A World Bank Group
Flagship Report

JANUARY 2020

GlobalEconomic
Prospects
Slow Growth,
Policy Challenges

JANUARY 2020

GlobalEconomic
Prospects© 2020 International Bank for Reconstruction and Development / The World Bank
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DOI: 10.1596/ 978-1-4648-1468-6

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The cutoff date for the data used in this report was December 20, 2019.

Summary of Contents
Chapter 1

Global Outlook: Fragile, Handle with Care ............................................................ 1

Special Focus 1

Price Controls: Good Intentions, Bad Outcomes ...................................................49

Chapter 2

Regional Outlooks ..............................................................................................61

Special Focus 2

Low for How Much Longer? Inflation in Low-Income Countries ..........................177

Chapter 3

Fading Promise: How to Rekindle Productivity Growth .......................................191

Chapter 4

The Fourth Wave: Rapid Debt Buildup. .............................................................251

Statistical Appendix .......................................................................................................................299
Selected Topics .............................................................................................................................306

III

Table of Contents
Foreword....................................................................................................................................... xiii
Acknowledgments .......................................................................................................................... xv
Executive Summary....................................................................................................................... xvii
Abbreviations ................................................................................................................................ xix
Chapter 1

Global Outlook: Fragile, Handle with Care ............................................................1
Summary .............................................................................................................3
Major economies: Recent developments and outlook ............................................... 7
United States..................................................................................................7
Euro Area ......................................................................................................8Japan.............................................................................................................8
China ............................................................................................................9
Global trends .......................................................................................................9
Global trade ................................................................................................. 10
Financial markets.......................................................................................... 11
Commodity markets ..................................................................................... 12
Emerging market and developing economies.......................................................... 13
Recent developments..................................................................................... 13
Outlook....................................................................................................... 15
Risks to the outlook ............................................................................................ 25
Summary of global outlook and risks............................................................... 25
Rising trade barriers and protracted policy uncertainty ...................................... 26
A deepening slowdown in major economies .................................................... 27
Financial stress in EMDEs ............................................................................. 29
Geopolitical and region-specific downside risks ................................................ 30
Upside risks ................................................................................................. 31
Policy challenges................................................................................................. 32
Challenges in advanced economies.................................................................. 32
Challenges in emerging market and developing economies ................................ 34
Box 1.1 Recent developments and outlook for low-income countries........................ 16
Box 1.2 Regional perspectives: Recent developments and outlook ............................ 23
References.......................................................................................................... 42

VSpecial Focus 1

Price Controls: Good Intentions, Bad Outcomes .................................................. 49
Introduction ...................................................................................................... 51
Use of price controls ........................................................................................... 51
Challenges of price controls ................................................................................. 55
Policy implications ............................................................................................. 57
References ......................................................................................................... 58

Chapter 2

Regional Outlooks ............................................................................................. 61
East Asia and Pacific .......................................................................................... 63
Recent developments........................................................................................ 63
Outlook.......................................................................................................... 65
Risks .............................................................................................................. 67
Box 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers .............. 69
Europe and Central Asia .................................................................................... 77
Recent developments........................................................................................ 77
Outlook.......................................................................................................... 79
Risks ............................................................................................................. 80
Box 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers ........ 84
Latin America and the Caribbean ........................................................................ 95
Recent developments........................................................................................ 95
Outlook.......................................................................................................... 97
Risks .............................................................................................................. 98
Box 2.3.1 Labor productivity in Latin America and the Caribbean:
Trends and drivers ......................................................................................... 102
Middle East and North Africa ........................................................................... 111
Recent developments...................................................................................... 111
Outlook........................................................................................................ 112
Risks ............................................................................................................ 114
Box 2.4.1 Labor productivity in the Middle East and North Africa:
Trends and drivers ......................................................................................... 117
South Asia ....................................................................................................... 125
Recent developments...................................................................................... 125
Outlook........................................................................................................ 127
Risks ............................................................................................................ 128
Box 2.5.1 Labor productivity in South Asia: Trends and drivers .......................... 131

VISub-Saharan Africa ..........................................................................................141
Recent developments...................................................................................... 141
Outlook ........................................................................................................ 143
Risks ............................................................................................................ 144
Box 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers .............. 148
References........................................................................................................ 159

Special Focus 2

Low for How Much Longer? Inflation in Low-Income Countries ........................ 177
Introduction.....................................................................................................179
Evolution of inflation ........................................................................................180
Factors supporting inflation developments ...........................................................181
Monetary policy challenges ................................................................................182
Policy options going forward ..............................................................................185
References ........................................................................................................187

Chapter 3

Fading Promise: How to Rekindle Productivity Growth......................................191
Introduction .................................................................................................... 193
Evolution of labor productivity growth ............................................................... 196
Labor productivity convergence.......................................................................... 204
Sources of post-crisis slowdown in labor productivity growth ................................. 205
Long-run drivers of productivity growth.............................................................. 214
Prospects for productivity growth ....................................................................... 220
Policy implications............................................................................................ 222
Box 3.1 EMDE regional productivity trends and bottlenecks ................................. 198
Box 3.2 Sectoral sources of productivity growth ................................................... 209
Box 3.3 Patterns of total factor productivity: a firm perspective .............................. 215
Box 3.4 Debt, financial crises, and productivity.................................................... 223
Annex 3.1 Challenges of productivity measurement .............................................. 231
Annex 3.2 Data and growth accounting approach................................................. 232
Annex 3.3 Drivers of productivity....................................................................... 233
Annex 3.4 Data and methodology for sectoral productivity.................................... 239
Annex 3.5 Methodology for Box 3.3 ................................................................... 240
Annex 3.6 Local projection methodology for Box 3.4............................................ 240
References........................................................................................................ 241
VIIChapter 4

The Fourth Wave: Rapid Debt Buildup ............................................................... 251
Introduction ...................................................................................................... 253
Evolution of past waves of debt ............................................................................ 256
The current wave of debt in historical context ........................................................ 259
Rapid debt accumulation episodes ........................................................................ 276
What comes next? ............................................................................................... 278
Seven lessons ...................................................................................................... 282
Policy implications.............................................................................................. 283
Box 4.1 Similarities and differences between the previous three waves ....................... 260
Box 4.2 The fourth wave ..................................................................................... 266
Box 4.3 Debt and crises ....................................................................................... 272
Annex 4.1 Event study methodology..................................................................... 286
Annex 4.2 Regression methodology ...................................................................... 287
Annex 4.3 Case studies ........................................................................................ 288
References.......................................................................................................... 289

Statistical Appendix ....................................................................................................................... 299
Selected Topics ............................................................................................................................. 306
Figures

VIII

1.1

Global growth prospects ....................................................................... 5

1.2

Global risks and policy challenges .......................................................... 6

1.3

Advanced economies ............................................................................ 8

1.4

United States ...................................................................................... 9

1.5

Euro Area ........................................................................................... 9

1.6

China ............................................................................................... 10

1.7

Global trade ...................................................................................... 11

1.8

Global finance................................................................................... 12

1.9

Commodity markets .......................................................................... 13

1.10

EMDE recent developments ............................................................... 14

1.11

EMDE commodity exporters and importers ......................................... 15

1.1.1

Recent developments in low-income countries ...................................... 17

1.1.2

Outlook for per capita GDP and risks.................................................. 18

1.12

EMDE outlook ................................................................................. 21

1.13

EMDE per capita income growth and poverty ...................................... 22

1.2.1

Regional growth ................................................................................ 24

1.14

Balance of risks.................................................................................. 26

1.15

Rising trade barriers and protracted policy uncertainty ........................... 27

1.16

A deepening slowdown in major economies .......................................... 28

1.17

Financial stress in EMDEs .................................................................. 30

1.18

Other downside risks ......................................................................... 31

1.19

Upside risks ...................................................................................... 32

1.20

Monetary and financial policies in advanced economies ......................... 32

1.21

Fiscal policy in advanced economies..................................................... 33

1.22

Structural policies in advanced economies ............................................ 34

1.23

EMDE monetary and financial policy .................................................. 36

1.24

EMDE fiscal policy............................................................................ 37

1.25

EMDE structural policies—Governance, business climate,and GVC participation....................................................................... 39

1.26

EMDE structural policies—Productivity .............................................. 40

SF1.1

Price controls .................................................................................... 53

SF1.2

Price controls on imported and exported goods ..................................... 54

2.1.1

EAP: Recent developments ................................................................ 64

2.1.2

Recent developments, China............................................................... 65

2.1.3

EAP: Outlook and risks...................................................................... 66

2.1.1.1

Productivity in EAP compared with other country groups ...................... 70

2.1.1.2

Evolution of productivity in EAP ........................................................ 71

2.1.1.3

Factors underlying productivity growth in EAP..................................... 73

2.1.1.4

Prospects for productivity growth in EAP ............................................. 74

2.2.1

ECA: Recent developments................................................................. 78

2.2.2

ECA: Outlook and risks ..................................................................... 79

2.2.1.1

Productivity in ECA compared with other regions................................. 85

2.2.1.2

Evolution of productivity in ECA........................................................ 86

2.2.1.3

Factors supporting productivity growth in ECA .................................... 88

2.2.1.4

Drivers of productivity growth in ECA ................................................ 91

2.3.1

LAC: Recent developments................................................................. 96

2.3.2

LAC: Outlook and risks ..................................................................... 98

2.3.1.1

Evolution of labor productivity growth in LAC....................................103

2.3.1.2

Sources of productivity growth in LAC ...............................................105

2.3.1.3

Sectoral productivity in LAC .............................................................106

2.3.1.4

Drivers of labor productivity growth in LAC .......................................107
IX

X

2.4.1

MENA: Recent developments ............................................................112

2.4.2

MENA: Outlook and risks.................................................................113

2.4.1.1

Productivity in MENA in regional comparison ....................................118

2.4.1.2

Evolution of labor productivity in MENA ...........................................1192.4.1.3

Factors supporting productivity growth in MENA ...............................120

2.4.1.4

Policy challenges...............................................................................121

2.5.1

SAR: Recent developments ................................................................126

2.5.2

SAR: Outlook and risks.....................................................................127

2.5.1.1

Evolution of productivity growth in SAR ............................................132

2.5.1.2

Sectoral productivity and employment in SAR .....................................134

2.5.1.3

Drivers of productivity growth in SAR ................................................135

2.5.1.4

Policy options in SAR .......................................................................136

2.5.1.5

Productivity prospects in SAR ............................................................137

2.5.1.6

Constraints to productivity growth in SAR ..........................................139

2.6.1

SSA: Recent developments.................................................................142

2.6.2

SSA: Outlook and risks .....................................................................144

2.6.1.1

Productivity in SSA in regional comparison .........................................149

2.6.1.2

Evolution of labor productivity growth in SSA.....................................150

2.6.1.3

Sectoral productivity growth in SSA...................................................152

2.6.1.4

Drivers of productivity growth in SSA ................................................154

2.6.1.5

Prospects for productivity growth in SSA ............................................156

SF2.1

Inflation in low-income countries and poverty .....................................180

SF2.2

Factors supporting falling inflation in LICs..........................................181

SF2.3

Monetary policy challenges in LICs ....................................................185

3.1

Labor productivity, per capita income and poverty reduction.................194

3.2

Global productivity developments ......................................................195

3.3

Evolution of global productivity growth ..............................................197

3.1.1

Evolution of regional labor productivity ..............................................199

3.1.2

Sectoral contributions to regional productivity growth ..........................201

3.1.3

Potential bottlenecks to productivity growth........................................203

3.4

Distribution of productivity levels and convergence progress .................205

3.5

Decomposition of productivity growth................................................207

3.2.1

Agriculture, industry and services .......................................................210

3.2.2

Sectoral labor productivity .................................................................211

3.2.3

Between– and within-sector sources to productivity growth...................212

3.6

Sectoral productivity developments.....................................................214

3.3.1

Firm TFP and distance-to-frontier in EMDEs by industry .................... 216

3.3.2

Firm TFP by regions......................................................................... 217

3.3.3

Distance-to-frontier of TFP, firm characteristics, and regulations ........... 218

3.7

Impact of drivers on productivity growth ............................................ 220

3.8

Pre-crisis developments in productivity drivers and productivity growth . 221

3.9

Post-crisis slowdown of the drivers of productivity growth .................... 222

3.4.1

Productivity in debt accumulation episodes and financial crises.............. 224

3.10

EMDE infrastructure and education gaps............................................ 226

3.11

Developments in Fintech and Govtech ............................................... 226

3.12

Productivity growth: reform scenario .................................................. 227

3.13

Effect of governance reform spurts...................................................... 230

Annex 3.3.1 Productivity drivers in 2017, by region ............................................. 234
Annex 3.3.2 Productivity changes in productivity drivers, by region ....................... 237
4.1

Evolution of debt ............................................................................. 254

4.2

Debt in EMDEs............................................................................... 257

4.3

The fourth wave: Debt accumulation.................................................. 259

4.1.1

Comparison of previous waves ........................................................... 261

4.1.2

Changes in debt by sector and region.................................................. 263

4.1.3

GDP per capita in EMDEs during the four waves ................................ 264

4.4

The fourth wave: Vulnerabilities and use of borrowed funds.................. 265

4.2.1

The fourth wave: Debt developments ................................................. 267

4.5

Comparison of features of fourth wave and earlier waves: Debt .............. 270

4.6

Comparison of fourth wave and earlier waves: Policies and institutions... 271

4.7

Episodes of rapid debt accumulation in EMDEs .................................. 277

4.8

Crises during rapid debt accumulation episodes in EMDEs ................... 278

4.9

Macroeconomic developments during debt accumulation episodes......... 279

4.10

Fourth wave: Opportunities and risks ................................................. 280

Annex 4.1.1 Country examples of debt accumulation episodes ............................... 286

Tables

1.1

Real GDP ...........................................................................................4

1.1.1

Low-income country forecasts ............................................................. 19

1.2

Emerging market and developing economies......................................... 41

2.1.1

East Asia and Pacific forecast summary ................................................. 68

2.1.2

East Asia and Pacific country forecasts.................................................. 68

2.2.1

Europe and Central Asia forecast summary ........................................... 82
XI

2.2.2Europe and Central Asia country forecasts ............................................83

2.3.1

Latin America and the Caribbean forecast summary ............................. 100

2.3.2

Latin America and the Caribbean country forecasts.............................. 101

2.4.1

Middle East and North Africa forecast summary.................................. 115

2.4.2

Middle East and North Africa economy forecasts................................. 116

2.5.1

South Asia forecast summary ............................................................. 129

2.5.2

South Asia country forecasts .............................................................. 130

2.6.1

Sub-Saharan Africa forecast summary ................................................. 146

2.6.2

Sub-Saharan Africa country forecasts.................................................. 147

Annex 3.3.1 Variables included in the regressions and sources ............................... 238
Annex 3.4.1 Sectoral categorization .................................................................... 239
Annex 4.1.1 Comparison of debt accumulation episodes....................................... 287

XII

Foreword
Following a year during which weak trade andinvestment dragged the world economy to its
feeblest performance since the global financial crisis,
economic growth is poised for a modest reboundthis year. However, for even that modest uptick to
occur, many things have to go right.
Global growth is set to rise by 2.5 percent this year,
a small rise from an estimated 2.4 percent in 2019,as trade and investment gradually recover.
Emerging market and developing economies are
anticipated to see growth accelerate to 4.1 percent
from 3.5 percent last year. However, that
acceleration will not be broad-based: the pickup
is anticipated to come largely from a handful oflarge emerging economies stabilizing after deep
recessions or sharp slowdowns.
Even this tepid global rally could be disrupted byany number of threats. Trade tensions could
re-escalate. A sharper-than-expected growth slowdown in major economies would reverberate
widely. A resurgence of financial stress in large
emerging markets, an escalation of geopoliticaltensions, or a series of extreme weather events could
all have adverse effects on economic activity.
This edition of Global Economic Prospects analyzes
several topical themes underlying the fragile
outlook.
One is the largest, fastest, and most broad-based
wave of debt accumulation in advanced economies
as well as in emerging and developing economies in
the last 50 years. Public borrowing can be beneficial
and spur economic development, if used tofinance growth-enhancing investments. However,
although currently low interest rates mitigate risks,
the three previous waves of debt accumulation in
debt have ended badly.
A second is the widespread slowdown in
productivity growth over the last ten years. Growth
in productivity—output per worker—is essential toraising living standards. However, weaker
investment and efficiency gains, dwindling gains

from the reallocation of resources to moreproductive sectors, and slowing improvements inthe key drivers of productivity have sapped
momentum in this key driver of lasting growth.
Additional key themes explored in this edition
include price controls—which, despite goodintentions, can dampen investment and growth,worsen poverty outcomes, and lead to heavier fiscal
burdens—and the drivers of the long recent periodof low inflation among low-income countries andnecessary policies to maintain low and stableinflation.
These messages have serious implications for thegoals of eradicating poverty and sharing prosperity.
Even if the recovery in emerging and developing
economy growth were to take place as expected, percapita growth would advance at a pace too slow tomeet development goals.
Yet policymakers have it in their capacity to ensure
the recovery not only stays on track, but evensurprises to the upside. Recent policy actions—particularly those that have mitigated tradetensions—could augur a sustained reduction inpolicy uncertainty. Countries could pursue decisivereforms to bolster governance and business climate,improve tax policy, promote trade integration, andrekindle productivity growth, all while protecting
vulnerable groups. Building resilient monetary andfiscal frameworks, instituting robust supervisory
and regulatory regimes, and following transparent
debt management practices could reduce the risk ofshocks, or soften their impact, and strengthen
resilience against them.
As a philosopher once said, one swallow does not asummer make. There are signs that global growthskirted a rough patch and is recovering; it is up to
policy makers to make sure it thrives.
Ceyla Pazarbasioglu
Vice President
Equitable Growth, Finance, and Institutions
World Bank GroupXIII

Acknowledgments
This World Bank Group Flagship Report is a product of the Prospects Group in the Equitable Growth,
Finance and Institutions (EFI) Vice Presidency. The project was managed by M. Ayhan Kose and Franziska
Ohnsorge, under the general guidance of Ceyla Pazarbasioglu.
Global and regional surveillance work wascoordinated by Carlos Arteta. The primaryauthors of this report were Alistair Dieppe,
Justin-Damien Guénette, Jongrim Ha, Gene
Kindberg-Hanlon, Patrick Kirby, Peter Nagle,
Rudi Steinbach, Naotaka Sugawara, Temel
Taskin, Ekaterine Vashakmadze, Dana Vorisek,
Collette M. Wheeler, and Lei Sandy Ye.
Other contributors included John Baffes, Csilla
Lakatos, Alain Kabundi, Sergiy Kasyanenko,
Atsushi Kawamoto, Sinem Kilic Celik, Wee
Chian Koh, Hideaki Matsuoka, Yoki Okawa,
Cedric Okou, Franz Ulrich Ruch, and Shu Yu.
Research assistance was provided by Vanessa
Arellano Banoni, Yushu Chen, Zhuo Chen,
Khamal Antonio Clayton, Aygul Evdokimova,
Awais Khuhro, Yi Li, Shihui Liu, Maria Hazel
Macadangdang, Julia R. R. Norfleet, Vasiliki
Papagianni, Jankeesh Sandhu, Shijie Shi, Xinyue
Wang, Jinxin Wu, Heqing Zhao, and JunchengZhou. Modeling and data work were provided by
Rajesh Kumar Danda, Julia R. R. Norfleet, andShijie Shi.
The online publication was produced by PaulBlake, Graeme Littler, Venkat Ganeshan

Gopalakrishnan, and Torie Smith. Mark
Felsenthal and Alejandra Viveros managed media
relations and dissemination. Graeme Littler
provided editorial support, with contributions
from Adriana Maximiliano.
Regional projections and write-ups were
produced in coordination with country teams,
country directors, and the offices of the regional
chief economists.
The print publication was produced by Maria
Hazel Macadangdang, Adriana Maximiliano, andQuinn Sutton, in collaboration with Luiz H.
Almeida, Andrew Charles Berghauser, Aziz
Gökdemir, Michael Harrup, and Jewel
McFadden.
Many reviewers provided extensive advice andcomments. The analysis also benefited fromcomments and suggestions by staff members
from World Bank Group country teams andother World Bank Group Vice Presidencies aswell as Executive Directors in their discussion of
the report on December 17, 2019. However,both forecasts and analysis are those of the WorldBank Group staff and should not be attributed to
Executive Directors or their national authorities.

