 4.4 6.6 10.2 5.1 –16.7 –18.1 –10.8 –8.7

Latvia 12.2 10.3 –0.9 –2.2 6.5 10.1 15.9 10.6 –22.7 –22.9 –15.1 –8.3

Lithuania 7.9 8.9 3.9 0.7 3.8 5.8 11.3 6.2 –10.7 –14.6 –14.9 –8.7

Central Europe 6.2 6.1 4.6 3.6 2.1 3.4 4.9 3.5 –3.6 –3.7 –4.4 –5.

Czech Republic 6.8 6.6 4.0 3.4 2.5 2.8 6.7 3.4 –2.6 –1.8 –2.2 –2.5

Hungary 3.9 1.3 1.9 2.3 3.9 7.9 6.3 4.1 –6.1 –5.0 –5.5 –6.1

Poland 6.2 6.6 5.2 3.8 1.0 2.5 4.0 3.3 –2.7 –3.8 –4.7 –5.7

Slovak Republic 8.5 10.4 7.4 5.6 4.3 1.9 3.9 3.6 –7.1 –5.4 –5.1 –4.7

Southern and southeastern Europe 7.0 6.0 7.3 4.5 6.2 5.1 8.8 6.4 –10.7 –14.1 –14.8 –14

Bulgaria 6.3 6.2 6.3 4.2 7.4 7.6 12.2 7.0 –15.6 –21.4 –24.4 –21.5

Croatia 4.8 5.6 3.8 3.7 3.2 2.9 7.0 4.9 –7.9 –8.6 –10.1 –10.2

Romania 7.9 6.0 8.6 4.8 6.6 4.8 8.2 6.6 –10.4 –14.0 –13.8 –13.3

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP.
spending restraint, but there is little room for 

automatic stabilizers to operate in full. Continued fiscal consolidation would help widen 

these margins and unburden monetary policy. 

Public finances need to be put on a sustainable long-term path to meet the challenges 

posed by population aging and to support 

continued convergence with the euro area, 

particularly in the Slovak Republic, which is 

scheduled to adopt the euro in January 2009. 

Addressing remaining rigidities in the labor 

market would facilitate long-term fiscal adjustment while easing labor market constraints 

and wage pressures.

• In the Baltics, macroeconomic adjustment 

needs to run its course. Although domestic 

demand pressures are subsiding, especially in 

Estonia and Latvia, external imbalances still 

loom large, inflation is at double-digit levels, 

and confidence is weakening. It is important 

to resist the temptation to significantly ease 

fiscal policy in the downturn, considering that 

these economies’ high degree of openness 

would limit its impact on demand. It will be 

important to claw back unsustainable expenditure increases from the recent past and target structural balance over the medium term. 

There is also a need for heightened supervisory vigilance and contingency plans for 

financial institutions to deal with potentially 

significant loan losses from the economic 

downturn. This will require close collaboration between domestic and foreign prudential 

authorities, given the large share of foreignowned banks.

• In the SEE economies, action is needed to 

rein in rising external and internal imbalances, mindful of the more volatile external 

financing conditions. Like the Baltics several 

years ago, these countries are still enjoying 

“good times,” and fiscal and income policies 

need to avoid adding procyclical impulses 

to the already overheating private sector. 

Specifically, growth in public expenditures 

needs to be contained by keeping public 

wage increases in line with productivity 

growth and reducing the size of government, 

in conjunction with reforms to raise the efficiency of the public sector. With credit to the 

private sector growing at double-digit rates, 

maintaining high prudential standards—and 

rigorously applying them—is critical in 

order to prevent weakening credit standards. 

Again, close cross-border collaboration will 

be important, for the same reasons as in the 

Baltics.

Commonwealth of Independent States:

Managing the Commodity Price Boom

Real GDP growth has been strong in most 

countries of the Commonwealth of Independent States (CIS), underpinned by buoyant 

domestic demand that has been boosted by 

terms-of-trade gains in most countries in the 

region and expansionary macroeconomic policies. However, growth prospects are deteriorating as the region has been affected by the 

global financial turmoil, notably Kazakhstan 

and, more recently, Russia, which have been 

confronted by loss of confidence and a sharp 

turn in capital flows. Weakening demand from 

partner countries would add to the effects on 

growth of unsettled financial markets.

High world prices for food and fuel have 

contributed significantly to inflation pressures 

across the region. Partly as a result of the high 

weight of food in household consumption, 

headline inflation accelerated sharply during 

the first half of 2008, reaching nearly 15 percent by the summer (Figure 2.7). Moreover, 

core inflation has picked up around the region, 

reflecting the combination of strains from rising commodity prices and domestic demand 

pressures. Thus, there are concerns about overheating, with output consistently above potential and labor markets remaining tight. The 

policy stance generally continues to be expansionary across the region; interest rates have 

turned negative in real terms, while government 

spending continues to expand rapidly. High 

international commodity prices have continued to boost trade balances in net commodity 

exporters, while net commodity importers have 

Commonwealth of Independent States: Managing the Commodit
CHAPTER2  Country and Regional Persp

seen a marked weakening in their external positions. Azerbaijan, Kazakhstan, Russia, Turkmenistan, and Uzbekistan have benefited most from 

terms-of-trade gains, whereas terms of trade in 

Armenia, Moldova, and Tajikistan have worsened. In net food importers, food balances have 

deteriorated significantly, with deficits reaching precarious levels in some countries. Linked 

to the rising cost of food imports, the Kyrgyz 

Republic received an augmented disbursement 

from the IMF in May to help meet a larger than 

expected balance of payments shortfall.

In response to slowing external demand and 

emerging financial market pressure, real GDP 

growth is projected to slow from 71⁄4 percent this 

year to about 53⁄4 percent in 2009 (Table 2.6). 

Notwithstanding recent oil price declines, 

output is expected to grow at a solid pace in net 

energy exporters, where terms-of-trade gains 

should continue to boost demand, whereas net 

energy importers will likely see their growth 

momentum weaken noticeably. In Russia, the 

growth forecast for 2008 reflects a stronger-thanexpected performance early in the year, rising 

terms-of-trade gains, and a larger-than-expected 

fiscal stimulus package. But growth is set to 

weaken appreciably, reflecting slowing world 

demand and tightening financial conditions. 

In Kazakhstan, growth is expected to remain 

relatively subdued as the excesses of the earlier 

credit boom unwind, although the still-buoyant 

oil sector should continue to partially offset the 

effects of the credit crunch. In Ukraine, the 

growth forecast for 2008 reflects strong performance during the first half of the year, termsof-trade gains, and indications of a bumper 

harvest. Going forward, growth is projected to 

decelerate markedly, reflecting weaker export 

market growth, slowing real wage increases, 

moderating terms-of-trade gains, and higher 

financing costs.

Risks to the outlook have moved to the 

downside. Financial market uncertainties are a 

source of concern, notably for countries with 

rapidly expanding current account deficits, 

such as Ukraine, and for other countries that 

are already heavily reliant on capital inflows. 

Soaring food and fuel prices are causing divergence in external positions and 

contributing to rising inflation across the region, while expansionary policies

continue to stimulate demand.

Figure 2.7. Commonwealth of Independent States (CIS):

Managing the Commodity Price Boom

CPI Weights

(percent, CIS average)

Interest Rates

(percent)

 Sources: IMF, International Financial Statistics; and IMF staff calculations.

 CPI = consumer price index.

 Output gaps are estimated using a Hodrick-Prescott filtered potential GDP. See Box 1.3 

for more detail on the methodology and the estimation issues involved.

 Deposit rates. Real rates are computed using headline inflation.

 Shares in total exports minus shares in total imports.

Fuel

(5.5%)

Food

(44.0%) Other

(50.4%)

0

5

10

15

20

25 Inflation

(percent)

Ukraine, core

inflation

Russia, core 

inflation

Kazakhstan, 

core inflation

Aug.

08

2001 03 05 07

Headline inflation 

CIS average

Food Energy -30

-20

-10

0

10

20

30

40

50

Net Export Shares 60

(percent)

Russia

Ukraine Kazakhstan

CIS average

2003 04 05 06 07 08 -12

-8

-4

0

4

8

12

16

20

24

28

32 Trade Balances

(percent of GDP)

Russia

Ukraine Kazakhstan

CIS average

2003 04 05 06 07 08 0

2

4

6

8

10

12 Output Gaps

(percent of potential GDP)

Russia

Ukraine Kazakhstan CIS average

CIS Average

Russia

Ukraine

Kazakhstan

-15

-10

-5

0

5

10

15

20

Nominal

Real

4

1

2

3

2 3

1

4
Although further monetary and fiscal tightening 

is expected in the second half of 2008, the nearterm effects on activity and inflation will likely 

be limited. Accordingly, inflation risks are still 

on the upside for net commodity importers and 

exporters alike.

Inflation is now expected to be higher than 

projected in the April 2008 World Economic 

Outlook, 151⁄2 percent this year and 121⁄2 percent 

next year, reflecting intensifying price pressures amid persistently high commodity prices 

and little spare capacity. Against this backdrop, 

monetary policy effectiveness generally remains 

constrained by inflexible exchange rate regimes, 

although in Ukraine the exchange rate was 

recently revalued within a widened trading 

band. To compensate, governments in CIS 

countries have resorted to a variety of fiscal and 

trade measures in order to contain inflation 

pressures and alleviate the social impact of rising 

food prices. These measures include reducing 

or eliminating import taxes and tariffs on key 

food items, cutting back domestic consumption 

taxes or stepping up subsidies for food and fuel, 

introducing direct price caps on key food items, 

and imposing export taxes and quotas. Some 

countries have introduced short-term supplyoriented measures, mainly subsidies and other 

supports to agriculture. However, the fiscal costs 

of these measures have reached disconcerting 

proportions in a number of economies, raising 

concerns about fiscal sustainability.

For as long as inflation pressure is not 

projected to diminish markedly from presently 

elevated levels, stronger policy action is needed 

in many countries across the region in order 

to ensure that long-term inflation expectations 

remain firmly anchored. A comprehensive 

policy response would require a combination 

of monetary tightening and greater exchange 

rate flexibility, combined with a prudent fiscal stance. In particular, universal subsidies, 

which reinforce domestic demand pressures 

and burden public resources, could usefully be 

Commonwealth of Independent States: Managing the Commodit

Table 2.6. Commonwealth of Independent States (CIS): Real GDP, Consumer Prices, and Curre

Account Balance

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Commonwealth of 

Independent States 8.2 8.6 7.2 5.7 9.5 9.7 15.6 12.6 7.5 4.4 5.5 3.

Russia 7.4 8.1 7.0 5.5 9.7 9.0 14.0 12.0 9.5 5.9 6.5 3.4

Ukraine 7.3 7.6 6.4 2.5 9.1 12.8 25.3 18.8 –1.5 –3.7 –7.2 –9.2

Kazakhstan 10.7 8.9 4.5 5.3 8.6 10.8 17.6 9.8 –2.4 –6.9 4.3 3.3

Belarus 10.0 8.2 9.2 8.0 7.0 8.4 15.3 9.6 –3.9 –6.8 –5.9 –8.0

Turkmenistan 11.4 11.6 10.8 10.3 8.2 6.3 13.0 12.0 15.7 15.4 26.5 33.0

Low-income CIS countries 14.7 14.5 10.5 10.5 10.1 12.7 16.3 13.7 7.7 11.2 15.7 16.

Armenia 13.3 13.8 10.0 8.0 2.9 4.4 9.4 5.0 –1.8 –6.4 –9.7 –10.8

Azerbaijan 30.5 23.4 16.0 16.4 8.4 16.6 22.4 20.0 17.7 28.9 38.3 38.6

Georgia 9.4 12.4 3.5 4.0 9.2 9.2 10.0 7.6 –15.9 –20.0 –20.8 –18.7

Kyrgyz Republic 3.1 8.2 7.5 6.7 5.6 10.2 24.5 12.2 –3.1 –0.2 –4.2 –5.4

Moldova 4.8 4.0 6.5 6.5 12.7 12.4 13.7 9.7 –11.8 –17.0 –19.9 –19.1

Tajikistan 7.0 7.8 6.0 7.0 10.0 13.2 21.6 15.5 –2.8 –11.2 –8.5 –8.1

Uzbekistan 7.3 9.5 8.0 7.5 14.2 12.3 11.1 10.6 17.2 19.1 16.8 12.8

Memorandum

Net energy exporters3 8.2 8.7 7.2 6.0 9.7 9.4 14.5 12.1 9.1 5.9 7.5 4.8

Net energy importers4 8.0 8.1 7.0 4.2 8.5 11.4 21.5 15.4 –3.0 –5.6 –7.9 –9.6

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP. 

3Includes Azerbaijan, Kazakhstan, Russia, Turkmenistan, and Uzbekistan.

4Includes Armenia, Belarus, Georgia, Kyrgyz Republic, Moldova, Tajikistan, and Ukraine.
CHAPTER2  Country and Regional Persp

replaced with temporary and targeted measures 

to alleviate the social effects of soaring food and 

energy prices on vulnerable segments of the 

population. In this regard, export restrictions 

and other policy interventions that prevent the 

needed supply adjustment are likely to prove 

counterproductive.

Over the longer term, the region continues to 

face the challenge of reducing its sensitivity to 

commodity price shocks through diversification 

of the economy away from primary commodities. Further efforts to improve the business 

climate; increase competition, including in the 

food and energy sectors; strengthen domestic 

financial systems; and continue to build market 

institutions more broadly would foster stronger 

performance and reduce vulnerabilities associated with terms-of-trade shifts.

Sub-Saharan Africa: A Test of Poli

Frameworks

Economic growth in sub-Saharan Africa 

(SSA) is expected to moderate in the face of 

the financial turmoil and high energy and food 

prices, even though many SSA countries are 

benefiting from terms-of-trade gains resulting from the surge in other commodity prices. 

Overall, growth is projected to decline from 

near 7 percent in 2007 to just over 6 percent in 

2008–09. However, there are important crosscountry variations (Figure 2.8 and Table 2.7). 

Despite a weakening external environment, 

economic expansion in oil-exporting countries is expected to soften only moderately 

in 2008–09, with growth declining to about 

71⁄2 percent from near 8 percent in 2007, owing 

to a near 75 percent improvement in the terms 

of trade in 2008. For oil importers, the terms of 

trade would remain broadly stable in 2008, with 

higher oil prices offset by higher export prices 

for metals, coffee, cocoa, and cotton.10 However, 

10Oil importers stand to benefit from higher prices for 

metals (Botswana, Ghana, Guinea, Kenya, Mozambique, 

Senegal, South Africa, Togo, Uganda, and Zambia), coffee, cocoa, and cotton (Burundi, Côte d’Ivoire, Ethiopia, 

the countries hit hardest (Benin, The Gambia, 

Kenya, Madagascar, Rwanda, and Sierra Leone) 

are projected to experience a 15–20 percent 

deterioration in the terms of trade. In South 

Africa, SSA’s largest economy, electricity shortages early in 2008 and the need for a 500-basispoint rise in policy interest rates since mid-2006 

to contain inflation are expected to slow growth 

from 5 percent in 2007 to about 31⁄2 percent 

in 2008–09. The risks to the regional growth 

outlook are tilted to the downside and relate 

mainly to slower-than-expected growth in global 

demand and slowing capital inflows.

Recent sharp increases in food and fuel 

prices pose significant challenges for price 

stability across SSA. Inflation is expected to rise 

from about 7 percent in 2007 to near 12 percent 

in 2008, before easing to 91⁄2 percent in 2009, 

although the average masks significant variation 

across countries. Food price rises tend to have 

a large impact on inflation in SSA, reflecting 

a high share of food in consumer baskets (see 

Chapter 3). Domestic demand pressures, which 

have emerged in some SSA countries during 

the past several years of robust growth, may also 

be amplifying the initial impact of food and 

fuel price shocks through second-round effects 

on inflation.

Against the backdrop of rising inflation, 

the impact of higher food prices on poverty is 

a major concern as it risks undermining past 

progress in this area and putting social cohesion at risk. SSA countries’ strong dependence 

on imports of food and fuel as well as a high 

incidence of poverty make them most vulnerable to increases in prices of these commodities. Populations in these countries have few 

options to hedge against rising food prices, and 

the urban poor tend to suffer most. The IMF 

staff estimates that rising prices for imported 

food would have the largest impact on poverty in The Gambia, Ghana, Mauritania, and 

Swaziland owing to their high dependence on 

Kenya, Rwanda, Uganda, and Zambia, among others). 

For more details on the effects of the recent commodity 

price shock, see Chapter 3 and IMF (2008a).
imports and low incomes (IMF, 2008a). Some 

countries have responded to rising inflation 

pressures by tightening monetary policy, but 

many have reduced import tariffs and the valueadded tax on food or have imposed export 

taxes and other restrictions.

The external positions of oil-importing countries are also coming under pressure because 

of surging prices of imported food and fuel. 

Current account deficits in oil-importing countries are projected to deteriorate on average 

from about 5 percent of GDP in 2007 to about 

53⁄4 percent of GDP in 2008 and 61⁄4 percent 

in 2009. In South Africa, a widening current 

account deficit, which stood at 71⁄4 percent of 

GDP in the second quarter of 2008, is of particular concern. The deficit is financed largely 

through volatile portfolio flows, although low 

external debt and a flexible exchange rate 

should provide some resilience if capital flows 

were to reverse. By contrast, current account 

balances in oil-exporting countries are in surplus and are projected to strengthen further, 

from 8 percent in 2007 to 131⁄2 percent in 2008 

and about 8 percent in 2009.

The main challenge for the region is how to 

respond to the large commodity price shock 

and the threat of slowing capital inflows. Oilimporting countries, where the negative termsof-trade shock has weakened fiscal and external 

positions, need to adjust their monetary, fiscal, 

and income policies. Delaying the adjustment 

would put at risk not only macroeconomic stability but also recent achievements in improving 

policy and institutional frameworks, which have 

been largely responsible for SSA’s impressive 

growth performance in recent years.

• Countries with managed or fixed exchange 

rates need to tighten monetary policies 

through interest rate increases or administrative measures to prevent inflation expectations from becoming ingrained and to 

strengthen the balance of payments and 

fiscal positions. A reduction in public sector 

borrowing and greater exchange rate flexibility would support monetary tightening and 

ease pressure on the balance of payments.

Figure 2.8. Sub-Saharan Africa: The Mixed Blessing of

High Commodity Prices

Oil Importers

 Sources: Haver Analytics; IMF, Direction of Trade Statistics; and IMF staff calculations. 

 Consumer price index (CPI) inflation measured as percent change from a year earlier, 

left scale; fiscal balances measured as percent of GDP, right scale.

 Current account balances measured as percent of GDP, right scale; terms of trade 

measured as index, 2000 = 100, left scale.

1

At the aggregate level, growth in Africa is expected to show some resilience to the 

global slowdown, as many countries benefit from improvement in the terms of trade 

owing to a surge in fuel and nonfuel commodity prices, and net capital inflows to the 

region remain broadly steady. Nonetheless, there are dramatic differences in the 

economic performance of oil importers and exporters, with the former experiencing a 

significant deterioration in their current account and fiscal positions, as well as a 

sharp rise in inflation.

02 04 06 08

2

2000 -4

-2

0

2

4

6

8

10

12

-4

0

4

8

12

Fiscal balance

CPI 

inflation

1

0

4

8

12

16

20 GDP Growth

(percent)

2000 02 04 06 08

Oil exporters

-2

0

2

4

6

Net Capital Flows 8

(percent of GDP)

2000 02 04 06 08

Oil importers Sub-Saharan

Africa

Oil exporters

Oil importers

Sub-Saharan 

Africa

Fiscal Balance and Inflation

Oil Exporters

2000 02 04 06 08 -30

-20

-10

0

10

20

30

40

50

60

-6

-4

-2

0

2

4

6

8

10

12

Fiscal balance

Oil Importers

2000 02 04 06 08 20

40

60

80

100

120

-8

-6

-4

-2

0

2

Current account

balance

Terms of trade

2 Current Account and Terms of Trade

Oil Exporters

2000 02 04 06 08 0

50

100

150

200

250

300

-10

-5

0

5

10

15

20

Current account 

balance

Terms of trade

CPI 

inflation

Sub-Saharan Afrca: A Test of Policy Fr
CHAPTER2  Country and Regional Persp

• At the same time, targeted fiscal measures, 

including subsidies, are needed to help 

protect the poor. These measures would need 

to be complemented by steps to safeguard 

the long-term budget position, including a 

gradual yet full pass-through of international 

oil prices to consumer prices as better-targeted measures are put in place for the poor. 

Price controls on food items are likely to be 

ineffective, leading to shortages and budgetary costs rather than reduced inflation.

• Additional donor assistance is needed to 

cushion the impact of adjustment on vulnerable countries that have high poverty 

levels, limited access to foreign financing, 

low reserve cushions, and high external or 

public debt levels. Such funding would allow 

these countries to meet higher import bills 

and provide targeted budget assistance to the 

poorest segments of the population while the 

necessary adjustments are phased in. Over 

the longer term, donor assistance could seek 

to promote the development of domestic 

agriculture and sustainable social safety nets.

Oil-exporting countries face the challenge of 

managing the windfall gains from high commodity prices more successfully than during 

similar past episodes, which failed to produce 

permanent improvements in living standards. 

Rising current account surpluses and inflation 

Table 2.7. Selected African Economies: Real GDP, Consumer Prices, and Current Account Bala

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Africa 6.1 6.3 5.9 6.0 6.3 6.2 10.2 8.3 2.9 0.4 3.0 0.2

Maghreb 4.3 4.3 5.5 4.9 3.1 3.0 4.3 4.0 13.9 12.1 15.5 10.3

Algeria 2.0 4.6 4.9 4.5 2.5 3.6 4.3 4.0 24.8 22.8 28.1 19.8

Morocco 7.8 2.7 6.5 5.5 3.3 2.0 3.9 3.5 2.2 –0.1 0.4 –0.3

Tunisia 5.5 6.3 5.5 5.0 4.5 3.1 5.1 4.5 –2.0 –2.6 –3.4 –3.5

Sub-Sahara 6.6 6.9 6.1 6.3 7.3 7.1 11.9 9.5 –0.3 –3.0 –0.7 –2

Horn of Africa3 11.3 10.6 8.4 7.2 9.1 11.0 19.5 20.9 –13.4 –10.4 –6.3 –6.

Ethiopia 11.6 11.4 8.4 6.5 12.3 15.8 25.3 40.8 –9.1 –4.5 –5.0 –5.2

Sudan 11.3 10.2 8.5 7.7 7.2 8.0 16.0 10.0 –15.2 –12.6 –6.3 –6.7

Great Lakes3 7.1 7.0 6.5 7.6 10.4 9.2 15.7 7.9 –4.3 –4.5 –6.2 –7.8

Congo, Dem. Rep. of 5.6 6.3 10.0 10.3 13.2 16.7 17.5 15.1 –2.4 –1.8 –1.9 –12.6

Kenya 6.4 7.0 3.3 6.4 14.5 9.8 25.0 6.5 –2.3 –3.1 –6.1 –4.5

Tanzania 6.7 7.1 7.5 8.0 7.3 7.0 9.2 6.5 –7.7 –9.0 –9.8 –10.0

Uganda 10.8 7.9 9.8 8.1 6.6 6.8 7.3 7.8 –3.5 –2.8 –3.4 –5.8

Southern Africa3 11.0 12.9 10.6 9.4 11.5 10.0 11.3 9.4 12.8 6.7 9.6 8.

Angola 18.6 21.1 16.0 12.8 13.3 12.2 12.1 9.3 23.3 11.3 18.0 15.9

Zimbabwe4 –5.4 –6.1 . . . . . . 1,016.7 10,452.6 . . . . . . –7.0 –3.5 . . . . . .

West and Central Africa3 4.9 5.1 5.4 6.8 6.8 4.6 9.3 8.3 4.5 –0.8 3.1 –0.

Ghana 6.4 6.3 6.5 5.8 10.2 10.7 16.8 13.3 –9.0 –10.9 –13.1 –13.2

Nigeria 6.2 5.9 6.2 8.1 8.3 5.5 11.0 11.1 9.5 2.1 6.2 0.6

CFA franc zone3 2.8 4.2 4.3 5.6 3.2 1.4 5.6 4.1 0.1 –2.4 2.5 0.7

Cameroon 3.2 3.5 3.8 4.6 5.1 0.9 4.1 2.1 0.6 –1.9 1.3 –1.1

Côte d’Ivoire 0.7 1.6 2.9 4.7 2.5 1.9 5.6 5.7 2.8 –0.7 3.8 –0.6

South Africa 5.4 5.1 3.8 3.3 4.7 7.1 11.8 8.0 –6.5 –7.3 –8.0 –8.

Memorandum

Oil importers 5.9 5.3 5.0 5.0 6.3 6.6 10.9 8.5 –3.8 –4.9 –5.7 –6.3

Oil exporters5 6.5 7.9 7.4 7.6 6.4 5.5 9.2 8.0 13.1 7.9 13.5 8.1

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP.

3The country composition of these regional groups is set out in Table F in the Statistical Appendix.

4No projections for 2008 and beyond are shown. The inflation figure for 2007 represents an estimate.

5Includes Chad and Mauritania in this table.
pressures call for close coordination between 

monetary and fiscal policies. Saving a portion of 

oil revenues would help mitigate emerging inflation pressures while laying a solid financial basis 

for addressing vital infrastructure, health, and 

social needs. Monetary tightening could help 

stabilize inflation and guard against the development of a generalized inflation process. Allowing nominal exchange rates to appreciate would 

also help keep inflation under control.

Middle East: Overheating Still a Conc

Activity continues to grow at a robust pace in 

much of the Middle East, while inflation pressures either remain high or keep rising, particularly in a number of oil exporters. Real GDP 

growth in Middle Eastern countries is projected 

to weaken only modestly during 2008–09, from 

61⁄2 percent to just below 6 percent, with oil 

exporters and non-oil economies growing at a 

similar pace (Table 2.8). The effects of falling 

demand in advanced partner economies and 

increasing supply-side constraints in oil sectors are projected to be partly offset by robust 

domestic demand and activity in non-oil sectors. 

Rising oil and food prices are posing important 

budgetary challenges for many non-oil economies. By contrast, the global financial turmoil 

has had relatively little effect on the region thus 

far, beyond pressing stock markets to surrender 

earlier gains.

Economic growth is being sustained mainly 

by non-oil sectors, as capacity constraints are 

slowing oil output growth (Figure 2.9). Growth 

in oil exporters is being driven by private 

construction, retail trade, transportation, and 

financial services. Aside from the indirect effect 

of high oil revenues, the strength reflects an 

improved business environment that is fostering private investment and a buildup in public 

projects to alleviate infrastructure and housing 

bottlenecks that have accumulated with rapid 

population growth. Notwithstanding record oil 

prices, recent activity in the oil sector has been 

noticeably less buoyant than in the non-oil sector. Despite heavy investment, production and 

distribution capacity are rising slowly, owing 

to soaring investment costs, technological and 

geological constraints, and the depletion of 

existing fields. Still, oil and natural gas production capacity and, to a lesser extent, output are 

projected to expand moderately in 2008–09, 

with significant new capacity in Qatar and Saudi 

Arabia. Real GDP growth in the non-oil economies is benefiting from past efforts to improve 

their business environments. Sizable FDI inflows 

are boosting activity in Egypt and Jordan, while 

Lebanon is continuing to recover from the conflict in 2006.

Signs of overheating are multiplying. Inflation has reached double-digit rates even in some 

countries with traditionally low rates, such as 

Saudi Arabia, and exceeds 20 percent in Egypt 

and the Islamic Republic of Iran. The surge in 

inflation has occurred despite limited passthrough of high fuel and, to a lesser extent, 

food prices to domestic markets.11 Not surprisingly, although inflation is still driven mainly 

by foreign-determined prices (including the 

depreciation of the U.S. dollar against third 

currencies), pressures are increasingly spilling 

over into domestically determined prices of 

nontraded goods. This is particularly true in 

oil-exporting countries, where expanding and 

wealthier populations and an influx of foreign 

workers are putting pressure on services prices, 

notably for housing, and where governments 

are granting large wage hikes. However, other 

countries are beginning to see broader pressures 

too. Accordingly, despite the technical assumptions of no further real effective exchange rate 

depreciation and broadly unchanged prices for 

fuel and food, inflation is expected to retreat 

only moderately in 2009.

At the same time, robust domestic demand 

is driving an acceleration of imports across the 

region. Current account developments differ 

widely between oil exporters and non-oil economies. Oil exporters are recording large and 

11These items typically have a combined weight of 

between one-quarter and one-third in consumption baskets, with the bulk accounted for by food.

Middle East: Overheating till A
CHAPTER2  Country and Regional Persp

growing surpluses, generally projected to reach 

10–45 percent of GDP in 2008–09. In the other 

countries, deficits have widened in response to 

rising import costs—to double-digit levels in Jordan and Lebanon, but to still-low levels in Egypt.

The risks to the growth outlook remain 

broadly balanced. External risks are mostly to 

the downside and relate to weaker demand in 

advanced economies and potentially lower oil 

prices. Capital inflows to deficit countries could 

take a hit if global financial conditions were to 

worsen suddenly, and access to funding by major 

nonfinancial corporations could also become 

more difficult. Upside risks relate to still-robust 

domestic demand and foreign investors’ continued strong interest from within (notably the 

Gulf Cooperation Council—GCC—countries) 

and outside the region. However, if upside risks 

materialize and exacerbate inflation pressure, 

this would further complicate macroeconomic 

policies.

In this setting, warding off mounting inflation 

pressures requires addressing growing imbalances. Countries that are not pegging exchange 

rates to foreign currencies (for example, Egypt 

and the Islamic Republic of Iran) can further 

tighten monetary policy while enhancing its 

effectiveness through greater exchange rate 

flexibility. In countries with pegged exchange 

rates, monetary policy is imported from abroad, 

mainly from the United States. In many oil 

exporters, currencies are undervalued, although 

by varying degrees, and higher inflation is 

contributing to an appreciation of real effective 

exchange rates. The main driver of the undervaluation is the peg to the U.S. dollar, which 

has been depreciating while terms of trade have 

been improving. Once domestic prices have 

completely adjusted to the higher levels that 

are consistent with an appreciated real effective exchange rate, inflation pressures should 

subside, provided the peg and fiscal policy effectively anchor expectations.

The move to a new equilibrium could also be 

achieved through a revaluation of currencies, 

but this would be complex, particularly in the 

GCC countries, which plan to move to monetary 

union in 2010. First, revaluations would introduce uncertainty as to how policy would respond 

to external shocks in the future, undermining the value of the peg as a nominal anchor. 

Second, many pegging countries still need to 

Table 2.8. Selected Middle Eastern Economies: Real GDP, Consumer Prices, and Curr

Account Balance

(Annual percent change unless noted otherwise)

Real GDP Consumer Prices1 Current Account Balance2

2006 2007 2008 2009 2006 2007 2008 2009 2006 2007 2008 2009

Middle East 5.7 5.9 6.4 5.9 7.0 10.6 15.8 14.4 21.1 18.4 22.9 17.

Oil exporters3 5.6 5.7 6.2 5.9 7.6 10.7 16.8 14.3 24.2 21.2 26.0 19.

Iran, I.R. of 5.8 6.4 5.5 5.0 11.9 18.4 26.0 22.0 9.2 10.1 11.2 6.7

Saudi Arabia 3.0 3.5 5.9 4.3 2.3 4.1 11.5 10.0 27.9 25.1 32.5 23.8

United Arab Emirates 9.4 7.4 7.0 6.0 9.3 11.1 12.9 10.8 22.6 20.5 22.6 18.8

Kuwait 6.3 4.6 5.9 5.8 3.1 5.5 9.0 7.5 52.2 43.1 44.6 39.3

Mashreq 5.9 6.3 6.6 5.8 5.4 9.2 11.3 13.5 –1.7 –2.2 –3.1 –3.6

Egypt 6.8 7.1 7.2 6.0 4.2 11.0 11.7 16.1 0.8 1.5 0.6 –0.9

Syrian Arab Republic 4.4 3.9 4.2 5.2 10.4 4.7 8.0 7.0 –2.9 –1.4 –2.7 –2.9

Jordan 6.3 6.0 5.5 5.3 6.3 5.4 15.8 7.6 –11.3 –17.5 –18.5 –16.3

Lebanon — 4.0 6.0 5.0 5.6 4.1 11.0 6.2 –5.6 –12.7 –14.0 –13.7

Memorandum

Israel 5.2 5.4 4.3 2.8 2.1 0.5 4.8 3.3 5.9 3.2 0.4 0.5

1Movements in consumer prices are shown as annual averages. December/December changes can be found in Table A7 in the Statistical 

Appendix. 2Percent of GDP.

3Includes Bahrain, Islamic Republic of Iran, Kuwait, Libya, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, and 

Republic of Yemen.
develop both their capacity to conduct an independent monetary policy and the underlying 

financial market infrastructure. Third, volatility 

in oil markets could lead to volatile exchange 

rates under a floating regime, which might 

hamper some countries’ efforts to diversify their 

export bases. Some of these issues would be 

addressed by a switch from a peg to the U.S. dollar to a peg to a basket of currencies that better 

reflects the composition of trade and services 

exchange with the rest of the world. However, 

had such a peg been in place recently, it would 

not have made a major difference with respect 

to excess demand pressures, as is evidenced by 

Kuwait’s experience. In the long run, however, 

if inflation persists, a switch to a basket peg is 

an option worth exploring. Overall, the extent 

of the role of the exchange rate in managing 

demand pressure has to depend on country-specific circumstances, including the potential for 

adjustments in fiscal policy and GCC countries’ 

commitment to peg their currencies to the U.S. 

dollar in the period leading up to monetary 

union.

Fiscal policy will be central in restraining 

demand pressures in Middle Eastern economies. Many non-oil economies have significantly reduced debt levels over the past decade. 

Nonetheless, domestic debt levels remain high 

by international standards, and thus both conjunctural and medium-term requirements point 

toward fiscal tightening. In oil exporters, the 

rise in oil prices has provided fiscal room for a 

buildup in government spending but has added 

to pressures on domestic resources. As a result 

of rapidly rising expenditures on wages and 

subsidies, for example, the oil price at which 

countries’ budgets balance over the medium 

term has been on the rise. The specific fiscal 

policy requirements vary, but generally would 

need to emphasize cutbacks in current spending 

while continuing to support critical infrastructure projects that alleviate internal supply-side 

bottlenecks. Furthermore, fiscal policy can 

also help address the social challenges posed 

by high energy and food prices. The development of more-targeted transfer programs would 

-5

0

5

10

15

20

25

30

2005 06 07 08 09 10

15

20

25

30

2005 06 07 08 09 0

2

4

6

8

10

Lebanon

Yemen, 

Rep. of

Egypt

Jordan

Syrian Arab Rep.

UAE

Bahrain

Iran, I.R. of

Saudi Arabia

Oman

Qatar

Libya

Kuwait

-60

-40

-20

0

20

40

-150

-100

-50

0

50

100

Figure 2.9. Middle East: Managing Inflation Pressures

 Sources: IMF, International Financial Statistics; and IMF staff calculations.

 Oil exporters include Bahrain, Islamic Republic of Iran, Kuwait, Libya, Oman, Qatar,

Saudi Arabia, Syrian Arab Republic, United Arab Emirates, and Republic of Yemen.

Real GDP growth is forecast to remain buoyant, mainly on account of activity in

non-oil sectors. Inflation pressure is rising, and tighter fiscal policies are needed to

keep inflation expectations well anchored, particularly in non-oil economies, which 

are also experiencing widening current account deficits.

Real GDP Growth of Oil Exporters

(percent)

Oil Production and Exports

(millions of barrels a day)

Consumer Price Inflation

(percent)

1

United Arab

Emirates (UAE)

Iran, I.R. of

Saudi Arabia

Regional

average Egypt

Oil production

Oil exports

Oil sector

Non-oil sector

2003 04 05 06 07 08 09

2007 Fiscal Balance

1

Overall (percent of GDP)

Non-oil (percent of non-oil GDP;

right scale)

Middle East: Overheating till A
CHAPTER2  Country and Regional Persp

8

help tackle poverty and, in oil exporters, share 

the gains from higher oil prices. As progress 

is made, high outlays on general oil and food 

subsidies––reaching about 10 percent of GDP in 

Egypt and Saudi Arabia, for example––need to 

be rolled back.

Over the medium term, stronger macroeconomic policy frameworks and continued structural reforms are important for strengthening 

the resilience of Middle Eastern economies to 

shocks and to providing for a young and rapidly 

expanding population. Fiscal policies could 

usefully be imparted with a longer-term orientation––for example, with the help of mediumterm budgetary frameworks––because of the 

need to lower debt in non-oil economies and 

in preparation for lower oil revenues in some 

oil exporters. The capacity to conduct independent monetary policy and to support financial 

markets needs to be enhanced. As domestic 

financial systems become more complex, rising 

regulatory and supervisory challenges will need 

to be addressed. Moreover, many countries stand 

to benefit from further improvements in their 

business environments, including by increasing 

investment in education and strengthening the 

social safety net.

References

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Beechey, Meredith J., Benjamin K. Johannsen, and 

Andrew T. Levin, 2008, “Are Long-Run Inflation 

Expectations Anchored More Firmly in the Euro 

Area than in the United States?” Finance and 

Economics Discussion Paper No. 23 (Washington: 

Federal Reserve Board).

Blanchard, Olivier, 2004, “The Economic Future of 

Europe,” NBER Working Paper No. 10310 (Cambridge, Massachusetts: National Bureau of Economic Research).

———, 2007, “Adjustment within the Euro: The Difficult Case of Portugal,” Portuguese Economic Journal, 

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———, and Justin Wolfers, 2000, “The Role of Shocks 

and Institutions in the Rise of European Unemployment: The Aggregate Evidence,” Economic Journal, 

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Britton, E., P.G. Fisher, and J.D. Whitley, 1998, “The 

Inflation Report Projections: Understanding the Fan 

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(February), pp. 30–37.

Bruno, Michael, and Jeffrey Sachs, 1985, Economics of 

Worldwide Stagflation (Cambridge, Massachusetts: 

Harvard University Press).

Carabenciov, I., I. Ermolaev, C. Freedman, M. Juillard, O. Kamenik, D. Korsunmov, and D. Laxton, 

forthcoming, “A Small Quarterly Multi-Country 

Projection Model with Financial-Real Linkages and 

Oil Prices,” IMF Working Paper (Washington: International Monetary Fund).

Davis, Morris A., Andreas Lehnert, and Robert F. Martin, 2007, “The Rent-Price Ratio for the Aggregate 

Stock of Owner-Occupied Housing” (unpublished, 

December). Available at www.morris.marginalq.

com.

Decressin, Jörg, and Emil Stavrev, forthcoming, 

“Building Economic Union: Productivity and Current Accounts in 10 Years of EMU,” Working Paper 

(Washington: International Monetary Fund).

Dew-Becker, Ian, and Robert J. Gordon, 2008, “The 

Role of Labour Market Changes in the Slowdown 

of European Productivity Growth,” CEPR Discussion Paper No. 6722 (London: Centre for Economic Policy Research).

European Commission, 2008, EMU@10: Successes and 

Challenges After 10 Years of Economic and Monetary 

Union (Brussels).

International Monetary Fund (IMF), 2008a, “Food 

and Fuel Prices—Recent Developments, Macroeconomic Impact, and Policy Responses” (Washington, June and September). Available at www.

imf.org/external/np/pp/eng/2008/063008.pdf 

and imf.org/external/np/pp/eng/2008/091908.

pdf.

———, 2008b, Global Financial Stability Report: Financial Stress and Deleveraging—Macrofinancial Implications and Policy (Washington, October).

———, 2008c, Japan: Selected Issues, IMF Country 

Report No. 08/254 (Washington).

Laxton, Douglas, D. Rose, and Alasdair Scott, forthcoming, “Developing a Structured Forecasting and 

Policy Analysis System,” IMF Working Paper (Washington: International Monetary Fund).

Lee, Jaewoo, Gian Maria Milesi-Ferretti, Jonathan 

David Ostry, Alessandro Prati, and Luca Antonio 
Ricci, 2008, Exchange Rate Assessments: CGER Methodologies, IMF Occasional Paper No. 261 (Washington: 

International Monetary Fund).

Pisani-Ferry, Jean, Philippe Aghion, Marek Belka, 

Jürgen von Hagen, Lars Heikensten, André Sapir, 

and Alan Ahearne, 2008, Coming of Age: Report on 

the Euro Area, Bruegel Blueprint No. 4 (Brussels: 

Bruegel).

Stavrev, Emil, 2007, “Growth and Inflation Dispersions in EMU: Reasons, the Role of Adjustment 

Channels, and Policy Implications,” IMF Working 

Paper 07/167 (Washington: International Monetary 

Fund).

Stock, James, and Mark Watson, 1989, “New Indexes 

of Coincident and Leading Economic Indicators,” 

in NBER Macroeconomic Annual: 1989 (Cambridge, 

Massachusetts: MIT Press), pp. 351–94.

———, 2003, “How Did Leading Indicator Forecasts 

Perform During the 2001 Recession?” Federal Reserve 

Bank of Richmond Economic Quarterly, Vol. 89 (Summer), pp. 71–90.

Timmermann, Allan, 2006, “An Evaluation of the World 

Economic Outlook Forecasts,” IMF Working Paper 

06/59 (Washington: International Monetary Fund).

van Ark, Bart, Mary O’Mahony, and Gerard Ypma, 2007, 

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Zarnowitz, Victor, 1992, Business Cycles: Theory, History, 

Indicators, and Forecasting (Chicago: University of 

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Referenc

c h a pte r 3

Note: The main authors of this chapter are Thomas 

Helbling, Douglas Laxton, Valerie Mercer-Blackman, and 

Irina Tytell, with contributions by Kevin Cheng and Kevin 

Clinton. To-Nhu Dao, Nese Erbil, Emory Oakes, and 

Ercument Tulun provided research support.

Is Inflatio

Commodity Prices

This chapter examines the current commodity price 

boom and evaluates the risks that the associated relative price adjustment could ratchet up inflation, as 

during the 1970s. Despite some recent easing in commodity prices, many of the forces underlying the boom 

are still in place, and prices will likely remain at high 

levels by historical standards. Continuing inflation 

risks arise from the large increases in commodity prices 

that have not fed fully through the supply chain. Most 

vulnerable to risks of a ratcheting up in inflation are 

those economies with a high likelihood of second-round 

effects—where commodities account for a large share 

of final expenditure and where monetary policy has 

only limited credibility, where there are price pressures 

from other sources such as overheating, and where the 

macroeconomic policy response to rising inflation has 

been inadequate.

Could the large commodity price surge 

of the past year and a half signal an 

end to a decade or so of price stability 

and herald a return to the type of high 

inflation seen during the 1970s? This question 

continues to be widely debated, even as commodity prices have begun to ease since mid-July. 

In many economies, headline inflation rates 

remain at levels last seen 10 to 15 years ago, and 

core inflation is still rising, particularly in emerging and developing economies.

Although there is broad agreement that 

inflation risks have increased across the globe, 

the causes for concern differ among various 

analysts and policymakers. For some, the main 

concern is that the commodity price increases 

have been so broad-based, large, and rapid that 

perceptions of rising inflation could spill over 

into expectations for further prices increases, 

demands for higher wages, and thereby an 

increase in underlying inflation (second-round 

effects). Others focus on the fact that, in a 

number of emerging and developing economies, 

the pressures from surging commodity prices 

come on top of price pressures from economic 

overheating. This combination exacerbates the 

risks of second-round effects. The problem is 

particularly acute in commodity exporters for 

which the commodity price surge has been 

expansionary.

A third cause for concern is that the commodity price surge might not, in fact, be a pure 

supply shock but may instead be the consequence of global excess demand resulting from 

overly expansionary macroeconomic policies. 

As during the 1970s, soaring commodity prices 

may be an early indication that capacity is being 

overestimated in some countries. By mistakenly 

reading the price surges as entirely the result 

of sector-specific constraints, policymakers may 

amplify inflation pressures.

The chapter analyzes the commodity price 

boom and the implications for inflation prospects and risks. Specifically, it seeks to answer 

the following questions.

• Why are commodity prices so high, and will 

they stay high?

• What has been the impact of rising commodity prices on headline and core inflation 

across the globe? Which countries have been 

most affected? What are the risks of significant second-round effects, and what factors 

affect these risks?

• What should be the appropriate monetary 

policy response to rising commodity prices? 

Under what circumstances can inappropriate monetary policies in individual countries 

carry significant global implications?

The chapter concludes that the current 

commodity price boom has, broadly speaking, 

reflected the interaction of strong demand, low 
Chapter3  Is Inflation Back? Commodity Prices and

inventory and spare capacity levels, slow supply 

expansion in key sectors, and adverse supply 

shocks. Prospects for a slowing of global growth 

in 2008–09—partly in response to high commodity prices—and the resolution of weatherrelated supply constraints for key food crops 

have recently caused commodity prices to ease. 

However, some of the underlying forces behind 

the commodity price boom are still in place, 

notably strong growth in large emerging economies, low inventories, and supply constraints in 

key sectors. Barring an intense global downturn, 

these factors will likely limit the extent of further easing from recent price peaks and provide 

for continued price volatility.

Inflation risks will likely remain elevated 

for some time, even if commodity prices exert 

less direct inflation pressure than during the 

past year and a half, because the adjustment 

to the large increase in relative commodity prices is still in train. There have already 

been second-round effects in some economies, 

and some others remain at risk. Emerging 

and developing economies are generally more 

vulnerable to the main risk factors, including 

having a large share of commodities in final 

expenditure and having less-credible monetary 

management. Moreover, higher international 

prices, in particular for fuels, have not yet been 

fully passed through to domestic prices in many 

economies.

Notwithstanding the recent easing in commodity prices, a determined monetary policy 

response remains important in economies 

where inflation pressures were already elevated 

before the commodity price surge and where 

risks of second-round effects are high. Delaying the monetary policy response could lower 

the credibility of policymakers and thereby 

significantly worsen the inflation-output tradeoff. Other macroeconomic policies should 

be supportive, particularly if exchange-raterelated constraints limit the scope for monetary 

tightening.

The chapter is organized as follows. The next 

section examines the origins of and prospects 

for high commodity prices. The following section looks at the relationship between commodity price shocks and inflation at the country 

level, examining whether sustained increases 

in food and energy prices could reverse the 

recent “great moderation” in inflation across the 

globe. The analysis then focuses on the monetary policy implications of the commodity price 

shocks and the implications for global inflation 

dynamics. The summary and conclusions section 

also draws some policy implications.

Surging Commodity Prices: Origins an

Prospects

Commodity prices surged during the past year 

and a half (Figure 3.1, top and middle panels). The oil price more than doubled between 

December 2006 and mid-July 2008, although 

some of these gains have been reversed since, 

and food prices rose by more than 50 percent 

during this period. These surges came on top 

of large price increases during 2003–06. Overall, cumulative commodity price increases since 

2003 are broadly similar in magnitude to those 

recorded during the commodity price boom 

of the early 1970s (1971–74), the last major 

boom. More recent periods of sustained global 

growth—during the 1980s and the 1990s—were 

not accompanied by broad-based commodity 

price booms involving fuel and food commodities. This section compares the current commodity price boom to the early 1970s boom 

and then discusses current oil and food price 

developments and prospects.1

The Current Commodity Price Boom Compared

with the 1970s

Three common factors seem to underlie both 

booms. First, the origins can be traced to strong 

global growth (Figure 3.1, bottom panel).2

Prices for many commodities respond strongly 

1Appendix 3.1 provides a more detailed overview of 

recent commodity market developments and prospects. 2Among others, Radetski (2006) noted that the beginning of each significant, broad commodity price boom 

during the postwar period (1950–52, 1972–74, and 2003 
to changes in global growth or industrial activity. This reflects the role of commodities in 

global industrial activity—especially intermediate inputs in manufacturing such as metals and 

agricultural raw materials, but also oil—and, 

for other commodities such as food, the role of 

income as a determinant of demand.

The growth acceleration in emerging and 

developing economies during the past few 

years—driven by industrialization takeoff and 

strong per capita income increases from a 

low base—has likely altered the relationship 

between global activity and commodity prices 

during the current boom. The rotation in 

global growth toward these economies has catalyzed commodity demand because their growth 

has been relatively more commodity-intensive 

(Figure 3.2, top panel). The slowdown in the 

advanced economies has so far had less of an 

impact on commodity prices than during earlier downturns in these economies. That said, 

turning points in price cycles have historically 

been broadly synchronized with those in global 

economic activity.3

A second factor common to the 1970s boom 

and the current boom is that both started with 

lower-than-usual inventory and spare capacity 

levels (Figure 3.2, middle and bottom panels). 

In both booms, this lack of buffers amplified the 

price impetus from the pickup in commodity 

demand resulting from strong global growth.4

The reasons for low inventory and spare capacity 

levels in the current boom vary across commodity sectors, but in general, there was underinvestment and slow supply growth during the late 

until now) coincided with an acceleration in economic 

growth and industrial production. 3See Box 5.2 in the April 2008 World Economic Outlook. 4The presence of such interaction between strong 

demand and low initial buffer levels is likely one of the 

factors that turn a cyclical price upswing into a price 

boom, because differences in global growth between 

expansions are too small to plausibly explain the large 

differences in commodity prices observed during global 

upswings. See, among others, Deaton and Laroque 

(1992) and Radetski (2006) on the mechanics of commodity price cycles. 

1960 65 70 75 80 85 90 95 2000 05 40

60

80

100

120

140

160

180

200

220

1960 65 70 75 80 85 90 95 2000 05 0

50

100

150

200

250

300

350

400

450

0

100

200

300

400

500

600

700

-15

-10

-5

0

5

10

15

Metal and Agricultural Raw Materials in Real Terms 

(1990 = 100)

 Source: IMF staff calculations.

 Deflated by U.S. consumer price index (CPI).

 Shading denotes periods of global recession (identified by a monthly index of global

industrial production). 

 

Figure 3.1. Commodity Prices in Historical Context

Metals

Agricultural

raw materials

Food and Oil Prices in Real Terms

(1990 = 100)

Food

Oil

Commodity Prices and Industrial Production

Dec.

76

1,2

1,2

0

100

200

300

400

500

600

700

-15

-10

-5

0

5

10

15 Current Boom (Jan. 2003 = 100)

1970s

(Jan. 1970 = 100)

1970 72 74 2003 05 07 Aug.

08

Oil 

1

2

The current commodity price boom shares many common features with the most

recent major commodity price boom, during the early 1970s, including sharp 

increases in oil and food prices and an environment of strong global growth .

08:

Q3

08:

Q2

Food

Metals

Global industrial production

(year-over-year change, right scale)

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

1990s, following two decades of low commodity 

prices.

A third factor common to both booms has 

been that supply constraints put upward pressure on prices. The abrupt rise in oil prices in 

December 1973, together with the temporary 

reduction in oil production during the embargo 

by the Organization of Petroleum Exporting 

Countries (OPEC), has become the textbook 

case of a commodity supply shock. In the current boom, weather-related crop failures, for 

wheat in particular, have boosted food prices. 

Such shortfalls also propelled food prices during 

the earlier boom (Figure 3.3).

In the current boom, the supply-side constraints in commodity sectors other than 

agriculture were not the result of sharp, temporary supply reductions, but instead reflected 

protracted, inelastic supply responses in the face 

of higher demand and rising prices. In the oil 

market and, to a lesser extent, in some metals 

markets, “time-to-build” lags appear to have 

increased during the current cycle, as discussed 

below. In the face of rapidly growing demand, 

this slow capacity expansion has led to a perpetuation of low inventory and spare capacity levels, which have sustained the pressure on prices. 

This feature of the current boom has given rise 

to the notion of a “supercycle” in commodity 

prices—a period with secular trend increases 

in commodity prices because of the need for a 

substantial buildup in capacity.5

Speculation—the purchase of commodities 

intended for resale at a higher price rather than 

for commercial use—has been widely seen as 

a factor driving up commodity prices during 

both booms.6 In the 1970s, speculative inventory holdings appear to have risen for some 

5See Cuddington and Jerrett (2008) for a recent 

analysis. More generally, lags in the response of supply (as 

well as demand) to unexpected price changes can lead to 

price cycles (see, for example, Krautkraemer, 1998), with 

the length and amplitude of a cycle depending on differences between long- and short-term price elasticities and 

the lag structure as well as the magnitude of the initial 

unexpected change. 6See Harrison and Kreps (1978) or Feiger (1976) on 

definitions of speculation.

1970 75 80 85 90 95 2000 05

0

40

80

120

160

200

0

40

80

120

160

200

1970 75 80 85 90 95 2000 05

40

45

50

55

60

0

3

6

9

12

 Sources: British Petroleum Statistical Review of World Energy (2008); International 

Energy Agency; U.S. Department of Agriculture; U.S. Department of Energy; and IMF staff 

calculations.

 OPEC is the Organization of Petroleum Exporting Countries.

 Primary energy in millions of barrels of oil equivalent per GDP (expressed in billions of

2005 U.S. dollars).

 Grains include corn, rice, and wheat; metals include copper, lead, and zinc.

 Shading denotes periods of global recession (identified by a monthly index of global

industrial production). 

Figure 3.2. Marginal Change in Energy Intensity,

Commodity Inventories, and OPEC Oil Spare Capacity

1

2

Spare 

capacity

(millions of

barrels a day,

right scale)

OECD

Inventory 

cover (days,

left scale)

2003 04 05 06 07 -0.2

0.0

0.2

0.4

0.6

0.8

1.0

Change in Energy Use as a Ratio of GDP Growth 1.2

Inventory of Major Grains and Metals

Metal stocks (tens 

of thousands of metric 

tons, right scale)

Grain forward 

cover (days,

left scale)

3,4

OPEC Oil Spare Capacity and OECD Oil Inventory Cover

Advanced economies

Emerging and developing economies

Advanced economies (10-year average)

Emerging and developing economies (10-year average)

2

4

3

Strong commodity-intensive growth in emerging and developing economies was a 

major factor behind declining inventory levels and low OPEC oil spare capacity.

1

4
commodities, notably metals (see, for example, 

Cooper and others, 1975). In the current boom, 

however, inventory holdings of key commodities have generally remained low or have even 

declined, suggesting that prices have not been 

driven up by a speculative shift toward holdings 

of real assets, as in the earlier boom. Despite 

recent financial innovation in commodity markets, such as indexing, which has allowed investors to benefit from rising commodity prices 

without having to maintain physical inventory 

holdings, there is little discernible evidence that 

the buildup of related financial positions has 

systematically driven either prices for individual 

commodities or price formation more broadly 

(Box 3.1).

Nevertheless, financial factors and sentiment do play a role in commodity price formation. Financial variables such as interest rates 

affect commodity prices through their effects 

on physical demand and supply. Indeed, the 

recent decline in U.S. policy interest rates likely 

spurred commodity demand temporarily, as 

discussed below. Many commodity prices have 

traditionally been more flexible than either 

wages or prices for other goods, and therefore 

they tend to respond faster to such monetary 

policy impulses, with some scope for short-term 

price overshooting.7 Moreover, because most 

commodities are storable, they are real assets, 

and their prices are thus affected not only by 

current market conditions but also by future 

expectations. In the short term, such expectations can be influenced by sentiment and investor behavior, which can amplify short-term price 

fluctuations, as in other asset markets.

Whether the current commodity price boom 

will continue depends on the extent to which 

the current alignment prevails: strong demand, 

low inventory and spare capacity levels, and 

supply constraints. There are indications that 

some elements of this constellation have started 

7See Bordo (1980) on the commodity price response to 

monetary policy impulses, and Frankel (1986, 2006) and 

Akram (2008) on the effects of real interest rate changes 

on commodity prices.

-100

-50

0

50

100

150

200

50

60

70

80

90

-6

-3

0

3

6

9

12

-15

-10

-5

0

5

10

15

20

-15

-10

-5

0

5

10

15

20

-100

-50

0

50

100

150

200

50

60

70

80

90

 Sources: British Petroleum Statistical Review of World Energy (2008); U.S. Department of

Agriculture; U.S. Department of Energy; and IMF staff estimates.

 Organization of Petroleum Exporting Countries.

 Grains include corn, rice, and wheat.

Figure 3.3. Grain and Oil Demand, Production, and 

Inventories in Comparison

Strong demand and slow capacity expansion in key sectors, as well as supply

disruptions for major crops, are aspects of both the current and the 1970s

commodity price booms.

1970s Current Boom

World capacity

Global Oil Demand Growth

(annual change, percent)

Current Boom

-6

-3

0

3

6

9

12 1970s

1970 72 74 76 2003 05 07 09

07

Global Oil Supply Growth

(annual change, percent)

1970 72 74 76 2003 05 09

1970 72 74 76

Global Grain Production and Inventories

(annual change)

1970s Current Boom

2003 05 07

2

1

1

2

OPEC Non-OPEC

Industrial economies Emerging and developing economies

Production (millions of metric

tons, left scale)

Inventories (cover days, right scale)

09

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

Commodities have become an alternative 

asset class in recent years, with rapid growth 

in both open positions at futures exchanges 

and investments in commodity-indexed assets.1

This financialization of commodity markets 

is often thought to have affected commodity 

price behavior, although views about the extent 

of influence vary widely among analysts. One 

perspective is that financialization of commodities is largely beneficial and improves market 

efficiency and price discovery. Another view is 

that recent commodity price surges are largely 

driven by speculators and herd behavior among 

investors looking for alternative asset classes. 

This box analyzes the potential impact of 

investment flows on commodity price behavior. 

Specifically, it considers whether the evidence 

supports the notion that speculation in commodity-related financial assets has driven the 

recent commodity price booms. To shed further light, it also considers how other aspects 

of price formation, such as price volatility and 

comovements, have been affected by increased 

financial flows. 

How do financial factors affect price formation? Financial markets can affect commodity prices through two channels. First, certain 

financial variables—such as exchange rates and 

interest rates—can directly affect commodity 

supply and demand. For example, a weakening 

U.S. dollar and lower interest rates could raise 

demand and reduce production of commodities, thereby exerting price pressures.2 Second, 

transactions by financial investors, including 

speculators, might influence price behavior. A 

prominent controversy in this area relates to 

The main author of this box is Kevin Cheng.

1For example, the open interest of crude oil 

futures traded in the New York Mercantile Exchange 

(NYMEX) has increased by 155 percent during 

2003–08, with corresponding figures increasing by 

63 percent for gold. Investment in commodity-related 

assets has increased from below $10 billion in 1997 

to about $230 billion in the second quarter of 2008 

(Barclays Capital, 2008). 2See, for example, Box 1.4 of the April 2008 World 

Economic Outlook.

whether the recent commodity price boom has 

been underpinned by the rapid rise in investment in commodity-indexed assets by investors 

seeking to diversify their portfolios.

Box 3.1. Does Financial Investment Affect Commodity Price Behavior?

-40

-20

0

20

40

60

0

100

200

300

400

500

-40

-20

0

20

40

60

0

1

2

3

4

5

-150

0

150

300

450

0

2

4

6

8

-100

-50

0

50

100

150

0

30

60

90

120

150

-80

-60

-40

-20

0

20

40

60

0

2

4

6

8

10

12

14

 Sources: Bloomberg Financial Markets; U.S. Commodities 

Futures Trading Comission; and IMF staff calculations.

 WTI is West Texas Intermediate crude oil.

Net Noncommercial Futures Positions and 

Prices

(Four-week moving averages)

Heating Oil

(U.S. cents a gallon)

WTI Crude Oil

(U.S. dollars a barrel)

Copper

(U.S. dollars a pound)

-100

-50

0

50

100

150

200

250

300

0

300

600

900

Gold 1200

(U.S. dollars a 

troy ounce)

Corn

(U.S. dollars a 

bushel) 

Wheat

(U.S. dollars a 

bushel)

Net noncommercial long positions 

(thousand contracts, left scale)

Prices (right scale)

1995 2000 05 Aug.

08

1995 2000 05 Aug.

08

1995 2000 05 Aug.

08

1995 2000 05 Aug.

08

1995 2000 05 Aug.

08

1995 2000 05 Aug.

08

Box 3.1 figure 1

1

1
Because the fair value of commodities is difficult to determine, the issue of whether such 

behavior has driven prices away from fundamentals has been addressed through indirect 

approaches. One approach is to examine 

whether changes in commodity financial positions lead to commodity price changes using 

time-series analysis (“Granger causality tests”). 

Many recent studies in this vein, including in 

the October 2008 Global Financial Stability Report 

(IMF, 2008d), have not found evidence of systematic causality between positions and prices in 

either direction.3 Indeed, the direction of financial flows is often inconsistent with the direction 

of price movements. For example, while crude 

oil prices rose sharply in May and June 2008, 

net speculative positions declined (first figure). 

A second approach is to examine whether 

recent inventory behavior is consistent with the 

hypothesis that the recent price trends have 

been mostly driven by speculation. The basic 

intuition is as follows: For speculation to have a 

persistent effect on commodity prices, it must 

be accompanied by increasing physical hoarding of the commodities to keep spot markets in 

balance because consumption would decline 

at the higher prices (see Krugman, 2008).

Available data, however, suggest that, although 

inventories for some commodities increased 

somewhat in recent years, inventories for other 

commodities that had significant price appreciation declined or remained broadly stable 

(second figure, upper panel). In particular, 

although crude oil prices almost doubled 

during 2007–08, crude oil inventories among 

Organization for Economic Cooperation and 

Development (OECD) member countries 

remained flat during 2008. Overall, therefore, 

there is little evidence of a systematic inventory 

hoarding of commodities, although a caveat is 

that data on commodity inventories are poor 

and lack global coverage.

3See also Box 5.1 of the September 2006 World 

Economic Outlook or the Interim Report on Crude Oil by 

the Interagency Task Force on Commodity Markets 

(ITC, 2008). 

A third approach to assessing the impact 

of financial investment is to gauge the crosssectional relationship between price formation and investor activities before and after 

the financialization of commodities. To shed 

further light, this box examines the relationship between financialization and price levels 

across markets. It also extends the analysis to 

two other aspects of price formation:

• Volatility: The impact of speculators on 

price volatility has long been a source of 

controversy among economists. Some noteworthy economists—including Adam Smith, 

John Stuart Mill, and Milton Friedman—have 

argued that speculators provide liquidity, 

facilitate price discovery, and improve intertemporal allocation of resources by buying low and 

selling high, thereby stabilizing prices. Others 

contend that market participants can often be 

“irrational,” trading based on emotion, heuristics, and herd mentality, thereby increasing 

market volatility.

• Price comovement: Another hypothesis is that 

enhanced financialization of commodities can 

raise the degree to which commodity prices 

move together. The reason is that increased 

financial flows can amplify exposure of commodities to some common financial shocks, 

such as exchange rate and interest rate movements. Moreover, investors may lack familiarity 

with individual commodities, thereby leading them to allocate funds to commodities as 

a whole (the habitat/category theory). For 

example, investors can invest in commodities 

by buying a commodity index, which allocates 

funds across various commodities according to 

some specified weights, rather than by investing in specific commodities about which they 

may lack knowledge. Moreover, financialization 

of commodities can increase the correlation—

either positive or negative—between commodity prices and other asset prices, such as equity 

prices, purely on account of overall financial 

market conditions. 

To examine the possibility of a price impact, 

properties of weekly commodity price returns 

(weekly changes of price in logarithms) of 

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

9

50 commodities are examined before and after 

the takeoff in commodity investment. Because 

the recent commodity price and investment 

booms began roughly in 2003, the focus period 

is January 2003–June 2008, with the control 

period being July 1997–December 2002. To 

distinguish the extent of financialization, commodities are divided into two groups:

• Group A: These are commodities heavily 

traded in the financial markets. Specifically, 

a commodity is included in Group A if it is 

included in one of the four major commodity 

Box 3.1 (continued)

Aluminum Copper Lead Tin Zinc Gold Silver OECD oil Wheat Corn Soybeans Rice 0.0

0.4

0.8

1.2

1.6

2.0

0.0

0.4

0.8

1.2

1.6

2.0

 Sources: Bloomberg Financial Markets; International Energy Agency; London Metal Exchange; U.S. Department of Agriculture; and IMF 

staff calculations.

 An F-test at the 5 percent significance level indicates that the standard deviations of the two periods are statistically different, except for rice.

Inventories, Price Changes, and Volatility

1

Box 3.1 Figure 2

Total

Gold

WTI

Aluminum

Copper

Corn

Wheat Total

Barley

Coal

Iron

Palm oil

Rice

Tin S&P 500

-150

-100

-50

0

50

100

150

200

250

-150

-100

-50

0

50

100

150

200

250

Inventories

(2006 = 1) 2007

2008

Cumulative Price Appreciation of Commodities

(percent)

July 1997–January 2002 January 2003–June 2008

Group A Group B Equity

Total

Gold

WTI

Aluminum

Copper

Corn

Wheat Total

Barley

Coal

Iron

Palm oil

Rice

Tin S&P 500

0

1

2

3

4

5

6

0

1

2

3

4

5

6

Volatility of Commodity Price Returns

(standard deviations in percent)

Group A Group B Equity

1
indices.4 A total Group A price index is computed based on the average weights of the 

underlying four commodity indices. Also, six 

individual commodities within the group are 

examined in greater detail: gold, WTI crude 

oil, aluminum, copper, corn, and wheat.

• Group B: This includes all the commodities 

in the IMF commodity index that are not 

included in Group A.5 In addition to the total 

Group B price index calculation based on 

the IMF commodity weights, six of these are 

examined in greater detail: barley, coal, iron 

ore, palm oil, rice, and tin. 

Price Level

Prices of Group A commodities rose by less 

than 6 percent between 1997 and 2002, but 

they increased by about 120 percent during 

2003–08. Group B prices fell by about 12 percent during the first period, but rose by almost 

75 percent during the second period (first 

figure, middle panel). Indeed, many commodities without significant futures markets—such 

as iron ore and rice—have experienced more 

price appreciation than those with sizable 

futures markets, such as gold and crude oil. 

Furthermore, a simple cross-sectional regression indicates an almost flat and slightly negative relationship between price changes and 

changes in the speculative net long positions6

during 2003 (third figure, upper panel).

4The four commodity indices examined are the S&P 

Goldman Sachs Commodity index, Deutsche Bank 

Commodity Index, Dow Jones–AIG Commodity Index, 

and UBS Bloomberg Constant Maturity Commodity 

Index. Commodities included in Group A are Brent 

crude, natural gas, West Texas Intermediate (WTI) 

crude, gas oil, unleaded gasoline, heating oil, aluminum, copper, gold, lead, nickel, silver, zinc, cocoa, coffee, corn, cotton, lean hogs, beef, orange juice, soybean 

oil, soybeans, soybean meal, sugar, and wheat. 5These include bananas, barley, coal, fish, fish meal, 

groundnuts, hard logs, hard sawed wood, hides, iron 

ore, lamb, olive oil, palm oil, poultry, rapeseed oil, 

rice, rubber, shrimp, soft logs, soft sawed wood, sunflower oil, tea, tin, uranium, and wool. 6Following the classification of futures positions by 

type of trader by the U.S. Commodity Futures Trading 

Price Volatility

To gauge if greater financial investment 

has destabilized markets by increasing price 

volatility, measures of price volatility (standard 

deviations) were computed for each commodity group before and after 2003 (second 

figure, lower panel). The results are mixed. 

First, price volatilities for most commodities 

in Group A were higher after 2003, with the 

notable exception of crude oil, which has 

significantly declined despite being heavily 

traded. Second, volatilities for most commodities in Group B have also risen, despite 

the fact that they are not heavily financially 

traded, which suggests that the volatility 

increases in Group A may reflect factors other 

than the financialization of commodities. 

Furthermore, a simple cross-sectional equation 

is estimated by regressing return volatilities 

on changes in open interests of commodity 

futures during 2003–08.7 The results indicate a positive but weak relationship between 

return volatilities and the extent of financialization, suggesting that price volatility may be 

better linked to other variables, such as market 

tightness, stock levels, or geopolitical risks8

(third figure, lower panel).

Price Comovement

To gauge if there has been an increase 

in comovement of commodity prices and 

stock prices, weekly returns of selected commodities were regressed on a constant and an 

“explanatory” variable—including a return of 

another commodity within the same group, 

Commission (CFTC), net noncommercial futures 

positions are used as a measure of speculative positions in commodity futures markets. These positions 

are defined as the net of long and short positions of 

noncommercial traders. 7An open interest—defined as the total number of 

options and/or futures contracts that are not closed 

or delivered on a particular day—is used as a proxy 

for the degree of financialization. 8Haigh, Hranaiova, and Overdahl (2007) also find 

no evidence that increased commodity hedge fund 

trading has raised price volatility.

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

the return of the total group index (excluding 

the individual commodity under investigation), 

or the return of the S&P 500 stock market 

index. The extent of comovement is measured 

by the coefficient of determination or R2. 

Intuitively, if comovements were pri­maril

driven by commodity investment, especially 

indexing, the R2 for Group A commodities should be higher than for Group B and 

should increase after 2003, as financialization 

accelerated.9

The results do suggest increasing price 

comovements among some of the more financialized commodities (table). Overall, Group A 

commodities demonstrate a higher comovement than those in Group B both before and 

after 2003. Moreover, on average, comovement 

among Group A commodities has increased to 

a greater extent than among Group B commodities. Most notably, the explanatory power 

of gold returns for other Group A returns has 

increased significantly, rising from about 2 percent during 1997–2002 to over 20 percent during 2003–08, suggesting that gold increasingly 

comoves with other commodities in Group A. 

However, the explanatory power of crude oil 

for other Group A commodities has declined 

significantly since 2003.10 Finally, commodity 

returns in both groups do not seem closely 

related to stock returns in either period.11

In summary, although financialization may 

have led to increases in comovement between 

some commodities, particularly with respect 

to gold, no apparent systematic connection is 

found to either price volatility or price changes. 

These findings are consistent with recent studies in the area by the CFTC and others. Thus, 

there is little evidence to suggest that trading 

in futures markets has driven the price run-up 

or has destabilized the commodity markets during the first half of 2008.

9As a caveat, given the interlinkages among commodities (such as production-consumption substitution), it is possible that financialization could affect 

Group B indirectly through Group A, even though 

Group B commodities are not heavily traded. See, 

for example, Adrangi and Chatrath (2006) for more 

details. 10Using monthly data, however, WTI crude oil has a 

high explanatory power—over 30 percent—for other 

commodity returns in Group A, reflecting energy cost 

pass-through over a longer horizon. 11Büyüks ̧ahin, Haigh, and Robe (2008) also find 

that the relation between the returns on investable 

commodity and U.S. equity indices has not changed 

significantly in the past 15 years.

Box 3.1 (concluded)

0

30

60

90

120

150

180

Price Changes and Changes in Net Long 

Speculative Positions, 2003–08

(percent)

Financialization, Price Changes, and Volatility

 Sources: Bloomberg Financial Markets; U.S. Commodities 

Futures Trading Commission; and IMF staff calculations.

 West Texas Intermediate crude oil.

 Sample size may vary among commodities because data on

open interests for some commodities are not available before

2003. Commodities with no futures markets are shown as zero

changes in open interests.

1

Box 3.1 Figure 3

0

2

4

6

8

Volatility and Changes in Open Interest, 2003–08 10

(percent)

2

-1000 -500 0 500 1000 1500 2000

-100 0 100 200 300 400 500

Price changes Volatility (standard deviation)

Net long position changes

WTI

Wheat Gold

Corn y = –0.0083x + 100.8

R-squared = 0.0132

y = 0.0024x + 3.276

R-squared = 0.027

Wheat

Corn

Silver

Gold

Rice WTI

Open-interest changes

1

2
to unwind. Prospects for slowing global growth 

in 2008–09, the resolution of weather-related 

supply constraints for key food crops, and 

increased oil supply have led to some easing 

of commodity prices since mid-July. However, 

inventories and spare capacity both remain 

low, growth momentum in the large emerging 

economies remains strong, and some supply 

constraints still exist, which, barring a more 

severe global downturn, will likely limit the 

extent of further easing and provide for continued price volatility.

Within this general outlook, prospects vary 

for individual commodities. Fundamentally, 

these cross-commodity variations reflect different characteristics (such as a commodity’s 

storability or its relative position in the stages of 

production) and the fact that supply problems 

and inventory conditions tend to be commodityspecific. The role of common factors in shortterm commodity price fluctuations is generally 

limited even during booms, as reflected in the 

wide differences in the magnitude and timing of 

price increases (Table 3.1). Against this backdrop, the chapter now turns to developments 

and prospects for the two commodity groups 

that are most relevant for the global inflation 

outlook: oil and food.

Will Slowing Growth Ease Oil Pri

By mid-July 2008, oil prices had risen well 

above previous highs, to some 30 percent 

above the previous December 1979 record in 

Comovement among Returns (R-squares in percent)1

Group A

July 4, 1997–December 27, 2002 January 3, 2003–June 27, 20083

Gold WTI Aluminum Copper Corn Wheat Group A Gold WTI Aluminum Copper Corn Wheat Group A

WTI crude 0.9 6.6*

Aluminum 0.8 2.1 23.4* 4.8

Copper 2.9 1.6 43.4 19.5* 3.5 34.6

Corn 1.1 0.1 0.2 0.0 3.4 0.1 1.6* 1.2

Wheat 0.5 0.5 0.3 0.5 40.6 4.7 0.5 1.0 1.9 23.1

Group A2 2.3 7.2 0.5 40.1 2.5 36.5 21.0* 0.9* 2.5* 28.1* 0.0* 17.6

S&P 500 0.0 0.6 0.2 0.2 0.0 0.3 0.5 0.1 0.1 0.6 0.8 0.7 0.1 0.0

Average 6.7 7.2

Group B

July 4, 1997–December 27, 2002 January 3, 2003–June 27, 2008

Barley Coal Iron ore Palm oil Rice Tin Group B Barley Coal Iron ore Palm oil Rice Tin Group B

Coal 0.0 0.0

Iron ore 0.4 0.0 1.3 0.8

Palm oil 2.0 0.2 0.1 0.2 0.0 0.2

Rice 0.0 0.1 0.0 0.0 0.0 0.2 0.1 0.4

Tin 0.1 0.2 0.1 0.1 0.4 0.5 0.4 0.3 0.0 0.2

Group B2 0.4 56.3 0.0 17.4 0.1 0.4 0.6 73.1* 0.1 0.0* 1.1 0.2

S&P 500 0.9 1.0 0.3 0.1 0.4 0.0 1.4 0.7 2.7 0.0 0.4 0.1 0.8 2.3

Average 2.9 3.1

Sources: Bloomberg Financial Markets; and IMF staff calculations.

1A higher R-square indicates higher comovement. In bivariate regressions, R-squares are invariant to the choice of left- and righthand-side variables. For example, regressing gold on WTI yields the same R-square as regressing WTI on gold. 2Excluding the commodity of the column under investigation.

3An asterisk indicates that there is a structural break between the two periods according to the Chow test at the 5 percent 

significance level.

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

real terms, but have fallen since then. The 

rise in oil prices since early 2007 mirrored a 

noticeable tightening in market balances in 

a context of low buffer levels (low inventories, little spare capacity). OPEC production 

through most of 2007 was below 2006 levels, 

and non-OPEC production declined in the 

second half of 2007, while global oil demand 

continued to expand at a broadly unchanged 

pace. When oil market capacity is so tightly 

stretched, relatively small unexpected shifts in 

global supply (or demand) can have large price 

effects, given the generally very low short-term 

price responsiveness of oil demand.8 During 

8Short-term price elasticities of oil demand are generally believed to be low. The U.S. Department of Energy 

(Costello, 2006) considers them to be in the range of 

0.01 to 0.04 (absolute values)—whereas income elasticities are much higher. Similarly, Hamilton (2008) reports 

elasticities of 0.03 to 0.07 (absolute values), and values 

ranging from 0.03 to 0.08 were reported in the September 2005 World Economic Outlook. As a result, income 

effects have dominated price effects in oil demand. In 

a simple demand model with exogenous supply that 

ignores nonlinearities from low inventories and intertemporal considerations, such price elasticities imply that 

a reduction in oil production of 0.5 million barrels a 

day—roughly the amount of the reduction in non-OPEC 

supply during the second half of 2007—should lead to 

prices that are 10–60 percent higher (the calculations 

are based on 2007 production data). If longer-term price 

elasticities are higher than short-term ones, prices will 

overshoot their long-term increase in response to a supply reduction. 

the past year and a half, the price impact of 

shifts in global demand has been reinforced by 

the decreased pass-through to domestic prices 

in emerging and developing economies, which 

further reduced the already low short-term 

price elasticity of global oil demand.9 Other 

contributing factors include rising risks of supply disruptions in some major producers and 

geopolitical concerns.

Growing expectations that medium-term 

oi-market conditions will remain tight were 

likely an important factor in price increases 

during the past year. The pace of capacity 

expansion has been slow and has consistently 

fallen short of expectations in recent years, 

particularly outside OPEC (Figure 3.4, top 

panel).10 A broad consensus has emerged that 

the buildup of production capacity needed to 

accommodate the anticipated robust expansion of emerging and developing economies 

will remain sluggish because of cyclical, 

technological and geological, and policy 

9Oil consumers in many countries have been increasingly sheltered from rising world market prices. In a 

sample of 43 emerging and developing economies, fewer 

than half allowed full pass-through during 2007 (compared to three-quarters in 2006). See IMF (2008b). 10Capacity constraints in the downstream oil sectors, 

notably in refining, have also contributed to rising oil 

prices. However, the longer-term supply issues discussed 

here primarily relate to upstream investment, which is 

where the long-term constraints are most severe.

Table 3.1. Contributions of Common Factors to Commodity Price Fluctuations

(In percent)

1970–2008 

(June)

Booms

“Great Moderation Period”

1984–2008 (June)

“Period with No Oil Shocks”2

1972–74 1992–2002 (June)

2003–08 

(June)

Crude oil 1.6 1.9 3.6 2.6 3.2

Metals 37.9 29.6 34.5 27.7 63.7

Agricultural raw materials 23.9 1.3 21.8 13.0 12.5

Food 16.7 1.2 23.9 24.7 15.2

Meat 8.3 0.5 26.7 9.7 6.8

Cereals 18.9 1.7 11.9 22.8 12.4

Vegetable oils and protein meals 24.3 0.7 28.5 42.6 25.9

Other foods 7.8 3.0 24.5 6.9 5.2

Beverages 11.2 2.2 28.0 7.5 2.4

Sources: IMF, Primary Commodities database; and IMF staff calculations.

1Contributions are based on the first principal component of logarithmic changes of prices of 38 primary commodities in team terms 

(corrected for serial correlation and standardized). 2See Kilian (2008).
constraints.11 In effect, the time-to-build lags 

noted above have lengthened, although the 

scope to eventually achieve such a buildup 

remains in place—as reflected in the broadly 

constant ratio of proven oil reserves to current 

production, a measure of the long-term scarcity 

of oil (Figure 3.4, middle panel). Even so, 

future capacity will be built at a much higher 

cost than in the past, because of the sharply 

rising extraction costs in marginal fields, which 

have a substantial permanent component 

(Figure 3.4, bottom panel).

Even relatively small downward revisions in 

the expected path of future supply expansion 

caused by increased pessimism can imply large 

increases in expected future prices, given the 

relatively low price elasticity of oil demand 

noted above.12 Such expectations of higher 

prices must be reflected in higher spot prices 

today. Otherwise, producers would have incentives to leave oil reserves in the ground, and 

traders would have incentives to accumulate 

inventories, which could be sold later at higher 

prices. It is for this reason that some observers 

have referred to recent oil price increases as an 

“expected supply shock,” that is, a response to 

tighter medium-term market conditions (see, for 

example, Clarida, 2007).

What about the role of financial factors? 

Speculation and commodity financial investment 

are frequently mentioned as factors in recent oil 

price increases. However, there is little clear evidence that these factors have any systematic price 

impact. Both investment inflows into energy and 

oil funds and the net futures market positions of 

noncommercial investors, for example, peaked 

in late 2007 and have since declined. Nevertheless, shifts in sentiment may well have some 

impact on short-term price dynamics, particularly 

given the lack of timely information about global 

market conditions. In addition, recent financial 

11See Box 1.5 in the April 2008 World Economic Outlook. 12A more inelastic medium-term supply response 

because of longer time-to-build lags also implies that 

upward revisions to the expected path of global demand 

should have a larger impact on current spot prices.

20

25

30

35

40

45

50

55

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Revision to Forecast of Non-OPEC Supply Growth 1.8

(millions of barrels a day)

 Sources: Bloomberg Financial Markets; British Petroleum Statistical Review of World 

Energy (2008); Goldman Sachs Global, Inc. Equity Group; International Energy Agency; 

U.S. Bureau of Labor Statistics; U.S. Department of Energy; and IMF staff calculations.

 The forecast refers to a simple average from the Organization of Petroleum Exporting 

Countries (OPEC), the International Energy Agency, and the U.S. Energy Information 

Administration at the time of the forecast. 

 Marginal cost is defined as the average of the highest-cost (or bottom quartile) 

producers, based on a survey of listed oil companies. The producer price index (PPI) for oil 

inputs refers to the unweighted average of drilling oil and gas wells, oil and gas operation 

support activities, and oil field machinery and equipment.

Figure 3.4. Oil Supply Developments

2007

1

1

2

Oil production, which has consistently fallen short of expectations over the past four

years amid high production costs, has fostered concerns that tight market conditions

will last for some time, despite adequate reserve levels.

Sep.

08

2004 05 06 07

2008

2010

2009

2005 2006

0

20

40

60

80

100

80

120

160

200

Indicators of Oil Production Costs 240

(U.S. dollars a barrel)

U.S. PPI for oil inputs 

(1991 = 100, right scale)

2

1991 94 97 2000 06

Marginal cost of

producing a barrel of

oil (left scale)

03

Ratio of Proven Reserves to Global Production

(years) With Canadian 

oil sands

1980 85 1990 95 2000 05

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

conditions likely exerted some upward pressure. 

Both U.S. dollar depreciation and the decline in 

real policy interest rates tend to push oil prices 

upward. The effects are primarily short-term, 

with scope for overshooting, but longer-term 

effects are possible through the effects on physical oil demand and supply.13

Oil prices have eased recently on (1) 

increased OPEC production (primarily in 

Saudi Arabia); (2) data signaling a continued 

decline in U.S. oil demand that seems to reflect 

a growing demand response to high prices and 

not just slowing income; (3) prospects for lower 

growth in other major advanced economies; 

and (4) less-supportive financial conditions, 

given the U.S. dollar rebound. Looking ahead, 

oil demand growth is likely to moderate with 

the slower global growth envisaged for the 

second half of 2008 and for 2009. If recent 

production increases are sustained, near-term 

market conditions will thus be less tight and 

will support a decrease in prices below recent 

peaks, with some scope for further downward 

adjustment if the global downturn intensifies 

or the demand response to high prices further 

strengthens in advanced economies. Nevertheless, supply constraints and continued strong 

growth in emerging economies are likely to 

keep prices both well above pre-boom levels and 

subject to continued volatility.

High Food Prices Reflect a Combination of

Permanent and Temporary Factor

The food price boom that began around mid2006 intensified during the first four months of 

2008, driven largely by increases in the prices 

of six key food commodities: corn, wheat, rice, 

13Effective U.S. dollar depreciation can exert upward 

pressure on commodity prices through a number of channels. The empirical analysis in Box 1.4 in the April 2008 

World Economic Outlook suggests that a 1 percentage point 

depreciation raises oil prices (in U.S. dollars) by more 

than 1 percentage point. Lower short-term real interest 

rates reduce inventory holding costs and could induce 

shifts from money market instruments to commodities 

and other higher-yielding assets. See Bordo (1980), Frankel (1986, 2006), and Akram (2008).

soybeans and related products, rapeseed oil, and 

palm oil. Together, these commodities account 

for over 80 percent of the rise in the IMF’s food 

price index since early 2006, despite having a 

weight of only 40 percent.

The decline in global inventory levels for 

these food commodities over the past few 

years was important in setting the stage for 

the price surges. On the supply side, there was 

a decline in yield growth rates after the mid1980s, attributable in part to declining relative 

prices and low investment rates (Figure 3.5, 

top panel). The high levels of protection in 

agriculture in advanced economies and the bias 

in public expenditures in developing economies 

toward subsidies (instead of investment in agricultural infrastructure and research) contributed 

to this trend.14 On the demand side, there was a 

strong pickup in consumption, driven by rapid 

income growth in emerging and developing 

economies (Figure 3.5, middle panel).15

This analysis seeks to estimate the relative 

roles of a number of supply and demand factors 

in explaining the price increases of these six 

food commodities during 2006–08 (Figure 3.5, 

bottom panel). As a caveat, the exercise is based 

on simple partial equilibrium analysis and does 

not incorporate the price-driving effects of low 

inventories. Moreover, the uncertainty involved 

is considerable, given complex interactions 

across markets and time.16

• Weather Shocks: A series of weather-related 

supply shocks in 2006 and 2007, which occur 

less frequently than once a decade on average, 

severely reduced average wheat and rapeseed 

yields for two years. These include drought 

14In the OECD countries, progress has been slow in 

reducing overall support during the past 20 years, with 

the average transfer to agricultural producers as a share 

of farm-gate prices falling from 37 percent to 30 percent 

in 2005. See World Bank (2007). 15The composition of demand has also changed toward 

protein-rich foods, feeds, and oils in line with consumption trends in developing economies. 16The methodology is described in more detail in 

Appendix 3.2. Unless otherwise stated, references are to 

crop years (with the 2007 crop year running from mid2007 to mid-2008).
damage, particularly to wheat crops in Australia, eastern Europe, and northern Africa, 

which accounted for about 20 percent of the 

increase in wheat prices since 2006.17 The 

impact of weather shocks is generally temporary; indeed, the wheat area planted for the 

2008 crop year has risen sharply in response 

to high prices in the United States (Trostle, 

2008).

• Biofuel Production: Soaring demand from 

biofuel producers for corn and some vegetable oils was a second factor boosting food 

prices. Biofuel production expanded rapidly 

in response to rising fuel prices, as well as to 

ambitious biofuel mandates, government subsidies, and tariff protection in major advanced 

economies.

In particular, corn-based ethanol production soared in the United States. Almost 

30 percent of the U.S. corn crop was diverted 

toward the production of biofuels during 

2006–07, and this share is projected to rise to 

36 percent in 2008. Despite a strong production response, the additional demand 

pressure is estimated to account for some 

25–45 percent of the rise in international 

corn prices during this period, given a range 

of plausible values for the price elasticity of 

demand. Looking ahead, demand pressures 

from ethanol will likely continue to exert a 

rising effect on food prices unless policies are 

changed.

The price effect of biofuel production on 

rapeseed oil—the main biodiesel feedstock 

in Europe—has become less important over 

time. A reduction of EU subsidies amid 

a reexamination of biofuel policies and 

soaring vegetable oil prices rendered many 

biodiesel plants unprofitable, and demand 

for rapeseed oil for biodiesel use declined 

during 2007.18 Moreover, rapeseed oil repre17Indeed, without the bumper crops in soybeans, 

wheat, and corn in 2005, the price surge might well have 

occurred earlier. 18About 20 percent of global rapeseed oil demand is 

currently diverted toward biodiesel production. Use of 

soybean oil and palm oil for biodiesel production also 

grew in during this period but remained a very small 

fraction of total global use (an estimated 9 percent and 

3 percent in 2006 and 2008, respectively).

-3

0

3

6

9

12

15

0

40

80

120

160

Figure 3.5. Price Trends of Major Foods

 Sources: Food and Agriculture Organization; U.S. Department of Agriculture; World Bank

(2007); and IMF staff calculations.

 Demand growth from biofuels is excluded from the calculation for country groups.

 See Appendix 3.2 for details on the calculations.

 Including spillovers and substitution effects.

Contribution to Price Increases of Major Foods

(Cumulative 12-month percent change, 2006–07 and 2007–08 crop years)

Demand Growth during Recent Price Surge

(2006–07 crop years, percent)

1

Emerging economies

Developed economies

Developing economies

Biofuels

2006–07

Corn Rice Wheat Rapeseed 

oil

Palm oil

The recent price surge followed a period of declining yield growth in grains amid

sluggish investment in agriculture. Recent demand growth, mostly from emerging

economies, has shifted toward soybean oil and palm oil. In addition to biofuel 

growth, the price surge reflects a confluence of factors.

Soybean

oil

2

Corn Rice Wheat Rapeseed

oil

Soybean Palm oil

oil

0

1

2

3

4

1966–85 1986–2005

Average Annual Cereal Yield Growth in Agriculture, Pre- and Post-1985

(percent) Advanced economies

Emerging and developing economies

Supply shocks

Biofuel demand

Fuel and fertilizer costs

Export restrictions

Other factors3

2

3

1

Surging Commodity Prices: Origins and Pr
Chapter3  Is Inflation Back? Commodity Prices and

sents only a small and declining share of the 

market for edible oils.

• Pass-Through of Higher Energy Costs: A third 

factor pertains to the pass-through of higher 

energy costs directly to food prices, estimated 

to have accounted for about 20 percent of the 

rise in the prices of the six commodities. Agricultural production costs have been pushed 

higher by an almost tripling of fertilizer prices 

and a doubling of fuel prices since mid-2006. 

This is particularly true for corn, rapeseed, 

and rice, which are particularly energy-intensive crops.

• Trade Policy: More-restrictive trade policies 

have been a fourth factor pushing up food 

prices. Growing concerns about the domestic 

impact of rising food prices led a number of 

major food-exporting countries to impose 

export restrictions starting in mid-2007. 

The restrictions had a particularly strong 

effect on rice prices—accounting for about 

half of the overall price increase according 

to IMF staff estimates—but they also affected 

the prices of wheat and, to a lesser extent, 

palm oil and soybean oil. These policies also 

led to some short-term price overshooting, 

as they reportedly triggered panic buying 

and inventory hoarding.19 Many of these 

restrictions were subsequently removed, and 

some countries have released stocks. The 

removal of restrictions is likely to continue 

with more favorable harvests for rice and 

wheat.

Overall, the most important direct factor driving up food prices since 2006 has been rising 

energy costs, with trade restrictions following 

as a close second. However, the direct effects 

of these factors do not account for all of 

the observed increases in the prices of the 

affected crops. Mutually reinforcing indirect 

effects, which operate mainly through supply 

and demand substitution channels, have also 

contributed. Table 3.2 illustrates the spillover 

effects from the price increases for corn and 

rapeseed oil (which are inputs for biofuels), 

using demand and supply cross-price elasticities. The results suggest that these effects are 

particularly important in explaining price 

increases for soybeans and related products: a 

19Such effects are common when international markets 

are segmented and the share of trade in total production 

is small, as it is the case for rice, but they are not considered in the estimates. See FAO (2008).

Table 3.2. Selected Indicators of Spillovers across Major Food Commodity Pri

Corn Rice Wheat Soybean Oil Rapeseed Oil Palm Oil

Estimated percent price change resulting from a 

1 percent increase in the price of foods used for 

biofuels1

Corn 1.00 0.23 0.19 0.78 — —

Rapeseed oil — — 0.62 1.19 1.00 —

Concordance statistic of cyclical comovement2

With corn (Jan. 1957–May 2008) 100** 82** 61* 71** 74** 66**

With rapeseed oil (Jan. 1980–May 2008) 74** 76** 46 82** 100** 78**

Memorandum items (2007 crop year) 

Share of global production exported 13 6 18 29 10 72

Share of fuel and fertilizers in total production costs3 32 30 25 12 30 14

Sources: USDA (2008); Food and Agriculture Organization’s online database, FAOSTAT; Fedepalma (2008); North Carolina Solar Center (2006); 

and IMF staff calculations (see Appendix 3.2 for details). 1Derived from composite estimate of elasticities of substitution.

2The concordance statistic measures the proportion of time that prices of two commodities are in the same phase, with a range between 

0 and 100. A high value implies that their cycles are more synchronized, suggesting the two commodities are highly substitutable (Cashin, 

McDermott, and Scott, 1999). * = significance at the 10 percent level. ** = significance at the 5 percent level. 3Production costs for soybean oil and rapeseed oil refer to corresponding plant crop. Share of fuels used for transport not included.
1 percent increase in corn prices, all else equal, 

raises soybean prices by about three-quarters of 

a percent, as farmers substitute acreage from 

soybeans to corn and consumers switch from 

corn to soybean meal.20 If it were assumed that 

the increase in corn prices was unrelated to 

price rises for the other major foods, the indirect effects of higher corn prices would account 

for some 60 percent of the increase in soybean 

prices and about 20 percent of the increases in 

rice and wheat prices.

The resolution of weather-related supply disruptions in the current crop year and removal 

of export restrictions have already led to some 

easing of food prices. However, the pressure 

on food prices from high oil prices and further increases in biofuel production will likely 

remain, limiting the extent of the easing, and 

low inventories will continue to contribute to 

price volatility. Indeed, because of these more 

permanent factors, the duration of the present boom has already exceeded the length of 

the average food price boom by 12 months 

(Figure 3.6).

Commodity Price Shocks and Inflatio

Can the large relative price adjustments 

implied by the recent commodity price surges 

be accommodated without ratcheting up 

underlying inflation? The main concern is 

that a lengthy period of high headline inflation following the commodity price surges may 

unhinge inflation expectations.21 The broad 

context for such concern is the contrast in commodity price behavior during two key episodes 

in recent history. During the “great mod20Consumers in this case would be mostly meat and 

poultry producers, who use cornmeal and soymeal as 

animal feed. 21While relative price shifts do not generally lead to 

sustained changes in the overall price level, large and 

persistent temporary shocks, especially to the prices of 

essential commodities, may unhinge inflation expectations and spill over into underlying inflation. For a more 

formal discussion of the relationship between relative 

price changes and overall inflation, see Ball and Mankiw 

(1995) and Sims (2003). 

-100

0

100

200

300

400

500

600

-10

0

10

20

30

40

50

60

Figure 3.6. Duration and Amplitude of Food and Crude 

Oil Price Cycles

 Source: IMF staff calculations.

 Range of price increases during past trough-to-peak phases between January 1957 and 

June 2008.

 Months price is rising within the cycle compared with the average of past cycles.

 Rapeseed oil price series starts in January 1980.

1

Dura 2 tion versus average

(months, right scale)

Price increase during

current boom

(percent)

Corn Rice Wheat Rapeseed

oil

Palm

oil

Soybean 

oil

Food

Range of

price 

increases

(percent)

3

1

2

3

The current boom is already longer than average for most foods and for crude oil.

However, with the exception of crude oil and corn, the price increases are not 

exceptionally high.

Brent 

Crude oil

Commodity Price Shocks and Infl
Chapter3  Is Inflation Back? Commodity Prices and

10

Figure 3.7. Inflation around the World

(2008:Q2-over-2007:Q2 percent change)

 Source: IMF staff calculations.

 Food and fuel price contributions are calculated as, respectively, food and fuel inflation multiplied by the corresponding weight in the consumer price index

(CPI).

Headline inflation has risen, especially in emerging and developing economies, where the role of food prices is particularly significant. 

The contribution of energy prices is smaller in comparison, with stronger effects in advanced economies.