XV

Executive Summary
Global growth is projected to reach 2.5 percent in 2020, slightly faster than the post-crisis low registered lastyear. While growth could be stronger if reduced trade tensions lead to a sustained reduction in uncertainty, thebalance of risks to the outlook is to the downside. Growth in emerging market and developing economies(EMDEs) is also expected to remain subdued, continuing a decade of disappointing outcomes. A steep andwidespread productivity growth slowdown has been underway in EMDEs since the global financial crisis,
despite the largest, fastest, and most broad-based accumulation of debt since the 1970s. In addition, many
EMDEs, including low-income countries, face the challenge of phasing out price controls that impose heavy
fiscal burdens and dampen investment. These circumstances add urgency to the need to implement measures torebuild macroeconomic policy space and to undertake reforms to rekindle productivity growth. These efforts
need to be supplemented by policies to promote inclusive long-term growth and accelerate poverty alleviation.
Global Outlook: Fragile, Handle with Care.
Global growth is expected to recover to 2.5
percent in 2020—up slightly from the post-crisis
low of 2.4 percent registered last year amid
weakening trade and investment—and edge up
further over the forecast horizon. This projected
recovery could be stronger if recent policy
actions—particularly those that have mitigatedtrade tensions—lead to a sustained reduction inpolicy uncertainty. Nevertheless, downside risks
predominate, including the possibility of a reescalation of global trade tensions, sharp
downturns in major economies, and financial
disruptions in emerging market and developing
economies (EMDEs). The materialization ofthese risks would test the ability of policymakers
to respond effectively to negative events.
Associated policy challenges are compounded byhigh debt levels and subdued productivity
growth. Many EMDEs need to rebuild
macroeconomic policy space to enhance resilience
to possible adverse developments. They also needto pursue decisive reforms to bolster governanceand business climates, improve tax policy,
promote trade integration, and rekindle
productivity growth, while protecting vulnerable
groups. These policy actions would help foster
inclusive and sustainable long-term growth andpoverty alleviation.
Regional Prospects. Growth in almost all EMDE
regions has been weaker than expected, reflecting
downgrades to almost half of EMDEs. Activity inmost regions is expected to pick up in 2020-21,

but the recovery will largely depend on a reboundin a small number of large EMDEs, some ofwhich are emerging from deep recessions or sharp
slowdowns.
This edition of Global Economic Prospects also
includes chapters on the productivity growth
slowdown in EMDEs since the global financial
crisis and on the rapid debt buildup in these
economies over the same period, and special focus
pieces on the implications of price controls in
EMDEs and on the challenges of maintaining
low inflation in low-income economies (LICs).
Fading Promise: How to Rekindle Productivity
Growth. A broad-based slowdown in laborproductivity growth has been underway since theglobal financial crisis. In EMDEs, the slowdown
has reflected weakness in investment andmoderating efficiency gains as well as dwindling
resource reallocation between sectors. The pace ofimprovements in key drivers of laborproductivity—including education, urbanization,and institutions—has slowed or stagnated since
the global financial crisis and is expected toremain subdued. To rekindle productivity
growth, a comprehensive approach is necessary:facilitating investment in physical, intangible, andhuman capital; encouraging reallocation of
resources towards more productive sectors;
fostering firm capabilities to reinvigorate
technology adoption and innovation; andpromoting a growth-friendly macroeconomic andinstitutional environment. Specific policy

XVII

priorities will depend on individual country
circumstances.
The Fourth Wave: Rapid Debt Buildup. The
global economy has experienced four waves ofdebt accumulation over the past fifty years. The
first three ended with financial crises in many
EMDEs. During the current wave, which started
in 2010, the increase in debt in these economies
has already been larger, faster, and more broadbased than in any of the previous three waves.
Current low interest rates—which markets expect
to be sustained into the medium term—appear to
mitigate some of the risks associated with highdebt. However, EMDEs are also confronted byweak growth prospects, mounting vulnerabilities,
and elevated global risks. A menu of policy
options is available to reduce the likelihood of thecurrent debt wave ending in crises and, if crises
were to take place, to alleviate their impact.
Price Controls: Good Intentions, Bad
Outcomes. The use of price controls is
widespread across EMDEs, including for foodand key imported and exported commodities.
While sometimes used as a tool for social policy,
price controls can dampen investment andgrowth, worsen poverty outcomes, cause countries
to incur heavy fiscal burdens, and complicate the

XVIII

effective conduct of monetary policy. Replacing
price controls with expanded and better-targetedsocial safety nets, coupled with reforms toencourage competition and a sound regulatoryenvironment, can be both pro-poor and progrowth. Such reforms need to be carefullycommunicated and sequenced to ensure politicaland social acceptance. Where they exist, pricecontrol regimes should be transparent andsupported by well-capitalized stabilization funds
or national hedging strategies to ensure fiscal
sustainability.
Low for How Much Longer? Inflation in LowIncome Countries. Inflation in LICs has declined
sharply to a median of 3 percent in mid-2019from a peak of 25 percent in 1994. The drop has
been supported by the move to more flexible
exchange rate regimes, greater central bankindependence, and a generally more benign
external environment since the 1990s. However,
low LIC inflation cannot be taken for granted
amid mounting fiscal pressures and the risk of
exchange rate shocks. To maintain low and stableinflation, monetary and fiscal policy frameworks
need to be strengthened and supported by efforts
to replace price controls with more efficient
policies.

Abbreviations
ACP

African, Caribbean and Pacific Group of StatesAEadvanced economy

CDS

credit default swap

CPTPP

Comprehensive and Progressive Agreement for Trans-Pacific Partnership

DR-CAFTA

Central America-Dominican Republic Free Trade Agreement

EAP

East Asia and Pacific

ECA

Europe and Central Asia

ECB

European Central Bank

ECI

Economic Complexity Index

EMBI

Emerging Market Bond Index

EMDE

emerging market and developing economies

ERPTR

exchange rate pass-through ratio

EU

European Union

FAVAR

Bayesian factor-augmented vector autoregression

FCVfragility, conflict, and violence

FDI

foreign direct investment

GCC

Gulf Cooperation Council

GDPgross domestic product

GEP

Global Economic Prospects

GMM

generalized methods of moments

GNFS

goods and nonfactor services

GNI

gross national income

GST

goods and services tax

GVCs

global value chains

HIPC

Heavily Indebted Poor Countries

ICE

Intercontinental Exchange

ICRG

International Country Risk Guide

IMF

International Monetary FundIT

inflation targeting

LAC

Latin America and the Caribbean

LFPR

labor force participation rateLIC

low-income country

LSAP

Large-Scale Asset Purchase

MDRI

Multilateral Debt Relief Initiative

MENA

Middle East and North Africa

MEP

Maturity Extension Program

MIC

middle-income country

XIX

XX

NEER

nominal effective exchange rate

NPL

nonperforming loan

ONI

Oceanic Niño Index

OPEC

Organization of the Petroleum Exporting Countries

PMI

Purchasing Managers’ Index

PPP

purchasing power parity

REER

real effective exchange rate

RHS

right-hand side (in figures)

RMB

renminbi

SAR

South Asia Region

SSA

Sub-Saharan Africa

SSE

Shanghai Stock Exchange

TFP

total factor productivity

TiVA

trade in value added

USMCA

United States-Mexico-Canada Agreement

VAT

value-added tax

WAEMU

West African Economic and Monetary Union

WGIWorld Governance Indicators

WTO

World Trade Organization

CHAPTER 1

GLOBAL OUTLOOK
Fragile, Handle with CareG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

Global growth is expected to recover to 2.5 percent in 2020—up slightly from the post-crisis low of 2.4 percent
registered last year amid weakening trade and investment—and edge up further over the forecast horizon. This
projected recovery could be stronger if recent policy actions—particularly those that have mitigated tradetensions—lead to a sustained reduction in policy uncertainty. Nevertheless, downside risks predominate,including the possibility of a re-escalation of global trade tensions, sharp downturns in major economies, andfinancial disruptions in emerging market and developing economies (EMDEs). The materialization of theserisks would test the ability of policymakers to respond effectively to negative events. Associated policy challenges
are compounded by high debt levels and subdued productivity growth. Many EMDEs need to rebuild
macroeconomic policy space to enhance resilience to possible adverse developments. They also need to pursue
decisive reforms to bolster governance and business climates, improve tax policy, promote trade integration, andrekindle productivity growth, while protecting vulnerable groups. These policy actions would help foster
inclusive and sustainable long-term growth and poverty alleviation.

Summary
Global growth decelerated markedly in 2019, with
continued weakness in global trade andinvestment (Figures 1.1.A and 1.1.B). Thisweakness was widespread, affecting both advanced
economies—particularly the Euro Area—and
emerging market and developing economies(EMDEs). Various key indicators of economic
activity declined in parallel, approaching theirlowest levels since the global financial crisis(Figure 1.1.C). In particular, global trade in goods
was in contraction for a significant part of 2019,and manufacturing activity slowed markedly overthe course of the year; recent high-frequency
readings suggest some tentative stabilization of
manufacturing output at weak levels. To a lesser
extent, services activity also moderated. A broad
range of economies have experienced feeble
growth, with close to 90 percent of advanced
economies and 60 percent of EMDEs going
through varying degrees of deceleration last year.
Bilateral negotiations between the United States
and China since mid-October resulted in a Phase
One agreement—including a planned partial
rollback of tariffs—that has de-escalated tradetensions. This comes after a prolonged period ofrising trade disputes between the two countries,

Note: This chapter was prepared by Carlos Arteta and Patrick
Kirby, with contributions from Collette M. Wheeler, Justin-Damien
Guénette, Csilla Lakatos, Rudi Steinbach, and Ekaterine
Vashakmadze. Additional inputs were provided by John Baffes,
Sergiy Kasyanenko, Peter Nagle, and Franz Ulrich Ruch. Research
assistance was provided by Yushu Chen, Shihui Liu, Julia Norfleet,
Vasiliki Papagianni, Shijie Shi, and Jinxin Wu.

which has heightened policy uncertainty andweighed on international trade, confidence, andinvestment. As a result of the increase of tariffs
between the two countries over the past couple ofyears, a substantially higher share of world trade
has become subject to protectionist measures(Figure 1.1.D).
Financial market sentiment improved appreciably
toward the end of last year along with thealleviation of trade tensions. That said, it had beenfragile for most of 2019. Concerns about growthprospects triggered widespread monetary policy
easing by major central banks last year, as well as
flight to safety flows into advanced-economy bondmarkets. In a context of subdued inflation, thispushed global yields down—in some advanced
economies, further into negative territory—for
most of 2019. Heightened risk aversion
contributed to subdued EMDE capital inflows in
the second half of last year, as a number of
EMDEs faced renewed currency and equity pricepressures. The subdued outlook led to declines inmost commodity prices, which are expected toremain near current levels over the forecast period.
Against this international context, global growth
weakened to an estimated 2.4 percent last year—
the lowest rate of expansion since the global
financial crisis. With some recent data pointing toan incipient stabilization of economic conditions,
global growth is projected to edge up to 2.5
percent in 2020, 0.2 percentage point below
previous forecasts, as investment and trade
gradually recover. In particular, global trade
growth—which is estimated to have slowed
sharply from 4 percent in 2018 to 1.4 percent in

3

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

TABLE 1.1 Real GDP1

Percentage point differencesfrom June 2019 projections(Percent change from previous year)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

3.2

3.0

2.4

2.5

2.6

2.7

-0.2

-0.2

-0.2

2.4

2.2

1.6

1.4

1.5

1.5

-0.1

-0.1

0.0

United States

2.4

2.9

2.3

1.8

1.7

1.7

-0.2

0.1

0.1

Euro Area

2.5

1.9

1.1

1.0

1.3

1.3

-0.1

-0.4

0.0

Japan

1.9

0.8

1.1

0.7

0.6

0.4

0.3

0.0

0.0

4.5

4.3

3.5

4.1

4.3

4.4

-0.5

-0.5

-0.3

Commodity-exporting EMDEs

2.2

2.0

1.5

2.6

2.9

3.0

-0.6

-0.5

-0.1

Other EMDEs

6.2

5.8

4.8

5.1

5.2

5.2

-0.4

-0.4

-0.3

5.4

5.0

3.3

4.0

4.4

4.5

-0.9

-0.8

-0.6

6.5

6.3

5.8

5.7

5.6

5.6

-0.1

-0.2

-0.2

China

6.8

6.6

6.1

5.9

5.8

5.7

-0.1

-0.2

-0.2

Indonesia

5.1

5.2

5.0

5.1

5.2

5.2

-0.2

-0.2

-0.1

Thailand

4.0

4.1

2.5

2.7

2.8

2.9

-1.0

-0.9

-0.9

4.1

3.2

2.0

2.6

2.9

2.9

0.4

-0.1

0.0

Russia

1.6

2.3

1.2

1.6

1.8

1.8

0.0

-0.2

0.0

Turkey

7.5

2.8

0.0

3.0

4.0

4.0

1.0

0.0

0.0

Poland

4.9

5.1

4.3

3.6

3.3

3.1

0.3

0.0

0.0

1.9

1.7

0.8

1.8

2.4

2.6

-0.9

-0.8

-0.3

World
Advanced economies

Emerging market and developing economies

Other EMDEs excluding China
East Asia and Pacific

Europe and Central Asia

Latin America and the Caribbean
Brazil

1.3

1.3

1.1

2.0

2.5

2.4

-0.4

-0.5

0.2

Mexico

2.1

2.1

0.0

1.2

1.8

2.3

-1.7

-0.8

-0.6

Argentina

2.7

-2.5

-3.1

-1.3

1.4

2.3

-1.9

-3.5

-1.8

1.1

0.8

0.1

2.4

2.7

2.8

-1.2

-0.8

0.0

Saudi Arabia

-0.7

2.4

0.4

1.9

2.2

2.4

-1.3

-1.2

-0.1
0.0

Middle East and North Africa
Iran

3.8

-4.9

-8.7

0.0

1.0

1.0

-4.2

-0.9

Egypt2

4.2

5.3

5.6

5.8

6.0

6.0

0.1

0.0

0.0

South Asia

6.7

7.1

4.9

5.5

5.9

6.0

-2.0

-1.5

-1.2

India3

7.2

6.8

5.0

5.8

6.1

6.1

-2.5

-1.7

-1.4

Pakistan2

5.2

5.5

3.3

2.4

3.0

3.9

-0.1

-0.3

-1.0

Bangladesh2

7.3

7.9

8.1

7.2

7.3

7.3

0.8

-0.2

0.0

Sub-Saharan Africa

2.7

2.6

2.4

2.9

3.1

3.3

-0.5

-0.4

-0.4

Nigeria

0.8

1.9

2.0

2.1

2.1

2.1

-0.1

-0.1

-0.3

South Africa

1.4

0.8

0.4

0.9

1.3

1.5

-0.7

-0.6

-0.4

Angola

-0.1

-1.2

-0.7

1.5

2.4

3.0

-1.7

-1.4

-0.4

High-income countries

2.4

2.2

1.7

1.5

1.5

1.6

-0.1

-0.1

-0.1

Developing countries

4.8

4.4

3.7

4.3

4.5

4.5

-0.4

-0.4

-0.3

5.5

5.8

5.4

5.4

5.5

5.8

-0.3

-0.6

-0.6

BRICS

5.3

5.4

4.6

4.9

4.9

5.0

-0.5

-0.4

-0.4

World (2010 PPP weights)

3.9

3.7

2.9

3.2

3.3

3.4

-0.4

-0.3

-0.3

5.9

4.0

1.4

1.9

2.5

2.8

-1.2

-1.3

-0.7

23.3
5.5

29.4
1.7

-10.3
-4.7

-5.4
0.1

1.9
1.7

1.9
1.7

-6.9
-2.6

-3.9
0.2

1.2
0.3

Memorandum items:
Real GDP1

Low-income countries

World trade volume4
Commodity prices5
Oil priceNon-energy commodity price index

Source: World Bank.
Note: PPP = purchasing power parity; e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information. Consequently, projections presented here may differ
from those contained in other World Bank documents, even if basic assessments of countries’ prospects do not differ at any given moment in time. Country classifications and lists ofemerging market and developing economies (EMDEs) are presented in Table 1.2. BRICS include: Brazil, Russia, India, China, and South Africa. The World Bank has ceased producing a
growth forecast for Venezuela and has removed Venezuela from all growth aggregates in which it was previously included.
1. Headline aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. World growth rates based on purchasing power parity (PPP) weights attributea greater portion of global GDP to EMDEs relative to market exchange rates due to the PPP methodology, which uses an exchange rate that is calculated from the difference in the pricelevels of a basket of goods and services between economies.
2. GDP growth values are on a fiscal year basis. Aggregates that include these countries are calculated using data compiled on a calendar year basis. Pakistan's growth rates are based on
GDP at factor cost. The column labeled 2019 refers to FY2018/19.
3. The column labeled 2018 refers to FY2018/19.
4. World trade volume of goods and non-factor services.
5. Oil is the simple average of Brent, Dubai, and West Texas Intermediate. The non-energy index is comprised of the weighted average of 39 commodities (7 metals, 5 fertilizers, 27
agricultural commodities). For additional details, please see http://www.worldbank.org/en/research/commodity-markets.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

2019, by far the weakest pace since the global
financial crisis—is projected to firm throughout
2020 and reach 1.9 percent, assuming tradetensions do not re-escalate. In the near term,monetary policy across the world is generally
expected to remain accommodative; however,fiscal policy support is likely to fade (Figure
1.1.E).
Near-term projections for global growth mask
different contours in advanced economies and
EMDEs. Growth in advanced economies is
projected to slow to 1.4 percent this year—below
previous projections, in part reflecting lingering
weakness in manufacturing—and improve slightly
over the rest of the forecast horizon.
In contrast, after decelerating to an estimatedweaker-than-expected 3.5 percent last year, growthin EMDEs is projected to increase to 4.1 percent
in 2020—0.5 percentage point below previous
forecasts, reflecting downgrades to half of EMDEs
due in part to downward revisions to trade andinvestment growth. Nonetheless, the recovery inaggregate EMDE growth this year—whichassumes continued monetary policy support inmany economies, no major swings in commodity
prices, and generally benign borrowing costs—is
not envisioned to be broad-based: About a third of
EMDEs are expected to decelerate. Instead, it islargely predicated on a rebound in a small numberof large EMDEs, most of which are emerging
from deep recessions or sharp slowdowns but
remain fragile. Excluding this group of countries,
there would be almost no acceleration in EMDE
growth this year—and, with advanced economies
slowing, global growth would actually decelerate.
Going forward, EMDE growth is projected tostabilize at an average of 4.4 percent in 2021-22,
as trade and investment firm. In low-income
countries, growth is expected to remain littlechanged at 5.4 percent in 2020 and edge up to an
average of 5.7 percent later in the forecast horizon,
boosted by increased investment in infrastructure
and rebuilding efforts in some countries following
extreme weather-related devastation.
Even if the recovery in EMDE growth proceeds as
expected, per capita growth will remain well below
long-term averages and far from sufficient to meet

CHAPTER 1

5

FIGURE 1.1 Global growth prospects
Global growth decelerated last year to 2.4 percent—its slowest pace since
the global financial crisis—amid weakening trade and investment. Key
indicators deteriorated in parallel, in part reflecting heightened tradeprotectionism. While monetary accommodation has increased, fiscal
support is expected to wane. Global growth is projected to recover to 2.5
percent in 2020 and edge further up thereafter as trade and investment firmand EMDE activity rebounds; however, per capita growth in EMDEs willremain insufficient to meet poverty alleviation goals.
A. Global growth

B. Global trade, investment, andconsumption growth

C. Global indicators of activity in 2019

D. Global trade subject to newprotectionist measures

E. Stance of global fiscal andmonetary policy

F. Per capita income growth

Source: Bank for International Settlements; Consensus Economics; CPB Netherlands Bureau for
Economic Policy Analysis; Haver Analytics; International Monetary Fund; World Bank; World Trade
Organization.
Note: AEs = advanced economies; EMDEs = emerging market and developing economies.
A.B.E. Shaded areas indicate forecasts. Data for 2019 are estimates.
B.C. Trade measured as the average of export and import volumes.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
B. Data for 2015-16 are simple averages. Green lines indicate average over period 1990-2018.
C. Manu. = manufacturing. PMI = Purchasing Managers’ Index. PMI readings above 50 indicate
expansion in economic activity; readings below 50 indicate contraction. Last observation is 2019Q3
for GDP, October 2019 for industrial production and goods trade, and November 2019 for PMI.
D. Figure includes new import-restrictive measures, including tariff and non-tariff trade barriers.
Annual data are mid-October to mid-October.
E. Aggregates calculated using nominal U.S. dollar GDP weights. Fiscal impulse is the negativechange in general government cyclically adjusted primary balance. Policy rates are the December toDecember change. Sample includes 35 AEs and 77 EMDEs for fiscal impulse and 16 AEs and 21
EMDEs for policy rates. Policy rates for 2020 use the December 2019 Consensus Forecasts report for
central bank policy rates. When these are unavailable, the change in short-term yields is used.
F. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the
Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa.
Long-term average is calculated over the period 2000-19. Poverty rates represent latest data.
Click here to download data and charts.

6

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.2 Global risks and policy challenges
Current projections represent a benign but fragile outlook given ongoing
global headwinds. Downside risks predominate and increase the likelihoodof much weaker-than-expected global growth. However, recent policy
actions that have reduced trade tensions could lead to a sustained
mitigation of policy uncertainty and bolster investment. In advanced
economies, the room for monetary accommodation is limited. In EMDEs,
fiscal space is constrained by weak tax capacity and high debt levels,
which also hinders the ability to fund basic public services. Boosting
EMDE productivity, which has been on a downward trend in recent years,
is essential to foster long-term growth and poverty reduction.
A. Average share of EMDEs withannual growth accelerating by morethan 0.1 percentage point, 1962-2019

B. Probability of global growth being 1
percentage point below baseline

C. Impact of a 10-percent decrease in
U.S. policy uncertainty on investment
growthD. Monetary policy rate increases
during current and previous
expansions

E. Share of EMDEs with limited tax
revenues to fund basic public
services

F. Productivity growth

Source: Baker, Bloom, and Davis (2016); Bloomberg; Haver Analytics; International Monetary Fund;
National Bureau of Economic Research; Penn World Table; The Conference Board; World Bank.
A.F. Aggregates calculated using GDP weights at 2010 prices and market exchange rates.
A. AE = advanced economies. “Subdued trade” refers to growth below 2.5 percent. “Moderating
commodity prices” refers to a year-on-year contraction in the non-energy commodity index.
B. Probabilities computed from the forecast distributions of 12- and 24-month-ahead oil price futures;
S&P 500 futures, and term spread forecasts. Risk factor weights are derived from the model
described in Ohnsorge, Stocker, and Some (2016). Last observation is December 19, 2019.
C. Figure shows median impact. See Annex SF.1B of World Bank (2017a) for methodology.
D. U.S. expansions: 1991-2001, 2001-07, 2009-present. Euro Area expansions: 1999-2008, 2009-11,
2013-present. Calculations based on trough and peak of policy rates of each period. Last observationis November 2019 for the United States and 2019Q3 for the Euro Area.
E. Revenue threshold needed to provide basic public services is 15 percent of GDP, per Gaspar,
Jaramillo, and Wingender (2016). Unbalanced sample includes 70 EMDEs, of which 11 are LICs.
F. Figure shows 5-year moving averages. Productivity is defined as output per worker. Sample
includes 74 EMDEs and 29 advanced economies. Refer to Chapter 3 for details.
Click here to download data and charts.

poverty alleviation goals. More specifically,
income growth will be slowest in Sub-SaharanAfrica—the region where most low-income
countries are clustered and most of the world’s
poor live (Figure 1.1.F).
The near-term forecast for a pickup in EMDE
growth represents a benign, but fragile, scenario
given ongoing global headwinds such as slowing
advanced-economy growth, subdued global trade,and moderating commodity prices (Figure 1.2.A).
More generally, a deeper global downturn could
result if global trade tensions re-emerge, policy
uncertainty persists and becomes entrenched, or
activity in major economies deteriorates
significantly. Other risks include financial stress inlarge EMDEs, heightened geopolitical tensions, ora higher incidence of extreme weather events.
Amid these downside risks, the probability that
global growth in 2020 will be below baseline
projections is above its historical average (Figure
1.2.B). That said, the projected recovery could bestronger than expected if recent policy actions—particularly those that have alleviated U.S.-China
trade tensions—lead to a sustained reduction inpolicy uncertainty and bolster confidence, trade,
and investment (Figure 1.2.C).
Against the backdrop of a fragile outlook, thepolicy challenges confronting the global economy
are compounded by subdued productivity growthand high levels of debt (Chapters 3 and 4). In
advanced economies, the weakness of the current
expansion has made it difficult for central banks tocreate room for additional easing (Figure 1.2.D).
Low global interest rates and the associatedreduction in debt service burdens may provide
some countries with additional flexibility for the
implementation of structural reforms, such as
investments in public infrastructure or the
adoption of other growth-friendly policies. In
addition, governments can create further fiscal
space through better tax compliance andenforcement.
Most EMDEs are not well positioned to confront
negative shocks, since policy buffers generally
remain inadequate. While moderating inflation
has allowed many EMDEs to cut policy rates tosupport growth, underlying price pressures areG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

building in some cases, and policy space would befurther eroded in the event of renewed financial
market pressures. Many EMDEs, including LICs,
face the additional challenge of phasing out pricecontrols and their associated distortions amid
moderate inflation (Special Focus 1 and Special
Focus 2).
Although fiscal accommodation in some EMDEs
may be warranted in response to adverse
developments, record-high debt levels and fragile
public finance positions limit the ability to
implement countercyclical policy—indeed, a large
share of EMDEs, particularly LICs, do not evenhave the capacity to adequately fund basic public
services (Figure 1.2.E; Chapter 4). If faced withnegative shocks, authorities would need to ensurethat any fiscal support prioritizes growthenhancing spending and domestic revenue
mobilization to avoid further erosion of public
debt sustainability. Tax policy reforms thatbroaden the revenue base are needed to fundinvestment, which could be complemented bymeasures that help reduce inequality.
EMDE policymakers also need to pursue decisive
structural reforms, while protecting vulnerable
groups, to promote inclusive long-term growth.
Policy actions that improve EMDE governanceframeworks and business climates, and facilitate
integration in existing supply chains or spur thecreation of new ones, could help counter theadverse effects of weak global growth and subdued
international trade (World Bank 2019a). Measures
to improve connectivity, lower trade costs, andensure a stable and predictable legal environment
could facilitate this integration. A strong andstable multilateral trading system remains animportant foundation for robust growth in
EMDEs.
The downward trend in EMDE productivity
growth in recent years complicates these policy
challenges (Figure 1.2.F; Chapter 3). Measures toboost EMDE productivity growth are essential tofoster potential growth and ensure continued
progress in improving living standards andalleviating poverty. To rekindle productivity
growth, a comprehensive approach needs to be
employed involving policies that facilitate
investment in physical and human capital,

CHAPTER 1

encourage the reallocation of resources toward
more productive sectors, reinvigorate technology
adoption and innovation among firms, andpromote a growth-friendly macroeconomic andinstitutional environment. Within this fourpronged approach, specific policy priorities will
depend on country circumstances. In addition,
investments in green infrastructure can also help
achieve development goals and improve resilience
to climate change.

Major economies: Recent
developments and outlook
In major economies, activity has slowed moremarkedly than previously expected. Very weak
manufacturing activity has dampened growth inadvanced economies, and policy uncertainty
associated with trade tensions has also weighed on
activity in the United States and China.
The growth forecast for advanced economies has
again been revised down as a consequence of
weaker-than-expected trade and manufacturing
activity (Figure 1.3.A). Recent data show
particular weakness in investment and exports,
particularly in the Euro Area. This, along withbelow-target inflation in many economies, has
prompted a broad shift toward monetary policy
easing (Figure 1.3.B). Labor markets and theservices sector generally remain more resilient, but
the latter has shown signs of moderation (Figure
1.3.C). Aggregate activity is expected to edge
down in 2020, with continued softness ininvestment and trade (Figure 1.3.D).
United States
Growth has decelerated amid slowing investment
and exports (Figure 1.4.A). Notwithstanding therecent trade deal with China, rising tariffs haveincreased trade costs, while policy uncertainty has
weighed on investment and confidence (Baker,
Bloom, and Davis 2016; Fajgelbaum et al. 2019).
As in many other advanced economies, the U.S.
manufacturing sector has been very weak. Support
from tax cuts and changes in government
spending is expected to fade this year and becomea drag on growth thereafter (Figure 1.4.B; IMF
2019a).

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.3 Advanced economies
The growth forecast for advanced economies has been steadily revised
down, prompting a general shift toward monetary policy easing. Services
activity has so far been more resilient than investment and trade, but it has
also moderated. Activity is expected to edge down in 2020, with continued
softness in investment and trade.
A. Evolution of the growth forecast foradvanced economies

B. Monetary policy in advanced
economies

impacts of lingering uncertainty and a waning
contribution from tax cuts and government
spending, which are only partly offset byaccommodative monetary policy. The forecast ispredicated on tariffs staying at planned levels,
fiscal policy progressing as currently legislated, andthe heightened degree of policy uncertainty
gradually dissipating. Additional progress in U.S.China trade negotiations that leads to a further
reduction in trade policy uncertainty could resultin higher-than-expected U.S. growth.
Euro Area

C. Services sector expectations

D. Advanced-economy trade,investment, and consumption growth

Source: Bank for International Settlements; Haver Analytics; World Bank.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
Blue bars and orange diamonds denote forecasts in the January 2019, June 2019, and January 2020
editions of the Global Economic Prospects report.
B. Aggregate nominal policy rates calculated using moving real GDP weights at 2010 prices andmarket exchange rates. Sample includes 15 advanced economies. Last observation is November
2019.
C. Figure shows 3-month moving averages of PMI service business expectations for the year ahead.
PMI readings above 50 indicate expansion in economic activity; readings below 50 indicate
contraction. Last observation is November 2019.
D. Trade is the average of import and export volumes. Data for 2015-16 are simple averages.
Long-term average calculated over the period from 1990-2018. Shaded area indicates forecasts.
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Despite these headwinds, the labor market
remains robust and has benefited from a risingparticipation rate. Unemployment is near a fivedecade low and wage growth has been solid,fueling resilient consumption. Concerns about the
global outlook and persistent below-target
inflation have resulted in the Federal Reserve
cutting its policy rates by 75 basis points since
mid-2019.
Growth is expected to slow over the course of theforecast period, from 2.3 percent in 2019 to 1.8
percent in 2020 and 1.7 percent in 2021-22. In
the near term, the slowdown reflects the negative

Economic activity in the Euro Area has
deteriorated significantly. Several economies wereon the verge of recession at some point last year,with particular weakness in the German industrial
sector as it struggled with falling demand from
Asia and disruptions to car production (Figures
1.5.A and 1.5.B). Uncertainty concerning Brexit
also weighed on growth.
The ECB has provided monetary stimulus bypushing its policy rate deeper into negative
territory, restarting quantitative easing, andproviding inexpensive credit to banks. The overall
fiscal position of the Euro Area is expected to beroughly balanced over the forecast period,
providing little additional support to activity
despite existing space in some economies.
Growth is expected to slow to 1 percent in 2020,
0.4 percentage point down from previous
projections due to worse-than-expected incoming
data, especially industrial production. Growth isforecast to recover modestly to an average of 1.3
percent in 2021-22, assuming that policy support
gains traction, the Brexit process unfolds withminimal disruption, and there is no further
escalation in trade restrictions.
Japan
Activity in Japan declined sharply following theimpact of Typhoon Hagibis and the increase in
the value-added tax (VAT) in October last year.
The economy is also suffering from acute weakness
in manufacturing and exports, particularly thoseto China, alongside declining consumer
confidence. In response, the government is

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

providing significant support. Despite recent
weakness in activity, the unemployment rate
remains near multidecade lows, labor force
participation continues to climb, and per capita
income growth remains healthy.

FIGURE 1.4 United States

Growth is expected to slow from 1.1 percent in2019 to 0.7 percent in 2020, as anticipatory
purchases prior to the VAT increase in October
2019 are unwound. Growth in 2021-22 is
expected to average about 0.5 percent.

A. Selected activity indicators

9

Growth has decelerated, reflecting slowing investment and exports. Whilethe labor market remains robust, manufacturing activity has been
contracting, higher tariffs have increased trade costs, and policy
uncertainty has continued to weigh on investment. Support from tax cutsand government spending is expected to fade.
B. Change in the general government
cyclically-adjusted primary deficit

China
Growth has decelerated more than previously
expected amid cooling domestic demand andheightened trade tensions. Trade policy
uncertainty and higher tariffs on trade with theUnited States weighed on investor sentiment formost of 2019. Industrial production growth hasreached multiyear lows (Figure 1.6.A).

Source: Haver Analytics; International Monetary Fund; World Bank.
A. Last observation is October 2019 for shipments of durables and exports of goods and services,and 2019Q3 for national accounts data.
B. Shaded area indicates forecasts.
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FIGURE 1.5 Euro Area

Trade flows have weakened substantially. Imports,
especially those of intermediate goods, havedeclined, falling more than exports, partly
reflecting a deceleration in domestic demand. The
contraction in exports to the United States has
deepened, although shipments to the rest of theworld have been somewhat more resilient.
In response to the deceleration in activity,
monetary
policyhas
becomemore
accommodative, but regulatory tightening to
reduce non-bank lending has continued. The
government has also stepped up some fiscal
measures, including tax cuts and support for local
governments for public investment spending(Figure 1.6.B; World Bank 2019b). Total debt has
surpassed 260 percent of GDP, but the share ofnon-bank lending has continued to decline(World Bank 2019c).
After decelerating to an estimated 6.1 percent in
2019, growth is expected to moderate to 5.9
percent in 2020 and 5.8 percent in 2021—0.2
percentage point below previous projections inboth years. This is the first time China will registera pace of expansion below 6 percent since 1990,
amid a slowdown in labor productivity growthand continued external headwinds (Chapter 3;
World Bank 2018a). A permanent and lasting

Many economies in the region were on the verge of recession during mostof 2019. The German industrial sector remains particularly weak.
A. Contributions to Euro Area growth

B. Industrial production in the EuroArea and Germany

Source: Haver Analytics; World Bank.
A. “Other countries” includes Euro Area economies not listed. Data for 2019 are for 2019Q1-Q3 andare seasonally-adjusted annualized quarter-on-quarter growth rates.
B. Industrial production excludes construction. Last observation is October 2019.
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resolution of trade disputes with the United Statesthat builds upon recent progress could bolster
China’s growth prospects and reduce reliance on
policy support.

Global trends
International trade and investment have weakened
further, impeded by slowing global demand, as wellas heightened policy uncertainty and an overall
increase in the level of tariffs despite recent de-

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.6 China
Growth has continued to decelerate amid weakening industrial activity.
Imports have experienced a sharp decline. The government has also
stepped up fiscal support, including tax cuts and support to local
governments for public investment spending.
A. Import volume and industrial
production growth

B. General government gross debt
and decomposition of fiscal support
measuresSource: Haver Analytics; International Monetary Fund; World Bank.
A. Figure shows 12-month moving averages. Import data include only goods. Import volumes arecalculated as import values deflated by import price deflators. Import price deflators for October andNovember are estimates. Last observation is November 2019.
B. Gross debt consists of all liabilities that require payment or payments of interest and/or principal by
the debtor to the creditor at a date or dates in the future. This includes debt liabilities in the form ofSDRs, currency and deposits, debt securities, loans, insurance, pensions and standardized
guarantee schemes, and other accounts payable. “Other” includes other net expenditures (including
social security and State-Owned Enterprise funds). Fiscal support measures are World Bank staff
estimates. General government gross debt in 2019 are estimates.
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escalation. Major central banks have loosened policy
in response, with interest rates in many advanced
economies reaching unprecedented lows last year.
Financial conditions in EMDEs have generally
improved in parallel, except in economies perceived as
higher risk. Weak demand has pushed mostcommodity prices down, which has been partially
offset in some cases by supply restrictions.
Global trade
The sharp slowdown in the trade-intensive
manufacturing sector has continued to weigh on
global trade. Global goods trade spent a significant
part of 2019 in contraction, with especiallypronounced weakness in advanced economies and
EMDEs such as China and the rest of East Asia
(Figure 1.7.A). The severe decline in the
production of capital and intermediate goods in
G20 countries seen last year is consistent with
continuing weakness in trade and investment(Figure 1.7.B). Manufacturing export orders havebeen contracting since late 2018 and services
export orders, while more resilient, have also
decelerated (Figure 1.7.C). The softness in services
trade has so far been concentrated in global

financial transactions, construction, and travel
services, which together account for more than
one-third of world services trade (WTO 2019a).
The slowdown in trade and manufacturing stems
from a variety of factors. Weakening demand inEurope and Asia, in particular for trade-intensive
automobiles and technology products, and theslowdown in investment growth have been
importantdrags.
Protectionist
measures
implemented by G20 countries since 2018 haveaffected over $1 trillion worth of trade flows, ornearly 7 percent of global goods trade (Figure
1.7.D; WTO 2019b). The number of regulatory
restrictions affecting foreign direct investment
flows has also been on the rise, increasing by morethan a third in 2018 (UNCTAD 2019a).
Additionally, despite recent moderation, global
trade policy uncertainty remains near historic
highs (Ahir, Bloom, and Furceri 2018; Baker,
Bloom, and Davis 2019).
Trade tensions between the United States andChina escalated throughout most of 2019, andnew tariffs were implemented on the majority of
their bilateral trade. These tensions, and theensuing increase in policy uncertainty, haveresulted in sizable aggregate losses for world trade;while they have also had a positive impact onsome EMDEs through trade diversion, this impact
has been relatively small. Trade frictions have also
risen elsewhere, including between the UnitedStates and some of its other trading partners suchas the European Union (EU), as well as between
Japan and the Republic of Korea.
Nevertheless, negotiations between the UnitedStates and China since mid-October resulted in a
Phase One agreement between the two countries,
including plans to partially roll back a subset of
U.S. tariffs in exchange for Chinese commitments
to make additional purchases of U.S. products,
strengthen intellectual property protection, andpursue financial services liberalization. The recent
agreement, coupled with continued negotiations
and recent unilateral tariff reductions by China,
signals a notable de-escalation of trade tensions.
Moreover, protectionist measures implemented
since 2016 have been partially offset by various
liberalizing measures that affected 5 percent of

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

global goods trade in 2019. The U.S.-China Phase
One agreement, as well as other positive
developments—such as progress in the ratification
or implementation of the Africa Continental Free
Trade Agreement, the U.S-Japan trade agreement,
and the United States-Mexico-Canada Agreement—could give a much-needed boost to trade
growth.
In sum, growth in global goods and services tradeslowed sharply from 4 percent in 2018 to an
estimated 1.4 percent last year, by far the weakest
pace since the global financial crisis, and is
projected to firm throughout 2020 and reach 1.9
percent. Critically, these projections assume nofurther escalation or reduction of trade restrictions
going forward. An additional decline in tradetensions and the associated policy uncertainty—if,
for instance, ongoing U.S.-China negotiations
were to result in further reductions in tariffs—
could lead to a stronger-than-expected pickup in
global trade growth.

CHAPTER 1

11

FIGURE 1.7 Global trade
The slowdown in global goods trade has been broad-based, withparticularly pronounced weakness in EMDEs in the East Asia and Pacificregion. The marked decline in global capital and intermediate goods
production last year highlights the weakness in trade and investment.
Manufacturing export orders have continued to contract, and services
export orders have decelerated. Despite a recent de-escalation of tradetensions, the incidence of protectionist measures affecting global goods
trade has risen.
A. EMDE goods trade growth,
by region

B. Global production of capital andintermediate goods

C. Manufacturing and services export
ordersD. Global trade subject to newprotectionist measures

Financial markets
Global financing conditions eased considerably in2019 (Figure 1.8.A). Bond yields in advanced
economies fell to unprecedented lows,
notwithstanding a pickup toward the end of theyear amid improvement in market sentiment.
Close to $12 trillion of outstanding global debt—
nearly a quarter of the total stock, and almost
entirely from Western Europe and Japan—istrading at negative interest rates. Major central
banks, most notably the U.S. Federal Reserve andthe ECB, eased monetary policy last year in theface of softening global economic prospects,
heightened downside risks, and persistently low
inflation. Despite weak global investment,
corporate debt has been rising in many countries,
with particularly rapid growth in some riskier
categories, such as lending to highly leveraged
firms in the United States and the Euro Area (FSB
2019a).
In general, EMDE borrowing costs have fallen anddebt issuances have increased. Not all countries
benefited equally, however—EMDEs that alreadyhad low spreads experienced further declines,
while economies with low sovereign credit ratings

Source: CPB Netherlands Bureau for Economic Policy Analysis; Haver Analytics; World Trade
Organization; World Bank.
A. Other EAP = East Asia and Pacific excl. China, ECA = Europe and Central Asia, LAC = Latin
America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia,
SSA = Sub-Saharan Africa. Figure shows 3-month moving averages. Trade is the average of export
and import volumes. Last observation is October 2019.
B. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
Sample includes the G20 countries for which capital goods and intermediates goods data are
available. Last observation is October 2019.
C. Figure shows 3-month moving average. PMI readings above 50 indicate expansion in economic
activity, readings below 50 indicate contraction. Last observation is November 2019.
D. Figure includes new import-restrictive measures, including tariff and non-tariff trade barriers.
Annual data are mid-October to mid-October.
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suffered from a flight to safety (Figure 1.8.B).
Investors were particularly cautious about equity
markets in riskier EMDEs, which experienced
significant portfolio outflows during the period ofheightened trade tensions and global growthconcerns starting around August of last year,
before recovering more recently (Figure 1.8.C).
While equity and bond market developments in
EMDEs have diverged considerably according torisk perception, many EMDE currencies have

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.8 Global finance
Global financing conditions have eased considerably, as major central
banks have provided accommodation in response to softening economic
prospects. However, EMDEs with low credit ratings have not benefitted
from the global decline in borrowing costs. Prior to their recent recovery,
EMDE equity markets had been suffering significant outflows. A rising
share of EMDE currencies are at their lowest level against the U.S. dollar ina decade.
A. Global financing conditions

C. EMDE portfolio flows

B. Change in EMDE bond spreads, bycredit rating

D. Share of EMDE currencies at theirlowest level against the U.S. dollar
since 2009

Source: Bloomberg; Haver Analytics; Institute of International Finance; International Monetary Fund;
J.P. Morgan; World Bank.
A. Based on Goldman Sachs Financial Conditions Index for the United States, United Kingdom,Japan, Euro Area, India, Indonesia, Brazil, Mexico, Russia, and Turkey. Aggregates are calculatedusing GDP weights at 2010 prices and market exchange rates. Last observation is December 2019,
which includes data through December 17, 2019.
B. Figure shows change in unweighted annual averages of daily data from 2018 to 2019. Sample
includes 42 EMDEs. Countries are grouped based on Fitch long-term sovereign rating. S&P ratings
are used for countries not rated by Fitch (Belize, Senegal). Fitch and S&P use similar rating grades.
Bond spread shows percentage improvement in EMBI spreads versus a year ago. Last observation is
December 16, 2019.
C. Equity flows include Brazil, India, Indonesia, Pakistan, Philippines, Sri Lanka, South Africa,
Thailand, Turkey, and Vietnam. Debt flows include Hungary, India, Indonesia, Mexico, Poland, South
Africa, Thailand, and Turkey. Post-crisis average over January 1, 2010 to December 29, 2017. Lastobservation is December 16, 2019.
D. Figure shows 3-month moving average. To avoid excessive volatility, figure shows share ofcountries whose monthly average exchange rate against the U.S. dollar is within 5 percent of theirmost depreciated level. Sample includes 32 EMDEs. Last observation is December 2019, which
includes data through December 17, 2019.
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depreciated, and a growing share have fallen to
their lowest exchange rate with the U.S. dollar in a
decade (Figure 1.8.D).
Foreign direct investment (FDI) has continued its
downward trend, with some of the recent
weakness attributable to global policy uncertainty.
FDI weakened across all EMDE regions in thefirst half of 2019, with the decline being
particularly pronounced in EMDEs that had
earlier experienced financial pressures (UNCTAD
2019b). By contrast, remittances to EMDEs
continued to grow and recently surpassed FDI(World Bank 2019d).
Commodity markets
The prices of most commodities fell in 2019,
mainly reflecting the deterioration in the growth
outlook—especially that of EMDEs, which tend
to have a larger income elasticity of demand forcommodities (Figure 1.9.A; Baffes, Kabundi, andNagle forthcoming). Forecasts have been revised
down for most commodities in 2020 (Figure
1.9.B).
Oil prices averaged $61/bbl in 2019, a 10 percent
fall from 2018 and $5/bbl below previous
projections. Prices were supported by production
cuts by OPEC and its partners, including theDecember 2019 decision to remove 0.5 mb/d ofproduction on top of previous reductions of 1.2
mb/d implemented since January 2019.
Production has also been constrained in theIslamic Republic of Iran and the República
Bolivariana de Venezuela by a variety ofgeopolitical and domestic factors. However, thesepressures were offset by weakening oil demand, as
exemplified by downward revisions to demand
projections (Figure 1.9.C; IEA 2019).
Oil prices are forecast to decline slightly to an
average of $59/bbl in 2020 and 2021. U.S. supply
is expected to continue to increase in 2020 as new
pipeline capacity comes onstream. The greatest
downside risk to the forecast is a further
deterioration in growth. Current expectations arefor oil consumption growth to pick up to just over
1 percent in 2020, which is comparable to thepace of global oil demand seen during previous
global downturns (Figure 1.9.D). A critical upsiderisk to the forecast is the possibility of a further
significant reduction in trade tensions between the
United States and China, which could boost oil
demand prospects.
Prices for most base metals weakened in thesecond half of 2019, primarily reflecting weakerG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

global growth and trade tensions. Metals prices are
expected to decline further in 2020, reflecting
subdued industrial commodity demand. As withoil, a significant continued mitigation of U.S.China trade tensions presents a key upside risk tometals price projections. Agricultural prices
declined in the second half of 2019 on improved
weather conditions that ensured elevated stocklevels for grains. Agricultural prices are expected tostabilize in 2020, with risks to the forecast broadly
balanced.