1

1

Overall CPI Inflation

Less than 3%

3 to 7%

7 to 10%

Greater than 10%

No data

Food Price Contribution to Inflation

Less than 1%

1 to 3%

3 to 10%

Greater than 10%

No data

Fuel Price Contribution to Inflation

Less than 0.1%

0.1 to 0.5%

0.5 to 1%

Greater than 1%

No data
1

eration”—the long period of low and falling 

inflation rates from the 1990s until recently—

changes in commodity prices were relatively 

modest and temporary, whereas during the 

“great inflation”—the 1970s—these shocks were 

large and persistent, as they have been in the 

present period.22

Concerns about second-round effects remain 

relevant despite the recent easing of international commodity prices, because domestic 

price pressures will likely persist for some time 

as a result of the continuing feed-through of 

past commodity price increases and lingering overheating pressures in many emerging 

economies. This section examines the links 

between commodity prices and inflation over 

time and across a broad sample of economies. 

It highlights how the risks to the inflation 

outlook are linked to the credibility of monetary policy—its ability to effectively anchor 

expectations—to the magnitude and persistence of the commodity price shocks, and to 

structural factors.

Turning first to current events, the dramatic 

rise in headline inflation in recent months 

owes much to commodity price increases over 

the past year and a half, with food prices playing a particularly important role in emerging 

and developing economies (Figure 3.7). In 

comparison, the contribution of energy prices 

has been moderate, with stronger effects in 

advanced economies. Indeed, domestic food 

prices have accelerated primarily in emerging and developing economies, while energy 

22There is a growing literature on the sources of the 

“great moderation.” For example, Gerlach and others 

(forthcoming) attribute the “great moderation” primarily 

to improved monetary policies. The role of globalization 

is less clear. Falling manufactured goods prices, driven 

by rapid productivity gains from integration of large 

underutilized labor forces in emerging and developing economies, helped to make the process of reducing 

inflation less costly than it would otherwise have been. 

However, recently strong growth in demand for commodities has added to price pressures. The observed flattening of the Phillips curve (documented in Chapter 3 of 

the April 2006 World Economic Outlook) may be related to 

global competition but may also reflect better monetary 

management.

2000 02 04 06

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75

100

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150

-10

-5

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30

2000 02 04 06

-50

-25

0

25

50

75

100

125

150

-10

-5

0

5

10

15

20

25

30

Figure 3.8. Changes in International and Domestic 

Commodity Prices and Headline Inflation

(Year-over-year changes, in percent)

 Sources: IMF Primary Commodity Prices database; and IMF staff calculations.

 International food and fuel prices are converted into local currencies. Food and fuel price 

indices in the 1970s include a narrower set of commodities for data availability reasons.

Large changes in commodity prices characterized the 1970s, when inflation reigned 

in advanced economies. In comparison, recent price fluctuations have been modest, 

although in recent months domestic food prices have accelerated in emerging and 

developing economies, and domestic energy prices have surged in advanced

economies, while inflation has picked up around the world.

Emerging and Developing Economies, Recent Years

Advanced Economies, Recent Years

1970 72 74 76 78 80

-50

-25

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75

100

125

150

-10

-5

0

5

10

15

20

25

30

1970 72 74 76 78 80

-50

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0

25

50

75

100

125

150

-10

-5

0

5

10

15

20

25

30

Advanced Economies, 1970s

International Domestic

Food Fuel

1

1

Headline inflation (right scale)

2000 02 04 06

-50

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100

125

150

-10

-5

0

5

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15

20

25

30

08:

Q2

08:

Q2

08:

Q2

08:

Q2

2000 02 04 06

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Commodity Price Shocks and Infl
Chapter3  Is Inflation Back? Commodity Prices and

1

prices surged mainly in advanced economies 

(Figure 3.8).23 To date, however, underlying or 

core inflation has remained broadly stable in 

advanced economies, although it has risen significantly in the rest of the world, as discussed 

in Chapter 1 (see Figure 1.3).24 Inflation expectations have also begun to mount, especially in 

emerging economies, where wages have been 

on the rise amid generally tight labor markets.

What factors might affect the extent of transmission, or pass-through, from international 

commodity prices into domestic food and fuel 

retail prices? First, because domestic prices 

are denominated in local currencies, whereas 

world prices are typically denominated in dollars, exchange rate movements can amplify or 

mitigate the domestic impact of changes in 

world prices.25 Second, many economies levy 

taxes or grant subsidies on certain commodities, 

especially fuels, which, again, may amplify or 

23There are substantial differences across countries 

in the way food and fuel prices are treated in consumer 

price indices, especially across emerging and developing 

economies. The food baskets used to measure food inflation vary from country to country, with some countries 

including beverages and tobacco alongside food items, 

and other countries using narrower definitions including 

fresh but not processed foods. The measurement issues 

are even more acute in the case of fuel prices: definitions of the fuel component of the consumer price index 

(CPI) range from gasoline prices to prices for household 

utilities. 24Measuring core inflation is difficult. In theory, core 

inflation is defined as the underlying, or persistent, part 

of inflation that provides an indication of future inflation, 

although precise definitions vary (see, for example, Eckstein, 1981; and Bryan and Cecchetti, 1994). In practice, 

core inflation is commonly measured using the CPI that 

excludes food and energy prices, or their most volatile 

components, but these measures differ across countries. 

The variation in measurements of core inflation tends to 

be especially significant among emerging and developing 

economies, for which inferences about the underlying 

inflation need to be made with caution. 25De Gregorio, Landerretche, and Neilson (2007) 

argue that past oil shocks were often accompanied by 

depreciations that may have amplified their pass-through 

into domestic prices, whereas depreciations have been 

less common in the past few years, and many economies 

have, in fact, experienced appreciations that may have 

softened the pass-through.

mitigate the transmission (Box 3.2).26 Third, the 

extent to which the domestic economy is integrated with international commodity markets 

is important, because in more isolated markets, 

domestic supply conditions may dominate the 

role of world price changes (for example, for 

certain crops). Fourth, the cost structure of 

domestic production plays a very important role 

in the extent and timing of the pass-through 

to retail prices, because labor, transportation, 

and retailing costs account for a large part of 

the final price of many food items, especially in 

advanced economies, and the costs associated 

with the commodities themselves may be moderate in comparison.27

Changes in domestic prices of food and fuel 

may influence overall inflation both directly 

and indirectly. The direct (first-round) effects 

on headline inflation are determined by the 

weights of these commodities in the consumption basket. Although these effects are large in 

many—especially poor—economies, they eventually dwindle once international price changes 

are passed through, unless underlying, or core, 

inflation is affected.28 Such indirect (secondround) effects on core inflation depend on the 

26A number of emerging and developing economies 

rely on energy subsidies to limit the domestic consequences of international energy price shocks. However, 

the associated fiscal costs may be large, especially at times 

of significant pressures from international prices (see 

IMF, 2008b). Indeed, escalating fiscal costs have recently 

forced a number of countries to roll back some of these 

subsidies. Furthermore, the associated fiscal expansion 

and financing requirements for ensuing government 

deficits may themselves lead to inflation (Sargent and 

Wallace, 1985). 27Movements in domestic labor and transportation 

costs may vary and may either offset or reinforce pressures from commodity price changes. For example, 

labor costs in advanced economies followed a declining 

trend during the past couple of decades, in part due to 

increased access to the global pool of labor (see Jaumotte 

and Tytell, 2007). This may have helped offset higher 

energy and material costs in recent years. 28In fact, these effects are rarely immediate, because 

commodity price shocks may take considerable time to 

propagate through to final retail prices. For example, 

Rigobon (2008) estimates that oil price shocks typically 

take 9 to 12 months to pass through, and food price 

shocks can take up to 30 months. 
1

The boom in prices for food and energy has 

led to a wide range of fiscal responses across 

the globe aimed at mitigating the domestic 

impact. This box summarizes these responses 

and discusses their effectiveness in alleviating 

the impact of commodity price increases on the 

poor and their macroeconomic implications 

more broadly.

A recent IMF survey collected information 

on the fiscal responses of 161 countries to international price increases (IMF, 2008b). Among 

the survey’s findings were these: 

• Expenditure measures are more prominent 

in the case of fuels, whereas revenue measures dominate for food. More than onequarter of the surveyed countries increased 

fuel subsidies, and about one-fifth reduced 

fuel taxes. Fuel subsidies reached high 

levels in many countries this year, exceeding 

5 percent of GDP in Ecuador, Egypt, Turkmenistan, República Bolivariana de Venezuela, and Republic of Yemen. The picture is 

almost the opposite for food, with more than 

half the countries reducing food taxes and 

less than one-fifth increasing food subsidies.

• Exporting countries have used both tax and 

regulatory measures to contain increases in 

domestic food prices. These measures have 

included increases in export taxes, the introduction of export quotas, and even the imposition of an outright ban on certain exports. 

Notably, export bans and export taxes were 

imposed by key exporters of major cereals, 

including Argentina, China, India, Kazakhstan, Ukraine, and Vietnam. However, more 

recently a few major exporters have started 

to relax some export restrictions. Vietnam 

and Kazakhstan recently allowed export bans 

to expire on rice and wheat, respectively, 

and Ukraine has increased quotas on wheat 

exports.

• About a quarter of the surveyed countries 

recently increased financing for more targeted transfer programs, and 15 countries 

increased public sector wages and pensions 

partly in response to the price increases.

The total fiscal cost of these measures has 

been substantial; the median annualized 

increase in fiscal cost across the surveyed 

countries during 2007–08 was 0.7 percent of 

GDP. For about a quarter of the countries, 

the fiscal costs exceeded 1 percent of GDP, 

with higher food and fuel universal subsidies 

accounting for the bulk of this increase. By 

2008, the combined fiscal cost of these universal 

subsidies had become a major fiscal burden in 

many countries, particularly for fuel subsidies. 

For example, these subsidies now account for 

Box 3.2. Fiscal Responses to Recent Commodity Price Increases: An Assessment

The main author of this box is David Coady.

Mexico

Peru

Timor-Leste

Gabon

Trinidad and Tobago

Ukraine

Sudan

Bangladesh

Nepal

Senegal

Dominican Republic

Nigeria

India

Ethiopia

Angola

United Arab Emirates

Bolivia

Burundi

Malaysia

Pakistan

Oman

Cambodia

Tunisia

Jordan

Morocco

Indonesia

Turkmenistan

Egypt

Ecuador

Yemen

0 4 8 12 16

Universal Subsidies, 2008

(Percent of GDP)

 Source: IMF Fiscal Survey.

 For countries where only the food or the fuel subsidy is

displayed, data for the other type of subsidy are not available.

Food Fuel

Venezuela, Rep. Boliv. de

São Tomé and Príncipe

1

1

Commodity Price Shocks and Infl
Chapter3  Is Inflation Back? Commodity Prices and

1

extent to which expectations of future inflation 

get unhinged and higher wage demands are set 

in motion.29 This is partly linked to the relative 

29In the past, the risk of a wage-price spiral was 

exacerbated in many countries by wage indexation, 

with wages indexed to past inflation, which introduced 

an additional source of inflation persistence. However, 

indexation systems have been redesigned over the past 

decades, weakening the inflation effects. This said, the 

role of indexation is difficult to quantity given differences in wage-setting practices across countries. In some 

countries—especially where labor markets are already 

tight—transfer revenue indexation can indirectly affect 

wage negotiations and increase inflation risks. In addimagnitudes of demand and supply effects associated with commodity price shifts. On the one 

hand, higher food and energy prices raise costs 

and may lower productivity—a negative supply 

effect that puts upward pressure on inflation. 

On the other hand, they may cause expenditure 

switching from other goods and services—a 

negative demand effect that pushes inflation 

down. Although the supply effect tends to 

tion, in a number of countries, public sector wages are 

adjusted in response to increases in food and energy 

prices, which may contribute to a wage-price spiral.

at least 5 percent of GDP in 7 countries and at 

least 2 percent in another 17 countries (figure).

These measures are adopted, in part, because 

increases in the prices of food and fuel are 

seen to be particularly damaging to the poor. 

In general, the burden of food price increases 

tends to be highly regressive, but the burden 

of fuel price increases depends on the type of 

fuel. Indeed, recent IMF studies1 found that a 

doubling of rice prices results in a 12 percent 

decrease in real incomes for the poorest income 

quintile, compared with a 5 percent decrease for 

the richest quintile. In contrast, whereas a doubling of all fuel prices results in approximately 

a 10 percent decrease in income for all income 

groups, the impact of increases in gasoline 

prices is roughly proportional, but the impact of 

increases in kerosene prices is highly regressive.

Universal price subsidies are a fiscally costly 

approach to protecting the welfare of poor 

households. This is because a high proportion 

of the benefits from low food and fuel prices 

accrue to higher-income groups, reflecting the 

higher shares of these groups in total consumption. For example, IMF studies found that about 

64 percent of a subsidy for rice went to the 

top three income quintiles (IMF, 2008b); the 

1IMF (2008a) analyzes the case of Senegal, and 

Coady and others (2006) provide evidence for Bolivia, 

Ghana, Jordan, Mali, and Sri Lanka. 

corresponding shares for kerosene and gasoline subsidies were 55 percent and 92 percent, 

respectively (Coady and others, 2006). Switching 

to better-targeted mitigation measures can substantially reduce the associated costs while more 

effectively assisting the most affected segments 

of the population.

Furthermore, incomplete pass-through of 

international to domestic commodity prices 

distorts incentives for domestic consumers and 

producers and ultimately reinforces global price 

pressures. More specifically, reduced taxes and 

increased subsidies dilute the impact of higher 

international commodity prices on demand, 

and the imposition of export taxes and quotas 

reduces the gains to exporters from higher prices 

and therefore obstructs the supply response that 

would, in time, help bring prices down. The 

financing requirements implied by the high fiscal 

costs of subsidies will eventually either cause their 

reversal or lead to higher taxes—and therefore 

higher prices—for other goods and services. 

They could also feed into more general inflation 

pressures if the ensuing deficits are monetized 

or if they cause an excessive accumulation of 

government debt. For these reasons, the broadbrush fiscal intervention ongoing in a wide 

range of countries is not a viable substitute for 

an appropriate monetary policy response to help 

maintain macroeconomic stability in the face of 

commodity price fluctuations.

Box 3.2 (concluded)
1

dominate, the balance between the two effects 

is subject to some uncertainty, especially in net 

commodity importers. In net commodity exporters, a commodity price boom typically raises 

aggregate demand and intensifies inflation pressures, although exchange rate adjustments could 

mitigate this effect.

What factors influence the vulnerability of 

economies to inflation risks associated with 

commodity price shifts? Broadly, these could 

be grouped into structural and policy-related 

factors. A key structural factor is the intensity 

of use. Indeed, energy intensity—measured as 

energy consumption per unit of real GDP—has 

fallen by about 40 percent in advanced economies since the 1970s. In comparison, emerging 

and especially developing economies are considerably more energy-intensive (Figure 3.9).30

The difference between these two groups is even 

more dramatic when it comes to food consumption. Food represents over one-third of household consumption in emerging and developing 

economies, with the share ranging from just 

over 10 percent to almost 80 percent in some 

developing economies. In contrast, in advanced 

economies food amounts to only one-tenth of 

household consumption (half of what it was in 

the 1970s), and the share of raw material costs 

in total costs is considerably lower.

Among the policy-related factors that affect 

economies’ vulnerability to the inflationary 

impact of a commodity price shock, the credibility of monetary policy stands out. The quality 

of monetary management—approximated by 

an index of central bank autonomy31—has 

improved around the world, but it remains 

lower in emerging and especially developing 

economies than in advanced economies (Figure 3.10). More than 80 percent of emerging 

and developing economies maintain heavily 

managed exchange rate regimes, in sharp con30Energy intensity in emerging and developing economies is even higher when GDP is evaluated at market 

exchange rates. 31This index captures the ability of a central bank to 

pursue independent monetary policy and is based on 

Arnone and others (2007).

1970s 1980s 1990s 2000s 0

10

20

30

40

Food Consumption 50

(percent of household consumption)

 Sources: British Petroleum Statistical Review of World Energy (2008); UN National 

Accounts; U.S. Department of Agriculture; and IMF staff calculations.

Figure 3.9. The Relative Importance of Food and Energy

1970s 1980s 1990s 2000s 50

100

150

200

250

300

Energy Intensity 350

(tons of oil equivalent per million of GDP in 2007

purchasing-power-parity dollars)

Although the relative economic importance of energy and food consumption has 

broadly followed a declining trend, emerging and developing economies are both 

more energy-intensive and more dependent on food consumption than advanced

economies.

Advanced economies

Emerging economies

Developing economies

Commodity Price Shocks and Infl
Chapter3  Is Inflation Back? Commodity Prices and

1

trast to advanced economies, where exchange 

rates are now overwhelmingly floating.32

Although pegged exchange rates have helped 

many emerging and developing economies 

anchor inflation expectations in the past, they 

do constrain monetary policy responses, particularly when advanced and nonadvanced economies face very different cyclical conditions. The 

dissonance between the buoyant activity levels 

and easy policy stances that now characterize a 

range of emerging and developing economies is 

striking and reminiscent of the situation faced 

by advanced economies during the great inflation of the 1970s.

To assess the potential for second-round 

effects from changes in food and fuel prices and 

to relate them to the structural and policy characteristics of different economies, two related 

econometric exercises were conducted. The first 

one links core inflation to changes in prices of 

food and fuel, controlling for changes in the 

output gap (the Phillips curve).33 It is based on 

country-by-country estimations over a relatively 

extended time period and allows a comparison between current developments and those 

at the time of the great inflation in the 1970s. 

The second exercise directly links changes in 

expected inflation to changes in actual headline 

inflation and disaggregates the latter into core 

inflation and changes in domestic inflation rates 

for food and fuel.34 This exercise is based on a 

32This comparison is based on an updated classification 

of exchange rate regimes of Reinhart and Rogoff (2004). 

Inflexible exchange rate regimes include all de jure and 

de facto exchange rate pegs and bands and exclude 

currency unions. See also Ilzetzki, Reinhart, and Rogoff 

(forthcoming). 33See Blanchard and Galí (2007) for an analysis of oil 

price pass-through across industrialized economies. De 

Gregorio, Landerretche, and Neilson (2007) undertake a 

similar study for a sample of industrial and some emerging and developing economies. Both studies find that the 

pass-through from oil price changes to overall inflation 

has declined over time. 34Inflation expectations are typically measured in one of 

two ways. The first is based on surveys of consumers or professional forecasters, and the second is based on the difference in yields between conventional and inflation-linked 

bonds (see Soderlind and Svensson, 1997; Fung, Mitnick, 

and Remolona, 1999; and Shen and Corning, 2001). 

 Sources: Arnone and others (2007); Reinhart and Rogoff (2004, updated); and IMF staff 

calculations.

 The score for the 1970s is constructed using the methodology of Arnone and others

(2007) for a somewhat narrower set of indicators.

 Inflexible exchange rate regimes include all de jure and de facto exchange rate pegs and 

bands and exclude currency unions.

Figure 3.10. Monetary and Exchange Rate Policies

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

Average Score on Central Bank Autonomy 1.0

Although the quality of monetary management has improved around the world,

advanced economies score better in this area than emerging and developing

economies, the majority of which continue to maintain inflexible exchange rate

regimes.

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

Proportion of Countries with Inflexible Exchange Rates 1.0

Mean

Advanced 

economies in

the 1970s

Advanced 

economies in

the 2000s

Emerging 

economies in

the 2000s

Developing

economies in

the 2000s

Advanced 

economies in

the 1970s

Advanced 

economies in

the 2000s

Emerging 

economies in

the 2000s

Developing

economies in

the 2000s

95 percent

confidence

interval

1

2

1

2
1

panel of advanced and emerging economies and 

allows a comparison of performance depending 

on structural and policy characteristics of these 

economies over recent years.

The first set of estimations shows that the 

pass-through from international to domestic 

food prices and from domestic food prices into 

core inflation in emerging economies is comparable to that seen in advanced economies in the 

1970s and much higher than the pass-through 

observed in advanced economies more recently 

(Figure 3.11).35 In emerging economies, about 

one-half of the shock to domestic food prices 

ultimately makes its way through to core inflation, whereas in advanced economies, less than 

one-quarter passes through. These findings are 

in line with the high share of food in consumption and the relative importance of material 

costs in production across emerging economies 

and underscore these economies’ sensitivity to 

food price developments.

Turning to fuel prices, the pass-through from 

international to domestic prices is substantially 

lower in emerging than in advanced economies. 

The pass-through from domestic prices to core 

inflation has recently been markedly lower than 

Both of these measures have shortcomings: survey-based 

measures may reflect subjective and sometimes unfounded 

perceptions about inflation, while bond-based measures 

may reflect liquidity and inflation volatility premiums, as 

well as institutional features of specific bond markets. In 

this study, expected inflation is measured using inflation 

forecasts published by Consensus Economics, because 

bond-based measures are not available for a sufficiently 

broad set of countries. See Goretti and Laxton (2005) and 

Levin, Natalucci, and Piger (2004) for similar analyses. 35The sample consists of 25 emerging and 21 advanced 

economies (9 for the 1970–95 period). In order to limit 

contamination of the estimates by endogenous factors, 

the pass-through from domestic commodity prices to core 

inflation is estimated using only the variation in domestic 

prices that is due to changes in international prices as 

well as lagged effects of domestic price developments. It 

must be mentioned that the estimates vary considerably 

across countries, reflecting in part differences in data 

quality, measurement of inflation, and sample periods, 

especially across emerging economies. The estimated 

pass-through captures the full long-term pass-through 

and does not reflect any differences in the time path of 

the inflation responses. Appendix 3.3 provides a detailed 

description of this exercise.

 Source: IMF staff calculations.

 Weighted averages of country-by-country estimates using quarterly data. The

pass-through from international to domestic prices is estimated using bivariate 

regressions. The pass-through from domestic commodity prices to core inflation is

estimated using Phillips curve equations with domestic prices net of any influences other 

than international prices and their own lags. In both estimations, the full long-term

pass-through is calculated as the sum of coefficients on the current value and the four lags

of the independent variable divided by 1 minus the sum of coefficients on the four lags of

the dependent variable.

Figure 3.11. Commodity Price Pass-Through

(Full long-term response to a 1 percentage point change in commodity 

price inflation, in percentage points)

Food

The recent food price pass-through in emerging economies resembles that seen in

advanced economies in the 1970s, whereas the fuel price pass-through has been

markedly lower. The recent pass-through to core inflation has been moderate for 

both food and fuel prices in advanced economies.

International to domestic prices Domestic prices to core inflation

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Fuel

Emerging economies

1995–2008

Advanced economies

1995–2008

Advanced economies

1970–95

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Emerging economies

1995–2008

Advanced economies

1995–2008

Advanced economies

1970–95

1

1

Commodity Price Shocks and Infl
Chapter3  Is Inflation Back? Commodity Prices and

1

during the 1970s, when over 20 percent of the 

price shock reached core inflation. The low 

pass-through coefficients may reflect a combination of factors, including declining energy 

intensity, widespread fuel subsidies and controls 

in emerging economies, and high fuel taxes in 

many advanced economies.36

The econometric analysis of the relationship 

between changes in expected and actual inflation suggests that differences in structural and 

policy vulnerabilities shape expectations across 

economies (Figure 3.12).37 In advanced economies, expectations appear to be well anchored: 

long-term inflation forecasts do not react to 

actual inflation. Expectations are generally less 

well anchored in emerging economies, where 

expected inflation continues to be influenced 

by actual inflation even at long forecast horizons. Thus, when headline inflation increases 

by 1 percentage point, inflation expected in 

the following year rises by nearly 0.2 percentage point on average. Even as far as six to ten 

years into the future, inflation is still expected 

to rise by about 0.05 percentage point. In these 

economies, expectations also respond strongly 

to changes in domestic food price inflation, 

whereas energy price inflation does not appear 

to exert significant effects, likely reflecting the 

relative shares of food and energy in consumption. In economies where food accounts for a 

large share of household consumption, there is 

a particularly sizable increase in expected inflation in response to changes in actual headline 

and food price inflation.

36In addition, comovement between food and energy 

prices could make the two effects hard to disentangle. 

Indeed, energy price changes contribute significantly 

to the dynamics of food prices, as pointed out in the 

preceding section. Furthermore, measurement issues 

in domestic food and especially fuel prices noted above 

could attenuate the estimated pass-through coefficients. 37The estimations are based on a panel of semiannual 

observations beginning in 2003. The sample includes 14 

advanced and 21 emerging economies. In order to disentangle the effects of core inflation from those of changes 

in commodity prices, only the variation in core inflation 

that is not due to changes in food and fuel prices is used 

in the analysis. More information on this exercise is provided in Appendix 3.3.

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

Inflation expectations appear significantly better anchored in advanced economies

than in emerging economies, especially those with a high share of food in the CPI. In

emerging economies, inflation targeting seems to have recently been more effective 

than alternative monetary policy frameworks in anchoring expectations.

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

 Sources: Consensus Forecasts; and IMF staff calculations.

 Based on statistically significant coefficients from panel regressions with fixed effects,

using semiannual data since 2003. The measure of core inflation is net of food and fuel

inflation.

Emerging Economies

Emerging Economies with a 

Low Weight of Food in CPI

1 year 3 years 5 years 6-10 years 0.00

0.05

0.10

0.15

0.20

0.25

Emerging Economies with a 

High Weight of Food in CPI

Emerging Economies That

Target Inflation

Emerging Economies That Do

Not Target Inflation

Advanced Economies

Figure 3.12. Changes in Expected Inflation in Response

to Changes in Actual Inflation

(Expected inflation 1, 3, 5, and 6–10 years ahead; percentage point 

responses to a 1 percentage point change in actual inflation)

1

1

Headline Core Food Fuel
1

The transmission of commodity price shocks 

into expected inflation appears to depend crucially on the conduct of monetary policy. Specifically, inflation targeting appears to have been 

quite effective in anchoring inflation expectations: beyond the one-year horizon, expectations respond very little to changes in actual 

inflation. In contrast, non-inflation-targeting 

countries—many of which formally or informally target nominal exchange rates—seem less 

successful in anchoring expectations. This said, 

the apparent benefits of inflation targeting may 

reflect in part the general quality of domestic 

monetary management in these countries and 

their levels of development more broadly (but 

even so, achieving the targets has recently 

become more difficult).38 In addition, other 

country-specific factors­—such as the extent o

labor market flexibility and the conduct of fiscal policy—may also influence the response of 

expectations to actual inflation.

Will the recent food and energy price surges 

lead to a sustained increase in inflation rates 

across the globe? The findings of this analysis 

may give reason to be optimistic, particularly 

for the advanced economies and emerging 

economies that have adopted inflation targeting. At the current juncture, inflation risks are 

also diminished by the economic slowdown, 

especially in advanced economies, although 

overheating pressures linger in many emerging 

and developing economies. That said, empirical relationships based on past data may not 

provide reliable guidance for the future, even 

if one assumes that monetary policy credibility 

will continue to improve and that global integration and competition will continue to rise. 

Recent commodity-market-related shocks have 

been larger and more persistent than they were 

over the sample period used for the estima38Inflation targeting in emerging economies is 

discussed in Chapter 4 of the September 2005 World 

Economic Outlook. In a more recent study, Mishkin and 

Schmidt-Hebbel (2007) suggest that inflation targeting helps countries to lower inflation, to strengthen 

monetary policy and, in particular, to reduce inflationary 

effects of oil price shocks.

tions, and for this reason, actual future passthrough may surprise on the upside, unless the 

global slowdown intensifies.39 The risks of such 

surprises are intimately linked to expectations 

of future inflation and the ability of monetary 

policies to anchor them effectively, as discussed 

in the following section.

Monetary Policy Responses to 

Commodity Price Shock

Monetary policy mistakes can have serious 

consequences in the presence of permanent 

commodity price shocks, as demonstrated by 

the great inflation of the 1970s in the advanced 

economies. Given already increasing inflation 

and easy monetary conditions, the appropriate 

response at that time to the oil price shock—an 

adverse supply shock—would have been to 

tighten. Instead, the inflation surge was exacerbated by a continued easing of the monetary 

policy stance, which further increased inflation expectations and eroded policy credibility. 

Since that experience, central banks have been 

very aware that monetary policy should not 

accommodate second-round effects of adverse 

supply shocks.40

It is well established that the appropriate response of monetary policy to supply 

shocks depends on the cyclical position of an 

economy and the degree of policy credibility. 

For example, with a high degree of capacity 

utilization and low policy credibility, dangers of 

pass-through into core inflation are relatively 

high. This has implications for the appropri39In addition, although the flexibility of domestic labor 

markets will in all probability continue to improve, the 

anti-inflationary role of the global labor market may 

eventually weaken, as labor markets in emerging and 

developing countries mature and their wages catch up to 

advanced economy levels. 40Many economists have noted the substantial decline 

in the volatility of important macroeconomic variables 

since the 1980s, including, for example, Bernanke (2004) 

and King (2005). Kumhof and Laxton (2007) estimate 

that about one-half of the higher output variability in the 

1970s and early 1980s relative to 1995–2007 can be attributed to inefficient monetary policy and one-half to larger 

supply and demand shocks. 

Monetary Policy Responses to Commodity Price 
Chapter3  Is Inflation Back? Commodity Prices and

1

ate monetary policy responses to the recent 

surges in food and energy prices. As discussed 

in Chapter 1, many emerging economies have 

been showing signs of overheating, along with 

easing monetary conditions. Short-term nominal interest rates are below nominal income 

growth—partly because expansionary U.S. monetary conditions have been imported along with 

exchange rate constraints on monetary policy, 

as noted above (Figure 3.13). At the same time, 

monetary policy credibility in these countries is 

more fragile. To bring inflation under control 

and avoid a boom-bust cycle, monetary conditions will have to tighten in affected countries. 

As outlined in Box 3.3, this would also have 

some moderating influence on commodity 

demand at the global level and on international 

commodity prices.

Monetary Policy Credibility and Inflation Dynamics

Simulations based on models with endogenous credibility and capacity constraints can 

provide useful guidance for using monetary 

policy to respond to adverse supply shocks in 

the face of different degrees of policy credibility, 

different cyclical positions, and different levels 

of initial inflation. The analysis is based on a 

small open economy macroeconomic model in 

which inflation behavior and inflation expectations depend on the credibility of monetary 

policy. Credibility is determined endogenously 

and depends on the evolving track record of 

inflation relative to the long-run target.41 This, 

in turn, affects the extent of second-round 

effects of supply shocks in the model, because 

the extent to which inflation shocks feed into 

expectations depends on current and past inflation. With full credibility, inflation expectations 

are entirely forward-looking, implying that a 

permanent increase in commodity prices has 

little effect on expectations. If credibility is low, 

however, expectations depend mostly on cur41See Alichi and others (forthcoming) for a description 

of the model and its properties.

-3

0

3

6

9

12

15

-3

0

3

6

9

12

15

Figure 3.13. Activity, Interest Rates, and Inflation

(Percent change from one year earlier unless otherwise noted)

 Source: IMF staff calculations.

 For data availability reasons, money market rates are used in place of policy rates for a 

number of countries.

 Inflexible exchange rate regimes include all de jure and de facto exchange rate pegs, 

bands, and crawling pegs or bands that are narrower than ±2 percent. See Reinhart and 

Rogoff (2004) and Ilzetzki, Reinhart, and Rogoff (forthcoming).

Low or negative real interest rates were a feature of the inflationary period in the 

1970s in advanced economies, in contrast to the period of stabilization that followed

in the 1980s. Recently, real interest rates have turned negative in emerging

economies—especially those with inflexible exchange rates—alongside substantially

more buoyant activity than in advanced economies.

Emerging Economies,

Recent Years

Advanced Economies,

Recent Years

-3

0

3

6

9

12

15

-3

0

3

6

9

12

Advanced Economies, 1970s Advanced Economies, 1980s 15

-3

0

3

6

9

12

15

-3

0

3

6

9

12

Emerging Economies with 15

Inflation Targeting

Emerging Economies with

Inflexible Exchange Rates

Real GDP Policy interest rate (percent)

1970 72 74 76 78 1980 82 84 86 88

2000 02 04 06 08:

Q2

2000 02 04 06 08:

Q2

2000 02 04 06 08:

Q2

2000 02 04 06 08:

Q2

Headline inflation

1

2

1 1

2
rent and past inflation, and they are affected by 

shocks to current inflation.

The model determines the optimal monetary 

policy response—through changes in the shortterm interest rate—given the central bank’s 

policy objectives. These relate to deviations from 

the inflation target, output gaps, and short-term 

variability in interest rates.

The model postulates that the central bank 

sets interest rates to minimize variability along all 

three dimensions. With adverse supply shocks, 

the difficulties in setting policies arise because 

inflation and output initially move in opposite 

directions and because monetary policy tightening reduces both output and inflation in the 

short term. The central bank’s policy preferences 

determine how it trades off gains from reducing 

inflation against the costs of lower output.

Supply Shocks and Policy Credibili

In a first simulation, the supply shocks hit the 

model economy when inflation is at the target 

rate of 2 percent and the initial level of the output gap is zero.42 With high initial credibility—

reflecting conditions prevailing in advanced 

economies—inflation rises to more than 3 percent following the inflationary supply shock 

(Figure 3.14, left panels). The optimal policy 

response brings inflation back to the target 

within eight quarters—which is in line with conventional estimates of the lags involved in the 

transmission of monetary policy. The interest 

rate has to rise temporarily to a peak of about 

5 percent—about 1 percentage point above the 

neutral rate of 4 percent assumed in the model. 

In the low-credibility case, the general picture is 

roughly similar, although inflation rises by more, 

and the interest rate increase required to bring 

inflation back to target is proportionally higher 

(Figure 3.14, right panels).

42The experiments are based on supply shocks that 

either permanently increase or decrease the price of 

commodities. The output gap is defined so that a positive value is excess demand and associated with inflation 

pressures. 

0 8 16 24

0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24

0

1

2

3

4

0 8 16 24

0

1

2

3

4

Figure 3.14. Stylized Advanced Economy with Adverse 

and Favorable Supply Shocks

(Percent; quarters on the x-axis)

 Source: IMF staff calculations.

Adverse and favorable supply shocks are broadly symmetric in their impact on 

inflation and output and monetary policy implications if credibility is high.

Credibility Stock

Year-over-Year Inflation

0 8 16 24

2

3

4

5

6

7

0 8 16 24

2

3

4

5

6

7

Nominal Interest Rate

Adverse shock Favorable shock

High Credibility Low Credibility

0 8 16 24

0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Output Gap

0 8 16 24

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Monetary Policy Responses to Commodity Price 
Chapter3  Is Inflation Back? Commodity Prices and

Policymakers around the world have recently 

shown much concern about heightened inflation pressures, with sharp spikes in oil and food 

prices starting to feed into headline and even 

core inflation in a large number of countries. 

The question is whether monetary policy 

arrangements, specifically the dollar standard 

that has many countries pegging their currencies to the U.S. dollar (formally or informally 

through heavily managed exchange rates) can 

be held partly accountable for this development. And, if yes, what would be the impact of 

adopting alternative approaches?

Under current monetary policy arrangements, 

the United States exports its monetary policy 

stance to a significant proportion of the global 

economy, when other countries either peg their 

exchange rates or intervene in foreign exchange 

markets. But the world is currently facing highly 

asymmetric shocks, with the United States and 

the euro area being slowed by financial strains 

and terms-of-trade losses and much of the rest 

of the world continuing to expand at historically 

high rates. A monetary policy that is appropriate for the United States, namely, relatively low 

nominal and real interest rates, is therefore 

highly inappropriate elsewhere. 

This box seeks to answer two questions. First, 

if the most significant exchange rate pegs continue for the time being, is it in the best interests of the United States to take into account 

the effects of its monetary policy on the world 

economy? Second, given current circumstances, 

what difference would it make to the behavior 

of the world economy and of individual economies if the countries that now peg to the dollar 

moved to more flexible exchange rate regimes? 

Monetary Policy and Core Inflation

This attempt to answer these questions 

involves illustrative dynamic simulations that 

use the Bank of Canada’s version of the Global 

Economy Model (BoC-GEM).1 This is a fiveThe main authors of this box are Michael Kumhof, 

Douglas Laxton, and Dirk Muir. 1See Lalonde and Muir (2007).

region dynamic stochastic general equilibrium 

model that separately specifies each region’s 

monetary policy regime as either a peg to the 

U.S. dollar or as an inflation-targeting regime.2

The latter is characterized by an interest rate 

reaction function whereby nominal interest 

rates are raised when inflation accelerates. 

These characterizations of monetary policies are 

not intended to be an accurate depiction of policies but rather a useful stylized representation 

that can help shed light on the issues. A critical 

feature of BoC-GEM for this investigation is its 

assumption of significant nominal rigidities in 

manufacturing and services but no nominal 

rigidities in the oil and commodity sectors.3 This 

implies that if monetary policy is solely concerned with domestic stabilization of price and 

output volatility, it should not attempt to pursue 

a strict short-run target that includes oil and 

commodity inflation, but should instead focus 

on stabilizing “core inflation” in the remaining 

sectors, which is therefore our baseline assumption. Finally, given that reduced spare capacity 

and low supply elasticities appear to have been 

major factors behind the recent volatility of oil 

and food prices, the model introduces factor 

adjustment costs that limit the short-term supply 

response in these sectors. As a result, following 

a positive shock that raises global demand, there 

will first be a spike in prices and only later a 

significant output response.

The baseline simulation is shown as the black 

lines in the figure. In the initial period, the 

United States lowers its interest rate by 2.5 percentage points in response to a contractionary 

2The regions are United States (21.2% of world 

GDP using purchasing-power-parity (PPP) weights), 

emerging Asia (24.8%), commodity exporters 

(15.2%), Canada (1.8%), and remaining countries 

(37.0%). The simulations do not address the issue 

of transitions from one monetary regime to another. 

They also do not address aspects of monetary policy 

other than the pure timing of interest rate changes 

such as questions of portfolio preferences for reserve 

assets in different currencies or questions of financial 

system regulation and control of credit expansion. 3The commodity sector includes but is not limited 

to food production.

Box 3.3. Monetary Policy Regimes and Commodity Prices
shock to consumption and investment demand 

and elevated concern about financial sector 

stability. This monetary intervention dampens 

the effects of the demand shock, with output 

falling up to 1.2 percent below potential in the 

year following the shock. 

At the same time, demand in emerging Asia 

(EA) and a group of oil-exporting countries 

(GOEC) continues to grow rapidly.4 Using 

exchange rate pegs, these regions’ interest 

rates cannot be raised in a countercyclical 

fashion, and instead they fall almost one for 

one with U.S. rates.5 Together with the highly 

inflationary effects of the shocks, this leads to 

sharply lower real interest rates that amplify 

rather than dampen the output effects of the 

shocks. GDP in these regions therefore expands 

sharply, by 4.5 percent for EA and 2.8 percent 

for GOEC, while inflation, both headline and 

core, increases by about 4.5 and 2.0 percentage 

points, respectively. This additional demand 

originates in regions representing only about 

20 percent of world GDP,6 but because growth is 

very commodity-intensive in EA and GOEC, this 

exerts strong upward pressure on international 

oil and commodity prices, which rise by 14 percent and 5.3 percent, respectively. This in turn 

accounts for the moderate increase shown in 

U.S. headline inflation of 0.4 percentage point 

in the initial period, despite the U.S. slowdown. 

The reason is that these highly flexible prices 

immediately pass through into headline inflation. Core inflation does fall with demand, but 

after about a year, it picks up as some oil and 

commodity inflation feeds through. 

The dynamics of inflation in the baseline are 

almost entirely due to the underlying demand 

shocks and their amplification by monetary 

policy, rather than to the initial large spikes in 

oil and commodity prices that are due to supply4The figure only shows simulation results for EA; 

results for GOEC are very similar. 5The small observed difference is due to a foreign 

exchange risk premium that is increasing each 

region’s net foreign liability position. 6World GDP expands by about 1.4 percent.

0 10 20 30 40 -2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

0 10 20 30 40

-2

0

2

4

6

8

0 10 20 30 40

-4

-2

0

2

4

6

0 10 20 30 40

0

1

2

3

4

5

0 10 20 30 40

-3

-2

-1

0

1

2

3

Box 3.3 figure 1

Adjustment Scenarios1

 Source: IMF staff calculations.

 Plots show deviations from control. Nominal interest rate and 

inflation are in percentage points. Real GDP and prices are in

percent. Quarters on the x-axis.

1

United States Emerging Asia

U.S. targets core and oil price inflation

Baseline

0 10 20 30 40 -3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5 Nominal Interest Rate

Real GDP Real GDP

0 10 20 30 40 -0.8

-0.4

0.0

0.4

0.8 Headline Inflation

U.S. Dollar Oil Price

0 10 20 30 40 -4

0

4

8

12

16 U.S. Dollar Commodity

Price 

Flexible exchange rates everywhere

0 10 20 30 40

-4

-2

0

2

4

Core Inflation 6

0 10 20 30 40 -1.0

-0.5

0.0

0.5

1.0 Core Inflation

Nominal Interest Rate

Headline Inflation

Monetary Policy Responses to Commodity Price 
Chapter3  Is Inflation Back? Commodity Prices and

A positive disinflationary shock has roughly 

symmetric implications for the inflation rate 

and for the other key variables. Thus, there is 

a transitory decline in inflation, which allows 

the central bank to lower the interest rate 

temporarily.

side rigidities.7 When the underlying shocks are 

to demand, flexible commodity prices therefore 

constitute a bellwether for underlying imbalances and overheating, rather than representing 

a problem in and of themselves. The situation 

would be very different, of course, if the underlying shocks were shocks to supply, an issue that 

is not addressed here.

Should the United States Account for the Global 

Impact of Its Monetary Policy?

The U.S. Federal Reserve could in principle 

take account of the effects of its monetary 

policy on inflation in the rest of the world. But, 

because targeting a measure of overall world 

inflation is not a realistic option for an institution with a mandate for domestic price and 

output stability, we consider a scenario in which 

the Fed, in addition to responding to domestic 

core inflation, also responds to oil price inflation. The corresponding simulations are shown 

as the blue lines in the figure. Monetary policy 

now is much less accommodative, with nominal interest rates dropping initially by around 

1.5 percentage points instead of 2.5 percentage 

points. They then quickly rise to 0.3 percentage point above the neutral rate in response to 

upward pressure on oil prices. 

Relative to the baseline, under this policy rule, 

the U.S. output gap deteriorates by 1.2 percentage points, with a cumulative 10-year difference 

in output losses of 3 percent. On the other hand, 

a less-accommodative U.S. monetary policy significantly mitigates the boom-and-bust cycle in EA 

and GOEC, with a 1 percent reduction in excess 

7This requires a simulation, not shown here, that 

eliminates supply-side rigidities in energy and commodities. The only major resulting difference is that 

the maximum increase in real oil prices is 3.5 percent 

instead of 14 percent and the maximum increase in 

commodity prices is 4 percent instead of 5.3 percent.

demand in the first year. The impact on world 

oil prices is large, with the peak increase reduced 

from 14 percent to 4 percent. A U.S. focus on 

oil price inflation is not only contractionary at 

home, but it also induces much greater volatility 

in inflation, with the benefit again accruing to 

EA and GOEC, whose inflation volatility falls by 

about a third. Adopting a more global measure 

of inflation, while of significant benefit to EA 

and GOEC, is therefore highly undesirable for 

the United States. But these regions have a far 

more powerful option at their disposal to help 

themselves without requiring sacrifices from others—the move from fixed to flexible exchange 

rates.

What Are the Benefits of Flexible Exchange 

Rate Regimes?

The red lines in the figure illustrate a 

scenario in which EA and GOEC also follow 

an inflation-targeting regime with flexible 

exchange rates, which causes them to sharply 

increase nominal interest rates in response to 

their demand shocks. This roughly halves the 

output expansion in these regions, with inflation rates only one-quarter of their baseline values. The effect on U.S. output, through reduced 

demand for U.S. exports, is less than 0.05 percent in the first year and virtually zero thereafter; the same is true for U.S. core inflation. 

But initial U.S. headline inflation rises by only 

half as much as under an exchange rate peg, 

principally because lower demand outside the 

United States causes the oil price to rise much 

less strongly, by 9 percent instead of 14 percent. 

This provides the answer to the second of 

our questions: Under current circumstances, 

flexible exchange rates would indeed make a 

large difference for countries now pegging to 

the dollar, with beneficial effects for output and 

inflation stabilization, including the stabilization 

of oil and commodity prices. 

Box 3.3 (concluded)
Supply Shocks and Existing Inflation Pressu

In a second simulation, the same shock hits 

an economy where there are excess demand 

pressures and inflation is already significantly 

above target. Initial inflation is assumed to be 

at 8 percent, which is above the long-run target 

of 3 percent—similar to the inflation pressures from overheating faced today by some 

emerging economies. With initial inflation 

above target and with low policy credibility, 

an adverse supply shock will have larger 

second-round effects on inflation (Figure 3.15, 

right panels). As expected, an aggressive 

immediate interest rate response is needed to 

bring inflation back to target, with rates rising 

to 14 percent, an increase substantially larger 

than the increase in inflation. Interest rates 

also need to remain higher for longer, and the 

negative output gap is longer-lived. Thus, even 

if policy responds appropriately, an inflationary 

supply shock in conjunction with low credibility 

results in a period of stagflation. By way of 

comparison, if credibility is higher, inflation can be brought back to target with a less 

aggressive interest rate response and a lower 

output cost (Figure 3.15, left panels). In contrast, in the case of a favorable, disinflationary 

supply shock, it is optimal to reduce inflation 

over a shorter time period than otherwise, as 

the more rapid gains from credibility lower the 

output costs of reducing inflation.

Supply Shocks with Delayed Monetary Polic

Responses

In a final simulation, monetary policy is 

assumed to fall behind the curve. Specifically, 

optimal policy is overruled for two quarters by 

a decision to hold interest rates constant in the 

face of an inflationary supply shock. In the case 

of on-target initial inflation and high policy 

credibility, the picture does not change materially from the path under the optimal responses 

discussed above (Figure 3.16, left panels). The 

delay does mean, however, that the interest rate 

has to rise by more than otherwise. In contrast, 

0 8 16 24

0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24 0

2

4

6

8

10

0 8 16 24 0

2

4

6

8

10

Figure 3.15. Stylized More-Vulnerable Emerging

Market Economy with Adverse and Favorable Supply 

Shocks

(Percent; quarters on the x-axis)

 Source: IMF staff calculations.

The symmetry between adverse and favorable supply shocks disappears if monetary

policy credibility is low and initial inflation is already above target. A more aggressive

immediate interest rate response is needed to bring inflation back to target after an

adverse supply shock.

Credibility Stock

Year-over-Year Inflation

0 8 16 24 4

8

12

16

0 8 16 24 4

8

12

16

Nominal Interest Rate

Adverse shock Favorable shock

High Credibility Low Credibility

0 8 16 24

0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24

-4

-3

-2

-1

0

1

Output Gap

0 8 16 24

-4

-3

-2

-1

0

1

Hypothetical baseline forecast before additional shocks

Monetary Policy Responses to Commodity Price 
Chapter3  Is Inflation Back? Commodity Prices and

in the case of excessive initial inflation and low 

credibility, the delay in raising interest rates 

in response to an adverse supply shock causes 

inflation to ratchet upward and to remain persistently higher than the long-term target. The 

damage to credibility means that significantly 

larger interest rate increases and a more prolonged negative output gap are needed to bring 

inflation back to target.43 At the same time, the 

time horizon for inflation stabilization lengthens, which increases the risks of possible future 

adverse supply shocks.

If the adverse supply shock resulted in an 

upward trend in commodity prices rather than 

a one-time permanent increase in prices, the 

monetary policy challenges associated with low 

credibility and existing inflation pressures would 

increase further. Simulations (not reported) that 

consider a more persistent rise in commodity 

prices show that such shocks would require even 

more aggressive monetary policy responses. The 

costs from falling behind the curve would be 

even greater with such a supply shock.

To sum up, the simulation results underline the overarching importance of monetary 

policy credibility. When credibility is low, the 

short-run inflation-output trade-off is worse, 

which implies that the policy interest rate must 

increase more vigorously in response to adverse 

supply shocks with second-round effects. Inappropriate actions or delays can quickly undermine credibility and make achieving price 

stability more difficult.

Summary and Conclusion

The world economy has experienced the 

broadest and most sustained commodity price 

boom since the early 1970s. The boom has 

largely been driven by the interaction of strong 

global growth, a lack of sector-specific spare 

43Historical experience supports this result. For example, in the United States and Canada in the early 1980s, 

short-term interest rates rose well above 20 percent, following the adoption of anti-inflation policies by the U.S. 

Federal Reserve and the Bank of Canada.

0 8 16 24 0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24 0

2

4

6

8

10

12

0 8 16 24 0

2

4

6

8

10

12

Figure 3.16. Potential Costs of Delaying Interest Rate

Hikes

(Percent; quarters on the x-axis)

 Source: IMF staff calculations.

Delaying the monetary policy response to adverse supply shocks implies that interest

rates have to rise more in the end to bring inflation back to target, with possible

disruptions to credibility building.

Credibility Stock

Year-over-Year Inflation

0 8 16 24 3

6

9

12

15

18

0 8 16 24 3

6

9

12

15

18

Nominal Interest Rate

With delay Without delay

Advanced Economies,

High Credibility

More-Vulnerable 

Emerging Economies, 

Low Credibility

0 8 16 24 0.0

0.2

0.4

0.6

0.8

1.0

0 8 16 24 -4

-3

-2

-1

0

1

2

3

Output Gap

0 8 16 24 -4

-3

-2

-1

0

1

2

3
capacity and low inventories from the onset of 

the boom, and slow supply responses. In addition, commodity-specific factors have contributed 

to the recent surge in food prices, including 

demand related to biofuel production, supply 

disruptions for major crops, and trade restrictions. Cross-commodity price linkages have reinforced the price momentum, with rising energy 

prices spilling into food prices. In contrast, the 

increasing role of commodities as alternative 

financial assets has had little, if any, discernible 

systematic impact on prices, although shifts in 

market sentiment can affect short-term price 

dynamics, and financial variables such as interest 

rates can affect prices through their effects on 

physical demand and supply.

Recent developments suggest that some of the 

factors driving the current boom appear to be 

unwinding. Prospects of slowing global growth in 

2008–09—partly in response to high commodity 

prices—the resolution of weather-related supply constraints for key food crops this year, and 

increased oil supply have already led to some easing of commodity prices. However, supply constraints and low inventories are likely to remain 

in place for some time, and the momentum of 

demand growth in large emerging economies 

remains robust. The extent of any further easing 

of prices will depend on the evolving balance 

between supply factors and global growth, with 

considerable scope for price volatility.

Barring a sharp drop in commodity prices, 

inflation risks will remain elevated for some 

time. The adjustment to the earlier commodity 

price surge is still in train in many economies, 

and the challenge remains to accommodate 

these relative price changes without secondround effects, that is, without spillovers into 

underlying inflation.

The chapter’s empirical findings on the 

pass-through from food and fuel prices to core 

inflation and inflation expectations suggest 

that the risks of second-round effects depend 

importantly on the credibility of monetary 

policy and its ability to anchor expectations and 

the weight of commodities—especially food—in 

final expenditure. Emerging and developing 

economies score lower along these dimensions, 

and a number of them are thus at greater risk, 

notwithstanding some offset from generally 

more flexible labor markets. Such risk concerns 

are corroborated by the recent increases in core 

as well as headline inflation in some of these 

economies. Although inflation risks are dimminishing in advanced economies owing to the 

deflationary impact of the financial turmoil, 

these countries may not be immune to inflation risks. Because the recent commodity price 

shocks have been larger and more persistent 

than they were during the period used in the 

analysis, the actual pass-through may surprise 

on the upside, unless the global slowdown 

intensifies.

There are growing signs that monetary policy 

has not yet responded adequately to the risks of 

rising inflation in some emerging and developing economies. Real policy interest rates in 

many of these economies are low, even in the 

face of strong growth rates, recent increases in 

core inflation, and relatively higher risks of second-round effects from recent commodity price 

increases. In some countries, this partly reflects 

exchange-rate-related constraints on monetary 

policy, which resulted in some economies having imported the expansionary U.S. monetary 

policy stance. In turn, monetary policies that are 

insufficiently tight to contain strong domestic 

demand may recently have put some additional 

pressure on international commodity prices.

As the chapter’s simulation results highlight, 

delays in responding to rising inflation can 

erode monetary policy credibility, particularly if 

inflation expectations are not well anchored—

which the chapter suggests remains the case 

for many emerging economies. As a result, 

more aggressive monetary policy responses may 

ultimately be needed to bring inflation back to 

target, at a higher cost in terms of output than 

would have been involved in a timely monetary 

policy response. Such dynamics are generally 

reinforced by higher initial inflation levels or 

inflation pressure from tightening capacity 

constraints. At the same time, even with a timely 

response, the time needed to reduce inflation 

Summary and Conclusio
Chapter3  Is Inflation Back? Commodity Prices and

is likely to be longer with low policy credibility, making an economy more vulnerable to 

future adverse supply shocks. This highlights 

the importance of an adequate monetary policy 

response to the rising inflation seen in the wake 

of recent commodity price surges, especially 

where current inflation is already high (“above” 

target) for other reasons, notably overheating, 

and where policy credibility is low.

Appendix 3.1. Recent Commodity Market 

Developments

The main author of this appendix is Valerie MercerBlackman, with contributions from To-Nhu Dao and 

Nese Erbil.

Commodity prices rose by 33 percent during 

the first six months of 2008, led by soaring fuel 

prices, before softening in the third quarter of 

the year. Oil prices continued to rise rapidly 

over most of this period, and they remain at 

high levels by historical standards, notwithstanding some recent declines. Food prices surged in 

the first quarter of 2008, led by wheat and rice, 

but stabilized thereafter, as prices of these two 

grains started to decline. Prices of agricultural 

raw materials and beverages increased only 

moderately overall, whereas base metals prices 

broadly stabilized (Figure 3.17, top panel).

Fuel Prices Leading the Surg

Oil prices reached an all-time record high (in 

both nominal and real terms) of $143 a barrel 

on July 11, and then declined to just over $100 

by mid-September.44 Oil prices in euros also 

reached record highs, although the rise was 

24 percentage points less than in dollar terms 

during the first six months of 2008 (Figure 3.17, 

second panel).

Despite rising slightly from their lows in late 

2007 in terms of forward cover, OECD stocks 

44Unless otherwise stated, oil prices refer to the IMF’s 

Average Petroleum Spot Price, which is a simple average 

of the prices for the West Texas Intermediate, Dated 

Brent, and Dubai Fateh grades.

50

100

150

200

250

300

350

400

450

 Sources: Bloomberg Financial Markets; and IMF staff estimates.

Commodity Price Indices

(January 2003 = 100)

Food

Figure 3.17. Commodity and Petroleum Prices

Metals

Beverages

2003 04 05 06 07 08

Energy

Agricultural

raw materials

20

40

60

80

100

120

Average Petroleum Spot Prices in Selected Currencies 140

(price a barrel) in U.S.

dollars

in SDRs

2001 02 03 04 05 07

in euros

60

70

80

90

100

110

120

130

New Yo 140 rk Mercantile Exchange West Texas Intermediate Crude Oil (WTI) 

Forward Curves

(U.S. dollars a barrel)

As of April 18, 2007

12 24 36 48 60

06

Spot

As of January 3, 2008

As of September 19, 2008

Months after pricing date

09

Aug.

08

0

20

40

60

80

100

120

Prices of Energy Commodities 140

(U.S. dollars a barrel of oil equivalent) Oil

U.S. gas

EU gas

1992

Australian coal

94 96 98 2000 02 04 06 Aug.

08
remained at relatively low levels in the first half 

of 2008. Reflecting this and the recent easing 

of broad market conditions (see below), the 

futures price curve moved from backwardation 

in the first quarter to a very mild contango in 

recent weeks, a constellation that provides incentives for further near-term inventory buildup 

(Figure 3.17, third panel). Nevertheless, shifts of 

the futures curve have dominated movements 

along the curve in terms of magnitudes, particularly in mid-September, when broad financial 

market volatility spilled into oil markets.

Diesel prices have risen much faster than gasoline prices, reflecting strong demand growth for 

this product relative to global refining capacity. 

Consequently, refining margins for diesel have 

generally been much higher than for gasoline, 

although gasoline crack spreads temporarily 

shot up in mid-September on hurricane-related 

temporary refining outages. Prices of other fuel 

products have followed crude oil prices, albeit 

with a lag (Figure 3.17, bottom panel). Coal 

prices, in particular, rose by 70 percent during 

the first six months of 2008, the largest increase 

among all energy products. This reflected shortterm factors (such as supply disruptions early in 

the year), bottlenecks in major shipping ports, 

and the gradual substitution from coal in power 

generation away from more expensive fuel oil.

World oil consumption moderated slightly after 

seven consecutive years of rising prices, rising by 

roughly 0.7 million barrels a day (mbd) during 

the first half of 2008 (year over year), compared 

with 1 mbd during 2005–07. Consumption in 

OECD member countries declined by 1.0 mbd 

during this period, primarily in the United States, 

but rose by 1.5 mbd in non-OECD countries, led 

by China, the Middle East, and Latin America 

(Table 3.3, and Figure 3.18, top panel). Across 

different products, demand for transportation 

fuels (gasoline and diesel) has grown the most, 

driven by increased vehicle ownership in emerging and developing economies amid continued 

fuel subsidies and price controls.45 However, 

45In many economies that have limited the fuel price 

pass-through, the fiscal burden from fuel subsidies has 

48

50

52

54

56

58

60

62

-1

0

1

2

3

4

5

6

7

60

65

70

75

80

85

90

95

100

105

-2

0

2

4

6

8

70

75

80

85

90

Semiannual World Oil Consumption

(millions of barrels a day; year-over-year change on left scale)

Figure 3.18. World Oil Market Balances and Oil Futures 

Price

United States

2004 05 06 07

Total consumption

(right scale)

OECD Inventory Demand Forward Cover

(days)

Averages 2003–07

Actual

08 09:

H2

China Other advanced economies

Emerging and developing economies

Semiannual World Oil Capacity and Production

(millions of barrels a day; year-over-year change on left scale)

Saudi Arabia

2004 05

Total capacity

(right scale)

06 07 08

CIS Other OPEC countries

Other non-OPEC countries

4

2004 05 Jul.

08

06

 Sources: Bloomberg Financial Markets; International Energy Agency; U.S. Energy

Information Agency; and IMF staff estimates.

 CIS is the Commonwealth of Independent States. OPEC is the Organization of

Petroleum Exporting Countries.

 Includes OPEC natural gas liquids.

 Band is based on averages for each calendar month during 2003–07 and a 40 percent 

confidence interval based on deviations during this period.

 From futures options.

1

07

Brent Crude Oil Futures Prices as of September 19, 2008

(U.S. dollars a barrel)

5

2006 07 08 30

90

180

60

150

Futures

50 percent confidence interval

70 percent confidence interval

90 percent confidence interval

Aug.

09

09:

H2

2

2

120

4

Biofuels

1 

5

3

 OECD is the Organization for Economic Cooperation and Development. 3

Appendix 3.1. Recent Commodity Market Deve
Chapter3  Is Inflation Back? Commodity Prices and

1

gasoline consumption in the United States fell 

by 1.7 percent in the first half of 2008—the first 

drop in at least 15 years—and has continued 

to fall to date, according to preliminary data, 

been increasing. Indeed, major product importers such 

as the Islamic Republic of Iran, Malaysia, and Pakistan 

have increased domestic prices by about 20 percent in 

response to this rising burden. 

reflecting constrained incomes amid weakening 

economic activity and, increasingly, a demand 

response to one of the sharpest pickups in gasoline prices in recent U.S. history.

Oil production increased by 1.6 mbd during the first half of 2008, as OPEC production 

increased by 1.7 mbd (year over year), partly on 

account of the organization’s September 2007 

decision to raise output as of November 2007. 

Table 3.3. Global Oil Demand and Production by Regi

(Millions of barrels a day)

Year-over-Year Percent Change

2006 2007

2008 2007 2008 2008 2007 2008

Proj. H1 H2 H1 2006 2007 Proj. H1 H2 H1

Demand

OECD 49.6 49.2 48.4 49.0 49.3 48.1 –0.5 –0.8 –1.3 -1.0 –0.6 –1.9

North America 25.4 25.5 24.8 25.5 25.5 24.8 –0.6 0.5 –2.6 1.1 –0.2 –3.4

Of which:

United States 21.0 21.0 20.3 21.1 22.0 20.2 –0.5 0.0 –3.5 0.8 0.8 3.9

Europe 15.7 15.3 15.2 15.1 15.5 15.0 0.1 –2.4 –0.4 –3.6 –1.2 –0.4

Pacific 8.5 8.3 8.4 8.4 8.3 8.5 –1.4 –1.6 1.1 –2.4 –0.8 0.9

Non-OECD 35.5 36.9 38.3 36.6 37.1 38.2 4.0 3.8 3.8 3.7 3.9 4.0

Of which:

China 7.2 7.5 8.0 7.5 7.6 7.9 7.8 4.6 5.6 4.8 4.3 4.9

Other Asia 9.0 9.3 9.5 9.3 9.2 9.6 2.3 2.8 2.4 2.2 3.5 3.6

Former Soviet Union 4.1 4.1 4.2 4.0 4.2 4.1 3.4 1.7 2.5 2.1 1.3 2.2

Middle East 6.2 6.5 6.9 6.5 6.6 6.8 4.0 4.7 5.9 5.5 4.0 6.0

Africa 3.0 3.1 3.1 3.1 3.1 3.2 0.9 3.9 1.7 3.0 4.7 2.2

Latin America 5.3 5.6 5.9 5.5 5.7 5.8 4.6 5.2 4.3 4.8 5.7 4.7

World 85.1 86.1 86.8 85.7 86.5 86.2 1.3 1.1 0.8 1.0 1.3 0.7

Production

OPEC (Current composition)1 36.3 35.9 35.5 36.4 37.2 0.8 –0.9 –2.1 0.2 4.7

Of which:

Saudi Arabia 10.4 10.0 9.9 10.1 10.4 –1.5 –4.4 –7.0 –1.8 5.6

Nigeria 2.5 2.3 2.3 2.4 2.1 –5.2 –4.8 –4.4 –5.1 –8.2

Venezuela 2.8 2.6 2.6 2.6 2.6 –5.8 –7.8 –9.6 –5.9 –0.7

Iraq 1.9 2.1 2.0 2.2 2.4 4.9 9.9 5.3 14.3 23.9

Non-OPEC 49.2 49.6 49.9 49.8 49.4 49.7 1.1 0.9 0.6 1.6 0.1 –0.2

Of which:

North America 14.3 14.2 14.1 14.1 14.1 14.2 0.6 0.4 –1.6 –0.3 –0.4 1.0

North Sea 4.8 4.6 4.3 4.7 4.5 4.4 –7.6 –5.0 –6.6 –5.6 –4.4 –5.5

Russia 9.8 10.1 10.0 10.1 10.1 10.0 2.2 2.4 –0.5 3.2 1.6 –0.8

Other former Soviet Union 2.4 2.7 2.9 2.7 2.7 2.9 11.1 12.0 8.4 16.9 7.5 6.5

Other non-OPEC 17.9 17.9 18.6 18.3 18.0 18.3 2.3 0.4 3.5 2.3 –0.2 –0.4

World 85.5 85.6 85.4 85.8 86.9 1.0 0.1 0.0 0.2 1.9

Net Demand2 –0.4 0.5 0.3 0.7 –0.

Sources: International Energy Agency, Oil Market Report (September 2008); and IMF staff calculations. 1Includes Angola (subject to quotas since January 2007) and Ecuador (rejoined OPEC in November 2007, after having suspended its membership from December 1992 to October 2007). 2Net demand is the difference between demand and production. It includes a statistical difference. A positive value indicates a tightening of 

market balances.
Within OPEC, production increases in Saudi 

Arabia (thereby risinig above the September 

2007 production quota), a pickup in Iranian 

exports, and production recovery in Iraq more 

than offset output losses in Nigeria (from 

continued attacks on production facilities) and 

sluggish Venezuelan output. In contrast, nonOPEC crude oil supply fell by 0.1 mbd, reflecting mostly unexpected falls in Russian output 

and field declines in the North Sea and Mexico. 

In addition, liquid fuel supply has benefited 

from important increases in OPEC natural gas 

liquids (NGLs, not subject to quotas) and biofuels, which contributed one-quarter of the net 

increase in supply during the first half of 2008 

(Figure 3.18, second panel).

In the near term, oil market conditions 

may ease further. On an annual basis, the 

International Energy Agency (IEA) forecasts 

global demand growth at 0.8 mbd in 2008 and 

0.7 mbd in 2009, down from 1.1 mbd in 2007. 

Non-OPEC supply is expected to pick up by 

1.2 mbd during the second half of 2008 (compared to the same period in the previous year), 

before decreasing again gradually in 2009. The 

completion of a host of new projects, particularly in Saudi Arabia, should temporarily lift 

OPEC spare capacity levels. The easing may not 

be long-lasting, however. In its recent MediumTerm Oil Market Report, the IEA expects OPEC 

spare capacity (as a share of global consumption) to fall to below 2008 levels by 2012, as 

OECD demand recovers in the outer years and 

supply growth trends remain limited (partially 

because of increased field decline rates).

With the moderate easing of market conditions—at least through end-2009—but with 

inventories and spare capacity still low, prices 

are expected to remain high, albeit below recent 

peaks. Oil futures options prices suggest a much 

wider range of uncertainty about price prospects than in recent years. As shown in the fan 

chart (Figure 3.18, bottom panel), the 90 percent confidence interval for end-2008 oil prices 

ranges from about $60 a barrel to more than 

$165 a barrel, a much wider range than typically 

observed.

Rising Food Prices Driven by Prices of Major Crops

Grain and vegetable oil prices picked up 

sharply during the first half of 2008 amid trade 

restrictions and tight supplies, leading to a 

23 percent increase in the IMF’s food price 

index during the first six months of 2008. 

Wheat prices reached record-high nominal 

levels in early March of this year following poor, 

drought-related crops in 2006 and 2007 but 

have declined since, as more favorable weather 

conditions led to a bumper crop this year. Rice 

prices began to rise in late 2007, as consumers 

in developing economies switched from highpriced wheat and corn toward cheaper rice. 

Price increases accelerated in early 2008, when 

major exporters started to impose trade bans 

(Figure 3.19, top panel).46

Corn and soybean prices have remained high 

so far in 2008, with a short-lived spike in June 

when floods in the U.S. Midwest (the largest 

producing region in the world) led to fears of 

crop damage. Other agricultural product prices 

have also risen, although much more gradually, 

partly because supplies and inventory levels have 

so far remained more comfortable. Meat and 

poultry prices have risen due to higher animal 

feed costs. Food prices are expected to remain 

high, given continued demand pressures, particularly for corn-based ethanol. As limited acreage 

moves from corn to wheat and soybeans on the 

margin in response to relative price movements, 

corn production is expected to fall slightly in 

2009 from 2008 (Figure 3.19, second panel). 

Moreover, high oil prices will affect agricultural 

production costs more broadly in the coming 

years, in particular through the effect on higher 

fertilizer prices.

Metal Prices Stabilize

Divergent trends in fundamentals explain 

the widely varying performance of base metal 

46Rice is mostly consumed domestically, and the share 

of global trade to consumption is very small (with large 

importers receiving the bulk of rice from only one or two 

producers). 

Appendix 3.1. Recent Commodity Market Deve
Chapter3  Is Inflation Back? Commodity Prices and

markets through 2008 to date. Iron ore47

prices increased by 66 percent, and copper and 

aluminum prices rebounded by 17 percent and 

21 percent, respectively, but zinc and nickel 

prices declined sharply. While demand for 

copper and aluminum, which are more widely 

traded than the other metals, has weakened, 

supply in key producers (Chile, China, and 

South Africa) has been adversely affected by 

disruptive power shortages. In contrast, zinc 

and nickel inventory levels at the London 

Metals Exchange have recovered in the face of 

declining demand and rising production (Figure 3.19, third panel).

Looking ahead, base metal prices should 

ease in 2008 and 2009, as demand growth is 

expected to weaken with slowing global industrial production and the end of the Olympic 

Games construction run-up in China (Figure 3.19, bottom panel). However, continued 

supply-side problems will likely provide for tight 

copper and aluminum market balances for 

some time.

Appendix 3.2. Accounting for Food Price 

Increases, 2006–08

The main author of this appendix is Valerie MercerBlackman, with contributions from Stephen Tokarick.

This section describes the methodology used 

in estimating the impact of the various demand 

and supply factors on the prices of the six key 

commodities discussed in the main text (as 

shown in Figure 3.5, third panel). For tractability, the analysis is based on simple partial equilibrium approaches.

The amount of weather-related supply shortfalls, qi

sh, was determined by the deviation of 

global production from trend, based on annual 

crop data since 1990.48 The percent change in 

47Iron ore prices are determined by annual contracts 

among producers and steel makers. The April 2008 

increase largely reflected soaring mining costs over the 

previous year and strong demand. 48Typically, shortfalls (negative deviations) were the 

result of lower yields, not reductions in planted acreage, 

 Sources: Bloomberg Financial Markets; World Bureau of Metal Statistics; and IMF staff 

calculations.

 Inventories refer to the sum of global stocks of copper, aluminum, tin, zinc, nickel, and 

lead monitored by the London Metal Exchange. Price refers to a composite index of these 

metals.

Figure 3.19. Developments in Food and Metal Markets

900

1200

1500

1800

2100

2400

2700

3000

3300

40

80

120

160

200

Metal Inventories and Prices 240

Inventories

(left scale)

2002 03 04 05 06 07

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

6000

World Copper and Aluminum Consumption Growth by Regions 7000

(thousands of metric tons)

United States

2000 01 02 03 04 05 06 07

China Other advanced economies

Emerging and developing economies

Metal price index, 2005=100

Thousands of metric tons

Metal price index 

(right scale)

1

1

Aug.

08

80

120

160

200

240

280

Selected Food Prices 320

(index, January 2006 = 100)

2006 07 08 09

Corn

Wheat

Soybeans

Futures curves as of 

September 19, 2008

-50

0

50

100

150

200

Production of Major Crops 250

(difference from previous year in millions of metric tons)

Corn

Soybean

Rice

Wheat

Total Crops

1995–2000

average

01 02 03 04 05 06 07 08 09
the global price of commodity i , as a result of 

the supply shortfall qi

sh was calculated as

pi = %DPi = ei

m *qish = ∑wc

[(ei,c

D*(Ci,c/Mi,c) c

 – ei

S*(Qi,c/Mi,c))]*qish, (1)

where ε

m , the global import demand elasticity of commodity i, is a weighted average of 

the import elasticities of demand of the main 

importing countries (where wc is the import 

weight of country c). This depends on the 

elasticities of demand (ε

i,c ), and supply ( εi,c ) of 

country c, respectively. Mi,c is total imports, Ci,c is 

total consumption, and Qi,c is total production 

of the commodity i in country c.

The price impact of higher energy prices was 

calculated using the contribution of fuel and 

fertilizers to the production cost of each food 

commodity, as reported by the U.S. Department 

of Agriculture (USDA). For the 2007 and 2008 

crop years, the costs were estimated based on 

the IMF commodity price projections, assuming 

that other costs grow at trend. The corresponding cost shares for palm oil and rapeseed oil are 

based on Fedepalma (2008) and North Carolina 

Solar Center (2006) estimates, respectively. The 

calculations assume full pass-through of higher 

costs to prices and a similar cost structure in 

crop production across the globe.

The price impact of increased biofuel 

demand was calculated for food items for which 

more than 1 percent of the crop was used as 

including for wheat and rapeseed oil, thereby corroborating the approach.

biofuel feedstock (which excluded wheat).49

The expansion of demand attributed to biofuels 

is then expressed as the percentage difference 

between the growth in total demand for the 

crop (di) and demand growth excluding biofuels 

(denoted as di

b

).50 The price impact (in percent) was then calculated as

%DPi = (di – di

b

)*(1/ei

D), (2)

where ei

D is the own-price elasticity of demand 

for the crop. A range of elasticity estimates from 

various sources were used (Table 3.4). Moreover, 

because a by-product of corn-based ethanol is 

distiller’s dried grains with solubles (DDGS), 

which is used for animal feed (about 30 percent 

of every bushel of corn used in production), 

this additional supply was deducted from the 

demand for biofuel use.

To measure the impact of trade restrictions, a 

slightly modified version of the trade model in 

Tokarick (2003) was used. Supply and demand 

are modeled as constant elasticity functions, 

using elasticities from Gardiner, Roningen, and 

Liu (1989). Data on commodity trade values 

were taken from the UN COMTRADE database. 

Production value data were estimated using 

49The shares of biofuel feedstocks were calculated using 

USDA data (adjusting the share of each crop used for 

industrial purposes) and IEA data on biofuel production. 50This definition takes into account two competing 

aspects. On the one hand, it is demand change, not 

demand levels, that has the greatest impact on prices. 

On the other hand, it avoids measuring the change from 

such a low base (given that biofuels are a small share of 

total demand), which would exaggerate the impact of 

demand growth for biofuel use on price. 

Appendix 3.2. Accounting for Food Price Increases, 

Table 3.4. Elasticity Estimates Used for Price Calculat

Own-Price-Demand 

Elasticity

Own-Price-Supply 

Elasticity

Cross-Price Elasticity 

of Supply with Wheat

Cross-Price Elasticity of 

Demand with Soybeans1

Corn –0.21 to –0.43 0.50 –0.08 to –00.1 0.36 to 0.54

Rice –0.38 0.32 . . . . . .

Wheat –0.3 0.48 . . . . . .

Soybeans/soybean oil –0.31 to –0.48 0.23 –0.03 . . .

Rapeseed oil –1.2 0.58 –0.62 to –0.8 0.57

Palm oil –0.47 0.21 . . . . . .

1 Soybeans are important substitutes for corn on the supply and demand sides. The cross-supply of corn and soybeans estimates range 

between –0.27 and –0.3.
Chapter3  Is Inflation Back? Commodity Prices and

volume data from the USDA’s FAS database and 

IMF price indices.

These direct effects, which can be considered 

initial shocks, together explain about half of the 

total price increase of these foods during the 

period considered (2006 and 2007 crop years). 

It would be impossible to account fully for the 

indirect effects of the shocks. However, it is 

possible to get a sense of the relative magnitude 

of the cross-effects due to supply and demand 

substitution and comovements. Two indicators 

are considered (see Table 3.2):

• For substitution across commodities: Assuming 

symmetry and no second-order effects, the 

impact of a price increase in commodity j, 

DPj, on commodity price i, DPi, is given by

DPi ei,j DPj —— = —— * (——), (3) Pi ei Pj

where εi,j is the cross-price elasticity of supply (demand) between commodities i and

j, and ε is the own-price elasticity of supply 

(demand) of commodity j, assuming commodities i and j are substitutes in production 

(consumption).

• Comovement across time: This was determined 

using the concordance statistic. The statistic 

was estimated for all commodity price pairs 

using monthly data from January 1957 to May 

2008 (starting in 1980 for rapeseed oil), using 

the methodology of Cashin, McDermott, 

and Scott (1999). The concordance statistic 

between commodities i and j, defined as the 

proportion of time two commodities are on 

the same phase of the cycle, is denoted as

T T

Ci,j = T –1{∑(Si,t *Sj,t) + ∑(1 – Si,t) (1 – Sj,t)}, (4) t=1 t=1

where Si,t is a binary random variable taking 

the value unity when the price of commodity 

i, Pi is in a boom phase and zero when it is in 

a slump phase. The same definition applies to 

Sj. T is the sample size and Cij e{0,1} measures 

the proportion of time the two series are in 

the same phase.

The elasticity estimates used in the calculation are weighted, global composites of 

individual country elasticities taken from 

Gardiner, Roningen, and Liu (1989). Plausible 

elasticity ranges for soybean oil and European rapeseed oil were also taken from the 

FAPRI/GOLD model estimates in Westhoff 

and Young (2000) and Arnade, Kelch, and 

Leetmaa (2002). Estimates and ranges used 

are shown in Table 3.4.

Appendix 3.3. Estimating Inflationary

Effects of Commodity Price Shoc

The main author of this Appendix is Irina Tytell.

This section outlines the methodology behind 

the two econometric exercises discussed in the 

main text and in Figures 3.11 and 3.12.

Commodity Price Pass-Throug

The pass-through coefficients shown in 

Figure 3.11 are obtained using quarterly data 

for 25 emerging economies and 21 advanced 

economies (9 for the 1970–95 period). First, 

the pass-through from international to domestic prices of food and fuel is estimated using 

country-by-country bivariate regressions of the 

following form:

4 4

πt

domestic = a + ∑bi

πt–i

domestic + ∑dπt–iworld + et . (1) i=1 i=0

In these equations π stands for the annualized quarter-over-quarter log difference (in 

percent) in, respectively, food or fuel prices (the 

equations also include seasonal dummies). The 

reported pass-through coefficients reflect the 

full long-term pass-through from international 

to domestic prices:

4

∑di i=0 price pass-through = ———— . (2) 4

1 – ∑bi t=1

Second, the pass-through from domestic food 

and fuel prices to core inflation is estimated 
using the following generalized Phillips curve 

equations for each country:51

4 4

πt = a + ∑biπt–i + ∑gi(yt–i – y*t–i) + ∑fipt–

food

i=1 i=0 i=0

4

+ ∑jipt–

fuel + et (3) i=0

4

∑fi i=0 food price pass-through = ——— . 4

1 – ∑bi i=1

4

∑ji i=0 food price pass-through = ———4

1 – ∑bi i=1

As above, π stands for the annualized quarterover-quarter log difference (in percent) in core, 

food, and fuel prices, while y and y* denote the 

annualized quarter-over-quarter log difference 

(in percent) in, respectively, real and potential GDP (the equations also include seasonal 

dummies).52 In order to limit contamination 

of the estimates by endogenous factors, the 

pass-through from domestic commodity prices 

to core inflation is estimated using predicted 

values of domestic food and fuel inflation from 

the first-stage bivariate regressions. In this way, 

domestic food and fuel prices reflect only the 

variation that is due to changes in international 

prices and lagged effects of domestic price 

developments, rather than movements in labor, 

transportation, and retailing costs that may have 

common origins with overall inflation.

The resulting pass-through coefficients are 

aggregated across countries using weighted 

averages, with weights inversely proportional to 

the standard errors of the corresponding coun51This approach is similar to the one used by De Gregorio, Landerretche, and Neilson (2007) to estimate passthrough from the world oil price to domestic inflation. 

See also Blanchard and Galí (2007). 52Core inflation is based on the CPI excluding food 

and energy prices. OECD data on potential GDP are used 

for OECD countries, and the Hodrick-Prescott filtered 

trend is employed to estimate potential GDP for nonOECD countries.

try-specific coefficients.53 Given considerable 

variation across individual—especially emerging—economies that reflects in part differences 

in data quality, measurement of inflation, and 

sample periods, this approach is designed to 

give more weight to more precisely estimated 

pass-through coefficients.

Expectations and Actual Inflatio

The responses of expectations to actual 

inflation shown in Figure 3.12 are based on a 

semiannual panel data set for 14 advanced and 

21 emerging economies that covers the period 

starting in 2003. The exercise links changes in 

expected inflation to changes in actual headline 

inflation and disaggregates the latter into core 

inflation and changes in domestic inflation rates 

for food and fuel:54

Dpi,

expected = l

 + qDpi,

headline + ei,t

= mi + aDpi,

core + bDpi,food

+ gDpi,

fuel + ni,t . (4)

In these equations, Δπ denotes first differences in expected inflation at various horizons

(1, 3, 5, and 6–10 years ahead) and actual inflation (headline, as well as its core, food, and fuel 

components) in percentage points. The data on 

inflation expectations are obtained from Consensus Economics and are based on surveys of 

professional forecasters published twice yearly in 

March/April and September/October. To correspond to these frequencies, the data on actual 

inflation refer to the first and third quarters of 

each year and are measured in year-over-year 

terms. To better disentangle the impact of food 

and fuel from core inflation, a residual from a 

regression of core on food and fuel inflation (in 

first differences) is used in place of actual core 

inflation. The equations also include country53In dynamic models, aggregating country-by-country 

estimates is preferable to aggregating the underlying data 

or using pooled panel regressions, as shown by Pesaran 

and Smith (1995). 54See Goretti and Laxton (2005) and Levin, Natalucci, 

and Piger (2004) for similar analyses, although without 

the disaggregation of headline inflation into core, food, 

and fuel components.

Appendix 3.3. Estimating Inflationary Effects of Commodit
Chapter3  Is Inflation Back? Commodity Prices and

and year-fixed effects. The reported results 

include only the coefficients that are statistically 

significant at the 10 percent level.

The sample of emerging economies is further 

split by the weight of food in the consumer price 

index (CPI) and by the type of monetary policy 

regime.55 Countries are grouped into those with 

high (low) food weights if the weight of food 

in their CPI is above (below) 25 percent. By 

this definition, Chile, China, Colombia, Hong 

Kong SAR, India, Indonesia, Peru, Romania, 

Russia, Taiwan POC, Turkey, and Ukraine have 

a high weight of food in the CPI; Brazil, Czech 

Republic, Hungary, Korea, Mexico, Poland, 

Singapore, Slovak Republic, and Thailand have 

a low food weight. With respect to the type of 

monetary policy regime, inflation targeters are 

defined as countries that introduced this regime 

prior to the beginning of the sample period and 

excludes more recent inflation targeters. Therefore, Brazil, Chile, Colombia, Czech Republic, 

Hungary, Korea, Mexico, Peru, Poland, and 

Thailand are classified as inflation targeters, whereas China, Hong Kong SAR, India, 

Indonesia, Romania, Russia, Singapore, Slovak 

Republic, Taiwan POC, Turkey, and Ukraine are 

classified as non-inflation-targeters.

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Developments, Macroeconomic Impact, and Policy 

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Available at www.imf.org/external/np/pp/

eng/2008/063008.pdf and www.imf.org/external/

np/pp/eng/2008/091908.pdf.

———, 2008c, Global Financial Stability Report: Containing Systemic Risks and Restoring Financial Soundness,

(Washington, April).

Referenc
Chapter3  Is Inflation Back? Commodity Prices and

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c h a pte r 4

This chapter examines why some episodes of financial 

stress lead to economic downturns and others have 

only a limited impact on the overall economy. The 

analysis indicates that episodes of financial turmoil 

characterized by banking sector distress are more likely 

to be associated with severe and protracted downturns 

than episodes of stress centered mainly in securities or 

foreign exchange markets. Countries with more arm’slength financial systems seem particularly vulnerable 

to sharp contractions in economic activity, because of 

the greater procyclicality of leverage in their banking 

systems. This chapter draws implications for economic 

prospects in the United States and the euro area and 

considers how policy responses could help limit the 

output consequences of the current and future financial crises.

The financial turmoil that began in the 

summer of 2007 has mutated into a 

full-blown crisis, encompassing broader 

securities markets and the banking 

systems of several advanced economies. How will 

macroeconomic activity be affected, and what 

can policymakers do both to reduce the economic consequences of this crisis and to forestall 

such crises in the future? Although past episodes 

of stress in banking, securities, and/or foreign 

exchange markets have only sometimes been 

associated with economic downturns (Figure 4.1, 

top panel), these downturns have tended to be 

more severe (Figure 4.1, bottom panel).

An important concept in assessing the impact 

of financial stress on economic activity is the 

role of financial cycles, which have been a constant feature of the economic landscape despite 

the evolution of financial systems through innovation and regulatory changes. Financial systems 

are inherently procyclical, with growth in credit, 

leverage, and asset prices often reinforcing the 

underlying economic dynamic—and in some 

cases leading to a buildup of financial imbalances followed by a sharp correction (see Borio, 

2007; Goodhart, 1996; and Minsky, 1992).

The impact of financial cycles on the real 

economy remains subject to debate in both 

academic and policy circles. One strand of 

research emphasizes the role of the financial 

accelerator in amplifying the effects of financial 

cycles on the real economy through its effect 

on the value of collateral and thereby on the 

willingness of the financial system to provide 

credit to the economy (Bernanke and Gertler, 

1995; Bernanke, Gertler, and Gilchrist, 1999; 

and Kiyotaki and Moore, 1997). In this view, 

shocks that affect the creditworthiness of borrowers tend to accentuate swings in output. 

Another branch of inquiry focuses on lenders’ 

balance sheets and the relationship between 

bank capital and aggregate credit, the so-called 

bank capital channel (Bernanke, Lown, and 

Friedman, 1991; Kashyap and Stein, 1995; Peek 

and Rosengren, 1995; and Altunbas, Gambacorta, and Marqués, 2007). When bank capital 

is eroded, banks become more reluctant to 

lend and may be forced to deleverage, leading to sharper economic downturns. Another 

area of analysis is the extent to which the role 

of the financial accelerator in the economy 

varies with the type of financial system (World 

Economic Outlook, September 2006; and Rajan 

and Zingales, 2003). The general trend toward 

greater reliance on arm’s-length financing and 

less reliance on relationship-based lending may 

have left economies better able to absorb financial stress, as both households and firms can 

now substitute away from banks to markets (and 

thus benefit from the so-called twin engines of 

the financial system).

Financial Stress and Ec

The main authors of this chapter are Subir Lall, 

Roberto Cardarelli, and Selim Elekdag, with support from 

Angela Espiritu and Gavin Asdorian. Hyun Song Shin 

provided consultancy support. Jörg Decressin and Tim 

Lane were chapter supervisors.
Chapter4  Financial Stress and Economic Dow

1

Against this background, this chapter 

addresses the following questions: Why are some 

periods of financial stress associated with slowdowns, or even recessions, while others appear 

to have little impact on the real economy? What 

role is played by the size or location of financial imbalances or the state of households’ and 

firms’ balance sheets? Has financial innovation 

reduced the role of banks in propagating shocks 

from the financial system to the real economy?

To answer these questions, this chapter analyzes episodes of financial stress and economic 

cycles in 17 advanced economies during the 

past three decades.1 It draws lessons from these 

episodes by differentiating among them on the 

basis of the conditions in place at the start of 

the financial stress episode, including the state 

of households’ and firms’ balance sheets and 

the dynamics of credit and asset prices in the 

run-up to the stress episode; the type of financial stress involved (that is, whether related to 

banks, securities markets, or foreign exchange 

markets); and the policy responses. Taken 

together, these factors provide a comprehensive window on the channels and mechanisms 

through which financial stress affects economic 

cycles.2 While establishing the causality between 

financial stress and economic downturns poses 

an inherently difficult challenge, the analysis in 

the chapter makes an attempt to address this 

issue by explicitly accounting for the effects of 

the most common types of shocks studied in the 

macroeconomic literature.

The key findings of this chapter are the 

following:

1The countries included in this study are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Norway, Spain, Sweden, 

Switzerland, United Kingdom, and United States. 2This broader approach complements recent research 

on the empirical relationship between asset prices—such 

as equity and house prices and bond spreads—and the 

dynamics of output during the course of the business 

cycle (see Cihák and Koeva Brooks, forthcoming; and 

Claessens, Kose, and Terrones, 2008) or between bank 

capital, lending, and output (see Bayoumi and Melander, 

2008; and Kashyap, Rajan, and Stein, 2008, in the context 

of the United States). 

 Sources: Haver Analytics; OECD, Analytic Database; OECD, Economic Outlook (2008); 

and IMF staff calculations.

 Measured as the cumulative output loss when output was below the Hodrick-Prescott trend for 

slowdowns and cumulative output loss until recovery for recessions.

Figure 4.1. Financial Stress and Output Loss

Only about half the episodes of financial stress identified in Chapter 4 for advanced

economies over the past three decades were followed by economic downturns.

Slowdowns and recessions preceded by financial stress tend to be longer and more

severe. 

-5

-4

-3

-2

-1

0

Preceded 

by financial

stress

Not 

preceded 

by financial

stress

Preceded

by financial 

stress

Not 

preceded

by financial 

stress

Slowdowns Recessions

Cumulative Output Loss during Slowdowns and Recessions

(median; percent of GDP)

0

20

40

60

80

100

120

1980s

1990s

2000s

All Followed by

slowdowns

Followed by

recessions

Other

Number of Financial Stress Episodes

1

1
• Episodes of financial turmoil characterized 

by banking distress are more often associated 

with severe and protracted downturns than 

episodes of stress centered mainly in securities 

or foreign exchange markets.

• The likelihood that financial stress will be 

followed by a downturn appears to be associated with the extent to which house prices 

and aggregate credit rise in the period before 

the financial stress. Moreover, greater reliance on external financing by households and 

nonfinancial firms is associated with sharper 

downturns in the aftermath of financial stress.

• Countries with more-arm’s-length financial 

systems appear to be vulnerable to sharper 

contractions in economic activity in the wake 

of banking stress, because leverage in the 

banking system appears to be more procyclical in countries characterized by greater 

financial innovation.

• The importance of core financial intermediaries in transmitting financial shocks to the 

real economy suggests that policies that help 

restore the capital base of these institutions 

within a strong framework of financial stability 

can help alleviate downturns.

• The patterns of asset prices and aggregate 

credit in the United States during the current 

episode of financial stress appear similar to 

those of previous episodes that were followed by recessions. In particular, changes in 

the pattern of household net borrowing—a 

measure of reliance on external financing—

closely track the trajectory of past recessions. 

Nonfinancial firms entered the turmoil from 

a relatively strong position. Combined with 

the large losses sustained by core banking 

institutions, these factors suggest that the 

United States continues to face considerable 

recession risks, even though real interest rates 

are low by the standards of financial-stressdriven recessions. In the euro area, households’ relatively strong balance sheets offer 

some protection against a sharp downturn, 

despite the sizable increases of asset prices 

and credit ratios preceding the financial 

turmoil.

The rest of the chapter is structured as follows. The next section elaborates the concept of 

financial stress that is employed in this chapter, 

and uses this concept to identify episodes of 

financial turmoil during the past three decades. 

The chapter then analyzes the behavior of 

economic cycles following these financial stress 

episodes. The section that follows discusses the 

factors that differentiate episodes that were 

associated with economic downturns from those 

that were not. Following the macro-level analysis is a micro analysis, using bank-level data, of 

the procyclicality of investment banks’ and commercial banks’ leverage in both arm’s-length 

and relationship-based financial systems. The 

chapter then focuses on six of the most wellknown episodes of banking-related financial 

stress and places the current financial turmoil 

in historical context. The concluding section 

outlines some implications for policy.

Identifying Episodes of Financial Stre

Financial systems—both financial institutions and the channels of intermediation—have 

historically been prone to periods of rapid 

expansion followed by corrections.3 To better 

understand the impact of financial cycles on 

the economy, it is useful to look for previous 

episodes of financial stress that share common 

features with the current one.

The current episode began in early 2007 as 

a generally orderly repricing of risk for assets 

linked to U.S. subprime mortgages. By the 

summer, it had rapidly escalated into a severe 

liquidity squeeze in the banking systems in the 

United States and western Europe and was causing serious dislocations in the interbank fund3See Kindleberger and Aliber (2005) for a history of 

financial crises. A well-known exposition of this procyclical feature of financial systems is Minsky’s financial 

instability hypothesis (Minsky, 1992), which posits that 

financial markets encourage businesses and individuals to 

take on too much risk, generating ruinous boom-and-bust 

cycles.

Identifying Episodes of Financial
Chapter4  Financial Stress and Economic Dow

ing market.4 The crisis mutated again more 

recently, as heavy credit losses raised questions 

about the capital strength of many banks. Moreover, the stress has spread across various market 

segments in emerging as well as advanced 

economies, with these segments marked by a 

loss of liquidity, falling valuations, rising risk 

aversion, and heightened volatility. Foreign 

exchange markets have also been affected by 

heightened uncertainty about the safety and 

soundness of global financial assets and the 

impact of financial stress on economic performance. Given these key features of the current 

financial market turmoil, any characterization 

of previous financial stress episodes should take 

into account conditions in the banking sector, 

the state of nonbank intermediation through 

equities and bonds, and the behavior of foreign 

exchange markets.

The academic literature on financial crises 

has relied largely on historical narratives of 

well-known systemic banking crises, when bank 

capital was eroded, lending was disrupted, 

and there was often the need for significant 

public intervention (see, for example, Caprio 

and Klingebiel, 2003). An extension of this 

approach is to augment the narratives about 

banking crises with narratives of currency 

crises, when reserves were depleted and/or 

there was a significant change in the exchange 

rate mechanisms (see, for example, Kaminsky 

and Reinhart, 1999; and Reinhart and Rogoff, 

2008). Pure securities market stress episodes 

have not been examined as comprehensively, 

especially those involving multiple countries, 

4For a detailed analysis of the interbank funding market in the context of the current turmoil, see Chapters 1 

and 2 of the October 2008 Global Financial Stability Report

(IMF, 2008). The supply of liquid funds in the interbank 

market dried up because many banks were in need of 

such funds, and those with surplus funds refrained from 

lending, owing to concerns about the creditworthiness of 

their counterparts. This affected both commercial banks, 

which rely largely on retail deposits to fund their lending 

activities, and investment banks and broker-dealers, which 

rely more on wholesale funding markets. Universal banks, 

which combine features of both commercial and investment banks, faced similar constraints. 

although studies for single countries are instructive (Shiller, 1999).

Although such narrative approaches to identifying financial crises provide a rich database of 

episodes, they are less well suited to the purposes of this chapter for a number of reasons. 

First, these are the episodes known to have 

had large output consequences and/or to have 

required significant public intervention. Less 

attention has been given to “near misses”—episodes of financial stress with little macroeconomic impact—which could serve as useful 

counterfactuals. Second, the episodes identified 

are typically of considerable length and involve 

stresses of varying intensity, making it difficult to identify both when the financial stress 

peaked and whether there was any meaningful 

causal relationship to an economic downturn. 

Finally, even the most comprehensive databases 

focus on banking crises and currency crises and 

pay little attention to pure securities market 

stresses or liquidity squeezes, such as the U.S. 

stock market crash of 1987 and the collapse of 

U.S. hedge fund Long-Term Capital Management (LTCM) in 1998. With leverage in banking systems linked to securitization, it would 

seem important to simultaneously analyze the 

banking and securities channels of intermediation to determine the degree of interaction 

between the two.

To overcome these limitations, this chapter 

identifies episodes of financial stress as extreme 

values of a composite variable—the “Financial 

Stress Index” (FSI)—which is built using marketbased indicators in real time and of high frequency.5 The FSI for each country is constructed 

as an average of the following indicators:

• three banking-related variables: the “beta” of 

banking sector stocks;6 the spread between 

5A similar approach is used by Illing and Liu (2006).

6The beta of banking stocks is a measure of the correlation between the total returns to the banking sector stock 

index and the overall stock market index. A beta greater 

than 1—indicating that banking stocks move more than 

proportionately against the overall stock market—suggests 

that the banking sector is relatively risky. The FSI computes the betas as the coefficient on the rolling returns of 
interbank rates and the yield on treasury 

bills—the so-called TED spread, which measures the premium banks charge each other 

over treasury bill rates; and the slope of the 

yield curve;

• three securities-market-related variables: 

corporate bond spreads, stock market returns, 

and time-varying stock return volatility; and

• one foreign exchange variable: time-varying 

effective exchange rate volatility.7

The advantage of utilizing such an index is 

the ability to more precisely date by quarter 

the start, peak, and end of a financial stress 

episode and thereby to calculate its duration. 