CHAPTER 1

13

FIGURE 1.9 Commodity markets
Most commodity prices fell in 2019, and forecasts for 2020 have beenrevised down. Despite oil supply disruptions, deteriorating expectations fordemand growth have put downward pressure on oil prices. A further
softening in growth prospects is the key downside risk to oil demand andprice forecasts, while a sustained reduction of trade tensions represents amajor upside risk.
A. Commodity price indexes

B. Commodity price forecast revisions

C. Change in oil demand forecasts

D. Oil demand and price growtharound periods of economic downturn

Emerging market anddeveloping economies
The outlook for EMDEs has weakened significantly.
As trade and investment firm, EMDE growth isprojected to pick up to 4.1 in 2020—0.5 percentage
point below previous forecasts—and stabilize at 4.4
percent in 2021-22, with the pace of the recovery
restrained by soft global demand and structural
constraints, including subdued productivity growth.
The near-term rebound in EMDE growth will bemainly driven by a projected pickup in a small
number of large countries. Per capita income growth
will remain well below long-term averages, making
progress toward poverty alleviation and development
goals more challenging.
Recent developments
EMDEs have continued to experience substantial
weakness, with industrial production, trade flows,
and investment decelerating sharply last year
(Figures 1.10.A to 1.10.C). While services activity
has been appreciably more resilient thanmanufacturing, it has also moderated (Figure
1.10.D). Growth has been particularly anemic in
EMDEs that have experienced the lingering effectsof varying degrees of financial pressures or otheridiosyncratic factors in the past couple of years.1
This weakness has also spread to other economies

1 These EMDEs include: (1) countries that have had an increase in
their J.P. Morgan EMBI credit spread of at least one standard
deviation above the 2010-19 average at any time since April 2018
(Argentina, Brazil, Egypt, Gabon, Jordan, Lebanon, Mexico, Nigeria,
South Africa, Sri Lanka, Tunisia, Turkey); or (2) countries that havebeen subject to sanctions (Iran, Russia). Additional details about thisclassification can be found in World Bank 2019e.

Source: Energy Information Administration (EIA); International Energy Agency (IEA); Kose andTerrones (2015); Organization of Petroleum Exporting Countries (OPEC); World Bank.
A. Last observation is November 2019.
C. Figure shows evolution of oil demand forecasts for 2019 by source. Diamonds show forecasts foroil demand in 2020.
D. Figure shows oil demand by component of global business cycle from 1971 to 2018. Over the time
period, there have been four global recessions, defined as a contraction in growth, in 1975, 1982,
1991, and 2009, and three global slowdowns, defined by very low output growth, in 1998, 2001, and2012 (Kose and Terrones 2015).
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that had previously shown resilience. In all,growth in about 60 percent of EMDEs isestimated to have slowed last year. In many
economies, subdued economic activity has beensomewhat cushioned by still-resilient consumption
and a shift toward more supportive monetary
policy.
Growth in EMDEs that experienced recent
financial or country-specific stresses remains feeble(Kose and Ohnsorge 2019). To different degrees,
these economies continue to face heightened
policy uncertainty and various domestic
challenges. With notable exceptions, activity has
started to firm somewhat; however, the recovery in

14

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.10 EMDE recent developments
EMDEs have continued to experience substantial weakness, which has
spread to countries that, until recently, had shown resilience. Industrial
production, trade flows, and investment have decelerated sharply. Whileservices activity has been appreciably more resilient than manufacturing, it
has also moderated.
A. Industrial production growth

B. Export and import volume growth

C. Investment growth

D. Manufacturing and services PMIs

Source: Haver Analytics; J.P. Morgan; World Bank.
A.-C. EMDEs under earlier pressure include: a) countries that have had an increase in their J.P.
Morgan EMBI credit spread of at least one standard deviation above the 2010-19 average at any timesince April 2018 (Argentina, Brazil, Egypt, Gabon, Jordan, Lebanon, Mexico, Nigeria, South Africa, Sri
Lanka, Tunisia, Turkey), or b) countries that have been subject to sanctions (Iran, Russia).
A. Figure shows 3-month moving averages. Dashed horizontal lines indicate the March 2006 to
October 2019 averages. Industrial production growth for EMDEs under earlier pressure includes
those countries in the group for which data are available. Last observation is October 2019, which isestimated for Tunisia.
B. Import and export data are volumes of goods and non-factor services. Aggregate growth rates
calculated using GDP weights at 2010 prices and market exchange rates. “Latest” indicates 2019 fullyear estimate.
C. Investment is defined as gross fixed capital formation. EMDEs under earlier pressure includes
those countries in the group for which data are available. “Latest” indicates 2019Q1-Q3 simple
average. Last observation is 2019Q3.
D. Figure shows 6-month moving averages. Manufacturing and services output are measured byPurchasing Managers’ Index (PMI). PMI readings above 50 indicate expansion in economic activity;
readings below 50 indicate contraction. Horizontal line indicates expansionary threshold. Lastobservation is November 2019.
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most of these economies is proceeding at amarkedly slower pace than previously envisioned.
Some easing of lending conditions, as well as
progress on the reform agenda, are beginning tosupport a modest pickup in Brazil. In the RussianFederation, monetary policy easing and public
infrastructure projects from the National Projects
program are buoying activity. In Turkey, activity
is rebounding from earlier financial turmoil at afaster-than-expected pace as domestic demand
improves; however, the pickup remains fragile
amid subdued confidence and investment. In

Mexico, easing monetary policy is providing some
support to growth. In contrast, activity inArgentina has been contracting amid high policy
uncertainty in the aftermath of severe financial
stress in mid-2019. In Iran, sanctions have been
weighing significantly on growth.
Growth in other EMDEs has generally softenedowing to global and domestic headwinds.
Economies that are deeply integrated into global
and regional production and trade networks—
most notably in Asia and Europe—particularly
suffered from global trade tensions anddecelerating trade flows last year (Philippines,
Thailand; World Bank 2019f, 2019g, 2019h).
Tighter credit conditions in the non-banking
sector are contributing to a substantial weakeningof domestic demand in India, while activity in
Pakistan has decelerated in response tocontractionary monetary policy intended torestore domestic and external balances. In somecountries, capacity constraints are also limiting
growth (Poland, Romania). Other economies haveexperienced temporary setbacks to construction
and infrastructure projects (Costa Rica, Panama),
the effects of natural disasters (Guatemala, Papua
New Guinea), and the negative impact of social
unrest (Bolivia, Chile).
Commodity exporters
Growth in commodity exporters slowed from 2
percent in 2018 to an estimated 1.5 percent in
2019, 0.6 percentage point below earlier forecasts,
reflecting softer-than-projected commodity prices,oil production cuts, decelerating investment in
extractive sectors, and weakness in the largest
countries that earlier experienced financialpressures or other country-specific stresses—
particularly Argentina, Brazil, Iran, and Russia(Figure 1.11.A). Weakening global demand andongoing domestic challenges—including large
macroeconomic imbalances and domestic policy
uncertainty—continue to discourage investment
and delay recovery in many commodity exporters(Nigeria, South Africa; World Bank 2019h).
Despite supportive fiscal policy and stable non-oil
activity, difficulties in the oil sector andheightened geopolitical tensions are weighing on
activity in oil exporters in the MENA region

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

(Algeria, Iran, Saudi Arabia; World Bank 2019i).
In other commodity exporters with more policy
space, countercyclical policy measures have been
partly offsetting the drag from weakening global
demand and lower commodity prices, resulting instable or moderately slower growth (Indonesia,
Peru).

CHAPTER 1

FIGURE 1.11 EMDE commodity exporters and importers
Growth in both commodity exporters and importers decelerated last year.
In both groups, growth remains particularly subdued in the largest EMDEs
that earlier experienced varying degrees of financial or country-specificstresses.
A. Growth in commodity exporters

B. Growth in commodity importers,
excluding China

Commodity importers
Growth in commodity importers excluding China
eased from 5 percent in 2018 to an estimated 3.3
percent in 2019—0.9 percentage point below
previous projections and the slowest rate since theglobal financial crisis (Figure 1.11.B). This
slowdown in part reflected a marked decelerationin Turkey due to earlier financial stress, in Mexico
due to heightened policy uncertainty, and in India
due to a tightening of domestic non-bank credit
conditions. Policy adjustments to address
macroeconomic imbalances in Pakistan also
weighed on aggregate growth in this group.

Source: World Bank.
A.B. Data for 2019 are estimates. Aggregate growth rates calculated using GDP weights at 2010
prices and market exchange rates. Shaded areas indicate forecasts. Green lines indicate 2000-19
simple averages.
Click here to download data and charts.

caused by two tropical cyclones and weaker-thanexpected coal production.

For many commodity importers, momentum lastyear was weaker than expected, reflecting declining
exports and investment, only partly offset by more
accommodative monetary policy stances and fiscal
support measures (Philippines, Thailand; WorldBank 2019j). Nonetheless, growth in many
commodity importers remains solid due to robust
private consumption and supportive policies in acontext of subdued inflation and resilient capitalflows(Bangladesh, Cambodia, Vietnam).
Moreover, decelerating activity in somecommodity importers also reflected a narrowing of
positive output gaps (Poland, Romania).

Activity in other LICs, however, has beensomewhat more robust, reflecting improved
harvests (Malawi, Nepal), as well as continued
services sector strength and solid public andprivate investment growth (Guinea-Bissau,
Uganda). Nonetheless, softer external demand andlower agricultural prices have dampened export
revenues and slowed growth in some countries(Madagascar, Rwanda).

Low-income countries

EMDE growth is expected to experience a
moderate cyclical recovery from an estimated 3.5
percent last year to 4.1 percent in 2020—0.5
percentage point lower than previously projected(Figure 1.12.A). Forecasts for almost all regions
and half of EMDEs have been downgraded forthis year, largely reflecting weaker-than-expectedexports and investment (Box 1.2; Chapter 2).
EMDE growth is projected to stabilize at an
average rate of 4.4 percent in 2021-22, as tradeand investment firm. These baseline projections
are predicated on resilient consumption, adiminishing drag from earlier pressures in some

The recovery in low-income countries (LICs) has
faltered amid softening external demand, weaker
commodity prices, political instability, anddevastation from extreme weather events (Box 1.1;
Steinbach 2019; World Bank 2019e). Growthamong fragile LICs, in particular, has slowedmarkedly. In the Democratic Republic of Congo,
falling metals prices stifled mining activity, while
the Ebola outbreak in the conflict-affected
northeastern region has persisted. Subdued growth
in Mozambique reflected widespread damage

15

Outlook
Growth outlook

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

BOX 1.1 Recent developments and outlook for low-income countries
Growth in low-income countries (LICs) has faltered in 2019, falling to 5.4 percent. The slowdown partly reflects global factors(softening external demand and weaker commodity prices), and idiosyncratic factors (political instability and devastation fromextreme weather events). Growth is expected to firm over the forecast horizon, reaching an average of 5.7 percent in 2021-22.
This pickup assumes improved stability, recovery from extreme weather events, continued investment in infrastructure, and theimplementation of structural reforms and measures to strengthen business environments. Nonetheless, projected growth will beinsufficient to markedly reduce poverty, particularly in LICs affected by fragility, conflict, and violence. Risks to the outlook
include slower-than-expected growth in major trading partners, rising debt vulnerabilities, and growing insecurity.

Recent developments
Economic activity. The recovery in low-income countries(LICs) stalled in 2019 as global and idiosyncratic factors
dampened activity. The global backdrop reflectedsoftening external demand and weaker commodity prices,while activity in some countries was weighed down further
by political instability and extreme weather events. Growthin LICs fell to an estimated 5.4 percent, 0.3 percentage
point lower than previous forecasts (Figure 1.1.1.A).
The weaker-than-expected performance reflected a marked
slowdown in activity among fragile LICs.1 Growth in the
Democratic Republic of Congo decelerated as weakening
external demand and lower metal prices weighed onexports. The conflict-affected northeastern region of thecountry is grappling with the second-largest Ebola
outbreak on record, which began in the middle of 2018.
In Haiti, growth is estimated to have contracted in 2019
amid severe political instability, rapid exchange rate
depreciation, elevated inflation, and rising food insecurity
exacerbated by drought. Similarly, in Liberia, theestimated contraction in activity last year reflected theerosion of incomes from elevated inflation, weak harvests,
and moderating mining production due to lower
commodity prices. In Mozambique—which has been on a
reduced growth path since 2016—slowing growth in 2019was largely due to the devastation caused by last year’s
cyclones alongside moderating coal production. In
addition to their heavy human toll, the cyclones have likely
reversed recent gains in poverty reduction in affectedeconomies (Malawi, Mozambique; Baez, Caruso and Niu
2019; World Bank 2019k).
Activity also slowed among other LICs (Benin, Burkina
Faso, Madagascar, Rwanda, The Gambia, Tajikistan). In
Rwanda—one of the fastest growing economies in theworld—growth edged down as weakening external

Note: This box was prepared by Rudi Steinbach. Research assistance
was provided by Hazel Macadangdang.
1 Fragile LICs are those affected by fragility, conflict, and violence,
according to the World Bank’s Harmonized List of Fragile Situations.

demand and lower commodity prices constrained export
revenues; however, sustained public investment helped
offset some of this weakness. Nonetheless, activity
remained resilient, or strengthened, among some LICs.
Improved harvests supported rising agricultural production(Malawi, Nepal), and services sector activity continued toaccelerate (Guinea-Bissau, Uganda). In Malawi,
agricultural production strengthened despite the impact of
Cyclone Idai, reflecting improved tobacco and maize
harvests in unaffected districts. In Ethiopia—the largest
LIC economy—agricultural production slowed whileconstrained hydroelectric power generation due to low
dam levels dampened industrial activity; however, theseweaknesses were more than offset by continued robust
services sector activity, particularly in travel, banking, andtelecommunications. On the demand side, activity was
supported by robust private consumption helped by strong
harvests (Malawi, Nepal), and solid investment growth—both public and private (Guinea-Bissau, Uganda). Despitea sharp fall in aluminum prices, growth edged up in
Guinea, partly due to continued infrastructure investment
in mining-related activities. In Sierra Leone, theresumption of iron ore production helped boost activity.
External positions. Current account balances widened
among more than half of LICs. In some countries, larger
deficits reflected weaker exports related to softening
external demand and lower international commodity
prices (Guinea-Bissau, Rwanda, Togo). Elsewhere, deficits
widened primarily due to imports of capital goods related
to large infrastructure investment projects (Mozambique,Togo, Uganda). Imports associated with cyclone-related
reconstruction added to existing deficits (Malawi,
Mozambique). In Ethiopia, however, the current accountdeficit narrowed amid improved services exports—largely
transport services with Addis Ababa increasingly becoming
a key regional hub—and as fiscal consolidation
contributed to slower import growth. By the second half of2019, capital flows into LICs appear to have weakened
noticeably, as growing concerns over global growthprospects and heightened trade tensions weighed oninvestor sentiment. As a result, international reserves in the
median LIC have weakened somewhat and remain below
the three-months-of-imports benchmark in about one-

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

BOX 1.1 Recent developments and outlook for low-income countries (continued)
FIGURE 1.1.1 Recent developments in low-income countries
Growth in low-income countries (LICs) has fallen to 5.4 percent in 2019 amid rising domestic and external headwinds.
Growth is, however, expected to firm to an average of 5.7 percent in 2021-22, reflecting improved stability, recovery fromextreme weather events, and continued investment in infrastructure. Fiscal deficits deteriorated sharply among LICs affectedby fragility, conflict, and violence. Subdued inflation has allowed some central banks to easy policy rates.
A. GDP growth

B. Fiscal deficits

C. Policy rates

Source: Haver Analytics; World Economic Outlook, International Monetary Fund; Reserve Bank of Malawi; World Bank.
Note: LICs = low-income countries. FCV LICs are LICs affected by fragility, conflict, and violence.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
B. Unweighted averages. Sample includes 27 LICs.
C. Reflects data up to December 19th, 2019. Prior to April 2017, data for Mozambique reflects the money market rate.
Click here to download data and charts.

quarter of countries—leaving these countries more
vulnerable to negative shocks.
Fiscal positions. LIC fiscal balances deteriorated, on
average, in 2019 with the average deficit widening to an
estimated 3 percent, from 2.6 percent in 2018 (Figure
1.1.1.B). Fiscal deficits mostly widened among fragile
LICs, partly reflecting low domestic revenue mobilization
while public spending remained elevated. In the
Democratic Republic of Congo, efforts to containspending were not sufficient to offset the decline in fiscal
revenues resulting from the weaker mining sector
performance. In contrast, increased fiscal consolidation
supported by greater revenue mobilization, as well as
broad-ranging tax administration reforms have helped
deficits improve in several LICs (Burkina Faso, Ethiopia,
Malawi, Mali).

Outlook for 2020-22
Economic growth. Growth in LICs is projected to remainunchanged at 5.4 percent in 2020, before firming to an
average of 5.7 percent in 2021-22. Forecasts for this year
and next are 0.6 percentage point lower than previous
projections, reflecting weaker external demand, lower
commodity prices, and policy tightening among somelarge LICs. The expected pickup is predicated on no
further deceleration in external demand and a stabilizationof commodity prices, albeit at lower levels.

Among fragile LICs, growth is forecast to rise to 3.7
percent in 2020, from 3.2 percent in 2019, in part due toimproved political stability in some countries,
strengthening business environments, and as the lingering
effects of extreme weather events wane. In Afghanistan,greater political stability following elections in late 2019 is
expected to help support activity. Notable businessenvironment reforms in Togo will continue to bolster
growth (World Bank 2020). In Chad and Mozambique,investment in new production capacity should spur growthand boost exports, more than offsetting softer commodity
prices and weaker external demand. In the Democratic
Republic of Congo, however, growth is projected tomoderate further as lower metal prices—particularly forcobalt—continue to suppress mining production.
In other LICs, economic activity is expected to remain
resilient, with growth above 6 percent over the forecasthorizon. In countries such as Benin and Rwanda, theexpansion will be supported by public investment ininfrastructure, strong agricultural growth, and increased
private sector activity as reforms continue to bolster thebusiness environment. Accommodative monetary policy
stances amid relatively subdued inflation will further
support activity in some countries (Malawi, Tanzania;
Special Focus 2; Figure 1.1.1.C). In Uganda, growth will
be boosted by public and private infrastructure
investments, as well as in energy projects, as the country

17

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BOX 1.1 Recent developments and outlook for low-income countries (continued)
FIGURE 1.1.2 Outlook for per capita GDP and risks
Growth in per capita incomes is expected to firm to an average of 2.9 percent in 2021-22; however, it will be markedly
weaker among LICs affected by fragility, conflict, and violence. For these countries, per capita growth will be insufficient tomake significant progress in poverty alleviation. Productivity in LICs is a mere 2 percent of the advanced-economy average,
reflecting low productivity in comparatively larger agricultural sectors. Labor shifting to more productive sectors has been an
important source of productivity growth in LICs. Debt sustainability concerns remain elevated, with a rising number ofcountries in debt distress. Insecurity, conflicts, and insurgencies, are leading to an increase in displaced populations.