Moreover, such an index facilitates the identification of four fundamental characteristics of 

financial stress events: large shifts in asset prices 

(stock and bond market returns); an abrupt 

increase in risk/uncertainty (stock and foreign 

exchange volatility); abrupt shifts in liquidity 

(TED spreads); and the health of the banking 

system (the beta of banking sector stocks and 

the yield curve, which affects the profitability of 

intermediating short-term liabilities into longterm assets). Looking at these subcomponents 

can help identify which types of financial stress 

episodes have been associated with larger output 

consequences: banking-related, securities-market-related, currency-related, or a combination 

of these.

This chapter uses financial market (assetprice-based) variables to identify periods when 

the financial sector is under strain and its 

ability to intermediate may be impaired. This 

strategy has three major advantages over using 

a quantity-based index (an approach that the 

corporate finance literature might suggest). 

each country’s banking sector subindex regressed on the 

returns of the country’s overall stock market index. The 

FSI considers the beta only during periods when returns 

are negative to focus on adverse shocks to banks. Accordingly, in high-stress episodes, this indicator would reflect 

an unusually large drop in banking stock prices relative 

to overall market prices. 7All variables are weighted by the inverse of their variance in order to reduce the impact on the overall index 

of the more volatile variables. See Appendix 4.1 on the 

construction of the index.

First, asset-price-based variables are easy to 

monitor and compute on a comparable basis 

across a large set of countries. Second, movements in broader financial asset prices can be 

expected to have a greater impact on the ability 

of financial firms to supply intermediation 

services than on the ability of specific nonfinancial firms to fund new investment, which is 

much more closely tied to developments in their 

particular sector. Third, it is useful to initially 

consider a broad range of financial stress events 

using asset prices and then use quantity-based 

variables to identify which of those financial 

stress episodes are associated with a significant 

economic downturn. The underlying hypothesis 

is that only a subset of the universe of assetprice-based stress episodes reflects true underlying distress in the balance sheets of financial 

intermediaries that have an impact on overall 

economic activity by restricting the supply of 

credit; others merely reflect normal market 

corrections.

Using the seven variables described above, 

the FSI is constructed for each of the 17 countries in the sample. Episodes of financial 

stress are identified as those periods when the 

index for a country is more than one standard 

deviation above its trend (identified using 

the Hodrick-Prescott filter), which signals 

that one or more of the banking, securities, 

and/or foreign exchange market variables 

shifted abruptly.

Overall, 113 financial stress episodes during 

the past 30 years were identified in the 17 countries considered in this chapter (Table 4.1). Of 

these, 43 episodes were driven mainly by stress 

in the banking sector (that is, the banking variables accounted for most of the FSI increase), 

50 episodes reflected primarily turmoil in the 

securities market, and 20 episodes reflected 

primarily turmoil in the foreign exchange 

market. In some cases, stresses in one segment 

of the financial system eventually migrated into 

the other segments. For example, in 17 of the 

70 episodes that reflected stress primarily in 

the securities or foreign exchange markets, the 

banking variables accounted for at least oneIdentifying Episodes of Financial
Chapter4  Financial Stress and Economic Dow

third of the spike in the FSI. This implies that 

there are actually 60 episodes in the sample with 

banking-related financial stress—episodes for 

which banks were either the most or the second 

most important factor, contributing at least onethird of the FSI spike.

Overall, the FSI appears to accurately capture 

global financial episodes.8 The current financial crisis, which began in 2007, has a significant global dimension, affecting virtually all 

countries in the sample (Figures 4.2 and 4.3). 

Earlier episodes that simultaneously affected the 

majority of countries in the sample include the 

1987 stock market crash, the Nikkei/junk bond 

collapse in the late 1980s, the Scandinavian 

banking crises in 1990, the European exchange 

rate mechanism crisis of 1992, and the collapse 

of LTCM, but the current episode seems to have 

the widest impact.

The FSI also accurately captures the fact 

that, although the origins of the current episode were in the banking sector, by early 2008 

the crisis had broadened significantly to affect 

8Overall, of the 113 episodes of financial stress identified in the sample, 87 episodes simultaneously affected 

two or more countries.

40

50

60

70

80

50

60

70

80

90

100

 Source: IMF staff calculations.

 

Japan

Figure 4.2. Financial Stress Index

(Shaded areas denote financial stress episodes)

The financial stress index appears to capture the current episode of financial stress

in all countries in the sample.

Germany

70

75

80

85

90

95

100 United States

1980 85 90 95 2000 05 08:

Q3

50

60

70

80

90

United Kingdom 100

1980 85 90 95 2000 05 08:

Q3

1980 85 90 95 2000 05 08:

Q3

1980 85 90 95 2000 05 08:

Q3

50

60

70

80

90 France

1980 85 90 95 2000 05 08:

Q3

50

60

70

80

90

Canada 100

1980 85 90 95 2000 05 08:

Q3

Table 4.1. Descriptive Statistics on Financial Stre

Episode

Duration of 

Episodes

(average; 

quarters)

Number of Episodes1

Total 1980s 1990s 2000s Current

Financial stress 113 37 42 34 16 2.4

Of which:

Banking 43 12 19 12 4 2.4

Securities markets 50 19 12 19 11 2.4

Foreign exchange 20 6 11 3 1 2.4

Memo:

Banking related 60 16 25 19 10 2.6

Of which:

Above median arm’s 

length 31 9 13 9 4 2.4

Below median arm’s 

length 27 7 11 9 5 2.9

Source: IMF staff calculations.

1The countries included are Australia, Austria, Belgium, Canada, 

Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Norway, 

Spain, Sweden, Switzerland, United Kingdom, and United States. 
the securities and foreign exchange markets as 

well (Figure 4.4). Moreover, the index indicates that all past episodes of banking-related 

stress also had significant securities market 

components.

The country-specific FSIs for the 17 countries 

considered in this chapter show that the FSI 

peaks, which correspond to periods of financial 

stress, generally overlap accurately with wellknown financial stress episodes in these countries over the past three decades, including the 

current episode (see Figure 4.2). Specifically, 

the FSI captures over 90 percent of the banking 

crises and over 80 percent of the currency crises 

identified in the literature.9

Given that spikes in the FSI are associated 

with well-known events such as stock/bond market collapses or banking crises, could the index 

simply mirror changes in other fundamentals 

that can directly affect the economic cycle? Considering four types of such shocks—to oil prices, 

labor productivity, fiscal policy, and monetary 

policy—the evidence indicates that spikes in the 

FSI are not correlated with oil, labor productivity, or macroeconomic policies (see Figure 4.3, 

bottom three panels).10 There does appear to be 

a greater correlation with monetary policy, but 

this can be expected, given that monetary policy 

(as measured by the term spread) is a subcomponent of the FSI itself, where it is included 

because financial stress appears to be associated 

9The FSI captures 100 percent of all episodes identified 

in the literature if the duration of episodes is interpreted 

more broadly—that is, if the period around the peak of 

financial stress is expanded by a few quarters. 10The definition of these shocks mirrors the definition 

of financial stress episodes: (1) Shocks to oil prices or 

labor productivity are defined as instances when changes 

in these indicators are one standard deviation above 

trend; (2) fiscal policy shocks, when the ratio of government net lending/borrowing to GDP is one standard 

deviation above trend; and (3) monetary policy shocks, 

when the inverse term spread is one standard deviation above trend. In all cases, the deviations from trend 

are calculated using Hodrick-Prescott filters. Figure 4.3 

reports the first principal components of these fiscal, 

monetary, and labor productivity indicators across the set 

of countries considered in the chapter.

0

20

40

60

80

Share of Countries Experiencing Financial Stress 100

1980 85 90 95 2000 05 08:

Q1

LTCM

collapse

Stock 

market

crash

Nikkei/junk 

bond collapse

ERM 

crisis

Scandinavian

banking 

crises

Figure 4.3. Financial Stress and Shocks

Financial stress episodes tend to be synchronized around systemic events, as shown 

by their clustering in a few peaks. Episodes of financial stress generally correspond 

to cases of monetary policy shocks, but they don't correlate well with other shocks.

-60

-40

-20

0

20

40

60

1980 85 90 95 2000 05 08:

Q1

Oil Prices1

-4

-3

-2

-1

0

1

2

3

Labor Productivity 4

1980 85 90 95 2000 05 08:

Q1

4

 Sources: Haver Analytics; IMF, Commodity Price System database; OECD, Analytic

Database; OECD, Economic Outlook (2008); and IMF staff calculations.

 Note: ERM = exchange rate mechanism; LTCM = Long-Term Capital Management.

 Oil prices are scaled by U.S. inflation (CPI) and represent deviations from

Hodrick-Prescott (HP) trend.

 Monetary policy is measured using the inverse term spread’s deviations from HP trend. 

 Fiscal policy is measured using government net lending’s deviations from HP trend.

 Labor productivity of the total economy is measured as the ratio of real GDP and total 

employment and represents deviations from HP trend. Data are not available for Austria,

Belgium, Denmark, Spain, and Switzerland.

1

2

3

4

1980 85 90 95 2000 05 08:

Q1

-3

-2

-1

0

1

2

3

Macroeconomic Policies 4

Monetar 2y

3 Fiscal

Identifying Episodes of Financial
Chapter4  Financial Stress and Economic Dow

with the monetary policy stance as reflected in 

the term spread.

Overall, the FSI appears to be a comprehensive indicator that successfully identifies the 

main episodes of financial stress for the sample 

of countries under consideration and provides a 

sound basis for examining the macroeconomic 

consequences of such stress.

Financial Stress, Economic Slowdow

and Recession

How many of the financial stress episodes 

identified using the FSI were followed by an 

economic slowdown or an outright recession? How did episodes that were followed by 

economic downturns differ from those that 

were not?

• An episode of financial stress is followed by 

an economic “slowdown” if the level of real 

GDP falls below trend (identified using the 

Hodrick-Prescott filter) within six quarters of 

the onset of financial stress.

• An episode of financial stress is followed by 

a “recession” if a peak-to-trough business 

cycle, identified using the methodology 

described in Harding and Pagan (2002) and 

the April 2003 World Economic Outlook, begins 

within six quarters of the onset of financial 

stress.11

This chapter seeks to identify the main characteristics of financial stress episodes that were 

eventually followed by economic downturns, 

not to assess whether financial stress “causes” 

economic downturns, in recognition of the 

significant analytical and empirical challenges in 

establishing causality.12 Nevertheless, the analysis 

attempts to control, to some extent, for other 

shocks—namely, monetary, fiscal, oil price, and 

11For example, in the United States, the most recent 

recession was in 2001, whereas the most recent slowdown 

was when GDP fell below trend during 2007:Q4–2008:Q1. 12For example, many shocks affect both the financial 

system and the economy, and although the financial 

system may amplify the shocks, it would be hard to disentangle the direct effects of the shocks from the amplification effects.

0 10 20 30 40 50 0 10 20 30 40 50

Australia

Figure 4.4. Contribution of Banking, Securities, and 

Foreign Exchange to Current Financial Stress Episode 

The episode of financial stress that started in 2007 has become more broad-based,

with contributions from banking, securities, and foreign exchange markets. Previous

episodes also show strong contributions from both banking and securities markets.

Austria

Belgium

Canada

Denmark

France

Germany

Italy

Japan 

Netherlands

Norway

Spain

Sweden

Switzerland

United Kingdom

United States

Banking

Securities

Foreign Exchange

Most Recent Quarter 

of Current Episode

Average of

Previous Episodes

 Source: IMF staff calculations.

 The sum of the components equals the level of the Financial Stress Index in the given 

period. See Appendix 4.1 for details.

1

1
labor productivity shocks—that may affect the 

relationship between financial stress and economic cycles.

Based on the definitions above, of the 113 

fi­nan­cial stress episodes identified here, 29 

followed by slowdowns and 29 by recessions. The 

remaining 55 episodes were not followed by an 

economic downturn (Table 4.2).

The average lag between the onset of 

financial stress and the subsequent downturn 

was about seven months. However, this average masks substantial variations: about half the 

downturns occurred within a quarter of the 

beginning of financial stress, but it took more 

than a year for a downturn to materialize during one-fourth of the episodes (Figure 4.5).

Most important, the slowdowns and recessions that were preceded by financial stress 

episodes were longer in duration and, partly as 

a result, were more severe than those that were 

not. Median cumulative output losses (relative 

to trend or until recovery) were about 3 percent of GDP for slowdowns following financial 

stress and about 41⁄2 percent of GDP for recessions following financial stress, significantly 

larger than for slowdowns and recessions that 

were not preceded by financial stress (about 

11⁄2 percent and 21⁄4 percent, respectively) (see 

Figure 4.1).

The occurrence of financial stress tends to 

change the patterns of downturns (Figure 4.6), 

as apparent by examining the dynamics of 

selected macroeconomic variables at the beginning of the downturn.13 In particular, when 

preceded by financial stress, economic slowdowns tend to be characterized by a flattening 

in consumption growth, by investment that 

follows a boom-and-bust cycle, by appreciable 

turnarounds of current account balances, and 

by falling inflation and real interest rates. The 

pattern changes are more pronounced for 

slowdowns and less pronounced for recessions, 

perhaps suggesting that the latter may be triggered to a greater extent by the interaction of 

financial stress with other shocks.

Why Are Some Financial Stress Episo

Associated with Economic Downturns

Only about half the episodes of financial 

stress identified using the FSI were followed by 

economic slowdowns or recessions. What deter13The charts use a window of 12 quarters and show 

only “complete” episodes (episodes spanning at least 12 

quarters from start to finish). Therefore, they include 

only those downturns that started between 1983:Q1 and 

2005:Q1.

Table 4.2. Descriptive Statistics on Financial Stress, Slowdowns, and Recess

Duration (average; quarters) Output Loss Lag until 

Downturn3

(average; 

N quarters)

Financial 

stress

Slowdown or 

recession1

(average; percent of GDP)

Cumulative2 Average

Financial stress 113 2.4

Of which:

Followed by slowdown 29 2.7 7.6 –7.6 –0.7 2.4

Banking-related 18 3.2 8.4 –9.3 –0.8 1.8

Followed by recession 29 3.0 6.8 –13.8 –1.2 2.3

Banking-related 17 4.0 7.6 –19.8 –1.5 2.0

Others 55 2.0

Slowdown not preceded by financial stress4 109 5.1* –4.1* –0.6

Recession not preceded by financial stress4 31 3.1* –5.4* –0.9

1Slowdown duration: number of quarters during which GDP is below trend; recession duration: number of quarters until GDP is at or exceeds 

peak level. 2Slowdown output loss: cumulative output loss below trend; recession output loss: cumulative output loss until recovery. 

3Number of quarters between start of financial stress and slowdown or recession.

4Asterisks indicate difference from slowdowns preceded by financial stress significant at 10 percent or better.

Financial Stress, Economic Slowdown, and Reces
Chapter4  Financial Stress and Economic Dow

mines whether a financial stress episode will be 

followed by a downturn? What characterizes the 

stress episodes that precede the most severe and 

prolonged downturns? To answer these questions, those financial stress episodes that were 

followed by downturns are compared against 

each other along two dimensions: (1) the characteristics of the financial stress episode itself 

and, in particular, the nature of the financial 

shock (whether related to the banking system, 

securities market, or foreign exchange market) 

and (2) the financial position of financial intermediaries, households, and firms at the beginning of the episode.

Is Banking-Related Financial Stress Different

Banking system stress is associated with 

larger output consequences than stress episodes related purely to the securities or foreign 

exchange markets, in which the banking system 

remains largely unaffected (Figure 4.7). About 

60 percent of those financial stress episodes that 

are followed by downturns are banking-related. 

Moreover, slowdowns and recessions preceded 

by banking-related stress tend to last longer and 

be associated with larger average GDP losses 

than those preceded by other types of financial stress or by no financial stress at all (see 

Table 4.2).

Bank asset growth slows significantly when 

banking-related financial stress episodes are 

followed by recessions or slowdowns, compared 

with financial stress episodes that are not followed by economic downturns (Figure 4.8, 

top panel). In general, downturns tend to be 

associated with a fall in the demand for credit, 

but during slowdowns or recessions associated 

with banking-related financial stress, the cost 

of capital is significantly higher (Figure 4.8, 

bottom panel).14 While the issue of reverse 

14The cost of capital is defined here as a weighted average of the real cost of equity, the real cost of debt, and 

real lending rates, using as weights the relative shares of 

equity, bonds, and loans in nonfinancial corporate liabilities. See Appendix 4.1 for details.

0 1 2 3 4 5 6

0

2

4

6

8

10

12

14

16

Figure 4.5. Lag between Financial Stress and Downturns

(Start of financial stress episode at t = 0; quarters on the x-axis; number of

slowdowns or recessions that begin in the given quarter on the y-axis)

In most cases economic downturns materialize soon (within one quarter) after the 

start of a financial stress episode, but longer lags have been observed.

 Source: IMF staff calculations.

Number of slowdowns

Number of recessions
causality between recessions and financial stress 

is difficult to address empirically, suggesting 

appropriate caution in interpreting these results, 

these findings are consistent with the view that 

a reduction in the supply of credit—the classic 

credit crunch or credit squeeze—is a key factor 

associating banking-related financial stress episodes with economic downturns.

Do Initial Conditions Matter?

Having examined the nature of the shocks, the 

analysis now assesses whether the likelihood of 

a downturn depends on initial conditions. The 

evidence suggests that the credit and asset price 

dynaamics and the financial position of financial 

intermediaries, households, and firms preceding 

the episode are important in determining the 

economic impact of a financial shock.

• House prices and the credit-to-GDP ratio 

both tend to rise significantly faster during 

the upswing of the financial cycle in those 

stress episodes that are followed by slowdowns 

or recessions (Figure 4.9). Statistical tests 

confirm that financial turmoil is more likely 

to be followed by economic slowdown or 

outright recession when it is preceded by a 

more rapid buildup in house prices and credit 

(Figure 4.10).

• Firms tend to be more heavily dependent 

on external sources of funding—that is, with 

higher net borrowing ratios—in the run-up 

to financial stress episodes that are followed 

by economic downturns (see Figures 4.9 and 

4.10). A higher initial reliance on external 

funding makes firms more vulnerable during the downswing of the financial cycle, and 

may force them to adjust their spending plans 

more drastically in the aftermath of financial 

stress, setting the stage for a larger impact on 

the real economy.

• Only financial stress episodes that are followed by recessions (not by slowdowns) seem 

to be characterized by a “more exposed” 

household sector in terms of reliance on 

external financing (see Figure 4.9). Indeed, 

the median household net borrowing ratio 

-12 -8 -4 0 4 8 12 92

94

96

98

100

102

104

106

108

110 Consumption

(100 at t = 0)

-12 -8 -4 0 4 8 12 92

94

96

98

100

102

104

106

108

110

Residential Investment 112

(100 at t = 0)

-12 -8 -4 0 4 8 12 80

85

90

95

100

105

110

115

120 Nonresidential Investment

(100 at t = 0)

-12 -8 -4 0 4 8 12 -1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Current Account 2.0

(percent of GDP)

-12 -8 -4 0 4 8 12 2

3

4

5

6 Inflation

(percent difference in CPI from

one year earlier)

-12 -8 -4 0 4 8 12 -2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0 Change in Real Interest Rates

(percentage points)

Slowdowns preceded

by financial stress

Recessions preceded

by financial stress

Figure 4.6. Selected Macrovariables around Economic 

Downturns with and without Financial Stress

(Median; start of economic contraction at t = 0; quarters on the x-axis)

The occurrence of financial stress changes the patterns of economic downturns. In

particular, the shape of slowdowns seems to be affected.

 Sources: Haver Analytics; IMF, International Financial Statistics database; OECD, Analytic

Database; and IMF staff calculations.

 All in real terms. The sample is constant for all quarters.

 

Slowdowns not preceded

by financial stress

Recessions not preceded

by financial stress

1

1

Financial Stress, Economic Slowdown, and Reces
Chapter4  Financial Stress and Economic Dow

1

(in deviation from trend) is significantly 

higher at the outset of financial stress episodes that are followed by recessions than 

during those that are followed by slowdowns 

or by no decline in economic activity (see 

Figure 4.10).

The analysis suggests that when the financial cycle turns, as signaled by the onset of 

stress in financial markets, there is a greater 

likelihood of a downturn in economic activity the larger the initial financial imbalances—when firms and households are more 

exposed to a decline in credit and asset prices. 

In particular, households’ exposure seems to 

be associated with more severe contractions in 

economic activity.

The degree to which initial financial imbalances and firms’ and households’ reliance on 

external funding explain the severity of ensuing economic downturns can be investigated 

more formally. The cumulative loss of output 

in the aftermath of financial stress episodes is 

regressed on (1) the run-up in credit and asset 

prices before the onset of the financial stress, 

(2) firms’ and households’ net borrowing ratios 

at the start of the episodes, and (3) a proxy for 

the severity of the financial shock, namely, the 

duration of the stress episode.

The main results of the regressions are presented in Table 4.3:

15

• Firms’ net borrowing ratio at the onset of the 

financial stress episode enters significantly 

in almost all specifications, confirming the 

importance of the link between firms’ initial 

reliance on external credit and the severity of 

the decline in economic activity.

• Households’ net borrowing ratio at the onset 

of the financial stress episode is statistically 

significant when considered alone but loses 

significance when firms’ net borrowing 

position is added. It continues to affect the 

severity of the output losses, however, when 

15Slowdown severity is measured using the cumulative 

output loss during the period during which output is 

below trend; see Table 4.1 for further details. Recession 

severity is measured by losses until recovery.

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

All financial stress episodes

Banking-related

financial stress

Figure 4.7. Banking-Related Financial Stress, 

Slowdowns, and Recessions

(Median; quarters on the x-axis)

Non-banking-related

financial stress

Financial Stress

(real GDP percent change from one year earlier)

The most severe economic downturns are associated with banking-related financial

stress.

1

Quarters before and after the start of financial stress

Slowdowns preceded

by banking-related financial stress

Recessions preceded

by banking-related financial stress

Slowdowns not preceded

by financial stress

Recessions not preceded

by financial stress

Quarters before and after the start of economic downturn

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

-2

-1

0

1

2

3

4

5

 Sources: Haver Analytics; OECD, Analytic Database; OECD, Economic Outlook (2008); and 

IMF staff calculations.

 The difference between banking-related and non-banking-related episodes is significant 

at a minimum of 10 percent for quarters 0, 1, 2, 3, and 4. The sample is constant for all 

quarters.

 The difference between slowdowns preceded by banking-related financial stress and 

slowdowns not preceded by financial stress is significant at a minimum of 10 percent for 

t – 6 to t + 6. The sample is constant for all quarters.

1

2

Slowdowns and Recessions

(real GDP percent change from one year earlier)

2
interacted with the duration of the financial 

episode, suggesting that households’ position 

matters especially when the economy is hit by 

a sustained financial shock.

This chapter seeks to identify factors that 

determine whether or not episodes of financial 

stress are followed by economic downturns. 

Therefore, a key challenge is to determine the 

origins of the shocks that hit the economy and 

set off the complex interactions between the 

financial and real sectors. The fact that financial sector stress precedes an economic downturn does not mean that financial stress drives 

subsequent real sector developments: because 

financial market participants are forward-looking, financial stress may be merely a manifestation that they anticipate a fundamental 

deterioration in the real sector. To address this 

problem, the chapter considers four types of 

shock that could be considered fundamental, 

exogenous deteriorations in the real sector 

environment: oil-price shock, monetary policy 

shock, fiscal policy shock, and labor productivity shock. As shown in Figure 4.11, when one of 

these shocks is combined with financial stress, 

there is a more severe downturn than when 

there is the shock alone. This suggests that 

financial stress has a separately identifiable 

impact.

Has Financial Innovation Affected the

Interplay between Financial Stress and

Economic Cycles

Banks continue to be important in explaining why certain financial stress episodes are 

associated with greater output consequences 

(see Figure 4.7). Why do banks remain crucial 

despite financial innovation and the emergence of nonbank sources of funding? Financial innovation would seem able to reduce the 

pivotal role of banks by providing alternative 

channels for firms and households to access 

financing, channels that loosen collateral constraints for borrowers and soften the adverse 

impact of financial stress on the cost of capital 

for banks. However, even though the role of 

Recessions Slowdowns Other 0.00

0.05

0.10

0.15

0.20

0.25

Recessions Slowdowns Other -0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Figure 4.8. Cost of Capital and Bank Asset Growth

around Banking Financial Stress Episodes

(Average; change from one year before to one year after the start of

financial stress)

Banking-related financial stress episodes associated with recessions are 

characterized by lower bank asset growth and higher cost of capital in the aftermath.

 Sources: OECD, Analytic Database; and IMF staff calculations.

 p-value reported refers to the two-tailed t-tests on the difference between the two 

averages.

 Banking-related financial stress episodes followed by slowdowns or recessions.

 Financial stress episodes not followed by slowdowns or recessions.

Change in Cost of Capital

(percentage points)

Change in Bank Assets

(percent)

p-value = 0.05

p-value = 0.05

3

p-value = 0.00

2

3

2 2

2 2

3

1

1

1

Has Financial Innovation Affected the Interplay between Financial Stress and Econom
Chapter4  Financial Stress and Economic Dow

banks has evolved over time, their symbiotic 

relationship with securities markets remains 

an essential feature of many financial systems, 

especially those characterized by arm’s-length 

financing (World Economic Outlook, September 2006).16 As a result, episodes of banking 

distress continue to affect nonbank sources of 

financing as well.

To explain the continuing importance of 

banks, it is revealing to explore the procyclical behavior of bank leverage around financial 

cycles. In particular, how banks manage their 

leverage during upturns and downturns in the 

cycle appears fundamental to explaining why 

banking stress translates into a reduced credit 

supply, a higher cost of capital, and a softening of economic activity. The hypothesis is as 

follows: When banks overextend their balance 

sheets during booms, on the back of higher 

asset values and lower perceived risk, there is 

a buildup of financial imbalances and a rapid 

expansion in activity, which further boosts asset 

values and reduces perceived risk, thereby fostering another round of lending and economic 

expansion.17 Under such conditions, a financial 

shock that either increases risk or reduces the 

return on assets could prompt a cycle of severe 

deleveraging, with banks sharply reducing their 

lending (or their growth in lending) as bank 

capital falls, prompting an economic slowdown 

that feeds back into a further reduction in 

credit supply.

The procyclicality of leverage is more pronounced when banks are more exposed to 

fluctuations in the market value of assets—for 

example, through their holdings of securities and 

16Banks increasingly depend on market-based funding 

sources to finance their assets (such as through their 

certificates of deposit and off-balance-sheet commercial 

paper programs). Conversely, investment banks and 

increasingly commercial banks also remain at the center 

of the originate-to-distribute model of securitized financing, providing credit through repurchase facilities to 

hedge funds and other leveraged intermediaries to invest 

in securities markets. 17This is in line with Minsky’s financial instability 

hypothesis (Minsky, 1992).

-12 -8 -4 0 4 8 12 -15

-10

-5

0

5

10

15

20

-12 -8 -4 0 4 8 12 -3

-2

-1

0

1

2

3

4

House Prices

(percent deviation from trend;

quarters on the x-axis)

Stock Prices

(percent deviation from trend;

quarters on the x-axis)

 Sources: IMF, International Financial Statistics database; OECD, Analytic Database; and 

IMF staff calculations.

 All in real terms, except for household and nonfinancial corporate net lending ratios. The

sample is constant for all quarters and years.

1

Figure 4.9. Selected Macrovariables around Financial 

Stress Episodes

(Median; start of financial stress episode at t = 0; x-axis as stated)

Financial stress episodes followed by slowdowns or recessions tend to be preceded

by rapid buildups in asset prices and credit ratios and are associated with higher 

initial net borrowing from nonfinancial firms and (in cases of recession) from 

households.

-12 -8 -4 0 4 8 12 -2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-12 -8 -4 0 4 8 12 -2

-1

0

1

2

3

Credit/GDP

(percent deviation from trend; 

quarters on the x-axis)

Interest Rate

(change from one year

earlier; percentage points; 

quarters on the x-axis)

Others

Followed by slowdowns only

Followed by recessions only

-3 -2 -1 0 1 2 3 -1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Household Net 

Lending/Borrowing

(deviation from trend; years 

on the x-axis)

-3 -2 -1 0 1 2 3 -1.5

-1.0

-0.5

0.0

0.5

1.0

Nonfinancial Corporate Net 

Lending/Borrowing

(deviation from trend; years 

on the x-axis)

1
their repurchase facilities.18 Because this is typical 

for nondepository financial intermediaries, especially investment banks, there should be evidence 

of procyclical leverage among them (Shin, 2008). 

On the other hand, commercial banks should be 

less prone to procyclically adjusting their balance 

sheets during asset price or liquidity booms and 

busts, because they rely less on wholesale funding and more on retail deposits, and also because 

they are less subject to mark-to-market changes in 

the value of their assets.

Evidence confirms that investment banks’ 

leverage tends to be procyclical: they expand 

their leverage when they are expanding their 

assets (Figure 4.12, upper panel).19 The evidence is less uniform on the procyclicality 

of commercial banks, which rely much more 

on retail deposits and whose main activity is 

making long-term, illiquid loans (Figure 4.12, 

lower two panels). However, the evidence does 

suggest that commercial banks tend to be more 

procyclical when operating in more-arm’slength financial systems, where a greater share 

of intermediation occurs through financial 

markets rather than through traditional relationship-based (and bank-dominated) activities 

(Figure 4.13). Thus, more-arm’s-length financial 

systems are associated with overall more procyclical bank behavior, and as a result may be 

more vulnerable to banking stress.20 Of note, 

the proportion of banking-related financial 

stress episodes occurring in more-arm’s-length 

financial systems has remained about equal to 

the proportion occurring in more-relationshipbased systems (see Table 4.1).

Indeed, slowdowns and recessions tend to be 

deeper in economies with more-arm’s-length 

financial systems, although the duration of such 

18In a systemic crisis, it would be difficult for all banks 

to adjust their leverage simultaneously, because there 

would be few buyers for these assets among other banks; 

the only likely buyers would be cash-rich investors who do 

not rely on bank leverage to fund their positions. 19See also Adrian and Shin (2008a and 2008b).

20This is consistent with the findings in Chapter 3 of 

IMF (2008), showing that fair value accounting tends to 

lead to more procyclical movements in financial intermediaries’ balance sheets.

Recessions Slowdowns Other -2.0

-1.5

-1.0

-0.5

0.0

0.5

Recessions Slowdowns Other -0.8

-0.6

-0.4

-0.2

0.0

Recessions Slowdowns Other

-6

-4

-2

0

2

4

6

8

Recessions Slowdowns Other -20

-10

0

10

20

30

 Sources: Haver Analytics; IMF, International Financial Statistics database; OECD, Analytic

Database; and IMF staff calculations.

 Note: p-value reported refers to the two-tailed t-tests on the difference between the two 

averages.

 Financial stress episodes not followed by slowdowns or recessions.

Household Net 

Lending/Borrowing

(percent of gross disposable 

income; deviation from trend 

one year before start of

financial stress)

Nonfinancial Corporate

Net Lending/Borrowing

(percent of GDP; deviation from

trend one year before start of

financial stress)

Real House Prices

(cumulative percent

deviation from trend over six 

quarters before start of

financial stress)

Credit

(percent of GDP; cumulative 

percent deviation from trend 

over six quarters before start 

of financial stress)

Figure 4.10. Initial Conditions of Financial Stress 

Episodes

(Average)

Faster buildup in the credit ratio and house prices and higher nonfinancial corporate 

borrowing are statistically significant indicators of the likelihood that financial stress

episodes will be followed by slowdowns or recessions. Household net borrowing is

significantly associated only with subsequent recessions.

p-value = 0.02

p-value = 0.04

p-value = 0.05

p-value = 0.00

p-value = 0.00

p-value = 0.05

p-value = 0.00

1

1 1

1 1

Has Financial Innovation Affected the Interplay between Financial Stress and Econom
Chapter4  Financial Stress and Economic Dow

downturns is broadly similar in both types of 

systems (Figure 4.14), indicating that deleveraging matters and that its impact depends on the 

degree of procyclicality in the banking system.21

This implies that more-arm’s-length systems are 

vulnerable to sharper contractions in activity following banking stress. Consistent with this channel, the leverage of banks in more-arm’s-length 

systems also tends to fall more sharply than that 

of banks in other types of financial systems, 

albeit from a lower starting level. This casts 

doubt on the presumption that arm’s-length systems can better soften the blow from financialstress-driven economic downturns because of 

21 Diverging experiences with economic cycles by 

economies characterized by arm’s-length versus relationship-based financial systems may also reflect contrasts in 

other areas, notably in the degree of flexibility in labor 

and product markets and the types of social welfare systems (see World Economic Outlook, October 2006).

the availability of the twin engines for financial 

intermediation (banks and markets).

Arm’s-length financial systems do offer several advantages over relationship-based systems 

in terms of reallocating resources in response 

to changing economic opportunities (see World 

Economic Outlook, October 2006). However, as 

the current crisis underscores, the trend toward 

greater securitization in more-arm’s-length systems, while permitting portfolio diversification 

to offset the costs of monitoring the idiosyncratic risks that are inherent in traditional relationship-based systems, does not eliminate the 

need for banks and markets to independently 

assess the risk of their exposures. Indeed, a 

lack of information about the value and risk of 

many securitized products, and about the losses 

subsequently associated with these products, 

appears to have played a significant role in 

amplifying the current crisis.

Table 4.3. Cross-Section Regressio

Dependent Variable: Cumulative Output Loss1 [1] [2] [3] [4] [5] [6] [7] [8] [9]

Duration of financial stress 1.324 1.591 0.641 1.023 0.963 1.377 1.034 0.888 0.711

(0.10) (0.10) (0.43) (0.33) (0.30) (0.09) (0.20) (0.07) (0.30)

Real interest rate2 0.89 0.808 0.47 0.835 0.877 0.87 0.841 0.887 0.869

 (0.00) (0.01) (0.12) (0.01) (0.00) (0.00) (0.01) (0.00) (0.01)

Nonfinancial corporate net borrowing ratio3 2.0 1.753 0.849 1.986 1.439

(0.01) (0.02) (0.35) (0.00) (0.07)

Household net borrowing ratio3 1.05 0.48 0.668 –1.086 –0.89

(0.10) (0.44) (0.33) (0.22) (0.32)

Real house price4 14.304

(0.01)

Real stock price4 –0.785

(0.72)

Credit ratio4 –1.09

(0.90)

Household net borrowing ratio x duration 0.593 0.561

(0.04) (0.04)

Nonfinancial corporate net borrowing ratio x duration 0.229 0.136

(0.43) (0.48)

Constant –2.014 –0.803 2.076 0.809 0.482 –1.877 –0.727 –1.161 –0.519

(0.45) (0.79) (0.39) (0.74) (0.86) (0.50) (0.78) (0.64) (0.85)

N 42 40 52 52 52 40 40 40 40

Adjusted R-squared 0.418 0.287 0.254 0.128 0.126 0.418 0.42 0.493 0.485

Note: Robust p-values in parentheses.

1Dependent variable is cumulative output loss in episodes of financial stress followed by slowdowns or recessions.

2Average of real interest rates during six quarters before the financial stress.

3Net borrowing ratios one year before the financial stress (deviation from trend).

4Cumulative percent deviation from trend over six quarters before the financial stress.
The Current Financial Crisis in Historica

Context

This section compares the current episode 

of financial stress to six well-known episodes of 

banking-related financial stress that occurred 

in advanced economies during the 1990s. These 

episodes affected Finland, Norway, Sweden, the 

United Kingdom, and the United States in the 

early 1990s and Japan throughout the 1990s. 

Given the importance of banking distress in the 

current financial turmoil, these episodes can 

serve as a useful benchmark for analyzing the 

current conjuncture and for gauging its potential macroeconomic impact.22

Examining the initial conditions before the 

onset of these six episodes confirms the main 

findings of the event analysis, namely, that 

financial stress episodes are more likely to be 

followed by severe economic downturns when 

they occur in the context of a rapid buildup in 

credit and house prices and a heavier reliance 

on credit by firms and households. Broadly 

speaking, the episodes characterized by the 

largest asset price and credit booms were 

in the Nordic countries in the early 1990s, 

where increases in credit ratios, assets prices, 

and bank assets were abnormally high (see 

Table 4.4, top panel). At the same time, borrowing by households and firms was initially much 

larger in Finland and Norway than in the other 

countries. Japanese firms relied extensively on 

external financing, but this was balanced at 

least partially by the saving patterns of households. In contrast, before the crises in the 

Anglophone countries, asset price imbalances 

were moderate and balance sheets were not 

under tremendous strain.

The countries with larger financial imbalances 

and balance sheet vulnerabilities at the onset 

of an episode experienced more severe output 

contractions (see Table 4.4, lower panel). The 

most dramatic collapses in asset prices, bank 

asset growth, and credit occurred in the coun22This approach is in line with other studies, including Reinhart and Rogoff (2008). See also J.P. Morgan 

Research (2008).

-12 -8 -4 0 4 8 12 -1

0

1

2

3

4

-12 -8 -4 0 4 8 12 -1

0

1

2

3

4

5

-12 -8 -4 0 4 8 12 -1

0

1

2

3

4

5

-12 -8 -4 0 4 8 12 0

1

2

3

4

5

Preceded by financial Preceded by shock only

stress and shock

Figure 4.11. Financial Stress and Economic Downturns:

Controlling for Four Main Shocks

(Median of GDP percent change from one year earlier; start of economic 

contraction at t = 0; quarters on the x-axis)

Economic downturns tend to be more severe when preceded by financial stress

episodes.

 Sources: Haver Analytics; IMF, Commodity Price System database; OECD, Analytic

Database; OECD, Economic Outlook (2008); and IMF staff calculations.

 Oil prices are scaled by U.S. inflation (CPI). Oil-price shock is identified if oil price is

one standard deviation above Hodrick-Prescott (HP) trend.

 Monetary policy is measured using the inverse term spread. Monetary policy shock is

identified if the inverse term spread is one standard deviation above HP trend.

 Fiscal policy is measured using government net lending. Fiscal shock is identified if

government net lending is one standard deviation above HP trend.

 Labor productivity of the total economy is measured as the ratio of real GDP and total 

employment. Productivity shock is identified if labor productivity is one standard deviation 

below HP trend. Data are not available for Austria, Belgium, Denmark, Spain, and 

Switzerland.

1

2

3

Oil-Price Shock1 Monetary Policy Shock2

Fiscal Policy Shock 3 Labor Productivity4

4

The Current Financial Crisis in Historical Co
Chapter4  Financial Stress and Economic Dow

tries with the largest buildups of financial imbalances. These countries also suffered the deepest 

and longest recessions.

Moreover, households and firms in these 

countries also generally experienced a stronger 

deleveraging process. Deleveraging to reduce 

the stock of debt through higher savings and 

consequently lower consumption and investment had direct implications for the dynamics 

of growth. The degree of corporate deleveraging, in particular, corresponds remarkably well 

with the length and depth of the recessions—

underscored by the contrast between the Nordic 

and Anglophone country groups.

These historical experiences underline the 

key role of policy responses to financial stress. 

Policies appropriate to restore sound financial 

intermediation are discussed in Box 4.1, based 

around four main principles. First, there must 

be a sound framework in place for ensuring 

financial stability, which encompasses a framework for intervention and appropriate legal, 

institutional, and procedural mechanisms to 

deal with distress. Second, policy responses must 

be rapid and involve the early recognition of 

losses, a quick assessment of the scale of the 

problem, and timely measures to ensure that 

financial institutions are adequately capitalized. 

Third, the adverse impact of financial stress on 

the real economy may need to be contained 

directly, in order to preserve or restore the 

health of households’ and firms’ balance sheets. 

Finally, adequate safeguards must be in place 

to limit the fiscal cost of support and prevent 

the creation of inappropriate incentives for the 

longer term that could lead to excessive reliance 

on publicly financed bailouts.

Implications for the Current Crisis in the United

States and Euro Ar

Figure 4.15 compares data for the current 

crisis in the United States and euro area against 

the medians of selected macroeconomic variables around the beginning of the six major 

financial stress episodes examined above and 

against the averages for these variables across all 

Figure 4.12. The Procyclicality of Leverage in

Investment and Commercial Banks

(Annual change, percent)

 Sources: Bankscope; and IMF staff calculations.

 B and R refer to the linear regression, excluding outliers, of asset growth over

leverage growth.

-4 -3 -2 -1 0 1 2 3

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Asset growth

Leverage growth

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-0.4

-0.2

0.0

0.2

0.4

0.6

Asset growth

Leverage growth

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-0.4

-0.2

0.0

0.2

0.4

Asset growth

Leverage growth

Investment banks’ leverage (assets divided by equity) is procyclical; for commercial 

banks, the evidence is mixed across countries.

Linear regression

Linear regression, excluding outliers

Median regression

Commercial Banks: Germany (top 10 banks)

B = 0.18

R = 0.04

1

Commercial Banks: United States (top 50 banks)

B = 0.32

R = 0.12

1

Top 50 Investment Banks

B = 0.47

R = 0.26

1

1

2

2

2

2
financial stress episodes that were followed by 

recessions. The current imbalances and adjustments appear generally much smaller than those 

for the six episodes examined here, except for 

U.S. residential real estate investment and the 

U.S. current account.23 The patterns of credit 

and asset prices in the United States prior to 

the current crisis are very similar to those for 

the typical financial-stress-driven recession. 

The deleveraging process by households in the 

United States is proceeding faster than in the 

typical recession, although deleveraging by firms 

seems to be proceeding somewhat more slowly 

and from a stronger initial position. Finally, 

although bank assets remained robust during 

the second half of 2007, partly reflecting the 

reintermediation of off-balance-sheet commitments, the ratio of credit to GDP declined significantly in the first quarter of 2008, suggesting 

that the pace of deleveraging may have picked 

up (see also Chapter 1).

The current crisis is different for the United 

States in important ways from previous episodes. Corporate balance sheets and firms’ 

reliance on external financing were on a more 

solid footing entering the current crisis, which 

should provide some resilience. However, the 

sheer size of the U.S. mortgage market, which 

is at the heart of the crisis, and the role of residential investment suggest that household saving and consumption behavior may play a much 

larger role in the current downturn than in the 

past. On a positive note, the policy stance in the 

United States has been proactive, as exemplified 

by the aggressive cuts in policy rates and the 

measures taken to shore up liquidity in both 

commercial banks and investment banks. Moreover, banks have raised substantial amounts of 

capital, although continuing declines in the 

mark-to-market value of assets suggest that 

substantially more capital will be needed before 

the financial system can resume significant 

discretionary lending.

23However, a strong drop in residential real estate 

investment is a distinguishing feature of almost all U.S. 

recessions (see Leamer, 2007).

Figure 4.13. Procyclical Leverage and Arm’s-Length

Financial Systems

 Source: IMF staff calculations.

There is more evidence of procyclical leverage by commercial banks in arm’s-length

financial systems.

0.2 0.3 0.4 0.5 0.6 0.7 0.8

-0.1

0.0

0.1

0.2

0.3

0.4

Arm’s-length index

Degree of procyclical leverage

(from median regressions)

Belgium

Austria

Denmark

United Kingdom

Canada

Netherlands

Australia

United States

France

Germany

Spain Norway

Finland

Japan

Italy

Sweden

The Current Financial Crisis in Historical Co
Chapter4  Financial Stress and Economic Dow

In the euro area, the adjustment of house 

prices and credit has thus far been milder than 

in the United States, but there is evidence that 

the adjustment is gathering momentum. The 

net borrowing pattern of firms in the euro area 

is similar to that in the United States: starting 

from a stronger base than typical for a financial-stress-driven recession but weakening. However, euro area households are in a considerably 

stronger position, and this is a distinguishing 

feature of financial stress episodes that are not 

followed by recessions. The euro area’s vulnerability may also be somewhat reduced because 

the financial systems in many countries tend to 

be less arm’s-length than in the United States.

Within the euro area, there are important 

intercountry differences. Credit growth is on a 

more pronounced downward path in Ireland 

and Spain than in other countries. A number 

of countries have experienced unusually large 

run-ups in house prices and residential investment (see Box 1.2 and Chapter 2), and activity in this sector is slowing markedly. Finally, 

although Germany is experiencing large external surpluses, there are large current account 

deficits in a number of countries, including 

Greece, Portugal, Spain, and, to a lesser extent, 

Ireland (see Box 2.1).

Overall, these results suggest that the economic impact of financial stress may be greater 

in the United States than in the euro area. 

The U.S. economic downturn may well become 

more severe and could evolve into a recession. 

The evidence for the euro area is more consistent with the pattern for a slowdown than a 

recession, and the dynamics also appear to be 

evolving with some lag.

Conclusions

This chapter uses the Financial Stress 

Index to analyze episodes of stress in banking, securities, and foreign exchange markets 

in 17 advanced economies during the past 

30 years. The focus is on attempting to identify factors that determine the extent to which 

financial stress affects economic activity.

-3 -2 -1 0 1 2 3

-1

0

1

2

3

4

5

 Sources: Haver Analytics; IMF, International Financial Statistics database; OECD, 

Analytic Database; OECD, Economic Outlook (2008); and IMF staff calculations.

 The sample is constant for all years.

 Difference in medians is statistically significant for all values of t. Bank leverage is

defined as assets divided by equity.

 

Financial Stress Followed by Recessions: Output

(real GDP percent change from one year earlier)

-3 -2 -1 0 1 2 3

12

14

16

18

20

22

24

26

28 Financial Stress Followed by Recessions: Bank Leverage

(percent)

Evidence of more procyclical leverage in arm’s-length financial systems may explain 

differences in spillovers from financial crises to the real economy.

Figure 4.14. Arm’s-Length Financial Systems, GDP 

Growth, and Bank Leverage

(Median; start of financial stress episode at t = 0; years on the x-axis)

Countries with above-median arm’s-length financial systems

Countries with below-median arm’s-length financial systems

1

1

Difference between medians

statistically significant at time t = 1 

2

2
The analysis finds that financial stress is 

often, but not always, a precursor to economic 

slowdown or recession. A rapid expansion of 

credit, a run-up in house prices, and heavy 

borrowing by households and firms all increase 

the likelihood that stress in the financial 

system will lead to a more severe economic 

downturn. Banking stress, in particular, tends 

to lead to greater effects on activity, despite 

financial innovation that has increased the 

role of securities markets in many countries. 

This can be explained by the procyclicality of 

leverage, especially for investment banks but 

also for commercial banks in many countries. 

Indeed, economies with financial systems 

dominated by more-arm’s-length transactions, 

as opposed to traditional relationship-based 

intermediation, tend to exhibit higher procyclical leverage, indicating that the amplifying 

role of financial systems in propagating shocks 

is more pronounced. As a result, when shocks 

affect core financial institutions, the subsequent 

downturns tend to be deeper in more-arm’slength financial systems. Even so, arm’s-length 

systems are not generally more prone to such 

shocks, and such systems are better able to 

reallocate resources across various sectors of 

the economy in response to changing economic 

opportunities.

Based on a comparison of the current episode of financial stress with previous episodes, 

there remains a substantial likelihood of a 

Table 4.4. Six Major Periods of Financial Stress and Economic Contract

Initial Condition

Asset price buildup1 Net lending ratio3

Equity prices House prices Credit/GDP Bank assets2 Households Firms

Finland, early 1990s 80.0 36.1 16.6 21.0 –6.5 –5.1

Sweden, early 1990s 68.5 17.5 19.1 27.2 . . . . . .

Norway, early 1990s 73.9 26.5 18.8 27.6 –6.9 –3.6

Japan, 1990s 54.4 12.2 7.4 22.4 5.3 –5.1

United Kingdom, early 1990s 19.9 22.9 2.5 16.1 –2.3 –3.4

United States, early 1990s 14.5 4.9 3.1 9.5 –0.4 –0.3

Average 51.9 20.0 11.3 20.6 –2.1 –3.5

Current episode

United States 27.7 5.9 3.0 9.9 1.4 –0.7

Euro area 44.0 2.9 4.5 9.7 –0.5 –0.4

Japan4 25.1 5.4 6.4 –0.8 0.1 –2.9

United Kingdom 29.4 3.2 5.1 11.2 –0.8 –0.8

Outcomes

Asset price decline

Macroeconomic deleveraging7 Equity 

prices

House 

prices

Credit/

GDP

Bank 

assets6 Households Firms Output loss8

Quarters to 

recovery

Finland, early 1990s –85.9 –39.8 –16.8 –5.1 16.2 17.0 –13.6 27

Sweden, early 1990s –69.5 –20.1 –21.3 –4.9 . . . . . . –5.8 19

Norway, early 1990s –76.9 –24.6 –2.7 –12.5 16.5 8.5 –3.9 12

Japan, 1990s –58.5 –11.1 –6.8 –8.5 0.5 15.4 –5.1 19

United Kingdom, early 1990s –21.4 –23.3 –5.6 –6.5 9.6 4.4 –2.6 13

United States, early 1990s –21.0 –4.8 –3.8 –5.4 0.8 0.6 –1.3 5

Average –55.5 –20.6 –9.5 –7.2 8.7 9.2 –5.4 15.8

1Trough-to-peak changes before the start of the crisis in the detrended Hodrick-Prescott (HP) filter level of the variables.

2Maximum percent deviation from detrended (HP filter) levels of bank assets before the start of the crisis.

3Deviation from HP trend the year before the crisis.

4Data in net lending ratios are for 2006.

5Peak-to-trough changes after the start of the crisis in the detrended (HP filter) level of the variables.

6Minimum percent deviation from the detrended (HP filter) level of bank assets after the start of the crisis.

7Trough-to-peak changes in detrended (HP filter) net lending ratios.

8Output loss is measured as the loss from peak to trough in percent of peak level of output.

Conclusion
Chapter4  Financial Stress and Economic Dow

1

-12 -8 -4 0 4 8 12 -1

0

1

2

3

4

5

-12 -8 -4 0 4 8 12 -2

-1

0

1

2

3

4

5

Output

(GDP percent change from one 

year earlier; quarters on the 

x-axis)

Consumption

(percent change from one year

earlier; quarters on the x-axis)

-3 -2 -1 0 1 2 3 -4

-3

-2

-1

0

1

2

3

4

-12 -8 -4 0 4 8 12 -10

-5

0

5

10

Household Net 

Lending/Borrowing Ratio

(deviation from trend; years on

the x-axis)

House Prices

(percent deviation from trend; 

quarters on the x-axis)

Figure 4.15. The Current Financial Stress Episode in the United States and Euro Area in Historical Context

(Start of financial stress episode at t = 0; x-axis as stated)

Credit ratios and asset prices are mirroring previous episodes, but bank asset growth remains resilient in both the United States and the euro area. Initial corporate 

financial positions have been stronger than in past episodes, but are deteriorating. A correction in household financial positions is under way in the United States. The 

slowdown of consumption and investment growth mirrors earlier episodes in the United States, although it has only recently started in the euro area. 

Median across financial stress episodes followed by recessions

Recent Episodes: United States Euro area

Median across financial stress episodes for six case studies

-12 -8 -4 0 4 8 12 -15

-10

-5

0

5

10

-12 -8 -4 0 4 8 12 -30

-20

-10

0

10

Residential Investment

(percent change from one year

earlier; quarters on the x-axis)

Nonresidential Investment

(percent change from one year

earlier; quarters on the x-axis)

-3 -2 -1 0 1 2 3 -3

-2

-1

0

1

2

3

-12 -8 -4 0 4 8 12 -4

-2

0

2

4

6

8

Nonfinancial Corporate

Net Lending/Borrowing Ratio

(deviation from trend; years on

the x-axis)

Credit

(percent of GDP; percent

deviation from trend; quarters

on the x-axis)

-12 -8 -4 0 4 8 12 -8

-6

-4

-2

0

2

Current Account

(percent of GDP; quarters on 

the x-axis)

-3 -2 -1 0 1 2 3 -10

-5

0

5

10

15

20

-12 -8 -4 0 4 8 12 -20

-15

-10

-5

0

5

10

15

20

Stock Prices

(percent deviation from trend; 

quarters on the x-axis)

Bank Assets

(percent deviation from trend;

years on the x-axis)

 Sources: European Central Bank; Haver Analytics; IMF, International Financial Statistics database; OECD, Analytic Database; OECD, Economic Outlook (2008); and IMF staff calculations. 

 All in real terms, except for household and nonfinancial corporate net lending ratios and bank assets. 1

-12 -8 -4 0 4 8 12 0

2

4

6

8

10

12

Real Interest Rate

(percent; quarters on the x-axis)

1
How can policymakers respond to financial 

stress, including the current global financial 

turmoil, in a way that ensures that the financial 

system is restored to health, while limiting the 

fallout on the economy and avoiding longterm moral hazard? Well-timed interventions 

aimed at financial institutions and borrowers 

can help restore balance sheets and incentives, 

mitigate the negative shock to the economy 

of a financial system under stress, and help to 

restart productive investment. But in undertaking these interventions, governments face the 

key challenge of restoring financial intermediation while keeping the costs to taxpayers down, 

avoiding misallocations of capital, and maintaining proper incentives. 

General Principles of Intervention

The experience from past episodes of financial system distress suggests that the effectiveness and cost of policy responses depend on 

four key elements:1

• Having a sound framework for ensuring 

financial sector stability helps prevent and 

contain financial stress. Key elements of this 

framework include (1) pre-crisis sanctions on 

undercapitalized financial institutions that 

pose systemic risks; (2) legal and institutional 

mechanisms to deal swiftly with weak financial institutions, such as bank-specific bankruptcy regimes; (3) well-defined tools and 

processes for closing and rapidly reopening 

banks; and (4) an effective deposit insurance 

system.

• Speed is of the essence to minimize the 

impact on the real economy. Too often, 

regulatory forbearance and liquidity support 

have been used to help insolvent financial 

institutions recover—only to have it become 

The author of this box is Luc Laeven. This box 

draws heavily on Calomiris, Klingebiel, and Laeven 

(2005). 1For an overview of existing literature on crisis 

resolution policies, see Hoelscher and Quintyn (2003) 

and Honohan and Laeven (2005).

clear later that delaying decisive intervention 

increased the stress on the financial system 

and the economy. To avoid this, policymakers 

should force the early recognition of losses 

and take steps to ensure that financial institutions are adequately capitalized.

• The adverse impact of financial system distress on the real economy may need to alleviated through measures that directly support 

firms and households—for example, through 

targeted debt relief programs to distressed 

borrowers and corporate restructuring 

programs.

• Steps should be taken to limit the costs 

and moral hazard implications of these 

policy responses. Shareholders must first 

absorb losses by a write-down of their equity 

capital. In the case of large losses, creditors 

also need to contribute by reducing and 

restructuring their claims. Borrowers must 

absorb some of the costs, especially if they 

have been imprudent. Mechanisms that link 

government support (such as preferred stock 

purchases) to privately raised capital can also 

help identify those banks that are truly worth 

saving and limit future distortions arising 

from moral hazard. 

Specific Policy Responses

Policymakers can employ a wide range of 

specific emergency measures (aimed at containing the crisis) and restructuring tools (aimed at 

restoring the normal functioning of the credit 

system and rebuilding banks’ and borrowers’ 

balance sheets). 

Emergency measures include (1) regulatory 

capital forbearance, (2) emergency liquidity support, (3) government deposit guarantees, and (4) suspension of convertibility of 

deposits.2 Each of these actions can have very 

2 Examining a sample of 40 banking crisis episodes, Laeven and Valencia (forthcoming) show that 

emergency measures have often included emergency 

liquidity support and government deposit guarantees. 

Regulatory capital forbearance—suspending prudenBox 4.1. Policies to Resolve Financial System Stress and Restore Sound Financi

Intermediation

Conclusion
Chapter4  Financial Stress and Economic Dow

different consequences on the supply of credit 

and thus on economic activity. The appropriate 

policy measure depends on whether the trigger 

for the crisis is a loss of depositor confidence, 

the (regulatory) recognition of existing insolvency, or the knock-on effects of asset price 

volatility, including exchange rate or house 

price pressures. Even during the emergency 

phase, however, longer-term implications must 

be taken into account—the risk being that 

restoring stability in the heat of the crisis may 

lead central banks to extend loans to some 

financial institutions that are almost certain to 

prove insolvent. 

Specific resolution policies include (1) recapitalizating financial institutions, (2) using asset 

management companies (AMCs) to resolve 

distressed loans, (3) offering debt forgiveness, 

and (4) providing incentives for loan loss writeoffs.3 Countries typically apply a combination of 

resolution strategies—with some directed more 

toward financial institutions and others geared 

more toward borrowers—and in the process the 

government often incurs substantial fiscal costs.4

Here are some experiences with these types of 

resolution policies. 

Recapitalization: Measures aimed at quickly 

improving the capital bases of financial institutions do not directly improve debtor capacity, 

but they make it easier for banks to recognize 

losses and thereby facilitate corporate restructuring. Government-assisted recapitalizations 

can, however, create moral hazard for shareholders, especially if government intervention 

tial regulations and allowing technically insolvent 

banks to continue operating—is also a rather common 

response. By contrast, measures aimed at avoiding 

bank runs through deposit freezes and bank holidays 

are rarely used. 3 Laeven and Valencia (forthcoming) show that 

bank recapitalization occurred in three-quarters of the 

crises they considered, with an average fiscal cost of 

6 percent of GDP. AMCs were set up in slightly more 

than half of the episodes in their database. 4The average fiscal cost of government intervention 

in the cases studied by Laeven and Valencia (forthcoming) is about 16 percent of GDP.

is small relative to the negative net worth of 

recipient institutions. Looking at the recapitalization schemes adopted in the United 

States (starting in 1933) and Japan (1998 and 

1999) helps illustrate some key issues.5 In the 

United States, the program mainly involved the 

purchase of preferred stock to enhance bank 

capital and included appropriate screening and 

incentives for participants so that only banks 

worth saving and those that managed their risk 

and capital structure more prudently received 

taxpayer funds. Moreover, banks receiving 

assistance were monitored to ensure that they 

made proper use of public aid. In Japan, the 

first program (launched in 1998) involved only 

small amounts, was mostly targeted to purchases 

of subordinated debt and loans, and was broadly 

spread across the banking system. A more successful recapitalization program was launched 

in 1999, which involved much larger purchases 

of preferred stocks, included more rigorous 

benchmarks, and participation was more narrowly focused.6

Asset management companies (AMCs): The main 

objective of government-owned AMCs is to 

accelerate financial restructuring by taking 

over nonperforming assets from banks. Two 

examples of successful AMCs are Securum and 

Retrieva in Sweden, created in 1992 to manage 

the problem loans of two major Swedish banks, 

Nordbanken and Gota Bank. Both companies managed to recover much of their initial 

investment by selling off their assets. Factors 

that contributed to their success include an 

efficient judicial system, which allowed them 

5The two Japanese programs together involved 

public purchase of ¥10 trillion (2 percent of GDP) of 

bank capital. 6The specific form of bank recapitalization often 

depends on the country’s insolvency regime for 

financial institutions. In many countries today such 

regimes do not allow for a speedy resolution of 

crises but rather prolong them. Another lesson for 

successful bank recapitalization is that bank capital 

regulations must be enforced rigorously, which can 

involve imposing limitations on the distribution of 

dividends.

Box 4.1 (concluded)
sharp downturn in the United States, given 

the similarities between the current dynamics of asset prices, credit ratios, and household 

financial positions and previous episodes that 

were followed by recession. Mitigating factors 

are the rapid monetary policy response and 

a relatively low real interest rate. In the euro 

area, by contrast, the relatively strong position 

of households offers some protection against a 

sharp downturn, despite the appreciable run-up 

in asset prices and the credit ratio ahead of 

the current financial turmoil. The euro area’s 

vulnerability to a deeper downturn may also be 

somewhat reduced because many of its financial 

systems are less arm’s-length, as evidenced most 

notably by the much smaller role for the originate-to-distribute mortgage banking business 

model.

One factor that helps predict whether a 

financial stress episode will lead to a downturn 

is the buildup in balance sheet vulnerabilities 

associated with rising asset prices and credit. 

Policymakers therefore need to be alert to these 

indicators during the upswing of the financial 

cycle. Prudential measures and monetary policy 

should be used to address buildups that may 

leave the economy vulnerable to greater output 

losses in the event of a severe shock.

Should significant financial stress affect the 

core of the banking system, the early recognition of losses and measures to support the 

speedy restoration of capital can help reduce 

the output consequences. At the same time, 

policymakers must seek to avoid longer-term 

moral hazard implications of any strategy to 

restore financial stability.

Conclusion

to force insolvent debtors into bankruptcy; 

the real-estate-related nature of their assets, 

which made it easier to restructure; and the 

strong governance mechanisms and skilled 

management teams in place at the companies. 

However, other countries have found it harder 

to realize these advantages, in part owing to 

weak legal, regulatory, and political institutions—banks’ assets often are transferred to 

the AMC at prices abovemarket value, resulting 

in backdoor bank recapitalization and creating 

moral hazard.

Debt forgiveness: Key advantages of this 

measure are its simplicity and speed—debt 

forgiveness recognizes loan losses up front 

and thus provides immediate relief to borrowers. At the same time, however, debt forgiveness poses incentive problems because it does 

not impose losses on borrowers and bank 

shareholders. It can also undermine trust in 

monetary institutions and the rule of law, as 

it can violate monetary standards and interfere in private contracting. Whether it works 

ultimately depends on the frequency of its use 

and the specific circumstances of financial 

distress.7 Because of the risks of moral hazard, 

however, debt forgiveness should be considered 

only as a last resort.

Loan loss write-off programs: Loan loss write-off 

programs are directed at supporting borrowers. 

Although they can be implemented quickly, 

loan loss write-offs may worsen incentives for 

prudent behavior as they do not impose losses 

on banks or their borrowers.

Overall, the mix of policy responses will 

ultimately be crisis-specific and must reflect 

a variety of factors, including the nature and 

depth of the financial crisis and the specific 

country circumstances. The four principles for 

intervention outlined here have proven to have 

general applicability and should be followed in 

every crisis, including the current one.

7 The U.S. experience in the 1930s, when gold payment clauses in debt contracts were abrogated, shows 

that debt forgiveness can help solve coordination 

problems in renegotiating debt. While few individual 

creditors were willing to voluntarily remove these 

clauses, when they were forced to do so collectively, 

the improvement in aggregate economic circumstances left both creditors and debtors better off.
Chapter4  Financial Stress and Economic Dow

Appendix 4.1. Data and Methodology

The main authors of this appendix are Angela Espiritu and 

Gavin Asdorian.

Table 4.5. Dat

Variable Source Frequency

GDP Haver Analytics, 

Organization for 

Economic Cooperation 

and Development 

(OECD) Analytical 

Database

Quarterly

Average petroleum spot 

price

World Economic 

Outlook database

Quarterly

CPI inflation Haver Analytics, OECD 

Analytical Database

Quarterly

Real private consumption OECD Analytical 

Database

Quarterly

Money supply OECD Analytical 

Database

Quarterly

Interest rate Haver Analytics, 

International Financial 

Statistics (IFS) 

database

Quarterly

Banking sector equity 

index

Thomson Datastream, 

Haver Analytics

Monthly

Stock market index OECD Monthly

3-month London 

interbank offered rate 

(LIBOR) or commercial 

paper rate

Haver Analytics Monthly

Government short-term 

rate

Haver Analytics Monthly

Government bond yields Haver Analytics, 

Thomson Datastream

Monthly

Corporate bond yields Thomson Datastream, 

Haver Analytics

Monthly

Real effective exchange 

rate

International Monetary 

Fund

Monthly

Residential investment OECD Analytical 

Database

Quarterly

Nonresidential investment OECD Analytical 

Database

Quarterly

Current account OECD Analytical 

Database

Quarterly

Real house prices OECD Quarterly

Credit IFS database Quarterly

Household net lending OECD Annual

Government net lending OECD Annual

Nonfinancial corporate net 

lending

OECD Annual

Bank assets OECD Annual

Bank equity OECD Annual

The Financial Stress Ind

This section of the appendix describes the 

components and the methodology used to construct the Financial Stress Index (FSI). The FSI 

is an equal-variance weighted average of seven 

variables, grouped into three categories.

Banking Secto

• Banking sector β: rolling 12-month covariance of the year-over-year percent change of a

country’s banking sector equity index and its 

overall stock market index, divided by the rolling 12-month variance of the year-over-year 

percent change of the overall stock market 

index. Sources: Thomson Datastream, Haver 

Analytics, and OECD.

• TED spread: three-month LIBOR or commercial paper rate minus the government shortterm rate. Source: Haver Analytics.

• Inverted term spread: government shortterm rate minus government long-term rate. 

Sources: Thomson Datastream and Haver 

Analytics.

Securities Marke

• Corporate spread: corporate bond yield minus 

long-term government bond yield. Sources: 

Thomson Datastream and Haver Analytics.

• Stock decline: stock index at t – 1 minus stock 

index at t, then divided by stock index at t – 1. 

Source: OECD.

• Time-varying stock volatility: GARCH(1,1) volatility of overall stock market index monthly 

return. Source: OECD.

Foreign Exchang

• Time-varying real effective exchange rate 

volatility: GARCH(1,1) volatility of real effective exchange rate monthly percent change. 

Source: IMF.

All components are originally in monthly 

frequency. The index is constructed by taking 

the average of the components after adjusting 
for the sample mean and standardizing by the 

sample standard deviation. The index is then 

rebased so that it ranges from 0 to 100. Finally, 

it is converted into quarterly frequency by taking the average of the monthly data. The FSI is 

available for 17 advanced economies starting in 

1980.24

Episodes of financial stress are identified 

when the index is one standard deviation above 

its trend. Episodes that are only two quarters 

apart are considered a single episode. To classify the cause of an episode of financial stress—

either banking-related, securities-related, or 

foreign-exchange-related—we look at the 

change between the FSI from the quarter prior 

to the start of the episode and the maximum 

value of the FSI within the episode. If most of 

the increase stems from banking sector components, the FSI is classified as “banking.” The 

same rule applies if the change results mainly 

from the securities markets components or 

the foreign exchange component. Moreover, if 

banking contributes at least one-third of the 

change in the FSI, the episode is also classified 

as “banking-related.”

The Cost of Capita

“Cost of capital” is defined in this chapter 

as a weighted average of the real cost of bank 

loans, the real cost of debt, and the real cost 

of equity, using as weights the relative shares 

of equity, bonds, and loans in nonfinancial 

corporate liabilities. The cost of capital is based 

on the calculation outlined in Box 4 on p. 37 

of the European Central Bank’s (ECB’s) March 

2005 Monthly Bulletin. The real cost of bank 

loans, real cost of debt, and real cost of equity 

are derived as follows:

• Real cost of bank loans: bank lending rates 

minus one-year-forward Consensus inflation 

forecast. Sources: IFS, ECB, and Consensus 

Economics.

24Data on long-term corporate bond yields for Greece, 

Ireland, New Zealand, and Portugal were not available 

and therefore were excluded from the sample.

• Real cost of debt: corporate bond yield minus 

one-year-forward Consensus inflation forecast. 

Sources: Thomson Datastream, Haver Analytics, and Consensus Economics.

• Real cost of equity: derived using a model 

specified in Box 2 on p. 76 of the ECB’s 

November 2004 Monthly Bulletin. Using available data for the other variables, the real cost 

of equity, ht, can be calculated using the following equation:

Dt[(1 + g) + 8(gt

IBES – g)] Pt

 = ———————————, ht

 – g

where

 Pt = real stock price,

 Dt = the current level of real dividends,

gt

IBES = I/B/E/S long-term earnings-per-share 

growth forecast minus Consensus longterm inflation forecast,

 g = long-term growth rate of real corporate earnings, assumed constant at 

2.5 percent.

The overall cost of capital is calculated as a 

weighted average of these three components 

with the weights defined, respectively, as loans, 

debt, and equity as shares of nonfinancial corporate liabilities as reported in the OECD national 

accounts data.

Bankscope Data

Two data sets were constructed using banklevel data obtained from the Bankscope 

database.25 The first data set included only 

investment banks as classified by the Bankscope 

database (“Investment Bank/Securities House”). 

The second data set, referenced in the chapter 

as “commercial banks,” included banks with the 

following Bankscope classifications: Commercial 

Bank, Savings Bank, Cooperative Bank, Real 

Estate/Mortgage Bank, and Medium & Long 

Term Credit Bank.

25Bankscope database published by Bureau van Dijk 

Electronic Publishing (BvDEP): www.bvdep.com.

Appendix 4.1. Data and Meth
Chapter4  Financial Stress and Economic Dow

Sample of Bank

Investment banks

The sample of banks contained banks that 

were among the top 50 investment banks globally in terms of total assets in one or more years 

from 1988 to 2007.

Commercial banks

The sample of banks consisted of banks that 

were among the top 10 banks26 in terms of 

total assets for each country in one or more 

years from 1988 to 2007. Also included were 

any banks that were acquired by or that merged 

with a top-10 bank. (See below for an explanation of accounting for mergers and acquisition.)

The number of commercial banks in each 

country used in the sample was chosen to provide a representative sample of banking activity within each country. Table 4.6 summarizes 

the average yearly share of total bank assets 

(as reported by the OECD) represented by the 

banks in the sample.

Consolidated Versus Unconsolidate

Balance Sheet

Investment banks

Data from consolidated statements were used 

for investment banks. If consolidated data were 

unavailable, data from unconsolidated statements were used.

Commercial banks

In order to isolate as much as possible the 

domestic activities of commercial banks, unconsolidated bank data were used for commercial 

banks in the sample. If unconsolidated statements were unavailable, data from consolidated 

data were used.

Data from multiple statements for the same 

bank were combined to form a single set of 

bank-level data if the statement types (consolidated or unconsolidated) were the same. In 

26Top 30 banks for United Kingdom and Japan; top 

50 banks for United States.

addition, the data were cleaned (by country, 

in the case of commercial banks) by excluding 

observations in which the growth rate of total 

assets was above the 95th percentile or below 

the 5th percentile.

Mergers and Acquisitions

For consistency, banks that were acquired by 

or merged with banks included in the original 

sample set were also included in the data set. 

For years prior to a merger or acquisition, the 

banks involved were treated as separate banks; 

for years subsequent to a merger or acquisition, 

the bank resulting from the merger or acquisition was naturally a single bank in the database. 

In order to calculate level changes or growth 

rates of a bank in the year of a merger or acquisition, a data point was constructed for the year 

prior to the merger or acquisition by summing 

the data values of the banks involved in the 

merger or acquisition.

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Table 4.6. Average Yearly Share of Total Ba

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Country

Number of Top Banks 

a Year in Sample 

Percent of Country’s 

Total Bank Assets

Australia 10 78 

Austria 10 71 

Belgium 10 94 

Canada 10 88 

Denmark 10 92 

Finland 10 79 

France 10 73 

Germany 10 65 

Italy 10 40 

Japan 30 74 

Netherlands 10 90 

Norway 10 78 

Spain 10 78 

Sweden 10 94 

Switzerland 10 64 

United Kingdom 30 67 

United States 50 60 
——— , 2008b, “Liquidity and Leverage,” Staff Report 

No. 328 (New York: Federal Reserve Bank of New 

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Matters: Empirical Evidence on U.S. Macro-Financial Linkages,” IMF Working Paper 08/169 (Washington: International Monetary Fund).

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Black Box: The Credit Channel of Monetary Policy 

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(Autumn), pp. 27–48.

———, and Simon Gilchrist, 1999, “The Financial 

Accelerator in a Quantitative Business Cycle Framework,” Handbook of Macroeconomics, Vol. 1C (Amsterdam: North-Holland), pp. 1341–93.

Bernanke, Ben, Cara Lown, and Benjamin Friedman, 

1991, “The Credit Crunch,” Brookings Papers on Economic Activity: 2, Brookings Institution, pp. 205–47.

 Borio, Claudio, 2007, “Change and Constancy in the 

Financial System: Implications for Financial Distress 

and Policy,” BIS Working Paper No. 237 (Basel: 

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Calomiris, Charles W., Daniela Klingebiel, and Luc 

Laeven, 2005, “Financial Crisis Policies and Resolution Mechanisms: A Taxonomy from Cross-Country 

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and Resolution, ed. by Patrick Honohan and Luc 

Laeven (Cambridge, Massachusetts: Cambridge 

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Caprio, Gerard, and Daniela Klingebiel, 2003, 

“Episodes of Systematic and Borderline Financial 

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Hoelscher, David S., and Marc Quintyn, 2003, Managing Systemic Banking Crises (Washington: International Monetary Fund).

Honohan, Patrick, and Luc Laeven, eds., 2005, Financial Crises: Containment and Resolution (Cambridge, 

Massachusetts: Cambridge University Press).

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Stress in a Developed Country: An Application to 

Canada,” Journal of Financial Stability, Vol. 2 (October), pp. 243–65.

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Financial Stability Report: Financial Stress and Deleveraging—Macro-Financial Implications and Policy 

(Washington, October).

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“Rethinking Capital Regulation,” paper prepared for 

the Federal Reserve Bank of Kansas City symposium, 

“Maintaining Stability in a Changing Financial System,” Jackson Hole, Wyoming, August 21–23.

Kashyap, Anil, and Jeremy Stein, 1995, “The Impact of 

Monetary Policy on Bank Balance Sheets,” CarnegieRochester Conference Series on Public Policy, Vol. 42 

(June), pp. 151–95.

Kindleberger, Charles, and Robert Aliber, 2005, 

Manias, Panics, and Crashes: A History of Financial 

Crises (Hoboken, New Jersey: John Wiley & Sons, 

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Cycles,” Journal of Political Economy, Vol. 105 (April), 

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Paper (Washington: International Monetary Fund).

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on Housing, Housing Finance, and Monetary 

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Journal of Money, Credit and Banking, Vol. 27, No. 3, 

pp. 625–38.

Referenc
Chapter4  Financial Stress and Economic Dow

Rajan, Raghuram, and Luigi Zingales, 2003, 

“Banks and Markets: The Changing Character 

of European Finance,” CEPR Discussion Paper 

No. 3865 (London: Centre for Economic Policy 

Research).

Reinhart, Carmen, and Kenneth Rogoff, 2004, “The 

Modern History of Exchange Rate Arrangements: 

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———, 2008, “Is the 2007 U.S. Sub-Prime Financial 

Crisis So Different? An International Historical 

Comparison,” NBER Working Paper No. 13761 

(Cambridge, Massachusetts: National Bureau of 

Economic Research).

Shiller, Robert J., 1999, “Measuring Bubble Expectations and Investor Confidence,” NBER Working Paper No. 7008 (Cambridge, Massachusetts: 

National Bureau of Economic Research).

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Runs: A Case Study of Northern Rock,” Working 

Paper (unpublished; Princeton, New Jersey: Princeton University).
c h a pte r 5

This chapter investigates whether fiscal policy should 

be used to combat business cycle fluctuations, especially 

downturns. Can discretionary fiscal policy successfully 

stimulate output? Or does it do more harm than good? 

New evidence presented here, from emerging as well as 

advanced economies, indicates that the effects of fiscal 

stimulus can be positive, albeit modest. But policymakers must be very careful about how stimulus packages are implemented, ensuring that they are timely 

and that they are not likely to become entrenched and 

raise concerns about debt sustainability. The chapter 

concludes with a discussion of how automatic stabilizers could be made more effective and how governance 

improvements could reduce “debt bias” concerns related 

to discretionary actions.

I

n recent months, as economies have been 

buffeted by falling asset prices, rising costs 

for raw materials and credit, and waning 

confidence, there have been renewed calls 

for governments to actively use fiscal policy to 

support efforts taken by central banks to prevent 

sharp declines in activity. Once again, there is a 

lively debate about the appropriate role of fiscal 

policy in managing the business cycle, especially 

during a downturn: Are discretionary fiscal 

actions helpful, or do they sometimes do more 

harm than good? When is a discretionary package most effective? When is it better simply to 

let automatic stabilizers do the job?

The debate over the appropriate role of fiscal policy in managing the business cycle has 

persisted for many years. One school of thought 

argues that taxes, transfers, and spending can 

be used judiciously to lean against fluctuations 

in economic activity, especially to the extent that 

economic fluctuations are mainly due to markets falling out of equilibrium instead of reacting to changes in fundamental factors such as 

productivity. Others contend that fiscal policy 

actions are generally either ineffective or make 

things worse, because the actions are ill timed 

or they create damaging distortions. This latter 

point of view has dominated the debate over the 

past two decades; consequently, fiscal policy has 

taken a backseat to monetary policy. But there 

also has been a recognition that there are times 

when monetary policy needs the support of fiscal stimulus, such as when nominal interest rates 

approach zero or the channels of monetary 

policy transmission are in some way impeded.

Against this background, this chapter takes 

a fresh look at the role of fiscal policy during 

economic downturns. The main objectives are 

to (1) analyze how fiscal policy has typically 

responded during downturns; (2) examine the 

effects on economic activity of fiscal stimulus 

during downturns; (3) identify the main factors that affect the outcomes of fiscal policy 

interventions; and (4) offer policy suggestions, 

in light of both empirical evidence and insights 

from theoretical work, on (a) whether and when 

to use discretionary fiscal policy, (b) the implications of using various fiscal policy instruments, 

and (c) the appropriate balance between automatic stabilizers and discretionary actions.

This chapter seeks to contribute to the considerable literature on fiscal policy as a countercyclical tool in three ways. First, it specifically 

evaluates whether discretionary fiscal policy 

responses to downturns have been timely and 

temporary. Second, whereas most previous 

studies have focused on the effects of policy in 

advanced economies, this chapter also looks 

at evidence for emerging economies. Finally, 

the chapter complements the empirical analysis with simulation analysis designed to assess 

how fiscal multipliers depend on the choice of 

The main authors of this chapter are Alasdair Scott 

(team leader), Steven Barnett, Mark De Broeck, Anna 

Ivanova, Daehaeng Kim, Michael Kumhof, Douglas 

Laxton, Daniel Leigh, Sven Jari Stehn, and Steven 

Symansky, with support from Elaine Hensle, Annette 

Kyobe, Susanna Mursula, and Ben Sutton.

Fiscal Plicy as a
Chapter5  Fiscal Policy as a Countercyclical

1

fiscal instruments and the characteristics of the 

economy.

The policy record shows that discretionary 

fiscal policy has been more timely than some 

critiques suggest. But there are valid concerns 

about whether fiscal stimulus packages will be 

temporary and the implications for the path 

of government debt. Empirical evidence suggests that discretionary fiscal stimulus has a 

moderately positive effect on output growth 

in advanced economies. However, the effects 

appear to be constrained in emerging economies. This might be because of credibility 

issues, especially debt concerns. Simulation 

experiments show that fiscal multipliers can 

vary considerably, depending on the instrument 

used, the degree of monetary policy accommodation, and the type of economy. Consistent 

with the empirical evidence, increases in interest 

rate risk premiums as a result of debt concerns 

can render fiscal multipliers negative, suggesting 

that discretionary fiscal stimulus may do more 

harm than good.

Does this mean there is no role for countercyclical fiscal policy? In practice, the extent of 

automatic stabilizers has been related to the size 

of government, but more extensive government 

is generally associated with lower growth. Given 

this dichotomy, it is worth investigating further 

whether countercyclical fiscal rules and the fiscal 

policy framework can be designed to increase 

the ability of fiscal policy to smooth fluctuations in output and income over the course of 

business cycles—without increasing the size of 

government or placing debt stability at risk.

The chapter is organized as follows. The next 

section provides a brief review of the empirical 

and theoretical literature on the role of fiscal 

policy in stabilizing output. The following two 

sections present, first, the results of new empirical work that characterizes how fiscal policy 

has been used in both advanced and emerging 

economies and then an analysis of its effects. 

The subsequent section uses formal simulation-based analysis to examine the effectiveness 

of various stimulus options and the effects of 

various macroeconomic factors when the policy 

is implemented. The concluding section offers 

some policy suggestions.

Understanding the Fiscal Policy Debat

Fiscal policy can work in two general ways to 

stabilize the business cycle. One way is through 

automatic stabilizers, which arise from parts of 

the fiscal system that naturally vary with changes 

in economic activity—for example, as output 

falls, tax revenues also fall and unemployment 

payments rise.1 Discretionary fiscal policy, on the 

other hand, involves active changes in policies 

that affect government expenditures, taxes, and 

transfers and are often undertaken for reasons 

other than stabilization.

By their nature, automatic stabilizers play an 

immediate role during downturns. But they are 

usually by-products of other fiscal policy objectives. As such, the size of automatic stabilizers 

tends to be associated with the size of government (see, for example, Fatás and Mihov, 2001), 

suggesting that an increase in the size of government can help dampen output volatility (see 

Galí, 1994). However, many argue that a larger 

government acts as a drag on growth over the 

longer term. Hence, there is a potential conflict between increasing stability and increasing 

economic efficiency. Moreover, the effectiveness 

of automatic stabilizers may be more a matter of 

proper design than size.

Because automatic stabilizers are often limited 

in scope—Box 5.1 reviews the extent of automatic stabilizers across economies—the active 

use of discretionary fiscal measures is often 

promoted as a countercyclical tool. Skeptics, 

however, question governments’ ability to deliver 

well-timed measures as well as the macroeconomic effects of discretionary fiscal measures 

and the longer-term implications for fiscal 

sustainability.

1Hence, the strength of automatic stabilizers depends 

on the size of transfers (such as the scope of unemployment insurance), the progressivity of the tax system, and 

the effects of taxes and transfers on labor participation 

and demand for workers and capital.
How important are automatic stabilizers? 

This box looks at their quantitative impact 

on the fiscal balance, especially in comparison with discretionary fiscal policy. First, the 

impact of automatic stabilizers on the primary 

balance varies across countries. The volatility in the primary balance is more a result of 

changes in discretionary policy than of automatic stabilizers. However, for many countries, 

changes in discretionary policy are not well 

synchronized with the business cycle, suggesting that automatic stabilizers are often a more 

important source of systematic countercyclical 

policy actions.

Automatic stabilizers are measured using 

the change in the cyclical balances estimated 

in the event analysis in the main text of this 

chapter.1 The impact of automatic stabilizers on fiscal outcomes varies across countries 

and is positively related to both government 

size and output volatility. Government size is a 

good proxy for the size of automatic stabilizers, 

and provides the horizontal axis in the first 

figure.

2 Realized volatility in the cyclical balance—­measured as the standard deviation o

the change in the cyclical balance—is roughly 

equal to government size times the volatility in the output gap. The first figure shows 

that even though emerging economies have 

smaller governments, they tend to experience 

higher volatility in the cyclical balance than 

advanced economies. This is largely because 

emerging economies have more volatile output 

gaps. However, looking separately at emerging 

economies and advanced economies (to control 

for the higher output volatility in emerging 

economies), there is a positive relationship 

between government size and cyclical balance volatility—that is, countries with larger 

The main author of this box is Steven Barnett.

1The elasticity-based measure is used for the analysis 

in this box. The sample period is 1992–2007. 2Balassone and Kumar (2007), Box 4.2, explains 

why this holds. This general finding is robust to 

income elasticity assumptions.

automatic stabilizers have more variation in the 

cyclical balance.3

Changes in discretionary fiscal policy, 

however, account for more of the volatility of 

primary balances than automatic stabilizers. On 

average, the volatility of the cyclically adjusted 

balance is about three times greater than that 

of the cyclical balance. This is true for advanced 

economies and for emerging economies. But 

the extent to which these policy changes play a 

countercyclical role depends on how well they 

are synchronized with the business cycle. To 

examine this empirically, a measure of the cyclicality of fiscal policy discretion is compared with 

3Government size, however, is often found to 

be negatively correlated with output volatility (for 

example, Andrés, Doménech, and Fatás, 2008), which 

would dampen the otherwise mechanical positive 

relationship between government size and cyclical 

balance volatility. 

Box 5.1. Differences in the Extent of Automatic Stabilizers and Their Relationship wi

Discretionary Fiscal Policy

Understanding the Fiscal Policy De

10 20 30 40 50 60

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Volatility in Cyclical Balance

 Source: IMF staff calculations.

Standard deviation of change in cyclical balance

Government size (percent of GDP)

Emerging economies

Advanced economies
Chapter5  Fiscal Policy as a Countercyclical

the size of automatic stabilizers.4 The second figure shows that discretionary fiscal policy tends 

to be more countercyclical in advanced economies (when the countercyclicality of discretion 

is greater than zero), but is often procyclical in 

emerging economies (below zero). The units 

on the two axes are comparable and indicate 

the percentage point change in the respective 

balance (after dividing by 100) for a 1 percentage point increase in the output gap. If a country lies above the 45-degree line, it indicates that 

discretionary policy makes overall fiscal policy 

more countercyclical than automatic stabilizers 

4Cyclicality of fiscal policy is measured by a regression, run in first differences, with the cyclically 

adjusted primary balance as the dependent variable 

and the output gap as the explanatory one. A positive 

coefficient indicates a more countercyclical policy. 

This regression, however, is potentially problematic in 

that it ignores the relationship (endogeneity) between 

fiscal policy and the output gap.

do. As can be seen, this happens in only a few 

cases, including some of the Anglophone countries with smaller governments, as well as some 

of the Nordic ones with larger governments. 

However, there is little systematic evidence that 

countries with smaller governments compensate 

for weaker automatic stabilizers by using more 

discretion.

Together, these findings would suggest that 

(1) automatic stabilizers have, in general, 

played a more consistently countercyclical role 

than discretionary fiscal policy, and (2) changes 

in discretionary fiscal policy are either poorly 

timed or related to factors other than output 

stabilization. A caveat, however, is that fiscal 

policy discretion is measured by the cyclically 

adjusted balance, which, as discussed in the 

main text, is an imperfect proxy, because it may 

also capture factors unrelated to discretionary 

changes, notably asset price fluctuations.

Asset price movements directly affect 

financial transaction and capital gains taxes, 

but they also have broader, indirect revenue 

implications, notably through a wealth effect 

on consumption. To the extent that these 

movements do not fully track the business 

cycle (for example, amplified fluctuations relative to those of the output gap), the revenue 

effects will not be captured by conventional 

tax revenue elasticities and will be part of the 

cyclically adjusted component of revenue. In 

an unpublished study, the IMF staff prepared 

econometric estimates of the short-run sensitivity of cyclically adjusted tax revenue to 

house and equity price fluctuations in the G7 

countries. The cyclically adjusted revenue data 

are computed using the conventional adjustment methods, ensuring consistency of the 

results. The estimates suggest that a 1 percent 

decline in both house and equity prices could 

reduce total tax revenue by up to almost 1 

percent, with the house price decline accounting for most of the drop. The estimates also 

indicate that Canada, Japan, the United Kingdom, and the United States are more sensitive 

to house and equity price fluctuations than 

the continental European G7. 

Box 5.1 (concluded)

0 10 20 30 40 50 60

-100

-50

0

50

100

Cyclicality of Structural Balance

 Source: IMF staff calculations.

Cyclicality of discretion

Government size (percent of GDP)

Emerging economies

Advanced economies
These skeptics argue that discretionary fiscal 

measures cannot be delivered quickly enough 

by legislatures, especially compared with the 

speed with which a central bank can change 

its policy rate. Hence, there is a risk that fiscal 

stimulus will arrive just as the economy recovers 

from a downturn. Moreover, argue the critics, 

fiscal stimulus measures are not likely to be well 

targeted, but are likely instead to be directed 

to wasteful and distortionary public spending 

and revenue measures more responsive to the 

pressures of interest groups than the needs of 

the economy. Furthermore, they are not likely 

to be withdrawn sufficiently quickly to preserve fiscal sustainability. For instance, there is 

widespread evidence that fiscal policy in emerging and less developed economies is procyclical 

rather than countercyclical, in part because of 

political incentives to run larger deficits in good 

times, when financing is available (Talvi and 

Végh, 2000).

Even if fiscal stimulus can be delivered 

quickly, does that justify the use of discretionary 

fiscal policy? There is still considerable debate 

and little theoretical consensus. A textbook 

Keynesian position is that private consumption 

and investment are driven by current income, 

with the implication that output is highly 

responsive to changes in fiscal policy. But fiscal 

policy can be much less effective in an open 

economy, depending on the degree of capital 

mobility and the exchange rate regime, because 

fiscal stimulus might simply “leak out.” In addition to the standard crowding-out arguments, 

many neoclassical theorists emphasize the role 

of expectations about future income and taxes, 

arguing that fiscal multipliers are likely to be 

small because forward-looking households will 

figure out that temporary fiscal stimulus matters little to their lifetime income; multipliers 

may even be negative, if increased government 

expenditures lead to offsetting reductions in 

private consumption and investment.2 By con2For example, the well-known Ricardian equivalence 

critique of Barro (1974) argues that households and 

firms understand that deficits accompanied by future tax 

trast, recent work using so-called New Keynesian 

models argues that an increase in government 

consumption still can have positive consumption and real wage effects, if there are nominal 

and real rigidities and liquidity constraints (see, 

for example, Galí, 2006). These models also 

suggest that not all temporary fiscal measures 

are ineffective: policies that affect the incentive 

to switch the timing of consumption—such as 

changes in consumption taxes—are likely to be 

most effective when they are understood to be 

temporary rather than permanent.

In recent years, four factors may have become 

increasingly relevant:

• The extent of market rigidities: Rigidities in goods 

and labor markets may have decreased over 

time, as a result of microeconomic reforms, 

and access to credit may have become more 

widely available, reducing fiscal multipliers.

• The monetary policy framework: The impact of 

fiscal policy can be expected to increase if it 

is accommodated by monetary policy, thus 

alleviating the crowding-out effect.

• Globalization and openness: To the extent that 

economies are more integrated—that is, an 

increasing share of domestic demand falls on 

imported goods—discretionary fiscal policy 

will be less effective today than previously.

• Financial innovation: Deregulation of financial markets and increased access to global 

capital may have eased credit constraints on 

households and firms, with the implication 

that consumption and investment are less constrained by current income and less responsive to discretionary fiscal policy measures. 

However, cross-border financial integration 

can also reduce the sensitivity of interest rates 

to government borrowing and ease crowdingout effects.

Unfortunately, empirical work has not settled 

the theoretical debates. Estimates of fiscal multirises leave them no better off in net present value terms, 

and therefore they save rather than spend temporary 

(lump-sum) tax cuts. Neoclassical models often exhibit 

negative wealth effects following increases in government spending that are strong enough to reduce private 

consumption and investment.

Understanding the Fiscal Policy De
Chapter5  Fiscal Policy as a Countercyclical

Perhaps surprisingly, the empirical literature 

on the effects of fiscal policy does not provide a 

clear answer to the simple question of whether 

discretionary fiscal policy can successfully 

stimulate the economy during downturns. Estimates of the effects of fiscal policy on many key 

macroeconomic variables can differ not merely 

in degree but in sign. This box aims to show 

why demonstrating conclusively what happens as 

a result of discretionary fiscal policy is, in fact, 

extremely difficult. 

Any empirical work on this issue faces the 

following problems: (1) Every assessment of 

the impact of a policy change must take into 

account the economic circumstances when the 

policy was implemented. (2) A fiscal stimulus 

can be achieved by many different combinations of taxes, transfers, and spending, each of 

which can have different effects. (3) There will 

sometimes be a difference between the date on 

which a change in fiscal policy is measured from 

the data and the date on which the policy was 

common knowledge to households and firms. 

(4) Policy measures and economic activity are 

both endogenous—they depend on each other 

at the same time—and so it is not immediately 

clear what determines what just by looking 

at simple correlations. This last problem is 

arguably the most difficult to overcome. The 

researcher must somehow strip out those parts 

of changes in taxes, transfers, and spending that 

occur passively (such as from automatic stabilizers) from those that represent the true policy 

initiative, and use that measure of fiscal impulse 

to determine the effects on economic activity.

To illustrate, suppose overall fiscal policy, g, 

evolves according to

g = (a + b)y + h, (1)

where y is the output gap. For simplicity, one 

can think of g as representing only government 

expenditures, so that a stimulus occurs when 

g is positive. There are two reactions of fiscal 

policy to the state of the economy: an automatic 

component, represented by a, and a systematic discretionary component, represented by 

b. Unexpected discretionary fiscal policy is 

denoted by h.

Now suppose that the output process is 

y = dg + ε, (2

where  is the fiscal multiplier andε represents shocks independent of policy. There ar

two significant problems presented by this 

system. First, we have a classic simultaneity 

problem—attempting to assess the effects of 

fiscal policy on output by estimating (1) will 

result in biased estimates. The second problem is a measurement problem—the difficulty 

of distinguishing systematic discretionary 

policy changes from automatic stabilizers. The 

elasticity-based fiscal impulse measure can be 

thought of as using OECD estimates of  and

constructing

f

~

 = f – ay.

Estimating the cyclicality of this measure is 

equivalent to estimating the parameter b.

1,2

When examining the effectiveness of fiscal 

policy in the regression framework, a fiscal 

impulse measure that mistakenly includes cyclical changes generated by automatic stabilizers 

will lead to invalid inferences about the effects 

of discretionary fiscal policy. The second fiscal 

impulse measure therefore focuses entirely 

on η, the effects of unexpected fiscal policy

shocks.3

Other approaches in the literature attempt 

to address the same issues. Structural vector 

autoregressions (SVARs) use statistical criteria 

to estimate shocks to fiscal policy and measure 

1See also Galí and Perotti (2003) for an application 

of the same method. 2When looking at the reaction of fiscal policy in 

emerging economies, it is necessary to make the 

“zero-one” assumption of income elasticities of expenditures and revenues, which is a cruder approach to 

measuring  but conceptually the same. 3For precise details on how the fiscal impulse measures are constructed, see Appendix 5.1

Box 5.2. Why Is It So Hard to Determine the Effects of Fiscal Stim

The main author of this box is Alasdair Scott.
Understanding the Fiscal Policy De

how well those shocks can explain movements 

in output that are not accounted for by other 

economic shocks. Three problems are potentially relevant. As with reduced-form regressions, 

statistical assumptions need to be made to identify the fiscal shocks. Second, most VARs ignore 

the importance of debt dynamics in conditioning responses (whether or not a temporary rise 

in debt causes households and firms to expect 

future higher taxes is a key distinction between 

Keynesian and classical views on the effectiveness of discretionary fiscal policy).4 Finally, as 

with reduced-form regressions, VARs might not 

reliably be able to resolve the timing issue.

By contrast, “narrative” approaches estimate 

policy-driven changes in fiscal stimulus by looking directly at the historical record of legislation and public statements. The advantage of 

this approach is that careful attention can be 

directed to picking the timing of the shocks by 

examining carefully when policy decisions were 

made and announced. But such studies are very 

resource intensive, making their application 

across countries almost impossible. Further, 

they are subjective, just as VARs and reducedform analysis rely on identifying assumptions. In 

practice, analysis has centered around a small 

number of extraordinary episodes of military 

buildups, and there are questions as to how 

much can be learned from such episodes about 

discretionary fiscal policy during downturns.

A final approach examines specific “natural 

experiments,” such as the effects of tax rebates. 

4See Chung and Leeper (2007). Favero and Giavazzi 

(2007) do include debt stock.

The advantage of this approach is that it can 

be directed at specific episodes for which it is 

relatively easy to identify the policy change and 

its intent. The corresponding disadvantage is 

that, by examining a specific case, it can be hard 

to draw broader lessons for policy.

This empirical work provides a mixed 

picture of the ability of government spending to stimulate private demand.5 (There is 

less evidence about revenue-based measures.) 

Moreover, there appears to be a pattern 

between the method used and the qualitative 

results obtained. The table summarizes the 

results of a selection of prominent papers in 

the literature in terms of the signs of responses 

of key variables to discretionary increases in 

government spending.

In particular, SVAR-based studies in which 

fiscal interventions are identified by assuming that government spending is predetermined within the quarter (see Blanchard and 

Perotti, 2002) tend to find relatively strong 

positive effects, whereas narrative studies that rely on the reactions to episodes of 

extraordinary spending have tended to find 

much weaker, and even negative, relationships between episodes of fiscal stimulus and 

5Results from case studies usually find positive 

effects, but the effects are generally not as strong as 

those generated by VAR studies. Studies of the 1975 

tax rebates generally conclude that the effects were 

positive but modest (that is, short-run multipliers of 

about 0.2–0.5); see Modigliani and Steindel (1977) 

and Blinder (1981). Studies of the 2001 tax rebates 

have generated similar results; see Shapiro and 

Slemrod (2002). 

Assessment of Impacts of Discretionary Fiscal Policy Stimulus by Empirical Meth

Output Private Consumption

Private Investment 

in Durables

Private 

Capital Investment

VAR studies Neutral to positive Neutral to positive Negative to positive Negative to positive

Narrative studies Positive Negative Negative . . .

Case studies Positive Positive . . . . . .

Note: Studies placed in the vector autoregression (VAR) category include Fatás and Mihov (2001); Mountford and Uhlig (2002); 

Blanchard and Perotti (2002); and Galí, López-Salido, and Vallés (2007). Studies placed in the narrative category include Ramey and 

Shapiro (1998) and Edelberg, Eichenbaum, and Fisher (1999). Case studies include Johnson, Parker, and Souleles (2006).
Chapter5  Fiscal Policy as a Countercyclical

pliers cover a wide range, from positive through 

insignificant to negative.3 One reason is that 

taking account of all the appropriate conditioning factors can be very difficult. Another reason 

is methodological. Put simply, separating out 

changes in discretionary fiscal policy from automatic stabilizers and evaluating their effects is 

very difficult—in particular, fiscal policy simultaneously both responds to and causes changes 

in economic activity. This “endogeneity problem” poses a major challenge for estimating the 

effects of fiscal policy, as discussed in Box 5.2.

How Has Discretionary Fiscal Polic

Typically Responded

The previous section identified two types of 

critique of fiscal policy: skepticism that discretionary fiscal policy can be delivered efficiently, 

owing to political constraints, and doubts that 

it can be effective, for economic reasons. These 

critiques frame the empirical analysis in this 

3A typical range of expenditure multipliers would be 

from 0.5 (for example, Mountford and Uhlig, 2002) to 

about 1 (for example, Blanchard and Perotti, 2002). But 

Perotti (2007) has outliers as high as 4 and Krogstrup 

(2002) as low as –2.

section, which examines how fiscal policy has 

typically responded to downturns.

Defining economic downturns and measuring fiscal stimulus are inevitably somewhat 

subjective exercises. In the analysis that follows, 

downturns are defined as periods during which 

either the growth rate is negative or the output 

gap is unusually negative, the precise threshold depending on whether quarterly or annual 

data are used. This definition is arguably more 

sensible than defining a downturn simply in 

terms of negative growth, because that would 

miss periods during which output is significantly 

below potential but still rising.

The measures of fiscal stimulus used in this 

chapter all start with the primary fiscal balance, 

the difference between total general government revenues and expenditure net of interest 

payments on consolidated general government 

liabilities. Changes in the primary balance can 

arise passively, as revenues and expenditures rise 

and fall with economic activity, or actively, as 

governments make choices about tax, transfer, 

and spending policies. What is needed, therefore, is a measure of the cyclically adjusted primary balance, the intuition being that changes 

in the cyclically adjusted primary balance should 

reflect changes in policy. The first part of this 

consumption.6 Ramey (2008) suggests that this 

difference relates to the way that VARs treat 

timing—if discretionary fiscal policy measures 

are pre-announced, and households decrease

their spending right away (as predicted by neoclassical theory), VARs that measure the effect 

based on actual changes to fiscal balances or 

components might record a rise in the growth 

rate of consumption on that date. This would 

support a Keynesian view of fiscal policy, but 

in fact the growth in consumption is driven 

6Note, however, that narrative studies of the effects 

of tax changes find very large multipliers—see Romer 

and Romer (2007).

by recovery from the previous fall. Narrative approaches, on the other hand, take into 

account the moment discretionary measures 

are announced.7 Compared with these studies, 

the reduced-form approach employed in this 

chapter is conceptually closest to the SVAR 

approach of Blanchard and Perotti (2002); to 

the extent that the timing criticism applies to 

this paper and those like it, it also applies to 

our methodology. However, a comparative narrative study of all 41 economies in this study is 

beyond the scope of this chapter.

7But see also the rebuttal in Perotti (2007).

Box 5.2 (concluded)
section looks at the responses of fiscal policy 

to changes in economic activity, identifying 

automatic stabilizers with changes in the cyclical component of the primary balance and 

discretionary fiscal policy with changes in the 

cyclically adjusted primary balance.4 Constructing this measure requires two slightly different 

approaches, depending on the information 

available for the economies being analyzed.

Evidence on the Responsiveness of Fiscal Polic

The empirical investigation begins with 

analysis of advanced economies, for which long 

spans of fiscal data are available on a quarterly 

basis.5 Discretionary fiscal actions are those that 

change the cyclically adjusted budget balance, 

using estimates of the output gap together with 

estimates of income elasticities of revenues and 

expenditures to extract the cyclical component 

from the budget.6 Figure 5.1 presents a summary of policy responses in G7 economies over 

the past four decades. The numbers indicate 

that discretionary fiscal stimulus has been delivered in downturns, but it has been used much 

less frequently than automatic stabilizers and 

monetary policy. Discretionary fiscal stimulus 

has been used in about 23 percent of all downturn quarters—less than half as frequently as 

interest-rate easing—whereas automatic stabilizers are observed in well over 95 percent of 

downturns (upper panel).7 Discretionary policy 

4As defined in Box 5.1 and the event analysis, the 

cyclically adjusted balance is a residual and embodies all 

changes in the primary balance not removed by cyclical 

adjustment. This includes many factors not necessarily related to output stabilization, such as the impact of 

structural reform, one-off items, and other economic 

events (including asset price changes that are not cyclical 

in nature and could therefore be identified as “automatic” changes in the fiscal balance—see Jaeger and 

Schuknecht, 2007). 5For further details about the following analysis, see 

Leigh and Stehn (forthcoming). 6These elasticities are taken from the OECD Economic 

Outlook; see Appendix 5.1 for details. 7Note that automatic stabilizers do not necessarily 

ease in all downturns, because the applied definition of 

a downturn does not rule out an increase in growth or 

the output gap (as long as the output gap is unusually 

negative).

How Has Discretionary Fiscal Policy Typically Respo

Discretionary fiscal policy has been used less frequently than monetary policy and 

automatic stabilizers during downturns, and has taken longer to arrive. 

0 1 2 3 4 5

0 20 40 60 80 100

 Source: IMF staff calculations.

 G7 comprises Canada, France, Germany, Italy, Japan, United Kingdom, and United

States.

Share of downturns with easing in:

Lag after start of downturn until policies eased

Figure 5.1. How Often and Quickly Has Fiscal Stimulus

Been Used in G7 Economies?

Group of 7 (G7) Anglophone G7 countries Other G7

Cyclically adjusted 

primary balance

Cyclically adjusted 

current spending 

Cyclically adjusted

revenue

Cyclical primary 

balance 

Nominal policy 

interest rate

Capital spending 

Cyclically adjusted 

primary balance

Cyclically adjusted 

current spending 

Cyclically adjusted

revenue

Cyclical primary 

balance 

Nominal policy 

interest rate

Capital spending 

Quarters

Percent

1

1
Chapter5  Fiscal Policy as a Countercyclical

also arrives later, on average about two and a 

half quarters after the onset of a downturn, and 

about one and a half quarters after interest-rate 

easing (lower panel). Capital spending is particularly slow, with an arrival lag of almost four 

quarters. By contrast, automatic fiscal easing, 

proxied by a fall in the cyclical primary balance, 

occurred in almost all downturns in the quarter 

of the downturn itself.

The size of discretionary fiscal easing is also 

much smaller on average than that of automatic 

stabilizers. Figure 5.2 shows average impulse 

responses of discretionary fiscal measures, 

automatic stabilizers, and interest rates for the 

G7 economies, drawing from vector autoregressions (VARs) estimated for two samples, an 

“early” sample covering 1980:Q1–1991:Q4 and 

a “late” sample covering 1992:Q1–2007:Q4.8 In 

both samples, the discretionary fiscal easing is 

much smaller than the automatic stabilizers and 

is slower to arrive than both changes in interest 

rates and automatic stabilizers. However, a comparison of the two panels also suggests that the 

countercyclical response of discretionary fiscal 

policy has strengthened since the early 1990s.9

The responses of spending and revenue components in the early sample reflect a combination 

of mildly procyclical revenue increases, small 

countercyclical current spending increases, and 

large procyclical capital spending cuts. The 

greater degree of fiscal policy countercyclicality observed since the early 1990s is the result 

of cuts in revenues, larger increases in current 

spending, and smaller procyclical cuts in capital 

spending. The response of automatic stabiliz8See Appendix 5.1 for more details. Note that, unlike 

much of the VAR literature, the analysis presented here 

does not evaluate the response of growth to fiscal policy 

shocks. Rather, the focus is on the response of fiscal 

policy variables to changes in growth. 9In the early sample, discretionary fiscal policy is procyclical on impact and provides a cumulative procyclical 

contraction of around 0.1 percentage point of potential 

GDP over four quarters. In the later sample, even though 

discretionary policy still produces no stimulus on impact, 

it leads to a cumulative stimulus of 0.2 percentage point 

over four quarters. This finding is consistent with, for 

example, Galí and Perotti (2003) and World Economic 

Outlook (September 2003). 

0 1 2 3 4 5 6 7 8 9

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0 1 2 3 4 5 6 7 8 9

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

 Source: IMF staff calculations.

Early Sample (1980:Q1–91:Q4)

Late Sample (1992:Q1–2007:Q4)

Following an unexpected 1 percentage point fall in growth below potential, interest

rates and the automatic component of the fiscal balance ease on impact; 

discretionary fiscal stimulus takes longer to arrive. In recent years, discretionary 

fiscal policy has become more countercyclical.

Figure 5.2. How Strong Was the Fiscal Policy Response 

in G7 Economies?

(Percentage point deviation; quarters on x-axis; shock occurs in period

zero)

Interest rate Discretionary policy Automatic stabilizers
ers remained unchanged in the second sample, 

while that of monetary policy strengthened. 

Figure 5.3 shows that there are noticeable 

cross-country differences across advanced economies. Discretionary fiscal policy and monetary 

policy have been more timely and more countercyclical in the United States, Canada, and the 

United Kingdom (the G7’s three Anglophone 

countries) than in the rest of the G7. The 

other Organization for Economic Cooperation 

and Development (OECD) member countries 

display even weaker countercyclicality than the 

United States, Canada, and the United Kingdom 

in both monetary and discretionary fiscal policy.

Data Uncertainties and the Risk of Debt Bia

A concern that often arises regarding countercyclical fiscal activism is that policymakers 

may respond in an asymmetric manner, easing 

in downturns and not tightening sufficiently in 

upturns, implying a permanent increase in the 

public-debt-to-GDP ratio with potentially adverse 

consequences for long-run growth. To investigate whether fiscal policy in G7 countries has 

displayed such an asymmetric tendency, the VAR 

framework is adapted to allow for an asymmetric 

response to upturns and downturns (see Appendix 5.1). The results suggest that both fiscal 

policy and monetary policy are subject to an easing bias; that is, more easing during downturns 

than tightening during upturns (Figure 5.4). In 

contrast, automatic stabilizers respond in a symmetric way, with the easing observed in downturns almost exactly offset by tightening during 

upturns.

Hence, although discretionary fiscal policy 

has been actively used, there are valid concerns 

about debt bias. For illustration, a case study 

of tax-based stimulus legislation in the United 

States is provided in Box 5.3. The study finds 

that, although reasonably timely, 38 percent of 

cyclically motivated tax cuts were permanent.

An additional concern in the analysis of 

countercyclical fiscal activism is that policymakers face substantial uncertainties regarding the 

cyclical position and run the risk of destabilizing 

Are fiscal policy reactions different in emerging and advanced eco

0 1 2 3 4 5 6 7 8 9

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0 1 2 3 4 5 6 7 8 9

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

 Source: IMF staff calculations.

Change in interest rate

Change in discretionary fiscal balance

Following an unexpected 1 percentage point fall in growth below potential, 

Anglophone countries have provided both monetary and fiscal stimulus; the rest of

the Organization for Economic Cooperation and Development (OECD) countries have

provided a weaker monetary response and procyclical discretionary fiscal tightening. 

The figure displays policy responses for the late sample (1992:Q1–2007:Q4).

Figure 5.3. How Have Fiscal Policy Responses Varied

across Advanced Economies?

(Percentage point deviation; quarters on x-axis; shock occurs in period

zero)

Group of 7 (G7)

Other G7

Anglophone G7 countries

Other OECD
Chapter5  Fiscal Policy as a Countercyclical

1

the economy by responding to erroneously perceived downturns. This appears to be a serious 

problem, based on an assessment of the reliability of preliminary GDP estimates produced by 

national authorities.10 There is a strong negative 

relationship between preliminary growth estimates and subsequent revisions. Forty percent 

of preliminary estimates indicating negative 

quarter-over-quarter growth were subsequently 

revised to positive growth.11 Forecast efficiency 

tests find strong evidence of a bias toward pessimism in preliminary growth estimates.12

To investigate how fiscal policy in G7 countries has been affected by errors in growth 

estimates, the VAR framework is augmented 

with growth-estimation errors (see Appendix 

5.1). The results reported in Figure 5.5 confirm 

that both fiscal and interest rate policy have 

been affected by errors in preliminary growth 

estimates, with a 1 percentage point fall in 

perceived growth relative to final revised growth 

associated with an easing in interest rates and 

the discretionary fiscal-balance-to-potential GDP 

ratio by about 0.2 percentage point. This finding 

suggests that concern over policy errors is well 

founded, especially as fiscal policy decisions 

appear to be less easily reversed than monetary 

policy decisions, and fiscal policy errors bear 

potentially long-lived consequences for debt.

Are Fiscal Policy Reactions Different in 

Emerging and Advanced Economie

Some of the reservations about the application of discretionary fiscal policy may apply 

even more strongly in less advanced economies. 

Unfortunately, although the data in the previous 

section were available at quarterly frequency, 

consistent data for a broader set of econo10See Appendix 5.1. See also Cimadomo (2008) for 

further analysis of fiscal policy using real-time data. 11At the same time, 30 percent of quarters that, according to the final data actually had negative growth, showed 

positive growth in preliminary estimates. 12While remaining statistically significant, this bias 

appears to have declined in recent years, possibly reflecting the more stable and predictable growth environment.

0 1 2 3 4 5 6 7 8 9

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 1 2 3 4 5 6 7 8 9

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

 Source: IMF staff calculations.

Change in interest rate

Change in fiscal balances

Following a 1 percentage point shock to growth, both discretionary fiscal policy and 

monetary policy are subject to an easing bias, with more stimulus during downturns 

than tightening during upturns. In contrast, automatic stabilizers respond 

symmetrically to upturns and downturns. The figure displays policy responses for 

the late sample (1992:Q1–2007:Q4).

Figure 5.4. Is There a Bias toward Easing during

Downturns in G7 Economies?

(Percentage point deviation; quarters on x-axis; shock occurs in period

zero)

Upturn Downturn

Automatic stabilizers

Discretionary policy Automatic 

stabilizers

Discretionary policy
mies are available only at annual frequency. 

In what follows, the analysis uses a sample of 

21 advanced economies and 20 emerging economies, covering the period from 1970 to 2007.13

The definition of “downturn” is conceptually 

the same as used previously with the quarterly 

data, but “unusually negative” is now defined 

as below –0.5 standard deviation of the output 

gap, on account of the use of annual data.14

For advanced economies, OECD estimates of 

income elasticities of revenues and expenditures are used to calculate the cyclical balance. 

However, such estimates are not available for 

emerging economies, and so it is assumed 

that revenues move one-for-one with the business cycle, but expenditures do not—that is, 

the income elasticity of revenues is 1 and the 

income elasticity of expenditures is zero (see 

Appendix 5.1 for details). A fiscal expansion is 

then defined as a negative change in the cyclically adjusted primary balance of more than 

0.25 percentage point and a fiscal contraction 

as a positive change of more than 0.25 percentage point. When the change in the cyclically 

adjusted primary balance is less than 0.25 percentage point (either positive or negative), fiscal 

policy is considered neutral. Hence, we have 

three states for the fiscal stance: stimulus (397 

episodes), neutral (155 episodes), and tightening (437 episodes).

In addition to the assumptions necessarily 

imposed when choosing data sets and definitions, a number of caveats apply to analysis using 

these measures. In particular, the use of annual 

data limits the ability to accurately characterize 

fiscal interventions that begin and end within 

a year. Second, what is relevant is policymakers’ perceptions of the state of the economy in 

real time, which might differ substantially from 

inferences made using revised data, but, in the 

13See Appendix 5.1 for a list of economies and episodes 

of downturns. 14Correspondingly, upturns are defined as episodes 

during which the output gap is above 0.5 standard deviation. Potential output is measured using the HodrickPrescott filter, with λ set to 6.25, the value recommended 

in Ravn and Uhlig (2002).

Are fiscal policy reactions different in emerging and advanced eco

Following an erroneously perceived 1 percentage point fall in growth, both

discretionary fiscal policy and monetary policy have eased, particularly in

Anglophone countries. The figure displays policy responses for the late sample

(1992:Q1–2007:Q4).

0 1 2 3 4 5 6 7 8 9

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0 1 2 3 4 5 6 7 8 9

-0.4

-0.3

-0.2

-0.1

0.0

0.1

 Source: IMF staff calculations.

Change in interest rate

Change in fiscal balances

Figure 5.5. Did G7 Economies Respond to Erroneously 

Perceived Downturns?

(Percentage point deviation; quarters on x-axis; shock occurs in period

zero)

Group of 7 (G7) Anglophone G7 countries Other G7
Chapter5  Fiscal Policy as a Countercyclical

This box takes a closer look at whether fiscal 

interventions in the United States have been 

timely, temporary, and targeted (TTT). A 

recent data set compiled by Romer and Romer 

(2007) of all significant tax changes signed 

into law since 1945 permits a detailed analysis 

of this issue. By consulting official documents, 

Romer and Romer distinguish tax changes 

that were explicitly motivated by cyclical 

considerations from those motivated by other 

factors, including long-run growth support, 

debt reduction, and the financing of additional 

expenditures. Of all the 50 significant federal 

tax actions identified, 7 were assessed as cyclical, and, of these, 5 were tax cuts designed to 

stimulate short-run growth.

This box focuses on these five tax cuts, 

implemented between 1970 and 2002, as well 

as the Economic Stimulus Act, signed into law 

in February 2008. The box assesses how quickly 

after the onset of a downturn the tax cuts were 

legislated and implemented, how temporary 

they were, and how well targeted they were. In 

assessing how close to a downturn the tax cuts 

arrived, the analysis defines a downturn as in 

the main text. Growth data to assess the 2008:

Q2 stimulus are not yet available.

The main results are as follows:

• Timeliness: Four out of the five cyclically motivated tax cuts occurred within one quarter of 

a downturn (see table). In the case of 2002, 

the stimulus arrived three quarters after the 

downturn. The average implementation lag of 

tax cuts; that is, the delay between the signing 

of the legislation into law and its impact on 

revenue, was one quarter.

• Temporariness: Although only one of the six 

cyclically motivated tax cuts was permanent, 

the remainder contained a permanent 

component (see table). In particular, about 

79 percent of the tax cuts were designed to be 

temporary, with an average planned duration of two quarters. Some of the tax cuts 

were subsequently extended, so that a smaller 

proportion—62 percent—actually ended up 

Box 5.3. Have U.S. Tax Cuts Bee

The main authors of this box are Daniel Leigh and 

Sven Jari Stehn.

How Timely, Temporary, and Targeted Were the Tax

Legislated Tax Cut

Timeliness Temporariness1

Targeting

Date 

stimulus 

arrived

Name of 

act

Size of 

stimulus 

(percent

of GDP)

Date of 

nearest 

downturn2

Inside 

lag 

(quarters)3

Proportion temporary

Duration of temporary 

portion (quarters) Bangfor-thebuck score4 Planned Actual Planned Actual

1970:Q1 Tax Reform 1.2 1970:Q1 1 0 0 permanent 1.0

1975:Q2 Tax Reduction 3.6 1975:Q1 1 97 78 2.3 1.0 2.5

1977:Q3 Tax Reduction

and Simplification

1.0 1977:Q4 1 77 67 1.3 1.0 1.9

2001:Q3 Economic Growth 

and Tax Relief 

Reconciliation

1.7 2001:Q3 1 100 100 1.3 1.3 2.4

2002:Q2 Job Creation 

and Worker 

Assistance

1.7 2001:Q3 1 100 67 3.7 4.0 1.3

2008:Q2 Economic Stimulus 1.1 . . . 1 100 . . . 1.6 . . . 2.7

Mean 1.6 . . . 1 79 62 2.0 1.8 2.0

1Temporary stimulus is defined as a stimulus that expires. Actual duration may exceed planned duration because of legislated 

extensions. 2Downturn is defined as a quarter with negative or below-trend growth and an output gap more than one standard deviation below 

zero. 3Inside lag denotes the period between the date the stimulus was signed into law and the date it was implemented (quarter in which 

tax liabilities actually changed). 4Bang-for-the-buck score (3 = high, 2 = medium, 1 = low) indicates the degree of cost-effectiveness according to CBO (2008) 

classification.
absence of consistent real-time vintages of data, 

it is difficult to adjust for this difference.

Bearing these caveats in mind, the analysis 

identified the following stylized facts:

• Emerging economies respond during downturns with fiscal stimulus only half as frequently as advanced economies: 22 percent 

versus 41 percent (Figure 5.6, top panel). 

When emerging economies do implement fiscal stimulus, the response is slightly higher, as 

measured by changes in the cyclically adjusted 

primary balance as a percent of potential 

GDP (Figure 5.6, middle panel, first and third 

bars). But this is because downturns are larger 

(Figure 5.6, middle panel, second and fourth 

bars).

• In just over one-third of episodes, fiscal stimulus involved a mixture of revenue and expenditure changes. Of those that relied mainly on 

one kind of stimulus, expenditure measures 

dominate for both advanced and emerging 

economies (Figure 5.6, bottom panel).

• In emerging economies, changes in the overall primary balance are usually procyclical, 

despite countercyclical effects from automatic 

stabilizers (Figure 5.7, top panel).15 And they 

are more procyclical in downturns when 

15This finding is consistent with Kaminsky, Reinhart, 

and Végh (2004) and a number of other studies. It holds 

advanced economies are simultaneously experiencing downturns, consistent with rises in 

external financing premiums (Figure 5.7, bottom panel). In advanced economies, changes 

in the primary fiscal balance are, on average, 

countercyclical, mostly because of automatic 

stabilizers, as measured by changes in the 

cyclical balance.

The Macroeconomic Effects o

Discretionary Fiscal Policy

Having defined downturns and episodes of 

fiscal stimulus, this section turns to the central 

question: What are the macroeconomic effects 

of discretionary fiscal policy, especially during 

downturns? An event analysis identifies some 

of the basic patterns, using the same elasticitybased fiscal impulse measure as in the previous 

section, and then regressions provide a more 

systematic assessment of cause and effect.

An Event Analysis of Episodes of Downturns an

Fiscal Stimulu

The event analysis shows the dynamics of key 

macroeconomic variables—real GDP growth, the 

across both fixed and floating exchange rate regimes at 

the time of the episode.

The Macroeconomic Effects of Discretionary Fisca

being temporary, and 38 percent became 

permanent.

• Targeting: The targeting efficiency of each 

tax cut package is assessed using the costeffectiveness classification scheme of the 

Congressional Budget Office (CBO, 2008), 

which indicates the likely bang-for-the-buck 

impact on aggregate demand of a range of 

possible fiscal stimulus tools. Based on this 

classification scheme, three out of the six 

cyclically motivated tax cuts are classified as 

cost-effective. More than half of the content 

of these three tax packages consisted of personal direct transfers and personal lump-sum 

rebates—two fiscal tools assessed as being 

the most cost-effective by the CBO. The most 

recent, 2008, stimulus scored highest on this 

account, followed by 1975 and 2001. The 

least cost-effective stimulus measures were 

the 1970 and 2002 tax reductions, the bulk 

of which consisted of corporate lump-sum 

rebates and personal and corporate tax-rate 

reductions. 

Hence, for the most part, fiscal interventions 

in the United States have been timely, but not 

always temporary or well targeted. 

Box 5.3 (concluded)
Chapter5  Fiscal Policy as a Countercyclical

debt-to-GDP ratio, inflation, exchange rates, the 

current account, and money growth—around 

episodes of downturns. Table 5.1 and Figure 5.8

show how macroeconomic variables move 

together with fiscal stimulus before, during, and 

after downturns. As expected, the debt-to-GDP 

ratio increases following a fiscal stimulus and 

improves when it tightens, while current account 

balances improve in the downturn year when 

there is tightening and deteriorate when there is 

stimulus. But for other variables, the results are 

generally ambiguous. In particular, growth rates 

are larger in episodes without fiscal stimulus, but 

the change in growth rates from the downturn 

year to the first year after the downturn is somewhat larger when there is fiscal stimulus. These 

observations are common across advanced and 

emerging economies.

Table 5.2 shows median values of real GDP 

growth across all economies during episodes 

of downturns and fiscal stimulus for a number of variables that theory suggests could 

have important effects: public debt, current 

account balances, trade openness, and the 

exchange rate regime.16 Figure 5.9 shows the 

difference between growth rates in the year of 

the downturn and the year following. Looking 

across these conditioning factors, there is little 

discernible difference in the impact of fiscal 

policy from variations in the current account 

balance, openness to trade, and the exchange 

rate regime, despite what theory suggests. However, the level of public debt does appear to be 

associated with consistent differences in growth 

outcomes—economies that implement fiscal 

stimulus and have high public debt going into 

a downturn typically experience lower growth 

rates before and after the downturn year and 

16For the first three of these variables, the results are 

divided into “high” and “low” cases, based on the average 

for that variable three years before the recession episode. 

The thresholds for high and low are the median values of 

the overall sample, except debt, for which the threshold 

for high debt is 75 percent for advanced economies 

and 25 percent for emerging economies. Exchange 

rate regimes are categorized according to whether the 

exchange rate was fixed or floating in the first year of the 

downturn.

Advanced

economies

Emerging

economies

Both

Expenditure

Revenue

Total number of responding economies that pursued stimulus driven by:

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

Figure 5.6. Composition of Fiscal Stimulus during

Downturns for Advanced and Emerging Economies

The pie charts at the top show the types of fiscal policy response—stimulus,

neutral, or tightening—during episodes of downturns for advanced economies and 

emerging economies. The bar chart indicates the average size of fiscal stimulus. 

Areas indicate the average proportion of the total sample stimulus from changes in

revenues only, changes in expenditures, or both. The pie charts at the bottom

indicate the frequency of using revenue only, changes in expenditures, or both for 

advanced economies and emerging economies.

Average change in cyclically adjusted primary balance during downturns

Emerging

economies

Advanced 

economies

Total number of fiscal responses during downturns

6

34

23

5 7 3

Tightening

Neutral

Stimulus

47

24

68

64

16

10

Both

Expenditure

Revenue

1,2

Emerging

economies

Advanced 

economies

 Source: IMF staff calculations.

 Average change in cyclically adjusted primary balance associated with various types of

fiscal stimulus weighted by the share of fiscal stimulus cases of a particular type among 

countries that responded with fiscal stimulus during downturns.

 For each group of economies the left-hand column is the change in cyclically adjusted

primary balance in percent of GDP. The right-hand column is the change in cyclically

adjusted primary balance in percent of GDP scaled by the standard deviations of changes 

in output gap.

1

2

Unadjusted Scaled Unadjusted Scaled
less of a pickup in growth in the year following 

fiscal stimulus, whereas high-debt economies 

that implement fiscal tightening experience 

stronger gains in growth.

Turning to the ways fiscal policy was implemented, economies that employed a combination of revenue and expenditure stimulus 

experienced less-severe downturns compared 

with those that relied on revenue or expenditure 

measures alone, although revenue-based policies 

were associated with faster recoveries and higher 

growth in the years following (Table 5.3).17 Conversely, expenditure-based fiscal tightening was 

associated with higher growth in years following 

the downturn.

In summary, the event analysis indicates that 

taking into account debt and the composition 

of fiscal stimulus could be important to understanding the effects of fiscal policy. Conversely, 

it is difficult to see clear patterns with other variables that theory indicates could be important.

Regression Analysis

Event analysis records only associations 

between fiscal stance and the dynamics of 

the macroeconomic variable in question, but 

indicates nothing about causation between the 

variables.18 Further, by characterizing variables 

according to simple categories and considering 

them one by one in isolation from one another 

might hide important information about the 

size of and interaction between variables. A 

regression framework is used to address this.

The conceptual framework for these 

regressions is an examination of the effects 

of discretionary fiscal policy on real GDP growth, 

while controlling for the potential effects from 

monetary policy and other sources of demand 

17The small sample size of episodes involving revenue 

impulse, however, warrants caution in interpreting these 

results. 18Growth associations are a prime example: If there are 

lower growth rates in downturns when fiscal policy was 

very aggressive, is the appropriate conclusion that fiscal 

policy is not effective or that fiscal policy had to be very 

aggressive because the downturn was very severe?

The Macroeconomic Effects of Discretionary Fisca

ADV EME ADV EME ADV EME

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

 Source: IMF staff calculations.

 Average change in the balance scaled by the standard deviations of changes in output

gap. A good year is defined as a year in which the GDP-weighted average gap of advanced

economies is below the median GDP-weighted average gap of advanced economies across 

all years.

Responses of advanced and emerging economies, depending on position in cycle

The upper bar chart shows average fiscal policy responses for advanced (ADV) and 

emerging (EME) economies in (left to right) GDP downturn episodes, neutral 

episodes, and upturn episodes. A negative number indicates fiscal stimulus. 

Discretionary fiscal policy is associated with the cyclically adjusted primary balance.

The lower bar chart shows average fiscal policy responses in emerging economies 

when advanced economies are in upturns and downturns. In both charts, the 

average change in the balance is scaled by the standard deviations of changes in the 

output gap.

Figure 5.7. Fiscal Policy Responses in Downturns and 

Upturns

(Average change, percent of GDP)

Change in primary balance

Change in cyclically adjusted primary balance

Change in cyclical balance

Downturn Neutral Upturn

1

Good year Bad year

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Response of emerging economies to downturn, depending on position of

advanced economies in cycle1
Chapter5  Fiscal Policy as a Countercyclical

stimulus, and taking into account factors that 

might affect the transmission of fiscal stimulus.

The main regressor of interest is the fiscal 

impulse measure.19 Ideally, the fiscal impulse 

measure would pick up all discretionary changes 

in fiscal stance, whether from systematic reactions to the state of the economy or nonsystematic (that is, unexpected) discretionary actions. 

The systematic component of the fiscal impulse 

measure is, however, endogenous, which leads 

to problems with statistical inference. Moreover, 

as discussed in Box 5.2, it is very difficult to 

distinguish systematic changes in fiscal policy 

from automatic stabilizers. In principle, the 

elasticity-based fiscal impulse measure used in 

the previous section should achieve this, but 

19Note that all the variables are now continuous and no 

longer use the categories of the event analysis.

unless the elasticities are perfectly accurate for 

each period—and potential output is measured 

correctly—this type of fiscal impulse measure 

will likely suffer from additional, measurementerror-related endogeneity, undermining the 

validity of the regressions.

To reduce these endogeneity problems and 

check for robustness, a second fiscal impulse 

measure is used that focuses exclusively on 

the nonsystematic component of discretionary fiscal policy (as is also the case in the fiscal 

literature that uses structural vector autoregression (SVAR) and “narrative” approaches; see 

Box 5.2). This measure aims to identify unexpected changes in fiscal stance, based for each 

country on separate regressions of revenues 

and expenditures on output growth and a time 

trend—see Appendix 5.1 for details. (In what 

follows, this measure will be referred to as the 

Table 5.1. Macroeconomic Indicators around Downturns, with and without a Fiscal Impulse: All

Economies

Median

Number of 

Observations 

in Downturn

Three-Year 

Average before 

Downturn

One Year 

before 

Downturn

Year of 

Downturn

One Year 

after 

Downturn

Four-Year 

Average after 

Downturn

Real GDP growth

Fiscal stimulus 51 3.1 2.2 –0.1 3.6 3.2

Fiscal tightening 83 2.5 2.8 0.7 4.2 3.6

Change in debt-to-GDP ratio

Fiscal stimulus 43 –1.4 –0.5 2.2 1.1 0.8

Fiscal tightening 61 1.4 1.5 1.2 –0.9 –1.2

Change in cyclically adjusted 

 primary balance

Fiscal stimulus 51 0.0 –0.2 –1.1 0.0 0.2

Fiscal tightening 83 0.0 0.1 1.6 –0.2 0.2

Inflation

Fiscal stimulus 48 5.6 5.5 4.7 3.0 2.7

Fiscal tightening 78 7.1 6.2 5.2 5.0 5.1

Change in nominal exchange rate2

Fiscal stimulus 41 –0.6 0.0 2.9 –0.5 0.1

Fiscal tightening 72 4.6 3.3 7.9 3.5 2.3

Current account surplus

Fiscal stimulus 51 –2.4 –2.9 –0.8 –0.9 –1.2

Fiscal tightening 81 –0.9 –0.8 0.0 0.2 –0.1

Real money growth

Fiscal stimulus 32 5.0 2.6 1.7 4.2 4.8

Fiscal tightening 54 4.6 4.3 3.3 4.9 5.0

Note: For each variable, the median is recorded for the three categories of fiscal stance during the first year of the downturn: stimulus, neutral 

policy, and tightening. In each case, values are recorded for the average of the median three years before the downturn, one year before the 

downturn, the first year of the downturn itself, one year after the whole downturn episode, and the average for the four years after the downturn 

episode. Note that some downturns last for more than one year. In a multiyear downturn, the year after the downturn is the first year after the 

last downturn year. 1Fiscal impulse identified during the first year of a downturn as a decline in the cyclically adjusted primary balance to GDP below 0.25 percentage 

point of GDP. 2Exchange rate is given as local currency/U.S. dollars (+ sign denotes a depreciation).
The Macroeconomic Effects of Discretionary Fisca

regression-based fiscal impulse measure, to distinguish it from the elasticity-based measure.)

Other regressors include two lags of real GDP 

growth, to control for endogenous inertia in the 

economy; real money growth (contemporaneous 

and two lags), as a measure of monetary policy; 

changes in foreign demand (contemporaneous 

and two lags); and government size. These were 

found to be significant at the 10 percent level 

and were retained in all regression specifications 

that follow.

Table 5.4 presents the key results in terms 

of responses of real GDP to a 1 percent fiscal 

impulse, using both the elasticity-based and 

regression-based fiscal impulse measures.20 The 

values show the output effects in the year of the 

fiscal intervention and three years later, with a 

positive number indicating that positive fiscal 

stimulus raises output.21 The results for the baseline specification are presented in the first row. 

For both fiscal impulse measures, the estimated 

effect of fiscal stimulus on output growth in the 

baseline specification is weak—closer to zero 

than the Keynesian assumption of 1 or more—

and turns negative after three years. However, as 

can be seen in the second and third rows of the 

table, this conceals important differences across 

countries. In advanced economies, the multipliers are statistically significant and moderately 

positive—a 1 percentage point fiscal stimulus 

leads to an increase in real GDP growth of about 

0.1 percent on impact, and up to 0.5 percent 

above its level in year 0 after three years. This is 

broadly comparable with the effects found from 

previous SVAR studies and case studies. By contrast, although emerging economies see impact 

effects similar to those of advanced economies, 

the effects on output in the medium term are 

consistently negative across both fiscal impulse 

measures—for these economies, discretionary 

20See Appendix 5.1 for tables of coefficient estimates 

and regression diagnostics. 21Note, however, that the regressions underlying the 

first nine rows do not distinguish between fiscal stimulus 

and fiscal tightening—a negative effect on output from 

fiscal tightening is therefore assumed to be consistent 

with a positive effect from fiscal stimulus.

Figure 5.8. Macroeconomic Indicators after Downturns,

with and without a Fiscal Stimulus

The bar charts indicate changes in macroeconomic indicators from the year of

downturn to the first year after downturn.

 Source: IMF staff calculations.

 Fiscal stimulus during the first year of a downturn is defined as a decline in the cyclically

adjusted primary balance to GDP below 0.25 percentage point of GDP.

 Exchange rate is given as local currency/U.S. dollar (+ sign denotes a depreciation). 

Value for emerging economies with fiscal stimulus is –10.5; with fiscal tightening, –21.2.

Fiscal tightening

1

-8 -6 -4 -2 0 2 4 6

All Advanced economies Emerging economies

Fiscal stimulus

Neutral fiscal policy

-11 -6 -4 -2 0 2 4 6

Change in current 

account balance

Change in nominal 

exchange rate

Change in inflation

Change in real GDP

growth

Debt-to-GDP ratio

Cyclically adjusted

primary balance

Real money growth

2

2

1

-22 -6 -4 -2 0 2 4 6

Change in current 

account balance

Change in nominal 

exchange rate

Change in inflation

Change in real GDP

growth

Debt-to-GDP ratio

Cyclically adjusted

primary balance

Real money growth

2

Change in current 

account balance

Change in nominal 

exchange rate

Change in inflation

Change in real GDP

growth

Debt-to-GDP ratio

Cyclically adjusted

primary balance

Real money growth

2
Chapter5  Fiscal Policy as a Countercyclical

fiscal policy does indeed appear to do more 

harm than good.

The output responses shown in the next six 

rows of the table indicate that, overall, revenue-based stimulus measures seem to be more 

effective in boosting real GDP than expenditure-based measures, particularly in the medium 

term and in advanced economies. Expenditurebased impulses are found to have consistently 

negative effects in emerging economies after 

three years, perhaps reflecting concerns that, 

once implemented, increased expenditures are 

difficult to remove.

A key question is whether discretionary fiscal 

policy can successfully stimulate the economy 

during downturns. This is addressed in the final 

four rows of the table. When controlling for 

downturns, the general effects of fiscal interventions appear to be positive and, if anything, show 

slightly stronger effects than the baseline specification. However, it is possible that the results for 

these multipliers are driven by strong negative 

effects from fiscal tightening and do not reflect 

significantly positive effects from fiscal stimulus. 

When controlling specifically for the effects of 

fiscal stimulus, the effects are in fact consistently 

negative across the two fiscal impulse measures 

(although there is some improvement in output 

growth in the years that follow, such that the 

level of output is less negative than initially).

What could be driving such a different result? 

One concern is that the fiscal impulse measures 

are not adequately dealing with the endogeneity 

problem, especially the elasticity-based measure, 

which could lead to biased results.22 If it is not a 

measurement problem, the effects could depend 

on private sector expectations of the debt 

implications of the fiscal stimulus. The final two 

rows show how the effects depend on the level 

of public debt at the time of the intervention. In 

low-debt economies, the initial effect of a fiscal 

stimulus is negative, but there is a positive effect 

on growth in the years that follow, such that the 

net effect after three years is relatively negative when using the elasticity-based measure, 

and positive when using the regression-based 

measure. By contrast, in high-debt economies 

22For example, during downturns both fiscal revenues 

and output fall. To the extent that the regressions do 

not correct for the response of automatic stabilizers, 

an automatic response might be picked up as a fiscal 

stimulus, which, unsurprisingly, is identified as “ineffective” in the regressions. This is more likely in the 

elasticity-based approach—the assumption of unit-revenue elasticities for emerging economies, for example, 

may well be too low. This would tend to bias results, 

especially for the short run.

Table 5.2. Real GDP Growth and Fiscal Impulse under Various Initial Conditions: All Economies

Conditioning Variables3

Number of 

Observations in 

Downturn

Three-Year 

Average

before Downturn

One Year 

before 

Downturn

Year of 

Downturn

Real GDP Growth

One Year after 

Downturn

Four-Year 

Average after 

Downturn

Public debt

High 13 2.1 1.5 –0.1 2.7 2.0

Low 30 3.1 2.4 –0.3 3.6 3.2

Current account balance4

High 22 2.7 2.4 0.3 2.6 2.4

Low 27 3.2 2.0 –0.7 3.9 3.4

Openness to trade

High 24 3.0 2.6 –0.1 2.7 3.1

Low 25 3.4 1.6 –0.3 3.9 3.4

Exchange rate

Fixed 20 2.8 2.0 –0.3 3.1 3.0

Floating 26 3.1 1.9 0.2 3.7 3.3

1Fiscal impulse is identified during the first year of a downturn as a decline in the cyclically adjusted primary-balance-to-GDP ratio below 0.25 

percentage point of GDP. 2Initial conditions for variables are defined as a three-year average before the year of a downturn.

3The threshold for high debt is 75 percent for advanced economies and 25 percent for emerging economies. All other variable thresholds are 

the median of the variable across the sample. 4A positive value for the current account balance indicates a surplus; a negative value indicates a deficit.
the effect is consistently large and persistently 

negative. This suggests that concerns about fiscal 

sustainability may be dominating spending decisions, even if current fiscal policy would traditionally be thought of as stimulatory.23

Additional regressions were run that included 

interaction terms of the fiscal impulse measure 

and dummies indicating (1) high or low openness to trade; (2) high or low levels of financial 

development, as a measure of liquidity constraints; (3) fixed versus floating exchange rate 

regimes; and (4) high or low current account 

surpluses, as a measure of external sustainability. Higher levels of trade openness and financial development yield higher multipliers, and 

multipliers are higher under floating exchange 

rate regimes. These results run contrary to 

economic theory, suggesting that debt concerns 

might dominate the effectiveness of fiscal policy. 

Indeed, higher-than-average current account 

balances (generally surpluses) tend to be associated with larger multipliers.24 Finally, running 

the baseline regression using two different time 

subsamples yields a cautionary note: multipliers 

have apparently been weaker in recent years.25

The evidence from this analysis indicates that 

discretionary fiscal policy can successfully stimulate output growth, especially if it is revenuebased. But there are reasons for caution in 

employing stimulus packages during downturns, 

with evidence suggesting that, if it is to work at 

all, it will do so only when underlying fiscal positions are sound. This indicates that governments 

need to improve balances during upturns and 

make credible commitments that stimulus packages will not threaten debt sustainability.

23The effects of both fiscal stimulus and fiscal tightening are much worse when controlling for severe downturns. See Appendix 5.1 for more details. 24In the medium term, the multipliers are positive 

when using the regression-based fiscal impulse measure 

but still negative when using the elasticity-based fiscal 

impulse measure. 25This is particularly true for advanced economies. One 

potential explanation, consistent with both the empirical evidence and the simulations presented later, is that 

monetary policy has become less accommodative in those 

economies.

A Simulation-Based Perspective on Fiscal Sti

Figure 5.9. Changes in Real GDP Growth and Fiscal

Policies under Various Initial Conditions

The bar charts indicate changes in real GDP growth from year of downturn to first 

year after downturn, differentiated by macroeconomic conditions three years before

the downturn (debt, current account, openness to trade, and money growth) and by

the exchange rate regime and composition of fiscal impulse in the year of downturn. 

 Source: IMF staff calculations.

 The threshold for high debt is 75 percent for advanced economies and 25 percent for 

emerging economies. The thresholds for current account balance and trade openness are 

the median of the variable across the sample.

1

High Low Fixed Floating 1 1

0 1 2 3 4 5

Public debt

Current account balance

0 1 2 3 4 5

Openness to trade

Exchange rate

0 1 2 3 4 5

0 1 2 3 4 5

Fiscal stimulus

Neutral policy

Fiscal tightening

Fiscal stimulus

Neutral policy

Fiscal tightening

Fiscal stimulus

Neutral policy

Fiscal tightening

Fiscal stimulus

Neutral policy

Fiscal tightening
Chapter5  Fiscal Policy as a Countercyclical

1

A Simulation-Based Perspective on

Fiscal Stimulu

The previous section finds some evidence 

for moderately positive multipliers, but with 

important caveats about the type of economy, 

the composition of the fiscal impulse, and the 

level of debt. Clearly, there is a large number 

of potentially important factors that policymakers need to take into account when designing a 

discretionary fiscal policy action. The objective 

of this section is to examine, in a controlled 

setting, how the effects of fiscal stimulus depend 

on the structure of the economy in question.

The model used is an annual version of the 

Global Integrated Monetary and Fiscal Model 

(GIMF). GIMF is a multicountry dynamic stochastic general equilibrium model that includes 

a number of useful features relative to existing 

monetary business cycle models (such as both 

myopic and liquidity-constrained consumers 

and potential long-term productivity benefits 

from government investment) and a wide range 

of fiscal instruments affecting household and 

business intertemporal choices (government 

investment, labor taxes, consumption taxes, and 

transfers to households).26

26The country blocs are the United States, the euro 

area, Japan, emerging Asia, and the remaining economies. For a more detailed description of the model, see 

Kumhof and Laxton (2008).

The first exercise compares outcomes for key 

macroeconomic variables using various fiscal 

policy instruments for a large economy, calibrated to match the United States. The results 

are presented in Figure 5.10. The shock is a 

temporary fiscal expansion, calibrated to deliver 

a primary deficit that is 1 percent above baseline 

in year 1 and 0.5 percent above baseline in year 

2. Thereafter, a fiscal reaction function ensures 

that debt is brought back to its initial level by 

raising lump-sum taxes. The fiscal stimulus is 

completely unanticipated in the first year, but 

its time profile, including the further stimulus 

in year 2 and the longer-term implications for 

taxes, is fully understood once initiated. Each 

row of Figure 5.10 shows the reactions of, from 

left to right, GDP (in percentage deviations 

from the baseline), inflation and nominal interest rates (in percentage point deviations from 

baseline), and real interest rates (in percentage 

point deviations from baseline). Going down 

the figure, successive rows show the impact of 

various fiscal instruments: government investment, consumption taxes, labor income taxes, 

and transfers to households. In each panel, two 

responses are shown: one in which nominal 

interest rates are assumed to react to expected 

deviations of inflation from target, and one in 

which nominal interest rates are held constant 

for the initial two years, thereby accommodating 

the fiscal stimulus.

Table 5.3. Real GDP Growth and Fiscal Impulse by Composition: All Economies1

Conditioning Variables

Number of 

Observations 

in Downturn

Three-Year 

Average before 

Downturn

One Year 

before 

Downturn

Year of 

Downturn

Real GDP Growth

One Year 

after 

Downturn

Four-Year 

Average after 

Downturn

Fiscal stimulus

Revenue-based impulse 5 4.4 2.8 –0.7 3.6 4.1

Expenditure-based impulse 31 3.1 2.0 –0.4 2.9 3.0

Both expenditure and revenue 

impulses 15 3.0 1.6 0.6 4.1 3.5

Fiscal tightening

Revenue-based impulse 31 2.4 2.3 –0.2 3.3 3.1

Expenditure-based impulse 17 2.8 3.2 1.2 5.0 4.3

Both expenditure and revenue 

impulses 35 2.7 3.3 1.1 4.3 4.2

1Fiscal impulse is identified during the first year of a downturn as a decline in the cyclically adjusted primary balance to GDP below 

0.25 percentage point of GDP. 2Initial conditions for variables are defined as a three-year average before the year of a downturn.
In each case, there are no long-run changes 

in potential output; eventually, each of the 

variables will return to zero.27 Hence, the experiment focuses on the differences in the short-run 

impact of the policy measures. The results show 

the following:

• For the same increase in deficit, there are 

large differences in the size of short-run multipliers across instruments. On the assumption that it can be implemented immediately 

and efficiently, government investment has 

a larger effect than other measures.28 This 

is because it has a direct effect on aggregate 

demand, whereas the effects of taxes and 

transfers depend on propensities to consume. 

Investment also has the largest effect on inflation and real interest rates.

27This is also true for government investment, but in 

this case the effect on output is much more long lived, 

because government infrastructure capital has productive 

benefits that depreciate only slowly over time. 28This is in contrast to the empirical results, which 

showed more positive effects from revenue-based stimulus. In these simulations, private agents understand that 

debt will be maintained at initial levels. In practice, it 

could be that expenditure-based packages are more likely 

to be made permanent and therefore raise concerns 

about debt sustainability.

• The monetary policy regime plays a key role 

in the effectiveness of fiscal stimulus—with 

accommodation, the output multipliers 

are up to twice as large, and the effects are 

more persistent.29 Concomitantly, inflation 

is higher. The difference is least for labor 

taxes, because lowering labor taxes increases 

incentives for work as well as consumption. As 

a result, a supply response mutes the inflationary impact. It can also be shown that without 

monetary accommodation, multipliers are 

smaller when prices are more flexible.30 This 

is because inflation increases more strongly 

following the stimulus, thereby necessitating 

a more aggressive hike in interest rates that 

reduces the output response. With monetary 

accommodation, greater price flexibility has 

the opposite effect, because higher inflation 

implies a larger drop in real interest rates.

• Cuts in the consumption tax and temporarily higher household transfers have a clear 

29This is consistent with the view that fiscal policy could 

be most effective when monetary policy is least effective, 

such as when nominal interest rates are close to zero or 

the monetary harmonization mechanism is imparied. 30Simulations for different degrees of price flexibility 

are not shown in Figure 5.7.

A Simulation-Based Perspective on Fiscal Sti

Table 5.4. Responses of Real GDP to Discretionary Fiscal Policy Change

Real GDP Response

Elasticity-based fiscal impulse measure Regression-based fiscal impulse measure

Effect in: Year zero Year three Year zero Year three

(with respect to positive fiscal impulse by 1 percentage point of GDP)

Baseline specification 0.15 –0.16 0.08 –0.02

Country differences

Advanced economies only 0.12 0.13 0.11 0.51

Emerging economies only 0.21 –0.03 0.10 –0.09

Composition

Revenue-based policy changes 0.21 0.12 0.10 0.14

Expenditure-based policy changes 0.13 –0.21 0.06 –0.06

Composition: advanced economies only

Revenue-based policy changes 0.35 0.59 0.01 0.40

Expenditure-based policy changes –0.09 –0.26 0.15 0.52

Composition: emerging economies only

Revenue-based policy changes 0.23 0.23 0.13 0.17

Expenditure-based policy changes 0.20 –0.18 0.08 –0.23

Downturns 0.29 0.00 0.10 0.04

Fiscal stimulus only –1.30 –0.88 –0.87 –0.29

Fiscal stimulus only, high initial debt –1.75 –2.05 –1.05 –0.80

Fiscal stimulus only, low initial debt –0.96 –0.36 –0.65 0.13
Chapter5  Fiscal Policy as a Countercyclical

0 1 2 3 4 5 -1

0

1

2

3

 Source: IMF staff calculations.

Figure 5.10. Effect of Fiscal Expansion in a Large Economy

(Deviation from baseline; years on x-axis; shock occurs in year 0)

Impulse responses to 1 percent increase in deficit in year 1 and 0.5 percent increase in deficit in year 2.

0 1 2 3 4 5 -3

-2

-1

0

1

0 1 2 3 4 5 0

1

2

3

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

With monetary accommodation Without monetary accommodation

GDP

(percent)

Inflation (solid) and 

Nominal Interest Rate (dashed)

(percentage points)

Real Interest Rate

(percentage points)

Transfers

Government Investment

Consumption Taxes

Labor Taxes
“tilting” effect on output (output is above, 

then below baseline), because they provide 

incentives to bring forward consumption and 

investment.31 Cuts in labor taxes, on the other 

hand, generate a more consistently positive 

supply response.

If policy measures are made permanent and 

financed by an increase in debt, then long-run 

supply and debt effects become much more 

important. For all fiscal instruments, higher debt 

tends to crowd out private output because it 

leads to higher real interest rates.32 When there 

is a permanent increase in transfers, regardless 

of short-run monetary accommodation, the real 

interest rate rises in the long run, which reduces 

output or, at best, leaves it unchanged. Lower 

tax rates, on the other hand, reduce supply 

distortions, and therefore generate permanent 

increases in output, more so when labor taxes 

are lowered than when consumption taxes are 

lowered. Making lower tax rates permanent 

could raise the short-term impact, depending 

on the balance between the positive supply-side 

effect and negative interest rate effects. The 

effects from permanently higher government 

investment depend on whether the spending 

can generate a higher rate of return than if the 

resources were available to private investors.

How do the multipliers differ according to 

the characteristics of the economy? Additional 

simulations show the following:

• For any given size of fiscal stimulus, multipliers are lower in smaller and more open 

economies—see Figure 5.11—although those 

for labor taxes fall by less.33

31This is an example of a temporary fiscal policy 

change that is effective because of forward-looking expectations, showing that the “permanent income” criticism of 

temporary policy measures does not always hold. 32In small countries—that is, small enough that interest rates are exogenous—this is still likely to happen. 

The degree depends on changes in interest rate risk 

premiums. 33The empirical results in the previous section point to 

the opposite result: multipliers are higher in smaller and 

more open economies. This suggests that the measure 

of openness used in the regressions is picking up other 

effects not accounted for in these simulations.

• A higher share of liquidity-constrained consumers, as might be expected in most emerging economies, results in significantly larger 

multipliers.

• At the same time, fiscal stimulus may lead, in 

high-debt emerging economies, to an increase 

in real interest rates as market participants 

demand a higher interest rate risk premium. 

This reduces output multipliers, especially 

for revenue-based measures, as shown in 

Figure 5.12. If the increase in interest rate 

risk premiums is large enough, the multipliers are negative. It is possible that this is the 

mechanism driving the negative results of 

fiscal stimulus seen from the empirical work 

in Table 5.4.

These results indicate that the effects of fiscal 

stimulus are likely to vary considerably, depending on how the stimulus is implemented and on 

the type of economy. The results support the 

idea that the degree of monetary policy accommodation is important, which may have played 

a role in the smaller estimates of fiscal multipliers in recent years. This is not to say that fiscal 

policy cannot work; rather, it is likely to be most 

effective when monetary policy is constrained 

and ineffective (see also Blinder, 2006). The 

results also illustrate a potentially important 

mechanism by which concerns about public 

debt sustainability could lower fiscal multipliers 

to a point at which discretionary fiscal policy 

would do more harm than good.

Conclusions and Policy Considerations

This chapter addresses a simple question: 

What are the effects of fiscal policy during 

downturns? The analysis indicates that the 

answer is complicated and highly dependent on 

an economy’s characteristics.

One obvious appeal of discretionary fiscal 

policy is that governments can potentially have 

a quick effect on spending power, whereas the 

effects of monetary policy are subject to long 

and sometimes uncertain lags. And in practice, 

the policy record in advanced economies shows 

that discretionary fiscal policy has been used 

A Simulation-Based Perspective on Fiscal Sti
Chapter5  Fiscal Policy as a Countercyclical

0 1 2 3 4 5 -1

0

1

2

3

 Source: IMF staff calculations.

Figure 5.11. Fiscal Expansion in a Large Economy Compared with a Small Open 

Economy with Monetary Accommodation

(Deviation from baseline; years on x-axis; shock occurs in year 0)

Impulse responses to 1 percent increase in deficit in year 1 and 0.5 percent increase in deficit in year 2.

0 1 2 3 4 5 -3

-2

-1

0

1

0 1 2 3 4 5 0

1

2

3

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.25

0.00

0.25

0.50

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

Large economy Small open economy

GDP

(percent)

Inflation (solid) and 

Nominal Interest Rate (dashed)

(percentage points)

Real Interest Rate

(percentage points)

Transfers

Government Investment

Consumption Taxes

Labor Taxes
0 1 2 3 4 5 -0.5

0.0

0.5

1.0

 Source: IMF staff calculations.

Figure 5.12. Effect of Fiscal Expansion in a Small Economy with Market-Risk-Premium 

Reaction

(Deviation from baseline; years on x-axis; shock occurs in year 0)

Impulse responses to 1 percent increase in deficit in year 1 and 0.5 percent increase in year 2.

0 1 2 3 4 5 -2

-1

0

1

2

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

1.0

1.5

0 1 2 3 4 5 -2

-1

0

1

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.5

0.0

0.5

0 1 2 3 4 5 -2

-1

0

1

0 1 2 3 4 5 -1.0

-0.5

0.0

0.5

0 1 2 3 4 5 -2

-1

0

1

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

0 1 2 3 4 5 -0.5

0.0

0.5

1.0

GDP

(percent)

Inflation

(percentage points)

Real Interest Rate

(percentage points)

Transfers

Government Investment

Consumption Taxes

Labor Taxes

Fiscal expansion Fiscal expansion with small market reaction Fiscal expansion with large market reaction

Conclusions and Policy Consideratio
Chapter5  Fiscal Policy as a Countercyclical

actively, although not nearly as much as automatic stabilizers or monetary policy. However, 

discretionary measures have typically been 

implemented later than automatic stabilizers 

and changes in monetary stance; they are more 

often a response to downturns than upturns, 

sometimes more than necessary; and stimulus 

measures have often been made permanent, 

which has had adverse implications for fiscal 

sustainability.

An examination of the average effects of 

discretionary fiscal policy across a combined 

sample of advanced and emerging economies 

does not provide strong evidence of countercyclical effects on activity. However, a closer 

analysis suggests that the effects are moderately 

countercyclical in advanced economies. By 

contrast, there is only weak evidence for countercylical effects in emerging economies, and 

only initially, with indications that effects turn 

negative in subsequent years. Revenue-based 

stimulus measures seem to be more effective 

at boosting output than expenditure-based 

measures, especially in emerging economies, 

perhaps reflecting concerns that, once implemented, increased expenditures are difficult 

to remove.

These empirical findings are broadly consistent with simulations from a multiperiod 

general equilibrium model. The simulations 

show how the fiscal multiplier can vary from 

Keynesian (1 or greater) to negative, depending on the instrument used and the type of 

economy. In particular, the multiplier is lower 

when monetary reaction does not accommodate 

the fiscal stimulus and when there is a strong 

increase in risk premiums following fiscal stimulus (such as when concerns about servicing 

debt obligations are high). Increased government spending can be the most direct means 

of increasing output, if it can be implemented 

quickly. On the other hand, it is the most 

inflationary. Tax changes that provide greater 

rewards for work effort or incentives for bringing consumption forward might be nearly as 

effective in supporting economic activity, with 

less risk of inflation.

Given both the interest in fiscal policy as a 

countercyclical tool and the evidence that discretionary fiscal stimulus can have adverse effects, 

should governments rely more on automatic 

stabilizers? Or is it possible to design alternative 

countercyclical fiscal policy mechanisms that 

would respond symmetrically and more quickly 

to changes in the state of economy?34

There are two broad possibilities, each with its 

advantages and disadvantages.

Increasing the responsiveness of automatic stabilizers: The extent of passive automatic stabilizers 

could be augmented, for example, by increasing the progressivity of the revenue system. 

Such mechanisms would work automatically 

and would not necessarily increase the size 

of government. A related approach would be 

to change certain tax, transfer, or spending 

programs to introduce links to the state of 

the economy following simple rules, akin to 

the Taylor rule for setting interest rates. This 

could be done by implementing pre-approved 

temporary spending programs or raising 

unemployment insurance benefits once the 

unemployment rate reaches a certain threshold. 

An advantage of such an approach would be its 

transparency.

However, such schemes could also bring 

unintended consequences that would need 

to be weighed against possible stabilization 

benefits. A system of temporary consumption 

tax changes could lead to self-fulfilling falls in 

current consumption if tax cuts were anticipated. Proposals that call for automatic triggers 

in response to cyclical developments are also 

problematic because there are no completely 

reliable real-time measures of the state of the 

economy. Responses based on previous periods’ 

outcomes—such as an automatic tax rebate—

could be more accurate and less distortionary 

but might not be as well targeted as those that 

34This idea goes back at least to Musgrave (1959, p. 

512), who coined the phrase “formula flexibility” to 

describe a system in which changes in taxes and/or 

expenditures would be legislated in advance to respond 

to changes in income. More recently, versions have been 

advocated in Seidman (2003).
are based on current income. It might also be 

difficult to develop predetermined state-contingent spending programs that are a well-targeted 

and efficient use of public money (Solow, 2005). 

Furthermore, all of the above could introduce 

distortions: schemes to increase tax progressivity or tie unemployment insurance generosity to 

the state of the economy would likely alter work 

incentives, and might prove politically difficult 

to adhere to during upturns. Thus, they would 

have to flanked with measures to improve the 

targeting of support during downturns. Better 

targeting, however, is likely to pose administrative challenges that could prove expensive to 

address.

Changes in fiscal policy governance: Broader 

reforms could bolster the credibility of discretionary policy actions, in particular, to reduce 

the risk of debt bias. This might involve establishing an independent, nonpartisan government agency, such as already exist in many 

countries—a sort of “fiscal watchdog”—charged 

with identifying changes in the cyclical state of 

the economy, assessing the extent to which fiscal 

policy is consistent with medium-term objectives, 

and providing advice on various policy measures.35 This would minimize partisan judgment 

in the evaluation of economic information and 

would avoid relying solely on statistical measures of the state of the economy, which can be 

imprecise. In addition, this arrangement could 

increase the timeliness and temporariness of 

the fiscal impulse. Such agencies could also be 

entrusted with giving advice on which tax and 

expenditure parameters to vary, as they indeed 

already do in a number of countries.36

35For example, the objective of the Swedish Fiscal 

Policy Council is to provide an independent evaluation of 

the Swedish government’s fiscal policy, including whether 

fiscal policy is consistent with the state of the economy in 

the business cycle. 36Some have even proposed that governments delegate limited fiscal responsibility to these nonpartisan 

agencies, for exclusive use in macroeconomic stabilization (see Ball, 1997, and Calmfors, 2003). Under existing proposals, such agencies could vary certain tax or 

expenditure parameters, within certain limits set out by 

the legislative branch and on the basis of a narrow stabiClearly, a careful examination of the potential costs and risks of such systems would 

be required before implementing any such 

approaches. In addition to the choice of fiscal 

instruments and the administrative complexities 

of changing tax rates or expenditure plans, the 

system would have to be coordinated with monetary policy goals (see Taylor, 2000). Nonetheless, 

given the limitations of automatic stabilizers as 

currently implemented and the risks associated 

with discretionary fiscal policy, the idea deserves 

further examination.

Appendix 5.1. Data and Empirical

Methods

Evidence on the Responsiveness of Fiscal Polic

Quarterly data on the output gap and real 

GDP growth are taken from the OECD Economic 

Outlook, and are seasonally adjusted. Downturns 

are defined as quarters in which growth is either 

negative or below potential, with the output gap 

more than one standard deviation below zero. 

Changes in monetary policy are proxied by the 

quarterly change in the nominal short-term 

interest rate taken from the IMF’s International 

Financial Statistics database. All changes are 

quarter-over-quarter and unannualized. The 

analysis focuses on “large” changes in discretionary fiscal variables, defined as those that exceed 

0.25 percent of GDP a quarter. Similarly, discretionary changes in nominal short-term interest 

rates are defined as those that exceed 0.25 percent in one quarter.

The vector autoregression (VAR) is estimated 

for each country using quarterly data. The variables included in the VARs, and their ordering, 

are as follows: actual real GDP growth minus 

potential real GDP growth, inflation (based 

lization mandate supplemented by strict accountability 

requirements. A weakness of such proposals is they present a challenge regarding the role of government and 

they make it dificult to establish a dividing line between 

the agency and government in terms of countercyclical 

fiscal policy (see Debrun, Hauner, and Kumar, 2008, for 

a detailed survey).

Appendix 5.1. Data and Empirical
Chapter5  Fiscal Policy as a Countercyclical

on the GDP deflator), changes in the nominal 

interest rate, changes in the primary cyclically 

adjusted fiscal balance, and changes in the 

automatic (cyclical) fiscal balance. This ordering implies that although policy variables can 

respond to growth and inflation shocks within 

one quarter, the transmission lag from policy 

variables to growth and inflation is at least one 

quarter. Two lags of each variable are included 

in the VAR.

Data Uncertainties and the Risk of Debt Bia

For the purposes of testing for asymmetric 

responses, each VAR now includes the following variables: growth when the economy is in a 

downturn and zero otherwise; growth when the 

economy is in an upturn and zero otherwise; 

and the previously included variables, that is, 

inflation, changes in nominal interest rates, and 

changes in fiscal balances. As before, downturns 

are defined as quarters in which growth is either 

negative or below potential, with the output gap 

more than one standard deviation below zero. 

The results are robust to changing the ordering of the two halves of growth (downturns and 

upturns) in the VAR, to alternative ordering 

for the fiscal and monetary policy variables, 

and to including a time trend in each equation. 

Because the VAR is specified in first differences, 

any trend in fiscal balances over the sample 

period affects the constant term in the fiscal balance equation.

For the purposes of testing the reliability of 

preliminary GDP estimates, the analysis updates 

the estimates of Faust, Rogers, and Wright 

(2005), which used data ending in 1997. Revisions are defined as the difference between the 

data as they stood in the most recent OECD 

Monthly Economic Indicators (June 2008) and 

the data when they were first published in the 

Monthly Economic Indicators. For the United 

States, the United Kingdom, and Canada, 

preliminary data are available beginning 

in 1965:Q1. For Japan, the starting date is 1970:

Q1; for Italy and Germany, 1979:Q4; and for 

France, 1987:Q4.

For the purposes of evaluating the effect 

of growth estimation errors, each VAR now 

includes the preliminary estimation errors in 

addition to the previously included variables. 

The estimation errors are ordered in the 

VARs after growth and inflation but before the 

policy variables. The results are robust to alternative ordering among the errors and policy 

variables.

Policy Reactions in Emerging and Advanced

Economie

The analysis uses a sample of 21 advanced 

economies and 20 emerging economies from 

the IMF’s World Economic Outlook (WEO) 

database, covering the period from 1970 

to 2007.37 The sample includes annual data for 

general government revenues and expenditures 

(net of interest payments). Other macroeconomic data (for example, for external balances 

and inflation) are sourced from the World 

Bank’s World Development Indicators database, 

the WEO database, and other public sources. 

The list of economies and episodes of downturn is in Table 5.5 (with years of fiscal stimulus in bold).

In addition to the WEO data, an examination was made using the IMF’s Government 

Finance Statistics Manual (GFS) data. One 

advantage of this data set is that it offers 

greater disaggregation—revenues can be broken down into personal, corporate, consumption, and trade. Taxes and expenditures can be 

broken down into household, nonprofit institution, and corporate transfers (subsidies); interest; government employee wages; and other 

expenditures as well as capital spending. Moredisaggregated data potentially allow for finer 

distinctions regarding the income elasticities 

of taxes and spending and therefore a more 

accurate measure of automatic versus cyclical 

adjustments in revenues and expenditures. 

Extensive comparisons were made between 

37Owing to data limitations, India was dropped from 

the sample used for regressions. 
WEO and GFS data for the same selection of 

countries. Two major problems arose. First, to 

create a sufficiently long time series, various 

GFS vintages needed to be spliced together, 

leading to situations in which the components 

listed above jumped at the splice points, apparently simply because of reclassifications. This 

led to spurious measures of fiscal impulses, 

taking away the theoretical advantage of using 

these data. Second, long time series of GFS 

data are available only for central government. This can present a deceptive picture of 

changes in fiscal policy. For example, estimates 

of fiscal impulses at the central level of government were found to be countercyclical (with 

the output cycle) for all countries. This finding 

deserves more investigation, but is outside the 

scope of this study.

Fiscal Impulse Measures

The elasticity-based fiscal impulse measure 

used for the stylized facts, event analysis, and 

regressions is a cyclically adjusted primary balance, calculated as 

 Yt

real

capbt = rt – et

P ————, Yt

tr–real

where rt is the revenue-to-GDP ratio in period t, 

et

P is the primary expenditure-to-GDP ratio in 

period t, and Yt

real/Yttr–real is real output divided 

by potential (trend) output in period t. These 

estimates of the cyclically adjusted balance 

rely on output gap estimates derived using a 

time-series filter, which may not work well when 

supply shocks are frequent and large, as for 

many emerging economies. Applying the same 

elasticities across economies (as assumed for 

emerging economies), where one has a low elasticity of taxes to output and another has a high 

elasticity of taxes to output could lead to results 

implying that the former uses discretionary 

fiscal policy more actively than the latter, 

whereas in fact the cause is stronger automatic 

stabilizers.

The regression-based fiscal impulse measure 

used for the regressions is constructed as the 

Table 5.5. List of Countries and Downturn

Episode

Country Years in Downturn

Argentina 1975, 1976, 1978, 1981, 1982, 1985, 1988, 

1989, 1990, 1995, 1999, 2000, 2001, 2002

Australia 1972, 1978, 1982, 1983, 1991, 1992

Austria 1975, 1978, 1981, 1988, 1997

Belgium 1975, 1977, 1987, 1993, 2003

Brazil 1970, 1981, 1983, 1990, 1992

Canada 1975, 1982, 1991, 1992

Chile 1972, 1973, 1975, 1982, 1983, 1999

China 1976, 1990, 1991

Colombia 1976, 1977, 1983, 1985, 1991, 1992, 1999

Czech Republic 1990, 1991, 1992, 1997, 1998

Denmark 1974, 1975, 1980, 1981, 1983, 1988, 1993, 

2003

Egypt 1973, 1974, 1981

Finland 1977, 1978, 1991, 1992, 1993

France 1975, 1986, 1987, 1993, 1997

Germany 1975, 1982, 1989, 1990, 1993, 2003

Greece 1974, 1981, 1982, 1983, 1987, 1993

Hungary 1985, 1988, 1990, 1991

Iceland 1975, 1976, 1983, 1985, 1988, 1991, 1992, 

2003

India 1972, 1974, 1979, 1980, 1987, 2002

Indonesia 1998

Ireland 1975, 1976, 1983, 1993, 1994

Italy 1972, 1975, 1980, 1983, 1993, 2003

Japan 1974, 1975, 1987, 1994, 1998, 1999

Malaysia 1971, 1975, 1985, 1986, 1987, 1998

Mexico 1977, 1982, 1983, 1986, 1988, 1995, 2001

Netherlands 1975, 1980, 1981, 1982, 1993, 2003, 2005

New Zealand 1972, 1976, 1977, 1979, 1983, 1991, 1992, 

1998

Pakistan 1970, 1972, 2002, 2003

Poland 1980, 1981, 1982, 1984, 1990, 1991

Portugal 1975, 1984, 1985, 1986, 1993, 2003

Romania 1975, 1985, 1988, 1989, 1990, 1991, 1992,

1997, 1998, 1999

Slovak Republic 1990, 1991, 1992, 1993

Slovenia 1976, 1983, 1987, 1988, 1990, 1991, 1992

South Africa 1977, 1978, 1982, 1983, 1985, 1986, 1990, 

1991, 1992

Spain 1971, 1981, 1985, 1986, 1993

Sweden 1977, 1981, 1983, 1991, 1992, 1993, 2003

Switzerland 1975, 1976, 1978, 1982, 1983, 1991, 1993, 

2003

Turkey 1973, 1979, 1980, 1989, 1994, 1999, 2001

Ukraine 1987, 1990, 1991, 1992, 1993, 1994, 1996, 

1997, 1998, 1999

United Kingdom 1971, 1974, 1975, 1980, 1981, 1991, 1992

United States 1970, 1974, 1975, 1980, 1982, 1991

1Years in bold correspond to use of a fiscal stimulus in a 

downturn, with fiscal stimulus defined as a decline in the cyclically 

adjusted primary balance to GDP below 0.25 percentage point of 

GDP.

Appendix 5.1. Data and Empirical
Chapter5  Fiscal Policy as a Countercyclical

1

difference between a hypothetical primary 

deficit in period t assuming no changes in the 

economic environment and the actual primary 

deficit in period t–1. As a first step, note that 

the actual primary balance in period t can be 

expressed as a function of the discretionary 

policies, Pt, and the economic environment 

prevailing in that period, Et:

Bt = B(Pt,Et).

The change in the primary balance with respect 

to the previous year can then be decomposed as 

follows:

DBt = B(Pt, Et) – B(Pt–1, Et–1)

= [B(Pt, Et) – B(Pt, Et–1)] + [B(Pt, Et–1) 

 – B(Pt–1, Et–1)]

= DBt

E + DBtP.

The term B(Pt, Et–1) captures what the primary 

balance would have been under the period 

t policies, assuming the economic environment was the same as in period t–1. It is then 

possible to break the change in the balance 

into two elements. The first element, DBt

E, 

represents the fiscal effects of changes in the 

economic environment from Et–1 to Et

 . The 

second element, DBt

P, captures the change in 

the balance as a result of changes in discretionary policies.

In practice, the initial step for calculating the 

regression-based measure of fiscal impulse is to 

estimate the following equations, assuming real 

GDP growth is a good proxy for the economic 

environment:

Rt = aR + bR · growtht + gR · trendt + ut

Gt = aE + bE · growtht + gE · trendt + et,

where R is general government revenue in percent of GDP, G is general government primary 

expenditure in percent of GDP, growth is real 

GDP growth, trend is a time trend, and u and 

e are residuals. The growth-adjusted revenue, 

which indicates what the revenue would have 

been in period t if the growth rate remained 

unchanged from the previous period, is computed as Rt

(growtht–1) = aˆR + b

ˆ

R · growtht–1 + 

ˆ

gR · trendt + uˆt. The growth-adjusted primary 

expenditure is computed in the same way, as 

Gt (growtht–1) = aˆ E + b

ˆ

E · growtht–1 + ˆ

gE · trendt + eˆt. 

The measure for the primary balance that would 

have prevailed in period t if the growth rate had 

been equal to that in period t – 1, B(Pt, Et–1), 

can then be calculated as Rt

(growtht--1) – 

Et(growtht–1). The actual primary balance in the 

previous period, B(Pt–1, Et–1), is simply Rt–1 – Gt–1. 

The final step in the construction of the fiscal impulse measure is to take the difference 

between the growth-adjusted measure for the 

primary balance in period t and the primary balance in the previous period:

Fiscal impulset = [Rt(growtht–1) – Gt(growtht–1)]

 – [Rt–1 – Gt–1]

= (g

ˆR – gˆE) + (uˆt

 – uˆt–1) 

 – (eˆt

 – eˆt–1).

Note that although uˆt and eˆt can be expected 

to be uncorrelated with yt, uˆt–1 and eˆt–1 are correlated with yt

.

Regression Analysis

Dynamic panel regressions were run using the 

Arellano-Bond estimator.38

The multipliers presented in Table 5.4 

are derived from regression results shown 

in Table 5.6 (using the elasticity-based fiscal 

impulse measure) and Table 5.7 (using the 

regression-based fiscal impulse measure). Note 

that, because it is based on the primary balance, 

a negative change in the regression-based measure represents fiscal stimulus, so that a negative 

coefficient indicates that fiscal stimulus typically 

has a positive effect on real GDP growth. A positive coefficient on the expenditures-only fiscal 

impulse or a negative coefficient on the revenue-only fiscal impulse indicate positive effects 

on growth.

38Experiments were also run with single-equation 

regressions for individual economies. In most cases, 

the results were insignificant, indicating that there 

was insufficient variation in the short time samples to 

adequately differentiate the effects of fiscal stimulus on 

output growth.
Table 5.6. Discretionary Fiscal Policy and Growth: Regression Results with Arellano-Bond Dynamic

Panel Estimator Using Elasticity-Based Fiscal Impulse Measu

Appendix 5.1. Data and Empirical

Right-Hand-Side Variables

Baseline 

Specification

Country 

Differences,

Advanced 

Economies

Country 

Differences, 

Emerging 

Economies Downturns Components

Components, 

Advanced 

Economies

Components, 

Emerging 

Economies

Real GDP growth

Lag1 0.36 0.53 0.33 0.42 0.37 0.53 0.31

(4.18) (8.11) (3.11) (7.21) (4.08) (8.52) (2.87)

Lag2 –0.01 –0.04 0.06 0.11 0.02 –0.04 0.08

(–0.15) (–0.85) (1.11) (2.83) (0.49) (–0.67) (1.47)

Changes in cyclically adjusted 

primary balance (dCAPB)

–0.15 –0.12 –0.21 . . . . . . . . . . . .

(–1.93) (–1.89) (–2.51) . . . . . . . . . . . .

Lag1 0.14 0.01 0.13 . . . . . . . . . . . .

(3.03) (0.28) (2.01) . . . . . . . . . . . .

Lag2 0.13 0.05 0.12 . . . . . . . . . . . .

(3.78) (0.90) (3.44) . . . . . . . . . . . .

Changes in cyclically adjusted 

primary expenditure

. . . . . . . . . . . . 0.13 –0.09 0.20

. . . . . . . . . . . . (1.34) (–0.83) (1.59)

Lag1 . . . . . . . . . . . . –0.16 0.00 –0.21

. . . . . . . . . . . . (–2.66) (0.13) (–2.39)

Changes in revenue . . . . . . . . . . . . –0.21 –0.35 –0.23

. . . . . . . . . . . . (–3.42) (–3.97) (–3.35)

Lag1 . . . . . . . . . . . . 0.05 0.02 0.03

. . . . . . . . . . . . (1.10) (0.29) (0.49)

Lag2 . . . . . . . . . . . . 0.10 0.02 0.06

. . . . . . . . . . . . (2.23) (0.32) (1.31)

Neutral dummy x positive 

fiscal impulse x dCAPB

. . . . . . . . . –0.35 . . . . . . . . .

. . . . . . . . . (–2.84) . . . . . . . . .

Lag1 . . . . . . . . . –0.15 . . . . . . . . .

. . . . . . . . . (–1.45) . . . . . . . . .

Lag2 . . . . . . . . . 0.10 . . . . . . . . .

. . . . . . . . . (1.13) . . . . . . . . .

Neutral dummy x negative 

fiscal impulse x dCAPB

. . . . . . . . . –0.06 . . . . . . . . .

. . . . . . . . . (–0.71) . . . . . . . . .

Lag1 . . . . . . . . . 0.19 . . . . . . . . .

. . . . . . . . . (1.82) . . . . . . . . .

Lag2

. . . . . . . . . 0.13 . . . . . . . . .

. . . . . . . . . (1.74) . . . . . . . . .

Downturn dummy x positive 

fiscal impulse x high-debt 

dummy x dCAPB

. . . . . . . . . 1.75 . . . . . . . . .

. . . . . . . . . (2.36) . . . . . . . . .

Lag1 . . . . . . . . . –0.30 . . . . . . . . .

. . . . . . . . . (–0.54) . . . . . . . . .

Lag2 . . . . . . . . . –0.51 . . . . . . . . .

. . . . . . . . . (–0.98) . . . . . . . . .

Downturn dummy x positive 

fiscal impulse x low-debt 

dummy x dCAPB

. . . . . . . . . 0.96 . . . . . . . . .

. . . . . . . . . (3.59) . . . . . . . . .

Lag1 . . . . . . . . . –0.50 . . . . . . . . .

. . . . . . . . . (–3.60) . . . . . . . . .

Lag2 . . . . . . . . . –0.42 . . . . . . . . .

. . . . . . . . . (–2.19) . . . . . . . . .

Downturn dummy x negative 

fiscal impulse x high-debt 

dummy x dCAPB

. . . . . . . . . –0.44 . . . . . . . . .

(–2.05)

Lag1 0.44

(1.98)

Lag2 0.15

(1.59)
Chapter5  Fiscal Policy as a Countercyclical

Right-Hand-Side Variables

Baseline 

Specification

Country 

Differences,

Advanced 

Economies

Country 

Differences, 

Emerging 

Economies Downturns Components

Components, 

Advanced 

Economies

Components, 

Emerging 

Economies

Downturn dummy x negative 

fiscal impulse x low-debt 

dummy x dCAPB

. . . . . . . . . –0.52 . . . . . . . . .

. . . . . . . . . (–3.75) . . . . . . . . .

Lag1 . . . . . . . . . 0.50 . . . . . . . . .

. . . . . . . . . (2.39) . . . . . . . . .

Lag2 . . . . . . . . . 0.21 . . . . . . . . .

. . . . . . . . . (1.65) . . . . . . . . .

Deep downturn dummy x 

positive fiscal impulse x 

dCAPB

. . . . . . . . . 0.00 . . . . . . . . .

. . . . . . . . . (0.00) . . . . . . . . .

Lag1 . . . . . . . . . –0.80 . . . . . . . . .

. . . . . . . . . (–4.76) . . . . . . . . .

Lag2 . . . . . . . . . 0.84 . . . . . . . . .

. . . . . . . . . (4.00) . . . . . . . . .

Deep downturn dummy x 

negative fiscal impulse x 

dCAPB

. . . . . . . . . 0.28 . . . . . . . . .

. . . . . . . . . (1.53) . . . . . . . . .

Lag1 . . . . . . . . . 0.00 . . . . . . . . .

. . . . . . . . . (0.00) . . . . . . . . .

Lag2 . . . . . . . . . 0.57 . . . . . . . . .

. . . . . . . . . (3.63) . . . . . . . . .

Upturn dummy x positive 

fiscal impulse x dCAPB

. . . . . . . . . –0.80 . . . . . . . . .

. . . . . . . . . (–4.76) . . . . . . . . .

Lag1 . . . . . . . . . 0.84 . . . . . . . . .

. . . . . . . . . (4.00) . . . . . . . . .

Lag2 . . . . . . . . . 0.28 . . . . . . . . .

. . . . . . . . . (1.53) . . . . . . . . .

Upturn dummy x negative 

fiscal impulse x dCAPB

. . . . . . . . . 0.57 . . . . . . . . .

. . . . . . . . . (3.63) . . . . . . . . .

Lag1 . . . . . . . . . –0.86 . . . . . . . . .

. . . . . . . . . (–3.95) . . . . . . . . .

Lag2 . . . . . . . . . –0.57 . . . . . . . . .

. . . . . . . . . (–3.10) . . . . . . . . .

Real money growth 0.04 0.02 0.07 0.05 0.05 0.02 0.07

(1.67) (0.95) (2.16) (1.94) (1.96) (1.13) (2.16)

Lag1 0.02 0.01 0.03 –0.01 0.02 0.01 0.03

(0.91) (0.46) (1.02) (–0.46) (0.90) (1.35) (1.17)

Lag2 –0.02 0.00 –0.02 –0.02 –0.02 0.00 –0.02

(–1.30) (0.22) (–1.05) (–1.02) (–1.17) (–0.23) (–1.18)

Government size –0.03 –0.01 –0.04 –0.02 –0.03 –0.02 –0.04

(–1.97) (–0.99) (–1.56) (–2.16) (–1.88) (–1.55) (–1.45)

Trade-weighted growth of 

trading partners

0.35 0.10 0.42 0.17 0.33 –0.01 0.44

(2.06) (0.71) (1.81) (1.37) (1.96) (–0.06) (1.77)

EMU dummy2 –0.80 –0.13 . . . –0.67 –0.78 –0.12 . . .

(–2.35) (–0.50) . . . (–2.50) (–2.58) (–0.47) . . .

Number of observations 796 487 309 650 796 487 309

Number of countries 40 21 19 40 40 21 19

p-value for Sargan test of 

overidentifying restrictions

0.000 0.000 0.003 0.000 0.000 0.011 0.002

p-value for Hansen test of 

overidentifying restrictions

1.000 1.000 1.000 1.000 1.000 1.000 1.000

p-value for the test of 

no-second-order serial 

correlation

0.811 0.270 0.868 0.010 0.606 0.242 0.845

1Dependent variable is real GDP growth. All regressions also included a set of time dummies.

2EMU = European Monetary Union.

Table 5.6 (continued
Table 5.7. Discretionary Fiscal Policy and Growth: Regression Results with Arellano-Bond Dynamic

Panel Estimator Using Regression-Based Fiscal Impulse Measur

Right-Hand-Side Variables Baseline

Country 

Differences, 

Advanced 

Economies

Country 

Differences, 

Emerging 

Economies Downturns Components

Components, 

Advanced 

Economies

Components, 

Emerging 

Economies

Real GDP growth

Lag1 0.37 0.54 0.30 0.43 0.38 0.56 0.29

(3.86) (8.09) (2.67) (7.10) (3.73) (7.58) (2.51)

Lag2 –0.03 –0.01 0.04 0.12 –0.03 –0.01 0.05

(–0.71) (–0.18) (0.83) (3.04) (–0.68) (–0.25) (0.86)

Changes in cyclically adjusted 

primary balance (dCAPB)

–0.08 –0.11 –0.10 . . . . . . . . . ...

(–1.18) (–1.81) (–1.50) . . . . . . . . . ...

Lag1 0.04 –0.14 0.08 . . . . . . . . . ...

(0.79) (–3.18) (1.25) . . . . . . . . . ...

Lag2 0.06 –0.02 0.10 . . . . . . . . . ...

(1.89) (–0.33) (2.00) . . . . . . . . . ...

Changes in cyclically adjusted 

primary expenditure

. . . . . . . . . . . . 0.06 0.15 0.08

. . . . . . . . . . . . (0.84) (2.27) (0.87)

Lag1 . . . . . . . . . . . . –0.05 0.13 –0.14

. . . . . . . . . . . . (–1.13) (2.89) (–1.78)

Lag2 . . . . . . . . . . . . –0.06 –0.01 –0.12

. . . . . . . . . . . . (–1.62) (–0.20) (–1.77)

Changes in revenue . . . . . . . . . . . . –0.10 –0.01 –0.13

. . . . . . . . . . . . (–1.87) (–0.17) (–1.90)

Lag1 . . . . . . . . . . . . –0.02 –0.13 –0.01

. . . . . . . . . . . . (–0.36) (–1.85) (–0.18)

Lag2 . . . . . . . . . . . . 0.03 –0.08 0.03

. . . . . . . . . . . . (0.97) (–1.29) (0.53)

Neutral dummy x positive 

fiscal impulse x dCAPB

. . . . . . . . . –0.39 . . . . . . ...

. . . . . . . . . (–3.15) . . . . . . ...

Lag1 . . . . . . . . . –0.17 . . . . . . ...

. . . . . . . . . (–2.43) . . . . . . ...

Lag2 . . . . . . . . . 0.08 . . . . . . ...

. . . . . . . . . (0.79) . . . . . . ...

Neutral dummy x negative 

fiscal impulse x dCAPB

. . . . . . . . . 0.07 . . . . . . ...

. . . . . . . . . (0.51) . . . . . . ...

Lag1 . . . . . . . . . 0.03 . . . . . . ...

. . . . . . . . . (0.31) . . . . . . ...

Lag2 . . . . . . . . . 0.19 . . . . . . ...

. . . . . . . . . (2.28) . . . . . . ...

Downturn dummy x positive 

fiscal impulse x high debt 

dummy x dCAPB

. . . . . . . . . 1.05 . . . . . . . . .

. . . . . . . . . (2.58) . . . . . . . . .

Lag1 . . . . . . . . . –0.37 . . . . . . . . .

. . . . . . . . . (–1.12) . . . . . . . . .

Lag2 . . . . . . . . . –0.38 . . . . . . . . .

. . . . . . . . . (–1.54) . . . . . . . . .

Downturn dummy x positive 

fiscal impulse x low debt 

dummy x dCAPB

. . . . . . . . . 0.65 . . . . . . . . .

. . . . . . . . . (4.87) . . . . . . . . .

Lag1 . . . . . . . . . –0.53 . . . . . . . . .

. . . . . . . . . (–5.50) . . . . . . . . .

Lag2 . . . . . . . . . –0.33 . . . . . . . . .

. . . . . . . . . (–2.52) . . . . . . . . .

Appendix 5.1. Data and Empirical
Chapter5  Fiscal Policy as a Countercyclical

Table 5.7 (concluded

Right-Hand-Side Variables Baseline

Country 

Differences, 

Advanced 

Economies

Country 

Differences, 

Emerging 

Economies Downturns Components

Components, 

Advanced 

Economies

Components, 

Emerging 

Economies

Downturn dummy x negative 

fiscal impulse x high debt 

dummy x dCAPB

. . . . . . . . . –0.40 . . . . . . . . .

. . . . . . . . . (–1.93) . . . . . . . . .

Lag1 . . . . . . . . . 0.34 . . . . . . . . .

. . . . . . . . . (1.87) . . . . . . . . .

Lag2 . . . . . . . . . 0.14 . . . . . . . . .

. . . . . . . . . (1.29) . . . . . . . . .

Downturn dummy x negative 

fiscal impulse x low debt 

dummy x dCAPB

. . . . . . . . . –0.46 . . . . . . . . .

. . . . . . . . . (–3.24) . . . . . . . . .

Lag1 . . . . . . . . . 0.30 . . . . . . . . .

. . . . . . . . . (1.29) . . . . . . . . .

Lag2 . . . . . . . . . 0.21 . . . . . . . . .

. . . . . . . . . (1.69) . . . . . . . . .

Upturn dummy x positive 

fiscal impulse x dCAPB

. . . . . . . . . –0.91 . . . . . . . . .

. . . . . . . . . (–4.59) . . . . . . . . .

Lag1 . . . . . . . . . 0.86 . . . . . . . . .

. . . . . . . . . (3.67) . . . . . . . . .

Lag2 . . . . . . . . . 0.19 . . . . . . . . .

. . . . . . . . . (0.84) . . . . . . . . .

Upturn dummy x negative 

fiscal impulse x dCAPB

. . . . . . . . . 0.57 . . . . . . . . .

. . . . . . . . . (4.27) . . . . . . . . .

Lag1 . . . . . . . . . –0.91 . . . . . . . . .

. . . . . . . . . (–5.62) . . . . . . . . .

Lag2 . . . . . . . . . –0.37 . . . . . . . . .

. . . . . . . . . (–2.04) . . . . . . . . .

Real money growth 0.05 0.02 0.07 0.05 0.06 0.02 0.08

(1.92) (1.06) (2.41) (1.92) (2.19) (1.11) (2.65)

Lag1 0.01 0.01 0.03 –0.01 0.01 0.01 0.03

(0.83) (0.50) (1.03) (–0.43) (0.85) (0.50) (0.99)

Lag2 –0.02 0.00 –0.02 –0.02 –0.02 0.00 –0.02

(–1.37) (0.15) (–0.97) (–1.03) (–1.34) (0.27) (–0.94)

Government size –0.04 –0.01 –0.04 –0.02 –0.04 –0.01 –0.04

(–2.44) (–1.00) (–1.62) (–2.26) (–2.17) (–0.97) (–1.36)

Trade-weighted growth of 

trading partners

0.34 0.08 0.40 0.16 0.35 0.08 0.43

(1.93) (0.60) (1.77) (1.29) (1.93) (0.54) (1.69)

EMU dummy –0.79 –0.19 –0.71 –0.82 –0.19 . . . 

(–2.28) (–0.80) (–2.61) (–2.50) (–0.83) . . . 

Number of observations 796 487 309 650 796 487 309

Number of countries 40 21 19 40 40 21 19

p-value for Sargan test of 

overidentifying restrictions

0.000 0.000 0.001 0.000 0 0.004 0.003

p-value for Hansen test of 

overidentifying restrictions

1.000 1.000 1.000 1.000 1.000 1.000 1.000

p-value for the test of no 

second order serial 

correlation

0.790 0.254 0.758 0.019 0.739 0.428 0.641

1Dependent variable is real GDP growth. All regressions also included a set of time dummies.
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c h a pte r 6 Divergence of Current Acco

across Emerg

This chapter seeks to explain the divergence in current 

account behavior between emerging Asia and emerging Europe. It identifies financial liberalization and 

EU integration as the main drivers of the large and 

persistent deficits in emerging Europe but also raises 

concerns about risks of abrupt endings. In contrast, 

less open capital accounts and financial sectors 

contributed to surpluses in emerging Asia. To a large 

extent, however, these surpluses remain unexplained, 

raising questions about the role of exchange rates and 

the desire of some countries to build high levels of 

reserves after the Asian crisis.

The pattern of current account balances 

across emerging economies has become 

much more diverse in recent years than 

during the early 1990s, particularly 

between emerging Asia and Europe.1 Most of 

emerging Asia (especially after the 1997–98 

crisis), the Middle East, and some members of 

the Commonwealth of Independent States have 

reported large current account surpluses, while 

large current account deficits are observed 

mainly in emerging Europe and other countries 

such as Jordan, Pakistan, South Africa, Turkey, 

Vietnam, and a number of countries in Central America and the Caribbean. The current 

account deficits in Latin America and Africa 

remained on average at modest levels. Notably, 

The main authors of this chapter are Stephan Danninger and Florence Jaumotte. Joshua Aizenman and 

Christopher Meissner provided consultancy support, and 

Stephanie Denis and Patrick Hettinger provided research 

assistance. Jonathan Ostry supervised the chapter. 1Emerging Asia is defined to include the newly industrialized Asian economies, or NIEs (Korea, Hong Kong 

SAR, Singapore, and Taiwan POC), the Asian Tigers 

(Indonesia, Malaysia, the Philippines, and Thailand), 

China, and other Asia (India, Pakistan, Sri Lanka, and 

Vietnam). Emerging Europe includes central Europe 

(Czech Republic, Hungary, Poland, Slovak Republic, 

and Slovenia), southeastern Europe (Albania, Bulgaria, 

Croatia, Macedonia, FYR, and Romania), and the Baltics 

(Estonia, Latvia, and Lithuania).

virtually all of these emerging economies have 

achieved high growth during the past decade, 

irrespective of their current account positions.

The divergent current account patterns in 

emerging Asia and Europe have revived the 

long-standing debate over the connection 

between economic development and capital flows—the Lucas paradox (Lucas, 1990). 

Theory predicts that growth should lead to 

current account deficits for two reasons. On one 

hand, high growth and the resulting profitable investment opportunities should make the 

country attractive to foreign capital. On the 

other hand, if individuals want to smooth their 

consumption over time, prospects of continued 

high growth should lead to higher consumption 

today because income and consumption can 

be expected to rise further in the future. The 

traditional view that capital flows downhill to 

high-growth countries seems to hold for emerging Europe, whereas the opposite appears to be 

the case for emerging Asia after 1997–98.

The two patterns may also have different 

implications for macroeconomic stability. The 

path of the Asian countries, which combine 

rapid growth with current account surpluses, 

may seem safer, at least from the point of view 

of external vulnerability.2 However, there may 

be limits to how long export-led growth can be 

sustained, particularly if it is associated with a 

low exchange rate, because of the risks of capital misallocation, overheating, and rising inflation. In contrast, although sustained current 

account deficits could fuel overconsumption 

and be vulnerable to “sudden stops” in financial 

flows, they need not end abruptly if they reflect 

consumption smoothing or the financing of 

productive investment during episodes of high 

2The large current account surpluses may, however, 

entail a growth and/or welfare cost, but this issue is not 

examined in this chapter.
Chapter6  Divergence of Current Account Balances across Emerg

growth, as theory would predict (see, for example, Ghosh and Ostry, 1995, and Ostry, 1997).