A. Per capita GDP growth

B. Changes in LIC extreme poverty rates
between 2015 and 2020

C. LIC productivity and agriculture value
added

D. Contribution to aggregate productivity
growthE. LICs in debt distress

F. Internally displaced populations in
LICs

Source: APO productivity database; Easterly and Fischer (1994); Expanded African Sector, Groningen Growth Development Center; Haver Analytics; ILOSTAT;International Monetary Fund; Penn World Table; United Nations High Commissioner for Refugees (UNHCR); World Development Indicators, World Bank.
Note: Shaded area indicates forecasts. LICs = low-income countries. FCV = fragility, conflict, and violence.
A. Aggregate per capita growth rates calculated by dividing the total GDP at 2010 prices and market exchange rates for each subgroup by its total population. Sample
includes 25 LICs, 12 “FCV LICs”, and 13 “Other LICs”.
B. The number of people living on or below the international poverty line of $1.90 per day as a share of the total population. Data for 2020 are estimates and calculatedusing data from World Bank. “FCV LICs” and “Other LICs” samples each include 12 and 13 countries, respectively.
C. Productivity data based on 74 emerging market and developing economies (EMDEs), including 11 low-income countries (LICs). Blue bars show unweighted average
output per worker during 2013-18 relative to the advanced-economy average. Whiskers indicate interquartile range relative to the advanced-economy average. Agriculture
value added reflects 2018 data and is based on 132 EMDEs, including 23 LICs. Red bars show unweighted average share of agriculture in value added.
D. Growth “within sector” shows the contribution to aggregate productivity growth of each sector holding employment shares fixed. The ‘between sector’ effect shows thecontribution arising from changes in sectoral employment shares. Sample includes 46 EMDEs of which 8 are LICs.
E. Number of LICs eligible to access the IMF’s concessional lending facilities that are either at high risk of, or in, debt distress according to the joint World Bank-IMF Debt
Sustainability Framework for Low-Income Countries. The sample includes 28 LICs.
F. MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Internally Displaced Populations (IDPs) are persons or groups of persons who
have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict,situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.
Data reflects only internally displaced populations (IDPs) who are protected or assisted by UNHCR, and country totals are not necessarily representative of the entire IDP
population in that country. Sample includes 15 countries, of which 2 are in the Middle East and North Africa, 1 is in South Asia, and 12 are in Sub-Saharan Africa.
Click here to download data and charts.

prepares to export oil by 2023. Similarly, higher growth inNiger in 2022 reflects a sharp pickup in crude oil exports
as oil production is expected to quadruple from current
levels. Activity in Guinea will benefit from investments in
new mining production capacity. In Ethiopia, however,growth is expected to slow due to tighter fiscal andmonetary policy stances aimed at containing inflation.
Prospects for per capita income convergence and poverty
alleviation. Per capita GDP growth in LICs is expected toG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

BOX 1.1 Recent developments and outlook for low-income countries (continued)
TABLE 1.1.1 Low-income country forecastsa

Percentage point differencesfrom June 2019 projectionsd(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

Low-Income Country, GDPb

5.5

5.8

5.4

5.4

5.5

5.8

-0.3

-0.6

-0.6

Afghanistan

2.7

1.8

2.5

3.0

3.5

3.5

0.1

-0.2

-0.1

Benin

5.8

6.7

6.4

6.7

6.7

6.7

-0.1

0.2

0.2

Burkina Faso

6.3

6.8

6.0

6.0

6.0

6.0

0.0

0.0

0.0

Burundi

0.5

1.6

1.8

2.0

2.1

2.2

0.0

-0.1

0.1

Chad

-3.0

2.6

3.0

5.5

4.8

4.8

-0.4

-0.1

0.0

Congo, Dem. Rep.

3.7

5.8

4.3

3.9

3.4

3.6

-1.6

-2.6

-3.4

Ethiopiac

10.0

7.9

9.0

6.3

6.4

7.1

1.1

-1.9

-1.8

Gambia, The

4.8

6.6

6.0

6.3

5.8

5.5

0.6

1.1

0.8

Guinea

10.0

5.8

5.9

6.0

6.0

6.0

0.0

0.0

0.0

Guinea-Bissau

5.9

3.8

4.6

4.9

5.0

5.0

0.3

0.1

-0.5

Haitic

1.2

1.5

-0.9

-1.4

-0.5

1.4

-1.3

-3.0

-1.8

Liberia

2.5

1.2

-1.4

1.4

3.4

4.2

-1.8

-0.2

2.1

Madagascar

4.3

5.1

4.7

5.3

4.4

5.0

-0.5

0.0

-0.7

Malawi

4.0

3.5

4.4

4.8

5.2

5.3

-0.1

0.1

0.1

Mali

5.3

4.7

5.0

5.0

4.9

4.9

0.0

0.1

0.1

Mozambique

3.7

3.4

2.0

3.7

4.2

4.4

0.0

0.2

0.0

Nepalc

8.2

6.7

7.1

6.4

6.5

6.6

0.0

0.0

0.0

Niger

4.9

6.5

6.3

6.0

5.6

11.9

-0.2

0.0

0.0

Rwanda

6.1

8.6

8.5

8.1

8.0

8.0

0.7

0.1

0.5

Sierra Leone

3.8

3.5

4.8

4.9

4.9

5.0

-0.6

-0.5

-0.3

Tajikistan

7.1

7.3

6.2

5.5

5.0

5.0

0.2

-0.5

-1.0

Tanzania

6.8

5.4

5.6

5.8

6.1

6.2

0.2

0.1

0.0

Togo

4.4

4.9

5.3

5.5

5.5

5.5

0.3

0.3

0.4

Ugandac

3.9

5.9

6.1

6.5

5.9

6.0

0.0

0.0

0.1

Source: World Bank.
Note: World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here may differ
from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
a. Central African Republic, the Democratic People’s Republic of Korea, Somalia, Syria, and Yemen are not forecast because of to data limitations.
b. Aggregate growth rate calculated using GDP weights at 2010 prices and market exchange rates.
c. GDP growth based on fiscal year data. For Nepal, the year 2019 refers to FY2018/19.
d. Due to changes in the official list of countries classified as low income by the World Bank, the sample of LICs in this table is not comparable to June 2019. However,an identical sample is used for the comparison of the aggregate LIC GDP projection.
Click here to download data.

remain broadly unchanged at 2.5 percent in 2020, before
firming to an average of 2.9 percent in 2021-22. This paceis insufficient to yield substantial progress in poverty
reduction as growth in LICs is often not inclusive and theconversion of growth into poverty reduction is therefore
low (Christiaensen, Chuhan-Pole, and Sanoh 2013;
Christiaensen and Hill 2018; Figure 1.1.2.A). Amongfragile LICs—where the incidence of extreme poverty iseven higher—per capita GDP is expected to grow by amere 1 percent in 2020-22, after having contracted in 40
percent of cases last year. As a result, the number of people
in LICs living below the international poverty line of$1.90 per day will remain elevated, while continuing torise among fragile LICs (Figure 1.1.2.B).

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

BOX 1.1 Recent developments and outlook for low-income countries (continued)
To raise LIC growth over the medium term requires
sustained improvements in labor productivity (Chapter 3).
Labor productivity—average output per worker—in LICs
is a mere 2 percent of that in the average advanced
economy and one-tenth of the productivity level in the
average emerging market and developing economy(EMDE), and LIC productivity growth has been
persistently below that of EMDEs (Figure 1.1.2.C). This
partly reflects LICs’ heavy reliance on agricultural sectors,
including widespread subsistence farming, as well as themisallocation of resources—often caused by distortionary
price controls (Special Focus 1). Raising LIC aggregate
productivity will face several challenges. The reallocationof labor from mostly agriculture to higher-productivity
sectors such as mining and construction has been an
important driver of LIC productivity in the pre-crisis
period; however, this engine of productivity growth haslargely stalled following the collapse in global industrial
commodity prices (Figure 1.1.2.D). Moreover, longerterm prospects for commodity demand are weakening as
growth in China—the largest source of commodity
demand—slows and shifts towards less resource-intensive
sectors (World Bank 2018b). Climate change will poseincreasing challenges to efforts to raise productivity in the
agricultural sector, with large falls in crop yields expected
as global temperatures rise (Fuglie et al. 2019).
Risks. Risks to the outlook are firmly to the downside. A
faster-than-expected deceleration in growth of major world
economies and key trading partners—such as the UnitedStates, the Euro Area, or China—would adversely affectexport demand and investment in several LICs. Together,
these three economies account for four-tenths of both LIC
goods exports and foreign direct investment, and about
one-quarter of remittance inflows. Countries that dependon extractive industries—specifically metals producers—
would be hard-hit by a sharp slowdown in China, as it
accounts for more than half of global metals demand(World Bank 2018b).
LIC government debt reached 55 percent of GDP, on
average, in 2019—a 19 percentage point rise since 2013—
keeping debt sustainability concerns elevated (World Bank
2019l). By November 2019, 12 out of 28 LICs wereregarded as being in debt distress, or at high risk thereof,
under the IMF-World Bank debt sustainability
framework—two more than at the end of 2018 (Figure
1.1.2.C). The ratio of interest payments to GDP has
doubled since 2013, in part reflecting the rising share of
non-concessional debt as commercial creditors havebecome an important source of credit (Essl et al. 2019;

World Bank 2019m; World Bank and IMF 2018). Non–
Paris Club creditors have also become a more importantsource of financing over the past decade, especially in SubSaharan Africa (World Bank 2015). Increased access tomarket-based debt may also be increasing governments’
exposure to interest rate and refinancing risks. Sharp
increases in debt-servicing costs would undermine muchneeded fiscal consolidation efforts and absorb revenues
that could otherwise be used for productivity-enhancing
investments in health care, education, and infrastructure.
LICs’ weakening reserve buffers mean that renewed
episodes of financial stress, accompanied by an unexpected
tightening of international financial conditions, could
disrupt capital inflows, fuel disorderly exchange rate
depreciations, and raise financing costs. LICs with weaker
macroeconomic fundamentals, higher foreign-currencydenominated debt, or greater political risks would be mostvulnerable.
Insecurity, conflicts, and insurgencies—particularly in the
Sahel and conflict-affected economies in the Middle Eastand North Africa—may further weigh on economic
activity as well as food security in many countries if they
were to intensify (Burkina Faso, Central African Republic,
Chad, the Democratic Republic of Congo, Ethiopia, Mali,
Niger, Republic of Yemen, Somalia, South Sudan, Syrian
Arab Republic; FAO 2019). Moreover, the large
populations that are forcibly displaced by these conflicts
cluster in areas that often become a source of further
instability, with poverty rates being worse than in theirplaces of origin (Figure 1.1.2.D; Beegle and Christiaensen
2019).
Natural disasters related to growing climate extremes, suchas flooding or severe and prolonged drought episodes,
remain an important risk for many LICs, as agricultural
output often accounts for a high share of domestic value
added, and infrastructure is generally less resilient than inmore developed economies (World Bank 2019e).
Health crises are a continuous concern. Although the paceof new Ebola infections in the Democratic Republic ofCongo has slowed in the second half of 2019, efforts tocontain the second-largest outbreak in history have been
complicated by conflict (Wannier et al. 2019). As
evidenced by the West African Ebola outbreak of 2014-16,
the current outbreak poses a significant risk to economic
activity, particularly if it were to spread to major urban
centers, or to neighboring countries (De la Fuente, Jacoby,
and Lawin 2019).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

large economies, reduced policy uncertainty,
varying degrees of monetary policy support,
generally benign borrowing costs, no major swings
in commodity prices, no further deterioration in
global activity, and no new adverse shocks. They
therefore represent a benign but fragile scenario,
given the ongoing global headwinds of slowing
advanced-economy growth, subdued global trade,and declining commodity prices (Figure 1.12.B).
The expected pickup in aggregate EMDE growth
is not broad-based: A third of EMDEs areprojected to decelerate this year. Instead, it islargely predicated on a rebound in a small groupof large EMDEs, most of which are emerging
from deep recessions or sharp slowdowns caused
by earlier financial pressures or other idiosyncratic
factors. Indeed, about 90 percent of the pickup in
EMDE growth in 2020 is accounted for by justeight countries—Argentina, Brazil, India, Iran,
Mexico, Russia, Saudi Arabia, and Turkey—eventhough they represent just a third of EMDE GDP(Figure 1.12.C). Excluding these eight countries,
aggregate EMDE growth would experience almost
no acceleration. More generally, aggregate
economic slack in EMDEs will persist in the nearterm, and actual EMDE growth this year willremain below potential (Figure 1.12.D).
Projections for Argentina have been downgradedfollowing the severe financial market turmoil lastyear; the impact of this event is assumed togradually diminish over the forecast horizon. In
Brazil, Russia, and South Africa, elevated policy
uncertainty is expected to moderate; however,recovery in these countries is projected to be
fragile due to continued challenges associated with
the implementation of reforms, sanctions, orinfrastructure bottlenecks. Growth in some otherlarge economies (Egypt, India, Thailand) is
expected to pick up, supported by policy easing
and gradually improving business confidence in
response to recent reforms.
Growth in LICs is projected to remain littlechanged at 5.4 percent in 2020 and edge up to an
average of 5.7 percent in 2021-22. Forecasts forthis year and next are 0.6 percentage point lower
than previous projections, reflecting weaker
external demand, lower commodity prices, and

CHAPTER 1

21

FIGURE 1.12 EMDE outlook
EMDE growth is expected to recover moderately, reaching 4.1 percent in2020 and stabilizing at an average of 4.4 percent in 2021-22. This is abenign but fragile scenario given ongoing global headwinds. The
recovery will not be broad-based and will instead mainly be driven by aprojected pickup in a small number of large economies. Aggregate
economic slack in EMDEs will persist in the near term, with actual EMDE
growth expected to remain below potential.

A. Growth outlook

B. Average share of EMDEs withannual growth accelerating by morethan 0.1 percentage point, 1962-2019

C. Contributions to the change in
EMDE annual growth

D. EMDE growth

Source: J.P. Morgan; World Bank.
A.C. Data for 2019 are estimates. “Main drivers of pickup” includes the eight largest EMDEs that
account for 90 percent of the acceleration in EMDE growth between 2019 and 2020 (Argentina,
Brazil, India, Iran, Mexico, the Russian Federation, Saudi Arabia, and Turkey). Aggregate growth
rates calculated using GDP weights at 2010 prices and market exchange rates. Shaded areas
indicate forecasts.
A. Green lines indicate 2000-19 simple averages.
B. AE = advanced economies. “Subdued trade” refers to growth below 2.5 percent. “Moderating
commodity prices” refers to a year-on-year contraction in the non-energy commodity index.
D. Estimates of potential growth are from a multivariate filter model of World Bank (2018a). Aggregate
growth rates are calculated using GDP weights at 2010 prices and market exchange rates. Sample
includes 57 EMDEs. Data for 2020 are forecasts.
Click here to download data and charts.

policy tightening among some large LICs (Box1.1). The expected pickup later in the forecasthorizon assumes that activity among fragile LICs
recovers
aspolitical
stability
improves(Afghanistan, Guinea-Bissau), investments in new
capacity offset weaker external demand (Chad,
Mozambique), and as rebuilding efforts following
last year’s cyclones boost activity (Malawi,
Mozambique; World Bank 2019h). Among other
LICs, activity is expected to remain generally

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.13 EMDE per capita income growth andpoverty
Despite significant gains in poverty alleviation over the last three decades,
meeting the Sustainable Development Goals by 2030 appears out of reachfor many EMDEs, partly because of the recent loss of momentum in percapita income growth. In Sub-Saharan Africa, per capita growth isexpected to remain below 1 percent, exacerbating the concentration of
extreme poverty.
A. Sustainable Development Goals

B. Per capita growth in EMDEs

EMDE productivity growth is expected to persist
or deepen (Chapter 3; World Bank 2018a). Goingforward, EMDE potential growth is likely to bedampened by the lingering effects of past weak
investment and subdued investment prospects,
diminishing demographic dividends, and more
limited avenues for technological diffusion,
especially in the face of rising protectionism(World Bank 2019e).
Per capita income growth and poverty

C. Cumulative per capita income gains
and losses relative to 1990-2014 trend

D. Global extreme poverty

Source: United Nations; World Bank.
A. Sample includes 155 EMDEs. Orange lines indicate interquartile ranges. “Access to at least basic
sanitation” and “Under-5 mortality” data reflect 2017 and 2018, respectively.
B. Data for 2019 are estimates. Aggregate growth rates calculated using GDP weights at 2010 prices
and market exchange rates. EMDE sample includes 144 countries, with 83 commodity exporters.
C. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the
Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa.
Negative bars represent the cumulative shortfalls in regional per capita income growth from 2015 to2019 relative to the 1990-2014 average growth rate. For ECA, the average uses data for 1995-2014
to exclude the immediate aftermath of the collapse of the Soviet Union.
D. Data for South Asia in 2015 are estimates.
Click here to download data and charts.

resilient spurred by sustained public investment ininfrastructure along with greater private sector
activity (Benin, Rwanda, Uganda). In somecountries, more accommodative monetary policy
amid relatively subdued inflation will support
growth (Special Focus 2; Malawi, Tanzania).
However, in Ethiopia—the largest LIC—growthis expected to slow due to tighter fiscal andmonetary policy stances aimed at containing
inflation.
Longer-term growth prospects for EMDEs are also
challenging (Ruch 2019a). In particular, the postcrisis weakness in several fundamental drivers of

The number of people living in extreme poverty—
below $1.90 per day—has fallen by more than 1billion over the past three decades, and remarkableprogress has been made on several development
indicators. Yet, meeting the Sustainable
Development Goals (SDGs) by 2030 appears outof reach for many EMDEs (Figure 1.13.A).
Extreme poverty rates are estimated to exceed 30
percent of the population in one-quarter of
economies. Around 830 million people still live
without electricity. Approximately 2 billion people
do not have access to at least basic sanitation
services. In LICs, child mortality rates are around
triple their SDG target, while access to essential
health services remains deficient.
To meet the infrastructure-related SDGs alone
will require annual investment equivalent to 4.5
percent to 8.2 percent of low- and middle-income
countries’ GDP between 2015 and 2030
(Rozenberg and Fay 2019; Vorisek and Yu,
forthcoming). The severity of this challenge has
been amplified by the loss of momentum in
EMDE per capita income growth during recent
years (Figures 1.13.B and 1.13.C). Givensustained headwinds to activity, per capita income
growth in EMDEs is expected to stabilize around3.2 percent over the near term—well below longterm averages. Lower income growth will also
adversely affect poverty reduction efforts, andthere is already evidence that poverty reduction
has started to slow (Ruch 2019a; World Bank
2018c).
In about one-quarter of EMDEs—mostly
commodity exporters—per capita growth will beinadequate to prevent income gaps from widening
relative to advanced economies. In Sub-SaharanAfrica—home to 24 of the 31 LICs and almost 60
percent of the world’s extreme poor—per capita

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

BOX 1.2 Regional perspectives: Recent developments and outlook
Growth in almost all EMDE regions was weaker than expected in 2019, reflecting downgrades to more than half of EMDEs.
Activity in most regions is expected to pick up in 2020-21, but the recovery will largely depend on a rebound in a small numberof large EMDEs, some of which are emerging from deep recessions or sharp slowdowns.
East Asia and Pacific. Growth in the region is projected toslow from an estimated 5.8 percent in 2019 to 5.7 percent
in 2020 and moderate further to 5.6 percent in 2021-22.
Easier financing conditions and fiscal policy support will
partly mitigate the lingering impact of trade tensions amid
domestic challenges. In China, growth is expected to slow
gradually, from an estimated 6.1 percent in 2019, to 5.9
percent in 2020, and to 5.7 percent by 2022. In the rest of
the region, growth is expected to recover slightly to 4.9
percent in 2020 and firm further to 5 percent in 2021-22.
The balance of risks has improved, but risks to the outlook
are still tilted to the downside. They include a sharp
slowdown in global trade due to renewed escalation of
trade tensions amid a fragile global outlook; a sharperthan-expected slowdown in major economies; and asudden reversal of capital flows due to an abrupt
deterioration in financing conditions, investor sentiment,
or geopolitical relations. An upside risk to the forecast is
related to stronger-than-expected recovery of regional
investment and trade amid a sustained de-escalation of
trade tensions between China and the United States.
Europe and Central Asia. Growth in the region
decelerated to an estimated 2 percent in 2019, reflecting a
sharp slowdown in Turkey as a result of acute financial
market stress in 2018, as well as in the Russian Federation
amid weak demand and cuts in oil production. Regional
growth is projected to strengthen in 2020, to 2.6 percent,as activity recovers in Turkey and Russia, and to stabilize
to 2.9 percent in 2021-22. Key external risks to theregional growth outlook include spillovers from weakerthan-expected activity in the Euro Area and escalation of
global policy uncertainty. The region also remains
vulnerable to disorderly commodity and financial market
developments.
Latin America and the Caribbean. Growth in the region
slowed markedly in 2019, to an estimated 0.8 percent,held back by idiosyncratic factors in large economies,
headwinds from slowing global trade, and social unrest in
several countries. As activity in Brazil gathers pace amid
improving investment conditions, policy uncertainty in

Note: This box was prepared by Patrick Kirby with contributions
from Rudi Steinbach, Temel Taskin, Ekaterine Vashakmadze, Dana
Vorisek, Collette Wheeler, and Lei Ye. Research assistance was provided
by Hazel Macadangdang.