Against this background, the chapter looks 

more closely at factors underlying the recent 

divergence in current account balances across 

emerging economies and attempts to assess 

their sustainability.3 The analysis focuses in particular on explaining the divergence between 

emerging Asia and emerging Europe and 

attempts to answer the following questions:

• What components of the current and financial accounts have driven the recent trends 

in the various emerging regions? How have 

saving and investment evolved? How does 

this experience compare to previous episodes 

of growth spurts, including those of currently advanced economies when they were 

emerging?

• How can the different growth-current 

account configurations in emerging economies be explained? Do they reflect temporary 

economic shocks, macroeconomic policies, 

or structural factors? For instance, what are 

the roles of financial liberalization, barriers 

to access to foreign capital, and the exchange 

rate?

• Are the current large imbalances atypically 

persistent relative to previous spells of current account surpluses and deficits? How long 

will they be sustained? Do particular factors 

or policies (such as export growth or the 

exchange rate regime) contribute to whether 

they resolve smoothly or abruptly?

The chapter finds that much of the regional 

differences can be explained by structural 

factors, while also providing some support for 

the traditional view that high growth prospects 

attract foreign capital and lower the current 

account balance. In emerging Europe, the liberalization of the financial sector and the process 

of integration into the EU are the main drivers 

of the large current account deficits. In emerg3See various issues of the World Economic Outlook for 

complementary analysis of global imbalances (April 2005, 

September 2005, April 2006, April 2007, and October 

2007). 

ing Asia, structural factors also matter. Low net 

capital inflows are linked to the more limited 

openness of the capital accounts and financial 

sectors, to demographics (younger populations), and to differences in political structures. 

However, these factors only partially account for 

these economies’ surpluses. The residual current account surpluses are strongly associated 

with low exchange rates and large accumulations of reserves. However, it is difficult to establish whether these variables reflect deliberate 

policy action or other unidentified fundamental 

factors that both raised the current account and 

lowered the exchange rate since the Asian crisis 

in 1997–98.

The deficits in emerging Europe appear 

especially large and persistent relative to 

historical episodes, and the protracted surpluses in emerging Asia, such as those in 

China and Malaysia, are equally uncommon 

among emerging economies. Based on past 

experience, the very lengthy deficit episodes 

in emerging Europe can be partly explained 

by high growth prospects, highly open capital 

accounts, financial liberalization, and high 

initial net foreign asset positions. In general, 

however, the duration of these episodes is 

already reaching the upper end of expectations, 

raising questions about their sustainability. The 

chapter finds that the factors that may cause an 

abrupt end to these deficits include the region’s 

fixed exchange rate regimes and open capital 

accounts.

This chapter is organized as follows. The 

next section examines current account patterns 

in emerging economies by reviewing developments in the current account, financial account, 

and saving-investment balance. The following 

section uses empirical evidence to identify the 

main economic factors driving these current 

account imbalances, again focusing on emerging Europe and emerging Asia. The next section puts the duration of present imbalances in 

historical perspective and examines the determinants of the length of imbalance episodes. 

The concluding section offers some policy 

suggestions.
Recent Current Account Patterns in 

Emerging Economi

In the mid-1990s the main emerging regions 

all ran moderate current account deficits, but 

there is now an increasing divergence in current 

account balances across emerging regions (Figure 6.1). In particular, emerging Asia is accumulating large and increasing current account 

surpluses, on the order of 5 percent of GDP in 

2007, whereas emerging Europe is running large 

and growing current account deficits reaching 

on average 10 percent of GDP in 2007. Most 

other country groups (Latin America and a 

group consisting of other emerging economies) 

are experiencing moderate current account 

deficits or small surpluses. Oil exporters are also 

running large current account surpluses, but 

these are driven by the particular circumstances 

of countries that rely on a depletable resource 

and are analyzed separately in Box 6.1. Because 

their current account positions respond differently to economic determinants, and their 

saving and investment behavior is driven by 

different considerations (such as the size of 

reserves), these countries were omitted from the 

empirical analysis below.

Within emerging Asia the pattern is also heterogeneous, with some persistent large surpluses 

and a few substantial deficits. The aggregate 

surpluses for the region reflect different contributors at different times. In the aftermath of 

the Asian crisis, the crisis countries (Korea and 

the Asian Tigers) accumulated large surpluses 

following the loss of access to international 

capital flows and in an effort to rebuild reserves. 

More recently (starting around 2002–03), current account surpluses in several of the crisis 

countries have come down, with the marked 

exception of Malaysia, while China started accumulating large current account surpluses. China 

and Malaysia are the only two cases of persistent 

large surpluses (see below). By contrast, lowincome countries, such as India, Pakistan, Sri 

Lanka, and Vietnam, have mostly been running 

deficits, importing capital in accordance with 

theory. The three small NIEs (Hong Kong SAR, 

Figure 6.1. Patterns of Divergence in Current Account 

Balance 

(Percent of GDP; simple average)

Other Asia China

NIEs Asian Tigers

Emerging Asia2

Latin America

Emerging Europe1

The increasing divergence of current account imbalances in emerging economies 

is the result of a homogenous shift to longer deficits in emerging Europe and a 

more varied transition to surpluses in Asia following the Asian crisis, with initially

large improvements by the Asian Tigers and Korea and more recently large surpluses

in Malaysia and China.

1982 87 92 97 2002 07 -20

-15

-10

-5

0

5

10

15

20

1980 85 90 95 2000 05 -20

-15

-10

-5

0

5

10

15

20 By Region Emerging Asia

1994 97 2000 03 06 -20

-15

-10

-5

0

5

10

15

20

1982 87 92 97 2002 07 -20

-15

-10

-5

0

5

10

15

20 Selected Emerging Asian 

Countries

Emerging Europe

 Sources: IMF, Balance of Payments Statistics; and IMF staff calculations.

 Emerging Europe includes central Europe (Czech Republic, Hungary, Poland, Slovak 

Republic, and Slovenia), southeastern Europe (Albania, Bulgaria, Croatia, Macedonia, 

FYR, and Romania), and the Baltics (Estonia, Latvia, and Lithuania). 

 Emerging Asia includes newly industrialized Asian economies (NIEs—Hong Kong

SAR, Korea, Singapore, and Taiwan POC), Asian Tigers (Indonesia, Malaysia,

Philippines, and Thailand), China, and Other Asia (India, Pakistan, Sri Lanka, and Vietnam).

1

2

Korea Malaysia Central Europe

Indonesia India Southeastern Europe

China Vietnam Baltics

Recent Current Account Patterns in Emerging Ec
Chapter6  Divergence of Current Account Balances across Emerg

20

The current account surpluses of oil-exporting countries have widened significantly in the 

past few years, as oil prices soared. The average 

current account of oil exporters increased from 

less than 4 percent of GDP to more than 13 

percent between 2002 and 2007 (first figure). 

During the same period, the sum of the current accounts of those countries increased 

from less than $90 billion (0.3 percent of world 

GDP) to almost $500 billion (0.9 percent of 

world GDP). These surpluses are projected to 

increase further in 2008 as a result of the sharp 

increase in oil prices.1

This box explores the medium-run determinants of the current account balance for oil 

exporters and their differences and similarities 

to determinants in other countries. It draws 

on the so-called macroeconomic balance (MB) 

approach, which is based on the equilibrium 

relationship between current account balances 

and a set of fundamentals (measured, when 

The main authors of this box are Rudolfs Bems and 

Irineu de Carvalho Filho. 1The oil exporters are Algeria, Angola, Azerbaijan, 

Bahrain, Republic of Congo, Ecuador, Equatorial 

Guinea, Gabon, I.R. of Iran, Kazakhstan, Kuwait, 

Libya, Nigeria, Norway, Oman, Qatar, Russia, Saudi 

Arabia, Syrian Arab Republic, Turkmenistan, United 

Arab Emirates, Rep. Bolivariana de Venezuela, and 

Republic of Yemen.

relevant, as differences from trading partners’ 

averages). These fundamentals include variables such as the fiscal balance, demographics, 

the oil balance, and economic growth, which 

are all robust determinants of the current 

account balance (Lee and others, 2008). 

Before turning to the regression analysis, it 

is useful to highlight three macroeconomic 

dimensions along which oil exporters are substantially different from the rest of the world:

• Oil-exporting countries are exposed to 

wide fluctuations in their external accounts, 

because their exports, by definition, are 

relatively undiversified and oil prices fluctuate widely. Such volatility is directly reflected 

in the higher volatility of their terms of trade 

and current accounts as a percent of GDP 

(second figure). 

• The fiscal balance in oil-exporting countries 

is typically dominated by swings in fiscal 

revenues related to oil exports2 and is hence 

2Among other revenue sources, oil-related revenues 

include royalties on oil exploration, export taxes, oil 

companies’ corporate income taxes, and dividends of 

state-owned oil companies.

Box 6.1. Current Account Determinants for Oil-Exporting Countri

 Sources: IMF, Balance of Payments Statistics; and IMF staff 

calculations.

1970 75 80 85 90 95 2000 05

-10

-5

0

5

10

15

20

-0.6

-0.3

0.0

0.3

0.6

0.9

1.2

Current Account Behavior in Oil Exporters

Average current account

balance (percent of GDP, left axis)

Oil exporters' current account balance 

(percent of world GDP, right scale)

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

0.0

0.1

0.2

0.3

0.4

0.5

0.0

0.2

0.4

0.6

0.8

1.0

Average net oil exports (share of GDP)

 Source: IMF staff calculations.

 Goods and services terms of trade. 1

Oil Dependency and Volatility, 1970–2006

Standard deviation of terms-oftrade changes (right scale) 1

Standard deviation of

current account balances

Standard deviation of current account 

balance as a share of GDP

Current Account and Terms of Trade

Standard deviation of terms-oftrade changes1
2

very strongly correlated with the current 

account as well as being more volatile than 

for non-oil-exporters.

• Because oil revenues accrue from the sale of 

an exhaustible resource, transfers from one 

generation to another play an important 

role in ensuring intergenerational equity.3

To avoid sharp decreases in absorption once 

oil exports decline, countries aim to accumulate foreign assets and use income from 

such assets to offset the decreasing income 

from oil. Such transfers are more important 

for countries that expect to deplete their 

exhaustible resource endowment within a 

few decades.

To assess the current account determinants 

for exporters of exhaustible resources more 

formally, MB-type regressions are estimated 

building on the work presented in Lee and 

others (2008). Oil exporters are incorporated 

in the framework by allowing for (1) the 

non-oil fiscal balance as the relevant fiscal 

variable, in order to separate the effects of 

oil revenues and the non-oil fiscal balance 

on the current account; (2) a specific oil-balance coefficient for oil exporters, as well as for 

those exporters with more limited reserves, 

to capture intergenerational transfers and 

the delayed response of consumption and 

investment to changes in oil income; and (3) 

a specific lagged current account coefficient 

for oil exporters, to capture differences in 

persistence. The analysis also included tests 

for differences in the other coefficients.

There are two important caveats to the 

results. First, the quality of historical data 

for several oil exporters is problematic—in 

particular, the measurement of the non-oil fiscal balance is fraught with difficulties because 

the definition of the “oil sector” can differ 

across countries. Second, the non-oil sector in 

oil exporters may include oil-related activities 

(such as petrochemicals and fertilizers). This 

may imply a stronger link between the cur3See Bems and de Carvalho Filho (forthcoming), 

and Thomas, Kim, and Aslam (2008).

rent account and oil prices than pure oil sales 

would suggest and hence a higher positive 

coefficient on the oil balance in the current 

account regression.

Regression results from the extended MB 

framework are reported in the table.

4 The 

first column presents coefficients for a subset 

of developed and emerging market countries 

that excludes oil exporters, with the excep4The regression sample excludes Angola, Republic 

of Congo, Equatorial Guinea, Gabon, and Nigeria, 

based on average size and GDP per capita during the 

sample period.

Determinants of Current Account Balances in 

Oil-Exporting Countri

MB 

Sample

1970–2004

All 

Countries

1970–2004

All 

Countries

1970–2006

Old-age dependency –0.15*** –0.14* –0.15

Population growth –1.10** –0.98 –1.29**

Output growth –0.20** –0.19** –0.15**

Dummy for financial 

center 0.03*** 0.03*** 0.03***

 Non-oil fiscal balance/

GDP 0.20*** 0.20*** 0.21***

Non-oil fiscal balance/

GDP (oil exporters) 0.45** 0.50***

Relative income 0.02* 0.03** 0.02

Relative income, for 

oil exporters 0.08*** 0.08***

Volatility of terms of 

trade 0.01 0.07* 0.08*

Oil balance/GDP 0.20*** 0.28** 0.33***

Oil balance/GDP (oil 

exporters) 0.49*** 0.61***

Oil balance/GDP (oil 

exporters, limited 

reserves) 0.59*** 0.68***

Lagged oil balance/

GDP –0.11 –0.16

Lagged current 

account 0.37*** 0.38*** 0.42***

Lagged current 

account (oil 

exporters) 0.56*** 0.59***

Observations 359 430 483

R-squared 0.62 0.78 0.79

Source: IMF staff estimates.

Note: *, **, and *** denote significance at the 10 percent, 

5 percent, and 1 percent level, respectively.

Recent Current Account Patterns in Emerging Ec
Chapter6  Divergence of Current Account Balances across Emerg

2

tion of Norway, based on a sample spanning 

1970–2004, with each observation corresponding to a four-year average. The second and 

third columns present results for the entire 

sample of countries (with the third column 

adding 2005–06 as an additional observation). Estimated coefficients are, in general, 

statistically and economically significant and 

have expected signs and plausible magnitudes. 

Furthermore, the fit of the regression is very 

good, especially in light of the fact that fixed 

country effects are not included.

Focusing first on those variables that 

have similar effects on the current account 

balance in both groups of countries, the 

estimates imply that the effects of the dependency ratio (ratio of population above age 65 

to population between ages 30 and 64), population growth and per capita GDP growth are 

statistically and economically indistinguishable across oil exporters and importers. A 

higher dependency ratio reduces the current account balance, a 1 percentage point 

increase in the population growth rate relative to trading partners lowers the current 

account by about 0.7–1.0 percent of GDP, and 

a 1 percentage point increase in per capita 

GDP growth relative to trading partners lowers the current account by about 0.2 percent 

of GDP. 

As for the impact of other variables on the 

current account, there are statistically and 

economically significant differences between 

oil exporters and other countries:

• A 1 percentage point improvement in the 

(non-oil) fiscal balance leads to a 0.4–0.5 percentage point increase in the current account 

balance in percent of GDP for oil exporters, 

and to an increase of about 0.15 percentage 

point for other countries. This result is consistent with evidence that, in less financially 

developed countries, the relationship between 

fiscal balance and the current account balance is stronger.

• The current account balance responds 

more strongly to the oil balance in oil 

exporters than in oil importers. This result 

is consistent with the notion that, because 

oil is an exhaustible resource, the propensity 

to save out of an oil price windfall is higher. 

Also, oil typically plays a more central 

economic role in oil exporters than in oil 

importers—as a result, the same oil price 

shock implies a larger change in income 

for oil exporters. With adjustment costs to 

consumption and investment, the response 

of the current account to an oil price shock 

is likely to be larger for oil exporters, at least 

in the short run.

• Among oil exporters, the response of the 

current account to the oil balance is stronger 

in countries with lower oil and gas reserves 

(such as Algeria and Norway), consistent 

with the fact that their oil revenues are more 

temporary than for other exporters.

• An increase in relative income raises the 

current account balance significantly more 

in oil-exporting countries than in other 

countries—an oil-exporting country with 

income half the level in the United States 

will have, on average, a current account balance that is 3–4 percentage points of GDP 

smaller than that of a country with income 

equal to the U.S. level (the difference 

is 1⁄2–1 percentage point for other countries). A possible interpretation is that, in 

countries with volatile relative income and 

exhaustible resources, like oil exporters, a 

higher fraction of income would be saved in 

“good times” (and dissaved in “bad times”) 

because shocks to income are more likely to 

be temporary.

In conclusion, this preliminary evidence 

is broadly consistent with theoretical predictions. Oil-exporting countries are likely to 

have large external surpluses, particularly 

at times of peaks in production and high oil 

prices. This is consistent with the need to 

smooth consumption over time and between 

generations, in light of the exhaustibleresource nature of oil, as well as with the 

partly transitory nature of oil revenue booms 

and the presence of adjustment costs to consumption and investment.

Box 6.1 (concluded)
2

Singapore, and Taiwan POC) have been running very large current account surpluses, well 

above 10 percent of GDP, but they are very much 

special cases: all three have high income levels, 

and Singapore and Hong Kong SAR are financial centers, pointing to different determinants 

for their international capital flows. For the most 

part, these economies are omitted from the rest 

of the chapter.

In contrast to the Asian experience, the 

current account patterns in emerging Europe 

are more homogenous and include many large, 

persistent imbalances. Deficits are very large 

and growing in the Baltics and southeastern 

Europe, averaging 18 percent and 11 percent of 

GDP in 2007, respectively. The deficits in central Europe have stabilized at more moderate 

levels, around 5 percent of GDP on average.

Developments in the current account are 

mostly driven by the trade balance (Figure 6.2). 

In emerging Asia, the trade surplus accounts for 

most of the rise in the current account surplus, 

although an increase in net private transfers has 

added an extra percentage point to the current account balance since 1997 (mostly in the 

Philippines, Vietnam, and Pakistan). Similarly 

in emerging Europe, the trade deficit explains 

most of the increase in the current account 

deficit, with an additional 11⁄2 percentage points 

of deficit coming from a recent decline in net 

investment income. However, in recent years, 

the Czech Republic and Hungary have been 

running trade surpluses, with their current 

account deficits mostly driven by negative 

income balances.

Turning to financial flows, the large surpluses 

in emerging Asia have been associated with 

large outflows of non-foreign-direct-investment 

(non-FDI) capital and an unprecedented accumulation of reserves. Reserves have now reached 

39 percent of GDP and cover 9.2 months of 

imports. A by-product of the large accumulation of reserves in emerging Asia and the 

oil-producing countries has been the creation 

of large sovereign wealth funds (SWFs), which 

has potentially important implications for global 

capital flows and asset prices (Box 6.2). Emerg1994 96 98 2000 02 04 06 -12

-10

-8

-6

-4

-2

0

2

4

6

8

1994 96 98 2000 02 04 06

-12

-10

-8

-6

-4

-2

0

2

4

6

8

 Sources: IMF, Balance of Payments Statistics; and IMF staff calculations.

 Financial account transactions have been multiplied by –1. 

 Includes net portfolio investments and net other investments.

1

2

1982 87 92 97 2002 07 -12

-10

-8

-6

-4

-2

0

2

4

6

8

1982 87 92 97 2002 07

-12

-10

-8

-6

-4

-2

0

2

4

6

8

Emerging Asia

Figure 6.2. External Balances by Component 

(Percent of GDP; simple average)

Current account balance

Trade

Net income

Net transfers

Current account balance

Net foreign direct investment

Reserve accumulation

Current Account Balance Financial Account Balance

1

Net other investments2

Emerging Europe

Developments in the current account are mostly driven by the trade balance.

In emerging Asia the current transfer balance also improved after 1997, 

whereas in emerging Europe rising deficits were associated with a 

deterioration of the net income balance.

3

3

3 See footnotes 1 and 2 in Figure 6.1 for regional breakdowns.

Recent Current Account Patterns in Emerging Ec
Chapter6  Divergence of Current Account Balances across Emerg

2

This box discusses how large, persistent current account surpluses in several, mostly emerging, economies have resulted in sovereign wealth 

funds (SWFs) becoming key players in the global 

financial landscape (first figure). It also examines the possible impact of the growing role of 

SWFs on global capital flows, key asset prices, 

and financial markets more broadly.1

Although many SWFs have been around for 

many years, if not decades, there has been a 

sharp increase since 2000 in the number of SWFs 

and in the assets estimated to be under their 

management. The growing presence of SWFs is 

a result of sustained large current account surpluses in several Asian economies and oil-exporting countries. These surpluses—reflecting high 

commodity prices and favorable trade balances—

have translated into a rapid accumulation of 

foreign reserves by central banks. Reserves have 

reached a level that many countries have come 

to believe provides a sufficient cushion against 

financial or economic shocks. Although many of 

these countries still have enormous development 

needs, their absorptive capacity is limited. Therefore, quickly spending the oil- or export-related 

revenues may be inappropriate or unfeasible. 

Moreover, there is a growing sense that turning 

“resources in the ground” into financial assets 

is an important channel for transferring wealth 

across generations. 

As a result, many countries are seeking to 

enhance the returns on these large pools of 

funds. Rather than continuing to invest conservatively through sustained reserve accumulation, they are transferring these assets to SWFs 

with broader and more aggressive investment 

mandates. Estimates by market participants 

suggest that assets under management of SWFs 

range from $2 to $3 trillion—exceeding assets 

managed by hedge funds ($1.9 trillion)—and 

account for about one-fourth to one-third of 

foreign assets held by sovereigns. Although 

SWF assets remain small relative to total global 

The main authors of this box are Julie Kozack, 

Douglas Laxton, and Krishna Srinivasan. 1See Kozack, Laxton, and Srinivasan (forthcoming).

financial assets (about $190 trillion), they are 

large relative to mature market stock capitalization and the size of debt and capital markets 

in emerging economies. That said, part of 

SWFs’ portfolios is often invested in nonfinancial assets, such as real estate. SWF assets are 

projected to surpass the stock of global foreign 

exchange reserves in the not-so-distant future 

and to top $7 to $11 trillion by 2013. Thus it is 

clear that SWFs will play an increasingly prominent role in global finance. 

Against this background, a key concern is the 

impact of the growing presence of SWFs on the 

pattern of global capital flows, asset prices, and 

financial stability more generally. SWFs typically 

have medium- to long-term investment horizons, suggesting that they are less likely to make 

abrupt portfolio shifts that could affect market 

stability. Indeed, during the current financial 

market turmoil, SWFs have made large capital 

injections into systemically important financial 

institutions, suggesting that SWFs can play a 

stabilizing role in global financial markets. Yet 

Box 6.2. Sovereign Wealth Funds: Implications for Global Financial Marke

Number of SWFs established (right scale)

Current assets under management, billions of

U.S. dollars (left scale)

The Number of Sovereign Wealth Funds 

(SWFs) Has Grown Dramatically

0

200

400

600

800

1000

0

5

10

15

20

25

30

 Sources: Media and analyst reports; and IMF staff 

estimates.

 Current estimated assets under management for SWFs 

established in each time period.

1950s 1960s 1970s 1980s 1990s 2000–07

1

1
2

Recent Current Account Patterns in Emerging Ec

even a gradual shift toward greater portfolio 

diversification of reserve assets by sovereigns, 

including through SWFs, could have implications for the flow of funds between countries, 

the absolute and relative price of assets, and the 

evolution of global imbalances.2

Analyzing the potential impact of a diversification of sovereign reserves through SWFs 

is challenging because of the lack of reliable 

information for several large SWFs, notably concerning their asset allocations. To examine the 

possible implications of the growing presence 

of SWFs, illustrative scenarios of asset allocation 

were constructed for countries that are in the 

process of shifting away from holding reserves 

and toward diversifying their assets through 

SWFs.3,4 Two stylized, diversified portfolios—one 

replicating that of Norway’s Government Pension Fund (GPF-Global) and the other representative of well-established SWFs—are calibrated 

and compared with a stylized portfolio of 

2Foreign official investors are estimated to have kept 

10-year U.S. Treasury nominal yields 100 basis points 

lower than otherwise (Warnock and Warnock, 2006). 3The analysis assumes that countries that have 

recently established SWFs or have announced their 

intention to do so will channel a portion of their prospective foreign exchange inflows to their respective 

SWFs. Countries that have recently established or are 

in the process of establishing SWFs or SWF-type investment funds include Brazil, China, Korea, Russia, and 

Saudi Arabia; those that are considering the establishment of SWFs (according to market reports) include 

India, Japan, and Thailand. 4The new flows are calculated as the sum of each 

country’s current account balance and net private 

capital flows, based on World Economic Outlook projections for 2008–13. The analysis provides for a lower 

bound—which assumes that countries with recently 

established SWFs will invest 50 percent of newly available foreign currency inflows in their SWFs; and an 

upper bound—which assumes that in addition, countries that are considering establishing SWFs (based on 

market reports) invest 50 percent of newly available 

foreign currency inflows in their SWFs. The upper 

bound also assumes that 10 percent of the stock of 

existing reserves of the top 10 emerging economy 

reserve holders is shifted from reserves to SWF holdings during 2008–13. It is assumed that all new flows 

into SWFs are invested abroad.

foreign exchange reserve assets, with a view to 

assessing likely changes in the pattern of global 

capital flows and the impact on asset prices 

(second figure).5 To complement this scenario 

analysis, the exercise also estimates the impact 

of a modest shift away from dollar assets in 

the current stock of reserves for the 10 largest emerging economy reserve holders. A note 

of caution is warranted. As in many modeling 

exercises, the results are highly sensitive to 

the underlying assumptions. For instance, by 

assuming no portfolio shifts for long-established 

SWFs, the exercise provides only a partial picture of the possible magnitude of the impact on 

capital flows and asset prices arising from possible diversification strategies. Moreover, other 

sovereigns may choose to diversify their existing 

stock of reserve assets (and not just the top 10 

emerging market reserve holders as assumed in 

the exercise). Finally, while the two stylized portfolios aim to capture possible asset-allocation 

strategies, it must be recognized that in practice, 

SWFs are a diverse group with differing mandates, transparency, and governance structures. 

Even so, this limited exercise provides a sense 

of the direction and magnitude of the possible 

impact on markets.

The analysis suggests that the pattern of 

global capital flows would change significantly, 

with advanced economies facing lower capital 

inflows and emerging economies attracting 

substantially larger inflows (third figure). Relative to reserve assets, which are predominantly 

dollar-denominated and generally held in the 

form of U.S. Treasury bills or agency securities, 

the stylized SWF portfolios are more diversified 

5The stylized portfolio of a representative diversified SWF is based on market reports concerning 

asset allocation and currency composition. Currency 

Composition of Official Foreign Exchange Reserves 

(COFER) is an IMF database that records end-ofperiod quarterly data on the currency composition of 

official foreign exchange reserves. Aggregate COFER 

data are used to derive a stylized reserves portfolio, 

assuming that assets are allocated exclusively toward 

government bonds, according to the COFER currency 

composition.
Chapter6  Divergence of Current Account Balances across Emerg

2

Box 6.2 (concluded)

-160

-120

-80

-40

0

40

80

120

160

-160

-120

-80

-40

0

40

80

120

160

0 1 2 3 4 5 6 7 8 90

10

20

30

40

50

0 1 2 3 4 5 6 7 8 9 0

5

10

15

20

75

100

125

150

175

200

75

100

125

150

175

200

Simulation Results

2,3

U.S. dollar

Pound sterling

Other

Euro

Yen

2008 09 10 11 12 13 2008 09 10 11 12 13

 Source: IMF staff estimates.

 Based on the assumption that 50 percent of available foreign 

currency flows to countries listed in footnote 2 are placed with

the sovereign wealth fund (SWF) and invested in foreign assets.

 Includes Brazil, China, Korea, Russia (National Wealth Fund

only), and Saudi Arabia. 

 The lower bound of the range is based on the assumption 

described in footnote 1. The upper bound assumes that countries 

with prospective SWFs (based on media reports) also place 50

percent of available foreign exchange in SWFs to be invested

abroad. The upper bound also assumes that 10 percent of the 

stock of existing reserves of the top 10 emerging economy 

reserve holders is shifted from reserves to SWF holdings over

the period 2008–13.

 REER = real effective exchange rate.

1

2

3

4

2008 09 10 11 12 13 2008 09 10 11 12 13

Time from shock (years) Time from shock (years)

4

2,3

Outflows Generated

by a Norway-Like

Portfolio

Outflows Generated 

by a More

Diversified Portfolio

U.S. Real Interest

Rate (deviation from

baseline in basis 

points)

U.S. Dollar REER

(deviation from

baseline in percent)

Based on a 

Norway-Like Portfolio

Based on a More

Diversified Portfolio

Possible Change in the Currency Composition of

Capital Outflows from Selected SWF Countries

(billions of U.S. dollars)

1,2

Range of Possible Capital Outflows from U.S. Dollar 

Assets (billions of U.S. dollars)

Range of Possible Effects on U.S. Exchange and 

Interest Rates

COFER1

Norway2

Diversified Portfolio

Currency Composition of Stylized Portfolios

Other

U.S. dollar Euro

Pound sterling Yen

 Sources: COFER database; Norges Bank; and IMF staff 

estimates. 

 Aggregate data. COFER is an IMF database on the currency

composition of official foreign exchange reserves. 

 At present, Norway’s portfolio is invested in 47 percent 

equities and 53 percent bonds. 

 Stylized portfolio of a representative diversified SWF based on

market reports about their asset allocation and currency 

composition.

1

2

3

3
2

ing Asia remains a net importer of FDI, but net 

FDI inflows (in percent of GDP) are small compared with inflows to emerging economies in 

other regions, and they have not changed much 

since the beginning of the 1990s. In emerging 

Europe, the increasing current account deficits are covered to a large extent by net FDI, a 

relatively stable source of financing, although 

increases in deficits have outpaced net FDI during the past few years. Emerging Europe is also 

a large importer of non-FDI capital, including 

both bond-related and equity inflows. Overall 

reserves have accumulated at a rate of 2–3 percent of GDP a year.

Another way to understand changes in the 

current account balance is to look at developRecent Current Account Patterns in Emerging Ec

across both asset classes and currency exposure. 

This suggests reduced inflows into government 

bond markets, with attendant implications for 

interest rates. The shift away from reserve assets 

could have the most significant effect on markets in the United States, if countries diversify 

away from dollar holdings. 

• Estimates show that inflows into the United 

States could decline by 1⁄2–1 percent of U.S. 

GDP a year on average, depending on the number of countries in the sample and the assumption made regarding the currency composition 

of reserves for the 10 largest emerging economy 

reserve holders. The results also hinge on the 

asset-allocation strategy that is used to model 

investments by the prospective SWFs. 

• Portfolios that are more weighted to emerging economies—such as the stylized diversified portfolio—would result in lower flows 

into both dollar and euro assets, whereas 

flows to emerging economies would tend to 

increase substantially. By contrast, a portfolio 

similar to Norway’s SWF—which is heavily 

weighted toward investments in Europe—

would suggest somewhat lower investment in 

dollar assets and a less sizable, but still positive, inflow to emerging markets.

• To quantify the implications of the potential 

changes in the pattern of capital flows on 

interest rates and exchange rates relative to 

the baseline, simulations were undertaken 

using the IMF’s GIMF5 model.6 The results 

6Simulations were performed on a five-region version of the Global Integrated Monetary and Fiscal 

focus on the effects for the United States. 

They point to a 10–25 basis point increase 

in U.S. real interest rates and a 2–4 percent 

depreciation of the U.S. dollar in the long 

run. The model does predict a sharper 

depreciation of the dollar in the short run, 

of some 6–10 percent. The U.S. current 

account deficit could improve by 1⁄2–1 percentage point of U.S. GDP, a consequence 

of a higher country risk premium driven by 

lower demand for U.S. assets. In the rest of 

the world, higher capital inflows would lead 

to lower real interest rates (and thus a larger 

interest rate differential with the United 

States) and more appreciated currencies (in 

real effective terms), and domestic demand 

would be boosted. 

The model estimates do not suggest a disorderly depreciation of the U.S. dollar, nor a 

disorderly unwinding of global imbalances. In 

fact, they suggest that the effect of gradual portfolio shifts would be modest in the long run. 

However, the model estimates do not take into 

account possible second-round effects, as other 

investors react to the change in the behavior 

of SWFs. Overall, the results suggest that lower 

demand for U.S. assets would help lower the 

U.S. current account deficit and lower the value 

of the dollar.

Model (GIMF5). GIMF5 is an extended version of 

the Kumhof and Laxton model and includes separate 

models for the United States, euro area, Japan, emerging Asia, and “remaining countries.” See Kumhof and 

Laxton (2007).
Chapter6  Divergence of Current Account Balances across Emerg

2

ments in saving and investment (Figures 6.3 and 

6.4).4 In emerging Asia, the 1997–98 crisis led to 

a drastic drop in (mostly) private investment in 

Korea and the Asian Tigers. Saving also declined, 

especially public saving, but the decline was 

much smaller. In contrast, the rising surplus in 

China during the latter period (2003–06) was 

driven by a large rebound in private (mostly 

corporate) saving and a continued increase in 

public saving.5 Private and public investment 

also increased, although by smaller amounts. In 

emerging Europe, the current account deficits 

reflected a surge in private investment (mirroring a rise in FDI) and, to a lesser extent, 

in public investment, especially in the Baltics 

and southeastern Europe. Public saving also 

increased modestly in these countries (with the 

exception of central Europe), whereas private 

saving was relatively flat. As in emerging Asia, 

household dissaving has been offset by increased 

corporate saving, although in recent years, there 

has been some private dissaving on net.

Compared with other episodes of growth 

takeoffs, recent current account deficits in 

emerging Europe are quite large (Figure 6.5).6

Economies that experienced a growth takeoff at 

some point during the past 35 years had current 

account deficits of about 3 percent of GDP on 

average during the first eight years following the 

growth takeoff, compared with deficits averaging 

6–7 percent of GDP in emerging Europe over 

the equivalent period. A similar pattern, albeit 

with a smaller difference, was also observed during the growth takeoffs of a number of countries 

4The current account balance is the difference between 

national savings and gross investment. 5An argument that has been advanced to explain 

China’s surplus is a high household saving rate, reflecting 

the lack of social safety nets or habit-based consumption. 

However, the recent rise in China’s current account was 

associated with an increase in the corporate saving rate 

and not the household saving rate. Aziz and Cui (2007) 

argue that a declining labor income share—rather than 

an increasing household saving rate—has been the main 

factor behind the declining consumption share of GDP 

in China. 6Appendix 6.1 presents in more detail the criteria used 

to identify growth takeoffs and the countries and years 

during which these occurred.

Figure 6.3. Current Account Balance, Saving, and 

Investment

(Percent of GDP; simple average)

In Asia saving and investment declined after 1997, the latter abruptly in the Tigers. 

Although investment remained below pre-crisis levels, saving and investment were

driven up recently by increases in China, India, and others. In contrast, investment

grew rapidly in emerging Europe, especially in the lower-income countries, and

was coupled with modest gains in saving.

1

Baltics -15

-10

-5

0

5

10

15

-15

-10

-5

0

5

10

15

1990 95 2000 05

10

15

20

25

30

35

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

1990 95 2000 05

10

15

20

25

30

35

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

-15

-10

-5

0

5

10

15

Change in Current Account Balance, Saving, and Investment:

1999–2002 versus 1994–96

Current account balance 

(right scale)

Saving Investment

 Sources: CEIC Data Company Limited; UN National Account Statistics; and IMF staff 

calculations.

 See footnotes 1 and 2 in Figure 6.1 for regional breakdowns. 1

Emerging Asia Emerging Europe

Current account balance Saving Investment

NIEs China India,

others

Emerging

Europe

Central Southeastern

Emerging Asia Emerging Europe

2003–07 versus 1999–2002

Emerging 

Asia

NIEs Asian 

Tigers

China India,

others

-15

-10

-5

0

5

10

15

Central Baltics

Emerging Asia Emerging Europe 

Emerging

Europe

Southeastern

Emerging 

Asia

Asian 

Tigers
2

in emerging Asia, with the exception of China.7

In general, across growth takeoffs, the deepening of the current account deficit was associated 

with a surge in investment and a small offsetting 

increase in saving. By comparison, the takeoff in 

emerging Europe brought a larger acceleration 

in investment, an experience matched previously only by the takeoffs in the Asian Tigers 

during the early 1970s. Where emerging Europe 

stands out from the typical growth takeoff is with 

respect to the larger net inflows of FDI and the 

longer duration of deficit episodes.

Interestingly, the growth takeoffs identified 

here include a number of western European 

countries with earlier EU entry. These also experienced substantial current account deficits and 

net inflows of FDI, but on a much smaller scale 

than emerging Europe. This could partly reflect 

the fact that their capital accounts were only 

fully opened in the early 1990s, in most cases 

after their growth takeoffs.

The current account reversals in emerging 

Asia during the 1997–98 crisis also stand out 

relative to experiences in other crisis episodes. 

Compared with currency and banking crises that 

occurred since 1980, emerging Asian economies 

started from bigger deficits on average, and the 

adjustments in their current accounts and investment levels were much larger and much more 

abrupt (Figure 6.6).8 Part of these large reversals 

were subsequently undone. However, five years 

after the crisis, surpluses remained higher than 

in the aftermath of other crisis episodes.

One common characteristic of both emerging Asian and emerging European economies 

is their high growth rates. Figure 6.7 suggests, 

consistent with theory, that among high-growth 

countries (that is, with growth in per capita 

GDP above 2 percent a year), those countries 

with higher growth rates tend to have lower 

current account balances. This negative correlation holds true across all economies, but also 

7See also Chapter 2 of the April 2004 World Economic 

Outlook. 8Dates for the start of currency and banking crises are 

from Laeven and Valencia (forthcoming).

Recent Current Account Patterns in Emerging Ec

Baltics

-15

-10

-5

0

5

10

15

-15

-10

-5

0

5

10

15

Change in Public and Private Saving and Investment:

1999–2002 versus 1994–96

Figure 6.4. Saving and Investment by Components

(Percent of GDP; simple average)

Public saving Private saving

Emerging

Asia

NIEs Asian 

Tigers

China India, 

others

Emerging

Europe

Central Southeastern

Emerging Asia Emerging Europe

Baltics

1990 95 2000 05

0

5

10

15

20

25

1990 95 2000 05

0

5

10

15

20

25

Corporate Saving

1990 95 2000 05

-20

-10

0

10

20

30

1990 95 2000 05

-20

-10

0

10

20

30

Household Saving

Korea

Thailand

China

Philippines Czech Rep.

Slovak Rep.

Poland

Emerging Asia Emerging Europe

Romania

 Sources: CEIC Data Company Limited; IMF, Balance of Payments Statistics; UN

National Account Statistics; and IMF staff calculations.

 See footnotes 1 and 2 in Figure 6.1 for regional breakdowns.

 Indonesia, Poland, and Slovenia are excluded from group averages because of

missing data. As a result, data in Figure 6.4 do not add up to the levels in Figure 6.3.

 Private investment includes changes in inventories.

1

India Bulgaria

Public investment Private investment

Baltics -15

-10

-5

0

5

10

15

-15

-10

-5

0

5

10

15

2003–07 versus 1999–2002

NIEs Asian 

Tigers

China India,

others

Central

Emerging Asia Emerging Europe

2

2

2

Components of Private Saving Rates: 1990–2007

3

3

The emergence of a large current account surplus in China in recent years is the 

result of a sharp increase in corporate saving. Household saving declined in both 

regions throughout the past decade and is especially low in emerging Europe. 

1

Emerging

Europe

Southeastern

Emerging

Asia
Chapter6  Divergence of Current Account Balances across Emerg

2

within each emerging region (China is again a 

clear exception).9 The available evidence about 

current account developments in the advanced 

economies when they were emerging in the late 

19th and early 20th centuries indicates that capital flowed to high-growth countries (Box 6.3).

What Factors Have Contributed to Recen

Current Account Patterns?

The current account balances of emerging 

economies are affected by multiple factors.10

This section looks closely at cross-country data 

relating the level of the current account balance 

to a broad set of variables that may be important 

in determining the current account balances 

of emerging Europe and emerging Asia. The 

empirical analysis first attempts to explain the 

current account developments solely based on 

standard factors that have been highlighted 

in the literature as important determinants of 

current account balances. These determinants 

include the government balance, youth and 

old-age dependency ratios, the net foreign asset 

position, and growth opportunities proxied by 

the initial income level and lagged growth.11

9This finding holds also if all emerging economies are 

included rather than only high-growth emerging economies. Excluding China, capital was flowing in aggregate 

to emerging economies. This evidence contrasts with the 

recent literature, which has found a positive correlation between growth and the current account (see, for 

instance, Prasad, Rajan, and Subramanian, 2007; and 

Gourinchas and Jeanne, 2007). One possible explanation 

is that many of these studies do not include countries of 

emerging Europe and include a large number of African 

countries, for which most capital inflows are official aid 

inflows and not private capital inflows driven by market 

considerations. Recent research suggests that aid inflows 

can have an ambiguous or even negative impact on 

growth by raising the exchange rate and curbing growth 

prospects for the tradables sector (see, for example, 

Rajan and Subramanian, 2005). 10See for instance, Aristovnik (2006), Chinn and Ito 

(2006), Gruber and Kamin (2007, 2008), and Herrmann 

and Jochem (2005). 11See Lee and others (2008). Growth opportunities are 

expected to lower the current account through higher 

investment and lower saving. Similarly, high dependency 

ratios will lower the current account by lowering saving. 

In contrast, a government surplus will raise the current 

-12

-8

-4

0

4

8

12

-12

-8

-4

0

4

8

12

10

15

20

25

30

35

10

15

20

25

30

35

-12

-8

-4

0

4

8

12

-12

-8

-4

0

4

8

12 Real GDP per Capita Growth Current Account

Saving Investment

Net FDI Other Investment

Figure 6.5. Growth Takeoffs 

(Percent of GDP, simple average; years before and after crisis on x-axis )

The growth takeoff in emerging Europe since 1995 was associated with larger current 

account deficits and significantly higher net FDI inflows relative to comparable 

growth takeoffs in other countries.

Earlier EU entrants 2 Emerging Europe1

Growth takeoffs 3 Asian Tigers4

 Sources: IMF, Balance of Payments Statistics; and IMF staff calculations.

 The takeoff year for emerging Europe is assumed to be 1995. 

 For earlier EU entrants the takeoff years are as follows: Greece (1996), Ireland (1985), 

Portugal (1985), and Spain (1984).

 Growth takeoff is defined as the onset of a growth acceleration characterized by an

increase in the real per capita growth rate of at least 2 percent and an average growth rate

of at least 3.5 percent sustained over an eight-year horizon based on Hausmann, Pritchett,

and Rodrik (2005). The figure shows the median value for each variable across all growth

accelerations (excluding those in emerging Europe).

 Asian Tigers comprise Indonesia, Malaysia, Philippines, and Thailand, for which the 

takeoff year is 1973.

1

2

3

4

t 3 t t t + 3 + 6 t + 9 t + 12 t 3 t t t + 3 + 6 t + 9 t + 12

t 3 t t t + 3 + 6 t + 9 t + 12 t 3 t t t + 3 + 6 t + 9 t + 12

t 3 t t t + 3 + 6 t + 9 t + 12 t – 3 t t t + 3 + 6 t + 9 t + 12

–

– –

–

–
These variables explain a large share of current 

account patterns worldwide, but they are not 

able to account for the large surpluses in emerging Asia and the large deficits in emerging 

Europe. The empirical analysis then augments 

these standard factors with a set of additional 

variables that characterize financial sector developments that may have played a key role driving 

current account patterns during recent years.

Over the past 10 years, economies in emerging Europe have very rapidly liberalized their 

domestic financial systems and opened up their 

capital accounts (Figure 6.8).12 The combination of these two liberalizations was reflected in 

a surge in the number of foreign banks in these 

countries.13 Although emerging Asia also made 

some progress toward domestic financial liberalization, the financial systems of these economies 

remain much less liberal, with the exception 

of the NIEs. During the Asian crisis, the Asian 

Tigers and the NIEs also introduced restrictions on capital account transactions. More than 

10 years later, capital accounts remain generally 

account if it is not fully offset by a decrease in private 

saving and/or a rise in private investment. Finally, higher 

net foreign assets are expected to raise the current 

account by increasing net investment income. The analysis also includes a dummy variable for financial centers 

as these typically export capital, the oil balance, and time 

effects to capture developments that affect similarly all 

countries in a given time period. 12This chapter uses an index of domestic financial 

liberalization that combines information on interest rate 

controls, credit controls, competition restrictions, state 

ownership, quality of the banking supervision and regulation, policies to encourage the development of bond and 

equity markets, and policies to permit access by foreigners to the domestic stock market (Abiad, Detragiache, 

and Tressel, forthcoming). The capital account openness 

index is from Chinn and Ito (2006). These two indices 

are highly correlated, in part because domestic financial 

liberalization includes a measure of entry barriers to foreign investors. The significance of these variables is thus 

tested jointly in the regressions. 13Another reason for the increase in foreign bank 

ownership is comparatively better growth opportunities 

for parent banks, which face tighter income conditions in 

their home markets. Ayden (forthcoming) finds that tight 

spreads—the difference between lending and deposit 

rates—for parent banks in their home markets are associated with an increase of lending by their subsidiaries 

operating in central and eastern Europe.

What Factors Have Contributed to Recent Current Account Pat

-6

-4

-2

0

2

4

6

8

10

12

-6

-4

-2

0

2

4

6

8

10

12

15

20

25

30

35

40

15

20

25

30

35

40

Figure 6.6. Current Account Reversals around Crises

(Percent of GDP, simple average; years before and after crisis on x-axis)

Asian crisis

Other emerging economies with currency crises

Other emerging economies with joint currency and banking crises

Current Account Trade Balance

Gross Saving Gross Investment

1

 Sources: IMF, Balance of Payments Statistics; and IMF staff calculations.

 Asian crisis countries include Indonesia, Korea, Malaysia, Philippines, and 

Thailand. Other emerging economies with crises comprise Argentina, Brazil, Mexico, 

Russia, and Turkey, for which 15 crisis episodes were identified based on Laeven and 

Valencia (forthcoming) over the period 1980–2007.

1

The adjustment of the current account during the Asian crisis was more abrupt

compared with other crisis episodes. Five years after the crisis, a larger surplus 

remained in Asia than elsewhere.

t – 3 t t + 3 t + 5 t – 3 t t + 3 t + 5

t – 3 t t + 3 t + 5 t – 3 t t + 3 t + 5
Chapter6  Divergence of Current Account Balances across Emerg

very closed in these economies, with the exception of the NIEs.

Theory does not provide clear guidance on 

the sign of the net effect of financial sector 

liberalization and capital account openness 

on the current account. A more open capital 

account and a more developed financial system 

are likely to improve access to foreign capital 

for financing domestic investment, thereby 

lowering the current account.14 However, a 

more liberalized domestic financial system with 

greater intermediation opportunities may also 

encourage domestic saving, with an opposite 

effect on the current account. On the other 

hand, domestic financial liberalization can also 

imply better access to credit and new financial 

products, which tends to reduce both domestic 

saving and the current account. Hence, the net 

effect of financial sector liberalization and capital account openness on the current account is 

uncertain and remains an empirical question.

Another financial factor that may affect the 

current account is the financial depth of the 

economy, measured by the share of credit to 

the private sector and stock market capitalization in GDP.15 Greater financial depth could be 

a sign of a developed financial system, which 

would raise the current account if it stimulated 

domestic saving but could lower the current 

account if it attracts more foreign savings and 

thereby fuels domestic investment. Financial 

depth appears much greater in emerging Asia 

than in emerging Europe, although it has been 

increasing in both regions (with the exception 

of the Asian Tigers).

A factor that has received a lot of attention 

in the context of the Asian current account 

surpluses is exchange rate policy and preferences for accumulating reserves. However, it 

is difficult to find an exogenous measure of 

these policies, because the exchange rate and 

14In case of a crisis or if the country is not well managed, a more open capital account could also be associated with more capital outflows. 15See Chinn and Prasad (2003), Gruber and Kamin 

(2007, 2008), and Chinn and Ito (2006) for analyses that 

include this measure (and capital account openness).

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

-20

-15

-10

-5

0

5

10

15

20

25

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

-20

-15

-10

-5

0

5

10

15

20

25

Current acount balance (percent of GDP)

Figure 6.7. Current Account Balance and Real GDP per

Capita Growth1

 Sources: IMF, Balance of Payments Statistics; and IMF staff calculations.

 Countries with less than 2 percent real GDP per capita growth are not shown.

 

1

Fast-growing emerging economies tend to have lower current account balances.

Real GDP per capita growth

China

Vietnam

India

Singapore

Malaysia

Hong Kong SAR

Taiwan POC

Philippines

Pakistan

Sri Lanka

Indonesia

Thailand

Korea

Latvia

Estonia

Lithuania

Bulgaria

Hungary

Slovenia

Poland

Czech Republic

Croatia Albania

Romania

Slovak 

Republic

Emerging Asia

Emerging Europe

Latin America

Emerging Economies, Average Level 2001–07
reserves are simultaneously determined with the 

current account balance. Hence, these factors 

are not part of this formal analysis, although 

some evidence is provided about their potential 

role in determining the size of emerging Asia’s 

surpluses. Finally, the exchange rate regime 

itself (fixed versus flexible) could also affect the 

current account balance, with fixed exchange 

rate regimes potentially leading to (temporarily) larger imbalances in response to economic 

shocks. However, the direction of the effect is 

unclear, depending on the nature of the initial 

shock to the current account balance.

Empirical Analysi

The empirical analysis focuses on determinants of the medium-run current account 

balance (averaging data over four-year periods) 

and covers a panel of 58 (non-oil-exporting) 

advanced and emerging economies during 

1983–2006, including emerging Europe for the 

subperiod 1995–2006 (for data quality reasons; 

see Appendix 6.2 for more details).16 It starts 

by estimating a standard model of the current 

account and then augments it with a set of 

financial variables and a measure of political 

structure.17 Finally, special factors that have 

affected emerging Europe are introduced to 

reflect their specific circumstances.18

16The panel is unbalanced as the variables were not 

always available for all subperiods for all countries. 17The political structure index is the “Polity2” variable 

from the Polity IV Project (Marshall, Jaggers, and Gurr, 

2004). It covers a number of dimensions, including the 

presence of institutions and procedures through which 

citizens can express effective preferences about alternative 

policies and leaders and the existence of institutionalized 

constraints on the exercise of power by the executive. 18First, since the collapse of the Council for Mutual 

Economic Assistance (COMECON), most of these countries embarked on a process of EU integration involving 

greater macroeconomic stability and improved policies. 

Hence, progress toward EU integration may have given 

these countries privileged access to foreign capital. 

Second, investment needs in emerging Europe may have 

been especially large as the collapse of the COMECON 

led to a substantial depreciation of capital stocks while 

the labor force is well educated.

What Factors Have Contributed to Recent Current Account Pat

Figure 6.8. Patterns of Financial Development

In emerging Europe domestic financial market liberalization proceeded faster than in

emerging Asia (except for the newly industrialized Asian economies (NIEs), which 

were already at a more advanced stage). The opening up of capital accounts was 

associated with a rapid influx of foreign banks.

Emerging Asia Emerging Europe

NIEs

Asian Tigers and China

Other Asia

Central Europe

Southeastern Europe

Baltics

3

2

1995 97 99 2001 03 05

0.2

0.4

0.6

0.8

1.0

1995 97 99 2001 03 05 0.2

0.4

0.6

0.8

1.0 Financial Liberalization Financial Liberalization4

1998 2000 02 04 06

-2

-1

0

1

2

3

1998 2000 02 04 06 -2

-1

0

1

2

Capital Account Openness Capital Account Openness 3

1995 97 99 2001 03 05

0

10

20

30

40

50

60

70

80

1995 97 99 2001 03 05 0

10

20

30

40

50

60

70

80 Number of Foreign Banks

(percent of total banks)

Number of Foreign Banks

(percent of total banks)

5

1995 97 99 2001 03 05 0.0

0.5

1.0

1.5

2.0

2.5

3.0

1995 97 99 2001 03 05

0.0

0.5

1.0

1.5

2.0

2.5

3.0 Financial Depth Financial Depth 6 7

 Sources: Abiad, Detragiache, and Tressel (forthcoming); Beck, Demirgüç-Kunt, and 

Levine (2000, updated); Chinn and Ito (2006, updated); Claessens and others (2008);

and IMF staff calculations.

 See Appendix 6.1 for a definition of variables. See footnotes 1 and 2 in Figure 6.1 for 

 Excludes Taiwan POC.

 Excludes Macedonia, FYR.

 Excludes Croatia, Slovak Republic, and Slovenia.

 Excludes Pakistan and Sri Lanka.

 Excludes Vietnam.

 Excludes Albania.

1

2

3

4

5

6

7

1

regional breakdowns.
Chapter6  Divergence of Current Account Balances across Emerg

Current Accounts and Capital Flows: Sources, Size, 

and Persistence

The global economy experienced a golden 

age of integration from the middle of the 19th 

century until World War I. Numerous factors 

underpinned the changes: better communications due to the diffusion of the telegraph and 

the railroad, massive declines in shipping costs, 

unparalleled mass migrations, the spread of the 

gold standard, the consolidation of the British Empire, and increasing sophistication of 

London’s financial markets. The largest supplier 

of funds was Great Britain, which accounted for 

well over 50 percent of all capital outflows from 

the surplus countries.1 Other capital exporters 

were France, Germany, and the Netherlands. 

Controls on inflows had yet to be established. 

And investors were largely left alone to decide 

where to send their capital, although in some 

cases political aims in the surplus countries 

determined the direction of capital flows. The 

panels of the figure show the current account 

for surplus countries (or gross capital outflows) 

between 1870 and 1913 and for the principal 

capital importers: Argentina, Australia, Canada, 

and the United States (see Stone, 1999).

Capital inflows were often very persistent. Many 

of the important capital-importing countries 

sustained current account deficits for a decade or 

longer. Other countries that were on more fragile 

financial footing experienced more short-lived 

deficits. Meissner and Taylor (2006) estimate that 

extensive capital importers, such as Argentina, 

Australia, Canada, and the United States, sustained 

deficits for long periods, with half-lives for current 

account deficits of about three years compared 

with half-lives of roughly three-fourths of a year in 

The main author of this box is Christopher M. 

Meissner. 1See Obstfeld and Taylor (2004) for a long-run 

overview of capital markets; O’Rourke and Williamson 

(1999) for a historical examination of the first period 

of globalization; López Córdova and Meissner (2003) 

on the gold standard and trade, and Mitchener and 

Weidenmier (forthcoming) on the British Empire and 

trade.

Box 6.3. Historical Perspective on Growth and the Current Account

Determinants of British Capital Exports

Capital Flows and Motivations for Capital 

Exports, 1865–1913

 Sources: Clemens and Williamson (2004); and Bordo, Cavallo,

and Meissner (2007).

 Includes Argentina, Australia, Austria, Brazil, Canada, Chile,

Denmark, Egypt, Finland, Greece, Italy, India, Japan,

Netherlands, New Zealand, Norway, Portugal, Russia, South 

Africa, Spain, Sweden, the United States, and Uruguay.

 Difference from mean.

1

2

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2

-4

-2

0

2

4

6

8

Ratio of primary commodities to exports2

Ratio of British capital inflows to

GDP in receiving country2

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

-4

-2

0

2

4

6

8

Population growth rate2

1880 85 90 95 1900 05 10 -25

-20

-15

-10

-5

0

5

10

15

20

Current Account Balance of Main Capital Importers 25

(percent of GDP)

United States

Canada Australia

Argentina

1880 85 90 95 1900 05 10 -25

-20

-15

-10

-5

0

5

10

15

20

25

Current Account Balance of Main Capital Exporters

(percent of GDP) United Kingdom

France

Germany Netherlands

1

Ratio of British capital inflows to

GDP in receiving country2
What Factors Have Contributed to Recent Current Account Pat

smaller recipient countries (such as Chile, Finland, 

Japan, and Uruguay).

Determinants of Capital Flows

Argentina, Australia, Canada, and the United 

States were the main recipients of British capital 

flows. Capital from France and Germany went 

primarily to Russia, Turkey, and other European countries. Recent research by Clemens 

and Williamson (2004) on the motivations for 

British capital outflows finds that long-term 

growth prospects mattered most to investors. 

Capital was most likely to flow toward areas with 

high population growth rates and high rates of 

net immigration, areas that focused on exports 

of commodities based on significant natural 

resource endowments, and where the population was better educated (see bottom figure 

panels). Imperial relations, default history, and 

monetary stability were additional factors that 

accounted for a small fraction of the observed 

inflows. Similar economic motivations also 

played a dominant role for other capital exporters, such as Germany (Esteves, 2008).2

In the major recipients such as Canada, 

Argentina, and Australia, inflows supplemented 

low rates of domestic saving. Investment was 

predominantly directed toward key infrastructure projects (railroads, harbors, municipal 

services) and helped raise productive capacity. Countries with smaller inflows tended to 

use foreign capital for consumption purposes 

and to supplement or smooth low government 

revenues.3 Many of these countries also had 

2This finding challenges a long-held conviction that 

French and German capital flows were significantly 

determined by the political exigencies of Paris or 

Berlin. 3Investors in the first wave of globalization used 

many public sources to gain information about the 

quality of their investments. The Fenn on the Funds

investors’ manual provided short excerpts from past 

bond prospectuses for each and every sovereign 

borrower on the London market. Examples of such 

excerpts from countries that borrowed to plug revenue gaps or to fund costly wars included Russia (an 

issue to strengthen the special reserve fund), Japan 

(to pay charges on pensions), Egypt (Pasha loan for 

considerable amounts of bond issues dedicated 

to unspecified purposes.

Sustainability of Capital Flows and Financial Crises

Some of these large capital inflows ended 

abruptly with financial crises that temporarily brought growth below long-run trend rates 

(Catão, 2007). Bordo, Cavallo, and Meissner 

(2007) show that sudden stops or turnarounds 

in capital flows are associated with previously 

high levels of capital inflows and foreign currency exposure. By contrast, strong reserve positions, high export growth, and close political 

ties with the lender lower the likelihood of a 

sudden stop in capital inflows in any given year. 

In particular, larger borrowers with financial 

credibility or ties to the British Empire (such 

as Canada) were able to sustain capital inflows 

even at times of low international liquidity. 

Experiences after a crisis differ significantly, 

but were more severe in less open economies 

and in countries with underdeveloped financial 

sectors. The experiences of Argentina and Australia in the early 1890s exemplify this. Argentina had a major banking, currency, and debt 

crisis in 1890 known as the Baring crisis.4 Default 

settlement was not concluded for several years, 

and a weak financial system and low credibility 

with international investors suppressed foreign 

investment for another decade. Around the 

same time, Australia also had a major banking 

crisis that lasted for several years.5 Nevertheless, 

the component colonies never defaulted on 

their external obligations, and their credibility as 

borrowers helped them avoid a currency crisis.6

repayment of existing debt), and Austria (an issue in 

1851 to improve the value of the paper florin). 4The crisis started because of overly optimistic 

investment by the Baring Brothers Bank based in 

London, but it also witnessed an early credit boom 

generated by a small and poorly regulated domestic 

banking sector. It ended with a major banking crisis, a 

currency crash, and a debt default. 5The crisis in Australia was triggered by a drought, 

coupled with an earlier credit boom. 6Australia did not issue its own currency at that 

time, but private bank notes were allowed to become 
Chapter6  Divergence of Current Account Balances across Emerg

The standard model fits the data well overall 

but explains only a small part of the pattern 

of current account balances in emerging Asia 

and emerging Europe (Table 6.1, column a).19

In emerging Asia for the subperiod 2003–06, it 

would predict a current account balance below 

the sample average by 1.4 percentage points of 

GDP, whereas the current account balance was 

actually above the sample average by 3.3 percentage points.20 Similarly, in emerging Europe, 

the model would predict a current account balance only moderately below the sample average, 

by 1.8 percentage points of GDP, whereas the 

actual current account balance over the subperiod 2003–06 was 7.4 percentage points of GDP 

below the sample average.

The preferred model, including the financial 

factors and special effects for emerging Europe, 

has a much better fit, especially for emerging 

Europe.21 Based on the preferred model (shown 

in column e of Table 6.1), the main contributing 

factors to the large deficits in emerging Europe 

have been the financial variables, accounting 

19The dummy variables for post-crisis emerging Asia and 

emerging Europe remain large and highly significant. 20This calculation is based on the final model reported 

in column e of Table 6.1 and sums the contributions of 

the standard structural factors. 21There remains a large and statistically significant 

dummy variable for emerging Asia in the aftermath of 

the 1997–98 crisis.

for 4.6 percentage points (about 60 percent) 

of the 7.4 percentage point deficit (deviation 

from sample average) (Figure 6.9). Among these 

variables, domestic financial liberalization is the 

factor with the largest impact by far.22 Growth 

opportunities—defined as the scope for convergence through a low initial per capita income 

level and a high recent growth performance—

contributed a further percentage point to the 

deficit. Other minor factors included low net 

foreign assets, the fiscal balance, and a negative 

oil balance. After allowing for special European 

effects (described below), the unexplained 

residual for the region as a whole is less than 

half a percentage point.

In emerging Asia, structural factors are found 

to have helped raise the current account, but 

the impact is offset by other factors (in particular high growth opportunities). Thus, about 

75 percent of the current account surplus 

remains unexplained. Structural factors that 

have contributed to the current account surplus 

include the lack of financial liberalization, 

younger populations, and lower values for the 

22The effect of domestic financial liberalization also 

captures the removal of entry barriers to foreign capital. 

The high openness of the capital account also lowers 

the current account, as does the relatively low level of 

financial development in emerging Europe (presumably 

by depressing saving). However, the magnitudes of these 

two other effects are very small.

Although the Australian economy recovered 

only slowly, a rise in domestic saving was able to 

repay previous debts and stimulate investment. 

Conclusions

The period between 1870 and 1913 witnessed 

historically unprecedented levels of international capital flows. These flows were often 

legal tender during the crisis. 

long-lasting and financed key infrastructure 

projects in many large and credible borrowing 

countries. They were for the most part driven 

by the desire of investors in industrial countries 

to invest in fast-growing countries with strong 

growth prospects, and there were no examples 

of capital flowing uphill. There were several 

episodes of disastrous financial crises, in the 

wake of sudden stops of capital, especially when 

financial development was weak and countries 

were less open to trade.

Box 6.3 (concluded)
What Factors Have Contributed to Recent Current Account Pat

Table 6.1. Determinants of the Current Account Balance

(Percent of GDP)

Standard Model

Standard Plus 

Financial Factors

Standard Plus Financial Factors and 

Emerging Europe Factors

(a) (b) (c) (d) (e)

Standard variable

Net foreign assets (percent of GDP, lagged) 0.040 0.035 0.036 0.035 0.035

(5.29)*** (4.37)*** (4.47)*** (4.32)*** (4.45)***

General government balance (percent of GDP) 0.055 0.07 0.108 0.115 0.118

(0.87) (1.08) (1.59) (1.66)* (1.77)*

Oil balance 0.247 0.226 0.229 0.232 0.231

(3.17)*** (3.07)*** (3.11)*** (3.13)*** (3.16)***

Old-age dependency ratio –0.234 –0.178 –0.143 –0.136 –0.134

(–3.04)*** (–2.27)** (–1.80)* (–1.69)* (–1.68)*

Population growth –0.755 –0.755 –0.727 –0.682 –0.681

(–1.77)* (–1.88)* (–1.80)* (–1.65) (–1.69)*

Growth opportunities: 

Relative income per capita (lagged) 5.162 6.693 5.679 5.622 5.582

(3.33)*** (3.69)*** (3.06)*** (3.00)*** (3.03)***

Growth of GDP per capita (lagged) –0.135 –0.181 –0.173 –0.162 –0.167

(–1.89)* (–2.64)** (–2.58)** (–2.25)** (–2.59)**

Financial factors and political structure

Financial depth (percent of GDP, lagged) 0.839 0.795 0.804 0.820

(1.66)* (1.58) (1.59) (1.64)

Financial liberalization –3.034 –2.699 –2.719 –2.743

(–1.85)* (–1.64) (–1.65) (–1.68)*

Capital account openness –0.278 –0.239 –0.233 –0.229

(–1.49) (–1.24) (–1.20) (–1.25)

Joint significance of financial variables (p-value) 0.01** 0.04** 0.04** 0.03**

Political structure –0.140 –0.145 –0.145 –0.146

(–3.45)*** (–3.55)*** (–3.50)*** (–3.54)***

Emerging Europe facto

General government balance interacted with 

emerging Europe dummy variable

 –0.642 –0.108

(–4.58)*** (–0.28)

Financial liberalization interacted with emerging 

Europe dummy variable

 –4.739 –3.883 –4.484

(–0.82)2 (–1.54) (–4.47)***

General government balance interacted with EU 

integration

 –1.123 –1.319

(–1.62) (–5.32)***

Financial liberalization interacted with EU 

integration

 –0.077

(–0.01)

EU integration –1.182

(–0.09)

Regional factors (unexplained effects)

Emerging Europe dummy variable –4.096 –3.515 0.074

(–4.45)*** (–3.94)*** (0.01)2

Asian crisis shift 2.921 2.352 2.430 2.479 2.518

(3.66)*** (2.79)*** (2.90)*** (2.89)*** (3.03)***

Observations 215 215 215 215 215

Adjusted R-squared 0.54 0.57 0.58 0.58 0.59

Source: IMF staff calculations.

1Robust t statistics are in parentheses; *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent level, respectively. All 

regressions include a constant, a dummy variable for financial-center and time-fixed effects. The regressions are estimated by ordinary least squares. 2Jointly significant at the 1 percent level.
Chapter6  Divergence of Current Account Balances across Emerg

political structure index, with respective contributions at 1.6, 0.9, and 0.6 points. These factors 

were partly offset by high growth opportunities, 

which contributed to lower the current account 

balance by 1.8 points, and a negative oil balance 

(as well as a number of other minor factors).

The decomposition of the change in current 

accounts over time reveals a similar picture: 

financial liberalization and growth opportunities 

largely explain the widening of current account 

deficits in emerging Europe, but the increase in 

the current account surplus in emerging Asia 

remains largely unexplained. For both regions, 

developments in the rest of the world contributed to raise the current account balance.

Special Factors in Emerging Eur

What are the special factors at work in emerging Europe? To explore this, the preferred 

model allows for separate regression coefficients 

for emerging Europe, thereby reducing the 

emerging Europe dummy to zero (see Table 6.1, 

column c). The main differentiated effects stem 

from the fiscal balance and financial liberalization. First, financial liberalization is found to 

have a more pronounced impact on current 

account balances in emerging Europe than in 

the rest of the sample. Therefore, the large 

contribution of financial liberalization to the 

current account deficits in emerging Europe 

reflects both a higher level of financial liberalization and a more pronounced impact on the 

current account of a given degree of financial 

liberalization. Second, although a government 

surplus raises the current account for the sample as a whole (although not very significantly in 

a statistical sense), it lowers the current account 

in emerging Europe.

One possible explanation for these differentiated effects is the process of EU integration that most countries in emerging Europe 

undertook after the collapse of their trade ties 

with the former Soviet Union.23 EU integration 

23See Herrmann and Winkler (2008) for a discussion 

of the role of European economic integration in current 

Figure 6.9. Explaining the Current Account Balances of

(Percent of GDP)

The current account deficits of emerging Europe are mainly explained by financial

factors, whereas a large portion of the surpluses in Asia remains unexplained by

standard factors.

1

-8 -6 -4 -2 0 2 4 6

Emerging Europe Emerging Asia

Difference from Sample Average in 2003–06

-8 -6 -4 -2 0 2 4 6

Current account

Net foreign assets

Oil balance

Demographics

Political structure

Residual

General government balance

Growth opportunities2

Financial factors

3

Other

4

Current account

Net foreign assets

General government balance

Oil balance

Demographics

Political structure

Residual

Growth opportunities2

Financial factors3

Other5

Change, 2003–06 versus 1995–98

 Source: IMF staff calculations.

 The contribution of each variable in the top (bottom) panel is computed as the deviation 

of the variable from the sample average (the change in the variable between 1995–98 and 

2003–06) times the regression coefficient of the variable from column e of Table 6.1. See

 The contribution of growth opportunities is the sum of the contributions of relative per 

capita income and growth of per capita GDP.

 The contribution of financial factors is the sum of the contributions of financial 

liberalization, capital account openness, and financial depth.

 “Other” is the contribution of a dummy variable for financial centers.

 “Other” is the contribution of time-fixed effects.

1

2

3

4

5

Contribution from:

Contribution from:

Emerging Asia and Emerging Europe

Appendix 6.2 for countries included in regional breakdowns. 
was a major factor behind financial liberalization, as reflected in the large and rising western 

European ownership of banks in the region. 

Progress toward EU integration also involved 

greater fiscal discipline—one of the Maastricht 

criteria—which may have given these countries 

privileged access to foreign capital by signaling 

greater macroeconomic stability and improved 

policies. In order to test this hypothesis, a measure of the degree of European integration is 

built as a score for achieving different stages of 

the formal integration process, namely EU membership application, initiation of negotiation for 

EU membership, EU accession, entry into ERM 

II, and euro adoption.

Interacting the government balance variable with this measure of progress toward EU 

integration supports this interpretation: the 

negative impact of a fiscal surplus on the current account is stronger the closer the country 

is to EU accession (it makes the interaction 

with the simple dummy variable for emerging 

Europe insignificant) (see Table 6.1, column d). 

A smaller government deficit provides confidence to foreign investors of progress toward 

EU accession and lowers the risk premium as 

the integration process advances.24 The divergent fiscal performance between the Baltics and 

southeastern Europe, which have improved their 

fiscal position, and central Europe, where the 

fiscal position has deteriorated, explains much 

of the current account variation within emerging Europe (Figure 6.10).25

In contrast, the differentiated effect of financial liberalization on the current account balance of emerging Europe is not directly related 

to the institutional measure of European inteaccount deficits in emerging Europe. As in this analysis, 

they identify region-specific effects that have led to the 

emergence of what they refer to as “convergence clubs.” 24Another possible explanation is a procyclical response 

of the fiscal balance to the economic boom fueled by 

foreign capital inflows, which may be especially large in 

emerging Europe. 25Because opposite fiscal developments occurred in 

the various subregions of emerging Europe, this factor is 

however not a main contributor to the aggregate deficit 

position of emerging Europe.

Figure 6.10. Explaining Current Account Balances: 

(Percent of GDP; difference from sample average in 2003–06)

1

Emerging Europe

Central Europe Southeastern Europe Baltics

Emerging Asia

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

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

Current account

Net foreign assets

Oil balance

Demographics

Political structure

Residual

General government balance

Growth opportunities2

Financial factors3

Other 4

Current account

Net foreign assets

General government balance

Oil balance

Demographics

Political structure

Residual

Growth opportunities2

Financial factors3

Other 4

China Other Asia Asian Tigers and Korea 5

 Source: IMF staff calculations.

 The contribution of each variable is computed as the deviation of the variable from the 

sample average times the regression coefficient of the variable from column e of Table 6.1.

 The contribution of growth opportunities is the sum of the contributions of relative

income per capita and of per capita GDP growth.

 The contribution of financial factors is the sum of the contributions of financial 

liberalization, capital account openness, and financial depth.

 “Other” is the contribution of a dummy variable for financial centers.

 Excludes Vietnam due to data availability.

1

2

3

4

5

Within emerging Europe regional differences are explained mainly by diverging 

fiscal performances. In emerging Asia, regional differences are largely unexplained 

by structural factors.

Contribution from:

Contribution from:

Results by Subregion

 See Appendix 6.2 for countries included in regional breakdowns.

What Factors Have Contributed to Recent Current Account Pat
Chapter6  Divergence of Current Account Balances across Emerg

2

gration. However, there is some evidence that 

the broad entry of foreign banks, which characterized the process of financial liberalization 

in these countries, may account for the more 

pronounced impact on the current account, 

because foreign banks may have drawn more 

foreign capital with them and, more generally, 

may have facilitated better access to foreign 

capital (see Appendix 6.2).26

Explaining the Residual Current Account Surplu

in Emerging Asi

Although the model achieves some success 

in explaining the current account deficits of 

emerging Europe with structural factors, in 

particular domestic financial liberalization, the 

surpluses in post-crisis emerging Asia remain 

largely unexplained, even after augmenting 

standard structural factors by financial variables.27 Within emerging Asia, most of the 

structural factors, including growth opportunities, financial liberalization, political structure, 

and demography, had a similar impact on the 

current account balances of the various subregions, though they had a somewhat larger 

impact on China’s current account balance 

(see Figure 6.10). However, a large fraction of 

26Abiad, Leigh, and Mody (2007) find that financial 

integration played an important role in explaining the 

current account deficits in emerging Europe. Herrmann 

and Winkler (2008) also find evidence that the presence 

of foreign banks was an important contributor in explaining the difference between the current account balances 

of emerging Asia and emerging Europe. Mihaljek (2007) 

finds that foreign banks played an important role in the 

rising credit growth in central and eastern European 

economies by introducing new products, improving financial sector efficiency, and strengthening risk management. 27Additional variables were tested but were not statistically significant. These included the share of employment 

in agriculture and the productivity differential between 

agriculture and the rest of the economy (to capture the 

large pool of underemployed labor in emerging Asia), 

the share of subsidies and social transfers in GDP (as a 

proxy for social safety nets), an index of terms of trade 

and the standard deviation of this index (as a motive for 

precautionary saving), a measure of trade openness, the 

exchange rate regime, and a variable indicating the start 

of banking crises.

the current account surplus in the Asian Tigers 

(including Korea) and, to a lesser extent, in 

China is left unexplained by the structural factors. One factor often mentioned to explain the 

large surpluses in emerging Asia is the valuation 

of exchange rates. A measure of the deviation of 

the real effective exchange rate from its predicted level suggests that, since the Asian crisis, 

the Asian Tigers and China have had declining 

or low exchange rates relative to the predicted 

levels, although some correction has taken place 

since 2003 (Figure 6.11).28 Low-income Asian 

countries, on the other hand, have had low but 

appreciating exchange rates during most of 

the period. Various reasons for the low and/or 

declining exchange rates have been advanced, 

such as a desire to accumulate large reserves for 

precautionary motives, which may have been 

relevant for the crisis countries for some time 

following the Asian crisis, and a growth model 

based on exports (Aizenman, 2006, 2007; Becker 

and others, 2007; Cheung and Qian, 2007; and 

Jeanne, 2007).29

There is a clear negative correlation between 

the unexplained component of the current 

28The real effective exchange rate deviation is based on 

the equilibrium real exchange rate approach developed 

as part of the IMF Consultative Group on Exchange 

Rate Issues (CGER) assessment and is calculated as the 

residual from a regression of the consumer price index 

(CPI)-based real effective exchange rate on the productivity differential between tradables and nontradables 

(to capture the Balassa-Samuelson effect), other factors 

affecting relative prices (government consumption, trade 

restriction index, price controls, and commodity terms of 

trade), and net foreign assets (see Lee and others, 2008). 

The advantage of using the residual from the equilibrium 

real exchange rate approach is that, unlike a quantitybased measure of deviation from equilibrium, it does not 

use information about the size of the current account. 29Cheung and Qian (2007) find evidence of competitive hoarding of reserves in emerging Asia aimed at 

preventing a real exchange rate appreciation and hence 

a loss in competitiveness. Controlling for conventional 

variables, they estimate that a $1 increase in international reserves by one country has been associated with 

an increase of about $0.6 by the other countries in the 

region. Zhang (forthcoming) argues that the increase 

in the Chinese current account and, in particular, in 

corporate saving partly reflects disguised capital inflows 

(through over-invoicing for exports) in anticipation of an 

appreciation of the currency. 
account balance—after structural factors have 

been taken into account—and the deviation 

of the exchange rate from its predicted level, 

suggesting that a low exchange rate is associated with a higher current account balance 

(Figure 6.12).30 There is also a positive but 

weaker correlation between the stock of reserves 

(a proxy of preferences for reserve accumulation) and the current account balance. A simple 

regression (not shown) confirms that the deviation of the exchange rate from its predicted 

level and (to a lesser extent) the high stock of 

reserves cut the unexplained current account 

surplus in emerging Asia in half, to about 1 

percentage point of GDP.

However, such simple regressions do not give 

reliable results on causality because the deviation of the exchange rate from its predicted 

value is not truly exogenous, but is rather jointly 

determined with the current account. Therefore, it is hard to discern whether the low and/

or declining exchange rates in emerging Asia 

were the result of deliberate policy action or the 

endogenous outcome of unidentified fundamental factors which are omitted from the current 

account model and which impacted both the 

current account and the exchange rate. An 

exogenous measure of exchange rate policy is 

difficult to obtain.

Sustainability of Current Account

Imbalances

The large and long-lasting current account 

imbalances in emerging Europe and emerging 

Asia prompt two questions: How long can these 

imbalances be sustained? And are they likely to 

end abruptly or be resolved smoothly? The current account deficits of emerging Europe can 

largely be explained by structural and financial 

variables, but this does not mean that the defi30The semi-elasticity of the current-account-to-GDP 

ratio to the exchange rate is proportional to the country’s 

trade openness (Lee and others, 2008). Therefore, the 

measure of exchange rate deviation was interacted with 

the ratio of the sum of exports and imports (adjusted for 

trade in intermediate goods) to GDP.

Figure 6.11. Deviation from Predicted Real Effective

Exchange Rates 

(Percent)

1

 Source: IMF staff calculations.

 Based on the equilibrium real exchange rate approach developed as part of the IMF 

CGER assessment (Lee and others, 2008).

 NIEs are Hong Kong SAR, Korea, and Singapore. Asian Tigers are Indonesia,

Malaysia, Philippines, and Thailand. Other Asia includes India and Pakistan.

 Central Europe includes Czech Republic, Hungary, Poland, and Slovak Republic. 

Comparable data were not available for southeastern Europe. 

1

2

1995 96 97 98 99 2000 01 02 03 04 05 06 -40

-30

-20

-10

0

10

Emerging Asia 20

NIEs

Asian Tigers and 

Other Asia China

2

1995 96 97 98 99 2000 01 02 03 04 05 06 -15

-10

-5

0

5

10

15

Emerging Europe 20

Central Europe

Baltics

3

Relative to predicted levels, exchange rates in emerging Asia were undervalued 

in recent years, while they were overvalued in emerging Europe. More recently, 

exchange rates returned to equilibrium levels in central Europe, while the 

deviation from predicted levels increased in the Baltics.

3

Sustainability of Current Account Imbal
Chapter6  Divergence of Current Account Balances across Emerg

cits are sustainable indefinitely. The rapid opening of the financial sector in emerging Europe 

has not only accelerated access to capital, it has 

also facilitated a credit boom, with the attendant risk that funds are being channeled to less 

productive uses (Duenwald, Gueorguiev, and 

Schaechter, 2005; and Rioja and Valev, 2004). 

The chapter identifies and analyzes a number of 

historical episodes of large, persistent surpluses 

and deficits in order to draw lessons as to the 

likely persistence of the current imbalances in 

emerging Europe and emerging Asia.31

Large, persistent current account imbalances are defined as current account deficits or 

surpluses that exceed 3 percent of GDP for at 

least three years, provided that no large reversal 

occurs during that period.32 Using this criterion, 

there were 69 deficit episodes and 15 surplus 

episodes during 1960–2007, with a higher 

incidence during 1990–2005 (Figure 6.13; see 

Appendix 6.2 for a list of all episodes). Interestingly, while the vast majority of current account 

deficits in emerging Europe qualify as large and 

persistent imbalances, only Malaysia and China 

meet the criteria for a large and persistent surplus in the aftermath of the Asian crisis.33 Deficit episodes are further separated according to 

whether or not they were resolved abruptly, with 

abrupt endings characterized by an improvement of the current account of 4 percentage 

points of GDP in the year following the end of 

31There are few empirical studies of the persistence of 

current account imbalance episodes in emerging economies. Edwards (2007) reports that large current account 

surpluses exhibit little persistence. Aizenman and Sun 

(2008) find that the length of current account deficits is 

negatively related to the relative size of the deficit. 32See Appendix 6.2 for a detailed description of the 

methodology, which is based on the adjustment algorithm 

developed in Chapter 3 of the April 2007 World Economic 

Outlook. The criteria are similar to the ones used to define 

large reversals in the literature (see, for example, Freund 

and Warnock, 2005). 33Large, persistent surpluses are also identified for 

some of the NIEs. However, these are no longer considered emerging economies, and Singapore and Hong 

Kong SAR differ because they are financial centers.

Figure 6.12. Residual Current Account Balance,

Deviation of Real Effective Exchange Rate from

Predicted Level and Stock of Reserves 

(Percent of GDP unless noted otherwise)

-30 -20 -10 0 10 20

-10

-5

0

5

10

Deviation of real effective exchange rate from predicted level 

interacted with trade openness

y = –0.1797x – 0.101

R-squared = 0.138

0 5 10 15 20 25 30 35 40 45

-10

-5

0

5

10

Lagged stock of reserves (percent of GDP)

Residual current acount balance

(percent of GDP)

y = 0.0466x – 0.4619

R-squared = 0.0166

Residual current acount balance

(percent of GDP)

 Sources: IMF, Balance of Payments Statistics; IMF, International Financial Statistics; Lee 

and others (2008); and IMF staff calculations.

 The residual current account balance is the unexplained current account balance once 

structural factors are accounted for, based on the regression in column e of Table 6.1.

 Trade openness is measured as the ratio of the sum of exports and imports (adjusted 

for trade in intermediates) to GDP.

1

1 1

A low exchange rate and a high stock of reserves, possibly reflecting

preferences for reserve accumulation, are associated with a higher residual

current account balance.

2

2
the episode.34 About one-third of the completed 

deficit episodes ended abruptly.

The surplus episodes in China and Malaysia 

are historically atypical. There have been few 

large, persistent current account surpluses––they 

account for only one-quarter of all persistent 

imbalance episodes––and they occurred primarily in advanced economies. Earlier studies also 

find abrupt adjustments of surplus episodes to 

be rare (Edwards, 2007).35 The remainder of 

this section therefore focuses on deficit episodes, which are by far the most common type 

of large, persistent imbalances, especially in 

emerging economies.36

The ongoing deficits in emerging Europe 

stand out, because of both their length and 

their magnitude (Figure 6.14). On average, current account deficits in emerging Europe have 

lasted 91⁄2 years, about 3 years longer than in 

other emerging economies, and most of these 

episodes are still ongoing. Interestingly, the 

historical evidence shows that longer deficits are 

not necessarily more shallow than shorter ones 

(with the ongoing episodes in emerging European countries clearly fitting this pattern); they 

also are no more likely to end abruptly.37 This 

may reflect the fact that persistent deficits can 

also be a sign of economic strength, reflecting 

an abundance of investment opportunities or 

a catch-up in productivity, which attract larger 

inflows of foreign capital and lead ultimately to 

a smooth resolution.38

34For a similar definition, see Edwards (2007).

35Chapter 3 of the April 2007 World Economic Outlook

finds that surplus reversals in advanced and emerging 

economies were associated with accelerations in GDP 

growth and with real exchange rate appreciations. 36This pattern is consistent with the notion of capital 

flowing downhill to countries with greater growth opportunities and with recent findings in the literature (see 

World Economic Outlook, April 2007, and Edwards, 2007). 37The finding is robust to variations in the size of the 

adjustment, using either 2 or 3 percent of GDP as a 

threshold for identifying an abrupt adjustment. 38Another potential reason for a positive correlation 

between the length and depth of episodes is that longer 

periods of foreign borrowing tend to weaken net foreign 

asset positions, which in turn weigh negatively on the net 

0

10

20

30

40

50

60

0

2

4

6

8

10

12

14

16

0

10

20

30

40

50

60

Figure 6.13. Persistently Large Current Account Deficit 

and Surplus Episodes, 1960–2007

(Number of episodes unless noted otherwise)

1

The majority of imbalance episodes in emerging economies are deficits and 

they have become more common since the 1990s.

Deficits Surpluses

Emerging

economies

Advanced

economies

Emerging

economies

Advanced

economies

Emerging economy episodes

Advanced economy episodes

0

5

10

15

20

25

30

35 Deficits Surpluses

1960–

1974

1975–

1989

1990–

2005

1960–

1974

1975–

1989

1990–

2005

Malaysia

China 

Years in episodes

Surpluses

1995 2000 05 07

Persistent Episodes in Recent Periods

 Sources: IMF, Balance of Payments Statistics; and IMF staff estimates.

 Persistently large deficit and surplus episodes are defined as consecutive current 

account balances exceeding 3 percent of GDP for a minimum of three years, with no

large adjustment (improvement or deterioration) during the episode, as defined in

Chapter 3 of the April 2007 World Econonomic Outlook.

 Start year of episodes.

1

Czech Republic

Hungary

Slovenia

Bulgaria

Romania

Estonia

Latvia

Lithuania

Croatia

Deficits

Years in episodes

1995 2000 05 07

2 2

2

Sustainability of Current Account Imbal
Chapter6  Divergence of Current Account Balances across Emerg

The regional variations in the length of 

imbalances comes out clearly in survival functions from duration analysis (Figure 6.15), which 

report the estimated likelihood that a large 

deficit will continue at a given duration, based 

on a statistical analysis of observations following 

the Kaplan-Meier estimation for survival curves. 

The flatter curve for emerging Europe (top left 

panel) indicates higher likelihoods of remaining in a large deficit and hence implies longer 

durations than for other regions.39 A comparison of survival functions by different subgroups 

and characteristics shows that deficits last longer 

when the economy has a high initial net foreign 

asset position, a more open capital account, 

lower real per capita income, and higher GDP 

growth. There appears to be little direct evidence that the type of exchange rate regime 

influences the length of deficit episodes.

A more formal analysis of the duration of 

imbalances suggests that growth opportunities, 

the opening of the capital account, liberalization 

of the financial system, and initially high net 

foreign assets are important in explaining the 

length of deficit episodes in emerging Europe 

(Table 6.2; see Appendix 6.2 for more details).40

These are broadly the same factors that explain 

the greater magnitude of these economies’ 

deficits (see Figures 6.9 and 6.10). Higher 

growth opportunities, measured by a low initial 

level of income per capita and high growth 

observed during deficit episodes, offer more 

income component of the current account. See also Lane 

and Milesi-Feretti (2007). 39Berg, Ostry, and Zettelmeyer (2008) use a similar 

empirical approach, but analyze the duration and survival 

rates of growth upbreaks. 40The model does a good job at predicting that episodes will be longer in emerging Europe, on average two 

to three years longer than for other emerging economies, 

which is in line with current observations. Moreover, once 

structural factors are taken into account, regional factors 

are no longer significant (see Table 6.2, columns a and 

e). The regression analysis confirms that the depth of the 

current account deficit during the episode does not influence its length. Other factors that increase the length 

of deficits include slow activity in advanced economies, 

which frees capital to flow to emerging economies, and a 

higher score on the political structure index.

0 2 4 6 8 10 12 14

0

5

10

15

20

Nonabrupt Abrupt

0

5

10

15

Figure 6.14. Duration of Large, Persistent Current 

Account Deficits, 1960–2007 

(Years)

Episodes in emerging Europe tend to be longer than in other emerging economies.

Longer episodes are not more shallow measured by the average current account

deficit, nor do they end more abruptly. 

Episode Length by Region

0

5

10

15

Emerging 

Asia

Emerging 

Europe

Latin 

America

Other

25th–75th percentile range

Median length

Duration and Average Size of Deficit Episodes1

Central and southeastern Europe

Baltics

Average current account deficit (percent of GDP)

Average 

size

Average 

duration

Episode Length by Adjustment Type2

 Sources: IMF, Balance of Payments Statistics; and IMF staff estimates.

 Sample comprises only complete episodes in emerging economies. Incomplete

episodes from emerging Europe are included for comparison. Duration and size of deficit 

are not correlated.

 Abrupt adjustment is defined as an improvement of the current account balance by

more than 4 percent of GDP in the year after the episode ends. 

2

1

25th–75th percentile range

Median length

Advanced 

economies

Duration (years)
productive investment opportunities and hence 

tend to prolong the economy’s access to foreign 

capital. The openness of the capital account 

eases access to foreign capital, and financial 

liberalization may improve the intermediation of 

funds and hence make the deficit more sustainable.41 The empirical analysis also indicates that 

a weak contribution of net exports to real GDP 

growth tends to reduce the length of imbalance 

episodes.42

The estimated model and observed fundamentals can be used to predict the length of deficit episodes in emerging Europe. The forecasts 

suggest that most of these deficits have persisted 

longer than expected (Figure 6.16).43 Because 

most deficit episodes in emerging Europe are 

still ongoing, the specification used for predicting the duration is based on parameters excluding these countries (see Table 6.2, column f); 

this also ensures that their specific characteristics do not determine the results. The longest 

spells are predicted for Estonia, Romania, and 

Slovak Republic, with an average duration of 

8.9 years. The deficits in the remaining two Baltic countries (Latvia and Lithuania) are forecast 

to last 7.8 years, whereas significantly shorter 

spells (4.2 years) are predicted for Bulgaria and 

Hungary.44

41Three noteworthy factors do not empirically correlate 

with the duration of large deficits: (1) the depth of the 

current account deficits during the episode, although 

lower payment obligations on foreign liabilities—measured by the average net income balance—improve 

the ability to continue foreign borrowing; (2) the type 

of capital inflows, in particular, the average size of FDI 

inflows; and (3) the type of exchange rate regime (fixed 

versus flexible), although the latter does help explain 

how the imbalance episodes are resolved (see further 

below). See Appendix 6.2 for details on results related to 

domestic financial liberalization. 42Data limitations made it difficult to build a large 

enough sample to test directly for the impact of deviations of the real exchange rate from its predicted value. 

The results over a small sample suggest that an exchange 

rate overvaluation tends to shorten deficit episodes, but 

the coefficient was not statistically significant. 43Financial globalization, through its positive effect on 

the availability of external financing, could also explain 

the longer-than-expected borrowing episodes. 44If predictions were made “in sample,” that is, based 

on an estimated model also including data from emerging Europe, expected deficit lengths would be significantly larger and more than double for the Baltics.

Figure 6.15. Survival Functions of Deficit Episodes 

(Number of years on x-axis, percent on y-axis)

1

The main factors associated with prolonged foreign borrowing (that is, high survival

rates) are high initial net foreign asset positions, capital account openness, and 

favorable growth opportunities.

0 2 4 6 8 10 12 14 16 18 0

20

40

60

80

100

0 2 4 6 8 10 12 14 16 18

0

20

40

60

80

100

Low

High

Regions2 Net Foreign Assets 2

Emerging 

Asia

Emerging

Europe

All emerging 

economies

0 2 4 6 8 10 12 14 16 18

0

20

40

60

80

100

 Source: IMF staff estimates.

 Kaplan-Meier survival function estimates. High and low levels are defined relative to

the median level.

 Difference in survival functions is statistically significant at the 10 percent level or

less (log-rank test).

1

0 2 4 6 8 10 12 14 16 18

0

20

40

60

80

100

Per Capita GDP 

Low

High

Capital Account Openness Index2

Low

High

0 2 4 6 8 10 12 14 16 18 0

20

40

60

80

100

0 2 4 6 8 10 12 14 16 18

0

20

40

60

80

100

GDP Growth Exchange Rate Regime

Fixed

Intermediate

Flexible

Low

High

2

Sustainability of Current Account Imbal
Chapter6  Divergence of Current Account Balances across Emerg

Economies in emerging Europe have been 

able to attract foreign capital for sustained 

periods in part because of favorable initial 

conditions (for example, high net foreign assets 

compared to other persistent deficit episodes) 

and, in most countries, a rapid opening of 

capital accounts. However, the average growth 

contribution from net exports has been low 

compared to sustained borrowing episodes in 

other emerging economies. This is likely related 

to strong exchange rates in several of these 

countries and may also reflect low productivity 

growth in the tradables sector, as a large share 

of investment has been going into the nontradTable 6.2. Duration Regressions of Persistent and Large Current Account Deficits

Regional 

Factors 

Only

Regional 

and 

Standard 

Factors

Regional, 

Standard, 

and Other 

Factors

Regional,

Standard, 

and Other 

Factors

Baseline with 

Standard and 

Other Factors

Baseline with 

Standard and 

Other Factors, 

Excluding 

Emerging 

Europe

(a) (b) (c) (d) (e) (f)

Standard variable

Net foreign assets (percent of GDP) initial level 1.00 0.98 0.98 0.98* 0.96**

(–0.16) (–1.02) (–1.37) (–1.79) (–2.21)

Log of per capita GDP initial level 1.08 1.96 8.33*** 3.13*** 4.84***

(0.16) (1.13) (2.86) (2.74) (3.24)

Average current account balance 0.97 1.03 1.09 1.17 1.02

(–0.25) (0.29) (0.70) (1.23) (0.13)

Average net income account balance 0.67* 0.68* 0.74 0.83 0.83

(–1.89) (–1.68) (–1.60) (–1.22) (–1.01)

Average output gap (advanced economies) 1.01** 1.01*** 1.01*** 1.02*** 1.01***

(2.33) (3.18) (3.16) (4.07) (3.42)

Financial factors and political structure2

Average capital account openness 0.62*** 0.52*** 0.58*** 0.47***

(–2.68) (–3.58) (–3.14) (–3.91)

Average political structure 0.92** 0.93 0.89*** 0.92**

(–1.99) (–1.61) (–2.90) (–2.13)

Growth performance factors

Average real GDP per capita growth 0.84 0.77** 1.01

(–1.15) (–2.11) (0.11)

Average net export growth contribution3 0.58** 0.75* 0.67**

(–2.99) (–1.94) (–2.51)

Regional factors

Emerging Europe4 0.13* 0.07** 0.04** 0.13

(–1.87) (–2.26) (–2.48) (–1.41)

Emerging Asia4 2.72* 2.34 0.9 11.2

(1.70) (0.89) (–0.09) (1.58)

Latin America 2.35* 1.28 0.57 3.49

(1.66) (0.29) (–0.53) (0.96)

Other emerging markets 5.49** 6.13** 2.03 4.52

(1.98) (2.04) (0.74) (1.50)

Episodes 48 48 48 48 48 49

Number of failures 31 31 31 31 31 30

Mean squared error5 22.3 12.7 14.4 6.8 7.3 3.3

Source: IMF staff calculations.

1Note: t statistics are in parentheses; *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent level, respectively. 

Averages are computed as mean values over the deficit episode. Weibull regression. Coefficients report odds ratio with values smaller (larger) 

than 1 measuring lower (higher) risks of an episode ending, implying longer (shorter) durations of persistent deficits.

2The effect of domestic financial sector liberalization is explored in Appendix 6.2.

3Net export growth contribution is defined as the average annual real GDP growth rate during the episode attributable to changes in net export 

balance.

4See Table 6.4 in Appendix 6.2 for countries included in regional breakdowns.

5Mean squared forecast error for episode length of complete episodes.
ables sector (see Figure 6.11).45 There are some 

warning signs in the Baltics and Bulgaria that 

productivity growth has slowed (albeit from high 

levels) and has been especially low in industry 

since 2003 (Figure 6.17).

An extension of the empirical model examining how imbalance episodes have been resolved 

in the past suggests that the Baltics and Bulgaria 

are at a higher risk of an abrupt ending of their 

deficits because of the very high openness of 

their capital accounts and their fixed exchange 

rate regimes.46 This vulnerability is heightened 

by their strong exchange rates, especially for 

the Baltics. In general, a more open capital 

account has been associated with prolonged 

deficit episodes that tend to end abruptly. Fixed 

exchange rate regimes are also associated with 

abrupt endings, but these episodes tend to be 

shorter (see Appendix 6.2 for details).47 Among 

the countries that had very open capital accounts 

and experienced an abrupt ending of their 

deficit is Malaysia (1995), whereas Thailand 

(1982) experienced an abrupt ending under a 

fixed exchange rate regime. On the other hand, 

a higher value on the political structure index is 

associated with longer-lasting and more smoothly 

ending episodes.