Mexico fades, and the recession in Argentina eases after
bouts of severe market stress, regional growth is projected
to rise to 1.8 percent in 2020 and about 2.4 percent in
2021. This recovery will not be sufficient to reverse thegrowing per capita income gap with advanced economies
in some LAC economies. Moreover, the regional outlook
is subject to significant downside risks, including frommarket volatility and adverse market responses to weakfiscal conditions; deeper-than-expected spillovers fromslowdowns in Argentina, China, and the United States;
heightened social unrest; and disruptions from natural
disasters and severe weather.
Middle East and North Africa. Regional growth
decelerated to an estimated 0.1 percent in 2019.
Geopolitical and policy constraints on oil sector
production slowed growth in oil-exporting economies,
despite support from public spending. Growth in oil
importers remained stable, as reform progress and resilient
tourism activity were offset by structural and external
headwinds. Regional growth is projected to pick up to 2.4
percent in 2020 and to about 2.8 percent in 2021-22, as
infrastructure investment and business climate reforms
proceed. Risks are tilted firmly to the downside—
geopolitical tensions, escalation of armed conflicts, slowerthan-expected pace of reforms, or weaker-than-expected
growth in key trading partners could heavily constrain
activity.
South Asia. Growth in the region is estimated to havedecelerated to 4.9 percent in 2019, reflecting a sharperthan-expected and broad-based weakening in domestic
demand. In India, activity was constrained by insufficientcredit availability, as well as by subdued private
consumption. Regional growth is expected to pick up
gradually, to 6 percent in 2022, on the assumption of amodest rebound in domestic demand. While growth in
Bangladesh is projected to remain above 7 percent throughthe forecast horizon, growth in Pakistan is projected tolanguish at 3 percent or less through 2020 as
macroeconomic stabilization efforts weigh on activity.
Growth in India is projected to decelerate to 5 percent in
FY2019/20 amid enduring financial sector issues. Key risks
to the outlook include a sharper-than-expected slowdown
in major economies, a reescalation of regional geopoliticaltensions, and a setback in reforms to address impairedbalance sheets in the financial and corporate sectors.

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BOX 1.2 Regional perspectives: Recent developments and outlook (continued)
FIGURE 1.2.1 Regional growth
Growth in almost all EMDE regions was weaker than expected in 2019, reflecting downgrades to more than half of EMDEs.
Activity in most regions is expected to pick up in 2020-21, but the recovery will largely depend on a rebound in a small
number of large EMDEs, some of which are emerging from deep recessions or sharp slowdowns.

A. Regional growth, weighted average

B. Regional growth, unweighted average

C. Regional investment, weighted
average

D. Regional exports, weighted average

E. GDP growth in ECA, weighted average

F. GDP growth in LAC, weighted average

Source: World Bank.
A.-D. Bars denote latest forecast; diamonds correspond to January 2020 forecasts in the Global Economic Prospects report. Average for 1990-2019 is constructed
depending on data availability. For Europe and Central Asia, the long-term average uses data for 1995-2019 to exclude the immediate aftermath of the collapse of theSoviet Union.
A.C.D.E.F. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Since the largest economies account for about 50 percent of
GDP in some regions, weighted averages predominantly reflect the developments in the largest economies in each region. Shaded areas indicate forecasts.
B. Unweighted average regional growth is used to ensure broad reflection of regional trends across all countries in the region.
Click here to download data and charts.

Sub-Saharan Africa. Growth in the region moderated to aslower-than-expected 2.4 percent in 2019. Activity wasdampened by softening external demand, heightened
global policy uncertainty, and falling commodity prices.
Domestic fragilities in several countries further constrained
activity. Growth is projected to firm to 2.9 percent in2020 and strengthen to 3.2 percent in 2021-22—notably
weaker than previous projections. The growth pickup ispredicated on improving investor confidence in some large
economies, a strengthening cyclical recovery amongindustrial commodity exporters along with a pickup in oil

production, and robust growth among several exporters ofagricultural commodities. Nonetheless, these growth rates
will be insufficient to make significant progress in reducing
poverty in many countries in Sub-Saharan Africa,
highlighting the need for lasting improvements in laborproductivity to bolster growth over the medium term.
Downside risks to the outlook include a sharper-thanexpected deceleration in major trading partners; increased
investor risk aversion and capital outflows triggered byelevated debt burdens; and growing insecurity.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

income growth over the forecast horizon is
expected to remain below 1 percent. In contrast,
per capita incomes are forecast to rise close to 5
percent per year in East Asia and Pacific andSouth Asia. As a result, the rapid declines in thenumber of extreme poor living in these two fastgrowing regions are likely to continue over thenear term. Absent major policy efforts to lift percapita growth, global extreme poverty will become
increasingly concentrated in Sub-Saharan Africa(Figure 1.13.D; Beegle and Christiaensen 2019;
World Bank 2018c).

Risks to the outlook
Global growth, which weakened to an estimated 2.4
percent in 2019, is projected to edge up to 2.5
percent this year, following an expected recovery of
trade and investment. Despite a recent notablereduction in the threat of protectionism, risks to theglobal outlook remain on the downside. A reescalation of global trade tensions could further weighon world activity. Amid financial sector
vulnerabilities, major economies could slow morethan expected. EMDEs remain at risk of financial
stress, especially those with elevated debt, while some
EMDE regions could be affected by geopoliticaltensions, social unrest, large swings in commodity
prices, or increasingly volatile weather patterns. On
the upside, further de-escalation of trade tensions
between the United States and China could continue
to mitigate global policy uncertainty and bolster
activity.
Summary of global outlook and risks
In light of softening trade and manufacturing,
global growth weakened to an estimated 2.4
percent last year. This was the slowest pace of
expansion since the global financial crisis—belowthat registered in 2012, when the Euro Area
suffered a serious debt crisis, and in 2015-16,
when many EMDE commodity exporters were
facing large declines in commodity prices andconcerns about China’s economy werewidespread. As international trade and investment
recover, global growth is projected to edge up to
2.5 percent in 2020—0.2 percentage point below
previous forecasts—and gradually firm over theforecast horizon, reaching 2.7 percent by 2022.

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Near-term projections for global growth mask
diverging contours for the forecasts for advanced
economies and EMDEs. Aggregate growth inadvanced economies is expected to slow from 1.6
percent in 2019 to 1.4 percent in 2020, primarilyreflecting a deceleration in the United States andanemic activity elsewhere. In contrast, EMDE
growth is envisioned to pick up from 3.5 percent
in 2019 to 4.1 percent this year, mostly as a resultof a pickup in a small number of large economies,some of which are emerging from deep recessions
or sharp slowdowns and whose outlooks are
therefore fragile. Absent this group of countries,
EMDE growth would be essentially stagnant and,with advanced economies decelerating, global
growth would actually slow. This indicates thatweaker-than-expected activity in this small set of
EMDEs could derail the expected recovery in
EMDE—and global—growth.
The contribution of EMDEs to the projected
pickup in global growth also hinges on theweighting methodology. Using market exchange
rates, as is done in these baseline projections,yields the aforementioned tepid recovery of global
growth. Using purchasing power parity (PPP),
however, places greater weight on EMDEs—
which are forecast to grow faster than advanced
economies—and thus results in a somewhat more
pronounced global pickup.
As a result of the greater emphasis on thecontribution of EMDEs—especially large, fastgrowing ones—to global activity, global growth isprojected at 3.2 percent in 2020 using PPP
weights, compared to 2.5 percent using market
exchange rates (Table 1.1). This is because
EMDEs are expected to account for 40 percent of
this year’s global output using market exchange
rates but 60 percent using PPP weights. In
particular, China’s share of global GDP in 2020 is
expected to be around 15 percent using market
exchange rates but 20 percent using PPP weights.
In fact, of the 0.7 percentage point difference in2020 global growth projections between the twoweighting methods, China accounts for over 50
percent, with the three next largest contributors to
the difference in global growth accounting for thevast majority of the remainder.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.14 Balance of risks
Amid heightened uncertainty about the economic outlook, risks to global
growth remain tilted to the downside. The probability of 2020 global growth
being a full 1 percentage point or more below baseline forecasts is almost
20 percent and above historical averages.

A. Probability distribution aroundglobal growth forecasts

B. Probability of global growth being
1 percentage point below current
baseline

Source: Bloomberg; World Bank.
A.B. The fan chart shows the forecast distribution of global growth using time-varying estimates of thestandard deviation and skewness extracted from the forecast distribution of three underlying risk
factors: Oil price futures, S&P 500 equity price futures, and term spread forecasts. Each of the risk
factor’s weight is derived from the model described in Ohnsorge, Stocker, and Some (2016). Values
for 2020 are computed from the forecast distribution of 12-month-ahead oil price futures, S&P 500
equity price futures, and term spread forecasts. Values for 2021 are based on 24-month-ahead
forecast distributions. Last observation is December 19, 2019.
Click here to download data and charts.

Regardless of the weighting scheme, baseline
projections for global growth represent a scenario
based on numerous benign assumptions. They
include no re-escalation of global trade tensions, amitigation in global policy uncertainty, no sharp
slowdown in major economies, no financial stressin large EMDEs, stability in commodity prices,
and—critically—the avoidance of policy missteps.
Accordingly, there is substantial uncertainty
surrounding these baseline projections (Figure
1.14.A).
On balance, risks to the outlook are on thedownside (Ruch 2019a). The trade conflict
between the United States and China could reescalate, and trade tensions involving other major
economies could emerge. Policy uncertainty could
rise significantly and persistently. Some EMDEs
could suffer full-fledged financial crises.
Commodity markets could see disruptive swings.
The United States or the Euro Area could suffer
deepening slowdowns, or China could slow
sharply—and the potentially large associatedspillovers could substantially erode the EMDE
outlook. Importantly, many of these risks areintertwined.

The materialization of one or more of these risks
could lead to a more severe global downturn—asituation many economies are not adequately
prepared to confront (Ruch 2019b). Reflecting a
preponderance of downside risks, the probability
that global growth in 2020 will be at least one
percentage point below baseline projections isalmost 20 percent, above historical averages.
(Figure 1.14.B).
Although downside risks predominate, there isalso the possibility that major headwinds dissipate
and the expected recovery is stronger than
expected.
In
particular,
recentpolicydevelopments—particularly those that havemitigated U.S.-China trade tensions—could leadto a sustained reduction in policy uncertainty andbolster confidence, trade, and investment, which isan important upside risk to the outlook.
Rising trade barriers and protracted
policy uncertainty
After decades of trade liberalization, protectionist
measures have been implemented on a growing
share of global trade (WTO 2019b). At the sametime, the number of trade agreements coming into
effect has fallen sharply. Progress on theratification of important trade agreements such as
EU-MERCOSUR has stalled. The WTO disputesettlement system became deadlocked in
December, threatening a key pillar of the global
rules-based trading system. Without a wellestablished arbitration system, countries may use
damaging unilateral or retaliatory trade policies toresolve the increasing number of trade disputes(Figure 1.15.A). The rising number of trade
restrictions and the associated uncertainty aroundthem have contributed to the recent contraction in
global trade and the slowdown in global growth.
The ratio of global trade-to-GDP growth has
fallen below 1, far exceeding the slowdown thatwould be expected from the ongoing maturationof global value chains (Figure 1.15.B).
Additional tariffs have been imposed on the
majority of bilateral trade between the UnitedStates and China over the past year. Despite theannouncement of the Phase One trade agreementthat resulted in the cancellation of planned tariffincreases, re-escalation remains possible—many

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

commitments, including items related to the
expansion of bilateral trade, intellectual property,
and technology transfer, may be difficult to
enforce.
The United States and China together account fornearly 40 percent of global GDP, nearly a quarterof global trade, and an even larger share of capital
goods trade (Figure 1.15.C). Accordingly,
renewed disruption to U.S.-China economic ties
could result in damage not only to these two
economies but to the rest of the world, as its
effects would propagate through trade, financial,and commodity linkages. There is also the risk that
trade tensions could extend to a broader set ofcountries. The imposition of U.S. tariffs on
automobiles and parts imports would impact a
globally important sector that is already struggling,
likely resulting in retaliation. The global
multilateral trading system could be put at risk by
a continuous rise in trade barriers stemming frommany countries.
In the longer run, protectionism would haveserious negative consequences for the global
economy, including by contributing to a further
decline in the trade intensity of global growth,
reducing productivity growth, and lowering real
incomes (Barattieri, Cacciatore, and Ghironi2018). The fragmentation of global value chains
would cause efficiency losses for producers andhigher prices for consumers. Exporting firms,
which tend to be more productive than exclusively
domestic firms, may need to redesign their supply
chains using costlier inputs and bearing the cost ofwriting off stranded assets (Atkin, Khandelwal,
and Osman 2017; Bernard and Jensen 2004).
Despite recent progress in the resolution of trade
conflicts, the impact of rising protectionism on
global growth has been magnified by protracted
policy uncertainty and a decline in confidence(Figure 1.15.D). A further increase in trade policy
uncertainty could continue to be a material
contributor to the softening of global growth(Caldara et al. 2019). Companies that are
uncertain about the framework for doing businessin the future are reluctant to invest, often
preferring to delay major, irreversible decisions
until the uncertainty has been resolved (Handleyand Limão 2015; Stokey 2016). If, in contrast to

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27

FIGURE 1.15 Rising trade barriers and protracted policy
uncertainty
After decades of trade liberalization, there has been a marked increase inprotectionist measures and trade disputes, contributing to a slowdown inglobal trade growth. A re-escalation of U.S.-China trade tensions, or adeterioration in trade relations involving a broader set of countries, could
substantially heighten policy uncertainty and further damage business
confidence and activity.
A. Trade disputes

B. Ratio of global trade to GDP growth

C. U.S. and China share of global
indicators, in 2018

D. Global trade policy uncertainty andbusiness confidence

Source: Ahir, Bloom, and Furceri (2018); Haver Analytics; International Monetary Fund; Organisation
for Economic Co-operation and Development; World Bank; World Trade Organization.
A. Figure shows monthly average of active disputes.
B. Shaded area indicates forecasts. Trade measured as the average of import and export volumes.
C. Trade measured as the average of goods exports and imports. Capital goods trade includes capital
goods and transport equipment.
D. Trade policy-related uncertainty is an index presented in Ahir, Bloom, and Furceri (2018) for 143
countries on a quarterly basis. Business confidence data are end of period and include 7 advanced
economies and 5 EMDEs. Aggregate business confidence calculated using GDP weights at 2010
prices and market exchange rates. Last observation is 2019Q3 for trade policy uncertainty. Business
confidence data for 2019Q4 use October 2019.
Click here to download data and charts.

baseline assumptions, policy uncertainty was torise further, the resulting impact on investment
would have critical consequences for activity inboth the short and long term.
A deepening slowdown in major economies
The United States, the Euro Area, and China are
the world’s largest economies. All three suffered amarked deceleration of activity in 2019 and facedownside risks (Figure 1.16.A). A deepening
slowdown in any of these economies would
worsen economic prospects in countries around28

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.16 A deepening slowdown in major
economies
Activity decelerated substantially in major economies in 2019. The U.S.
corporate sector and the Euro Area banking sector exhibit vulnerabilities
that could contribute to a deeper slowdown, which would have sizable
spillovers and increase the probability of a global downturn. In China,
private debt as a share of GDP is well above levels observed prior to
slowdowns in other EMDEs. Stress in the financial system could lead toeither a crisis or an extended period of slow growth as deleveraging dragson activity.
A. Growth in the United States, EuroArea, and China

B. U.S. financial vulnerability
indicators

the world through direct trade linkages andcommodity prices, as well as through financial andconfidence channels. This could derail the
anticipated recovery in EMDE growth (WorldBank 2016a). The Latin America and the
Caribbean region would be particularly impacted
by a sharp deceleration in the United States, while
economies in Europe and Central Asia would be
disproportionately affected by deepening weakness
in the Euro Area. Spillovers from a slowdown in
China would have sizable effects on the country’s
trading partners and in commodity producers(Ahmed et al. 2019; Stocker et al. 2018; WorldBank 2016a).
United States

C. Measures of health for Euro Areabanks

D. Probability of global downturn
given U.S. or Euro Area recession

E. Private sector debt in China
compared with peaks in other EMDEs

F. Share of EMDEs slowing after
reaching debt peaks

Source: Bank for International Settlements; Center for Economic Policy and Research; Economic
Cycle Research Institute; European Central Bank; Haver Analytics; Institute of International Finance;
International Monetary Fund; Kose and Terrones (2015); Laeven and Valencia (2018); National
Bureau of Economic Research; Shiller (2015); World Bank.
A. Data are seasonally adjusted for the United States and the Euro Area, and not for China.
B. Last observation is December 2019 for Shiller Price-to-Earnings (P/E) ratio and 2019Q2 for debt.
C. Return on equity is calculated using the average of 2008 to 2018. Euro Area aggregates calculatedusing nominal U.S. dollar GDP weights of France, Germany, Italy, and Spain, as available. Capital
adequacy ratio and non-performing loans are calculated using the average of 2009 to 2017.
Advanced economy aggregates calculated using available data for 37 advanced economies.
D. Figure shows the probability of a global downturn occurring given a U.S. or Euro Area recession.
Probabilities are based on annual data—the number of years with events divided by the total numberof years. U.S. recessions dated by National Bureau of Economic Research. Euro Area recessions
dated by the Center for Economic and Policy Research. German recessions are used prior to theformation of the Euro Area. From 1958 to 2018, there have been four global recessions, in 1975,
1982, 1991, and 2009, and three global slowdowns, in 1998, 2001, and 2012.
E. Debt peaks defined as the highest value of private non-financial credit to GDP over the period
1960Q1 to 2019Q2. Sample includes 15 EMDEs. For China, the last observation is 2019Q2.
F. Economies must have experienced a currency, systemic banking, or sovereign debt crisis within
two years after reaching the peak debt-to-GDP ratio. A slowdown is defined as a 1 percentage point
or more drop in GDP growth between the two years before and the two years after peak debt-to-GDP
ratio. Sample includes 15 EMDEs from 1960Q1 to 2019Q1.
Click here to download data and charts.