45While it is true that improvements in transportation, 

financial services, and utility sectors enhance productivity, a significant amount of investment is taking place in 

real estate and retail trade, with less clear productivityenhancing benefits (Rahman, 2008). See also Bems and 

Schellekens (2007). 46Determinants and implications of current account 

reversals were discussed in Chapter 3 of the April 2007 

World Economic Outlook. This analysis finds that current 

account reversals were preceded by a positive output gap 

and had varied implications for output growth: contractionary reversals were associated with low openness to 

trade and large initial deficits. In contrast, expansionary 

reversals were associated with larger-than-average total 

real depreciations and increases in savings rates (mainly 

public), which allowed investment rates to be sustained. 47This finding complements recent empirical findings 

on the persistence of current account imbalances under 

different exchange rate regimes. Chinn and Wei (2008) 

find no direct link between exchange rate regimes and 

current account persistence. This result is qualified by 

Ghosh, Terrones, and Zettelmeyer (2008), who report 

that large reversals appear correlated with fixed exchange 

rate regimes.

Figure 6.16. Predicted Duration and Actual Length of

Ongoing Deficit Episodes 

(Years)

Relative to a fundamentals-based model prediction, the ongoing episodes in

emerging Europe appear quite long, especially in comparison with data from 

other emerging economies.

Predicted mean duration plus/minus 2 standard deviations

Predicted mean duration

Actual length

 Source: IMF staff estimates.

 Based on column f of Table 6.2. 1

0

5

10

15

Model Excluding Emerging Europe and Advanced Economies 20

Bulgaria Hungary Latvia Lithuania Slovak

Republic

Romania Estonia

0

5

10

15

Model Excluding Emerging Europe 20

Bulgaria Hungary Latvia Lithuania Slovak

Republic

Romania Estonia

1

Sustainability of Current Account Imbal
Chapter6  Divergence of Current Account Balances across Emerg

Conclusions and Policy Implications

The growing divergence of current account 

imbalances in emerging economies has led to 

much discussion about the underlying causes 

and the implications for growth and sustainability. Some suggest that large surpluses in emerging Asia could imply that income convergence 

can be achieved without a need to borrow large 

amounts of foreign capital and therefore without the associated vulnerabilities to external 

stability. By contrast, emerging Europe’s ability 

to borrow foreign capital for long periods 

suggests that the standard growth model, with 

capital flowing downhill, remains relevant. This 

chapter explores the reasons for these diverging trends and assesses the sustainability of the 

growing current account imbalances.

The empirical analysis suggests that structural 

changes have been a main factor explaining the 

different regional trends. In emerging Europe, 

the large current account deficits are related to 

a rapid liberalization of domestic financial markets and open capital accounts, which attracted 

large capital inflows and prompted a rapid rise 

of foreign bank ownership. The process of integration into the EU also enhanced foreign capital inflows by improving prospects for economic 

and policy stability.

Economies in emerging Asia typically have 

less open capital accounts, and liberalization of 

domestic financial markets lags other regions. 

Several countries in emerging Asia also have 

different political structures and younger populations. These factors, and in particular the lack 

of financial liberalization, explain a substantial part of the current account surpluses in 

the region. As these countries move toward 

more financial liberalization in the future, this 

may help lower the surpluses by both raising 

consumption and increasing foreign financing of investment. However, a large fraction 

of the persistent current account surpluses in 

these economies remains unexplained. One 

candidate explanation is the undervaluation of 

their exchange rates. However, it is difficult to 

establish definitively whether the low exchange 

1993 95 97 99 2001 03 05 07

-15

-10

-5

0

5

10

15

1993 95 97 99 2001 03 05 07 -4

-2

0

2

4

6

8

Figure 6.17. Corporate Profitability and Productivity 

Growth 

Profitability

(return on assets)

Central Europe

China

Asian Tigers

and Korea

Nonfinancial Sector

Emerging Asia

Financial Sector

Emerging Asia

China

Baltics

Central Europe

Southeastern

Europe

1

Corporate profitability is robust in emerging Asia and emerging Europe. However,

productivity growth shows signs of slowing in the Baltics.

 Sources: Brooks and Ueda (2005, updated July 2008); and Groningen Growth and 

Development Centre, 60-industry database, September 2006. 

 Central Europe includes Czech Republic, Hungary, Poland, and Slovak Republic. 

Southeastern Europe includes Albania, Bulgaria, Croatia, Macedonia, FYR, and Romania.

Baltics are Estonia, Latvia, and Lithuania. Emerging Asia includes China, India,

Indonesia, Korea, Malaysia, Pakistan, Philippines, and Thailand. Asian Tigers are 

Malaysia, Indonesia, Philippines, and Thailand. 

 Central Europe includes Czech Republic, Hungary, Poland, Slovak Republic, and 

Slovenia. 

1

2

Per-Hour Labor Productivity Growth

1996 99 2002 05 0

2

4

6

8

10

12

14

16

1996 99 2002 05 0

2

4

6

8

10

12

14

16 Central Europe2 Baltics

Total Industry

Services

Total

Services

Industry

(Percent)
rate levels reflect deliberate policy action—for 

example, an attempt by some countries to build 

high levels of international reserves after the 

Asian crisis—or other unidentified factors that 

moved current accounts decisively into surplus 

after 1997–98.

As current account imbalances have 

increased, the duration of imbalance episodes 

has also lengthened, raising concerns about 

their sustainability. Indeed, the number of large, 

persistent current account deficits has risen rapidly since the 1990s, with many of these located 

in emerging Europe. The main economic factors that explain prolonged deficits are favorable 

initial net foreign asset positions, growth opportunities, and open capital accounts. By contrast, 

prolonged surpluses are rare among emerging 

economies.

Based on an analysis of historical patterns, 

ongoing deficits in emerging Europe are 

expected to last longer than in other regions, 

although most are already at or beyond the 

upper end of their expected duration. The basic 

characteristics of emerging European economies 

explain the prolonged length of their deficits, 

but this is no safeguard against hard landings. Risk factors for abrupt endings to deficits 

identified in the empirical analysis include 

fixed exchange rate regimes and open capital 

accounts, which are characteristic of many of 

these economies. These countries’ choice of a 

fixed exchange rate regime may be motivated by 

many factors, in particular, the desire to enter 

the euro area, but having made this choice, 

these countries need to protect themselves 

against external vulnerabilities by ensuring that 

product and labor markets are flexible, that 

strong financial regulatory and supervisory 

frameworks are in place, and that macroeconomic policies are consistent with domestic and 

external balance (see IMF, 2007).

The large surpluses in emerging Asia may be 

safer from the point of view of external vulnerability. However, they may also entail lower-thandesirable consumption over the near term and 

a less efficient allocation of capital, given that 

saving and investment choices are made within 

financial and corporate governance systems that 

need to be more responsive to market forces.48

A gradual return to equilibrium exchange rate 

levels would help address these concerns and 

help forestall the type of negative effects on 

productivity and growth that have been experienced in other countries that have grown 

rapidly over extended periods with high rates of 

investment. At the same time, as emphasized in 

the IMF-led Multilateral Consultation on Global 

Imbalances, a broader set of policies would help 

smooth the adjustment process, including rebalancing the components of aggregate demand 

and further financial liberalization to improve 

both access to credit and the quality of financial 

intermediation.

Appendix 6.1. Variable Definitions and

Data Sourc

The main authors of this appendix are Stephan Danninger 

and Florence Jaumotte.

This appendix provides further details on the 

construction of the variables used in Chapter 

6 and the sources of the data. The analysis is 

based on annual data from 1980 until most current. It covers countries with a 2006 level of real 

GDP per capita above $2,000 and a population 

of at least 2 million and excludes oil exporters 

(according to the IMF World Economic Outlook

definition).

Balance of Payments Data

The main source for balance of payments data 

is the IMF Balance of Payments Statistics, complemented by data from the IMF World Economic 

Outlook (WEO) database and the External 

Wealth of Nations Mark II database created by 

Lane and Milesi-Ferretti (2006) (for stock data 

on foreign assets and liabilities).

48See Box 3.2 in the September 2006 World Economic Outlook and Box 2.3 in the April 2007 World Economic Outlook. 

Appendix 6.1. Variable Definitions and Dat
Chapter6  Divergence of Current Account Balances across Emerg

2

Saving and Investmen

Saving and investment data are taken from 

the IMF WEO database. The breakdown of 

saving and investment into their public and 

private components is from the United Nations 

National Accounts Statistics database and IMF 

WEO database. Private saving is further disaggregated into corporate and household savings rates using the United Nations National 

Accounts Statistics database and, where necessary, the CEIC Asia database. Post-2003 data for 

China’s corporate and household saving rates 

are based on staff estimates.

Standard Determinant

The general government balance, the oil 

balance (defined as the difference between 

oil exports and imports), and real GDP per 

capita growth are from the IMF WEO database, whereas output per capita in constant 

purchasing-power-parity (PPP) terms is taken 

from the World Bank’s World Development Indicators. The latter is divided by the level in the 

United States to generate relative income per 

capita. Finally, population growth and the oldage dependency ratio are from the World Bank’s 

World Development Indicators.

Additional Factors

Financial factors

Financial depth is measured by the sum of 

credit to the private sector by deposit money 

banks and other financial institutions and 

stock market capitalization, divided by GDP. 

The source is a 2007 update of the Financial Structure Database prepared by Beck, 

Demirgüç-Kunt, and Levine (2000). Data for 

China are based on IMF staff calculations. The 

capital account openness index is taken from 

an update of Chinn and Ito (2006) and is based 

on principal components extracted from disaggregated capital and current account restriction 

measures in the IMF Annual Report on Exchange 

Arrangements and Exchange Restrictions. Financial 

liberalization is an index combining information on interest rate controls, credit controls, 

competition restrictions, state ownership, 

quality of the banking supervision and regulation, policies to encourage the development of 

bond and equity markets, and policies to permit 

access by foreigners to the domestic stock market. The index is from Abiad, Detragiache, and 

Tressel (forthcoming). Finally, the fraction of 

foreign banks is taken from Claessens and others (2008). A bank is considered foreign-owned 

if at least 50 percent of its shares are held by 

foreign nationals in a given year (only direct 

ownership is considered).

Exchange rate

The deviation of the real effective exchange 

rate from its predicted value is based on the 

equilibrium real exchange rate approach developed as part of the IMF CGER assessment and 

is calculated as the residual from a regression 

of the CPI-based real effective exchange rate on 

the productivity differential between tradables 

and nontradables (the so-called Balassa-Samuelson effect), other factors affecting relative prices 

(government consumption, trade restriction 

index, price controls, and commodity terms of 

trade), and net foreign assets (see Lee and others, 2008). Exchange rate deviation measures for 

the Baltics not available through the IMF CGER 

assessment are staff estimates based on a similar 

methodology. The classification of exchange rate 

regimes into fixed, intermediate, and flexible 

is a “de facto” IMF exchange rate regime index 

kindly provided by IMF staff member Harald 

Anderson.

Political factors

The political structure index is the “Polity2” 

variable from the Polity IV Project (Marshall, 

Jaggers, and Gurr, 2004). It covers a number of 

dimensions, including the presence of institutions and procedures through which citizens can 

express effective preferences about alternative 

policies and leaders and the existence of institutionalized constraints on the exercise of power 

by the executive.
The EU integration measure captures how 

far countries in emerging Europe (and Turkey) 

are along the different stages of the formal 

integration process, namely, EU membership 

application, initiation of negotiation for EU 

membership, EU accession, entry into ERM II, 

and euro adoption. A score of 0.2 is given for 

each stage; hence the maximum score is 1.

Vulnerability Indicator

Profitability of the nonfinancial sector is calculated as net income plus interest expense to 

last year’s assets, adjusted for CPI inflation. Profitability of the financial sector is earnings before 

extraordinary items and taxes in percent of total 

assets. The data are from a July 2008 update of 

Brooks and Ueda (2005) based on data from 

Worldscope and Datastream.

Data on hourly labor productivity growth in 

industry, services, and the aggregate economy 

for the Baltics and central Europe are from the 

Groningen Growth and Development Centre, 

60-industry database, September 2006.

Event Study: Growth Accelerations and Curren

Account Developments

To compare current account developments 

in emerging Europe with experiences from past 

growth accelerations, an event study analysis was 

conducted based on the definition of growth 

accelerations proposed by Hausmann, Pritchett, 

and Rodrik (2005). An event is defined as the 

onset of an acceleration in growth with the start 

date identified by two criteria:

• growth is rapid: gt,t+n ≥ 3.5 percent per year, 

with gt,t+8 = ln(yt+8) – ln(yt) being the real 

per capita GDP growth rate at time t over an 

eight-year horizon, and

• growth accelerates: Δ(g

) = (gt,t+8) – (gt–8,t) ≥

2.0 percent with Δ(g

) being the change in the 

growth rate at time t.

Once an acceleration in growth is under way, 

identification of the end of an acceleration is 

based on two criteria: the average growth rate 

declines below 2 percent, and growth in the 

year following the end of the event dips below 

3 percent.

These criteria were applied to the sample 

of non-oil-exporting emerging and advanced 

economies of this chapter between 1960 and 

2007.49 A total of 63 episodes were identified, of 

which 10 episodes from emerging Europe were 

dropped due to their overlap with the comparator countries. Data limitations excluded the use 

of another 38 episodes––29 accelerations had 

start dates prior to 1970—so that the final group 

of episodes comprised 15 growth accelerations 

including the following countries: Cameroon, 

China, Chile, Dominican Republic, Egypt, 

Finland, Greece, India, Indonesia, Ireland, Lao 

P.D.R., and Portugal. The average growth rate 

prior to the onset of the identified accelerations 

is –1.1 percent.

Appendix 6.2. Econometric Approac

The main authors of this Appendix are Stephan Danninger 

and Florence Jaumotte.

This appendix describes in greater detail the 

model underlying the econometric analysis of 

the determinants of the current account balances and its estimation. It also presents some 

additional results on the heterogeneity of coefficients across regions. Finally, it provides technical details of the duration analysis.

Determinants of the Current Account Balance

The model used in the empirical analysis 

relates the current account balance (expressed 

in percent of GDP) to a number of standard 

determinants and a range of new factors. The 

following equation is adopted as the specification for the analysis:

—— = 

CA NFA GGB NXoil a1 + a2(——) + a3(——) + a4(——) Y Y Y Y

49The algorithm for identifying growth accelerations 

was generously provided by Jeromin Zettelmeyer and Jean 

Salvati.

Appendix 6.2. Econometric 
Chapter6  Divergence of Current Account Balances across Emerg

Popold y + a5(——) + a6gpop + a7(——) + a8(gy) Popwa yUS

+ ∑ bkXk + g1EE + g2APC + g3FC

 k

+ ∑dt

l

 + e, (1)

 l

where CA is the current account balance, Y is 

nominal GDP, NFA is net foreign assets, GGB

is the general government balance, NXoil is the 

oil balance, Popold is the population ages 65 and 

over, Popwa is the working-age population, gpop is 

the population growth rate, y is GDP per capita 

in constant PPP terms, gy is the growth rate of 

real per capita income, EE is a dummy variable 

taking the value 1 for emerging Europe and 

zero otherwise, APC is a dummy variable taking 

the value 1 for emerging Asia starting in 1999 

(after the Asian crisis years), FC is a dummy variable taking the value 1 for financial centers, t is 

time-fixed effects, and X denotes a range of new 

factors added to the standard model in several 

stages (see main text). These are financial structure variables (financial depth, domestic financial liberalization, capital account openness) and 

a measure of political structure.

Following the literature (see, for example, 

Lee and others, 2008), a number of variables 

are calculated as deviations from the average for 

the rest of the world. These are the ratio of the 

general government balance to GDP, the demographic variables, the growth of GDP per capita, 

and the measure of political structure. Data are 

averaged over four years to focus on determinants of medium-term movements in the current account. In order to minimize endogeneity 

problems, net foreign assets, relative income 

per capita, and financial depth are measured in 

the year preceding the four-year period under 

consideration; the growth rate of real GDP per 

capita is measured over the four years preceding the current four-year period. The equations 

do not include country-fixed effects and retain 

the cross-sectional information since they will 

be used to explain differences between countries. Time-fixed effects are included to capture 

developments that affect similarly all countries 

in a given year (for example, the aggregate 

balance of savings and investment). The model 

is estimated using ordinary least squares and 

heteroscedasticity-robust standard errors.50

The sample of countries for which all variables used in the regressions were available consists of 58 advanced and emerging economies, 

of which 21 are advanced economies and 37 are 

emerging economies. Based on data availability, 

the following countries are included:

• Advanced economies: Australia, Austria, 

Belgium, Canada, Denmark, Finland, France, 

Germany, Greece, Ireland, Italy, Japan, 

Netherlands, New Zealand, Norway, Portugal, 

Spain, Sweden, Switzerland, United Kingdom, 

and United States.

• Emerging economies: Argentina, Bolivia, Brazil, Bulgaria, Chile, China, Colombia, Costa 

Rica, Czech Republic, Egypt, El Salvador, 

Estonia, Georgia, Hungary, India, Indonesia, 

Israel, Jamaica, Jordan, Korea, Latvia, Lithuania, Malaysia, Mexico, Morocco, Pakistan, 

Paraguay, Peru, Philippines, Poland, Romania, 

South Africa, Sri Lanka, Thailand, Tunisia, 

Turkey, and Uruguay.

The results of the estimations are reported in 

the text.

Additional Results: Heterogeneity between

Regions

Reflecting emerging Europe’s special circumstances, the empirical analysis allowed 

and tested for different coefficients for this 

group of countries relative to the rest of the 

sample. Equation (1) was extended to include 

interaction terms between each of its variables 

and a dummy variable for countries of emerging Europe. The hypothesis that the dummy 

variable for emerging Europe and all the 

interaction terms are zero (that is, that the 

effects of the variables are similar for emerging Europe and other sample countries) could 

50Results are robust to including the capital accountto-GDP ratio as an explanatory variable, to control for 

changes over time in the classification of capital transfers.
not be rejected except for the interaction terms 

involving the general government balance and 

domestic financial liberalization.51

While the text shows that the differentiated 

effect of the fiscal balance can be directly attributed to the EU integration process, this section 

provides more evidence on the reason for the 

differentiated impact of financial liberalization. 

Financial liberalization has a stronger negative 

impact on the current account in emerging 

Europe than in the rest of the sample. This 

likely reflects the much stronger presence of 

foreign banks. Some supportive evidence for this 

hypothesis was found using available data on the 

fraction of foreign banks from Claessens and 

others (2008) (the sample size falls to 77 observations). While domestic financial liberalization 

51The p-value for this test is 11 percent.

(interacted with the emerging Europe dummy) 

dominates the fraction of foreign banks when 

they are entered jointly, the fraction of foreign 

banks has a coefficient of a magnitude and 

significance similar to that of domestic financial 

liberalization (interacted with the emerging 

Europe dummy) when it is entered on its own 

(Table 6.3, columns c and d). This provides 

some supportive evidence that the stronger 

presence of foreign banks in emerging Europe 

may have contributed to the stronger impact of 

domestic financial liberalization on the current 

account.

Duration Analysis and Current Account 

Imbalances

This part provides greater detail on the identification of large, persistent current account 

Table 6.3. Explaining Differentiated Effects in Emerging 

Full Sample Restricted Sample Plus Foreign Bank Presence

(a) (b) (c) (d)

Financial factors and political structure

Financial depth (percent of GDP, lagged) 0.82 0.217 0.207 0.186

(1.64) (0.27) (0.25) (0.21)

Financial liberalization –2.743 –3.757 –3.823 –6.186

(–1.68)* (–0.64) (–0.64) (–1.17)

Capital account openness –0.229 –0.435 –0.444 –0.423

(–1.25) (–1.07) (–1.11) (–1.05)

Joint significance of financial variables (p-value) 0.03** 0.23 0.22 0.1

Political structure –0.146 –0.033 –0.03 –0.038

(–3.54)*** (–0.36) (–0.32) (–0.39)

Emerging Europe facto

General government balance interacted with EU 

integration –1.319 –1.348 –1.371 –1.443

(–5.32)*** (–3.43)*** (–3.33)*** (–3.35)***

Financial liberalization interacted with emerging Europe 

dummy –4.484 –3.533 –3.287

(–4.47)*** (–1.85)* (–1.35)

Fraction of foreign banks –0.476 –3.128

(–0.26) (–2.27)**

Regional factors (unexplained effects)

Asian crisis shift 2.518 4.2 4.192 4.509

(3.03)*** (3.21)*** (3.19)*** (3.76)***

Observations 215 77 77 77

Adjusted R-squared 0.59 0.66 0.65 0.64

Source: IMF staff calculations.

Note: Robust t statistics are in parentheses; *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent level, respectively. 

All regressions control for net foreign assets, the general government balance, the oil balance, the old-age dependency ratio, population growth, 

the relative income per capita, growth of GDP per capita, a dummy for financial centers, a constant, and time-fixed effects. The regressions are 

estimated by ordinary least squares. See footnotes 1 and 2 in Figure 6.1 for regional breakdowns.

Appendix 6.2. Econometric 
Chapter6  Divergence of Current Account Balances across Emerg

imbalances and describes the econometric methodology and additional results from the duration analysis discussed in the main text.

The method for identifying large, persistent 

current account imbalances is based on the 

approach developed in Chapter 3 of the April 

2007 World Economic Outlook but with modified 

parameters. For this chapter, the cutoff values 

for large current account imbalance episodes 

are a deficit or surplus of 3 percent of GDP or 

larger for at least three years and during which 

no current account reversal occurs.52 The latter 

criterion ensures that the end of an episode 

is dated at the onset of any large adjustments, 

regardless of whether the imbalance crosses 

the 3 percent of GDP threshold. Table 6.4 lists 

all large, persistent current account imbalance 

episodes that meet these criteria.

Duration Analysis

A duration analysis was performed to relate 

different fundamental determinants to the 

length of current account imbalances. Due to 

the small number of persistent surpluses, the 

analysis was limited to deficits.

The empirical approach models the hazard 

rate of the duration of an imbalance episode, 

which is equivalent to the conditional probability that an episode ends in the next period, 

given a set of determinants x:

λ(t, x(t)) = limh-≥0 F(t ≤ T< t+h | x)/h = f(t|x) 

/(1 – F(t|x(t))).

Formally, the hazard rate is defined as the 

ratio of the density function f(t|x) of the duration T, and the survival function 1 – F(t, x), 

where F(t,x) is the cumulative distribution function of T.

53 The empirical implementation for 

estimating the hazard rate is based on a proportional hazard rate model:

52A description of the method used for identifying current account reversals and persistent imbalances can be 

found in Appendix 3.1 of the April 2007 World Economic 

Outlook. 53 For details on duration analysis concepts, see Kiefer 

(1988) and Wooldridge (2002).

λ(t, x(t)) = exp(βx(t)) λ0(t),

with a Weibull specification for the baseline 

hazard rate λ0= p tp–1. The parameters p and  are

estimated via maximum likelihood and determine 

the shape of the baseline hazard rate function λ0

and the size of proportional shifts in the baseline 

hazard rate related to determinants x(t). Due to 

concerns about endogeneity of fundamental factors with respect to the length of an episode, the 

model uses time-invariant controls x.

54

The empirical analysis is based on 48 large, 

persistent current account deficits and covers episodes from both advanced (35 percent) 

and emerging economies (65 percent). The 

main results are reported in the text. Other 

explanatory variables and specifications were 

explored––for instance capital account openness 

has a larger effect at higher levels of per capita 

income—but did not improve the model’s fit. 

Additional results in Table 6.5 pertain to the 

role of domestic financial liberalization on the 

duration of episodes, which were omitted in 

the main text due to reduced country coverage. The analysis shows that domestic financial 

sector liberalization increases the length of an 

episode (column a), but that this effect disappears once measures of political structure 

and capital account openness are included 

(column b). There is however evidence that 

the speed of liberalization adds to the episode 

length (column c), but the same is not true for 

the speed of capital account liberalization and 

change in political institutions (not shown). This 

finding is consistent with the interpretation of 

panel regression results presented in the main 

text, which highlight the large effect of domestic 

financial sector liberalization on capital inflows 

in the past decade.

Deficit Episodes and Resolution of Imbalance

To explore the link between duration of 

persistent deficits and their resolution, a compet54A specification using time-varying controls was estimated and generated similar results to the ones reported 

in the main body of the chapter.
Country

Start 

Year

End 

Year

Length 

in Years

Average Current

Account 

Balance

(percent of 

GDP)

Deficit Episodes

Advanced economies

United States 1999 2007 9 –4.8

Denmark 1979 1986 8 –3.7

Norway 1974 1977 4 –8.8

Canada 1989 1993 5 –3.7

Greece 1979 1985 7 –4.6

Greece 1996 2007 12 –6.8

Ireland 1969 1981 13 –6.2

Portugal 1996 2007 12 –8.0

Spain 1974 1976 3 –3.8

Spain 1990 1992 3 –3.5

Spain 2000 2007 8 –5.8

Australia 1981 2007 27 –4.6

New Zealand 1979 1984 6 –6.4

New Zealand 1992 2007 16 –5.4

Emerging economie

Bolivia 1983 1987 5 –7.6

Bolivia 1990 1992 3 –6.2

Bolivia 1995 1998 4 –6.1

Brazil 1971 1974 4 –4.9

Brazil 1977 1982 6 –6.4

Brazil 1999 2001 3 –4.1

Chile 1981 1984 4 –10.1

Chile 1996 1998 3 –4.5

Costa Rica 1967 1974 8 –8.5

Costa Rica 1977 1981 5 –12.2

Costa Rica 1987 1989 3 –5.8

Costa Rica 1997 2007 11 –4.5

Dominican Republic 1967 1973 7 –5.9

Dominican Republic 1978 1980 3 –7.8

El Salvador 2003 2007 5 –4.8

Guatemala 1987 1990 4 –5.0

Guatemala 1996 2007 12 –5.3

Honduras 1975 1980 6 –7.4

Honduras 1991 1996 6 –6.3

Honduras 1999 2007 9 –3.9

Mexico 1974 1981 8 –4.0

Panama 1997 1999 3 –8.1

Panama 2003 2007 5 –5.6

Paraguay 1967 1974 8 –9.8

Paraguay 1977 1987 11 –6.4

Peru 1990 1995 6 –6.0

Jamaica 1967 1984 18 –6.7

Jamaica 2002 2007 6 –11.0

Israel 1962 1964 3 –9.3

Israel 1968 1975 8 –7.7

Table 6.4. List of Persistently Large Current Account Imbalance Episod

Country

Start 

Year

End 

Year

Length 

in Years

Average Current

Account 

Balance

(percent of 

GDP)

Emerging economie

(continued) Deficit Episodes (continued)

Israel 1978 1982 5 –5.7

Egypt 1970 1975 6 –5.3

Sri Lanka 1986 1994 9 –5.3

Indonesia 1967 1971 5 –3.7

Korea 1965 1974 10 –11.5

Malaysia 1991 1995 5 –6.4

Pakistan 1988 1996 9 –3.7

Philippines 1976 1982 7 –5.6

Singapore 1977 1980 4 –7.8

Thailand 1977 1981 5 –6.4

Thailand 1990 1996 7 –7.0

Tunisia 1980 1984 5 –6.7

Albania 1999 2007 9 –6.6

Bulgaria 1999 2007 9 –9.1

Czech Republic 2000 2003 4 –5.5

Slovak Republic 1996 2007 12 –7.3

Estonia 1995 2007 13 –9.6

Latvia 2000 2007 8 –12.2

Hungary 1995 2007 13 –6.5

Lithuania 2001 2007 7 –7.9

Croatia 2001 2007 7 –6.7

Macedonia, FYR 1994 2004 11 –5.9

Romania 1995 2007 13 –6.3

Surplus Episodes

Advanced economies

Belgium 2001 2007 7 3.5

Denmark 2001 2007 7 2.8

Netherlands 1988 1997 10 4.1

Netherlands 2001 2007 7 5.7

Norway 1991 1997 7 4.3

Norway 2001 2007 7 14.7

Sweden 1999 2007 9 5.4

Switzerland 1984 2007 24 8..3

Japan 1991 2007 17 2.9

Finland 2005 2007 3 4.3

Emerging economie

Argentina 2004 2007 4 2.8

Egypt 2004 2007 4 3.2

Hong Kong SAR 1967 1975 9 11.9

Hong Kong SAR 1985 1989 5 7.4

Malaysia 2002 2007 6 12.7

Singapore 1998 2007 10 20.2

Namibia 1993 2007 15 5.7

China 2002 2007 6 6.1

Source: IMF staff calculations.

Note: Large, persistent imbalances defined as a current account imbalance of at least 3 percent of GDP lasting for a minimum of three years 

based on method reported in Appendix 3.1 in the April 2007 World Economic Outlook.

Appendix 6.2. Econometric 
Chapter6  Divergence of Current Account Balances across Emerg

ing risks model was estimated using the same 

set of determinants as in Table 6.2. The empirical specification follows an approach proposed 

by Lunn and McNeil (1995) and explores the 

hazard rates for different exit types (abrupt and 

non-abrupt endings). The model assumes that in 

each period the total exit risk can be separated 

into two additively separable risks for abrupt and 

non-abrupt endings. The approach adds interaction terms between fundamental determinants 

and an exit type variable, which allows estimation 

of differences in hazard rates by exit type. The 

model is implemented through a semiparametric 

Cox proportional hazard model.

Table 6.6 presents estimation results for a 

baseline and two competing risks model specifiTable 6.5 Duration Analysis and Domestic

Financial Sector Liberalization

Standard and 

Other Factors

(a)

Standard and 

Other Factors

(b)

Standard and 

Other Factors

(c)

Standard factor

Net financial assets 

initial level 

(percent of GDP)

0.97* 0.97* 0.97*

(–1.80) (–1.92) (–1.90)

Log of per capita 

GDP initial level

2.44** 3.47*** 3.19**

(2.12) (2.65) (2.36)

Current account 

balance average

1.16 1.22 1.11

(1.47) (1.53) (0.76)

Net income account 

balance average

0.96 0.9 0.8

(–0.26) (–0.62) (–1.19)

Output gap average 

(advanced 

economies)

1.01*** 1.01*** 1.01***

(2.92) (3.66) (3.47)

Financial factors and political structure

Domestic 

financial sector 

liberalization 

average

0.03*** 0.26 2.61

(–3.35) (–1.02) (0.59)

Change in domestic 

financial sector 

liberalization

0.02***

(–2.59)

Capital account 

openness average

0.63** 0.48**

(–2.18) (–2.85)

Political structure 

average

0.92** 0.92**

(–2.16) (–1.98)

Growth performance factors

Real GDP per 

capital growth 

average

0.9 0.83 1.00

(–0.90) (–1.39) (0.01)

Real export growth 

average

0.81 0.78 0.76*

(–1.34) (–1.58) (–1.69)

Observations 43 43 43

Source: IMF staff calculations.

Note: Z statistics are in parentheses; *, **, and *** denote 

significance at the 10 percent, 5 percent, and 1 percent level, 

respectively. Averages computed as mean values over the deficit 

episode, changes are computed as the average difference of 

variable value between the beginning and end of the episode. 

Coefficients indicate odds ratio with smaller (larger) values than one 

measuring lower (higher) risk of an episode completion implying 

longer (shorter) expected durations of episodes.

Table 6.6. Duration Analysis and Risk of Abrupt

and Non-Abrupt Endin

Standard 

Model

Competing Risks 

Model

(a) (b) (c)

Factors for common hazard

Net financial assets initial level 

(percent of GDP) 

0.98 0.97** 0.98

(–1.38) (–2.03) (–1.10)

Log of per capita GDP initial 

level

2.16** 2.48** 2.14*

(2.27) (2.28) (1.72)

Net income account balance 

average

0.85 0.94 0.81

(–1.19) (–0.36) (–1.12)

Output gap average (advanced 

economies)

1.01*** 1.01*** 1.01***

(4.22) (3.37) (3.11)

Capital account openness 

average

0.66*** 0.80 0.78

(–3.00) (–1.42) (–1.58)

Political structure average

0.92** 0.88*** 0.89***

(–2.55) (–3.22) (–3.02)

Real GDP per capita growth 

average

0.84 0.77*** 0.79**

(–1.62) (–2.85) (–2.23)

Real export growth average

0.86 0.93 0.8

(–1.57) (–0.51) (–1.25)

Flexibility of exchange rate 

regime average

3.50***

(2.70)

Factors of hazard with abrupt endings

Net financial assets initial level 

(percent of GDP) 

1.05** 1.04

(2.19) (1.54)

Log of per capita GDP initial 

level

0.37 0.41

(–1.04) (–0.93)

Net income account balance 

average

0.64 0.74

(–1.21) (–0.84)

Output gap average (advanced 

economies)

0.99 0.99

(–1.20) (–1.41)

Capital account openness 

average

0.39* 0.38*

(–1.79) (–1.74)

Political structure average

1.15* 1.17

(1.65) (1.52)

Real GDP per capita growth 

average

1.58 1.54

(1.12) (1.05)

Real export growth average

0.84 1.17

(–0.71) (0.41)

Flexibility of exchange rate 

regime average

0.077*

(–1.71)

Observations 96 96 96

Episodes 48 48 48

Source: IMF staff calculations. 

Note: Z statistics are in parentheses; *, **, and *** denote 

significance at the 10 percent, 5 percent, and 1 percent level, 

respectively. Averages computed as mean value over the current 

account deficit episode. Coefficients report odds ratio with smaller 

(larger) values than 1, indicating decreased (increased) risk of an 

episode ending.
cations. The coefficients of the standard determinants (column a) are similar to the ones 

presented in the main body of the chapter using 

a Weibull specification (see Table 6.2). Note 

however that the number of observations is twice 

as large compared to the original duration model 

specification since each observation is entered 

twice to allow for different (competing) risks’ 

effects. The results in columns (b) and (c) report 

hazard rate models for a common baseline hazard rate (top panel) and differences for hazards 

with abrupt endings (lower panel). The interaction effects specification implies that the total 

hazard rate for abrupt endings is determined by 

the sum of the direct and the interaction effects.

The majority of explanatory variables do 

not have significant additive risk factors (lower 

panel) and hence do not indicate different 

hazard rates by exit types. Significant interaction 

effects are however found for net foreign assets, 

capital account openness, and political structure.55 Combining the direct and interaction 

effects, the results indicate that longer spells 

due to greater capital account openness bear an 

increasing risk of abrupt endings (column b in 

Table 6.6). The results are different for net foreign assets and political structure due to the offsetting signs of the direct and interaction effects. 

They imply that higher values on both indicators 

increase the length of non-abrupt episodes, but 

there are no effects for abrupt endings.

In column c of Table 6.6, a variable capturing the flexibility of the exchange rate regime 

is introduced. The direct effect of this variable on the length of non-abrupt episodes is 

negative, whereas the interaction effect has 

the opposite sign and more than offsets the 

direct effect. This implies that a more flexible 

exchange rate regime reduces the length of 

episodes that end non-abruptly—supporting 

the view that flexibility reduces persistence—

and that fixed regimes are linked to shorter 

episodes that end more abruptly.

55For net foreign assets a test of the joint significance 

of the direct and interaction effects is rejected.

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Xc h a pte r

chapter

IMF Executive Board Discussi

Outlook, SEPTE

The following remarks by the Acting Chair were made at the conclusion of the Executive 

Board’s discussion of the World Economic Outlook on September 19, 2008.1

An ne 

Global Prospects and Policies

Executive Directors noted that, after a fouryear expansion, the world economy is slowing 

rapidly, buffeted by many forces, notably a deep 

financial crisis, a housing downturn in a number 

of advanced economies, and a surge in commodity prices until recent months. Many advanced 

economies are close to recession, and emerging economies are also generally decelerating. 

Looking ahead, Directors agreed that continuing 

strained financial conditions will weigh heavily on 

global growth prospects. With credit defaults still 

on the rise, pressures on financial institutions’ 

capital positions will remain exceptionally high. 

The necessary process of deleveraging will therefore be difficult and protracted, and credit availability will be very tight. Directors underscored 

the unusual uncertainties in the global outlook 

and the difficulty of predicting the severity of 

the slowdown. Nevertheless, assuming that the 

many public initiatives under way are successful 

in lowering financial market strains and rebuilding confidence, it is the IMF staff view that the 

still relatively sound nonfinancial corporate balance sheets in advanced economies and resilient 

domestic demand in emerging economies should 

support the beginning of a global economic 

recovery later in 2009, as the effects of the commodity price shocks unwind and housing activity 

in the United States stabilizes.

1The IMF staff provided an informal briefing to the 

Executive Board on October 3, 2008, on the revisions to 

the World Economic Outlook made in light of the developments in global financial markets and commodity markets 

since the Executive Board discussion of September 19, 

and this summing up reflects the updates provided and 

the ensuing discussion at that time.

Directors noted that the uncertainty surrounding the economic outlook is exceptionally large, 

with risks clearly to the downside. The principal risk relates to the potential for a further 

deterioration in financial conditions and more 

protracted balance-sheet adjustments. Directors 

acknowledged that, as the events of recent weeks 

demonstrate, the financial crisis has not yet run 

its full course, and other major disturbances may 

still lie ahead. Risks of an intensified negative 

feedback loop between the financial system and 

the real sectors of advanced economies have 

risen, as have the risks of stronger spillovers to 

emerging markets. A further marked weakening 

of commodity prices could pose a downside risk 

to already slowing growth in several commodityexporting emerging economies.

Directors underscored the complex challenges facing policymakers at this juncture in 

light of the uncertain outlook and the financial 

turmoil. First and foremost, policymakers in 

the advanced economies will need to address 

stresses in financial markets in a coherent and 

consistent manner, while adjusting macroeconomic policies with a view to striking the right 

balance between supporting growth and combating inflation. The financial authorities should 

continue to give priority to stabilizing financial 

conditions, including guarding against systemic 

failures through liquidity provision and prompt 

intervention when needed, while also being 

mindful of the need to avoid moral hazard. This 

likely will require broad and well-coordinated 

policy actions. On monetary policy, given tight 

credit conditions, growing unemployment fears, 

subdued wage growth, and still-well-anchored 

inflation expectations, the IMF staff sees scope 
ANNEX  IMF Executive Board Discussion of the 

for easing in advanced economies with relatively 

higher real rates of interest. A number of other 

Directors, however, favored a cautious approach 

to easing, pointing to the risks of second-round 

effects from the recent increases in inflation 

and the challenge of ensuring that expectations 

remain well anchored. On fiscal policy, Directors 

welcomed the timely support being provided to 

a number of economies. However, they recalled 

the importance of medium-term consolidation 

and thus suggested that further fiscal initiatives 

should be limited to dealing with financial problems as needed.

In the wake of recent commodity price 

declines and the projected slowdown in global 

activity, Directors expected headline inflation to moderate in emerging and developing 

economies. This means that macroeconomic 

policies could stay on hold in an increasing 

number of these economies, or even ease if the 

outlook threatened to deteriorate further. In 

other economies, many Directors observed that 

underlying inflation pressures remain an issue. 

They observed that these economies face greater 

risks of second-round effects than advanced 

economies, owing to the higher share of food 

and fuel in consumption baskets and low spare 

capacity. Many Directors therefore considered 

that policymakers in these countries should lean 

toward tightening the overall stance of macroeconomic policies. Additionally, these Directors underscored that monetary policy should 

take the lead role in short-term stabilization in 

economies with inflation targeting and flexible 

exchange rate management, while recognizing 

that country circumstances differ and that open 

capital markets can pose special challenges in 

these cases. They recognized the challenges 

facing countries with heavily managed exchange 

rate regimes that are now in effect importing an accommodative monetary policy stance 

from the United States, while still experiencing 

strong growth and current account surpluses. A 

number of Directors observed that more flexible 

exchange rates would help in some cases by 

providing scope for adjusting monetary conditions and fostering a rebalancing of demand in 

their economies. Several other Directors stressed 

that the choice of the exchange rate regime 

depends on broader considerations, and that in 

the countries with fixed exchange rates, fiscal 

policy would have to take the lead in relieving 

any excess demand pressures that threaten the 

viability of the peg. Several Directors argued 

that more monetary and fiscal tightening may 

not be needed, as headline inflation is likely to 

moderate in the near term and a slowing global 

economy would alleviate overheating concerns. 

Advanced Economie

Although the U.S. economy continued to 

grow at a moderate pace in the first half of 2008, 

Directors agreed that activity is likely to slow 

appreciably in the second half. Given the exceptionally unsettled financial market conditions, 

most Directors expected a prolonged period of 

very low growth, followed by a gradual recovery that would begin later in 2009. The pickup 

would be driven by a turnaround in private 

consumption and residential investment as commodity and housing prices stabilize, although 

financial conditions are expected to remain 

tight. Directors expected underlying price pressures to be contained as economic slack rises in 

the coming months.

Against this background, Directors acknowledged that the U.S. authorities’ accommodative 

monetary policy thus far has provided support 

to the economy in the face of financial stress 

and the continuing housing correction. They 

thought that further easing should not be ruled 

out in view of the deteriorating outlook. By the 

same token, once economic recovery gains traction and financial conditions stabilize, policies 

should shift toward a less supportive stance. The 

fiscal stimulus package has also provided a useful and timely boost to activity. However, given 

long-term fiscal challenges, further policy initiatives should be focused on providing support for 

the banking and housing sectors as needed to 

maintain stability. Directors welcomed the recent 

intervention in government-sponsored enterprises (GSEs) as an important step to ensure 
IMF Executive Board Discussion of the Outloo

the continued availability of housing financing, 

while stressing the need for fundamental GSE 

reform over the medium run. More broadly, 

they welcomed the authorities’ recent additional 

efforts to ensure an orderly resolution of the 

ongoing crisis, while remaining mindful of the 

need to contain moral hazard.

Directors observed that western European 

economies have slowed considerably. Higher oil 

and food prices have undercut real disposable 

incomes, while tighter financial conditions have 

raised the costs of household mortgages and 

slowed investment. In some countries, particularly the United Kingdom and Spain, weak housing has weighed heavily on economic activity. 

Although European banks entered the turmoil 

from a position of strength, they have been 

exposed in varying degrees to losses on their 

holdings of U.S. mortgage-related assets, tightening liquidity conditions, and deteriorating credit 

quality. As in the United States, financial institutions have been shaken by recent events, with 

some requiring public support. Given the need 

for further deleveraging to rebuild confidence, 

Directors thought that overall credit conditions 

will likely remain tight for some time. Growth is 

expected to remain weak for a prolonged period 

of time before starting to recover gradually later 

in 2009, as credit markets and commodity prices 

stabilize and confidence is restored.

 With the prospect of a further weakening of 

economic activity and generally well-anchored 

inflationary expectations, as well as high risk 

premiums, many Directors considered that 

inflation pressures would subside over the near 

term, providing scope to ease monetary policies. 

Underscoring the benefits of rules-based policies 

and the medium-term need for consolidation, 

Directors encouraged firm adherence to national 

and EU fiscal policy frameworks, which would 

generally weigh against fiscal stimulus packages, 

unless downside risks materialize. Furthermore, 

a decisive commitment to concerted action with 

respect to addressing growing financial sector 

strains would go a long way toward restoring 

more orderly conditions in financial markets. 

Directors observed that the near-term outlook 

for Japan has deteriorated. Although financial 

conditions have tightened to a lesser extent 

than in other major economies owing to the 

lower exposure of Japanese banks to securitized 

products, Directors expected that high food and 

fuel prices and weaker external growth would 

weigh on consumer and business activity. Most 

Directors considered that, with the economy 

weakening and underlying price pressures well 

contained, the monetary stance should remain 

accommodative. For fiscal policy, the priority 

remains medium-term consolidation owing to a 

rapidly aging population and rising public debt—

although automatic stabilizers could be allowed 

to operate in the event of a sharp downturn. 

Emerging and Developing Economi

Growth of emerging Asian economies is 

expected to moderate over the near term, but 

to remain around trend in many cases. However, Directors emphasized the increasing risk 

that growth could slow more markedly owing to 

intensified financial market stress and a sharper-than-anticipated slowdown in trading partner 

countries. On the other hand, in some countries inflation could remain at elevated levels, 

despite the recent easing in commodity prices, 

as spare capacity has largely been eroded. Thus, 

countries’ policies need to be tailored to their 

particular circumstances, which are diverse. Most 

Directors were of the view that countries with 

heavily managed exchange rate regimes would 

benefit from shifting to more flexible exchange 

rate management that would provide more 

scope for monetary adjustment and foster global 

rebalancing. At the same time, Directors underscored that global rebalancing would require a 

mix of appropriate and complementary policies and could not be left solely to adjustments 

in exchange rates. Fiscal restraint could help 

reduce inflation concerns, while continued 

efforts at fiscal consolidation remain an important priority for other countries in the region. 

Directors observed that Latin American 

economies face a challenging combination of 
ANNEX  IMF Executive Board Discussion of the 

slowing activity, volatile financial conditions, and 

still-­elevated inflation pressures. While acknowledging growing downside risks to growth, largel

reflecting external factors, many Directors 

stressed that the priority for policies in a number of countries remains to quell the surge of 

inflation. While tightening has occurred, notably 

in countries with inflation-targeting regimes, 

more action will likely be needed in some 

countries where real interest rates have become 

significantly negative and policy credibility is 

being eroded. Shifts in international oil and 

food prices should be allowed to pass through to 

the domestic market, using targeted programs to 

protect the poor.

Directors noted that, after a prolonged economic expansion, activity in emerging Europe 

has started to moderate, with the Baltic countries 

undergoing sharp corrections as earlier booms 

have started to unwind. Some countries with 

large current account deficits could be vulnerable 

to a reversal of capital inflows. Containing inflation pressures also remains a concern, particularly in southeastern European economies. This 

underlines the importance of macroeconomic 

policies that steer economies toward a “soft landing,” as well as of prudential and regulatory policies to contain balance sheet vulnerabilities. 

Real GDP growth remains strong in the Commonwealth of Independent States, underpinned 

by terms-of-trade gains and expansionary macroeconomic policies, although external conditions are becoming more noticeably difficult for 

several countries. Directors agreed that stronger 

policy action is needed in many countries in the 

region to rein in rising inflation pressures and 

address external pressures. A comprehensive 

policy response would require a combination 

of monetary tightening and greater exchange 

rate flexibility, combined with a prudent fiscal stance. Over the longer term, Directors 

emphasized that the region should continue to 

reduce its vulnerability to commodity price shifts 

through diversification of the economy away 

from primary commodities.

Directors were encouraged that growth in subSaharan Africa is expected to show resilience 

to the global slowdown, but expressed concern 

about the impact of the recent surge in food 

and fuel prices on poverty and about risks of a 

lower pace of financing and investment inflows. 

The oil-exporting countries in this region face 

the challenge of managing the windfall gains 

from high commodity prices. A number of oilimporting countries, where the negative termsof-trade shock has weakened fiscal and external 

positions, will need to tighten their macroeconomic policies, while stepped-up donor support 

will be essential to help low-income countries 

cope with high commodity prices.

Directors observed that activity in the Middle 

East continues to grow at a robust pace, supported by higher oil prices, an improving business environment, and a buildup in government 

spending in oil-exporting countries. Inflation 

pressures either remain high or have risen 

considerably. Directors recommended focusing 

public spending on infrastructure to address 

supply bottlenecks, while some cautioned that 

rising inflation pressures may require exercising 

greater restraint over current spending to counterbalance strong private demand growth. A few 

Directors suggested that some countries should 

consider moving away from U.S. currency pegs. 

At the same time, Directors emphasized the 

need to continue strengthening macroeconomic 

policy frameworks and pursuing structural 

reforms that are key to the region’s mediumterm prospects. Financial sector reforms would 

be important to develop financial systems that 

can support high and sustained growth and 

more independent monetary policy. 

Other Issue

Regarding the strains in financial markets, 

Directors emphasized that the immediate tasks 

are to safeguard financial stability, restore 

healthy financial balance sheets by encouraging the rebuilding of capital bases, and guard 

against systemic failures through liquidity 

provision and intervention as needed. They 

emphasized that this would require broad and 

well-coordinated policy action. Over a longer 
IMF Executive Board Discussion of the Outloo

horizon, determined efforts will be needed to 

build firmer underpinnings for financial intermediation, learning lessons from the weaknesses 

revealed by the present period of turbulence. 

Central objectives include ensuring more effective and resilient risk management by individual 

institutions, developing new securitization 

techniques to improve incentives, and strengthening accounting and credit ratings systems to 

raise transparency. Another important task will 

be to strengthen approaches to crisis management and resolution, including clarifying roles 

of different official agencies, bolstering deposit 

insurance schemes, and ensuring adequate 

intervention instruments, while taking care to 

avoid exacerbating moral hazard. In many of 

these areas, but particularly for the purpose of 

preventing, managing, and resolving financial 

stress, coordination across national boundaries 

will be crucial, given the ever-larger international integration of financial institutions and 

markets.

Directors generally saw merit in giving consideration to introducing a macroeconomic 

element in the financial prudential framework to 

weigh against the inherent procyclicality of credit 

creation. Many Directors saw merit in the possibility of extending monetary policy frameworks 

to provide for “leaning against the wind” of asset 

price movements. However, a number of other 

Directors pointed to the complexities that this 

involves and questioned the potential benefits.

Directors emphasized that joint multilateral efforts will be crucial to relieve strains in 

commodity and financial markets in a lasting 

way. Many Directors agreed with the IMF staff’s 

analysis in Chapter 3, which finds little concrete 

evidence that rising investor interest in commodities as an alternative asset or speculation 

has had a systematic or lasting impact on prices, 

although swings in market sentiment may have 

contributed to short-term price dynamics. Some 

Directors, however, had further questions and 

requested additional analysis, based on data 

refinements. Directors considered that the focus 

should be on policies to encourage stronger supply and demand responses to improve market 

balances, while avoiding measures that could 

exacerbate market tightness in the short term. 

Directors stressed that the multilateral strategy 

endorsed by the IMFC in 2005 and elaborated 

by the Multilateral Consultation on Global 

Imbalances in 2006 and 2007 remains relevant 

but should be applied flexibly. In this context, 

they acknowledged that the issues related to 

global imbalances are shifting. U.S. fiscal consolidation remains a key medium-term objective, 

but countercyclical fiscal stimulus and public 

support for the housing and financial sectors 

have been warranted to alleviate the current 

slowdown and stabilize markets. For their part, 

the euro area and Japan should press ahead 

with product and labor market reforms to raise 

potential growth in their economies. China 

needs to build on the progress in boosting 

domestic demand and continue to contribute 

to addressing global imbalances. The recent 

increase in surpluses of Middle Eastern oil 

exporters is an inevitable counterpart of higher 

oil prices, given absorptive capacity constraints, 

and it appears that the recycling of surpluses 

to importing economies has generally worked 

well so far. Nevertheless, it will be important to 

ensure a transparent and open environment 

for capital flows, including through finalizing 

a set of good practices for sovereign wealth 

funds. IMF staff analysis shows that there has 

been further—albeit uneven—progress toward 

realignment of major world currencies. The real 

effective exchange rate of the U.S. dollar is now 

judged to have moved broadly into line with 

medium-run fundamentals, and this should help 

lower the U.S. current account deficit. However, 

the dollar realignment is mostly attributable to 

the depreciation against the euro rather than 

against the currencies of major current account 

surplus countries. Also, the real effective 

exchange rate of the euro remains on the strong 

side of fundamentals. Directors noted the IMF 

staff’s assessment that China’s exchange rate is 

still substantially undervalued. At this juncture, 

it is important to resist protectionist pressures 

and to make progress toward multilateral trade 

liberalization by unblocking the Doha Round. 

The Statistical Appendix presents historical data, as well as projections. It comprises five sections: Assumptions, What’s 

New, Data and Conventions, Classification of Countries, and Statistical Tables.

The assumptions underlying the estimates and 

projections for 2008–09 and the medium-term 

scenario for 2010–13 are summarized in the 

first section. The second section presents a brief 

description of changes to the database and statistical tables. The third section provides a general 

description of the data and of the conventions 

used for calculating country group composites. 

The classification of countries in the various 

groups presented in the World Economic Outlook is 

summarized in the fourth section. 

The last, and main, section comprises the 

statistical tables. Data in these tables have been 

compiled on the basis of information available through early October 2008. The figures 

for 2008 and beyond are shown with the same 

degree of precision as the historical figures 

solely for convenience; because they are projections, the same degree of accuracy is not to be 

inferred.

Assumptions

Real effective exchange rates for the advanced 

economies are assumed to remain constant at 

their average levels during the period August 

18 to September 15, 2008. For 2008 and 2009, 

these assumptions imply average U.S. dollar/SDR conversion rates of 1.596 and 1.567, 

U.S. dollar/euro conversion rates of 1.52 and 

1.50, and yen/U.S. dollar conversion rates of 

106.5 and 108.3, respectively.

It is assumed that the price of oil will average 

$107.25 a barrel in 2008 and $100.50 a barrel in 

2009.

Established policies of national authorities are 

assumed to be maintained. The more specific 

policy assumptions underlying the projections 

for selected advanced economies are described 

in Box A1.

With regard to interest rates, it is assumed that 

the London interbank offered rate (LIBOR) 

on six-month U.S. dollar deposits will average 

3.2 percent in 2008 and 3.1 percent in 2009, that 

three-month euro deposits will average 4.8 percent in 2008 and 4.2 percent in 2009, and that 

six-month Japanese yen deposits will average 

1.0 percent in 2008 and 1.2 percent in 2009.

With respect to introduction of the euro, on 

December 31, 1998, the Council of the European Union decided that, effective January 1, 

1999, the irrevocably fixed conversion rates 

between the euro and currencies of the member 

states adopting the euro are as follows. 

See Box 5.4 of the October 1998 World Economic Outlook for details on how the conversion 

rates were established.

Statistic

1 euro = 13.7603 Austrian schillings

= 40.3399 Belgian francs

= 0.585274 Cyprus pound1

= 1.95583 Deutsche mark

= 5.94573 Finnish markkaa

= 6.55957 French francs

= 340.750 Greek drachma2

= 0.787564 Irish pound

= 1,936.27 Italian lire

= 40.3399 Luxembourg francs

= 0.42930 Maltese lira3

= 2.20371 Netherlands guilders

= 200.482 Portuguese escudos

= 239.640 Slovenian tolars4

= 166.386 Spanish pesetas

1Established on January 1, 2008. 2Established on January 1, 2001. 3Established on January 1, 2008. 4Established on January 1, 2007.
The short-term fiscal policy assumptions used in 

the World Economic Outlook are based on officially 

announced budgets, adjusted for differences 

between the national authorities and the IMF 

staff regarding macroeconomic assumptions and 

projected fiscal outturns. The medium-term fiscal projections incorporate policy measures that 

are judged likely to be implemented. When the 

IMF staff has insufficient information to assess 

the authorities’ budget intentions and prospects for policy implementation, an unchanged 

structural primary balance is assumed, unless 

otherwise indicated. Specific assumptions used 

in some of the advanced economies follow (see 

also Tables B5–B7 in the Statistical Appendix for 

data on fiscal and structural balances).1

United States. The fiscal projections are based 

on the administration’s fiscal year 2009 budget 

and mid-session review. The fiscal projections 

do not reflect the impact of the most recent 

government interventions in financial markets, 

instead accounting only for the $25 billion in 

support for the government-sponsored enterprises estimated by the Congressional Budget 

Office (CBO) in July 2008. Adjustments are 

made to account for differences in macroeconomic projections as well as IMF staff assumptions about (1) additional defense spending 

1The output gap is actual minus potential output, 

as a percent of potential output. Structural balances 

are expressed as a percent of potential output. The 

structural budget balance is the budgetary position 

that would be observed if the level of actual output 

coincided with potential output. Changes in the 

structural budget balance consequently include effects 

of temporary fiscal measures, the impact of fluctuations in interest rates and debt-service costs, and other 

noncyclical fluctuations in the budget balance. The 

computations of structural budget balances are based 

on IMF staff estimates of potential GDP and revenue 

and expenditure elasticities (see the October 1993 

World Economic Outlook, Annex I). Net debt is defined 

as gross debt minus financial assets of the general 

government, which include assets held by the social 

security insurance system. Estimates of the output gap 

and of the structural balance are subject to significant 

margins of uncertainty.

based on analysis by the CBO, (2) slower 

compression in the growth rate of discretionary spending, and (3) continued alternative 

minimum tax relief beyond fiscal year 2009. 

Projections also assume that proposed Medicare 

savings are achieved only partially and that personal retirement accounts are not introduced.

Japan. The medium-term fiscal projections 

assume that expenditure and revenue of the 

general government (excluding social security) 

are adjusted in line with the current government target to achieve primary fiscal balance 

(excluding social security) by fiscal year 2011.

Germany. Projections reflect the measures 

announced in the Stability Program Update 

2007. Projections for 2008 include a loss in 

revenue owing to corporate tax reform and a 

cut in social security contribution rates (unemployment insurance). Over the medium term, 

health expenditures accelerate because the 

population is aging and health care reform 

measures have not been taken.

France. For 2008, the fiscal projections are 

based on the budget law and assume higher 

social security spending growth, largely owing 

to higher-than-targeted increases in health care 

outlays. Medium-term projections reflect the 

authorities’ official tax revenue forecast but 

assume different spending (less deceleration) 

and nontax revenue profiles, consistent with an 

unchanged policy assumption.

Italy. For 2009, the projection is based on 

the IMF staff’s evaluation of the fiscal package 

that anticipated the 2009 budget, as passed in 

August 2008. For 2010–13, a constant primary 

structural balance is assumed.

United Kingdom. The medium-term revenue 

projections are consistent with the IMF staff’s 

macroeconomic assumptions. The expenditure 

projections assume that after the planned consolidation, set out in the 2008 budget, expenditures will continue in terms of the percent of 

GDP through 2012–13.

Canada. Projections use the baseline forecasts in the 2008 federal budget and the 2007 

Box A1. Economic Policy Assumptions Underlying the Projections for Select

Economie

Statistical Appe
Economic Statement. The IMF staff makes 

some adjustments to this forecast for differences in macroeconomic projections. The IMF 

staff forecast also incorporates the most recent 

data releases from Statistics Canada, including 

provincial and territorial budgetary outturns 

through the first quarter of 2008. 

Australia. The fiscal projections through 

fiscal year 2011/12 are based on the budget 

published in May 2008. For the remainder of 

the projection period, the IMF staff assumes 

unchanged policies.

Austria. Projections for 2008 are based on current policies. For the medium term, fiscal policy 

assumptions take into account announced 

future policy measures, including tax cuts, that 

are judged likely to be implemented.

Belgium. Projections for 2008 are IMF staff 

estimates based on the 2008 budget voted by the 

Parliament in May 2008 and adjusted for macroeconomic assumptions. Projections for 2009 are 

IMF staff estimates, adjusted for macroeconomic 

assumptions and assuming unchanged policies.

 Brazil. The fiscal projections for 2008 are 

based on the 2008 budget guidelines law and 

recent pronouncements by the authorities 

regarding their policy intentions. For the outer 

years, the IMF staff assumes unchanged policies, 

with a further increase in public investment in 

line with the authorities’ intentions.

China. Projections for 2008 are based on 

IMF staff estimates and data for the first three 

months, with some adjustment for the IMF 

staff’s definition of overall budget balance. 

For 2009–13, IMF staff projections assume 

that spending, especially in social sectors, will 

increase, with the deficit roughly constant at its 

projected 2008 level (about 1 percent of GDP).

Denmark. Projections for 2008 and 2009 are 

aligned with the latest official budget estimates 

and the underlying projections, adjusted where 

appropriate for the IMF staff’s macroeconomic 

assumptions. For 2009–13, the projections incorporate key features of the prior medium-term 

fiscal plan as embodied in the authorities’ November 2007 Convergence Program submitted to the 

European Union. The projections imply convergence of the budget toward a close-to-balanced 

position from an initial surplus position. This is 

consistent with the authorities’ projection of a 

closure of the output gap over the medium term, 

as well as being in line with their objectives for 

long-term fiscal sustainability and debt reduction.

Greece. Projections are based on the 2008 

budget, the latest Stability Program, and other 

forecasts and data provided by the authorities.

Hong Kong SAR. Fiscal projections for 2007–10 

are consistent with the authorities’ mediumterm strategy as outlined in the fiscal year 

2007/08 budget, with projections for 2011–13 

based on the assumptions underlying the IMF 

staff’s medium-term macroeconomic scenario.

India. Estimates for 2007 are based on data on 

budgetary execution. Projections for 2008 and 

beyond are based on available information on 

the authorities’ fiscal plans, with some adjustments for the IMF staff’s assumptions.

Korea. The fiscal projections reflect the 2008 

budget and the five-year medium-term budget 

for 2009–13, with some adjustment for measures 

announced since the passage of the budget as 

well as the IMF staff’s assumptions and macroeconomic projections. 

Mexico. Fiscal projections for 2008 build 

on the authorities’ budget and also take into 

consideration higher-than-budgeted oil prices. 

Projections for 2009 and beyond are based on 

IMF staff calculations in line with the Federal 

Government Fiscal Responsibility Law, requiring 

a zero overall balance according to the traditional budget definition.

Netherlands. The fiscal projections build on 

the 2007 budget, the latest Stability Program, 

and other forecasts provided by the authorities.

New Zealand. The fiscal projections through 

fiscal year 2011/12 are based on the 2008 budget, released in May 2008. For the remainder 

of the projection period, the IMF staff assumes 

unchanged policies. The New Zealand fiscal 

account switched to generally accepted accounting principles beginning in fiscal year 2006/07, 

with no comparable historical data.

Statistical Appe
What’s N

No changes have been introduced for this 

issue of the World Economic Outlook.

Data and Conventions

Data and projections for 183 countries form 

the statistical basis for the World Economic Outlook (the World Economic Outlook database). 

The data are maintained jointly by the IMF’s 

Research Department and area departments, 

with the latter regularly updating country projections based on consistent global assumptions.

Although national statistical agencies are 

the ultimate providers of historical data and 

definitions, international organizations are also 

involved in statistical issues, with the objective 

of harmonizing methodologies for the national 

compilation of statistics, including the analytical 

frameworks, concepts, definitions, classifications, 

and valuation procedures used in the production of economic statistics. The World Economic 

Portugal. Fiscal projections for 2008–10 are 

based on unchanged policies specified in the 

2008 Budgetary Policy Steering Report. They take 

into account the anticipated savings from the 

reforms that have already been introduced (for 

example, of the social security system and public 

sector administration reform). Beyond 2011, no 

further consolidation is assumed, and the structural primary balance is kept unchanged.

Russia. Fiscal projections for Russia are based 

on the 2008 budget, the authorities’ proposed 

medium-term budget for 2009–11 and, for later 

years, the ceiling for the non-oil deficit of the 

federal government as imposed by the budget 

code. Differences in expenditure projections 

between those of the IMF staff and of the 

authorities for 2009 and beyond reflect mainly 

different assumptions for real GDP, inflation, 

and revenues.

Singapore. For fiscal year 2007/08, expenditure projections are based on budget numbers, 

whereas revenue projections reflect the IMF 

staff’s estimates of the impact of new policy 

measures, including an increase in the goods 

and services tax. Medium-term revenue projections assume that capital gains on fiscal reserves 

will be included in investment income.

Spain. Fiscal projections through 2010 are 

based on the 2008 budget and policies outlined 

in the authorities’ updated Stability Program 

2007–10, adjusted for the IMF staff’s macroeconomic assumptions, information from recent 

statistical releases, and official announcements. 

In subsequent years, fiscal projections assume 

unchanged policies.

Sweden. Fiscal projections are based on information provided in the 2009 Fiscal Policy Bill 

(April 2008), with adjustments reflecting incoming fiscal data and the IMF staff’s views on the 

macroeconomic environment.

Switzerland. Projections for 2008–13 are based 

on IMF staff calculations, which incorporate 

measures to restore balance in the federal 

accounts and strengthen social security finances.

Monetary policy assumptions are based on the 

established policy framework in each country. 

In most cases, this implies a nonaccommodative 

stance over the business cycle: official interest 

rates will increase when economic indicators 

suggest that inflation will rise above its acceptable rate or range, and they will decrease when 

indicators suggest that prospective inflation 

will not exceed the acceptable rate or range, 

prospective output growth is below its potential 

rate, and the margin of slack in the economy is 

significant. On this basis, the London interbank 

offered rate on six-month U.S. dollar deposits 

is assumed to average 3.2 percent in 2008 and 

3.1 percent in 2009 (see Table 1.1). The rate 

on three-month euro deposits is assumed to 

average 4.8 percent in 2008 and 4.2 percent 

in 2009. The interest rate on six-month Japanese 

yen deposits is assumed to average 1.0 percent 

in 2008 and 1.2 percent in 2009.

Box A1 (concluded)

Statistical Appe

2
Statistical Appe

Outlook database reflects information from 

both national source agencies and international 

organizations. 

The comprehensive revision of the standardized System of National Accounts 1993 (SNA), the 

IMF’s Balance of Payments Manual, Fifth Edition

(BPM5), the Monetary and Financial Statistics 

Manual (MFSM), and the Government Finance 

Statistics Manual 2001 (GFSM 2001) represented 

significant improvements in the standards of 

economic statistics and analysis.1 The IMF was 

actively involved in all these projects, particularly 

the Balance of Payments, Monetary and Financial 

Statistics, and Government Finance Statistics manuals, which reflects the IMF’s special interest in 

countries’ external positions, financial sector 

stability, and public sector fiscal positions. The 

process of adapting country data to the new 

definitions began in earnest when the manuals 

were released. However, full concordance with 

the manuals is ultimately dependent on the provision by national statistical compilers of revised 

country data, and hence the World Economic 

Outlook estimates are still only partially adapted 

to these manuals.

In line with recent improvements in standards 

for reporting economic statistics, several countries have phased out their traditional fixed-baseyear method of calculating real macroeconomic 

variables levels and growth by switching to a 

chain-weighted method of computing aggregate 

growth. Recent dramatic changes in the structure of these economies have caused these countries to revise the way in which they measure 

real GDP levels and growth. Switching to the 

chain-weighted method of computing aggregate 

growth, which uses current price information, 

allows countries to measure GDP growth more 

1Commission of the European Communities, International Monetary Fund, Organization for Economic 

Cooperation and Development, United Nations, and 

World Bank, System of National Accounts 1993 (Brussels/

Luxembourg, New York, Paris, and Washington, 1993); 

International Monetary Fund, Balance of Payments Manual, 

Fifth Edition (Washington, 1993); International Monetary 

Fund, Monetary and Financial Statistics Manual (Washington, 2000); and International Monetary Fund, Government 

Finance Statistics Manual (Washington, 2001).

accurately by eliminating upward biases in new 

data.2 Currently, real macroeconomic data for 

Albania, Australia, Austria, Azerbaijan, Belgium, 

Bulgaria, Canada, Cyprus, the Czech Republic, Denmark, Estonia, the euro area, Finland, 

France, Georgia, Germany, Greece, Hong Kong 

SAR, Iceland, Ireland, Italy, Japan, Kazakhstan, 

Lithuania, Luxembourg, Malta, the Netherlands, 

New Zealand, Norway, Poland, Portugal, Russia, 

Slovenia, Spain, Sweden, Switzerland, the United 

Kingdom, and the United States are based on 

chain-weighted methodology. However, data 

before 1996 (Albania), 1994 (Azerbaijan), 1995 

(Belgium), 2000 (Bulgaria), 1995 (Cyprus), 

1995 (Czech Republic), 2001 (Estonia), 1995 

(euro area), 1996 (Georgia), 1991 (Germany), 

2000 (Greece), 1990 (Iceland), 1995 (Ireland), 

1994 (Japan), 1994 (Kazakhstan), 1995 (Luxembourg), 2000 (Malta), 1995 (Poland), 1995 

(Russia), 1995 (Slovenia), and 1995 (Spain) 

are based on unrevised national accounts and 

subject to revision in the future.

The members of the European Union have 

adopted a harmonized system for the compilation of national accounts, referred to as 

ESA 1995. All national accounts data from 

1995 onward are presented on the basis of the 

new system. Revision by national authorities 

of data prior to 1995 to conform to the new 

system has progressed but, in some cases, has 

not been completed. In such cases, historical 

World Economic Outlook data have been carefully 

adjusted to avoid breaks in the series. Users of 

EU national accounts data prior to 1995 should 

nevertheless exercise caution until such time as 

the revision of historical data by national statistical agencies has been fully completed. See Box 

1.2 of the May 2000 World Economic Outlook.

Composite data for country groups in the 

World Economic Outlook are either sums or 

weighted averages of data for individual countries. Unless otherwise indicated, multiyear aver2Charles Steindel, 1995, “Chain-Weighting: The New 

Approach to Measuring GDP,” Current Issues in Economics 

and Finance (Federal Reserve Bank of New York), Vol. 1 

(December).
ages of growth rates are expressed as compound 

annual rates of change.3 Arithmetically weighted 

averages are used for all data except inflation and 

money growth for the emerging and developing 

economies group, for which geometric averages 

are used. The following conventions apply.

• Country group composites for exchange 

rates, interest rates, and the growth rates of 

monetary aggregates are weighted by GDP 

converted to U.S. dollars at market exchange 

rates (averaged over the preceding three 

years) as a share of group GDP.

• Composites for other data relating to the 

domestic economy, whether growth rates or 

ratios, are weighted by GDP valued at PPPs as 

a share of total world or group GDP.4

• Composites for data relating to the domestic economy for the euro area (15 member 

countries throughout the entire period unless 

otherwise noted) are aggregates of national 

source data using GDP weights. Annual data 

are not adjusted for calendar day effects. For 

data prior to 1999, data aggregations apply 

1995 European currency unit exchange rates.

• Composite unemployment rates and employment growth are weighted by labor force as a 

share of group labor force.

• Composites relating to the external economy 

are sums of individual country data after 

conversion to U.S. dollars at the average market exchange rates in the years indicated for 

balance of payments data and at end-of-year 

market exchange rates for debt denominated 

in currencies other than U.S. dollars. Composites of changes in foreign trade volumes 

3Averages for real GDP and its components, employment, per capita GDP, inflation, factor productivity, 

trade, and commodity prices are calculated based on the 

compound annual rate of change, except for the unemployment rate, which is based on the simple arithmetic 

average. 4See Box A2 of the April 2004 World Economic Outlook 

for a summary of the revised PPP-based weights and 

Annex IV of the May 1993 World Economic Outlook. See 

also Anne-Marie Gulde and Marianne Schulze-Ghattas, 

“Purchasing Power Parity Based Weights for the World 

Economic Outlook,” in Staff Studies for the World Economic 

Outlook (International Monetary Fund, December 1993), 

pp. 106–23.

and prices, however, are arithmetic averages 

of percent changes for individual countries 

weighted by the U.S. dollar value of exports 

or imports as a share of total world or group 

exports or imports (in the preceding year).

For central and eastern European countries, 

external transactions in nonconvertible currencies (through 1990) are converted to U.S. dollars 

at the implicit U.S. dollar/ruble conversion rates 

obtained from each country’s national currency 

exchange rate for the U.S. dollar and for the ruble. 

All data refer to calendar years, except for the 

following countries, which refer to fiscal years: 

Australia (July/June), Egypt (July/June), Haiti 

(October/September), Islamic Republic of Iran 

(April/March), Mauritius (July/June), Myanmar 

(April/March), Nepal (July/June), New Zealand 

(July/June), Pakistan (July/June), Samoa (July/

June), and Tonga (July/June).

Classification of Countries

Summary of the Country Classificatio

The country classification in the World Economic 

Outlook divides the world into two major groups: 

advanced economies and emerging and developing economies.5 Rather than being based on 

strict criteria, economic or otherwise, this classification has evolved over time with the objective 

of facilitating analysis by providing a reasonably 

meaningful organization of data. Table A provides 

an overview of these standard groups in the World 

Economic Outlook, showing the number of countries in each group and the average 2007 shares of 

groups in aggregate PPP-valued GDP, total exports 

of goods and services, and population. 

A few countries are currently not included in 

these groups, either because they are not IMF 

members and their economies are not monitored by the IMF or because databases have 

5As used here, the term “country” does not in all cases 

refer to a territorial entity that is a state as understood 

by international law and practice. It also covers some territorial entities that are not states, but for which statistical 

data are maintained on a separate and independent basis.

Statistical Appe
Statistical Appe

Table A. Classification by World Economic Outlook Groups and Their Shares in Aggregate GDP, Expo

of Goods and Services, and Population, 2007

(Percent of total for group or world)

Number of

Countries GDP

Exports of Goods

and Services Population

Advanced

economies World

Advanced

economies World

Advanced

economies World

Advanced economies 31 100.0 56.3 100.0 66.2 100.0 15.2

United States 37.9 21.3 14.5 9.6 30.7 4.7

Euro area 15 28.6 16.1 44.3 29.4 32.3 4.9

Germany 7.7 4.3 13.9 9.2 8.4 1.3

France 5.6 3.2 6.1 4.0 6.3 1.0

Italy 4.9 2.8 5.4 3.6 6.0 0.9

Spain 3.7 2.1 3.4 2.3 4.6 0.7

Japan 11.7 6.6 7.1 4.7 13.0 2.0

United Kingdom 5.9 3.3 6.5 4.3 6.2 0.9

Canada 3.5 2.0 4.4 2.9 3.3 0.5

Other advanced economies 12 12.5 7.0 23.2 15.3 14.4 2.2

Memorandum

Major advanced economies 7 77.2 43.5 57.9 38.3 73.8 11.2

Newly industrialized Asian economies 4 6.6 3.7 13.4 8.9 8.4 1.3

Emerging and 

developing 

economies World

Emerging and 

developing 

economies World

Emerging and 

developing 

economies World

Emerging and developing economies 141 100.0 43.7 100.0 33.8 100.0 84.

Regional groups

Africa 47 6.8 3.0 7.4 2.5 15.0 12.7

Sub-Sahara 44 5.3 2.3 5.5 1.9 13.6 11.5

Excluding Nigeria and South Africa 42 2.8 1.2 2.8 0.9 10.1 8.6

Central and eastern Europe 13 9.3 4.0 13.6 4.6 3.2 2.8

Commonwealth of Independent States2 13 10.2 4.5 10.2 3.4 5.1 4.3

Russia 7.3 3.2 6.8 2.3 2.6 2.2

Developing Asia 23 46.1 20.1 39.3 13.3 62.3 52.9

China 24.8 10.8 23.2 7.8 24.0 20.4

India 10.5 4.6 4.1 1.4 21.2 18.0

Excluding China and India 21 10.8 4.7 12.1 4.1 17.1 14.5

Middle East 13 8.7 3.8 14.3 4.8 4.3 3.7

Western Hemisphere 32 18.9 8.3 15.1 5.1 10.0 8.5

Brazil 6.4 2.8 3.2 1.1 3.4 2.9

Mexico 4.7 2.1 5.0 1.7 1.9 1.6

Analytical groups

By source of export earnings

Fuel 24 19.3 8.4 26.8 9.1 11.0 9.4

Nonfuel 117 80.7 35.2 73.2 24.7 89.0 75.5

of which, primary products 20 1.7 0.7 2.1 0.7 4.0 3.4

By external financing source

Net debtor countries 116 55.1 24.1 47.1 15.9 64.8 55.0

of which, official financing 30 3.4 1.5 2.4 0.8 10.6 9.0

Net debtor countries by debtservicing experienc

Countries with arrears and/or 

rescheduling during 2002–06 49 9.7 4.3 6.9 2.3 17.1 14.5

Other net debtor countries 67 45.3 19.8 40.3 13.6 47.7 40.5

Other group

Heavily indebted poor countries 31 1.8 0.8 1.2 0.4 8.4 7.1

Middle East and North Africa 19 10.5 4.6 16.4 5.6 6.5 5.5

1The GDP shares are based on the purchasing-power-parity (PPP) valuation of country GDPs. The number of countries comprising each group 

reflects those for which data are included in the group aggregates. 2Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and 

similarities in economic structure.
Statistical Appe

not yet been fully developed. Because of data 

limitations, group composites do not reflect 

the following countries: the Islamic Republic of 

Afghanistan, Bosnia and Herzegovina, Brunei 

Darussalam, Eritrea, Iraq, Liberia, Montenegro, 

Serbia, Somalia, Timor-Leste, and Zimbabwe. 

Cuba and the Democratic People’s Republic of 

Korea are examples of countries that are not 

IMF members, whereas San Marino, among 

the advanced economies, and Aruba, Marshall 

Islands, the Federated States of Micronesia, and 

Palau, among the developing economies, are 

examples of countries for which databases have 

not been completed.

General Features and Composition of 

Groups in the World Economic Outlo

Classification

Advanced Economie

The 31 advanced economies are listed in 

Table B. The seven largest in terms of GDP—

the United States, Japan, Germany, France, 

Italy, the United Kingdom, and Canada—

constitute the subgroup of major advanced 

economies, often referred to as the Group of 

Seven (G7). The 15 members of the euro area

and the four newly industrialized Asian economies

are also distinguished as subgroups. Composite data shown in the tables for the euro area 

cover the current members for all years, even 

though the membership has increased over 

time.

In 1991 and subsequent years, data for 

Germany refer to west Germany and the eastern 

Länder (that is, the former German Democratic 

Republic). Before 1991, economic data were 

not available on a unified basis or in a consistent manner. Hence, in tables featuring data 

expressed as annual percent change, these apply 

to west Germany in years up to and including 1991, but to unified Germany from 1992 

onward. In general, data on national accounts 

and domestic economic and financial activity 

through 1990 cover west Germany only, whereas 

data for the central government and balance of 

payments apply to west Germany through June 

1990 and to unified Germany thereafter.

Table C lists the member countries of the 

European Union, not all of which are classified as 

advanced economies in the World Economic Outlook.

Emerging and Developing Economi

The group of emerging and developing 

economies (141 countries) includes all countries 

that are not classified as advanced economies.

The regional breakdowns of emerging and 

developing economies—Africa, central and eastern 

Europe, Commonwealth of Independent States, developing Asia, Middle East, and Western Hemisphere—

largely conform to the regional breakdowns in 

the IMF’s International Financial Statistics. In both 

classifications, Egypt and Libya are included in 

the Middle East region rather than in Africa. In 

addition, the World Economic Outlook sometimes 

refers to the regional group of Middle East and 

Table B. Advanced Economies by Subgr

Major Currency Areas

Other Subgroups

Euro area

Newly industrialized 

Asian economies

Major advanced 

economies Other advanced economies

United States Austria Italy Hong Kong SAR1 Canada Australia New Zealand 

Euro area Belgium Luxembourg Korea France Denmark Norway 

Japan Cyprus Malta Singapore Germany Hong Kong SAR1 Singapore 

Finland Netherlands Taiwan Province Italy Iceland Sweden 

France Portugal of China Japan Israel Switzerland 

Germany Slovenia United Kingdom Korea Taiwan Province 

Greece Spain United States of China

Ireland

1On July 1, 1997, Hong Kong was returned to the People’s Republic of China and became a Special Administrative Region of China.
Statistical Appe

North African countries, also referred to as the 

MENA countries, whose composition straddles 

the Africa and Middle East regions. This group 

is defined as the Arab League countries plus the 

Islamic Republic of Iran (see Table D).

Emerging and developing economies are also 

classified according to analytical criteria. The 

analytical criteria reflect countries’ composition of export earnings and other income from 

abroad; exchange rate arrangements; a distinction between net creditor and net debtor countries; and, for the net debtor countries, financial 

criteria based on external financing sources and 

experience with external debt servicing. The 

detailed composition of emerging and developing economies in the regional and analytical 

groups is shown in Tables E and F.

The analytical criterion, by source of export 

earnings, distinguishes between categories fuel

(Standard International Trade Classification—

SITC 3) and nonfuel and then focuses on nonfuel 

primary products (SITCs 0, 1, 2, 4, and 68).

The financial criteria focus on net creditor 

countries, net debtor countries, and heavily indebted 

poor countries (HIPCs). Net debtor countries 

are further differentiated on the basis of two 

additional financial criteria: by official external 

financing and by experience with debt servicing.

6 The 

6During 2002–06, 49 countries incurred external payments arrears or entered into official or commercial bank 

debt-rescheduling agreements. This group of countries 

HIPC group comprises the countries considered 

by the IMF and the World Bank for their debt 

initiative, known as the HIPC Initiative, with the 

aim of reducing the external debt burdens of all 

the eligible HIPCs to a “sustainable” level in a 

reasonably short period of time.7

is referred to as countries with arrears and/or rescheduling 

during 2002–06. 7See David Andrews, Anthony R. Boote, Syed S. Rizavi, 

and Sukwinder Singh, Debt Relief for Low-Income Countries: 

The Enhanced HIPC Initiative, IMF Pamphlet Series, No. 51 

(Washington: International Monetary Fund, November 

1999).

Table D. Middle East and North African Countr

Algeria Jordan Morocco Syrian Arab Republic

Bahrain Kuwait Oman Tunisia

Djibouti Lebanon Qatar United Arab Emirates

Egypt Libya Saudi Arabia Yemen, Rep. of

Iran, I.R. of Mauritania Sudan

Table E. Emerging and Developing Economies

Region and Main Source of Export Earni

Fuel

Nonfuel Primary 

Products

Africa Algeria Botswana

Angola Burkina Faso

Congo, Rep. of Burundi

Equatorial Guinea Chad

Gabon

Nigeria

Sudan

Congo, Dem. 

Rep. of

Guinea

Guinea-Bissau

Malawi

Mauritania

Mozambique

Sierra Leone

Zambia

Commonwealth 

of Independent 

State

Azerbaijan Mongolia

Kazakhstan Tajikistan

Russia Uzbekistan

Turkmenistan

Developing Asia Papua New Guinea

Solomon Islands

Middle East Bahrai

Iran, I.R. of

Kuwait

Libya

Oman

Qatar

Saudi Arabia

Syrian Arab Republic

United Arab Emirates

Yemen, Rep. of

Western

Hemispher

Ecuador Chile

Trinidad and Tobago Guyana

Venezuela, Rep. 

Boliv. de

Suriname

Note: Mongolia, which is not a member of the Commonwealth of 

Independent States, is included in this group for reasons of geography and similarities in economic structure.

Table C. European Un

Austria Finland Latvia Romania

Belgium France Lithuania Slovak Republic

Bulgaria Germany Luxembourg Slovenia

Cyprus Greece Malta Spain

Czech Republic Hungary Netherlands Sweden

Denmark Ireland Poland United Kingdom

Estonia Italy Portugal
Statistical Appe

Heavily

Indebted

Poor

Countries

Net External Position

Net

creditor

Net

debtor1

Africa

Maghreb

Algeria *

Morocco *

Tunisia *

Sub-Saha

South Africa *

Horn of Afric

Djibouti *

Ethiopia • *

Sudan *

Great Lakes

Burundi • *

Congo, Dem. Rep. of * *

Kenya *

Rwanda • *

Tanzania • *

Uganda * *

Southern Afric

Angola *

Botswana *

Comoros •

Lesotho *

Madagascar • *

Malawi • *

Mauritius *

Mozambique * *

Namibia *

Seychelles *

Swaziland *

Zambia • *

West and Central Afric

Cape Verde *

Gambia, The * *

Ghana • *

Guinea * *

Mauritania * *

Nigeria *

São Tomé and Príncipe * *

Sierra Leone • *

CFA franc zone

Benin * *

Burkina Faso • *

Cameroon * *

Central African Republic • *

Chad * *

Congo, Rep. of • *

Côte d’Ivoire *

Equatorial Guinea *

Gabon *

Guinea-Bissau * *

Mali * *

Niger • *

Senegal * *

Togo *

Heavily

Indebted

Poor

Countries

Net External Position

Net

creditor

Net

debtor1

Central and eastern Europ

Albania *

Bulgaria *

Croatia *

Czech Republic *

Estonia *

Hungary *

Latvia *

Lithuania *

Macedonia, FYR *

Poland *

Romania *

Slovak Republic *

Turkey *

Commonwealth of 

Independent States

Armenia •

Azerbaijan *

Belarus *

Georgia *

Kazakhstan *

Kyrgyz Republic *

Moldova *

Mongolia •

Russia *

Tajikistan *

Turkmenistan *

Ukraine *

Uzbekistan *

Developing Asia

Bhutan •

Cambodia •

China *

Fiji *

Indonesia *

Kiribati *

Lao PDR *

Malaysia *

Myanmar *

Papua New Guinea *

Philippines *

Samoa *

Solomon Islands •

Thailand *

Tonga •

Vanuatu *

Vietnam •

South Asi

Bangladesh •

India *

Maldives *

Nepal •

Pakistan *

Sri Lanka •

Table F. Emerging and Developing Economies by Region, Net External Position, and Status as H

Indebted Poor Countries
Statistical Appe

Heavily

Indebted

Poor

Countries

Net External Position

Net

creditor

Net

debtor1

Middle Eas

Bahrain *

Iran, I.R. of *

Kuwait *

Libya *

Oman *

Qatar *

Saudi Arabia *

United Arab Emirates *

Yemen, Rep. of *

Mashreq

Egypt *

Jordan *

Lebanon *

Syrian Arab Republic *

Western Hemisphe

Mexico *

South Americ

Argentina *

Bolivia *

Brazil • *

Chile *

Colombia *

Ecuador *

Paraguay *

Heavily

Indebted

Poor

Countries

Net External Position

Net

creditor

Net

debtor1

Peru *

Uruguay •

Venezuela, Rep. Boliv. de *

Central America

Costa Rica *

El Salvador •

Guatemala *

Honduras * *

Nicaragua * *

Panama *

Caribbean

Antigua and Barbuda *

Bahamas, The *

Barbados *

Belize *

Dominica *

Dominican Republic *

Grenada •

Guyana * *

Haiti • *

Jamaica *

St. Kitts and Nevis *

St. Lucia *

St. Vincent and the Grenadines •

Suriname *

Trinidad and Tobago *

Table F (concluded

1Dot instead of star indicates that the net debtor’s main external finance source is official financing.

2Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and 

similarities in economic structure.
Statistical Appe

List of Table

Outpu

A1. Summary of World Output 259

A2. Advanced Economies: Real GDP and Total Domestic Demand 260

A3. Advanced Economies: Components of Real GDP 261

A4. Emerging and Developing Economies by Country: Real GDP 263

Inflation

A5. Summary of Inflation 267

A6. Advanced Economies: Consumer Prices 268

A7. Emerging and Developing Economies by Country: Consumer Prices 269

Financial Policies

A8. Major Advanced Economies: General Government Fiscal Balances and Debt 273

Foreign Trad

A9. Summary of World Trade Volumes and Prices 274

Current Account Transaction

A10. Summary of Balances on Current Account 276

A11. Advanced Economies: Balance on Current Account 277

A12. Emerging and Developing Economies by Country: Balance on Current Account 278

Balance of Payments and External Financin

A13. Emerging and Developing Economies: Net Capital Flows 282

A14. Emerging and Developing Economies: Private Capital Flows 283

A15. Emerging and Developing Economies: Reserves 284

Flow of Funds

A16. Summary of Sources and Uses of World Savings 286

Medium-Term Baseline Scenar

A17. Summary of World Medium-Term Baseline Scenario 290
Output: Summ

Table A1. Summary of World Out

(Annual percent change)

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

World 2.9 4.7 2.2 2.8 3.6 4.9 4.5 5.1 5.0 3.9 3.0 4.

Advanced economies 2.7 4.0 1.2 1.6 1.9 3.2 2.6 3.0 2.6 1.5 0.5 2.5

United States 3.1 3.7 0.8 1.6 2.5 3.6 2.9 2.8 2.0 1.6 0.1 2.3

Euro area . . . 3.8 1.9 0.9 0.8 2.1 1.6 2.8 2.6 1.3 0.2 2.2

Japan 1.5 2.9 0.2 0.3 1.4 2.7 1.9 2.4 2.1 0.7 0.5 1.7

Other advanced economies2 3.4 5.2 1.7 3.3 2.5 4.0 3.3 3.8 3.9 2.2 1.6 3.6

Emerging and developing economies 3.2 5.9 3.8 4.8 6.3 7.5 7.1 7.9 8.0 6.9 6.1 6.

Regional groups

Africa 2.3 3.5 4.9 6.2 5.4 6.5 5.8 6.1 6.3 5.9 6.0 5.4

Central and eastern Europe 1.2 4.9 0.4 4.2 4.8 6.9 6.1 6.7 5.7 4.5 3.4 5.0

Commonwealth of Independent States3 . . . 9.1 6.1 5.2 7.8 8.2 6.8 8.2 8.6 7.2 5.7 5.6

Developing Asia 7.2 7.0 5.8 6.9 8.2 8.6 9.0 9.9 10.0 8.4 7.7 8.8

Middle East 4.3 5.5 2.6 3.8 7.1 5.8 5.7 5.7 5.9 6.4 5.9 5.4

Western Hemisphere 2.9 4.1 0.7 0.5 2.2 6.1 4.7 5.5 5.6 4.6 3.2 4.2

Memorandum

European Union 2.0 3.9 2.1 1.4 1.5 2.7 2.2 3.3 3.1 1.7 0.6 2.8

Analytical groups

By source of export earnings

Fuel –0.2 7.0 4.3 4.7 6.9 7.7 6.9 7.1 7.4 6.7 5.9 5.0

Nonfuel 4.2 5.7 3.7 4.8 6.1 7.5 7.2 8.1 8.2 6.9 6.1 7.3

of which, primary products 3.1 3.3 4.0 3.5 4.8 7.2 5.8 5.1 5.8 5.4 5.3 5.4

By external financing source

Net debtor countries 3.1 4.7 2.2 3.2 4.6 6.3 6.1 6.8 6.6 5.6 4.7 5.8

of which, official financing 4.3 4.8 4.2 3.9 5.3 6.4 7.0 7.2 7.1 6.4 5.7 6.4

Net debtor countries by debtservicing experienc

Countries with arrears and/or

rescheduling during 2002–06 3.3 3.3 2.6 1.6 5.7 6.7 6.7 6.7 6.5 5.8 4.9 5.5

Memorandum

Median growth rate

Advanced economies 3.0 3.9 1.9 1.8 1.9 3.7 2.9 3.4 3.7 1.8 1.2 3.0

Emerging and developing economies 3.3 4.3 3.6 4.0 4.9 5.5 5.6 6.2 6.0 5.5 5.0 5.1

Output per capit

Advanced economies 2.0 3.3 0.5 1.0 1.3 2.6 1.9 2.4 1.9 0.9 –0.1 1.9

Emerging and developing economies 1.6 4.6 2.4 3.4 4.9 6.1 5.8 6.6 6.7 5.6 4.8 5.5

World growth based on market exchange rates 2.4 4.2 1.5 1.9 2.7 4.0 3.4 3.9 3.7 2.7 1.9 3.

Value of world output in billions of U.S. doll

At market exchange rates 27,383 31,916 31,677 32,954 37,048 41,677 45,022 48,665 54,585 62,054 64,168 82,523

At purchasing power parities 31,729 41,748 43,659 45,634 48,252 52,000 55,924 60,610 65,281 69,229 72,395 93,305

1Real GDP.

2In this table, “other advanced economies” means advanced economies excluding the United States, euro area countries, and Japan.

3Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Statistical appen

2

Table A2. Advanced Economies: Real GDP and Total Domestic Dem

(Annual percent change)

Average Fourth Quarter1

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Real GDP

Advanced economies 2.7 4.0 1.2 1.6 1.9 3.2 2.6 3.0 2.6 1.5 0.5 2.5 2.6 0.7 1.0

United States 3.1 3.7 0.8 1.6 2.5 3.6 2.9 2.8 2.0 1.6 0.1 2.3 2.3 0.8 0.4

Euro area . . . 3.8 1.9 0.9 0.8 2.1 1.6 2.8 2.6 1.3 0.2 2.2 2.1 0.4 0.6

Germany 2.3 3.2 1.2 — –0.2 1.2 0.8 3.0 2.5 1.8 — 1.7 1.7 0.7 0.6

France 1.9 3.9 1.9 1.0 1.1 2.5 1.9 2.2 2.2 0.8 0.2 2.8 2.2 –0.1 0.8

Italy 1.4 3.7 1.8 0.5 — 1.5 0.6 1.8 1.5 –0.1 –0.2 1.3 0.1 –0.1 0.2

Spain 2.8 5.1 3.6 2.7 3.1 3.3 3.6 3.9 3.7 1.4 –0.2 3.3 3.2 0.1 0.1

Netherlands 3.1 3.9 1.9 0.1 0.3 2.2 2.0 3.4 3.5 2.3 1.0 2.2 4.1 0.8 2.3

Belgium 2.3 3.8 0.9 1.4 1.0 2.7 2.0 2.9 2.8 1.4 0.2 2.4 2.4 0.4 0.9

Austria 2.5 3.7 0.5 1.6 0.8 2.5 2.9 3.4 3.1 2.0 0.8 2.2 2.7 1.2 1.0

Finland 1.5 5.0 2.6 1.6 1.8 3.7 2.8 4.9 4.5 2.5 1.6 2.3 4.0 1.6 1.9

Greece 1.9 4.5 4.5 3.9 5.0 4.6 3.8 4.2 4.0 3.2 2.0 3.5 3.6 2.5 2.4

Portugal 3.4 3.9 2.0 0.8 –0.8 1.5 0.9 1.4 1.9 0.6 0.1 1.8 1.9 0.4 0.2

Ireland 6.9 9.2 5.8 6.4 4.5 4.7 6.4 5.7 6.0 –1.8 –0.6 4.1 5.6 –3.3 2.1

Luxembourg 4.7 8.4 2.5 4.1 2.1 4.9 5.0 6.1 4.5 2.3 1.8 3.4 3.1 1.3 2.4

Slovenia . . . 4.1 3.1 3.7 2.8 4.4 4.1 5.7 6.1 4.3 3.7 3.7 4.9 3.2 6.1

Cyprus 3.7 5.0 4.0 2.1 1.9 4.2 3.9 4.0 4.4 3.4 2.8 4.1 4.4 2.4 3.6

Malta 5.0 –1.0 –1.6 2.6 –0.3 1.1 3.5 3.1 3.7 2.8 2.3 3.4 3.7 2.0 2.5

Japan 1.5 2.9 0.2 0.3 1.4 2.7 1.9 2.4 2.1 0.7 0.5 1.7 1.4 0.2 0.9

United Kingdom 2.2 3.9 2.5 2.1 2.8 2.8 2.1 2.8 3.0 1.0 –0.1 3.1 2.9 –0.3 0.7

Canada 2.4 5.2 1.8 2.9 1.9 3.1 2.9 3.1 2.7 0.7 1.2 2.8 2.8 0.3 1.7

Korea 6.1 8.5 3.8 7.0 3.1 4.7 4.2 5.1 5.0 4.1 3.5 4.7 5.9 2.4 5.2

Australia 3.3 3.5 2.1 4.2 3.0 3.9 2.8 2.7 4.2 2.5 2.2 3.6 4.2 1.9 2.5

Taiwan Province of China 6.5 5.8 –2.2 4.6 3.5 6.2 4.2 4.9 5.7 3.8 2.5 5.0 6.4 2.5 5.8

Sweden 1.7 4.4 1.1 2.4 1.9 4.1 3.3 4.1 2.7 1.2 1.4 3.0 2.4 0.7 1.7

Switzerland 1.1 3.6 1.2 0.4 –0.2 2.5 2.5 3.4 3.3 1.7 0.7 1.7 3.7 0.1 1.7

Hong Kong SAR 3.5 8.0 0.5 1.8 3.0 8.5 7.1 7.0 6.4 4.1 3.5 5.0 7.0 2.1 6.1

Denmark 2.4 3.5 0.7 0.5 0.4 2.3 2.5 3.9 1.7 1.0 0.5 1.6 1.5 3.1 –3.0

Norway 3.6 3.3 2.0 1.5 1.0 3.9 2.7 2.5 3.7 2.5 1.2 2.2 4.7 1.4 0.9

Israel 5.4 8.9 –0.3 –0.6 1.8 5.0 5.1 5.2 5.4 4.3 2.8 3.7 5.8 2.4 4.2

Singapore 7.5 10.1 –2.4 4.2 3.5 9.0 7.3 8.2 7.7 3.6 3.5 5.5 5.4 4.0 4.4

New Zealand 2.5 3.8 2.6 4.9 4.1 4.5 2.7 1.9 3.2 0.7 1.5 2.9 3.7 — 1.5

Iceland 2.2 4.3 3.9 0.1 2.4 7.7 7.4 4.4 4.9 0.3 –3.1 3.3 6.4 –1.3 –6.0

Memorandum

Major advanced economies 2.5 3.6 1.0 1.2 1.8 2.9 2.3 2.7 2.2 1.2 0.1 2.2 2.0 0.5 0.6

Newly industrialized Asian economies 6.1 7.7 1.2 5.5 3.2 5.9 4.8 5.6 5.6 4.0 3.2 4.9 6.1 2.6 5.4

Real total domestic demand

Advanced economies 2.7 4.0 1.1 1.7 2.1 3.3 2.6 2.8 2.2 0.8 0.1 2.4 2.1 0.1 0.4

United States 3.3 4.4 0.9 2.2 2.8 4.1 3.0 2.6 1.4 0.1 –0.9 2.2 1.4 –0.6 –0.3

Euro area . . . 3.4 1.2 0.4 1.5 1.9 1.8 2.6 2.2 0.8 0.1 2.1 2.0 0.2 0.5

Germany 2.3 2.2 –0.5 –2.0 0.6 –0.1 — 2.1 1.1 0.6 –0.2 1.3 1.3 0.2 0.5

France 1.6 4.4 1.7 1.1 1.8 3.4 2.7 2.4 2.9 0.9 0.6 2.8 2.6 0.3 1.0

Italy 1.3 3.0 1.6 1.3 0.8 1.3 0.8 1.8 1.3 –0.2 –0.4 1.5 0.1 — –1.1

Spain 2.7 5.3 3.8 3.2 3.8 4.8 5.1 5.1 4.2 0.8 –1.9 3.0 3.6 –1.3 –1.0

Japan 1.4 2.4 1.0 –0.4 0.8 1.9 1.7 1.6 1.0 –0.3 0.4 1.8 0.1 –0.4 1.0

United Kingdom 2.2 3.9 3.0 3.2 2.9 3.4 1.9 2.6 3.6 0.8 –0.6 3.2 3.5 –1.1 0.3

Canada 1.8 4.8 1.2 3.2 4.6 4.2 4.8 4.7 4.3 2.9 1.6 3.0 6.3 0.9 1.5

Other advanced economies 4.1 5.5 0.3 3.9 1.4 4.8 3.3 3.7 4.5 2.8 2.4 3.8 4.6 2.6 2.3

Memorandum

Major advanced economies 2.5 3.7 1.1 1.3 2.2 3.1 2.3 2.5 1.7 0.3 –0.3 2.2 1.6 –0.4 0.2

Newly industrialized Asian economies 5.9 7.8 –0.1 4.5 0.1 4.9 2.6 4.0 4.4 3.1 3.2 4.3 5.0 3.3 3.5

1From fourth quarter of preceding year.
Output: Advanced Econo

Table A3. Advanced Economies: Components of Real G

(Annual percent change)

 Ten-Year Averages

1990–99 2000–09 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Private consumer expenditure

Advanced economies 2.8 2.2 3.8 2.3 2.2 1.9 2.8 2.5 2.7 2.6 0.9 —

United States 3.3 2.5 4.7 2.5 2.7 2.8 3.6 3.0 3.0 2.8 0.5 –0.9

Euro area . . . 1.4 3.1 2.0 0.9 1.3 1.6 1.6 1.8 1.5 0.3 –0.1

Germany 2.4 0.2 2.4 1.9 –0.8 0.1 0.1 0.2 1.0 –0.4 –1.0 –1.1

France 1.6 2.2 3.6 2.6 2.4 2.0 2.5 2.6 2.3 2.4 0.8 0.4

Italy 1.6 0.8 2.4 0.7 0.2 1.0 0.7 0.9 1.1 1.4 –0.3 –0.3

Spain 2.5 3.0 5.0 3.4 2.8 2.9 4.2 4.2 3.9 3.5 1.1 –1.3

Japan 1.9 1.2 0.7 1.6 1.1 0.4 1.6 1.3 2.0 1.5 0.7 0.6

United Kingdom 2.5 2.6 4.7 3.1 3.5 3.0 2.9 1.9 2.1 3.0 2.0 —

Canada 2.2 3.5 4.0 2.3 3.6 3.0 3.3 3.7 4.3 4.5 3.9 2.2

Other advanced economies 4.1 3.2 5.6 2.6 3.9 1.1 3.5 3.4 3.5 4.2 2.2 1.7

Memorandum

Major advanced economies 2.6 2.0 3.6 2.2 2.0 2.0 2.6 2.3 2.5 2.3 0.6 –0.3

Newly industrialized Asian economies 5.9 3.4 7.4 3.3 5.1 –0.3 2.4 3.4 3.8 4.3 2.1 2.1

Public consumption

Advanced economies 1.9 2.2 2.5 2.8 3.3 2.3 1.8 1.2 1.7 2.1 2.2 1.7

United States 1.1 2.1 1.7 3.1 4.3 2.5 1.5 0.3 1.6 1.9 2.5 1.7

Euro area . . . 1.9 2.4 2.1 2.4 1.8 1.4 1.5 2.0 2.2 1.8 1.6

Germany 2.0 1.0 1.4 0.5 1.5 0.4 –0.7 0.4 0.6 2.2 2.0 2.0

France 1.7 1.6 1.9 1.2 1.9 2.0 2.3 1.2 1.3 1.4 1.4 1.6

Italy 0.2 1.8 2.2 3.9 2.4 1.9 2.2 1.9 0.8 1.2 0.8 0.4

Spain 3.3 4.6 5.3 3.9 4.5 4.8 6.3 5.5 4.6 4.9 3.9 2.9

Japan 3.0 1.7 4.3 3.0 2.4 2.3 1.9 1.6 –0.4 0.7 0.4 1.0

United Kingdom 1.2 2.6 3.1 2.4 3.4 3.5 3.4 1.7 1.6 1.8 2.3 2.6

Canada 0.9 2.8 3.1 3.9 2.5 3.1 2.0 1.5 3.8 3.7 4.1 0.9

Other advanced economies 3.6 2.8 2.4 3.1 3.6 2.1 1.9 2.4 3.2 2.8 3.4 3.0

Memorandum

Major advanced economies 1.5 1.9 2.3 2.7 3.2 2.3 1.6 0.9 1.2 1.7 2.0 1.5

Newly industrialized Asian economies 5.2 3.4 2.9 3.7 4.5 2.4 1.8 3.0 4.0 3.6 4.4 3.9

Gross fixed capital formation

Advanced economies 3.4 1.7 5.0 –0.8 –1.5 2.2 4.6 4.5 3.8 1.8 –0.5 –1.9

United States 5.1 0.8 6.1 –1.7 –3.5 3.2 6.1 5.8 2.0 –2.0 –2.7 –4.6

Euro area . . . 2.1 4.9 0.5 –1.4 1.4 2.4 3.0 5.0 4.3 2.2 –0.6

Germany 2.7 1.0 3.0 –3.7 –6.1 –0.3 –0.3 1.1 7.7 4.3 5.2 –0.5

France 1.4 2.8 7.2 2.4 –1.7 2.2 3.6 4.4 4.8 4.9 1.1 –0.1

Italy 1.1 1.8 6.3 2.7 3.7 –1.2 2.3 0.7 2.5 1.2 0.2 0.2

Spain 3.3 3.6 6.6 4.8 3.4 5.9 5.1 7.0 7.1 5.3 –1.9 –6.0

Japan — –0.3 1.2 –0.9 –4.9 –0.5 1.4 3.1 1.3 –0.6 –2.3 –0.4

United Kingdom 2.3 1.9 2.7 2.6 3.6 1.1 4.9 2.2 6.0 7.1 –4.0 –6.1

Canada 1.9 4.5 4.7 4.0 1.6 6.2 7.8 9.2 7.1 3.9 1.3 0.2

Other advanced economies 4.9 4.0 6.8 –4.4 3.8 2.8 7.3 4.7 5.4 6.6 3.5 3.5

Memorandum

Major advanced economies 3.2 1.1 4.8 –0.6 –2.6 1.9 4.3 4.4 3.2 0.5 –1.3 –2.8

Newly industrialized Asian economies 7.1 3.6 10.5 –5.9 2.2 2.3 7.8 2.0 4.0 5.1 2.8 5.5
Statistical appen

Table A3 (concluded

Ten-Year Averages

1990–99 2000–09 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Final domestic demand

Advanced economies 2.5 2.0 3.6 1.8 1.5 2.1 2.9 2.7 2.7 2.2 0.7 –0.3

United States 3.3 2.1 4.5 1.8 1.8 2.8 3.8 3.1 2.6 1.8 0.3 –1.1

Euro area . . . 1.7 3.4 1.7 0.7 1.4 1.7 1.8 2.5 2.3 1.0 0.1

Germany 2.4 0.6 2.0 0.4 –1.4 0.1 –0.1 0.4 2.2 1.1 1.0 –0.3

France 1.6 2.2 3.9 2.2 1.4 2.1 2.7 2.6 2.5 2.7 1.0 0.6

Italy 1.2 1.2 3.1 1.7 1.3 0.7 1.4 1.1 1.3 1.4 — –0.1

Spain 2.8 3.4 5.4 3.9 3.2 4.0 4.8 5.2 4.9 4.2 0.7 –1.9

Japan 1.5 0.9 1.4 1.2 –0.2 0.5 1.6 1.9 1.4 0.9 –0.1 0.4

United Kingdom 2.2 2.5 4.1 2.9 3.5 2.8 3.3 1.9 2.6 3.4 1.0 –0.5

Canada 1.8 3.6 4.0 2.9 3.0 3.7 3.9 4.4 4.8 4.2 3.3 1.5

Other advanced economies 4.2 3.3 5.4 0.9 3.7 1.7 4.0 3.4 3.8 4.6 2.7 2.5

Memorandum

Major advanced economies 2.5 1.8 3.6 1.7 1.3 2.0 2.8 2.5 2.4 1.8 0.5 –0.4

Newly industrialized Asian economies 6.1 3.4 7.6 0.8 4.1 0.8 3.5 3.0 3.9 4.5 2.6 3.3

Stock building

Advanced economies — — 0.1 –0.6 — 0.1 0.3 –0.1 — –0.1 –0.2 0.1

United States 0.1 — –0.1 –0.9 0.4 — 0.4 –0.1 — –0.4 –0.1 0.3

Euro area . . . –0.1 — –0.5 –0.3 0.1 0.2 — 0.1 — –0.2 —

Germany –0.1 –0.2 –0.1 –0.9 –0.6 0.5 — –0.4 — 0.1 –0.6 0.1

France — — 0.5 –0.4 –0.4 –0.3 0.7 0.1 –0.1 0.3 –0.2 —

Italy — — –0.2 0.1 — 0.1 –0.1 –0.2 0.5 –0.1 0.3 –0.3

Spain –0.1 — –0.1 –0.1 — –0.1 — –0.1 0.2 –0.1 0.1 —

Japan –0.1 0.1 1.0 –0.2 –0.3 0.2 0.3 –0.1 0.2 0.1 –0.2 —

United Kingdom — — –0.1 0.1 –0.3 0.2 — — — 0.2 –0.2 —

Canada — — 0.8 –1.7 0.2 0.8 0.1 0.3 –0.1 0.1 –0.4 0.2

Other advanced economies — — 0.1 –0.5 0.1 –0.2 0.6 –0.1 –0.1 — 0.2 –0.1

Memorandum

Major advanced economies — — 0.2 –0.6 0.1 0.1 0.3 –0.1 0.1 –0.1 –0.2 0.1

Newly industrialized Asian economies –0.1 — 0.1 –0.8 0.3 –0.6 1.1 –0.4 0.1 –0.1 0.4 –0.1

Foreign balance1

Advanced economies — — –0.1 — –0.2 –0.5 –0.3 –0.2 0.1 0.3 0.7 0.5

United States –0.2 — –0.9 –0.2 –0.7 –0.4 –0.7 –0.2 — 0.6 1.4 1.0

Euro area . . . 0.2 0.5 0.7 0.5 –0.7 0.2 –0.2 0.2 0.4 0.4 0.2

Germany — 1.0 1.1 1.7 2.0 –0.8 1.4 0.7 1.0 1.4 1.3 0.2

France 0.3 –0.4 –0.5 0.1 –0.1 –0.7 –0.9 –0.8 –0.3 –0.8 — –0.4

Italy 0.1 –0.1 0.6 0.2 –0.8 –0.8 0.2 –0.3 — 0.1 0.2 0.1

Spain –0.2 –0.5 –0.4 –0.2 –0.6 –0.8 –1.7 –1.6 –1.2 –0.7 0.5 1.7

Japan 0.1 0.5 0.5 –0.8 0.7 0.7 0.8 0.3 0.8 1.1 0.9 0.1

United Kingdom — –0.2 — –0.5 –1.1 –0.1 –0.7 0.1 0.1 –0.7 0.2 0.5

Canada 0.6 –0.9 0.6 0.7 –0.1 –2.5 –0.9 –1.7 –1.3 –1.5 –2.1 –0.4

Other advanced economies 0.3 0.7 0.7 0.9 0.2 1.2 0.5 0.9 1.0 0.6 0.4 0.3

Memorandum

Major advanced economies — 0.1 –0.2 — –0.2 –0.4 –0.2 –0.1 0.1 0.4 0.9 0.5

Newly industrialized Asian economies 0.1 1.6 0.3 1.1 1.1 3.0 1.8 2.5 2.2 1.9 1.3 0.5

1Changes expressed as percent of GDP in the preceding period.
Output: Emerging and Developing 

Table A4. Emerging and Developing Economies, by Country: Real G

(Annual percent change)

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Africa 2.3 3.5 4.9 6.2 5.4 6.5 5.8 6.1 6.3 5.9 6.0 5.4

Algeria 1.5 2.2 2.7 4.7 6.9 5.2 5.1 2.0 4.6 4.9 4.5 5.2

Angola 0.6 3.0 3.1 14.5 3.3 11.2 20.6 18.6 21.1 16.0 12.8 0.1

Benin 4.9 4.9 6.2 4.5 3.9 3.1 2.9 3.8 4.6 5.1 5.7 6.0

Botswana 6.0 8.4 4.7 5.3 6.4 6.6 4.7 3.4 5.7 5.3 4.6 5.0

Burkina Faso 5.1 1.8 6.6 4.7 7.3 4.6 7.1 5.5 3.6 4.5 5.6 6.0

Burundi –1.3 –0.9 2.1 4.4 –1.2 4.8 0.9 5.1 3.6 4.5 5.0 5.5

Cameroon2 0.3 4.2 4.5 4.0 4.0 3.7 2.3 3.2 3.5 3.8 4.6 5.3

Cape Verde 6.1 7.3 6.1 5.3 4.7 4.3 6.5 10.8 6.9 6.0 6.5 6.7

Central African Republic 0.4 1.8 0.3 –0.6 –7.6 1.0 2.4 4.0 4.2 3.5 4.5 5.0

Chad 3.2 –0.9 11.7 8.5 14.7 33.6 7.9 0.2 0.2 0.4 5.0 2.4

Comoros 1.5 1.4 3.3 4.1 2.5 –0.2 4.2 1.2 0.5 0.5 1.7 4.5

Congo, Dem. Rep. of –5.6 –6.9 –2.1 3.5 5.8 6.6 7.9 5.6 6.3 10.0 10.3 7.3

Congo, Rep. of 0.8 7.6 3.8 4.6 0.8 3.5 7.8 6.2 –1.6 9.1 12.1 1.4

Côte d’Ivoire 3.5 –4.6 — –1.6 –1.7 1.6 1.9 0.7 1.6 2.9 4.7 6.4

Djibouti –1.2 0.5 2.0 2.6 3.2 3.0 3.2 4.8 5.3 5.9 6.9 7.2

Equatorial Guinea 29.2 18.2 63.4 19.5 14.0 38.0 9.7 1.3 21.4 7.4 4.6 1.2

Eritrea . . . –12.4 8.8 3.0 –2.7 1.5 2.6 –1.0 1.3 1.2 2.0 4.8

Ethiopia 2.6 5.9 7.7 1.2 –3.5 9.8 12.6 11.6 11.4 8.4 6.5 7.7

Gabon 2.4 –1.9 2.1 –0.3 2.4 1.1 3.0 1.2 5.6 3.9 7.0 1.5

Gambia, The 4.2 5.5 5.8 –3.2 6.9 7.0 5.1 6.5 6.3 5.5 6.0 5.5

Ghana 4.5 3.7 4.2 4.5 5.2 5.6 5.9 6.4 6.3 6.5 5.8 6.8

Guinea 4.2 2.9 3.8 4.2 1.2 2.3 3.0 2.4 1.8 4.5 4.7 5.3

Guinea-Bissau 0.6 7.5 0.2 –7.1 –0.6 2.2 3.2 1.8 2.5 3.2 3.1 3.9

Kenya 2.1 0.6 4.7 0.3 2.8 4.6 5.9 6.4 7.0 3.3 6.4 6.5

Lesotho 4.0 2.3 1.8 2.8 2.7 4.2 2.9 7.2 4.9 5.2 5.4 5.2

Liberia . . . 29.3 2.9 3.7 –31.3 2.6 5.3 7.8 9.5 8.6 14.3 12.0

Madagascar 1.6 4.5 6.0 –12.4 15.3 3.3 4.7 6.6 6.4 2.1 7.5 6.1

Malawi 3.9 0.8 –4.1 1.9 4.2 5.0 2.3 8.2 7.9 7.1 7.8 6.5

Mali 5.5 –3.2 12.1 4.3 7.2 2.4 6.1 5.3 3.1 4.8 5.2 4.5

Mauritania 2.6 1.9 2.9 1.1 5.6 5.2 5.4 11.4 1.0 5.0 6.8 6.2

Mauritius 5.8 7.2 4.2 1.5 3.8 4.8 3.1 3.6 4.2 6.6 6.2 5.1

Morocco 2.6 1.8 7.6 3.3 6.3 4.8 3.0 7.8 2.7 6.5 5.5 6.0

Mozambique 6.4 1.5 12.3 9.2 6.5 7.9 8.4 8.7 7.0 6.5 6.7 6.5

Namibia 3.8 3.5 2.4 6.7 3.5 6.6 4.7 3.9 3.6 3.9 4.2 4.6

Niger 1.1 –2.6 7.4 5.3 7.7 –0.8 7.4 5.2 3.2 4.4 4.5 4.4

Nigeria 2.6 5.3 8.2 21.2 10.3 10.6 5.4 6.2 5.9 6.2 8.1 7.0

Rwanda –0.1 8.1 8.5 11.0 0.3 5.3 7.1 5.5 6.0 6.0 5.6 5.6

São Tomé and Príncipe 1.2 0.4 3.1 11.6 5.4 6.6 5.7 6.7 6.0 5.8 6.0 8.0

Senegal 2.7 3.2 4.6 0.7 6.7 5.9 5.6 2.3 4.8 4.3 5.8 5.4

Seychelles 4.8 4.3 –2.3 1.2 –5.9 –2.9 1.2 5.3 5.5 2.5 3.0 3.8

Sierra Leone –7.8 3.8 18.2 27.4 9.5 7.4 7.3 7.4 6.8 5.5 5.9 6.5

South Africa 1.4 4.2 2.7 3.7 3.1 4.9 5.0 5.4 5.1 3.8 3.3 5.0

Sudan 2.7 8.4 6.2 5.4 7.1 5.1 6.3 11.3 10.2 8.5 7.7 4.7

Swaziland 3.7 2.0 1.0 1.8 3.9 2.5 2.2 2.9 3.5 2.6 2.5 2.5

Tanzania 3.1 4.9 6.0 7.2 6.9 7.8 7.4 6.7 7.1 7.5 8.0 7.5

Togo 1.6 –1.3 –2.3 –0.3 5.2 2.4 1.3 4.1 2.1 2.5 3.5 4.0

Tunisia 5.0 4.7 5.0 1.7 5.6 6.0 4.0 5.5 6.3 5.5 5.0 5.8

Uganda 6.3 5.4 5.2 8.6 6.6 6.8 6.3 10.8 7.9 9.8 8.1 6.0

Zambia –0.6 3.6 4.9 3.3 5.1 5.4 5.3 6.2 6.3 5.8 6.4 6.0

Zimbabwe3 2.0 –7.3 –2.7 –4.4 –10.4 –3.6 –4.0 –5.4 –6.1 . . . . . . . . .
Statistical appen

Table A4 (continued

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Central and eastern Europe4 1.2 4.9 0.4 4.2 4.8 6.9 6.1 6.7 5.7 4.5 3.4 5.

Albania –0.5 7.3 7.0 4.2 5.8 5.7 5.8 5.4 6.0 6.1 6.3 6.1

Bosnia and Herzegovina . . . 5.2 3.6 5.0 3.5 6.3 3.9 6.9 6.8 5.5 5.0 4.0

Bulgaria –5.4 5.4 4.1 4.5 5.0 6.6 6.2 6.3 6.2 6.3 4.2 6.5

Croatia . . . 2.9 4.4 5.6 5.3 4.3 4.3 4.8 5.6 3.8 3.7 4.8

Czech Republic –0.3 3.6 2.5 1.9 3.6 4.5 6.3 6.8 6.6 4.0 3.4 4.5

Estonia . . . 9.6 7.7 7.8 7.1 7.5 9.2 10.4 6.3 –1.5 0.5 5.2

Hungary 0.1 5.2 4.1 4.4 4.2 4.8 4.1 3.9 1.3 1.9 2.3 3.4

Latvia . . . 6.9 8.0 6.5 7.2 8.7 10.6 12.2 10.3 –0.9 –2.2 3.5

Lithuania . . . 4.1 6.6 6.9 10.3 7.3 7.9 7.9 8.9 3.9 0.7 5.5

Macedonia, FYR . . . 4.5 –4.5 0.9 2.8 4.1 4.1 4.0 5.0 5.5 5.0 4.0

Montenegro . . . . . . 1.1 1.9 2.5 4.4 4.2 8.6 9.7 7.5 5.0 4.0

Poland 2.6 4.3 1.2 1.4 3.9 5.3 3.6 6.2 6.6 5.2 3.8 5.1

Romania –2.5 2.1 5.7 5.1 5.2 8.5 4.2 7.9 6.0 8.6 4.8 6.0

Serbia . . . 4.5 5.4 3.6 2.8 8.2 6.0 5.6 7.1 6.0 6.0 5.5

Slovak Republic . . . 1.4 3.4 4.8 4.8 5.2 6.6 8.5 10.4 7.4 5.6 4.7

Turkey 3.9 6.8 –5.7 6.2 5.3 9.4 8.4 6.9 4.6 3.5 3.0 5.0

Commonwealth of Independent States4,5 . . . 9.1 6.1 5.2 7.8 8.2 6.8 8.2 8.6 7.2 5.7 5.

Russia . . . 10.0 5.1 4.7 7.3 7.2 6.4 7.4 8.1 7.0 5.5 5.5

Excluding Russia . . . 6.6 8.9 6.6 9.1 10.8 7.7 10.2 9.8 7.6 6.2 5.7

Armenia . . . 6.0 9.6 13.2 14.0 10.5 14.0 13.3 13.8 10.0 8.0 6.0

Azerbaijan . . . 6.2 6.5 8.1 10.5 10.4 24.3 30.5 23.4 16.0 16.4 –1.8

Belarus . . . 5.8 4.7 5.0 7.0 11.4 11.4 10.0 8.2 9.2 8.0 5.0

Georgia . . . 1.9 4.7 5.5 11.1 5.9 9.6 9.4 12.4 3.5 4.0 5.0

Kazakhstan . . . 9.8 13.5 9.8 9.3 9.6 9.7 10.7 8.9 4.5 5.3 6.8

Kyrgyz Republic . . . 5.4 5.3 –0.0 7.0 7.0 –0.2 3.1 8.2 7.5 6.7 6.3

Moldova . . . 2.1 6.1 7.8 6.6 7.4 7.5 4.8 4.0 6.5 6.5 6.0

Mongolia –0.4 3.9 0.2 4.7 7.0 10.6 7.3 8.6 9.9 9.0 8.1 5.3

Tajikistan . . . 8.3 10.2 9.1 10.2 10.6 6.7 7.0 7.8 6.0 7.0 7.0

Turkmenistan . . . 18.6 20.4 15.8 17.1 14.7 13.0 11.4 11.6 10.8 10.3 10.2

Ukraine . . . 5.9 9.2 5.2 9.6 12.1 2.7 7.3 7.6 6.4 2.5 6.5

Uzbekistan . . . 3.8 4.2 4.0 4.2 7.7 7.0 7.3 9.5 8.0 7.5 6.0
Output: Emerging and Developing 

Table A4 (continued

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Developing Asia 7.2 7.0 5.8 6.9 8.2 8.6 9.0 9.9 10.0 8.4 7.7 8.8

Afghanistan, I.R. of . . . . . . . . . . . . 15.1 8.8 16.1 8.2 11.5 7.5 8.3 7.2

Bangladesh 4.8 5.6 4.8 4.8 5.8 6.1 6.3 6.5 6.3 7.0 5.6 6.7

Bhutan 5.3 7.2 6.8 10.9 7.2 6.8 7.0 8.8 17.9 6.6 5.7 6.7

Brunei Darussalam . . . 2.9 2.7 3.9 2.9 0.5 0.4 4.4 0.6 –0.5 2.8 3.0

Cambodia . . . 8.8 8.1 6.6 8.5 10.3 13.3 10.8 10.2 7.0 6.0 7.4

China 9.9 8.4 8.3 9.1 10.0 10.1 10.4 11.6 11.9 9.7 9.3 10.0

Fiji 5.8 –1.8 2.0 3.2 1.0 5.5 0.7 3.3 –3.1 2.5 2.0 2.7

India 5.6 5.7 3.9 4.6 6.9 7.9 9.1 9.8 9.3 7.9 6.9 8.0

Indonesia 4.1 5.4 3.6 4.5 4.8 5.0 5.7 5.5 6.3 6.1 5.5 6.7

Kiribati 4.3 3.9 3.2 8.1 –1.3 –1.5 1.7 2.4 2.0 3.7 2.5 1.1

Lao PDR 6.4 5.8 5.7 5.9 6.1 6.4 7.1 8.1 7.9 7.5 6.8 7.1

Malaysia 7.1 8.7 0.5 5.4 5.8 6.8 5.3 5.8 6.3 5.8 4.8 6.0

Maldives 6.5 4.8 3.5 6.5 8.5 9.5 –4.6 18.0 7.6 6.5 6.5 6.0

Myanmar 6.0 13.7 11.3 12.0 13.8 13.6 13.6 12.7 5.5 2.0 6.0 4.0

Nepal 4.9 6.1 5.6 0.1 3.9 4.7 3.1 3.7 3.2 4.7 5.5 5.5

Pakistan 4.0 4.3 2.0 3.2 4.8 7.4 7.7 6.9 6.4 5.8 3.5 6.0

Papua New Guinea 4.5 –2.5 –0.1 –0.2 2.2 2.7 3.4 2.6 6.2 5.8 4.7 2.6

Philippines 2.8 6.0 1.8 4.4 4.9 6.4 5.0 5.4 7.2 4.4 3.8 5.5

Samoa 2.0 5.0 8.1 5.5 2.1 2.4 6.0 1.8 6.0 4.5 4.0 3.5

Solomon Islands 4.3 –14.3 –8.0 –2.8 6.5 8.0 5.0 6.1 10.3 7.3 4.0 3.2

Sri Lanka 5.2 6.0 –1.5 4.0 5.9 5.4 6.2 7.7 6.8 6.1 5.1 5.0

Thailand 5.1 4.8 2.2 5.3 7.1 6.3 4.5 5.1 4.8 4.7 4.5 6.0

Timor-Leste . . . 15.5 16.5 –6.7 –6.2 0.3 2.3 –3.4 19.8 2.5 0.6 3.8

Tonga 1.5 5.4 2.6 3.0 3.2 1.4 5.4 0.6 –3.2 1.2 2.6 1.6

Vanuatu 3.6 2.7 –2.5 –7.4 3.2 5.5 6.5 7.2 6.5 6.0 5.5 3.0

Vietnam 7.4 6.8 6.9 7.1 7.3 7.8 8.4 8.2 8.5 6.3 5.5 7.4

Middle East 4.3 5.5 2.6 3.8 7.1 5.8 5.7 5.7 5.9 6.4 5.9 5.

Bahrain 4.8 5.2 4.6 5.2 7.2 5.6 7.9 6.5 6.0 6.3 6.0 6.0

Egypt 4.1 5.4 3.5 3.2 3.2 4.1 4.5 6.8 7.1 7.2 6.0 6.9

Iran, I.R. of 5.1 5.1 3.7 7.5 7.2 5.1 4.7 5.8 6.4 5.5 5.0 4.5

Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Jordan 4.2 4.3 5.3 5.8 4.2 8.6 7.1 6.3 6.0 5.5 5.3 6.0

Kuwait 0.1 4.7 0.2 3.0 17.3 10.7 11.4 6.3 4.6 5.9 5.8 6.0

Lebanon 5.4 1.7 4.5 3.3 4.1 7.5 1.1 — 4.0 6.0 5.0 5.0

Libya –2.4 3.7 –4.3 –1.3 13.0 4.4 10.3 6.7 6.8 7.1 8.1 7.9

Oman 4.9 5.5 7.5 2.6 2.0 5.3 6.0 6.8 6.4 7.4 6.0 5.6

Qatar 4.2 10.9 6.3 3.2 6.3 17.7 9.2 15.0 15.9 16.8 21.4 5.5

Saudi Arabia 3.1 4.9 0.5 0.1 7.7 5.3 5.6 3.0 3.5 5.9 4.3 5.0

Syrian Arab Republic 5.6 2.3 3.7 5.9 1.1 2.8 3.3 4.4 3.9 4.2 5.2 4.7

United Arab Emirates 5.4 12.4 1.7 2.6 11.9 9.7 8.2 9.4 7.4 7.0 6.0 5.6

Yemen, Rep. of . . . 6.2 3.8 3.9 3.7 4.0 5.6 3.2 3.3 3.5 8.1 4.4
Statistical appen

Table A4 (concluded

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Western Hemisphere 2.9 4.1 0.7 0.5 2.2 6.1 4.7 5.5 5.6 4.6 3.2 4

Antigua and Barbuda 3.3 3.3 1.5 2.0 4.3 5.2 5.5 12.2 6.1 2.1 4.0 4.5

Argentina 4.2 –0.8 –4.4 –10.9 8.8 9.0 9.2 8.5 8.7 6.5 3.6 3.0

Bahamas, The 1.8 1.9 0.8 2.3 1.0 1.3 2.5 3.4 2.8 1.0 1.2 2.0

Barbados 0.4 2.3 –2.6 0.7 2.0 4.8 4.3 3.3 3.3 1.7 1.0 3.0

Belize 5.8 13.0 5.0 5.1 9.3 4.6 3.1 4.7 1.2 4.0 2.5 2.5

Bolivia 4.0 2.5 1.7 2.5 2.7 4.2 4.4 4.8 4.6 5.9 5.0 4.5

Brazil 1.7 4.3 1.3 2.7 1.1 5.7 3.2 3.8 5.4 5.2 3.5 4.0

Chile 6.4 4.5 3.5 2.2 4.0 6.0 5.6 4.3 5.1 4.5 3.8 5.0

Colombia 2.9 2.9 2.2 2.5 4.6 4.7 5.7 6.8 7.7 4.0 3.5 5.0

Costa Rica 5.4 1.8 1.1 2.9 6.4 4.3 5.9 8.8 7.3 4.0 3.5 5.5

Dominica 2.6 1.3 –4.2 –5.1 0.1 3.0 3.3 4.0 1.5 2.6 2.8 3.0

Dominican Republic 4.9 5.7 1.8 5.8 –0.3 1.3 9.3 10.7 8.5 4.7 2.8 6.3

Ecuador 2.2 2.8 5.3 4.2 3.6 8.0 6.0 3.9 2.5 3.0 3.0 4.5

El Salvador 4.9 2.2 1.7 2.3 2.3 1.9 3.1 4.2 4.7 3.0 2.6 4.5

Grenada 4.3 7.0 –3.0 1.6 7.1 –6.4 11.5 –1.1 4.3 3.7 4.2 4.2

Guatemala 3.7 2.5 2.4 3.9 2.5 3.2 3.3 5.2 5.7 4.5 4.0 4.5

Guyana 4.7 –1.3 2.3 1.1 –0.7 1.6 –1.9 5.1 5.5 4.6 4.5 3.6

Haiti 0.2 0.9 –1.0 –0.3 0.4 –3.5 1.8 2.3 3.2 2.5 4.0 4.0

Honduras 2.7 5.7 2.7 3.8 4.5 6.2 6.1 6.3 6.3 4.2 4.0 4.2

Jamaica 0.9 0.7 1.5 1.1 2.3 1.0 1.4 2.5 1.2 0.7 0.9 2.7

Mexico 3.3 6.6 –0.2 0.8 1.7 4.0 3.1 4.9 3.2 2.1 1.8 4.4

Nicaragua 3.1 4.1 3.0 0.8 2.5 5.3 4.4 3.9 3.8 3.0 3.5 5.0

Panama 6.1 2.7 0.6 2.2 4.2 7.5 7.2 8.5 11.5 8.3 7.8 6.5

Paraguay 2.4 –3.3 2.1 — 3.8 4.1 2.9 4.3 6.8 5.5 4.2 5.0

Peru 3.1 3.0 0.2 5.0 4.0 5.1 6.7 7.7 8.9 9.2 7.0 6.5

St. Kitts and Nevis 4.0 4.3 2.0 1.0 0.5 7.6 4.8 6.4 3.1 3.5 2.7 2.0

St. Lucia 3.1 — –4.1 0.6 3.5 3.8 4.4 4.9 1.7 2.3 3.1 4.4

St. Vincent and the Grenadines 3.5 2.0 –0.1 3.2 2.8 6.8 2.6 6.9 6.6 5.0 4.7 4.3

Suriname 0.5 –0.1 6.8 2.6 6.0 8.2 4.5 4.8 5.5 6.5 4.8 4.8

Trinidad and Tobago 3.9 7.6 3.8 7.9 14.4 7.8 6.1 12.2 5.5 5.0 4.5 3.7

Uruguay 3.2 –1.4 –3.4 –11.0 2.2 11.8 6.6 7.0 7.4 6.5 5.5 4.0

Venezuela 2.4 3.7 3.4 –8.9 –7.8 18.3 10.3 10.3 8.4 6.0 2.0 2.0

1For many countries, figures for recent years are IMF staff estimates. Data for some countries are for fiscal years.

2The percent changes in 2002 are calculated over a period of 18 months, reflecting a change in the fiscal year cycle (from July–June to January–December).

3Given recent trends, it is not possible to forecast GDP with any precision, and consequently no projections for 2008 and beyond are shown.

4Data for some countries refer to real net material product (NMP) or are estimates based on NMP. For many countries, figures for recent years are IMF staff estimates. The 

figures should be interpreted only as indicative of broad orders of magnitude because reliable, comparable data are not generally available. In particular, the growth of output 

of new private enterprises of the informal economy is not fully reflected in the recent figures. 5Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Inflation: Summa

Table A5. Summary of Inflati

(Percent)

 Average

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

GDP deflators

Advanced economies 2.6 1.5 1.9 1.6 1.7 2.0 2.1 2.1 2.1 2.2 1.7 1.9

United States 2.2 2.2 2.4 1.7 2.1 2.9 3.3 3.2 2.7 2.2 1.6 1.9

Euro area . . . 1.5 2.4 2.6 2.2 1.8 2.0 2.0 2.2 2.4 1.8 2.1

Japan 0.6 –1.7 –1.2 –1.5 –1.6 –1.1 –1.2 –1.0 –0.8 –0.6 0.3 1.1

Other advanced economies1 3.3 2.1 2.0 1.7 2.1 2.2 2.0 1.9 2.4 3.2 2.5 1.9

Consumer prices

Advanced economies 3.0 2.2 2.1 1.5 1.8 2.0 2.3 2.4 2.2 3.6 2.0 2.0

United States 3.0 3.4 2.8 1.6 2.3 2.7 3.4 3.2 2.9 4.2 1.8 2.1

Euro area2 . . . 2.2 2.4 2.3 2.1 2.1 2.2 2.2 2.1 3.5 1.9 1.9

Japan 1.2 –0.8 –0.7 –0.9 –0.3 — –0.3 0.3 — 1.6 0.9 1.5

Other advanced economies 3.5 1.8 2.1 1.7 1.8 1.7 2.1 2.1 2.1 3.9 3.0 2.2

Emerging and developing economies 51.3 8.5 7.7 6.8 6.6 5.9 5.7 5.4 6.4 9.4 7.8 4.

Regional groups

Africa 24.9 11.7 11.0 9.0 8.6 6.4 7.1 6.3 6.2 10.2 8.3 4.8

Central and eastern Europe 60.1 24.9 21.5 16.4 10.1 6.3 5.1 5.4 5.6 7.8 5.7 3.2

Commonwealth of Independent States3 . . . 24.1 20.3 14.0 12.3 10.4 12.1 9.4 9.7 15.6 12.6 7.5

Developing Asia 8.7 1.9 2.8 2.1 2.6 4.1 3.8 4.2 5.4 7.8 6.2 3.7

Middle East 10.9 4.1 3.8 5.3 6.1 7.1 6.2 7.0 10.6 15.8 14.4 8.2

Western Hemisphere 98.4 8.3 6.5 8.7 10.5 6.6 6.3 5.3 5.4 7.9 7.3 5.6

Memorandum

European Union 10.0 3.1 3.0 2.5 2.2 2.3 2.3 2.3 2.4 3.9 2.4 2.1

Analytical groups

By source of export earnings

Fuel 76.0 14.4 13.4 11.8 11.5 9.8 9.6 8.5 9.8 15.2 13.4 8.7

Nonfuel 45.1 7.1 6.3 5.6 5.5 5.0 4.8 4.7 5.6 8.1 6.5 3.9

of which, primary products 55.0 17.8 15.4 8.4 6.6 4.0 7.2 7.4 6.5 10.7 8.3 4.8

By external financing source

Net debtor countries 52.2 9.4 8.4 8.1 7.3 5.4 5.9 6.1 5.9 8.7 7.5 4.3

of which, official financing 17.5 4.2 4.3 4.7 6.6 7.3 7.9 7.5 8.9 15.6 12.9 6.0

Net debtor countries by debtservicing experienc

Countries with arrears and/or 

rescheduling during 2002–06 28.5 7.2 8.3 11.2 7.6 5.8 8.6 9.9 7.2 10.7 10.6 5.8

Memorandum

Median inflation rate

Advanced economies 2.8 2.7 2.5 2.3 2.1 1.9 2.1 2.2 2.1 3.8 2.6 2.0

Emerging and developing economies 10.1 4.0 4.7 3.3 4.3 4.5 5.7 5.6 6.2 9.6 7.4 4.0

1In this table, “other advanced economies” means advanced economies excluding the United States, euro area countries, and Japan.

2Based on Eurostat’s harmonized index of consumer prices.

3Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Statistical appen

Table A6. Advanced Economies: Consumer Pric

(Annual percent change)

Average End of Period

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Consumer Prices

Advanced economies 3.0 2.2 2.1 1.5 1.8 2.0 2.3 2.4 2.2 3.6 2.0 2.0 3.1 3.2 1.7

United States 3.0 3.4 2.8 1.6 2.3 2.7 3.4 3.2 2.9 4.2 1.8 2.1 4.1 3.1 1.6

Euro area1 . . . 2.2 2.4 2.3 2.1 2.1 2.2 2.2 2.1 3.5 1.9 1.9 3.1 2.9 1.7

Germany 2.4 1.4 1.9 1.4 1.0 1.8 1.9 1.8 2.3 2.9 1.4 1.7 3.1 2.4 0.8

France 1.9 1.8 1.8 1.9 2.2 2.3 1.9 1.9 1.6 3.4 1.6 1.8 2.8 2.6 1.6

Italy 4.1 2.6 2.3 2.6 2.8 2.3 2.2 2.2 2.0 3.4 1.9 2.0 1.8 4.6 1.6

Spain 4.3 3.5 2.8 3.6 3.1 3.1 3.4 3.6 2.8 4.5 2.6 2.4 4.3 3.6 2.2

Netherlands 2.3 2.3 5.1 3.8 2.2 1.4 1.5 1.7 1.6 2.9 2.6 2.0 1.6 2.9 2.6

Belgium 2.0 2.7 2.4 1.6 1.5 1.9 2.5 2.3 1.8 4.6 2.8 1.9 3.1 4.1 2.2

Austria 2.1 2.0 2.3 1.7 1.3 2.0 2.1 1.7 2.2 3.5 2.3 1.8 3.5 2.9 1.8

Finland 2.1 3.0 2.7 2.0 1.3 0.1 0.8 1.3 1.6 3.9 2.5 2.0 1.9 4.5 2.7

Greece 10.8 2.9 3.7 3.9 3.4 3.0 3.5 3.3 3.0 4.4 3.1 2.5 3.9 3.7 2.9

Portugal 5.7 2.8 4.4 3.7 3.3 2.5 2.1 3.0 2.4 3.2 2.0 2.1 2.4 3.2 2.0

Ireland 2.4 5.3 4.0 4.7 4.0 2.3 2.2 2.7 2.9 3.5 2.4 2.0 3.2 3.3 2.2

Luxembourg 2.2 3.2 2.7 2.1 2.0 2.2 2.5 2.7 2.3 3.7 1.8 2.0 3.4 2.8 2.0

Slovenia . . . 8.8 8.4 7.5 5.6 3.6 2.5 2.5 3.6 5.9 3.3 3.3 5.6 4.7 3.0

Cyprus 3.7 4.9 2.0 2.8 4.0 1.9 2.0 2.2 2.2 4.6 3.5 2.0 3.7 4.4 2.9

Malta 3.1 3.0 2.5 2.6 1.9 2.7 2.5 2.6 0.7 3.7 2.2 2.4 3.1 2.8 2.4

Japan 1.2 –0.8 –0.7 –0.9 –0.3 — –0.3 0.3 — 1.6 0.9 1.5 0.7 1.9 0.5

United Kingdom1 3.3 0.9 1.2 1.3 1.4 1.3 2.0 2.3 2.3 3.8 2.9 2.0 2.0 4.6 2.0

Canada 2.2 2.7 2.5 2.3 2.7 1.8 2.2 2.0 2.1 2.5 2.1 2.0 2.4 2.9 1.9

Korea 5.7 2.3 4.1 2.8 3.5 3.6 2.8 2.2 2.5 4.8 4.0 3.0 3.6 5.0 3.0

Australia 2.5 4.5 4.4 3.0 2.8 2.3 2.7 3.5 2.3 4.6 3.6 2.8 3.0 4.8 3.2

Taiwan Province of China 2.9 1.3 — –0.2 –0.3 1.6 2.3 0.6 1.8 4.2 2.5 1.5 3.3 4.3 1.8

Sweden 3.6 1.3 2.7 1.9 2.3 1.0 0.8 1.5 1.7 3.4 2.8 2.0 2.5 3.3 2.4

Switzerland 2.3 1.6 1.0 0.6 0.6 0.8 1.2 1.0 0.7 2.6 1.5 1.0 2.0 1.2 1.5

Hong Kong SAR 6.8 –3.7 –1.6 –3.0 –2.6 –0.4 0.9 2.0 2.0 4.8 4.3 3.0 3.8 3.8 5.1

Denmark 2.1 2.9 2.4 2.4 2.1 1.2 1.8 1.9 1.7 3.4 2.8 1.9 2.3 3.7 2.4

Norway 2.4 3.1 3.0 1.3 2.5 0.4 1.6 2.3 0.8 3.2 2.7 2.5 2.8 1.4 2.4

Israel 11.2 1.1 1.1 5.7 0.7 –0.4 1.3 2.1 0.5 4.8 3.3 2.0 3.4 5.1 1.9

Singapore 1.9 1.3 1.0 –0.4 0.5 1.7 0.5 1.0 2.1 6.5 3.3 1.7 3.9 5.6 2.6

New Zealand 2.1 2.6 2.6 2.6 1.7 2.3 3.0 3.4 2.4 4.2 3.8 2.2 3.2 4.8 2.9

Iceland 4.1 5.1 6.6 4.8 2.1 3.2 4.0 6.8 5.0 12.1 11.2 2.0 5.9 14.9 9.1

Memorandum

Major advanced economies 2.6 2.1 1.9 1.3 1.7 2.0 2.3 2.4 2.2 3.5 1.7 1.9 3.0 3.0 1.4

Newly industrialized Asian economies 4.7 1.2 1.9 1.0 1.4 2.4 2.2 1.6 2.2 4.8 3.5 2.5 3.6 4.7 2.9

1Based on Eurostat’s harmonized index of consumer prices.
Inflation: Emerging and Developing E

Table A7. Emerging and Developing Economies, by Country: Consumer Pric

(Annual percent change)

Average End of Period

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Africa 24.9 11.7 11.0 9.0 8.6 6.4 7.1 6.3 6.2 10.2 8.3 4.8 7.1 11.2 6.8

Algeria 17.3 0.3 4.2 1.4 2.6 3.6 1.6 2.5 3.6 4.3 4.0 2.9 4.4 4.2 3.9

Angola 463.0 325.0 152.6 108.9 98.3 43.6 23.0 13.3 12.2 12.1 9.3 4.5 11.8 11.0 8.0

Benin 7.2 4.2 4.0 2.4 1.5 0.9 5.4 3.8 1.3 8.8 6.5 2.8 0.3 13.1 3.7

Botswana 10.9 8.5 6.6 8.0 9.2 7.0 8.6 11.6 7.1 12.6 11.9 9.5 8.1 13.4 10.7

Burkina Faso 4.3 –0.3 4.7 2.3 2.0 –0.4 6.4 2.4 –0.2 9.5 5.0 2.0 2.3 5.5 3.5

Burundi 13.5 24.3 9.3 –1.3 10.7 8.0 13.4 2.8 8.3 24.3 15.4 5.2 14.7 23.7 9.8

Cameroon2 4.9 0.8 2.8 6.3 0.6 0.3 2.0 5.1 0.9 4.1 2.1 2.0 3.5 2.5 1.7

Cape Verde 7.3 –2.4 3.7 1.9 1.2 –1.9 0.4 4.8 4.4 5.7 4.9 2.0 3.4 6.5 4.1

Central African Republic 3.5 3.2 3.8 2.3 4.4 –2.2 2.9 6.7 0.9 8.5 6.7 2.5 –0.2 13.0 3.8

Chad 4.1 3.8 12.4 5.2 –1.8 –5.4 7.9 7.9 –8.8 5.0 3.0 3.0 0.8 2.9 3.0

Comoros 2.5 5.9 5.6 3.6 3.7 4.5 3.0 3.4 4.5 5.9 5.6 3.1 2.2 9.6 1.6

Congo, Dem. Rep. of 848.4 550.0 357.3 25.3 12.8 4.0 21.4 13.2 16.7 17.5 15.1 10.0 10.0 23.5 11.5

Congo, Rep. of 7.3 0.4 0.8 3.1 1.5 3.8 2.5 4.7 2.6 4.0 4.0 3.0 –1.7 5.0 3.0

Côte d’Ivoire 6.0 –0.4 4.4 3.1 3.3 1.5 3.9 2.5 1.9 5.6 5.7 3.0 1.5 9.0 3.0

Djibouti 4.2 1.6 1.8 0.6 2.0 3.1 3.1 3.5 5.0 8.1 6.0 3.0 5.0 8.1 6.0

Equatorial Guinea 6.1 4.8 8.8 7.6 7.3 4.2 5.7 4.5 2.8 6.4 5.5 4.0 3.7 6.5 5.2

Eritrea . . . 19.9 14.6 16.9 22.7 25.1 12.5 15.1 9.3 11.0 10.5 8.5 12.3 11.0 10.0

Ethiopia 7.1 6.2 –5.2 –7.2 15.1 8.6 6.8 12.3 15.8 25.3 40.8 9.1 15.1 55.3 18.0

Gabon 5.5 0.5 2.1 0.2 2.1 0.4 1.2 –1.4 5.0 5.1 5.7 2.5 5.9 5.1 5.5

Gambia, The 5.4 0.9 4.5 8.6 17.0 14.3 5.0 2.1 5.4 6.0 5.5 4.0 6.0 6.0 5.0

Ghana 26.8 25.2 32.9 14.8 26.7 12.6 15.1 10.2 10.7 16.8 13.3 5.0 12.7 17.8 13.0

Guinea 8.5 6.8 5.4 3.0 11.0 17.5 31.4 34.7 22.9 17.9 9.3 5.0 12.8 15.0 10.0

Guinea-Bissau 35.6 8.6 3.3 3.3 –3.5 0.8 3.4 2.0 4.6 9.6 6.2 3.0 9.3 9.8 3.0

Kenya 16.0 10.0 5.8 2.0 9.8 11.6 10.3 14.5 9.8 25.0 6.5 5.0 12.0 24.0 6.5

Lesotho 11.1 6.1 6.9 12.5 7.3 5.0 3.4 6.1 8.0 11.2 9.5 4.9 10.5 12.7 9.1

 Liberia . . . 5.3 12.1 14.2 10.3 3.6 6.9 7.2 11.4 19.2 11.4 5.0 11.7 18.8 8.5

Madagascar 16.4 10.7 6.9 16.2 –1.1 14.0 18.4 10.8 10.3 9.4 8.8 5.0 8.2 11.0 8.5

Malawi 29.0 29.6 27.2 17.3 9.6 11.4 15.5 13.9 7.9 8.2 7.9 6.7 7.5 8.2 7.0

Mali 3.8 –0.7 5.2 5.0 –1.3 –3.1 6.4 1.9 2.5 2.5 2.5 2.5 2.5 2.5 2.5

Mauritania 5.0 6.8 7.7 5.4 5.3 10.4 12.1 6.2 7.3 12.5 9.5 5.0 7.4 12.0 7.0

Mauritius 8.2 4.2 5.4 6.5 3.9 4.7 4.9 8.9 9.1 8.8 8.6 5.0 10.0 9.7 7.5

Morocco 4.4 1.9 0.6 2.8 1.2 1.5 1.0 3.3 2.0 3.9 3.5 2.6 2.0 3.9 3.5

Mozambique 31.8 12.7 9.1 16.8 13.5 12.6 6.4 13.2 8.2 10.1 8.4 5.4 10.3 9.2 8.1

Namibia 10.2 9.3 9.3 11.3 7.2 4.1 2.3 5.1 6.7 8.7 8.1 4.7 7.1 9.1 7.0

Niger 4.5 2.9 4.0 2.7 –1.8 0.4 7.8 0.1 0.1 8.3 3.5 2.0 4.7 6.4 3.5

Nigeria 28.5 6.9 18.0 13.7 14.0 15.0 17.8 8.3 5.5 11.0 11.1 8.5 6.6 13.1 9.8

Rwanda 16.3 3.9 3.4 2.0 7.4 12.0 9.0 8.9 9.1 12.0 8.0 5.0 6.6 15.5 5.0

São Tomé and Príncipe 39.2 11.0 9.2 10.1 9.8 13.3 17.2 23.1 18.5 25.9 19.8 5.0 27.6 24.5 16.0

Senegal 4.0 0.7 3.0 2.3 — 0.5 1.7 2.1 5.9 5.4 2.8 2.2 6.2 4.1 2.2

Seychelles 2.0 6.3 6.0 0.2 3.3 3.9 0.8 –1.4 5.7 27.3 28.7 3.0 16.8 21.2 35.2

Sierra Leone 42.6 –0.9 2.6 –3.7 7.5 14.2 12.1 9.5 11.7 15.3 13.9 6.5 13.8 15.7 12.1

South Africa 9.8 5.4 5.7 9.2 5.8 1.4 3.4 4.7 7.1 11.8 8.0 4.5 9.0 12.7 6.6

Sudan 75.2 8.0 4.9 8.3 7.7 8.4 8.5 7.2 8.0 16.0 10.0 4.5 8.8 12.0 8.0

Swaziland 9.5 7.2 7.5 11.7 7.4 3.4 4.8 5.3 8.2 12.7 8.9 5.3 9.8 13.5 7.5

Tanzania 21.3 6.2 5.1 4.6 4.4 4.1 4.4 7.3 7.0 9.2 6.5 5.0 6.4 8.3 5.0

Togo 6.0 1.9 3.9 3.1 –0.9 0.4 6.8 2.2 1.0 5.2 5.2 3.4 3.4 4.9 4.5

Tunisia 4.8 2.3 2.0 2.7 2.7 3.6 2.0 4.5 3.1 5.1 4.5 2.7 5.3 4.7 4.5

Uganda 16.3 5.8 4.5 –2.0 5.7 5.0 8.0 6.6 6.8 7.3 7.8 4.5 4.4 12.5 4.5

Zambia 68.3 26.1 21.7 22.2 21.4 18.0 18.3 9.0 10.7 11.8 8.6 5.0 8.9 12.7 9.0

Zimbabwe3 28.1 55.6 73.4 133.2 365.0 350.0 237.8 1,016.7 10,452.6 . . . . . . . . . 108,844.1 . . . . . .
Statistical appen

2

Table A7 (continued

Average End of Period

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Central and eastern Europe4 60.1 24.9 21.5 16.4 10.1 6.3 5.1 5.4 5.6 7.8 5.7 3.2 6.6 7.3 5.

Albania 34.7 — 3.1 5.2 2.3 2.9 2.4 2.4 2.9 4.0 3.0 2.8 3.1 3.9 3.0

Bosnia and Herzegovina . . . 5.0 4.5 0.3 0.5 0.3 3.6 6.1 1.5 8.5 5.2 2.5 4.9 8.1 3.5

Bulgaria 110.3 10.3 7.4 5.8 2.3 6.1 6.0 7.4 7.6 12.2 7.0 3.4 11.6 9.6 6.5

Croatia . . . 4.6 3.8 1.7 1.8 2.0 3.3 3.2 2.9 7.0 4.9 3.0 5.8 6.5 4.0

Czech Republic 13.9 3.8 4.7 1.8 0.1 2.8 1.8 2.5 2.8 6.7 3.4 2.0 5.4 5.7 3.4

Estonia . . . 4.0 5.8 3.6 1.3 3.0 4.1 4.4 6.6 10.2 5.1 2.9 9.6 6.9 5.2

Hungary 22.0 9.8 9.2 5.3 4.6 6.8 3.6 3.9 7.9 6.3 4.1 3.0 7.4 5.5 3.4

Latvia . . . 2.6 2.5 1.9 2.9 6.2 6.7 6.5 10.1 15.9 10.6 3.1 14.0 14.6 8.8

Lithuania . . . 1.1 1.6 0.3 –1.1 1.2 2.7 3.8 5.8 11.3 6.2 2.4 8.2 9.2 5.7

Macedonia, FYR . . . 6.4 5.5 2.2 1.2 –0.4 0.5 3.2 2.3 8.5 3.0 3.0 6.7 5.5 3.0

Montenegro . . . . . . 23.7 19.7 7.5 3.1 3.4 2.1 3.5 9.2 5.2 3.0 . . . . . . . . .

Poland 51.4 10.1 5.5 1.9 0.8 3.5 2.1 1.0 2.5 4.0 3.3 2.5 4.0 3.0 4.3

Romania 110.3 45.7 34.5 22.5 15.3 11.9 9.0 6.6 4.8 8.2 6.6 3.5 6.6 7.9 6.2

Serbia . . . 70.0 91.8 19.5 11.7 10.1 17.3 12.7 6.8 10.7 7.5 7.5 10.1 8.5 8.0

Slovak Republic . . . 12.2 7.2 3.5 8.4 7.5 2.8 4.3 1.9 3.9 3.6 2.5 2.5 4.1 3.0

Turkey 76.1 55.0 54.2 45.1 25.3 8.6 8.2 9.6 8.8 10.5 8.4 4.0 8.4 10.9 7.5

Commonwealth of Independent States4,5 . . . 24.1 20.3 14.0 12.3 10.4 12.1 9.4 9.7 15.6 12.6 7.5 13.0 14.8 10.

Russia . . . 20.8 21.5 15.8 13.7 10.9 12.7 9.7 9.0 14.0 12.0 7.7 11.9 13.8 10.5

Excluding Russia . . . 34.1 17.1 9.2 8.6 9.1 10.7 8.9 11.6 19.7 14.2 6.9 15.7 17.2 11.3

Armenia . . . –0.8 3.1 1.1 4.7 7.0 0.6 2.9 4.4 9.4 5.0 4.0 6.6 7.5 5.0

Azerbaijan . . . 1.8 1.5 2.8 2.2 6.7 9.7 8.4 16.6 22.4 20.0 15.0 19.5 25.0 15.0

Belarus . . . 168.6 61.1 42.6 28.4 18.1 10.3 7.0 8.4 15.3 9.6 7.2 12.1 16.3 6.3

Georgia . . . 4.0 4.7 5.6 4.8 5.7 8.3 9.2 9.2 10.0 7.6 6.0 11.0 8.0 8.0

Kazakhstan . . . 13.3 8.4 5.9 6.4 6.9 7.6 8.6 10.8 17.6 9.8 6.0 18.8 11.5 8.0

Kyrgyz Republic . . . 18.7 6.9 2.1 3.1 4.1 4.3 5.6 10.2 24.5 12.2 5.1 20.1 20.0 12.0

Moldova . . . 31.3 9.8 5.3 11.7 12.5 11.9 12.7 12.4 13.7 9.7 5.0 13.1 11.5 9.5

Mongolia . . . 11.6 6.2 0.9 5.1 7.9 12.5 5.1 9.0 26.0 18.8 5.3 15.1 27.5 10.0

Tajikistan . . . 32.9 38.6 12.2 16.4 7.2 7.3 10.0 13.2 21.6 15.5 6.0 19.8 18.0 13.0

Turkmenistan . . . 8.0 11.6 8.8 5.6 5.9 10.7 8.2 6.3 13.0 12.0 6.0 8.6 12.0 12.0

Ukraine . . . 28.2 12.0 0.8 5.2 9.0 13.5 9.1 12.8 25.3 18.8 5.8 16.6 21.6 14.7

Uzbekistan . . . 25.0 27.3 27.3 11.6 6.6 10.0 14.2 12.3 11.1 10.6 8.0 11.9 11.0 10.0
Table A7 (continued

Average End of Period

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Developing Asia 8.7 1.9 2.8 2.1 2.6 4.1 3.8 4.2 5.4 7.8 6.2 3.7 6.3 7.4 6.2

Afghanistan, I.R. of . . . . . . . . . 5.1 24.1 13.2 12.3 5.1 13.0 24.0 9.5 5.0 20.7 15.6 6.0

Bangladesh 6.4 2.5 1.9 3.7 5.4 6.1 7.0 7.1 9.1 10.1 10.0 4.7 9.6 10.5 9.4

Bhutan 9.8 4.0 3.4 2.5 2.1 4.6 5.3 5.0 5.2 7.7 5.0 3.9 4.8 6.4 4.5

Brunei Darussalam . . . 1.2 0.6 –2.3 0.3 0.9 1.1 0.2 0.3 0.8 1.0 1.2 . . . . . . . . .

Cambodia . . . –0.8 0.2 3.3 1.2 3.9 5.8 4.7 5.9 20.1 9.0 4.3 10.8 16.3 9.6

China 7.5 0.4 0.7 –0.8 1.2 3.9 1.8 1.5 4.8 6.4 4.3 3.3 6.6 4.5 5.5

Fiji 4.2 1.1 4.3 0.8 4.2 2.8 2.4 2.5 4.3 7.0 6.0 4.0 6.0 7.0 6.0

India 9.5 4.0 3.8 4.3 3.8 3.8 4.2 6.2 6.4 7.9 6.7 3.9 5.5 9.2 5.1

Indonesia 13.6 3.8 11.5 11.8 6.8 6.1 10.5 13.1 6.2 9.8 8.8 4.7 5.6 12.0 7.5

Kiribati 3.4 0.4 6.0 3.2 2.5 –1.9 –0.5 –0.2 0.2 1.0 1.5 2.5 3.7 2.8 2.5

Lao PDR 22.6 23.2 9.3 9.2 15.5 10.5 7.2 6.8 4.5 8.4 5.4 4.5 5.6 7.3 5.0

Malaysia 3.7 1.6 1.4 1.8 1.1 1.4 3.0 3.6 2.0 6.0 4.7 2.5 2.2 7.2 3.3

Maldives 8.4 –1.2 0.7 0.9 –2.8 6.3 3.3 3.5 7.4 15.0 4.0 3.0 10.3 9.5 3.5

Myanmar 26.9 –1.7 34.5 58.1 24.9 3.8 10.7 25.7 33.9 34.5 30.0 18.0 29.0 40.0 20.0

Nepal 9.8 3.4 2.4 2.9 4.7 4.0 4.5 8.0 6.4 8.0 8.5 4.0 5.1 13.4 5.3

Pakistan 9.6 3.6 4.4 2.5 3.1 4.6 9.3 7.9 7.8 12.0 23.0 7.0 7.0 21.5 20.0

Papua New Guinea 8.6 15.6 9.3 11.8 14.7 2.1 1.8 2.4 0.9 5.0 5.0 4.0 3.2 5.5 4.5

Philippines 9.6 4.0 6.8 2.9 3.5 6.0 7.7 6.2 2.8 10.1 7.0 3.5 3.9 12.2 4.3

Samoa 4.4 –0.2 1.9 7.4 4.3 7.9 1.9 3.8 6.0 7.1 5.1 3.0 5.1 6.0 4.7

Solomon Islands 10.7 6.9 7.6 9.3 10.0 6.9 7.4 11.2 7.7 15.1 8.8 5.0 10.9 6.7 6.5

Sri Lanka 11.2 6.2 14.2 9.6 9.0 9.0 11.0 10.0 15.8 23.7 20.0 14.8 18.7 22.2 17.7

Thailand 5.0 1.6 1.7 0.6 1.8 2.8 4.5 4.6 2.2 5.7 3.2 1.6 3.2 2.5 6.5

Timor-Leste . . . 63.6 3.6 4.8 7.0 3.2 1.8 4.1 7.8 4.0 3.5 3.2 7.8 4.0 3.5

Tonga 4.4 5.3 6.9 10.4 11.1 11.7 9.7 7.0 5.1 14.5 12.3 4.2 5.6 6.0 6.0

Vanuatu 3.2 2.5 3.7 2.0 3.0 1.4 1.2 2.0 3.9 3.8 3.0 2.5 4.1 3.8 2.5

Vietnam 19.2 –1.6 –0.4 4.0 3.2 7.7 8.2 7.5 8.3 24.0 15.0 6.0 12.6 25.0 11.0

Middle East 10.9 4.1 3.8 5.3 6.1 7.1 6.2 7.0 10.6 15.8 14.4 8.2 11.4 16.1 13.

Bahrain 0.8 –0.7 –1.2 –0.5 1.7 2.3 2.6 2.2 3.4 4.5 6.0 3.0 4.0 10.5 6.0

Egypt 10.7 2.8 2.4 2.4 3.2 8.1 8.8 4.2 11.0 11.7 16.1 7.5 8.6 20.2 12.0

Iran, I.R. of 23.5 12.8 11.3 15.7 15.6 15.3 10.4 11.9 18.4 26.0 22.0 15.0 22.5 24.0 22.0

Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Jordan 5.0 0.7 1.8 1.8 1.6 3.4 3.5 6.3 5.4 15.8 7.6 2.8 5.7 16.1 5.5

Kuwait 3.6 1.6 1.4 0.8 1.0 1.3 4.1 3.1 5.5 9.0 7.5 4.5 5.5 9.0 7.5

Lebanon 24.9 –0.4 –0.4 1.8 1.3 1.7 –0.7 5.6 4.1 11.0 6.2 2.2 6.0 8.6 3.8

Libya 6.2 –2.9 –8.8 –9.9 –2.1 1.0 2.9 1.4 6.2 12.0 10.0 6.0 7.3 12.0 10.0

Oman 1.6 –1.2 –0.8 –0.3 0.2 0.7 1.9 3.4 5.9 11.2 9.0 5.0 8.6 10.0 8.0

Qatar 2.9 1.7 1.4 0.2 2.3 6.8 8.8 11.8 13.8 15.0 13.0 5.0 13.8 15.0 13.0

Saudi Arabia 1.2 –1.1 –1.1 0.2 0.6 0.4 0.6 2.3 4.1 11.5 10.0 5.0 4.1 11.5 10.0

Syrian Arab Republic 7.2 –3.9 3.4 –0.5 5.8 4.4 7.2 10.4 4.7 8.0 7.0 5.0 4.8 –10.0 7.0

United Arab Emirates 3.6 1.4 2.7 2.9 3.2 5.0 6.2 9.3 11.1 12.9 10.8 3.4 . . . . . . . . .

Yemen, Rep. of 37.0 10.9 11.9 12.2 10.8 12.5 11.8 18.2 12.5 17.2 14.4 10.4 8.6 15.9 13.0

Inflation: Emerging and Developing E
Statistical appen

Table A7 (concluded

Average End of Period

1990–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013 2007 2008 2009

Western Hemisphere 98.4 8.3 6.5 8.7 10.5 6.6 6.3 5.3 5.4 7.9 7.3 5.6 6.3 8.5 6

Antigua and Barbuda 3.4 –0.6 1.7 2.4 2.0 2.0 2.1 1.8 1.4 3.0 2.0 2.0 5.1 3.0 2.0

Argentina 59.3 –0.9 –1.1 25.9 13.4 4.4 9.6 10.9 8.8 9.1 9.1 9.0 8.5 9.0 9.0

Bahamas, The 2.8 1.6 2.0 2.2 3.0 1.0 2.2 1.8 2.5 4.5 3.5 2.0 2.9 5.7 2.0

Barbados 2.9 2.4 2.6 –1.2 1.6 1.4 6.1 7.3 4.0 9.0 6.4 2.6 4.8 14.5 –2.7

Belize 2.0 0.7 1.2 2.2 2.6 3.1 3.7 4.2 2.3 4.6 3.3 2.5 4.1 4.0 2.5

Bolivia 10.4 4.6 1.6 0.9 3.3 4.4 5.4 4.3 8.7 14.3 10.6 4.0 11.7 13.0 9.9

Brazil 325.4 7.1 6.8 8.4 14.8 6.6 6.9 4.2 3.6 5.7 5.1 4.5 4.5 6.3 4.5

Chile 11.5 3.8 3.6 2.5 2.8 1.1 3.1 3.4 4.4 8.9 6.5 3.0 7.8 8.5 4.9

Colombia 22.0 9.2 8.0 6.3 7.1 5.9 5.0 4.3 5.5 7.3 5.5 3.4 5.7 7.2 4.9

Costa Rica 16.7 11.0 11.3 9.2 9.4 12.3 13.8 11.5 9.4 12.2 10.9 4.5 10.8 13.0 9.0

Dominica 2.3 0.9 1.6 0.1 1.6 2.4 1.6 2.6 2.7 2.2 1.5 1.5 3.4 2.4 1.5

Dominican Republic 14.2 7.7 8.9 5.2 27.4 51.5 4.2 7.6 6.1 12.3 10.7 7.9 8.9 14.3 7.7

Ecuador 38.6 96.1 37.7 12.6 7.9 2.7 2.1 3.3 2.3 8.5 5.1 2.5 3.3 9.5 4.0

El Salvador 10.4 2.3 3.8 1.9 2.1 4.5 4.7 4.0 3.9 7.6 7.4 3.0 4.9 9.0 6.0

Grenada 2.9 0.6 1.7 1.1 2.2 2.3 3.5 4.2 3.9 7.8 4.5 2.3 7.4 6.2 3.9

Guatemala 14.5 6.0 7.3 8.1 5.6 7.6 9.1 6.6 6.8 10.6 7.7 4.1 8.7 9.8 6.5

Guyana 21.8 6.1 2.7 5.4 6.0 4.7 6.9 6.7 12.2 8.6 6.8 5.0 14.0 9.0 7.0

Haiti 20.7 11.5 16.5 9.3 26.7 28.3 16.8 14.2 9.0 14.5 11.5 5.5 7.9 16.0 9.5

Honduras 19.5 11.0 9.7 7.7 7.7 8.1 8.8 5.6 6.9 11.2 10.3 5.5 8.9 12.1 8.4

Jamaica 26.7 8.1 6.9 7.0 10.1 13.5 15.1 8.5 9.3 20.2 15.4 4.5 16.8 18.3 12.0

Mexico 20.1 9.5 6.4 5.0 4.5 4.7 4.0 3.6 4.0 4.9 4.2 3.0 3.7 5.7 3.3

Nicaragua 66.5 9.9 4.7 4.0 6.5 8.5 9.6 9.1 11.1 20.5 11.4 7.0 16.9 17.1 9.2

Panama 1.1 1.4 0.3 1.0 0.6 0.5 2.9 2.5 4.2 9.2 5.9 4.0 6.4 9.6 4.8

Paraguay 16.2 9.0 7.3 10.5 14.2 4.3 6.8 9.6 8.1 10.5 5.6 3.0 5.9 8.6 5.0

Peru 112.1 3.8 2.0 0.2 2.3 3.3 1.6 2.0 1.8 5.6 4.4 2.0 3.9 5.8 3.5

St. Kitts and Nevis 3.5 2.1 2.1 2.1 2.3 2.2 3.4 8.5 4.5 2.6 4.0 2.2 2.1 4.5 3.5

St. Lucia 3.2 3.7 5.4 –0.3 1.0 1.5 3.9 3.6 1.9 7.2 4.9 2.2 6.8 6.0 3.9

St. Vincent and the Grenadines 3.1 0.2 0.8 0.8 0.2 3.0 3.7 3.0 6.9 9.3 6.2 3.4 8.3 9.6 3.8

Suriname 70.9 58.6 39.8 15.5 23.0 9.1 9.9 11.3 6.4 15.5 9.5 6.1 8.4 15.0 9.5

Trinidad and Tobago 5.9 3.6 5.5 4.2 3.8 3.7 6.9 8.3 7.9 10.1 10.0 6.0 7.6 11.5 8.5

Uruguay 45.1 4.8 4.4 14.0 19.4 9.2 4.7 6.4 8.1 6.8 6.2 5.0 8.5 7.0 6.5

Venezuela 46.1 16.2 12.5 22.4 31.1 21.7 16.0 13.7 18.7 27.2 33.5 30.0 22.5 32.0 35.0

1In accordance with standard practice in the World Economic Outlook, movements in consumer prices are indicated as annual averages rather than as December/December 

changes during the year, as is the practice in some countries. For many countries, figures for recent years are IMF staff estimates. Data for some countries are for fiscal years. 2The percent changes in 2002 are calculated over a period of 18 months, reflecting a change in the fiscal year cycle (from July–June to January–December). 

32007 represents an estimate. No projections for 2008 and beyond are shown becuase Zimbabwe is in hyperinflation, and inflation can no longer be forecasted in a 

meaningful way. Unless policies change, inflation can increase without limit. 4For many countries, inflation for the earlier years is measured on the basis of a retail price index. Consumer price indices with broader and more up-to-date coverage are 

typically used for more recent years. 5Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Financial Policies: Advanced Economi

Table A8. Major Advanced Economies: General Government Fiscal Balances and Deb

(Percent of GDP)

 Average

1992–2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Major advanced economies

Actual balance –2.8 –4.0 –4.8 –4.2 –3.4 –2.4 –2.2 –3.2 –3.7 –2.4

Output gap2 0.1 –0.1 –0.4 0.2 0.3 0.7 0.7 –0.1 –1.8 –0.1

Structural balance2 –2.7 –4.0 –4.5 –4.2 –3.4 –2.6 –2.5 –3.2 –2.9 –2.3

United Stat

Actual balance –1.8 –3.8 –4.8 –4.4 –3.3 –2.2 –2.7 –4.1 –4.6 –2.9

Output gap2 0.6 0.4 0.3 1.2 1.4 1.6 1.2 0.4 –1.6 —

Structural balance2 –2.0 –3.9 –4.9 –4.7 –3.8 –2.8 –3.1 –4.3 –3.9 –2.8

Net debt 47.7 38.1 41.5 43.0 43.4 42.5 43.2 46.3 50.7 56.9

Gross debt 64.2 56.1 59.4 60.4 60.7 59.9 60.7 61.5 65.4 70.4

Euro are

Actual balance –3.5 –2.6 –3.1 –3.0 –2.5 –1.3 –0.6 –1.5 –2.0 –0.8

Output gap2 –0.1 0.2 –0.9 –0.7 –1.0 –0.2 0.5 0.2 –1.1 —

Structural balance2 –3.0 –2.7 –2.7 –2.5 –2.1 –1.4 –0.9 –1.1 –1.1 –0.5

Net debt 58.3 58.3 59.7 60.2 60.6 58.7 56.9 56.4 57.1 54.3

Gross debt 70.3 68.2 69.3 69.7 70.3 68.6 66.5 69.9 70.6 66.7

Germany3

Actual balance –2.2 –3.7 –4.0 –3.8 –3.3 –1.5 –0.2 –0.3 –0.8 —

Output gap2 0.2 –0.2 –1.7 –1.9 –2.3 –0.8 0.4 1.0 –0.3 —

Structural balance2,4 –1.8 –2.9 –3.2 –2.8 –2.3 –1.2 –0.2 –0.8 –0.3 —

Net debt 46.8 54.3 57.7 60.0 61.8 60.2 57.7 56.1 56.8 55.8

Gross debt 54.3 59.6 62.8 64.7 66.4 66.0 63.2 76.4 77.0 73.5

France

Actual balance –3.7 –3.1 –4.1 –3.6 –3.0 –2.4 –2.7 –3.3 –3.9 –1.5

Output gap2 –0.8 0.7 –0.2 0.2 — 0.1 0.2 –1.0 –2.7 —

Structural balance2,4 –3.1 –3.5 –3.8 –3.5 –3.2 –2.3 –2.6 –2.7 –2.3 –1.5

Net debt 44.8 49.1 53.2 55.3 56.7 53.9 54.2 55.5 57.8 57.1

Gross debt 54.1 58.8 62.9 65.0 66.4 63.6 63.9 65.2 67.5 66.8

Italy

Actual balance –5.5 –2.9 –3.5 –3.5 –4.2 –3.4 –1.6 –2.6 –2.9 –2.4

Output gap2 –0.5 0.7 –0.5 –0.2 –0.8 –0.2 0.2 –0.6 –1.6 –1.0

Structural balance2,4 –5.3 –4.1 –3.4 –3.7 –4.0 –3.3 –1.8 –2.3 –2.1 –1.9

Net debt 109.5 102.1 101.5 100.8 102.7 102.7 101.1 101.3 102.5 101.9

Gross debt 114.9 105.7 104.4 103.8 105.8 106.5 104.0 104.3 105.5 104.9

Japan

Actual balance –4.6 –8.0 –8.0 –6.2 –5.0 –3.8 –3.2 –3.4 –3.9 –3.2

Excluding social security –6.2 –7.9 –8.1 –6.6 –5.4 –3.8 –2.7 –2.7 –3.4 –3.1

Output gap2 –0.5 –2.3 –2.2 –1.1 –0.9 –0.2 0.2 –0.7 –1.8 —

Structural balance2 –4.4 –7.0 –7.1 –5.7 –4.7 –3.7 –3.3 –3.1 –3.2 –3.2

Excluding social security –6.8 –7.3 –7.6 –6.4 –5.2 –3.8 –2.7 –2.6 –3.0 –3.1

Net debt 37.0 72.6 76.5 82.7 84.6 88.4 90.6 94.3 97.6 99.2

Gross debt 108.5 160.9 167.2 178.1 191.6 194.7 195.4 198.6 200.9 191.1

United Kingdo

Actual balance –3.0 –1.9 –3.3 –3.4 –3.3 –2.6 –2.7 –3.5 –4.4 –3.5

Output gap2 –0.4 –0.1 –0.1 0.1 –0.4 –0.1 0.3 –1.1 –3.2 —

Structural balance2 –2.5 –1.9 –2.9 –3.4 –3.0 –2.6 –2.8 –2.9 –2.8 –2.6

Net debt 37.1 32.0 33.7 35.6 37.4 38.1 38.3 37.6 38.5 39.7

Gross debt 42.6 37.2 38.5 40.3 42.1 43.3 44.1 43.4 44.3 45.8

Canada

Actual balance –2.7 –0.1 –0.1 0.9 1.5 1.3 1.4 0.7 0.6 1.2

Output gap2 –0.2 0.2 –0.7 –0.2 0.0 0.5 0.6 –1.4 –2.8 —

Structural balance2 –2.4 –0.2 0.2 1.0 1.5 1.1 1.1 1.2 1.7 1.2

Net debt 60.4 42.6 38.7 34.5 30.0 26.4 23.2 21.5 20.4 12.2

Gross debt 93.5 80.6 76.6 72.4 70.5 67.9 64.2 60.7 58.4 43.1

Note: The methodology and specific assumptions for each country are discussed in Box A1 in this Statistical Appendix.

1Debt data refer to end of year. Debt data are not always comparable across countries.

2Percent of potential GDP.

3Beginning in 1995, the debt and debt-service obligations of the Treuhandanstalt (and of various other agencies) were taken over by general government. This debt is 

equivalent to 8 percent of GDP, and the associated debt service to 1⁄2 to 1 percent of GDP. 4Excludes one-off receipts from the sale of mobile telephone licenses (the equivalent of 2.5 percent of GDP in 2000 for Germany, 0.1 percent of GDP in 2001 and 2002 for 

France, and 1.2 percent of GDP in 2000 for Italy). Also excludes one-off receipts from sizable asset transactions, in particular 0.5 percent of GDP for France in 2005.
Statistical appen

Table A9. Summary of World Trade Volumes and P

(Annual percent change)

 Ten-Year Averages

1990–99 2000–09 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Trade in goods and service

World trade

Volume 6.5 6.5 12.3 0.3 3.5 5.4 10.7 7.6 9.3 7.2 4.9 4.1

Price deflator

In U.S. dollars — 5.0 –0.5 –3.6 1.2 10.4 9.7 5.6 5.0 8.1 15.3 0.2

In SDRs –0.7 3.6 3.1 –0.1 –0.6 2.1 3.7 5.8 5.5 3.9 10.6 2.0

Volume of trad

Exports

Advanced economies 6.5 5.3 11.9 –0.5 2.4 3.3 9.1 6.1 8.4 5.9 4.3 2.5

Emerging and developing economies 7.5 9.2 13.8 2.6 6.9 10.5 13.9 10.8 11.0 9.5 6.3 7.4

Imports

Advanced economies 6.4 4.8 11.9 –0.5 2.7 4.1 9.4 6.4 7.5 4.5 1.9 1.1

Emerging and developing economies 6.5 11.2 13.8 3.2 6.2 10.0 16.0 12.0 14.7 14.2 11.7 10.5

Terms of trad

Advanced economies — –0.5 –2.6 0.4 0.8 1.0 — –1.5 –1.2 0.3 –1.8 –0.1

Emerging and developing economies –0.7 2.4 6.0 –2.3 0.3 1.1 3.0 5.2 4.9 1.7 5.5 –0.9

Trade in good

World trade

Volume 6.7 6.6 13.0 –0.4 3.7 6.3 10.9 7.6 9.3 6.5 5.2 4.4

Price deflator

In U.S. dollars –0.3 5.1 0.2 –3.9 0.6 9.9 9.8 6.1 5.7 8.2 16.0 –0.1

In SDRs –0.9 3.7 3.9 –0.4 –1.1 1.6 3.8 6.4 6.1 4.0 11.3 1.8

World trade prices in U.S. dolla

Manufactures 0.3 4.6 –5.3 –3.4 2.1 14.4 9.5 3.6 3.7 8.8 13.8 0.5

Oil — 18.8 57.0 –13.8 2.5 15.8 30.7 41.3 20.5 10.7 50.8 –6.3

Nonfuel primary commodities –2.2 6.9 4.2 –4.8 1.9 5.9 15.2 6.1 23.2 14.1 13.3 –6.2

Food –2.3 6.9 2.5 –2.0 3.5 6.3 14.0 –0.9 10.5 15.2 29.8 –5.8

Beverages –0.5 4.6 –18.4 –13.3 24.3 4.8 –0.9 18.1 8.4 13.8 27.5 –7.1

Agricultural raw materials –0.6 2.1 5.5 –3.4 –0.2 0.6 4.1 0.5 8.8 5.0 3.6 –2.7

Metals –4.1 11.5 13.2 –10.3 –3.5 11.8 34.6 22.4 56.2 17.4 –1.9 –8.4

World trade prices in SDR

Manufactures –0.3 3.2 –1.8 0.1 0.4 5.7 3.6 3.9 4.2 4.5 9.2 2.4

Oil –0.6 17.2 62.8 –10.7 0.8 7.1 23.6 41.6 21.0 6.4 44.7 –4.6

Nonfuel primary commodities –2.9 5.5 8.1 –1.3 0.2 –2.1 9.0 6.3 23.8 9.6 8.7 –4.5

Food –2.9 5.4 6.2 1.5 1.8 –1.7 7.8 –0.7 11.0 10.7 24.5 –4.1

Beverages –1.1 3.2 –15.4 –10.2 22.2 –3.1 –6.3 18.3 8.8 9.4 22.3 –5.4

Agricultural raw materials –1.2 0.7 9.4 0.1 –1.9 –7.0 –1.6 0.8 9.3 0.9 –0.6 –0.9

Metals –4.8 10.0 17.4 –7.0 –5.1 3.3 27.3 22.7 56.9 12.8 –5.9 –6.7

World trade prices in euros

Manufactures 0.7 1.1 9.3 –0.3 –3.1 –4.5 –0.4 3.4 2.9 –0.4 2.4 1.9

Oil 0.4 14.8 81.3 –11.1 –2.8 –3.3 18.9 41.0 19.5 1.4 35.6 –5.0

Nonfuel primary commodities –1.9 3.3 20.4 –1.8 –3.3 –11.6 4.8 5.9 22.3 4.5 1.9 –4.9

Food –2.0 3.3 18.3 1.1 –1.8 –11.2 3.7 –1.1 9.6 5.6 16.8 –4.5

Beverages –0.1 1.1 –5.7 –10.5 17.9 –12.5 –9.9 17.8 7.5 4.2 14.7 –5.8

Agricultural raw materials –0.3 –1.3 21.8 –0.4 –5.4 –16.0 –5.3 0.3 8.0 –3.8 –6.8 –1.3

Metals –3.8 7.8 30.7 –7.4 –8.4 –6.7 22.4 22.2 55.0 7.5 –11.8 –7.1
Foreign Trade: Summa

Table A9 (concluded

Ten-Year Averages

1990–99 2000–09 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Trade in good

Volume of trad

Exports

Advanced economies 6.4 5.2 12.7 –1.3 2.3 3.9 8.8 5.6 8.5 5.1 4.3 2.6

Emerging and developing economies 7.5 9.3 13.9 2.0 7.0 11.6 14.0 10.9 10.9 9.2 6.9 7.4

Fuel exporters 2.8 5.5 7.5 0.5 3.0 12.1 8.9 5.4 4.2 4.0 5.1 4.3

Nonfuel exporters 9.5 10.7 15.8 2.6 8.3 11.5 15.8 12.8 13.6 11.4 7.5 8.6

Imports

Advanced economies 6.7 5.0 12.6 –1.4 3.1 5.0 9.7 6.3 7.8 3.8 2.3 1.4

Emerging and developing economies 6.8 11.3 14.6 3.1 6.3 11.4 17.0 12.5 12.8 13.2 11.8 10.8

Fuel exporters –0.5 14.6 11.3 16.0 8.7 9.6 16.1 17.5 14.2 20.2 17.3 15.8

Nonfuel exporters 9.0 10.6 15.1 1.0 5.8 11.7 17.2 11.6 12.5 11.8 10.6 9.7

Price deflators in SDR

Exports

Advanced economies –1.1 2.7 0.3 –0.2 –0.9 2.5 3.0 3.7 4.2 3.7 9.3 1.8

Emerging and developing economies — 6.9 14.8 –1.0 0.1 1.4 7.5 13.9 10.8 5.4 16.1 1.6

Fuel exporters 0.5 13.8 43.7 –7.3 0.6 4.2 17.1 33.2 18.1 8.0 33.1 –1.1

Nonfuel exporters –0.2 4.3 6.1 1.4 –0.1 0.5 4.3 7.0 7.8 4.3 9.6 2.6

Imports

Advanced economies –1.4 3.2 3.4 –0.8 –1.9 1.2 3.1 5.6 5.6 3.5 10.9 1.7

Emerging and developing economies 0.7 4.1 6.6 1.1 –0.6 0.3 4.1 6.8 6.6 4.2 10.6 2.3

Fuel exporters 1.0 3.8 1.9 0.4 0.9 0.7 4.2 7.3 7.8 4.6 8.1 2.3

Nonfuel exporters 0.5 4.2 7.5 1.2 –0.9 0.2 4.1 6.7 6.3 4.2 11.2 2.2

Terms of trad

Advanced economies 0.3 –0.5 –3.0 0.6 1.0 1.2 –0.1 –1.8 –1.4 0.2 –1.4 0.1

Emerging and developing economies –0.7 2.6 7.7 –2.1 0.7 1.0 3.3 6.7 4.0 1.1 4.9 –0.7

Regional groups

Africa –0.4 5.6 13.7 –3.5 0.2 2.8 4.0 14.4 9.7 3.9 16.6 –3.2

Central and eastern Europe –0.6 0.2 –2.3 3.5 0.9 — 1.4 –0.8 –1.5 1.1 –0.6 0.5

Commonwealth of Independent States3 –2.2 7.7 23.3 –2.6 –2.0 9.0 12.1 14.6 8.9 2.8 17.5 –3.6

Developing Asia –0.2 –0.8 –3.9 0.9 0.5 –0.6 –1.9 –0.8 –0.1 –0.7 –2.7 1.7

Middle East 0.3 8.3 39.7 –8.3 1.7 –0.8 10.6 25.4 5.9 1.7 18.6 –3.2

Western Hemisphere –0.9 3.1 7.4 –4.2 1.7 3.1 5.9 5.8 8.8 2.2 4.3 –2.8

Analytical groups

By source of export earnings

Fuel exporters –0.5 9.7 41.1 –7.6 –0.2 3.4 12.4 24.1 9.5 3.2 23.1 –3.3

Nonfuel exporters –0.7 0.1 –1.3 0.1 0.8 0.2 0.2 0.4 1.4 0.1 –1.4 0.3

Memorandum

World exports in billions of U.S. doll

Goods and services 5,755 13,121 7,880 7,612 7,992 9,309 11,299 12,834 14,759 17,130 20,770 21,622

Goods 4,583 10,560 6,349 6,076 6,355 7,429 9,022 10,295 11,903 13,751 16,860 17,560

Average oil price4 — 18.8 57.0 –13.8 2.5 15.8 30.7 41.3 20.5 10.7 50.8 –6.3

In U.S. dollars a barrel 18.20 54.07 28.2 24.3 25.0 28.9 37.8 53.4 64.3 71.1 107.3 100.5

Export unit value of manufactures5 0.3 4.6 –5.3 –3.4 2.1 14.4 9.5 3.6 3.7 8.8 13.8 0.5

1Average of annual percent change for world exports and imports.

2As represented, respectively, by the export unit value index for the manufactures of the advanced economies; the average of U.K. Brent, Dubai, and West Texas Intermediate 

crude oil prices; and the average of world market prices for nonfuel primary commodities weighted by their 2002–04 shares in world commodity exports. 3Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.

4Average of U.K. Brent, Dubai, and West Texas Intermediate crude oil prices.

5For the manufactures exported by the advanced economies.
Statistical appen

Table A10. Summary of Balances on Current Accou

(Billions of U.S. dollars)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Advanced economies –264.8 –200.9 –213.2 –209.3 –206.1 –392.2 –454.0 –368.8 –430.2 –268.6 –199.5

United States –417.4 –382.4 –461.3 –523.4 –625.0 –729.0 –788.1 –731.2 –664.1 –485.9 –479.1

Euro area1 –35.2 8.3 49.8 48.4 120.3 46.7 32.9 29.3 –65.5 –54.3 –14.6

Japan 119.6 87.8 112.6 136.2 172.1 165.7 170.4 211.0 194.3 179.2 153.3

Other advanced economies2 68.2 85.4 85.7 129.6 126.5 124.5 130.8 122.1 105.2 92.4 141.0

Memorandum

Newly industrialized Asian economies 38.9 47.5 55.3 80.5 81.9 75.0 84.4 106.1 84.1 81.8 102.8

Emerging and developing economies 86.5 41.2 76.9 144.5 215.1 445.9 617.0 634.2 784.9 612.9 493.

Regional groups

Africa 8.1 1.0 –8.8 –4.1 2.1 15.6 27.8 4.0 40.1 3.6 –46.7

Central and eastern Europe –31.4 –15.5 –23.1 –36.8 –57.6 –59.4 –87.7 –120.7 –164.4 –174.2 –218.5

Commonwealth of Independent States3 48.3 33.0 30.3 36.0 63.8 88.3 97.7 74.3 127.9 80.9 –106.9

Developing Asia 38.6 36.6 64.6 82.5 89.3 161.5 277.6 403.4 380.0 410.2 697.3

Middle East 71.5 39.9 30.3 59.1 97.0 204.7 253.9 257.0 438.6 365.0 280.3

Western Hemisphere –48.5 –53.9 –16.3 7.8 20.6 35.2 47.7 16.2 –37.3 –72.6 –112.1

Memorandum

European Union –82.9 –25.3 18.7 21.2 65.0 –10.7 –60.5 –111.0 –232.1 –223.0 –243.0

Analytical groups

By source of export earnings

Fuel 151.7 83.5 61.6 106.4 185.8 353.9 439.6 405.5 711.3 558.8 239.5

Nonfuel –65.2 –42.3 15.3 38.1 29.4 92.0 177.5 228.6 73.6 54.1 253.8

of which, primary products –2.5 –3.8 –4.6 –3.0 0.2 0.9 9.2 10.3 –1.7 –4.3 –5.2

By external financing source

Net debtor countries –95.9 –72.5 –34.5 –30.7 –68.6 –99.8 –104.4 –179.4 –322.0 –371.6 –503.9

of which, official financing –5.1 –3.9 –4.2 –5.8 –5.5 –6.2 –6.5 –17.0 –26.7 –27.7 –32.5

Net debtor countries by debtservicing experienc

Countries with arrears and/or

rescheduling during 2002–06 –8.4 –5.2 12.1 15.0 0.1 –9.7 0.7 –14.7 –36.8 –48.3 –67.4

World1 –178.4 –159.7 –136.3 –64.8 9.0 53.7 163.0 265.3 354.7 344.3 293.

Memorandum

In percent of total world current

account transactions –1.1 –1.0 –0.8 –0.3 — 0.2 0.6 0.8 0.9 0.8 0.5

In percent of world GDP –0.6 –0.5 –0.4 –0.2 — 0.1 0.3 0.5 0.6 0.5 0.4

1Reflects errors, omissions, and asymmetries in balance of payments statistics on current account, as well as the exclusion of data for international organizations and a 

limited number of countries. Calculated as the sum of the balance of individual euro area countries. See “Classification of Countries” in the introduction to this Statistical 

Appendix. 2In this table, “other advanced economies” means advanced economies excluding the United States, euro area countries, and Japan.

3Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Current Account: Advanced Econom

Table A11. Advanced Economies: Balance on Current Accou

(Percent of GDP)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Advanced economies –1.0 –0.8 –0.8 –0.7 –0.6 –1.1 –1.3 –0.9 –1.0 –0.6 –0.4

United States –4.3 –3.8 –4.4 –4.8 –5.3 –5.9 –6.0 –5.3 –4.6 –3.3 –2.8

Euro area1 –0.6 0.1 0.7 0.6 1.2 0.5 0.3 0.2 –0.5 –0.4 –0.1

Germany –1.7 — 2.0 2.0 4.7 5.2 6.1 7.6 7.3 6.8 6.5

France 1.6 1.9 1.4 0.8 0.6 –0.6 –0.7 –1.2 –2.8 –2.7 –2.9

Italy –0.5 –0.1 –0.8 –1.3 –0.9 –1.6 –2.6 –2.5 –2.8 –2.4 –1.0

Spain –4.0 –3.9 –3.3 –3.5 –5.3 –7.4 –8.9 –10.1 –10.1 –7.7 –5.6

Netherlands 1.9 2.4 2.5 5.5 7.5 7.1 8.2 6.8 5.6 5.1 4.2

Belgium 4.0 3.4 4.6 4.1 3.5 2.6 2.7 2.1 — –1.1 –0.9

Austria –0.7 –0.8 2.7 1.7 2.1 2.0 2.4 3.2 2.8 2.4 2.1

Finland 8.1 8.6 8.8 5.1 6.5 3.6 4.6 4.6 3.4 2.9 2.3

Greece –7.8 –7.2 –6.5 –6.6 –5.8 –7.4 –11.1 –14.1 –14.0 –14.1 –11.5

Portugal –10.2 –9.9 –8.1 –6.1 –7.6 –9.5 –10.1 –9.8 –12.0 –12.7 –11.2

Ireland –0.4 –0.6 –1.0 — –0.6 –3.5 –3.6 –5.4 –5.0 –4.4 –3.2

Luxembourg 13.2 8.8 10.5 8.2 11.9 11.1 10.5 9.9 8.6 8.2 5.8

Slovenia –2.7 0.2 1.0 –0.8 –2.7 –2.0 –2.8 –4.9 –4.7 –4.7 –3.7

Cyprus –5.2 –3.3 –3.7 –2.2 –5.0 –5.6 –5.9 –9.7 –9.7 –7.8 –4.5

Malta –13.1 –4.1 2.5 –3.1 –5.8 –8.7 –8.2 –5.4 –7.7 –6.4 –3.5

Japan 2.6 2.1 2.9 3.2 3.7 3.6 3.9 4.8 4.0 3.7 2.8

United Kingdom –2.6 –2.1 –1.7 –1.6 –2.1 –2.6 –3.4 –3.8 –3.6 –3.4 –3.0

Canada 2.7 2.3 1.7 1.2 2.3 1.9 1.4 0.9 0.9 — 1.3

Korea 2.4 1.7 1.0 2.0 4.1 1.9 0.6 0.6 –1.3 –0.7 —

Australia –3.8 –2.0 –3.7 –5.3 –6.1 –5.8 –5.3 –6.2 –4.9 –4.3 –5.0

Taiwan Province of China 2.8 6.5 8.9 10.0 6.0 4.9 7.2 8.6 7.8 6.5 4.9

Sweden 4.0 4.3 5.0 7.2 6.7 6.8 8.5 8.5 6.4 5.8 4.1

Switzerland 12.3 7.8 8.3 12.9 12.9 13.6 14.7 16.6 9.3 8.7 12.0

Hong Kong SAR 4.1 5.9 7.6 10.4 9.5 11.4 12.1 13.5 11.7 10.3 6.7

Denmark 1.4 3.1 2.5 3.4 3.1 4.4 2.9 1.1 1.3 1.8 1.7

Norway 15.0 16.1 12.6 12.3 12.7 16.3 17.3 15.4 19.1 18.0 17.7

Israel –0.8 –1.1 –0.8 1.2 2.4 3.2 5.9 3.2 0.4 0.5 1.5

Singapore 11.6 12.5 12.6 23.2 16.7 18.6 21.8 24.3 19.1 17.0 17.5

New Zealand –5.1 –2.8 –3.9 –4.3 –6.4 –8.5 –8.7 –8.2 –9.3 –8.1 –5.6

Iceland –10.2 –4.3 1.5 –4.8 –9.8 –16.1 –25.4 –14.6 –18.2 –13.7 –5.5

Memorandum

Major advanced economies –1.6 –1.4 –1.5 –1.5 –1.4 –1.8 –2.0 –1.5 –1.3 –0.9 –0.7

Euro area2 –1.5 –0.3 0.8 0.4 0.8 0.2 — 0.3 –0.5 –0.5 –0.2

Newly industrialized Asian economies 3.5 4.6 5.0 6.9 6.4 5.2 5.3 6.2 4.7 4.3 4.0

1Calculated as the sum of the balances of individual euro area countries.