In the United States, growth is expected todecelerate as earlier tariff increases, lingering
uncertainty, and fiscal policy all exert a drag on
activity. High corporate debt and elevated equity
valuations increase the economy’s susceptibility toa more severe downturn (Figure 1.16.B). In thecurrent environment of low rates, some high-yieldborrowers have benefited from investors’ search foryield. For example, leveraged loan issuance has
increased rapidly, with borrowers benefitting fromlow spreads and loose lending standards. This
increase has been facilitated by financial
institutions bundling many lower-rated loans into
more highly rated securities known as
collateralized loan obligations (Federal Reserve
Board 2019). A sudden decline in the perceived
creditworthiness of borrowers could lead to a rapid
fall in asset valuations and a localized credit
crunch (Bank of England 2019). More generally,rising interest rates could slow activity across theentire corporate sector. Consumption has been thesole pillar supporting economic growth in recent
quarters, but this would be undermined iftightening credit conditions and declining
business confidence—for example, triggered byfurther increases in policy uncertainty—slowed
hiring and wage growth.
Euro Area
The Euro Area economy has already weakened
considerably. Vulnerabilities in the banking
system could lead to a further slowdown, giventhat banks are the region’s primary source of creditG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

and—despite some recent improvement—
continue to suffer from low profitability andelevated levels of non-performing loans (Figure
1.16.C). Negative interest rates in the region
could further undermine bank profitability anderode financial stability, possibly impacting
sovereign borrowing costs through the “sovereignbank” nexus (Arteta et al. 2016; Feyen and
Zuccardi 2019; Molyneux, Reghezza, and Xie
forthcoming). An unexpected bank failure—
generated, for example, by exposure to Germany’s
struggling industrial sector or sharp movements inasset prices following Brexit—could trigger
broader financial stress and an associated loss ofconfidence. As with the United States, a severe
slowdown in the Euro Area would substantially
increase the probability of a more severe global
downturn (Figure 1.16.D).
China
China’s primary vulnerability is its high and risingstock of private debt in its increasingly complex
and interconnected financial system (Arteta andKasyanenko 2019; IMF and World Bank 2017).
Credit to non-financial corporates and households
as a share of GDP nearly doubled in the last
decade, reaching about 210 percent in the firstquarter of 2019, well above the share observed
prior to previous growth slowdowns and financial
crises in other EMDEs (Figure 1.16.E). The
effectiveness of credit in stimulating growthappears to be declining, which implies that thebenefits of any further increase in credit would
diminish while risks would rise (Chen and Kang
2018). Rising defaults in local banks or in the
shadow banking system, a collapse in property
prices, or large capital outflows alongside a sharp
adjustment in asset prices could all ripple throughthe highly leveraged financial system. This risk isonly partly mitigated by the country’s low relianceon external financing and ample capacity for fiscal
and monetary support.
Alternatively, while a crisis could be avoided with
policy support given China’s sizable policy buffers,
the transition toward consumer-led and less creditdriven growth may lead to an extended period of
subdued growth in the absence of deep structuralreforms. Moreover, private deleveraging may act asa persistent drag on activity, as is commonly the

CHAPTER 1

case following periods of rapid debt accumulation(Figure 1.16.F; Kose, Sugawara, and Terrones2019).
Financial stress in EMDEs
EMDE debt burdens for both public and private
borrowers have grown considerably in recent years
as part of the most recent global wave of debt
(Figure 1.17.A; Chapter 4). Generally benign
global financial conditions have reduced debtservice burdens for many EMDEs, but they may
also be encouraging further debt accumulation,with prospect of persistently low advancedeconomy interest rates pushing some foreign
lenders to look for higher returns in EMDEs. In
some areas, debt is increasingly flowing to riskier
borrowers. Elevated debt can make economies
vulnerable to large depreciations, capital outflows,
financial stress, and abrupt policy tightening,particularly when it is financed from abroad. In
addition, solvency risks in the non-bank financial
sector are mounting in some large EMDEs (Arteta
and Kasyanenko 2019).
Recent credit booms in EMDEs have largely beenused to fund consumption rather than investment(Figure 1.17.B; Chapter 4; Arteta and Kasyanenko
2019). This carries the risk that rising debt will not
be matched by rising growth, increasing the
likelihood and impact of a loss of investor
confidence. When such a loss is combined with anelevated proportion of debt denominated inforeign currency, capital flight and depreciation
would add to existing debt sustainability concerns
and magnify the negative feedback loop (Bruno
and Shin 2018).
In the past, EMDEs have been vulnerable to abroad-based strengthening of the U.S. dollar(Figure 1.17.C). Amid rapidly increasing nonfinancial-sector debt, sharp dollar appreciation due
to interest rate differentials or generalized flight tosafety can expose currency and maturity
mismatches and trigger widespread corporate
insolvencies (Caballero, Fernández, and Park
2019; Chui, Kuruc, and Turner 2016). Large
depreciations are associated with higher borrowing
costs, and monetary authorities are often required
to tighten to stabilize currencies or resist thepassthrough of higher import costs to domestic

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FIGURE 1.17 Financial stress in EMDEs
EMDE debt burdens have grown considerably in recent years for bothpublic and private borrowers; however, recent credit booms have generally
not been accompanied by rising investment. A loss in investor confidence
could lead to an increase in bond spreads, as could a sharp U.S. dollar
appreciation arising from flight to safety or other factors.
A. EMDE debt levels

B. Investment surges during recent
credit booms

tightly integrated into the global financial system.
While this has benefits, it also facilitates thecontagion of global financial shocks both toforeign-currency and, to a lesser extent, localcurrency debt markets (Agur et al. 2018; Arteta
and Kasyanenko 2019; Cerutti, Claessens, andPuy 2019). The risk of contagion is further
amplified by constrained policy room for crisis
response and weaker buffers against external
shocks.
Geopolitical and region-specific downsiderisks

C. Bond spreads and exchange rates

D. Bond spreads in previous episodesof stress

Source: Bank for International Settlements; Haver Analytics; International Monetary Fund; J.P.
Morgan; Kose et al. (2017); World Bank.
A. Aggregate for foreign-currency-denominated debt is calculated using moving GDP weights at 2010
prices and market exchange rates, excluding 2002-05 due to missing data. “Latest” indicates 2019Q2
for government debt and corporate debt, and 2018 for foreign-currency-denominated debt.
B. A credit boom is defined as an episode during which the cyclical component of the nonfinancial
private sector credit-to-GDP ratio (using a Hodrick-Prescott filter) is larger than 1.65 times its
standard deviation in at least one year. The episode starts when the cyclical component first exceeds
one standard deviation and ends in a peak year (“0”) when the nonfinancial private sector credit-toGDP ratio declines in the following year. Consumption and investment surges are defined as periods
when the cyclical component of the consumption-to-GDP/investment-to-GDP ratio is at least one
standard deviation above the HP-filtered trend. See Chapter 4 for more details.
C. NEER = nominal effective exchange rate. Bond spreads are represented by J.P. Morgan’s
Emerging Market Bond Index (EMBI). Last observation is December 18, 2019.
D. “t=0” indicates May 2013, June 2015, and March 2018. Bond spreads are represented by J.P.
Morgan’s Emerging Market Bond Index (EMBI).
Click here to download data and charts.

inflation (Figure 1.17.D). Similarly, large swings
in commodity prices can potentially lead to
disruptive currency movements and balance of
payments difficulties for vulnerable EMDEs.
The risk of contagion of country-specific financial
distress across markets may be growing. Foreign
portfolio investors and global mutual funds are
becoming more active in local bond markets,
accounting for an increasing share of localcurrency-denominated sovereign bonds. As aresult, EMDE financial markets are now more

Downside risks to the global outlook arecompounded by various geopolitical and regionspecific concerns. Geopolitical risks remain acuteglobally and in several regions (Ruch 2019a). The
disruption in Saudi oil production in midSeptember highlights the potential for renewed
tensions in the Middle East. In addition, if
skirmishes in Eastern Europe and in South Asia
escalate, there could be important consequencesfor growth in the associated regions.
Amid geopolitical concerns, a sustained disruptionin oil production may increase energy prices, tothe detriment of affected suppliers and commodity
importers. While commodity producers left
unaffected by the disruption could potentiallybenefit from higher prices, these benefits can beundone if the price increase is accompanied byheightened volatility (van Eyden et al. 2019).
Alternatively, regions with a large presence of oil
producers, particularly MENA, would be adversely
affected by a sharp fall in oil prices resulting fromweaker-than-expected demand amid subdued
global growth. A sudden increase in supply—
reflecting, for instance, increased production in
the United States—could also lead to a moremeaningful decline in prices. Such a decline could
lead to substantial fiscal tightening, as was the casein 2014-16 (Figure 1.18.A; Stocker et al. 2018).
Falls in metals or agricultural prices could follow asimilar pattern and would also have a serious
impact on economies in regions such as SubSaharan Africa, Latin America and the Caribbean,or Europe and Central Asia. While regions withlarge numbers of commodity importers would faceG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

a positive terms-of-trade shock, these gains would
likely be diffused across many economies, only
partially offsetting the relatively larger losses facedby commodity exporters.
Social unrest has been on the rise in a growing
number of countries in various regions, motivatedby discontent about some combination of
inequality, slow growth, governance, andeconomic policy. Unrest has the potential todisrupt activity and damage infrastructure. It may
also make fiscal consolidation efforts morechallenging for governments trying to easetensions.
Climate change is increasing the frequency ofsevere weather events and lowering agricultural
productivity in some regions (IPCC 2018). As
such, its impact is more detrimental for regionsthat have large numbers of countries with lessresilient infrastructure and a larger share of
agricultural production. These countries tend to bepoor and can ill-afford the lost infrastructure andincome that accompanies extreme weather andpoor harvests. Similarly, regions with large coastal
populations are at risk, not only from extreme
weather, but also from rising sea levels. Climatechange also presents risks to the financial system insome EMDEs, as the need to incorporate climate
risks into asset valuations and insurance coveragecalculations increases the risk of mispricing(Figure 1.18.B). Rapid repricing is possible, forexample, as more information becomes available
about what assets are most at risk from rising sea
levels or less habitable weather conditions (NGFS
2018).
Upside risks
Although downside risks predominate, there isalso the possibility that the global recovery isstronger than expected. Existing headwinds togrowth—including those related to policy
uncertainty—could further dissipate, or additional
macroeconomic policy support could be deployed
in response.
Heightened policy uncertainty exerted a notabledrag on activity throughout 2019, much of it
related to concerns about rising trade barriers. The

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31

FIGURE 1.18 Other downside risks
A sustained decline in the price of oil or other commodities could lead to
substantial fiscal tightening in commodity exporters, as was the case in
2014-16. Climate change is increasing the frequency of severe weather
events and the volatility of agricultural conditions. Rising losses fromsevere weather events related to climate change increase the risk offinancial instability.

A. Change in overall fiscal balance inoil-exporting EMDE sub-groups, from2014-16

B. Rising frequency and costs fromnatural disasters

Source: International Monetary Fund; Munich Reinsurance Company; World Bank.
A. Exchange rate classification is based on the IMF’s Annual Report on Exchange Arrangements andExchange Restrictions database, in which countries are ranked 0 (no separate legal tender) to 10(free float). “Pegged” denotes countries ranked 1 to 6. “Floating” denotes countries ranked 7 to 10.
Sample includes 27 oil-exporting EMDEs, based on data availability. Change in overall fiscal balanceis measured from 2014-16. Above average and below average oil revenue groups are defined bycountries above or below the sample average of oil revenues as a share of GDP based on 2014 data.
B. Global natural disasters and economic losses statistics from Munich Reinsurance Company
including loss estimation based on Property Claim Services (PCS). The 30-year average represents
1988-2017. 5-year average represents 2014-2018. Losses adjusted to inflation based on local CPI.
Click here to download data and charts.

recent trade agreement between the United States
and China that reverses some tariff increases could
be the beginning of a constructive process leading
to a sustained reduction in policy uncertainty andtrade barriers. This could significantly improve
confidence and unlock pent-up demand forinvestment, bolstering growth (Figure 1.19.A).
Similarly, rapid progress on the post-Brexit tradenegotiations between the United Kingdom andthe European Union could lift a cloud on
Europe’s outlook.
Central banks provided significant accommodation over the course of 2019, which is expected
to contribute to the pickup in activity over thenear term. On a global level, falling policy rates
have coincided with declining inflation, suggestingthat there is scope for further monetary easing,
mainly for some EMDEs (Figure 1.19.B). In
addition to the potential boost to growth frommonetary policy, some major advanced economies
with sufficient space could choose to provide
additional fiscal support.

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FIGURE 1.19 Upside risks

Policy challenges

Sustained progress in the resolution of U.S.-China trade tensions would
reduce policy uncertainty, which could unlock pent-up demand forinvestment. A continued decline in global inflation could open the door tofurther monetary stimulus.
A. Impact of a 10-percent decrease in
U.S. policy uncertainty on investment
growthB. Change in global inflation andinterest rates over the last year

Source: Baker, Bloom, and Davis (2016); Bank for International Settlements; Bloomberg; Haver
Analytics; World Bank.
A. Figure shows median growth impact of 10 percent fall in U.S. economic policy uncertainty (EPU).
See Annex SF.1B of World Bank (2017a) for details on the methodology.
B. Calculations based on change in year-on-year global inflation and nominal interest rate between
November 2018 and November 2019. Aggregate nominal interest rate calculated using GDP weights
at 2010 prices and market exchange rates. Unbalanced samples include 35 advanced economies
and 77 EMDEs, including 39 low-income countries, for nominal interest rates and include 36
advanced economies and 112 EMDEs for inflation. Last observation is November 2019.
Click here to download data and charts.

FIGURE 1.20 Monetary and financial policies
in advanced economies
Weak growth and low inflation have prevented major central banks fromremoving policy accommodation in the post-crisis period. As a result,policy rates are at or close to their effective lower bounds in many
economies. Longer-term yields have also fallen, limiting the remaining
room for other policy tools, such as forward guidance and quantitativeeasing.
A. Monetary policy rate increases
during current and previous
expansions

B. Policy rates and 10-year sovereign
yields

Source: Bank of Japan; Bloomberg; European Central Bank; Federal Reserve System; Haver
Analytics; National Bureau of Economic Research; World Bank.
A. U.S. expansions: 1991-2001, 2001-07, 2009-present. Euro Area expansions: 1999-2008, 2009-11,
2013-present. Calculations based on trough and peak of policy rates of each period. Last observationis November 2019 for the United States and 2019Q3 for the Euro Area.
B. Figure shows data as of December 18, 2019.
Click here to download data and charts.

Challenges in advanced economies
Very low interest rates highlight the limited roomthat advanced-economy central banks have to provide
additional accommodation. If persistent, they may
also erode the health of financial institutions.
However, low borrowing costs have loosened some of
the constraints on fiscal policy allowing for increased
public investment or other support in countries withfiscal space, if needed. Fiscal positions could also be
improved through better tax compliance andenforcement. Productivity growth in advanced
economies has declined due to weak investment
growth and aging populations. Reducing policy
uncertainty would buttress capital formation.
Monetary and financial policies
The combination of feeble growth and stubbornly
subdued inflation in the post-crisis period has
made it difficult for major central banks to remove
policy accommodation (Figure 1.20.A). Policy
rates remain very low in most countries, and close
to their effective lower bound, greatly limiting theability to further cut rates. Other policy tools,
such as policy guidance or quantitative easing,have been used to help lower long-term interest
rates as short-term rates approached their lowerbound (Woodford 2012). However, the limits ofthese tools may also have been reached, with longterm yields in many economies, including
Germany and Japan, now below zero (Figure
1.20.B). The downward trend in interest rates, andthe associated challenges for monetary policy,
appears to be a persistent phenomenon, driven inpart by a fundamental weaknesses in investment
demand across advanced economies (Rachel andSummers 2019; Williams 2016).
A number of ideas have been put forward to
improve the traction of monetary policy,
including targeting price levels or nominal GDP
rather than inflation, stimulating activity throughdirect transfers to households, and eliminating thelower bound by subordinating paper money tocentral-bank electronic money (Agarwal andKimball 2019; Buiter 2014; Mertens andWilliams 2019). These come with their own risks

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

and tradeoffs, including the difficulty oftransitioning from one framework to anotherwhile maintaining the credibility and public
understanding that is essential for the effective
operation of monetary policy.
Aside from the constraints it places on monetary
policy, an extended period of low or negativeinterest rates may also be detrimental to the healthof financial institutions, as their interest rate
margins become squeezed (Arteta et al. 2016;
Brunnermeier and Koby 2019). For banks, lowinterest rates can reduce profitability—and
therefore resilience in the face of negative
shocks—and encourage greater risk taking. Nonbank financial institutions, which account for an
increasing share of credit issuance, are also
affected. Pension funds and insurance companies
often have fixed future liabilities and may becompelled to invest in riskier and less liquid assets
in order to meet their nominal return targets.
Increased lending to over-leveraged borrowers may
be sowing the seeds for future financial stress,
especially given uncertainty about non-bankbehavior and its impact on the financial system
during a downturn (IMF 2019b). Regulatory
reforms have made the global financial system
more resilient since the global financial crisis;
however, prudential authorities need to remain
vigilant to risks originating from the growing
importance of non-bank financial institutions, andbe wary of vulnerabilities being masked bytechnological innovations and complex financial
products (FSB 2019b).
Fiscal policy
In many advanced economies, households aredeleveraging and corporate investment is weak,leaving aggregate demand unusually dependent on
government borrowing (Figure 1.21.A). Further
fiscal support may become necessary given the
combination of slowing activity, elevateddownside risks, and limited room for monetary
policy accommodation. Many countries arecarrying persistent deficits, however, despite thebudgetary benefits of the global decline in interest
rates.
One growth-friendly approach that advanced
economies can take to improve their fiscal

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33

FIGURE 1.21 Fiscal policy in advanced economies
In many advanced economies, households are deleveraging andcorporate investment is weak, leaving aggregate demand unusuallydependent on government borrowing. Public investment can bolster
growth in both the short and long term, and increase the stock of public
capital, which has fallen in a number of economies.

A. Change in debt over GDP since2009, by sector

B. Public capital stock

Source: Institute of International Finance; International Monetary Fund; World Bank.
A. Figure shows the change in the debt-to-GDP ratio since 2009. Sector aggregates are calculatedusing GDP weights at 2010 prices and market exchange rates. Sample includes 23 advanced
economies. Last observation is 2019Q2.
B. Lines represent the ratio of general public capital stock to GDP, in billions of constant 2011
international dollars. Last observation is 2017.
Click here to download data and charts.

positions is through better tax compliance andenforcement (OECD 2019a). Preventing corporate tax avoidance through profit shifting is one
way to broaden the revenue base, especially withrespect to companies that provide digital services
in a given jurisdiction without any physicalpresence (World Bank 2018d). Providing tax
agencies with more resources to bring down tax
non-compliance could increase revenues while
helping reduce inequality (Sarin and Summers2019).
Should governments choose to provide fiscal
support, the focus should be on spending that has
a high multiplier. This could include transfers to
low-income individuals, as well as to regional
governments, whose spending tends to be morecredit constrained and procyclical (Whalen andReichling 2015). These multipliers may beparticularly large when interest rates areconstrained by their effective lower bound, andcan benefit other countries through spillovers,
especially if action is taken in an internationally coordinated fashion (Auerbach and
Gorodnichenko 2013; Wieland 2010; Woodford
2011). By contrast, multipliers tend to be low
when debt levels are elevated (Huidrom et al.
2019).

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.22 Structural policies in advanced economies
Productivity has slowed in advanced economies, primarily due to thedecline in capital deepening, slowing gains in education and genderequality, and lower levels of innovation associated with a shrinking working-age population. Policymakers can help reverse this trend by fostering
innovation and human capital, as well as avoiding policy choices thathinder investment.

A. Contributions to labor productivity

B. Share of advanced economies witha slowdown in productivity drivers in
2008-17 relative to 1998-2007

Source: Barro and Lee (2015); Penn World Table; The Conference Board, United Nations; WorldBank.
A.B. Productivity defined as output per worker. Refer to Chapter 3 for details. Unbalanced sample
includes 29 advanced economies.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
B. Share of AEs where improvements in each driver of productivity were lower during 2008-2017 thanin the pre-crisis period 1998-2007 or improvements were negative. Variables corresponding to eachconcept are: Investment = investment-to-GDP ratio, Education = years of schooling, Demography =share of working-age population, Gender equality = female average years of education minus maleaverage years.
Click here to download data and charts.

Public investment may be an especially effective
form of fiscal support in many advanced
economies, as it can bolster growth in the shortterm by crowding in private capital, and in thelong term by increasing productivity growth andmitigating climate change (Bouakez, Guillard, andRoulleau-Pasdeloup 2017; Dreger and Reimers
2016; World Bank 2019j). The falling stock ofpublic capital as a share of GDP in some advanced
economies suggests the need to fill infrastructureneeds (Figure 1.21.B; Heintz 2010). To the extent
that it boosts demand and potential output,
borrowing to finance public investment may
ultimately have a limited impact on public debt
ratios (Abiad, Furceri, and Topalova 2015).
Structural policies
Potential growth has been slowing in advanced
economies due to a combination of demographic
trends and decelerating productivity growth. The
latter primarily reflects the appreciably diminishedrole of capital deepening as a contributor to
growth since the global financial crisis (Figure
1.22.A). There are a variety of tools policymakers

can use to help reverse this trend. Pursuing
growth-enhancing public investment, fostering
innovation, and increasing human capital can all
be effective means of boosting productivity(Chapter 3).
The simplest option, however, is to avoid policy
choices that actively hinder investment. The rise of
trade protectionism and the associated uncertainty
has made companies more reluctant to invest until
the framework for global trade is normalized(Handley and Limão 2015; World Bank 2017a).
A stable, predictable system based on a multilateralconsensus about the rules governing global trade
would foster investment and, thereby, strengthen
potential output.
Alongside weak investment, the other main dragson productivity in advanced economies are related
to slowing gains in education and gender equality(Figure 1.22.B). In addition, the working-age
share of the population continues to shrink, which
can slow productivity growth as younger
generations tend to adopt new technology more
rapidly (Chapter 3). This trend is expected tocontinue in coming decades, but could be partiallymitigated by allowing new migrants, who tend to
be prime-aged, in an orderly fashion and as
appropriate to country-specific circumstances.
Challenges in emerging market anddeveloping economies
While subdued inflation has allowed many EMDEs
to cut policy rates, a deterioration in investor
sentiment could require policy tightening. With thespace for fiscal support constrained by record-highdebt, tax policy reforms are needed to broaden the tax
base to fund growth-enhancing and climate-friendly
investment. Measures to improve governance andbusiness climates and phase out price controls canmake institutional environments more conducive to
growth. Encouraging EMDE integration in supply
chains could counterweigh the effects of weak global
trade. Bolstering productivity growth by encouraging
diversification and upgrading to high-value added,
technology-intensive industries will be critical to shore
up long-term growth. China’s key policy challenge isto address lingering disruptions associated with tradetensions while shifting to more balanced andsustainable growth.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Policy challenges in China
China’s authorities have provided monetary andfiscal support to mitigate the impact of higher
tariffs on bilateral trade with the United States andweakening global demand. The central bank has
eased policy mainly by cutting bank reserve
requirements. On the fiscal front, authorities havefocused on measures to accelerate investment
spending at the subnational level. A number of
initiatives to improve market access for foreign
investors and various reforms to improve thebusiness climate have also been implemented(World Bank 2019c, 2020).
China’s key policy challenge is to achieve apermanent and lasting resolution of trade tensions
while continuing to shift to more balanced growthand gradually reducing excessive leverage. This
would require enhancing productivity by boosting
investment in human capital; further improving
market access, competition, and financial
discipline; strengthening intellectual property
rights; reducing barriers to entry; continuing thegradual opening of China’s financial system tointernational investors; and fostering innovation(Chapter 3; World Bank 2018e; World Bank and
DRC 2019).
EMDE monetary and financial policies
Consistent with flagging global growth, negative
output gaps, and moderating inflation in many
EMDEs, including some LICs, nearly 75 percent
of EMDEs have lower policy rates now than at thestart of 2019, with more than half implementing
multiple cuts (Figure 1.23.A). Many EMDEs havespace to cut rates further as interest rates remain
relatively high and inflation below target (Figures
1.23.B and 1.23.C). However, the effectiveness ofmonetary policy in EMDEs is likely more limited
than in advanced economies, as the interest ratechannel may be weaker and the impact of externalfinancing conditions larger (Aoki, Benigno, andKiyotaki 2018; Choi et al. 2017).
Although global financing conditions havegenerally eased, policy uncertainty and risk
aversion have tightened financing conditions insome EMDEs. An abrupt change in market
sentiment could reignite capital outflows and