2Corrected for reporting discrepancies in intra-area transactions.
Statistical appen

Table A12. Emerging and Developing Economies, by Country: Balance on Current Acco

(Percent of GDP)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Africa 1.8 0.2 –1.9 –0.7 0.3 1.9 2.9 0.4 3.0 0.2 –2.2

Algeria 16.7 12.9 7.7 13.0 13.1 20.6 24.8 22.8 28.1 19.8 10.4

Angola 8.7 –16.0 –1.3 –5.2 3.5 16.8 23.3 11.3 18.0 15.9 10.7

Benin –7.7 –6.4 –8.4 –8.3 –7.2 –5.5 –5.7 –6.7 –9.8 –8.1 –5.5

Botswana 8.8 9.9 3.3 5.6 3.6 14.8 17.6 15.9 9.3 7.6 11.3

Burkina Faso –12.3 –11.2 –10.0 –8.7 –10.6 –11.7 –9.6 –8.3 –12.7 –12.1 –8.7

Burundi –8.6 –4.6 –3.5 –4.6 –8.4 –1.2 –14.5 –16.0 –21.9 –14.8 –10.8

Cameroon –1.4 –3.6 –5.1 –1.8 –3.8 –3.4 0.6 –1.9 1.3 –1.1 –4.9

Cape Verde –10.9 –10.7 –11.2 –11.2 –14.4 –3.4 –5.0 –9.1 –10.6 –10.9 –10.7

Central African Republic –1.3 –1.7 –1.6 –2.2 0.4 –6.2 –2.7 –4.4 –6.3 –5.9 –5.6

Chad –15.4 –31.3 –93.8 –47.8 –16.7 3.1 –10.6 1.7 10.0 –1.8 –2.9

Comoros 1.7 3.0 –1.7 –3.2 –4.6 –7.2 –6.1 –6.7 –8.1 –9.5 –7.9

Congo, Dem. Rep. of –4.0 –4.0 –1.6 1.0 –2.4 –10.4 –2.4 –1.8 –1.9 –12.6 –4.7

Congo, Rep. of 7.9 –5.6 0.6 –4.1 12.7 5.3 2.3 –19.3 10.7 21.4 9.4

Côte d’Ivoire –2.8 –0.6 6.7 2.1 1.6 0.2 2.8 –0.7 3.8 –0.6 –4.9

Djibouti –9.0 –2.9 –1.6 3.4 –1.3 –3.2 –14.7 –24.8 –33.5 –32.9 –13.8

Equatorial Guinea –15.8 –41.2 0.9 –33.3 –21.6 –6.2 7.1 4.3 12.5 2.8 3.8

Eritrea –0.6 –4.6 6.8 9.7 –0.7 0.3 –3.6 –3.7 –3.0 –2.1 2.6

Ethiopia –4.2 –3.0 –4.7 –1.4 –4.0 –6.0 –9.1 –4.5 –5.0 –5.2 –4.7

Gabon 19.7 11.0 6.8 9.5 11.2 22.9 18.7 14.8 17.0 18.1 6.8

Gambia, The –3.1 –2.6 –2.8 –5.1 –6.1 –15.1 –11.5 –12.5 –13.9 –12.5 –11.1

Ghana –8.4 –5.3 0.5 1.7 –2.8 –7.0 –9.0 –10.9 –13.1 –13.2 –12.2

Guinea –6.4 –2.7 –2.5 –0.2 –1.9 –0.6 0.5 –2.0 –6.3 –6.7 –6.7

Guinea-Bissau –5.6 –22.1 –10.7 –2.8 1.8 –5.1 –13.9 –2.2 0.2 –11.6 –11.8

Kenya –2.3 –3.1 2.2 –0.2 0.1 –0.8 –2.3 –3.1 –6.1 –4.5 –4.1

Lesotho –18.0 –12.5 –20.1 –12.3 –5.6 –7.3 4.4 3.6 0.1 –1.4 –3.0

Liberia –19.2 –25.0 –18.2 –35.4 –26.2 –39.1 –24.5 –18.7 –65.9 –43.9 –11.0

Madagascar –5.6 –1.3 –6.0 –4.7 –8.8 –10.6 –8.4 –13.9 –23.1 –21.2 –6.5

Malawi –5.3 –6.8 –12.5 –5.8 –7.3 –10.1 –6.4 –2.1 –8.2 –5.4 –7.3

Mali –10.0 –10.4 –3.1 –6.2 –8.4 –8.3 –3.7 –7.2 –6.6 –6.9 –3.4

Mauritania –9.0 –11.7 3.0 –13.6 –34.6 –47.2 –1.3 –11.4 –6.3 –3.0 5.4

Mauritius –1.5 3.4 5.7 2.4 0.8 –3.5 –5.3 –8.0 –4.7 –6.6 –3.5

Morocco –1.3 4.3 3.6 3.2 1.7 1.8 2.2 –0.1 0.4 –0.3 –0.1

Mozambique –16.2 –17.6 –18.8 –15.5 –8.9 –11.4 –9.2 –9.5 –13.6 –13.3 –12.1

Namibia 9.0 1.9 3.7 6.7 8.2 5.5 16.0 18.2 14.0 12.4 6.3

Niger –6.7 –5.1 –6.6 –8.3 –7.8 –9.3 –8.6 –7.7 –10.7 –20.6 –7.6

Nigeria 11.7 4.9 –13.1 –6.1 5.0 7.1 9.5 2.1 6.2 0.6 –3.5

Rwanda –6.5 –6.0 –10.7 –12.4 –1.9 –1.1 –7.3 –5.0 –9.3 –12.4 –10.1

São Tomé and Príncipe –17.5 –22.7 –17.0 –13.9 –16.8 –10.3 –41.3 –30.2 –29.6 –34.5 –30.2

Senegal –6.5 –4.3 –5.6 –6.1 –6.1 –7.8 –9.4 –10.4 –11.1 –11.4 –12.1

Seychelles –7.3 –23.4 –16.3 0.4 –7.0 –23.6 –17.2 –37.0 –38.3 –35.1 –44.0

Sierra Leone –8.8 –6.3 –2.0 –4.8 –5.8 –7.1 –3.5 –3.8 –6.3 –4.2 –3.2

South Africa –0.1 0.3 0.8 –1.1 –3.2 –4.0 –6.5 –7.3 –8.0 –8.1 –8.3

Sudan –8.4 –12.7 –10.3 –7.9 –6.5 –11.1 –15.2 –12.6 –6.3 –6.7 –5.8

Swaziland –4.9 –4.3 4.8 6.8 3.1 –4.1 –7.4 –1.4 –5.3 –2.0 –2.6

Tanzania –4.8 –4.5 –6.2 –4.2 –3.6 –4.1 –7.7 –9.0 –9.8 –10.0 –9.8

Togo –9.0 –9.3 –5.5 –4.2 –3.0 –5.3 –6.0 –6.4 –9.6 –8.5 –6.5

Tunisia –4.2 –5.1 –3.6 –2.9 –1.9 –1.1 –2.0 –2.6 –3.4 –3.5 –2.7

Uganda –6.7 –3.7 –4.6 –5.5 –3.0 –4.5 –3.5 –2.8 –3.4 –5.8 –3.7

Zambia –18.2 –19.9 –13.8 –14.7 –11.7 –10.0 –0.7 –7.1 –9.5 –6.6 –3.6

Zimbabwe1 0.6 –0.7 –3.1 –6.0 –7.8 –10.2 –7.0 –3.5 . . . . . . . . .
Table A12 (continued

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Central and eastern Europe –4.8 –2.5 –3.2 –4.1 –5.2 –4.6 –6.0 –6.6 –7.1 –7.2 –6.

Albania –3.7 –3.7 –7.2 –5.2 –4.0 –6.1 –5.6 –9.2 –10.5 –7.1 –5.0

Bosnia and Herzegovina –6.9 –12.5 –17.8 –19.4 –16.3 –18.0 –8.4 –12.7 –15.8 –13.5 –13.0

Bulgaria –5.6 –5.6 –2.4 –5.5 –6.6 –12.0 –15.6 –21.4 –24.4 –21.5 –12.9

Croatia –2.9 –3.7 –8.4 –6.2 –5.0 –6.3 –7.9 –8.6 –10.1 –10.2 –7.2

Czech Republic –4.7 –5.3 –5.7 –6.3 –5.3 –1.3 –2.6 –1.8 –2.2 –2.5 –2.8

Estonia –5.4 –5.2 –10.6 –11.3 –11.7 –10.0 –16.7 –18.1 –10.8 –8.7 –9.3

Hungary –8.4 –6.0 –7.0 –7.9 –8.4 –6.8 –6.1 –5.0 –5.5 –6.1 –6.5

Latvia –4.7 –7.5 –6.7 –8.2 –12.8 –12.4 –22.7 –22.9 –15.1 –8.3 –5.3

Lithuania –5.9 –4.7 –5.2 –6.9 –7.7 –7.1 –10.7 –14.6 –14.9 –8.7 –8.3

Macedonia, FYR –1.9 –7.2 –9.4 –4.1 –8.4 –2.6 –0.9 –3.0 –14.0 –13.8 –8.9

Montenegro . . . . . . . . . –6.8 –7.2 –8.5 –24.7 –39.6 –39.6 –36.8 –16.7

Poland –5.8 –2.8 –2.5 –2.1 –4.0 –1.2 –2.7 –3.8 –4.7 –5.7 –5.9

Romania –3.7 –5.5 –3.3 –5.8 –8.4 –8.9 –10.4 –14.0 –13.8 –13.3 –12.7

Serbia –1.8 –2.5 –8.2 –7.2 –12.1 –8.7 –10.0 –15.9 –18.6 –19.3 –15.4

Slovak Republic –3.3 –8.3 –8.0 –5.9 –7.8 –8.5 –7.1 –5.4 –5.1 –4.7 –3.0

Turkey –3.7 2.0 –0.3 –2.5 –3.7 –4.6 –6.0 –5.7 –6.5 –6.7 –5.5

Commonwealth of Independent States2 13.7 8.0 6.5 6.3 8.2 8.8 7.5 4.4 5.5 3.0 –2.

Russia 18.0 11.1 8.4 8.2 10.1 11.0 9.5 5.9 6.5 3.4 –3.2

Excluding Russia 1.5 –0.9 1.0 0.4 2.3 1.6 1.1 –0.5 2.3 1.8 1.1

Armenia –14.6 –9.5 –6.2 –6.8 –0.5 –1.0 –1.8 –6.4 –9.7 –10.8 –8.1

Azerbaijan –3.5 –0.9 –12.3 –27.8 –29.8 1.3 17.7 28.9 38.3 38.6 12.0

Belarus –3.2 –3.3 –2.2 –2.4 –5.2 1.4 –3.9 –6.8 –5.9 –8.0 –7.2

Georgia –7.9 –6.4 –6.8 –8.6 –8.9 –11.9 –15.9 –20.0 –20.8 –18.7 –11.9

Kazakhstan 3.0 –5.4 –4.2 –0.9 0.8 –1.8 –2.4 –6.9 4.3 3.3 3.3

Kyrgyz Republic –4.3 –1.5 –4.0 1.7 4.9 2.8 –3.1 –0.2 –4.2 –5.4 –2.0

Moldova –7.6 –1.8 –1.2 –6.6 –2.3 –10.3 –11.8 –17.0 –19.9 –19.1 –16.4

Mongolia –5.0 –13.0 –8.7 –7.1 1.3 1.1 7.0 2.5 –12.6 –12.6 0.1

Tajikistan –1.6 –4.9 –3.5 –1.3 –3.9 –2.7 –2.8 –11.2 –8.5 –8.1 –4.5

Turkmenistan 8.2 1.7 6.7 2.7 0.6 5.1 15.7 15.4 26.5 33.0 35.6

Ukraine 4.7 3.7 7.5 5.8 10.6 2.9 –1.5 –3.7 –7.2 –9.2 –5.7

Uzbekistan 1.8 –1.0 1.2 8.7 10.1 13.6 17.2 19.1 16.8 12.8 8.4

Current Account: Emerging and Developing E
Statistical appen

2

Table A12 (continued

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Developing Asia 1.7 1.5 2.4 2.7 2.6 4.0 5.9 7.0 5.4 5.2 5.8

Afghanistan, I.R. of . . . . . . –3.7 –15.7 –4.9 –2.8 –4.9 0.8 0.9 –3.0 –4.6

Bangladesh –1.4 –0.9 0.3 0.3 –0.3 — 1.2 1.1 1.0 0.9 –0.5

Bhutan –9.0 –8.5 –15.8 –23.6 –17.9 –30.4 –4.3 11.0 11.7 2.8 –28.6

Brunei Darussalam 50.0 48.4 41.2 47.7 48.6 52.8 56.3 48.8 55.0 55.6 56.7

Cambodia –2.8 –1.1 –2.4 –3.6 –2.2 –4.2 –1.1 –3.6 –10.3 –9.8 –5.7

China 1.7 1.3 2.4 2.8 3.6 7.2 9.4 11.3 9.5 9.2 9.9

Fiji –5.6 –7.0 –1.2 –6.1 –13.6 –14.0 –22.6 –15.5 –21.3 –21.4 –15.0

India –1.0 0.3 1.4 1.5 0.1 –1.3 –1.1 –1.4 –2.8 –3.1 –2.1

Indonesia 4.8 4.3 4.0 3.5 0.6 0.1 3.0 2.5 0.1 –0.1 –2.0

Kiribati –1.2 22.2 10.5 12.6 –3.4 –42.2 –27.6 –31.1 –43.7 –47.0 –53.6

Lao PDR –9.1 –12.8 –11.6 –12.1 –16.9 –17.4 –10.8 –17.3 –16.3 –16.5 –6.5

Malaysia 9.0 7.9 8.0 12.0 12.1 14.5 16.1 15.6 14.8 13.2 11.2

Maldives –8.2 –9.4 –5.6 –4.6 –16.2 –35.9 –40.3 –45.0 –48.3 –37.0 –4.0

Myanmar –0.8 –2.4 0.2 –1.0 2.4 3.7 9.5 6.8 3.6 1.6 –4.8

Nepal 2.9 4.5 4.2 2.4 2.7 2.0 2.2 0.5 1.9 1.7 –0.6

Pakistan –0.3 0.4 3.9 4.9 1.8 –1.4 –3.9 –4.8 –8.7 –6.4 –3.5

Papua New Guinea 8.5 6.5 –1.0 4.5 2.2 4.2 2.9 4.3 3.3 1.7 –3.7

Philippines –2.9 –2.4 –0.4 0.4 1.9 2.0 4.5 4.4 2.4 2.2 0.5

Samoa 1.0 0.1 –1.1 –95.3 –6.8 –1.6 –4.6 –6.1 –9.4 –8.4 0.2

Solomon Islands –10.1 –9.4 –6.5 9.1 23.5 –9.8 –5.6 –2.8 –6.8 –9.6 –11.9

Sri Lanka –6.3 –1.1 –1.4 –0.4 –3.1 –2.7 –5.3 –4.2 –7.5 –7.1 –7.0

Thailand 7.6 4.4 3.7 3.4 1.7 –4.3 1.1 6.4 3.1 2.0 –0.4

Timor-Leste –15.0 –19.3 –22.9 –21.4 14.8 61.0 192.2 253.3 230.5 178.1 94.3

Tonga –6.2 –9.5 5.1 –3.1 4.2 –2.6 –9.7 –10.4 –10.4 –8.8 –7.6

Vanuatu 2.0 2.0 –5.4 –6.6 –5.0 –7.4 –5.7 –9.9 –11.4 –13.2 –6.5

Vietnam 3.5 2.1 –1.7 –4.9 –3.5 –0.9 –0.3 –9.9 –11.7 –10.4 –6.3

Middle East 11.3 6.3 4.7 8.3 11.7 20.0 21.1 18.4 22.9 17.1 8.

Bahrain 10.6 2.8 –0.7 2.0 4.2 11.0 13.8 16.7 18.0 15.0 9.0

Egypt –1.2 — 0.7 2.4 4.3 3.2 0.8 1.5 0.6 –0.9 –2.7

Iran, I.R. of 13.0 5.2 3.1 0.6 0.9 8.8 9.2 10.1 11.2 6.7 –1.1

Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Jordan 0.7 0.1 5.7 12.2 0.8 –17.4 –11.3 –17.5 –18.5 –16.3 –11.5

Kuwait 38.9 23.9 11.2 19.7 30.6 46.6 52.2 43.1 44.6 39.3 39.1

Lebanon –17.2 –19.3 –14.1 –13.2 –15.5 –13.6 –5.6 –12.7 –14.0 –13.7 –9.3

Libya 29.8 12.3 3.0 19.9 22.3 38.4 45.8 34.0 36.5 29.5 24.3

Oman 15.5 9.8 6.7 3.8 2.4 15.2 12.1 8.0 10.1 5.6 5.4

Qatar 23.2 27.3 21.9 25.3 22.4 33.2 28.4 29.2 42.9 35.6 23.0

Saudi Arabia 7.6 5.1 6.3 13.1 20.8 28.7 27.9 25.1 32.5 23.8 8.6

Syrian Arab Republic 5.2 5.7 7.2 1.0 –1.9 –2.1 –2.9 –1.4 –2.7 –2.9 –4.2

United Arab Emirates 17.3 9.5 4.9 8.5 9.1 18.0 22.6 20.5 22.6 18.8 10.9

Yemen, Rep. of 13.8 6.8 4.1 1.5 1.6 3.8 1.1 –6.1 2.9 2.1 —
Current Account: Emerging and Developing E

Table A12 (concluded

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2013

Western Hemisphere –2.3 –2.7 –0.9 0.4 0.9 1.3 1.5 0.4 –0.8 –1.6 –1

Antigua and Barbuda –9.8 –8.0 –11.5 –12.9 –8.3 –12.4 –16.1 –19.4 –18.2 –14.6 –11.6

Argentina –3.2 –1.4 8.9 6.3 2.1 2.0 2.6 1.7 0.8 –0.6 –0.8

Bahamas, The –12.6 –11.6 –7.8 –8.6 –5.4 –14.3 –25.0 –21.9 –15.1 –12.8 –8.9

Barbados –5.7 –4.4 –6.8 –6.3 –12.4 –12.8 –8.7 –7.2 –9.9 –9.1 –6.4

Belize –20.3 –21.9 –17.7 –18.2 –14.7 –13.6 –2.1 –4.2 –4.1 –3.0 –6.0

Bolivia –5.3 –3.4 –4.1 1.0 3.8 6.5 11.3 13.1 12.1 7.4 4.7

Brazil –3.8 –4.2 –1.5 0.8 1.8 1.6 1.3 0.1 –1.8 –2.0 –1.7

Chile –1.2 –1.6 –0.9 –1.1 2.2 1.2 4.7 4.4 –1.1 –0.9 –2.5

Colombia 0.8 –1.2 –1.5 –1.1 –0.8 –1.3 –1.8 –2.9 –2.2 –1.9 –1.3

Costa Rica –4.3 –3.7 –4.9 –5.0 –4.3 –5.2 –4.9 –5.8 –7.8 –6.6 –5.7

Dominica –19.7 –18.4 –13.6 –12.8 –16.5 –28.0 –17.8 –23.6 –27.5 –22.9 –17.4

Dominican Republic –4.2 –3.0 –3.6 4.9 4.8 –1.4 –3.6 –5.4 –13.5 –12.4 –7.7

Ecuador 5.3 –3.2 –4.8 –1.5 –1.7 0.8 3.9 2.3 5.6 1.5 –0.1

El Salvador –3.3 –1.1 –2.8 –4.7 –4.0 –3.3 –3.6 –5.5 –6.1 –5.3 –3.9

Grenada –17.7 –19.7 –26.6 –25.3 –9.7 –31.4 –33.1 –32.6 –36.4 –35.1 –24.4

Guatemala –6.1 –6.7 –6.1 –4.6 –4.9 –4.5 –5.0 –5.0 –5.8 –5.9 –5.0

Guyana –14.1 –15.0 –11.9 –8.6 –9.3 –14.8 –19.4 –18.2 –22.2 –18.7 –9.4

Haiti –1.0 –2.0 –0.9 –1.6 –1.6 2.6 –1.4 –1.1 –3.0 –2.9 —

Honduras –7.1 –6.3 –3.6 –6.8 –7.7 –3.0 –4.7 –10.0 –13.9 –10.5 –7.9

Jamaica –5.0 –8.3 –12.7 –8.6 –7.3 –10.6 –11.7 –16.4 –16.0 –12.8 –8.0

Mexico –3.0 –2.6 –2.0 –1.2 –0.9 –0.6 –0.2 –0.6 –1.4 –2.2 –2.8

Nicaragua –20.1 –19.4 –17.7 –16.2 –14.5 –14.6 –13.6 –18.3 –23.9 –21.1 –16.4

Panama –5.9 –1.5 –0.8 –4.5 –7.5 –4.9 –3.2 –8.0 –11.7 –13.9 –8.9

Paraguay –2.3 –4.2 1.8 2.3 2.1 0.8 1.5 1.9 1.4 0.5 –0.3

Peru –2.8 –2.1 –1.9 –1.5 — 1.4 3.0 1.4 –2.0 –1.8 –1.3

St. Kitts and Nevis –21.0 –32.0 –39.1 –34.8 –20.1 –18.3 –21.1 –28.4 –30.6 –30.2 –24.8

St. Lucia –13.4 –15.6 –15.0 –14.7 –10.9 –17.1 –29.7 –29.1 –28.3 –26.7 –17.9

St. Vincent and the Grenadines –7.1 –10.4 –11.5 –20.8 –24.8 –22.3 –24.0 –26.5 –26.5 –22.8 –19.2

Suriname –3.8 –15.2 –5.6 –10.8 –2.1 –4.3 1.8 2.9 1.3 0.8 1.4

Trinidad and Tobago 6.6 5.0 0.9 8.7 12.5 23.7 25.2 25.8 22.3 19.7 9.2

Uruguay –2.8 –2.9 3.2 –0.5 0.3 — –2.4 –0.8 –2.6 –1.9 –1.4

Venezuela 10.1 1.6 8.2 14.1 13.8 17.7 14.7 8.8 8.5 3.4 —

1Given recent trends, it is not possible to forecast nominal GDP with any precision, and consequently no projections for 2008 and beyond are shown.

2Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Statistical appen

Table A13. Emerging and Developing Economies: Net Capital Fl

(Billions of U.S. dollars)

Average

1997–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Emerging and developing economie

Private capital flows, net2 118.9 71.6 75.5 77.1 162.5 236.5 248.7 223.0 632.8 528.6 286.6

Private direct investment, net 163.4 172.0 187.2 156.6 166.2 189.0 261.8 246.0 379.0 443.6 414.6

Private portfolio flows, net 52.0 16.0 –78.7 –91.9 –13.0 12.7 –20.4 –107.3 54.5 –6.6 –89.1

Other private capital flows, net –96.5 –116.3 –33.0 12.4 9.2 34.8 7.3 84.4 199.5 91.8 –38.7

Official flows, net3 18.1 –34.2 –0.4 –1.0 –50.5 –71.1 –109.9 –158.0 –140.7 –158.6 –135.4

Change in reserves4 –74.4 –138.0 –124.7 –195.1 –364.0 –508.4 –595.8 –754.3 –1256.1 –1270.1 –920.2

Memorandum

Current account5 –27.3 124.3 87.4 131.3 226.4 300.0 525.1 709.9 745.5 869.6 695.6

Africa

Private capital flows, net2 8.6 –3.1 2.3 1.8 3.9 13.1 26.3 36.0 39.6 43.7 62.3

Private direct investment, net 7.4 7.7 23.2 14.4 17.6 16.2 23.8 21.8 31.3 31.4 34.0

Private portfolio flows, net 6.9 –2.1 –7.9 –1.6 –0.4 6.0 3.6 18.4 12.1 8.3 11.0

Other private capital flows, net –5.6 –8.7 –13.1 –10.9 –13.3 –9.2 –1.1 –4.1 –3.6 4.3 17.5

Official flows, net3 2.1 1.0 0.1 3.9 1.1 –1.6 –5.3 –16.6 –1.8 6.5 3.7

Change in reserves4 –2.6 –13.4 –10.6 –5.7 –11.5 –31.9 –43.3 –54.2 –60.3 –99.3 –78.3

Central and eastern Europ

Private capital flows, net2 32.4 38.6 11.1 53.7 53.6 74.3 119.2 119.9 173.8 179.9 181.7

Private direct investment, net 18.1 23.5 24.0 24.5 17.1 36.1 51.7 64.3 74.8 77.3 81.8

Private portfolio flows, net 4.3 3.8 0.9 2.1 8.0 28.4 21.5 9.9 –7.7 10.4 15.5

Other private capital flows, net 10.0 11.4 –13.8 27.2 28.5 9.8 45.9 45.6 106.7 92.2 84.5

Official flows, net3 –1.5 1.6 6.0 –7.5 –5.1 –6.0 –7.9 –4.7 –2.4 –2.4 –2.2

Change in reserves4 –10.1 –6.2 –2.7 –18.1 –12.8 –14.7 –45.9 –22.8 –41.6 –22.9 –21.3

Commonwealth of Independent State

Private capital flows, net2 –7.2 –27.9 6.8 15.4 19.3 3.1 31.7 56.8 125.3 19.8 26.0

Private direct investment, net 5.4 2.3 4.9 5.2 5.4 13.0 11.5 21.0 26.0 28.7 35.0

Private portfolio flows, net 1.0 –10.0 –1.2 0.4 –0.5 4.4 –4.8 12.8 15.5 –0.2 5.4

Other private capital flows, net –13.6 –20.2 3.0 9.9 14.3 –14.3 25.0 23.1 83.8 –8.8 –14.5

Official flows, net3 –0.5 –5.8 –5.0 –10.5 –9.3 –7.4 –20.3 –29.7 –5.7 –8.4 –4.9

Change in reserves4 1.6 –20.4 –14.4 –15.1 –32.8 –54.9 –77.2 –128.6 –168.3 –129.9 –92.1

Emerging Asia

Private capital flows, net2 1.3 6.3 23.5 23.1 64.2 147.8 90.9 48.3 163.0 291.6 22.0

Private direct investment, net 63.1 61.6 54.0 53.3 70.4 64.5 104.3 96.5 160.4 224.5 181.2

Private portfolio flows, net 23.4 19.7 –50.1 –60.0 7.9 13.4 –9.3 –110.7 14.8 –24.8 –108.4

Other private capital flows, net –85.2 –75.0 19.5 29.9 –14.1 69.9 –4.0 62.5 –12.2 91.9 –50.7

Official flows, net3 10.7 –1.8 –13.0 2.8 –18.0 –13.4 –21.2 –22.0 –37.0 –9.5 –18.9

Change in reserves4 –59.0 –60.1 –87.7 –154.8 –236.7 –338.7 –288.3 –373.3 –662.8 –752.4 –546.2

Middle East

Private capital flows, net2 9.3 –5.2 –7.3 –22.1 2.6 –16.9 –57.5 –47.5 33.7 –99.6 –86.2

Private direct investment, net 7.2 6.0 12.3 9.2 17.5 10.2 18.3 15.1 7.0 8.0 11.8

Private portfolio flows, net –5.0 3.1 –12.6 –17.4 –17.0 –20.8 –36.5 –24.3 –12.7 –23.4 –31.2

Other private capital flows, net 7.1 –14.2 –7.1 –13.8 2.1 –6.3 –39.2 –38.2 39.4 –84.2 –66.9

Official flows, net3 1.3 –23.5 –13.9 –8.1 –24.2 –33.7 –24.4 –66.4 –93.9 –147.0 –115.3

Change in reserves4 –3.4 –31.3 –11.1 –2.9 –36.7 –46.2 –107.5 –126.0 –192.3 –191.7 –161.9

Western Hemisphe

Private capital flows, net2 74.5 62.8 39.2 5.1 19.0 15.2 38.1 9.5 97.4 93.2 80.8

Private direct investment, net 62.3 71.0 68.7 50.2 38.2 49.0 52.3 27.3 79.5 73.7 70.8

Private portfolio flows, net 21.3 1.5 –7.9 –15.3 –11.0 –18.7 5.1 –13.4 32.6 23.1 18.6

Other private capital flows, net –9.1 –9.6 –21.6 –29.8 –8.3 –15.1 –19.3 –4.4 –14.6 –3.6 –8.6

Official flows, net3 5.9 –5.8 25.4 18.4 4.9 –8.9 –30.8 –18.6 0.0 2.3 2.2

Change in reserves4 –0.9 –6.7 1.7 1.5 –33.6 –22.1 –33.8 –49.5 –130.8 –74.0 –20.3

Memorandum

Fuel-exporting countries

Private capital flows, net2 –4.8 –43.6 –6.4 –16.4 11.6 –18.4 –38.1 –3.8 120.9 –141.0 –103.1

Other countrie

Private capital flows, net2 123.7 115.2 81.9 93.6 150.9 254.9 286.8 226.7 511.9 669.6 389.6

1Net capital flows comprise net direct investment, net portfolio investment, and other long- and short-term net investment flows, including official and private borrowing. In 

this table, Hong Kong SAR, Israel, Korea, Singapore, and Taiwan Province of China are included. 2Because of data limitations, flows listed under private capital flows, net, may include some official flows.

3Excludes grants and includes overseas investments of official investment agencies.

4A minus sign indicates an increase.

5The sum of the current account balance, net private capital flows, net official flows, and the change in reserves equals, with the opposite sign, the sum of the capital 

account and errors and omissions. 6Consists of developing Asia and the newly industrialized Asian economies.

7Includes Israel.
External Financing: By Regional G

Table A14. Emerging and Developing Economies: Private Capital Flo

(Billions of U.S. dollars)

Average

1997–99 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Emerging and developing economie

Private capital flows, net 118.9 71.6 75.5 77.1 162.5 236.5 248.7 223.0 632.8 528.6 286.6

Inflow 253.1 314.3 160.1 167.8 414.8 634.7 838.9 1,276.9 2,017.0 1,344.3 1,293.6

Outflow –87.0 –242.3 –90.5 –90.4 –256.4 –400.3 –591.1 –1,053.8 –1,384.4 –815.7 –1,006.6

Africa

Private capital flows, net 8.6 –3.1 2.3 1.8 3.9 13.1 26.3 36.0 39.6 43.7 62.3

Inflow 20.4 5.7 14.5 14.0 18.0 25.8 45.3 70.2 69.5 78.9 93.1

Outflow –7.3 –8.8 –12.3 –12.2 –14.1 –12.7 –19.0 –34.1 –29.7 –34.9 –30.6

Central and eastern Europ

Private capital flows, net 32.4 38.6 11.1 53.7 53.6 74.3 119.2 119.9 173.8 179.9 181.7

Inflow 38.1 48.6 20.4 55.0 64.0 103.8 140.1 175.3 211.8 203.8 201.1

Outflow –1.5 –9.9 –9.3 –1.3 –10.5 –29.5 –20.9 –55.4 –38.0 –23.8 –19.4

Commonwealth of Independent State

Private capital flows, net –7.2 –27.9 6.8 15.4 19.3 3.1 31.7 56.8 125.3 19.8 26.0

Inflow 11.2 –5.5 11.0 22.6 46.4 63.2 112.3 161.7 282.8 182.4 195.0

Outflow –1.4 –22.3 –4.3 –7.1 –27.2 –60.1 –80.6 –104.9 –157.5 –162.6 –169.0

Emerging Asia

Private capital flows, net 1.3 6.3 23.5 23.1 64.2 147.8 90.9 48.3 163.0 291.6 22.0

Inflow 61.4 139.6 48.0 63.2 207.1 308.7 366.8 512.9 823.1 653.6 512.8

Outflow –56.2 –132.9 –29.9 –40.0 –147.2 –163.1 –278.0 –464.8 –660.7 –362.5 –490.8

Middle East

Private capital flows, net 9.3 –5.2 –7.3 –22.1 2.6 –16.9 –57.5 –47.5 33.7 –99.6 –86.2

Inflow 17.1 41.2 –3.7 –11.6 32.1 67.1 83.9 246.3 403.6 18.1 94.8

Outflow –7.1 –46.4 –4.1 –10.3 –29.4 –83.9 –140.1 –293.6 –369.7 –117.5 –180.8

Western Hemisphe

Private capital flows, net 74.5 62.8 39.2 5.1 19.0 15.2 38.1 9.5 97.4 93.2 80.8

Inflow 104.8 84.8 69.8 24.7 47.1 66.0 90.5 110.5 226.3 207.5 196.8

Outflow –13.6 –22.0 –30.6 –19.6 –28.1 –50.8 –52.4 –101.0 –128.8 –114.3 –116.0

1Private capital flows comprise direct investment, portfolio investment, and other long- and short-term investment flows. In this table, Hong Kong SAR, Israel, Korea, 

Singapore, and Taiwan Province of China are included. 2Consists of developing Asia and the newly industrialized Asian economies.

3Includes Israel.
Statistical appen

Table A15. Emerging and Developing Economies: Reserv

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Billions of U.S. dollars

Emerging and developing economies 801.1 896.1 1,072.9 1,395.6 1,848.5 2,339.6 3,095.8 4,308.4 5,552.7 6,459.

Regional groups

Africa 54.3 64.6 72.2 90.4 126.4 160.5 221.6 281.9 381.2 459.5

Sub-Sahara 35.3 35.8 36.3 40.2 62.6 83.3 116.2 144.3 192.0 233.5

excluding Nigeria and South Africa 19.0 19.0 22.7 26.3 32.2 36.2 50.6 62.5 82.3 106.8

Central and eastern Europe 91.2 91.3 121.7 149.1 172.0 202.2 240.2 281.7 304.6 325.9

Commonwealth of Independent States2 33.2 43.9 58.1 92.4 148.7 214.4 356.8 525.1 655.0 747.1

Russia 24.8 33.1 44.6 73.8 121.5 176.5 296.2 445.2 558.7 638.3

Excluding Russia 8.4 10.8 13.5 18.5 27.2 37.9 60.6 79.9 96.3 108.8

Developing Asia 320.7 379.5 496.2 669.7 933.9 1,155.5 1,489.3 2,106.9 2,830.4 3,360.3

China 168.9 216.3 292.0 409.2 615.5 822.5 1,069.5 1,531.3 2,201.3 2,701.3

India 38.4 46.4 68.2 99.5 127.2 132.5 171.3 249.6 254.0 243.8

Excluding China and India 113.4 116.9 136.0 161.1 191.2 200.5 248.5 326.0 375.1 415.2

Middle East 146.1 157.9 163.9 198.3 246.7 351.6 477.2 671.2 865.9 1,030.8

Western Hemisphere 155.7 158.8 160.7 195.6 220.8 255.5 310.7 441.5 515.5 535.9

Brazil 31.5 35.8 37.7 49.1 52.8 53.6 85.6 180.1 207.4 211.7

Mexico 35.5 44.8 50.6 59.0 64.1 74.1 76.3 86.6 97.1 105.9

Analytical groups

By source of export earnings

Fuel 192.0 216.6 232.8 310.2 436.7 626.1 931.6 1,310.8 1,716.2 2,034.4

Nonfuel 609.1 679.5 840.1 1,085.5 1,411.9 1,713.5 2,164.2 2,997.6 3,836.4 4,425.0

of which, primary products 26.2 25.2 26.5 27.6 29.2 31.9 39.8 42.5 56.3 61.6

By external financing source

Net debtor countries 420.0 443.7 527.0 644.6 745.4 825.8 1,024.2 1,364.5 1,515.1 1,601.3

of which, official financing 17.3 18.4 18.8 26.1 30.0 33.8 44.3 59.9 69.2 78.6

Net debtor countries by debtservicing experienc

Countries with arrears and/or

rescheduling during 2002–06 76.7 72.6 81.6 98.5 110.6 122.6 150.9 194.5 209.3 232.8

Other group

Heavily indebted poor countries 10.6 11.2 13.7 16.4 19.6 20.7 27.0 32.3 38.5 47.3

Middle East and north Africa 165.5 187.1 200.6 249.6 312.4 431.3 585.0 810.7 1,058.3 1,260.7
External Financing: Reserv

Table A15 (concluded

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Ratio of reserves to imports of goods and services3

Emerging and developing economies 44.9 49.7 55.4 60.6 62.8 66.3 73.5 83.5 84.7 88.

Regional groups

Africa 39.9 46.3 46.9 48.1 54.3 57.7 68.6 71.4 75.9 82.2

Sub-Sahara 34.4 34.1 31.3 27.9 35.2 38.5 45.5 47.1 49.8 54.5

excluding Nigeria and South Africa 34.8 32.6 37.0 35.7 35.2 32.0 39.0 39.3 40.5 47.5

Central and eastern Europe 35.5 35.7 42.0 39.8 35.1 35.5 34.6 32.2 27.0 26.9

Commonwealth of Independent States2 30.5 34.3 40.9 52.6 65.3 76.9 101.6 111.0 103.0 102.4

Russia 40.6 44.6 52.9 71.5 93.0 107.4 141.7 157.5 146.4 143.4

Excluding Russia 17.5 20.0 23.3 25.6 28.0 33.1 42.6 42.0 37.9 38.2

Developing Asia 49.1 58.3 68.1 74.5 79.5 81.8 89.7 106.4 113.3 118.8

China 67.4 79.7 89.0 91.1 101.5 115.5 125.4 148.0 166.0 174.2

India 52.6 65.0 90.0 107.1 97.0 72.8 76.3 88.2 71.8 61.9

Excluding China and India 34.5 37.9 41.8 45.1 43.7 38.7 42.7 49.3 45.8 47.0

Middle East 75.6 78.7 74.2 78.0 77.4 91.7 101.9 114.4 115.5 119.9

Western Hemisphere 35.8 37.1 40.1 47.3 44.3 42.2 43.5 52.1 49.5 48.2

Brazil 43.5 49.2 61.1 77.2 65.9 54.8 71.0 114.3 92.2 87.7

Mexico 18.6 24.2 27.3 31.4 29.8 30.5 27.4 28.3 27.1 28.5

Analytical groups

By source of export earnings

Fuel 64.6 66.3 63.8 72.4 81.0 94.1 114.1 124.1 125.3 128.9

Nonfuel 41.0 46.1 53.5 57.9 58.7 59.9 63.8 73.1 73.9 77.3

of which, primary products 65.3 62.6 63.3 59.0 49.3 43.9 48.1 41.6 43.2 43.0

By external financing source

Net debtor countries 36.9 39.6 45.6 48.3 44.3 40.6 42.5 46.7 41.1 40.2

of which, official financing 24.7 26.4 25.3 30.8 28.3 27.8 30.5 32.8 29.5 31.1

Net debtor countries by debtservicing experienc

Countries with arrears and/or

rescheduling during 2002–06 42.0 41.1 48.4 51.1 45.5 40.1 43.3 46.7 38.3 39.1

Other group

Heavily indebted poor countries 28.5 28.9 30.9 34.6 32.4 29.6 33.0 32.8 31.1 34.7

Middle East and north Africa 72.0 78.3 76.4 82.5 82.1 94.8 106.7 117.7 119.9 125.1

1In this table, official holdings of gold are valued at SDR 35 an ounce. This convention results in a marked underestimate of reserves for countries that have substantial gold 

holdings. 2Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.

3Reserves at year-end in percent of imports of goods and services for the year indicated.
Statistical appen

Table A16. Summary of Sources and Uses of World S

(Percent of GDP)

Averages Average

1986–93 1994–2001 2002 2003 2004 2005 2006 2007 2008 2009 2010–13

Worl

Savings 22.7 22.1 20.6 20.9 22.0 22.9 23.9 24.1 24.0 24.1 24.8

Investment 22.3 22.4 20.9 21.1 22.0 22.5 23.2 23.5 23.5 23.6 24.4

Advanced economies

Savings 22.2 21.6 19.1 19.1 19.8 20.1 20.7 20.5 19.6 19.6 19.9

Investment 22.7 21.8 19.9 19.9 20.5 21.0 21.4 21.2 20.7 20.2 20.5

Net lending –0.5 –0.2 –0.7 –0.8 –0.7 –0.9 –0.7 –0.7 –1.1 –0.7 –0.6

Current transfers –0.4 –0.5 –0.6 –0.6 –0.7 –0.7 –0.7 –0.8 –0.8 –0.8 –0.7

Factor income –0.3 0.1 0.2 0.1 0.4 0.8 1.1 0.8 0.3 0.3 0.2

Resource balance 0.2 0.2 –0.4 –0.4 –0.5 –0.9 –1.1 –0.7 –0.6 –0.2 —

United Stat

Savings 16.3 17.0 14.2 13.3 13.8 14.8 15.5 14.2 12.6 13.4 14.2

Investment 18.8 19.6 18.4 18.4 19.4 20.0 20.1 18.8 17.5 16.7 17.2

Net lending –2.6 –2.6 –4.2 –5.1 –5.5 –5.1 –4.6 –4.6 –4.9 –3.3 –3.0

Current transfers –0.4 –0.6 –0.6 –0.7 –0.7 –0.7 –0.7 –0.8 –0.8 –0.7 –0.7

Factor income –0.4 0.1 0.5 0.1 0.4 1.3 1.8 1.3 0.6 1.1 0.7

Resource balance –1.7 –2.2 –4.0 –4.5 –5.2 –5.7 –5.7 –5.1 –4.7 –3.6 –3.0

Euro are

Savings . . . 21.4 20.8 20.8 21.7 21.3 22.1 22.5 21.8 21.4 21.5

Investment . . . 21.0 20.0 20.1 20.4 20.8 21.6 22.1 22.2 21.8 21.9

Net lending . . . 0.5 0.8 0.7 1.3 0.5 0.5 0.4 –0.4 –0.4 –0.4

Current transfers1 –0.5 –0.7 –0.7 –0.8 –0.8 –0.9 –1.0 –1.0 –1.0 –1.0 –1.0

Factor income1 –0.3 –0.4 –0.9 –0.7 –0.1 –0.2 0.1 –0.3 –0.7 –0.9 –1.2

Resource balance1 1.0 1.6 2.4 2.1 2.2 1.7 1.2 1.6 1.2 1.4 1.6

Germany

Savings 23.8 20.5 19.3 19.4 21.8 22.1 23.7 25.9 25.9 24.8 24.8

Investment 21.8 21.4 17.3 17.4 17.1 16.9 17.6 18.3 18.6 18.0 18.2

Net lending 2.0 –0.9 2.0 2.0 4.7 5.2 6.1 7.6 7.3 6.8 6.6

Current transfers –1.6 –1.4 –1.3 –1.2 –1.3 –1.3 –1.2 –1.3 –1.3 –1.3 –1.3

Factor income 0.8 –0.3 –0.8 –0.7 0.9 1.1 1.6 1.7 1.1 0.6 0.5

Resource balance 2.8 0.8 4.1 3.9 5.0 5.3 5.6 7.1 7.5 7.5 7.3

France

Savings 20.4 20.7 20.5 20.0 20.3 19.6 20.7 20.9 19.5 19.2 19.0

Investment 20.7 18.8 19.0 18.9 19.6 20.3 21.1 22.1 22.8 23.0 23.4

Net lending –0.3 1.9 1.6 1.2 0.7 –0.6 –0.4 –1.1 –3.3 –3.7 –4.3

Current transfers –0.6 –0.8 –1.0 –1.1 –1.1 –1.3 –1.2 –1.2 –1.2 –1.2 –1.2

Factor income –0.5 0.7 0.8 1.2 1.2 1.2 1.9 1.6 0.6 0.3 –0.1

Resource balance 0.8 2.1 1.7 1.1 0.6 –0.5 –1.1 –1.5 –2.8 –2.8 –3.0

Italy

Savings 20.3 21.1 20.4 19.4 19.9 19.1 19.0 19.0 19.2 19.4 19.4

Investment 21.7 19.8 21.1 20.7 20.8 20.8 21.5 21.5 21.9 21.8 20.9

Net lending –1.3 1.4 –0.8 –1.3 –0.9 –1.6 –2.6 –2.5 –2.8 –2.4 –1.6

Current transfers –0.4 –0.5 –0.4 –0.5 –0.6 –0.7 –0.9 –0.9 –0.7 –0.8 –0.8

Factor income –1.6 –1.1 –1.2 –1.3 –1.1 –1.0 –0.9 –1.3 –1.3 –1.3 –1.3

Resource balance 0.7 3.0 0.8 0.6 0.7 — –0.7 –0.3 –0.8 –0.3 0.6

Japan

Savings 33.7 29.3 25.9 26.1 26.8 27.2 27.8 28.6 27.4 27.0 26.6

Investment 30.9 26.9 23.1 22.8 23.0 23.6 24.0 23.8 23.4 23.3 23.4

Net lending 2.7 2.3 2.9 3.2 3.7 3.6 3.9 4.7 4.0 3.8 3.2

Current transfers –0.1 –0.2 –0.1 –0.2 –0.2 –0.2 –0.2 –0.3 –0.2 –0.2 –0.2

Factor income 0.7 1.2 1.7 1.7 1.9 2.3 2.7 3.1 2.9 2.8 3.0

Resource balance 2.1 1.3 1.3 1.7 2.0 1.5 1.4 1.9 1.3 1.1 0.4

United Kingdo

Savings 16.1 16.1 15.3 15.1 15.0 14.7 14.2 14.6 13.4 12.7 13.4

Investment 18.8 17.4 17.1 16.7 17.1 17.3 17.6 18.4 17.1 16.1 16.4

Net lending –2.6 –1.3 –1.7 –1.6 –2.1 –2.6 –3.4 –3.8 –3.6 –3.4 –3.0

Current transfers –0.7 –0.8 –0.8 –0.9 –0.9 –0.9 –0.9 –1.0 –1.0 –1.1 –1.1

Factor income –0.4 0.3 1.7 1.5 1.5 1.7 0.8 0.6 0.9 0.6 0.6

Resource balance –1.6 –0.8 –2.6 –2.3 –2.7 –3.4 –3.3 –3.4 –3.5 –3.0 –2.5

Canada

Savings 17.2 19.8 21.0 21.2 23.0 23.9 24.3 24.1 23.5 22.7 23.4

Investment 20.6 19.6 19.3 20.0 20.7 22.0 22.9 23.3 22.6 22.7 22.4

Net lending –3.4 0.2 1.7 1.2 2.3 1.9 1.4 0.9 0.9 — 1.0

Current transfers –0.2 0.1 — — –0.1 –0.1 –0.1 –0.1 –0.1 –0.1 –0.1

Factor income –3.4 –3.2 –2.6 –2.5 –1.9 –1.6 –0.9 –0.9 –0.8 –1.0 –1.0

Resource balance 0.2 3.4 4.3 3.7 4.2 3.7 2.4 1.9 1.8 1.1 2.1
Flow of Funds: Summar

Table A16 (continued

Averages Average

1986–93 1994–2001 2002 2003 2004 2005 2006 2007 2008 2009 2010–13

Newly industrialized Asian economie

Savings 35.7 33.0 29.7 31.5 32.8 31.3 31.4 32.2 31.6 31.3 31.9

Investment 29.8 29.9 24.7 24.7 26.5 25.9 26.0 25.9 26.9 27.1 27.9

Net lending 5.9 3.1 5.0 6.8 6.3 5.3 5.3 6.3 4.7 4.3 4.0

Current transfers 0.1 –0.3 –0.7 –0.8 –0.7 –0.7 –0.7 –0.7 –0.7 –0.7 –0.7

Factor income 1.3 0.6 0.5 0.8 0.5 –0.1 0.3 0.9 1.0 0.8 1.0

Resource balance 4.5 2.8 5.2 6.7 6.6 6.1 5.8 6.1 4.4 4.2 3.6

Emerging and developing economie

Savings 24.3 24.2 26.3 28.0 29.7 31.5 33.0 33.4 33.7 33.3 33.4

Investment 25.3 25.0 25.0 26.0 27.3 27.3 28.2 29.3 29.7 30.4 31.3

Net lending –2.5 –0.8 1.2 2.0 2.4 4.2 4.8 4.1 4.1 2.9 2.1

Current transfers 0.5 0.9 1.4 1.6 1.5 1.6 1.5 1.5 1.3 1.2 1.1

Factor income –1.5 –1.6 –2.0 –2.0 –2.0 –1.6 –1.6 –1.4 –1.7 –1.3 –0.8

Resource balance –0.8 — 1.8 2.4 2.9 4.2 4.9 4.1 4.4 3.0 1.7

Memorandum

Acquisition of foreign assets 1.1 3.6 3.5 5.7 7.0 9.2 11.2 13.7 9.3 7.4 6.5

Change in reserves 0.3 1.1 2.3 3.8 4.7 5.0 5.5 7.9 6.4 4.3 3.4

Regional groups

Africa

Savings 18.0 18.5 20.3 21.4 22.9 24.2 26.1 25.0 27.3 25.5 24.6

Investment 19.5 20.2 22.1 21.8 22.9 22.5 23.3 24.5 24.2 25.1 25.8

Net lending –1.5 –1.6 –1.8 –0.4 0.1 1.7 2.7 0.5 3.1 0.4 –1.2

Current transfers 2.4 2.6 3.0 3.1 3.2 3.0 2.9 3.0 2.9 2.7 2.5

Factor income –3.6 –3.9 –4.6 –4.4 –5.0 –5.3 –5.0 –5.6 –5.6 –5.5 –4.6

Resource balance –0.3 –0.3 –0.1 0.9 1.9 4.0 4.8 3.1 5.8 3.2 0.9

Memorandum

Acquisition of foreign assets 0.1 2.6 2.8 3.3 4.4 5.8 6.9 6.5 8.5 5.9 4.1

Change in reserves 0.1 1.4 1.2 2.0 4.5 5.2 5.7 5.5 7.3 5.2 3.5

Central and eastern Europ

Savings 25.3 19.9 18.1 17.1 17.4 17.7 18.2 18.6 18.9 19.3 20.4

Investment 26.3 22.7 21.3 21.2 22.5 22.2 24.0 25.0 25.6 25.9 26.7

Net lending –1.0 –2.8 –3.2 –4.1 –5.1 –4.5 –5.8 –6.3 –6.7 –6.6 –6.3

Current transfers 1.4 1.7 1.7 1.5 1.4 1.4 1.4 1.3 1.2 1.3 1.4

Factor income –1.4 –1.2 –2.1 –2.5 –2.9 –2.5 –2.7 –2.8 –2.5 –2.4 –2.5

Resource balance –0.9 –3.4 –2.8 –3.2 –3.6 –3.4 –4.6 –4.8 –5.4 –5.5 –5.2

Memorandum

Acquisition of foreign assets 1.1 1.9 3.3 2.2 3.3 5.0 4.8 4.7 1.9 1.5 2.0

Change in reserves –0.4 1.6 2.5 1.4 1.3 3.5 1.6 2.3 1.0 0.9 1.3

Commonwealth of Independent States

Savings . . . 24.7 26.6 27.5 29.7 29.9 29.8 29.4 30.4 28.3 25.9

Investment . . . 21.0 20.2 21.2 21.5 21.1 22.4 25.1 25.0 25.5 26.7

Net lending . . . 3.6 6.4 6.3 8.3 8.8 7.3 4.3 5.4 2.8 –0.8

Current transfers . . . 0.4 0.6 0.6 0.5 0.6 0.5 0.4 0.3 0.3 0.2

Factor income . . . –2.0 –2.0 –2.8 –2.1 –2.8 –3.5 –3.0 –3.4 –3.0 –2.4

Resource balance . . . 5.2 7.9 8.4 9.9 11.0 10.4 6.9 8.4 5.5 1.5

Memorandum

Acquisition of foreign assets . . . 4.7 5.5 11.6 13.9 15.2 16.2 17.5 11.2 8.4 6.3

Change in reserves . . . 1.1 3.3 5.7 7.1 7.7 9.9 9.9 5.6 3.4 2.9
Statistical appen

Table A16 (continued

Averages Average

1986–93 1994–2001 2002 2003 2004 2005 2006 2007 2008 2009 2010–13

Developing Asia

Savings 28.8 32.7 33.7 36.6 38.4 41.4 43.8 45.1 44.6 44.9 46.3

Investment 31.4 32.4 31.2 33.8 35.9 37.3 37.9 38.1 39.2 39.7 40.6

Net lending –2.6 0.4 2.5 2.8 2.6 4.1 5.9 7.0 5.4 5.2 5.8

Current transfers 0.8 1.3 1.9 2.1 2.0 2.1 2.1 2.2 2.0 1.8 1.7

Factor income –1.8 –1.4 –1.5 –1.1 –1.0 –0.7 –0.6 –0.3 –0.3 — 0.6

Resource balance –1.6 0.4 2.1 1.8 1.6 2.6 4.4 5.2 3.6 3.4 3.5

Memorandum

Acquisition of foreign assets 2.4 5.9 5.2 6.1 7.3 9.6 11.4 14.7 12.1 9.2 9.1

Change in reserves 0.9 1.7 4.2 5.5 7.4 5.9 6.8 10.7 10.3 6.7 5.6

Middle Eas

Savings 17.6 25.5 28.4 32.5 35.7 42.7 43.6 43.3 47.4 42.8 38.4

Investment 23.8 22.4 23.7 24.2 24.0 22.7 22.6 24.9 24.5 25.7 26.5

Net lending –6.3 3.1 4.7 8.3 11.6 20.0 21.0 18.4 22.8 17.0 11.9

Current transfers –3.5 –2.9 –2.5 –2.2 –2.0 –1.7 –1.8 –1.6 –1.4 –1.4 –1.4

Factor income 2.3 2.6 0.5 0.2 0.3 1.5 2.4 2.7 1.3 2.3 3.8

Resource balance –5.1 3.3 6.7 10.3 13.3 20.3 20.4 17.3 22.9 16.1 9.5

Memorandum

Acquisition of foreign assets –0.5 4.8 2.6 12.8 17.5 26.2 36.6 42.6 20.2 18.3 13.4

Change in reserves –0.4 1.2 0.6 5.0 5.5 10.3 10.4 13.9 10.1 7.7 4.4

Western Hemisphe

Savings 18.6 17.7 19.3 19.8 22.0 22.0 23.2 22.7 22.0 21.5 21.4

Investment 19.1 20.7 19.9 19.2 20.8 20.5 21.6 22.4 23.1 23.4 23.8

Net lending –0.5 –3.0 –0.7 0.6 1.1 1.5 1.5 0.3 –1.1 –1.9 –2.3

Current transfers 0.8 0.9 1.7 1.9 2.0 2.0 2.0 1.8 1.5 1.5 1.5

Factor income –2.3 –2.7 –2.7 –3.0 –2.9 –2.2 –2.3 –2.1 –2.8 –2.5 –2.4

Resource balance 1.0 –1.2 0.3 1.6 2.0 1.7 1.9 0.6 0.2 –1.0 –1.4

Memorandum

Acquisition of foreign assets 0.7 1.7 1.2 2.9 2.8 3.0 3.2 6.1 3.2 2.1 1.6

Change in reserves 0.6 0.2 –0.1 1.8 1.0 1.3 1.6 3.6 1.7 0.4 0.3

Analytical groups

By source of export earnings

Fuel

Savings 26.6 26.1 28.9 31.1 34.1 38.2 38.6 37.0 39.3 35.4 31.4

Investment 28.8 22.4 23.5 23.3 23.4 22.4 22.9 25.3 24.3 25.1 26.0

Net lending –2.2 3.7 5.4 7.8 10.7 15.8 15.6 11.7 14.9 10.3 5.4

Current transfers –1.5 –1.9 –1.7 –1.4 –1.1 –0.9 –0.9 –0.9 –0.9 –0.9 –0.9

Factor income — –0.7 –1.7 –2.3 –2.1 –2.0 –1.9 –1.9 –2.6 –1.8 –0.6

Resource balance –0.7 6.3 8.8 11.5 13.9 18.7 18.4 14.5 18.3 13.0 6.9

Memorandum

Acquisition of foreign assets 0.2 5.0 3.0 11.7 14.8 20.0 24.3 26.3 15.5 12.7 9.0

Change in reserves –0.3 0.9 1.0 5.2 6.9 9.2 10.3 11.1 8.6 6.0 3.8

Nonfue

Savings 23.2 23.9 25.7 27.3 28.7 29.8 31.5 32.3 32.0 32.5 34.1

Investment 25.0 25.5 25.4 26.6 28.3 28.7 29.7 30.4 31.4 32.1 33.2

Net lending –2.1 –1.7 0.3 0.7 0.4 1.1 1.8 1.9 0.6 0.4 0.9

Current transfers 1.2 1.4 2.0 2.2 2.1 2.2 2.2 2.2 2.0 1.9 1.9

Factor income –1.7 –1.8 –2.0 –1.9 –1.9 –1.5 –1.5 –1.3 –1.4 –1.1 –0.8

Resource balance –0.8 –1.3 0.3 0.4 0.2 0.4 1.1 1.1 –0.1 –0.4 –0.2

Memorandum

Acquisition of foreign assets 1.3 3.4 3.6 4.4 5.1 6.4 7.5 10.1 7.3 5.6 5.7

Change in reserves 0.5 1.1 2.5 3.5 4.2 3.9 4.2 7.0 5.7 3.7 3.3
Flow of Funds: Summar

Table A16 (concluded

Averages Average

1986–93 1994–2001 2002 2003 2004 2005 2006 2007 2008 2009 2010–13

By external financing source

Net debtor countrie

Savings 20.8 19.6 20.1 20.8 21.7 21.9 23.0 23.3 22.9 23.0 23.9

Investment 22.9 22.2 20.9 21.4 23.0 23.4 24.4 25.3 26.0 26.3 27.3

Net lending –2.1 –2.6 –0.8 –0.6 –1.3 –1.5 –1.4 –2.0 –3.0 –3.3 –3.4

Current transfers 1.5 1.8 2.6 2.7 2.6 2.7 2.7 2.6 2.5 2.5 2.5

Factor income –2.4 –2.4 –2.4 –2.5 –2.7 –2.4 –2.5 –2.4 –2.6 –2.3 –2.4

Resource balance –1.1 –2.5 –1.0 –0.8 –1.2 –1.8 –1.7 –2.2 –3.0 –3.5 –3.5

Memorandum

Acquisition of foreign assets 0.8 1.7 2.4 3.0 2.9 3.1 4.2 6.2 2.6 2.0 2.1

Change in reserves 0.5 0.8 1.5 2.0 1.5 1.8 2.3 4.0 1.5 0.8 1.1

Official financin

Savings 14.2 17.6 20.2 21.1 22.3 23.2 23.9 23.2 22.4 22.0 22.5

Investment 17.1 20.5 22.0 23.2 24.2 24.8 25.6 27.0 27.4 26.4 26.3

Net lending –2.8 –2.9 –1.8 –2.1 –1.8 –1.6 –1.7 –3.8 –5.1 –4.4 –3.8

Current transfers 4.2 5.7 7.0 7.5 8.2 8.6 8.8 9.3 8.6 7.9 7.9

Factor income –0.8 –0.6 –0.5 –1.5 –0.8 –1.8 –2.0 –1.2 –1.0 –1.2 –1.4

Resource balance –6.3 –7.9 –8.3 –8.1 –9.2 –8.5 –8.5 –11.9 –12.7 –11.1 –10.3

Memorandum

Acquisition of foreign assets 0.5 2.8 1.5 4.6 2.8 4.2 3.6 8.5 4.4 3.3 3.9

Change in reserves 0.5 0.5 –0.2 3.0 1.2 2.1 2.9 4.2 2.0 1.8 2.4

Net debtor countries by debtservicing experienc

Countries with arrears and/or

rescheduling during 2002–06

Savings 17.1 19.3 21.0 23.1 21.6 21.9 23.7 23.3 22.4 22.2 22.8

Investment 23.1 22.3 18.7 20.6 21.4 22.6 23.6 24.3 24.3 24.7 25.3

Net lending –6.0 –3.0 2.3 2.5 0.2 –0.7 0.2 –0.9 –1.9 –2.4 –2.5

Current transfers 1.6 2.1 3.9 4.0 3.9 4.5 4.4 4.0 3.9 3.6 3.6

Factor income –5.6 –3.1 –4.0 –3.3 –4.1 –3.8 –3.7 –3.5 –3.1 –2.4 –2.5

Resource balance –1.9 –2.0 2.3 1.8 0.5 –1.3 –0.5 –1.4 –2.7 –3.7 –3.6

Memorandum

Acquisition of foreign assets 1.0 2.0 3.3 3.9 2.5 2.6 3.7 4.7 1.9 1.9 1.5

Change in reserves 0.2 0.5 0.9 2.3 1.5 1.7 2.2 3.3 0.9 1.3 1.1

Note: The estimates in this table are based on individual countries’ national accounts and balance of payments statistics. Country group composites are calculated as 

the sum of the U.S dollar values for the relevant individual countries. This differs from the calculations in the April 2005 and earlier World Economic Outlooks, where the 

composites were weighted by GDP valued at purchasing power parities as a share of total world GDP. For many countries, the estimates of national savings are built up from 

national accounts data on gross domestic investment and from balance-of-payments-based data on net foreign investment. The latter, which is equivalent to the current 

account balance, comprises three components: current transfers, net factor income, and the resource balance. The mixing of data source, which is dictated by availability, 

implies that the estimates for national savings that are derived incorporate the statistical discrepancies. Furthermore, errors, omissions, and asymmetries in balance of 

payments statistics affect the estimates for net lending; at the global level, net lending, which in theory would be zero, equals the world current account discrepancy. Despite 

these statistical shortcomings, flow of funds estimates, such as those presented in these tables, provide a useful framework for analyzing development in savings and 

investment, both over time and across regions and countries. 1Calculated from the data of individual euro area countries.

2Mongolia, which is not a member of the Commonwealth of Independent States, is included in this group for reasons of geography and similarities in economic structure.
Statistical appen

2

Table A17. Summary of World Medium-Term Baseline Sce

 Eight-Year Averages

Four-Year

Average

2006–09 2006 2007 2008 2009

Four-Year

Average

1990–97 1998–2005 2010–13

Annual percent change unless otherwise noted

World real GDP 2.9 3.6 4.2 5.1 5.0 3.9 3.0 4.

Advanced economies 2.6 2.6 1.9 3.0 2.6 1.5 0.5 2.5

Emerging and developing economies 3.3 5.2 7.2 7.9 8.0 6.9 6.1 6.9

Memorandum

Potential output

Major advanced economies 2.5 2.3 2.1 2.2 2.1 2.0 1.9 1.9

World trade, volume1 6.9 6.2 6.4 9.3 7.2 4.9 4.1 7.

Imports

Advanced economies 6.2 6.0 3.7 7.5 4.5 1.9 1.1 5.2

Emerging and developing economies 8.0 7.7 12.8 14.7 14.2 11.7 10.5 11.0

Exports

Advanced economies 6.9 5.2 5.2 8.4 5.9 4.3 2.5 5.3

Emerging and developing economies 8.3 8.4 8.5 11.0 9.5 6.3 7.4 10.1

Terms of trade

Advanced economies –0.1 –0.1 –0.7 –1.2 0.3 –1.8 –0.1 0.2

Emerging and developing economies –0.6 1.4 2.8 4.9 1.7 5.5 –0.9 –0.2

World prices in U.S. doll

Manufactures 1.3 1.6 6.6 3.7 8.8 13.8 0.5 1.5

Oil 0.9 13.6 17.2 20.5 10.7 50.8 –6.3 0.6

Nonfuel primary commodities 0.0 0.5 10.5 23.2 14.1 13.3 –6.2 –2.6

Consumer prices

Advanced economies 3.4 1.9 2.5 2.4 2.2 3.6 2.0 2.0

Emerging and developing economies 62.9 8.3 7.2 5.4 6.4 9.4 7.8 5.3

Interest rates (in percent)

Real six-month LIBOR2 3.1 1.6 1.8 2.1 2.6 1.0 1.5 3.2

World real long-term interest rate3 4.0 2.4 1.5 1.7 2.0 0.4 1.8 2.8

Percent of GDP

Balances on current account

Advanced economies — –0.7 –1.0 –1.3 –0.9 –1.0 –0.6 –0.5

Emerging and developing economies –1.6 1.2 4.0 4.9 4.1 4.1 2.9 2.2

Total external deb

Emerging and developing economies 34.2 35.8 25.4 26.6 26.9 24.2 23.8 23.5

Debt service

Emerging and developing economies 4.6 6.3 4.7 5.8 4.8 4.1 4.0 4.0

1Data refer to trade in goods and services.

2London interbank offered rate on U.S. dollar deposits minus percent change in U.S. GDP deflator.

3GDP-weighted average of 10-year (or nearest maturity) government bond rates for the United States, Japan, Germany, France, Italy, the United Kingdom, and Canada.
World Economic Outlook and S

for the World Economi

Selected Topics, 2

I. Methodology—Aggregation, Modeling, and Forecasting 

 World Economic Outlook

Revised Purchasing Power Parity Based Weights for the World Economic Outlook May 2000, Box A1

The Global Current Account Discrepancy October 2000, Chapter I,

Appendix II

How Well Do Forecasters Predict Turning Points? May 2001, Box 1.1

The Information Technology Revolution: Measurement Issues October 2001, Box 3.1

Measuring Capital Account Liberalization October 2001, Box 4.1

The Accuracy of World Economic Outlook Growth Forecasts: 1991–2000 December 2001, Box 3.1

On the Accuracy of Forecasts of Recovery April 2002, Box 1.2

The Global Current Account Discrepancy and Other Statistical Problems September 2002, Box 2.1

The Global Economy Model April 2003, Box 4.3

How Should We Measure Global Growth? September 2003, Box 1.2

Measuring Foreign Reserves September 2003, Box 2.2

The Effects of Tax Cuts in a Global Fiscal Model April 2004, Box 2.2

How Accurate Are the Forecasts in the World Economic Outlook? April 2006, Box 1.3

Drawing the Line Between Personal and Corporate Savings April 2006, Box 4.1

Measuring Inequality: Conceptual, Methodological, and Measurement Issues October 2007, Box 4.1

New Business Cycle Indices for Latin America: A Historical Reconstruction October 2007, Box 5.3

Implications of New PPP Estimates for Measuring Global Growth April 2008, Appendix 1.1

Measuring Output Gaps October 2008, Box 1.3

Assessing and Communicating Risks to the Global Outlook October 2008, Appendix 1.1

II. Historical Surv

 World Economic Outlook

The World Economy in the Twentieth Century May 2000, Chapter V

The Monetary System and Growth During the Commercial Revolution May 2000, Box 5.2

The Great Depression April 2002, Box 3.2

Historical Evidence on Financial Crises April 2002, Box 3.3

A Historical Perspective on Booms, Busts, and Recessions April 2003, Box 2.1

Institutional Development: The Influence of History and Geography April 2003, Box 3.1

External Imbalances Then and Now April 2005, Box 3.1

Long-Term Interest Rates from a Historical Perspective April 2006, Box 1.1

Recycling Petrodollars in the 1970s April 2006, Box 2.2

Historical Perspective on Growth and the Current Account October 2008, Box 6.3
Selected Topics, 2000–2

 Staff Studies for the

World Economic Outlook

Globalization and Growth in the Twentieth Century

Nicholas Crafts May 2000

The International Monetary System in the (Very) Long Run

Barry Eichengreen and Nathan Sussman May 2000

III. Economic Growth—Sources and Patters  World Economic Outl

How Can the Poorest Countries Catch Up? May 2000, Chapter IV

Trends in the Human Development Index May 2000, Box 5.1

Productivity Growth and IT in the Advanced Economies October 2000, Chapter II

Transition: Experience and Policy Issues October 2000, Chapter III

Business Linkages in Major Advanced Countries October 2001, Chapter II

How Do Macroeconomic Fluctuations in the Advanced Countries Affect 

the Developing Countries? October 2001, Chapter II

Confidence Spillovers October 2001, Box 2.1

Channels of Business Cycle Transmission to Developing Countries October 2001, Box 2.2

The Information Technology Revolution October 2001, Chapter III

Has the IT Revolution Reduced Output Volatility? October 2001, Box 3.4

The Impact of Capital Account Liberalization on Economic Performance October 2001, Box 4.2

How Has September 11 Influenced the Global Economy? December 2001, Chapter II

The Long-Term Impact of September 11 December 2001, Box 2.1

Is Wealth Increasingly Driving Consumption? April 2002, Chapter II

Recessions and Recoveries April 2002, Chapter III

Was It a Global Recession? April 2002, Box 1.1

How Important Is the Wealth Effect on Consumption? April 2002, Box 2.1

A Household Perspective on the Wealth Effect April 2002, Box 2.2

Measuring Business Cycles April 2002, Box 3.1

Economic Fluctuations in Developing Countries April 2002, Box 3.4

How Will Recent Falls in Equity Markets Affect Activity? September 2002, Box 1.1

Reversal of Fortune: Productivity Growth in Europe and the United States September 2002, Box 1.3

Growth and Institutions April 2003, Chapter III

Is the New Economy Dead? April 2003, Box 1.2

Have External Anchors Accelerated Institutional Reform in Practice? April 2003, Box 3.2

Institutional Development: The Role of the IMF April 2003, Box 3.4

How Would War in Iraq Affect the Global Economy? April 2003, Appendix 1.2

How Can Economic Growth in the Middle East and North Africa 

Region Be Accelerated? September 2003, Chapter II

Recent Changes in Monetary and Financial Conditions in the Major 

Currency Areas September 2003, Box 1.1

Accounting for Growth in the Middle East and North Africa September 2003, Box 2.1

Managing Increasing Aid Flows to Developing Countries September 2003, Box 1.3

Fostering Structural Reforms in Industrial Countries April 2004, Chapter III

How Will Demographic Change Affect the Global Economy? September 2004, Chapter III
Selected Topics, 2000–2

HIV/AIDS: Demographic, Economic, and Fiscal Consequences September 2004, Box 3.3

Implications of Demographic Change for Health Care Systems September 2004, Box 3.4

Workers’ Remittances and Economic Development April 2005, Chapter II

Output Volatility in Emerging Market and Developing Countries April 2005, Chapter II

How Does Macroeconomic Instability Stifle Sub-Saharan African Growth? April 2005, Box 1.5

How Should Middle Eastern and Central Asian Oil Exporters Use Their 

Oil Revenues? April 2005, Box 1.6

Why Is Volatility Harmful? April 2005, Box 2.3

Building Institutions September 2005, Chapter III

Return on Investment in Industrial and Developing Countries September 2005, Box 2.2

The Use of Specific Levers to Reduce Corruption September 2005, Box 3.2

Examining the Impact of Unrequited Transfers on Institutions September 2005, Box 3.3

The Impact of Recent Housing Market Adjustments in Industrial Countries April 2006, Box 1.2

Awash With Cash: Why Are Corporate Savings So High? April 2006, Chapter IV

The Global Implications of an Avian Flu Pandemic April 2006, Appendix 1.2

Asia Rising: Patterns of Economic Development and Growth September 2006, Chapter III

Japan’s Potential Output and Productivity Growth September 2006, Box 3.1

The Evolution and Impact of Corporate Governance Quality in Asia September 2006, Box 3.2

Decoupling the Train? Spillovers and Cycles in the Global Economy April 2007, Chapter 4

Spillovers and International Business Cycle Synchronization: 

A Broader Perspective April 2007, Box 4.3

What Risks Do Housing Markets Pose for Global Growth? October 2007, Box 2.1

Climate Change: Economic Impact and Policy Responses October 2007, Appendix 1.2

The Discounting Debate October 2007, Box 1.7

Taxes Versus Quantities Under Uncertainty (Weitzman, 1974) October 2007, Box 1.8

Experience with Emissions Trading in the European Union October 2007, Box 1.9

The Changing Dynamics of the Global Business Cycle October 2007, Chapter 5

Major Economies and Fluctuations in Global Growth October 2007, Box 5.1

Improved Macroeconomic Performance—Good Luck or Good Policies? October 2007, Box 5.2

IV. Inflation and Deflation, and Commodity Market

 World Economic Outlook

Cycles in Nonfuel Commodity Prices May 2000, Box 2.2

Booms and Slumps in the World Oil Market May 2000, Box 2.3

Commodity Prices and Commodity Exporting Countries October 2000, Chapter II

Developments in the Oil Markets October 2000, Box 2.2

The Decline of Inflation in Emerging Markets: Can It Be Maintained? May 2001, Chapter IV

The Global Slowdown and Commodity Prices May 2001, Chapter I, 

Appendix 1

Why Emerging Market Countries Should Strive to Preserve Lower Inflation May 2001, Box 4.1

Is There a Relationship Between Fiscal Deficits and Inflation? May 2001, Box 4.2

How Much of a Concern Is Higher Headline Inflation? October 2001, Box 1.2

Primary Commodities and Semiconductor Markets October 2001, Chapter I, 

Appendix 1
Selected Topics, 2000–2

Can Inflation Be Too Low? April 2002, Box 2.3

Could Deflation Become a Global Problem? April 2003, Box 1.1

Housing Markets in Industrial Countries April 2004, Box 1.2

Is Global Inflation Coming Back? September 2004, Box 1.1

What Explains the Recent Run-Up in House Prices? September 2004, Box 2.1

Will the Oil Market Continue to Be Tight? April 2005, Chapter IV

Should Countries Worry About Oil Price Fluctuations? April 2005, Box 4.1

Data Quality in the Oil Market April 2005, Box 4.2

Long-Term Inflation Expectations and Credibility September 2005, Box 4.2

The Boom in Nonfuel Commodity Prices: Can It Last? September 2006, Chapter 5

Commodity Price Shocks, Growth, and Financing in Sub-Saharan Africa September 2006, Box 2.2

International Oil Companies and National Oil Companies in a Changing 

Oil Sector Environment September 2006, Box 1.4

Has Speculation Contributed to Higher Commodity Prices? September 2006, Box 5.1

Agricultural Trade Liberalization and Commodity Prices September 2006, Box 5.2

Recent Developments in Commodity Markets September 2006, 

Appendix 2.1

Who Is Harmed by the Surge in Food Prices? October 2007, Box 1.1

Refinery Bottlenecks October 2007, Box 1.5

Making the Most of Biofuels October 2007, Box 1.6

Commodity Market Developments and Prospects April 2008, Appendix 1.2

Dollar Depreciation and Commodity Prices April 2008, Box 1.4

Why Hasn’t Oil Supply Responded to Higher Prices? April 2008, Box 1.5

Oil Price Benchmarks April 2008, Box 1.6

Globalization, Commodity Prices, and Developing Countries April 2008, Chapter 5

The Current Commodity Price Boom in Perspective April 2008, Box 5.2

Is Inflation Back? Commodity Prices and Inflation October 2008, Chapter 3 

Does Financial Investment Affect Commodity Price Behavior? October 2008, Box 3.1

Fiscal Responses to Recent Commodity Price Increases: An Assessment October 2008, Box 3.2

Monetary Policy Regimes and Commodity Prices October 2008, Box 3.3

V. Fiscal Policy World Economic Outloo

Social Spending, Poverty Reduction, and Debt Relief in Heavily Indebted 

Poor Countries May 2000, Box 4.3

Fiscal Improvement in Advanced Economies: How Long Will It Last? May 2001, Chapter III

Impact of Fiscal Consolidation on Macroeconomic Performance May 2001, Box 3.3

Fiscal Frameworks in Advanced and Emerging Market Economies May 2001, Box 3.4

Data on Public Debt in Emerging Market Economies September 2003, Box 3.1

Fiscal Risk: Contingent Liabilities and Demographics September 2003, Box 3.2

Assessing Fiscal Sustainability Under Uncertainty September 2003, Box 3.3

The Case for Growth-Indexed Bonds September 2003, Box 3.4

Public Debt in Emerging Markets: Is It Too High? September 2003, Chapter III
Selected Topics, 2000–2

Has Fiscal Behavior Changed Under the European Economic and 

Monetary Union? September 2004, Chapter II

Bringing Small Entrepreneurs into the Formal Economy September 2004, Box 1.5

HIV/AIDS: Demographic, Economic, and Fiscal Consequences September 2004, Box 3.3

Implications of Demographic Change for Health Care Systems September 2004, Box 3.4

Impact of Aging on Public Pension Plans September 2004, Box 3.5

How Should Middle Eastern and Central Asian Oil Exporters Use April 2005, Box 1.6

Their Oil Revenues?

Financial Globalization and the Conduct of Macroeconomic Policies April 2005, Box 3.3

Is Public Debt in Emerging Markets Still Too High? September 2005, Box 1.1

Improved Emerging Market Fiscal Performance: Cyclical or Structural? September 2006, Box 2.1

When Does Fiscal Stimulus Work? April 2008, Box 2.1

Fiscal Policy as a Countercyclical Tool October 2008, Chapter 5

Differences in the Extent of Automatic Stabilizers and Their Relationship 

with Discretionary Fiscal Policy October 2008, Box 5.1

Why Is It So Hard to Determine the Effects of Fiscal Stimulus? October 2008, Box 5.2

Have the U.S. Tax Cuts been “TTT” [Timely, Temporary, and Targeted]? October 2008, Box 5.3

VI. Monetary Policy, Financial Markets, and Flow of Funds World Economic Outloo

Asset Prices and Business Cycle May 2000, Chapter III

Global Liquidity and Asset Prices May 2000, Box 3.2

International Capital Flows to Emerging Markets October 2000, Chapter II

Developments in Global Equity Markets October 2000, Chapter II

U.S. Monetary Policy and Sovereign Spreads in Emerging Markets October 2000, Box 2.1

Impact of the Global Technology Correction on the Real Economy May 2001, Chapter II

Inflation Targeting in Emerging Market Economies: Implementation 

and Challenges May 2001, Box 4.3

Financial Market Dislocations and Policy Responses After the 

September 11 Attacks December 2001, Box 2.2

Investor Risk Appetite December 2001, Box 2.3

Monetary Policy in a Low Inflation Era April 2002, Chapter II

The Introduction of Euro Notes and Coins April 2002, Box 1.3

Cross-Country Determinants of Capital Structure September 2002, Box 2.3

When Bubbles Burst April 2003, Chapter II

How Do Balance Sheet Vulnerabilities Affect Investment? April 2003, Box 2.3

Identifying Asset Price Booms and Busts April 2003, Appendix 2.1

Are Foreign Exchange Reserves in Asia Too High? September 2003, Chapter II

Reserves and Short-Term Debt September 2003, Box 2.3

Are Credit Booms in Emerging Markets a Concern? April 2004, Chapter IV

How Do U.S. Interest and Exchange Rates Affect Emerging Markets’ 

Balance Sheets? April 2004, Box 2.1

Does Financial Sector Development Help Economic Growth and Welfare? April 2004, Box 4.1

Adjustable- or Fixed-Rate Mortgages: What Influences a Country’s Choices? September 2004, Box 2.2
Selected Topics, 2000–2

What Are the Risks from Low U.S. Long-Term Interest Rates? April 2005, Box 1.2

Regulating Remittances April 2005, Box 2.2

Financial Globalization and the Conduct of Macroeconomic Policies April 2005, Box 3.3

Monetary Policy in a Globalized World April 2005, Box 3.4

Does Inflation Targeting Work in Emerging Markets? September 2005, Chapter IV

A Closer Look at Inflation Targeting Alternatives: Money and 

Exchange Rate Targets September 2005, Box 4.1

How Has Globalization Affected Inflation? April 2006, Chapter III

The Impact of Petrodollars on U.S. and Emerging Market Bond Yields April 2006, Box 2.3

Globalization and Inflation in Emerging Markets April 2006, Box 3.1

Globalization and Low Inflation in a Historical Perspective April 2006, Box 3.2

Exchange Rate Pass-Through to Import Prices April 2006, Box 3.3

Trends in the Financial Sector’s Profits and Savings April 2006, Box 4.2

How Do Financial Systems Affect Economic Cycles? September 2006, Chapter 4

Financial Leverage and Debt Deflation September 2006, Box 4.1

Financial Linkages and Spillovers April 2007, Box 4.1

Macroeconomic Conditions in Industrial Countries and Financial Flows to 

Emerging Markets April 2007, Box 4.2

What Is Global Liquidity? October 2007, Box 1.4

Macroeconomic Implications of Recent Market Turmoil: Patterns From 

Previous Episodes October 2007, Box 1.2

The Changing Housing Cycle and the Implications for Monetary Policy April 2008, Chapter 3

Assessing Vulnerabilities to Housing Market Corrections April 2008, Box 3.1

Is There a Credit Crunch? April 2008, Box 1.1

Financial Stress and Economic Downturns October 2008, Chapter 4

Sound Policies to Resolve Financial System Stress and Restore 

Sound Financial Intermediation October 2008, Box 4.1

The Latest Bout of Financial Distress: How Does It Change the Fundamentals 

Underpinning the Global Outlook? October 2008, Box 1.1

House Prices: Corrections and Consequences October 2008, Box 1.2

 Staff Studies for the

World Economic Outlook

Capital Structure and Corporate Performance Across Emerging Markets September 2002, Chapter II

VII. Labor Markets, Poverty, and Inequalit

 World Economic Outlook

Unemployment and Labor Market Institutions: Why Reforms Pay Off April 2003, Chapter IV

Regional Disparities in Unemployment April 2003, Box 4.1

Labor Market Reforms in the European Union April 2003, Box 4.2

The Globalization of Labor April 2007, Chapter 5

Emigration and Trade: How Do They Affect Developing Countries? April 2007, Box 5.1

Labor Market Reforms in the Euro Area and the Wage-Unemployment 

Trade-Off October 2007, Box 2.2

Globalization and Inequality October 2007, Chapter 4
Selected Topics, 2000–2

 Staff Studies for the

World Economic Outlook

EMU Challenges European Labor Markets

Rüdiger Soltwedel, Dirk Dohse, and Christiane Krieger-Boden May 2000

VIII. Exchange Rate Iss

 World Economic Outlook

The Pros and Cons of Dollarization May 2000, Box 1.4

Why Is the Euro So Undervalued? October 2000, Box 1.1

Convergence and Real Exchange Rate Appreciation in the EU 

Accession Countries October 2000, Box 4.4

What Is Driving the Weakness of the Euro and the Strength of the Dollar? May 2001, Chapter II

The Weakness of the Australian and New Zealand Currencies May 2001, Box 2.1

How Did the September 11 Attacks Affect Exchange Rate Expectations? December 2001, Box 2.4

Market Expectations of Exchange Rate Movements September 2002, Box 1.2

Are Foreign Exchange Reserves in Asia Too High? September 2003, Chapter II

How Concerned Should Developing Countries Be About G-3 

Exchange Rate Volatility? September 2003, Chapter II

Reserves and Short-Term Debt September 2003, Box 2.3

The Effects of a Falling Dollar April 2004, Box 1.1

Learning to Float: The Experience of Emerging Market Countries Since 

the Early 1990s September 2004, Chapter II

How Did Chile, India, and Brazil Learn to Float? September 2004, Box 2.3

Foreign Exchange Market Development and Intervention September 2004, Box 2.4

How Emerging Market Countries May Be Affected by External Shocks September 2006, Box 1.3

Exchange Rates and the Adjustment of External Imbalances April 2007, Chapter 3

Exchange Rate Pass-Through to Trade Prices and External Adjustment April 2007, Box 3.3

Depreciation of the U.S. Dollar: Causes and Consequences April 2008, Box 1.2

 Staff Studies for the

World Economic Outlook

Currency Crises: In Search of Common Elements

Jahangir Aziz, Francesco Caramazza, and Ranil Salgado May 2000

Business Cycle Influences on Exchange Rates: Survey and Evidence

Ronald MacDonald and Phillip Suragel May 2000

IX. External Payments, Trade, Capital Movements, and Foreign 

 World Economic Outlook

The Global Current Account Discrepancy October 2000, Chapter I,

Appendix II

Trade Integration and Sub-Saharan Africa May 2001, Chapter II

Sustainability of the U.S. External Current Account May 2001, Box 1.2

Reducing External Balances May 2001, Chapter I, 

Appendix 2
Selected Topics, 2000–2

The World Trading System: From Seattle to Doha October 2001, Chapter II

International Financial Integration and Economic Performance: Impact on 

Developing Countries October 2001, Chapter IV

Potential Welfare Gains From a New Trade Round October 2001, Box 2.3

Critics of a New Trade Round October 2001, Box 2.4

Foreign Direct Investment and the Poorer Countries October 2001, Box 4.3

Country Experiences with Sequencing Capital Account Liberalization October 2001, Box 4.4

Contagion and Its Causes December 2001, Chapter I,

Appendix

Capital Account Crises in Emerging Market Countries April 2002, Box 3.5

How Have External Deficits Adjusted in the Past? September 2002, Box 2.2

Using Prices to Measure Goods Market Integration September 2002, Box 3.1

Transport Costs September 2002, Box 3.2

The Gravity Model of International Trade September 2002, Box 3.3

Vertical Specialization in the Global Economy September 2002, Box 3.4

Trade and Growth September 2002, Box 3.5

How Worrisome Are External Imbalances? September 2002, Chapter II

How Do Industrial Country Agricultural Policies Affect Developing Countries? September 2002, Chapter II

Trade and Financial Integration September 2002, Chapter III

Risks to the Multilateral Trading System April 2004, Box 1.3

Is the Doha Round Back on Track? September 2004, Box 1.3

Regional Trade Agreements and Integration: The Experience with NAFTA September 2004, Box 1.4

Trade and Financial Integration in Europe: Five Years After the 

Euro’s Introduction September 2004, Box 2.5

Globalization and External Imbalances April 2005, Chapter III

The Ending of Global Textile Trade Quotas April 2005, Box 1.3

What Progress Has Been Made in Implementing Policies to Reduce 

Global Imbalances? April 2005, Box 1.4

Measuring a Country’s Net External Position April 2005, Box 3.2

Global Imbalances: A Saving and Investment Perspective September 2005, Chapter II

Impact of Demographic Change on Saving, Investment, and Current 

Account Balances September 2005, Box 2.3

How Will Global Imbalances Adjust? September 2005, 

Appendix 1.2

Oil Prices and Global Imbalances April 2006, Chapter II

How Much Progress Has Been Made in Addressing Global Imbalances? April 2006, Box 1.4

The Doha Round After the Hong Kong SAR Meetings April 2006, Box 1.5

Capital Flows to Emerging Market Countries: A Long-Term Perspective September 2006, Box 1.1

How Will Global Imbalances Adjust? September 2006, Box 2.1

External Sustainability and Financial Integration April 2007, Box 3.1

Large and Persistent Current Account Imbalances April 2007, Box 3.2

Multilateral Consultation on Global Imbalances October 2007, Box 1.3

Managing the Macroeconomic Consequences of Large and Volatile Aid Flows October 2007, Box 2.3

Managing Large Capital Inflows October 2007, Chapter 3

Can Capital Controls Work? October 2007, Box 3.1

Multilateral Consultation on Global Imbalances: Progress Report April 2008, Box 1.3
Selected Topics, 2000–2

How Does the Globalization of Trade and Finance Affect Growth? 

Theory and Evidence April 2008, Box 5.1

Divergence of Current Account Balances across Emerging Economies October 2008, Chapter 6

Current Account Determinants for Oil-Exporting Countries October 2008, Box 6.1

Sovereign Wealth Funds: Implications for Global Financial Markets October 2008, Box 6.2

X. Regional Issue

 World Economic Outlook

The Economic Impact of HIV/AIDS in Southern Africa October 2000, Box 1.4

Accession of Transition Economies to the European Union: 

Prospects and Pressures October 2000, Chapter IV

The IMF and the Transition Economies October 2000, Box 3.1

Previous EU Enlargements October 2000, Box 4.2

The Enhanced HIPC Initiative in Africa May 2001, Box 1.4

Large Current Account Deficits in EU Accession Countries May 2001, Box 1.5

Africa’s Trade and the Gravity Model May 2001, Box 2.2

The Implications of the Japanese Economic Slowdown for East Asia October 2001, Box 1.4

Relative Euro-Area Growth Performances: Why Are Germany and Italy 

Lagging Behind France? October 2001, Box 1.5

Economic Growth, Civil Conflict, and Poverty Reduction in Sub-Saharan Africa October 2001, Box 1.7

Information Technology and Growth in Emerging Asia October 2001, Box 3.3

The IT Slump and Short-Term Growth Prospects in East Asia October 2001, Box 3.5

The Effects of the September 11 Attacks on the Caribbean Region December 2001, Box 3.3

Debt Crises: What’s Different About Latin America? April 2002, Chapter II

Foreign Direct Investment in Africa September 2002, Box 1.6

Promoting Stronger Institutions and Growth: The New Partnership for 

Africa’s Development April 2003, Box 3.3

How Can Economic Growth in the Middle East and North Africa 

Region Be Accelerated? September 2003, Chapter II

Gulf Cooperation Council: Challenges on the Road to a Monetary Union September 2003, Box 1.5

Accounting for Growth in the Middle East and North Africa September 2003, Box 2.1

Is Emerging Asia Becoming an Engine of World Growth? April 2004, Box 1.4

What Works in Africa April 2004, Box 1.5

Economic Integration and Structural Reforms: The European Experience April 2004, Box 3.4

What Are the Risks of Slower Growth in China? September 2004, Box 1.2

Governance Challenges and Progress in Sub-Saharan Africa September 2004, Box 1.6

The Indian Ocean Tsunami: Impact on South Asian Economies April 2005, Box 1.1

Workers’ Remittances and Emigration in the Caribbean April 2005, Box 2.1

What Explains Divergent External Sector Performance in the Euro Area? September 2005, Box 1.3

Pressures Mount for African Cotton Producers September 2005, Box 1.5

Is Investment in Emerging Asia Too Low? September 2005, Box 2.4

Developing Institutions to Reflect Local Conditions: The Example of 

Ownership Transformation in China Versus Central and Eastern Europe September 2005, Box 3.1

How Rapidly Are Oil Exporters Spending Their Revenue Gains? April 2006, Box 2.1

EMU: 10 Years On October 2008. Box 2.1
Selected Topics, 2000–2

30

 Staff Studies for the

World Economic Outlook

The Great Contraction in Russia, the Baltics, and Other Countries of 

the Former Soviet Union: A View from the Supply Side

Mark De Broeck and Vincent Koen May 2000

XI. Country-Specific Analys

 World Economic Outlook

Turkey’s IMF-Supported Disinflation Program May 2000, Box 2.1

Productivity and Stock Prices in the United States May 2000, Box 3.1

India: Reinvigorating the Reform Process May 2000, Box 4.2

Risky Business: Output Volatility and the Perils of Forecasting in Japan October 2000, Box 1.2

China’s Prospective WTO Accession October 2000, Box 1.3

Addressing Barter Trade and Arrears in Russia October 2000, Box 3.3

Fiscal Decentralization in Transition Economies: China and Russia October 2000, Box 3.5

Accession of Turkey to the European Union October 2000, Box 4.3

Japan’s Recent Monetary and Structural Policy Initiatives May 2001, Box 1.3

Japan: A Fiscal Outlier? May 2001, Box 3.1

Financial Implications of the Shrinking Supply of U.S. Treasury Securities May 2001, Box 3.2

The Growth-Poverty Nexus in India October 2001, Box 1.6

Has U.S. TFP Growth Accelerated Outside of the IT Sector? October 2001, Box 3.2

Fiscal Stimulus and the Outlook for the United States December 2001, Box 3.2

Argentina: An Uphill Struggle to Regain Confidence December 2001, Box 3.4

China’s Medium-Term Fiscal Challenges April 2002, Box 1.4

Rebuilding Afghanistan April 2002, Box 1.5

Russia’s Rebounds April 2002, Box 1.6

Brazil: The Quest to Restore Market Confidence September 2002, Box 1.4

Where Is India in Terms of Trade Liberalization? September 2002, Box 1.5

How Important Are Banking Weaknesses in Explaining Germany’s Stagnation? April 2003, Box 1.3

Are Corporate Financial Conditions Related to the Severity of Recessions 

in the United States? April 2003, Box 2.2

Rebuilding Post-Conflict Iraq September 2003, Box 1.4

How Will the U.S. Budget Deficit Affect the Rest of the World? April 2004, Chapter II

China’s Emergence and Its Impact on the Global Economy April 2004, Chapter II

Can China Sustain Its Rapid Output Growth? April 2004, Box 2.3

Quantifying the International Impact of China’s WTO Accession April 2004, Box 2.4

Structural Reforms and Economic Growth: New Zealand’s Experience April 2004, Box 3.1

Structural Reforms in the United Kingdom During the 1980s April 2004, Box 3.2

The Netherlands: How the Interaction of Labor Market Reforms and 

Tax Cuts Led to Strong Employment Growth April 2004, Box 3.3

Why Is the U.S. International Income Account Still in the Black, 

and Will This Last? September, 2005, Box 1.2

Is India Becoming an Engine for Global Growth? September, 2005, Box 1.4

Saving and Investment in China September, 2005, Box 2.1
Selected Topics, 2000–2

3

China’s GDP Revision: What Does It Mean for China and 

the Global Economy? April 2006, Box 1.6

What Do Country Studies of the Impact of Globalization on Inequality Tell Us? 

Examples from Mexico, China, and India October 2007, Box 4.2

XII. Special To

 World Economic Outlook

Climate Change and the Global Economy April 2008, Chapter 4

Rising Car Ownership in Emerging Economies: Implications for Climate Change April 2008, Box 4.1

South Asia: Illustrative Impact of an Abrupt Climate Shock April 2008, Box 4.2

Macroeconomic Policies for Smoother Adjustment to Abrupt Climate Shocks April 2008, Box 4.3

Catastrophe Insurance and Bonds: New Instruments to Hedge Extreme 

Weather Risks April 2008, Box 4.4

Recent Emission-Reduction Policy Initiatives April 2008, Box 4.5

Complexities in Designing Domestic Mitigation Policies April 2008, Box 4.6