CHAPTER 1

currency depreciation, as well as force policy
interest rate hikes, exerting greater pressure on
economies still suffering the lingering effects ofprevious financial market stress. EMDEs withlarge external imbalances tend to be the mostvulnerable to financial stress, including those that
rely on short-term capital inflows to financecurrent accounts, borrow heavily in foreigndenominated currencies and from external lenders,
and lack adequate reserve coverage levels.
Many EMDEs lack buffers to confront financial
shocks—in nearly half of EMDEs, international
reserves are currently below levels that would beconsistent with reserve adequacy (IMF 2011; Kose
and Ohnsorge 2019). Among LICs, reserve
coverage has fallen to a two-year low (Figure
1.23.D). Following the taper tantrum of 2013,
depreciations were less severe in countries withlarger reserves, highlighting the importance of
restoring monetary buffers (BIS 2019). In
anticipation of renewed episodes of market
volatility, EMDE policymakers need to keep
expectations of longer-term inflation moderateand stable. This includes demonstrating a credible
commitment to inflation targets in economies thathave implemented such a framework (World Bank
2019n).
Since the global financial crisis, more than two
thirds of EMDEs have strengthened macroprudential policies to rein in the growth of credit
to non-financial corporations and households(Figure 1.23.E; Cerutti, Claessens, and Laeven
2017; Koh and Yu 2019; World Bank 2019o).
Supervisory and regulatory frameworks need to befurther strengthened to confront future shocks andshore up financial stability, especially in a context
where cross-border lending has shifted from banks
headquartered in advanced economies to EMDEheadquartered banks (Figure 1.23.F). Macroprudential measures, such as countercyclicalcapital buffers and limits on foreign-currency
borrowing, can help contain systemic risk inbanking and corporate sectors. Additionally,
carefully calibrated regulatory measures, such asreporting and licensing criteria, could help supportconfidence and resilience in new platforms that
expand the access to credit through financial
technology innovations (BIS 2017). However,
EMDEs will need to strike a careful balance when

35

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.23 EMDE monetary and financial policy
Moderating inflation and relatively high interest rates allowed many EMDEs
to cut policy interest rates to support growth—consistent with negative
output gaps and below-target inflation. Reserve coverage sharply fell in
2019, particularly in LICs, leaving many economies unprepared to respondto financial market shocks. Strengthening regulatory frameworks in EMDEs
is crucial, especially in a context where cross-border lending has shifted to
EMDE-headquartered banks.
A. Output gaps and policy interest
rate actions

B. Real interest rates

C. Inflation and inflation targeters,
2019

D. Reserve coverage

E. Macroprudential policies: Use offinancial institution-targeted
instruments

F. Sources of cross-border bank loans

Source: Bank for International Settlements; Consensus Economics; Haver Analytics; International
Monetary Fund; World Bank.
A. Output gaps aggregated using GDP weights at 2010 prices and market exchange rates and are
estimated from a multivariate filter model of World Bank (2018a). Figure shows number of EMDEs
with policy interest rates lower (higher) than start of the year. Sample includes 45 EMDEs. Countries
with fixed exchange rates are excluded. Data as of December 19, 2019.
B. Real interest rates are nominal interest rates less expected inflation. Expected inflation is the oneyear ahead forecast from Consensus Economics. Sample includes 17 EMDEs. Blue area shows
minimum and maximum. Last observation is November 2019.
C. Sample includes the 34 EMDEs with inflation targets and is based on data availability. Figure
shows the last observation, which is November 2019.
D. Figure shows number of months of reserve coverage. Data are 6-month moving averages of thesample median. Sample includes 66 EMDEs including 25 LICs. Last observation is October 2019.
The Assessing Reserve Adequacy (ARA) metric is based on IMF (2011).
E. Each bar represents share of EMDEs using at least one macroprudential tool that is financial
institution-targeted (for example, limits on foreign currency loans and leverage ratios).
F. Sample includes 115 EMDEs, excluding China. Due to data availability, 77 EMDEs are included in
2018. Lending by non-BIS banks is estimated as total bank loans and deposits from the IMF Balanceof Payments Statistics (excluding central banks) minus cross-border lending by BIS reporting banks.
This difference mostly accounts for the banking flows originating from non-BIS reporting countries.
Click here to download data and charts.

considering the trade-offs between managing
macroprudential risk and fostering financial
development (Krishnamurti and Lee 2014).
EMDE fiscal policy
Many EMDEs face narrowing fiscal space andmay struggle to quickly rebuild buffers, limiting
their options to address a severe downturn (Figure
1.24.A; Ruch 2019b). Aggregate EMDE debt
reached a historical high last year and is expected
to rise further (Chapter 4). Fiscal sustainability
remains a critical challenge in many EMDEs,
reflecting increased spending in commodity
exporters and reduced revenues in commodity
importers (Figures 1.24.B and 1.24.C). Should anegative shock occur, the scope for fiscal
accommodation may be constrained by the needto ensure long-term fiscal stability. The case forproviding fiscal support would be strengthened
where there are clear needs, such as infrastructure
gaps, and a transparent public expenditure review
process. In many cases, however, the expansion of
credit over the past decade has not been channeledinto investment, and was instead used to fundconsumption (Chapter 4; Arteta and Kasyanenko
2019).
In particular, EMDE commodity exporters needto grapple with lower commodity prices, especiallyin those oil exporters where fiscal breakeven prices
are higher than oil prices. In many commodity
exporters, fiscal revenues are not well diversified,leaving revenues highly dependent on commodity
production and exposed to global commodity
price volatility (Gunter et al. 2019).
For fiscally constrained economies, building tax
capacity is a crucial step towards mobilizing
domestic resources, providing essential public
services, pursuing appropriate redistributive
policies to address inequality, and building fiscal
buffers (Doumbia and Lauridsen 2019). This isparticularly true in LICs, 80 percent of which lackthe tax revenues to provide even basic services, letalone to meet the SDGs (Figure 1.24.D; Gaspar,
Jaramillo, and Wingender 2016). Overall,policymakers need to ensure that public spending
is cost effective and yields a positive growth
dividend, while also protecting critical social safety
nets and supporting climate-friendly measures.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Measures that help mitigate and adapt to climatechange, such as environmental tax reforms, canreap a triple dividend by lowering pollution,
raising welfare, and generating positive
externalities (World Bank 2019p).
In many EMDEs, tax policy reform is achallenging process. To protect the mostvulnerable, adjusting income tax brackets to rising
inflation can ease the tax burden and prevent theerosion of real net incomes. Harmonizing tax rates
across different savings instruments or a welldesigned earned income tax credit can support
labor participation and poverty reduction without
distorting the incentive to work and save (OECD2019a). When there is a clear rationale for tax
cuts, negative revenue effects can be partly offset
by measures that increase compliance—such as theintroduction of a withholding mechanism or asimplification of the tax structure—or that spur
innovation and investment—such as tax credits on
vocational education and research anddevelopment (Clavey et al. 2019; Correa andGuceri 2013; World Bank et al. 2015). Additional
measures that broaden the tax base, including
those that eliminate costly loopholes, can becomplemented with reforms that strengthen tax
administration and collection to reduce avoidance,base erosion, and profit shifting (Awasthi andBayraktar 2014; OECD 2017; Packard et al.
2019; Prichard et al. 2019; World Bank 2018d).
EMDEs with unsustainable fiscal positions canalso prioritize rebuilding policy space byimproving spending efficiency, by shifting
spending toward growth-enhancing, climatefriendly investment from unproductive current
spending, and by strengthening governance tocontain and eliminate wasteful spending (WorldBank 2017b). If public expenditure needs are
high, rebalancing the tax structure can provide
maneuvering room, particularly in economies withlower initial tax rates (Gunter et al. 2018, 2019).
The realization of costly fiscal risks to publicbalance sheets, such as contingent liabilities, could
be stemmed through use of macroprudential
measures that help ensure the resilience of thebanking sector. Building credible and transparent
medium-term expenditure frameworks that alignwith the strategic goals of the government is also

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37

FIGURE 1.24 EMDE fiscal policy
Fiscal deficits persist despite previous procyclical tightening in some
EMDEs, as weaker-than-expected growth hindered revenue collection.
Weak tax capacity has contributed to fragile fiscal positions, particularly in
LICs, highlighting the urgency for fiscally constrained economies to better
mobilize domestic resources or reform their tax structure to free up space
to finance growth-enhancing spending.
A. Fiscal impulses and output gaps

B. Fiscal sustainability gaps in EMDEs

C. Contribution to change in fiscal
balance, 2019

D. Share of EMDEs with limited tax
revenues to fund basic public
services

Source: International Monetary Fund; Kose et al. (2017); World Bank.
A. Output gaps are estimates from a multivariate filter model of World Bank (2018a). Average ofquarterly output gap data. Fiscal impulse is defined as the change in the structural fiscal deficit fromthe previous year. A decline in structural deficit (a negative fiscal impulse) is a fiscal consolidation—
countercyclical if implemented while output gaps are positive—while an increase in the structural
deficit (positive fiscal impulse) is a fiscal stimulus—countercyclical if implemented while output gaps
are negative.
B. Fiscal sustainability gaps are measured as the difference between the primary (overall) balance
and the debt-stabilizing primary (overall) balance. A negative bar indicates government debt is risingalong an accelerated trajectory.
C. Sample includes 152 EMDEs.
D. Figure shows the share of EMDEs with tax revenue-to-GDP ratios that are below 15 percent, thethreshold needed to provide basic public services, as identified in Gaspar, Jaramillo, and Wingender(2016). Basic services include road infrastructure, health care, and public safety. Sample varies due
to data limitations. In 2017, the sample includes 70 EMDEs, of which 11 are LICs.
Click here to download data and charts.

crucial (Koh and Yu 2019; Munoz and Olaberria2019).
EMDE structural policies
Over the long run, EMDE policymakers need toundertake the necessary structural reforms tobuttress potential growth. Inadequate governanceand business climates need to be improved tofoster an institutional environment that is more
conducive to growth. In a context of subdued
trade growth, further integration of EMDEs into

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global value chains needs to be promoted.
Critically, amid slowing capital deepening,productivity growth—an essential driver of longterm growth and poverty reduction—needs to berekindled. Many EMDEs, including LICs, face
the added challenge of phasing out distortionary
price control policies that impede growth anddevelopment. In tackling these challenges, careshould be taken to protect vulnerable populations
by improving social safety nets.
Moreover, investment in green infrastructure andits integration with traditional infrastructure canlower costs, help achieve development goals, andcontribute to improving infrastructure systems’
resilience to climate change (Browder et al. 2019).
Private sector financing to meet large infrastructure investment needs and foster capital formationand the leveraging of digital technologies topromote the inclusion, efficiency, and innovationof firms in EMDEs are all crucial in boosting
potential growth (World Bank 2016b).
Implementing governance and business climate
reforms
Governance reforms in EMDEs have stalled, andrenewed momentum is needed (World Bank
2018a). The number of countries whose ranking
for rule of law and control of corruption havesignificantly worsened in the last two decades
outnumber those whose rankings have improved(Figure 1.25.A). Strikingly, very few large EMDEs
had significant gains in any of the worldwide
governance indicators, nor did LICs as a group.
Strengthening institutional quality and governanceto protect property rights would encourage theshift from informal to more productive formal
activities (World Bank 2017c). Measures that
improve public sector efficiency through theprovision of high-quality and cost-effective public
goods also need to be considered as they can help
raise firm productivity (Giordano et al. 2015).
Since 2009, only about a third of EMDEs
increased their doing business score significantly,
with notable regional variations (Figure 1.25.B).
Reforms should aim to accelerate improvements inthe business climate by tackling burdensome
regulations and enhancing the ease of doing
business, in order to pave the way for more jobs,

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

higher incomes, and reduced poverty (WorldBank 2020).
Phasing out distortionary price controls
While introduced with the best social intentions,price control policies, often coupled with onerous
subsidies, pose important obstacles to growth anddevelopment in many EMDEs, including LICs
(Special Focus 1). The removal of these costly
controls can reduce misallocation of capital andlabor, spur investment, and increase competition
in sectors subject to these policies. Moreover,when paired with targeted social safety nets, theirremoval can help reduce poverty and inequality(Verme and Araar 2017). Some of the fiscalsavings from the reforms can be used to fundgrowth-enhancing education and infrastructure
spending.
Promoting integration into global value chains
The rise in the incidence of protectionist measuresover the past couple of years not only weighs on
global trade growth but could lead to thefragmentation of global supply chains and deprive
EMDEs of a key source of growth and poverty
reduction. Policy measures that help facilitate
trade in EMDEs by boosting their integration inexisting supply chains and spurring the creation of
new ones could provide a counterweight to theglobal slowdown in growth and trade (WorldBank 2019a). A 10-percent increase in GVC
participation is estimated to boost per capita
income growth by more than 10 percent, about
twice as much as standard trade (Figure 1.25.C).
Firms integrated in GVCs tend to be moreproductive and capital intensive; they represent
only about 15 percent of all trading firms, yetaccount for almost 80 percent of total trade. GVC
participation is positively associated with foreign
direct investment in EMDEs, as well as technology and knowledge transfers (Martínez‐Galánand Fontoura 2019; World Bank et al. 2017).
Reducing distortions to international trade cancontribute to boosting EMDE participation in
GVCs (Figure 1.25.D; OECD 2019b). The
liberalization of barriers (both tariff and non-tariff)
affecting imported intermediate inputs could
expand sources of supply available to EMDEs andG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

their ability to specialize. A one-standard-deviation
decrease in a country’s average manufacturing
tariffs—8 percentage points—is associated with anincrease in the country’s backward GVC
participation (captured by the foreign value-added
content of exports) of about 0.2 standard
deviations (Fernandes, Kee, and Winkler 2019).
Liberalizing barriers to services trade, which aresignificantly higher than those for goods trade, isalso important in promoting GVC growth.
Trade facilitation policies that improveconnectivity by enhancing trade and transport
logistics and lower trade costs can help EMDEs
better integrate into GVCs. For many goods
traded in GVCs, a day’s delay has costs equivalent
to a tariff of 1 percent or more. Improving
customs and border procedures, promoting
competition in transport services, and improving
port structure and governance are all strategies
that can help reduce trade costs related to timeand uncertainty (Pathikonda and Farole 2016).
Because GVCs thrive on the flexible formation of
networks of firms, a stable and predictable legal
environment and contract enforcement are crucial(Ignatenko, Raei, and Micheva 2019). Better
contract enforcement supports the supply ofbusiness services, which encourages thedevelopment of GVCs. The ability to enforce
contracts relating to intellectual property is also
important for more innovative and complex value
chains.
Complementary policies are also needed to ensurethat the gains from participation in GVCs areevenly distributed. These include labor market
policies to help workers who may be hurt by
structural change; mechanisms to ensurecompliance with labor regulations; appropriate tax
policies to attract GVCs without undermining tax
revenues; and environmental protection measures(Taglioni and Winkler 2016).

CHAPTER 1

FIGURE 1.25 EMDE structural policies—Governance,business climate, and GVC participation
The number of EMDEs whose rankings for some key governanceindicators have significantly worsened in the last two decades outnumber
those whose rankings have improved. Since 2009, only about a third of
EMDEs increased their Doing Business score significantly. This highlights
a critical need to foster institutional environments more conducive to
growth. Trade liberalization can help boost EMDEs’ participation in global
value chains and contribute to rising per capita incomes.
A. Change in Worldwide GovernanceIndicators, 1996 to 2018

B. Change in Doing Business scores,
2009 to 2019

C. Impact of 1 percent increase in
GVC participation on GDP per capita

D. Impact of input tariffs on GVC
participation

Source: World Bank.
A.B. A country significantly improved (deteriorated) if its rating increased (decreased) by two standard
errors over the indicated periods. For Worldwide Governance Indicators, standard errors are theaverage between the two periods. For Doing Business, standard errors are the cross-country
standard deviation of changes in scores.
A. Based on indicators from the Worldwide Governance Indicators (WGI) measuring aspects ofgovernance. The four indicators are government effectiveness, regulatory quality, rule of law, andcontrol of corruption.
B. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the
Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa.
C.D. Backward participation is defined as the share of foreign inputs in domestic value added.
Forward participation is the share of domestic value added in exports.
C. GDP per capita increase as a result of 1 percent increase in x-axis indicators. Blue vertical lines
indicate 95 percent confidence interval and red squares indicate point estimates. Estimates obtained
from a panel of standard Solow growth models augmented with measures of GVC using System
Generalized Method of Moments (World Bank 2019a). Panel includes 100 countries across income
groups for the period of 1990-2015. Non-GVC exports is defined as exports that neither include
foreign value-added nor are exports of domestic value added that are re-exported in other countries’
exports.
D. Figure shows standardized beta coefficients for each variable from each of the three separate
regressions listed. Results obtained from regressions using three-year lag of each determinant inaddition to country-year fixed effects and sectoral fixed effects.
Click here to download data and charts.

Fostering productivity growth
EMDE productivity growth has been in a broadbased downward trend in recent years (Figure
1.26.A; Chapter 3). This deceleration has
coincided with a slowdown in improvements inmany correlates of strong productivity growth(Figure 1.26.B) The structural tailwinds that39

boosted EMDE productivity growth prior to 2008
are fading. Output per worker in EMDEs is, on
average, less than one fifth than that of advanced
economies, and at current rates of productivity
growth the average EMDE would take over 100
years to close half of the productivity gap with

40

CHAPTER 1

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 1.26 EMDE structural policies—Productivity
EMDE productivity growth has been in a broad-based downward trend inrecent years. This deceleration has coincided with a slowdown in
improvements in many correlates of strong productivity growth. A reform
package that combines filling investment needs, boosting human capital,
and improving the adoption of new technologies could lift productivity
significantly. Fostering productivity is key to alleviate poverty.
A. EMDE productivity growth

C. EMDE productivity reform scenario

B. Share of EMDEs with a slowdown
in productivity drivers in 2008-17
relative to 1998-2007

D. Productivity growth and global
poverty

Source: Barro and Lee (2015); Observatory of Economic Complexity; Penn World Table; Rozenberg
and Fay (2019); The Conference Board; United Nations; World Bank.
Note: Productivity is defined as output per worker. Sample includes 29 advanced economies and 74
EMDEs. Refer to Chapter 3 for details. Aggregate growth rates calculated using GDP weights at 2010
prices and market exchange rates.
A. Figure shows 5-year moving averages.
B. Econ. complexity = economic complexity. Post-crisis slowdown defined as the share of economies
where improvements in each underlying driver of productivity during 2008-2018 was less than zero or
the pace of improvement during the pre-crisis period 1998-2007. Unbalanced sample of 74
economies. Variables corresponding to each concept are (sample in parentheses): Demography=share of working-age population, Investment =investment to GDP ratio, Innovation =patents per
capita, Gender equality = ratio of female labor market participation rate to male, Urbanization = urban
population (% total), Institutions = WGI Rule of Law, Income equality = (-1)*Gini coefficient, Education= years of schooling, ECI defined as Economic Complexity Index of Hidalgo and Hausmann (2009).
Orange line indicates 50 percent.
C. The reform scenario assumes: (1) Fill investment needs: the investment share of GDP increases
by 4.5 percentage points as in the Rozenberg and Fay (2019) “preferred” infrastructure scenario. The
increase is phased in linearly over 10 years; (2) Boost human capital: average years of education
increases in each EMDE at its fastest cumulative 10-year pace during 2000-08; (3) Reinvigorate
technology adoption: economic complexity (Hidalgo & Hausmann 2009) increases at the same pace
as its fastest 10-year rate of increase during 2000-08.
D. Poverty is defined as the extreme poor living at or below $1.90 per day, in 2011 PPP terms.
Click here to download data and charts.

advanced economies. In addition, cyclicalheadwinds, rising protectionist measures, andelevated policy uncertainty highlight theimportance of productivity-enhancing policies,
such as those that improve institutions, encourage
investment, and promote diversification.

Policies to boost sectoral diversification are crucial,
particularly for commodity exporters that havehistoricallyexperienced
low
productivity
growth—total factor productivity in commodity
exporters has contracted by around 0.8 percent per
year over the past four decades. Sectoral
diversification may encourage productivity gains
in sectors that are less dependent on volatilecommodity prices (Bahar and Santos 2018;
Frankel 2010). Removing bottlenecks and barriers
to investment in high value-added services sectors
provides opportunities for rapid catch-up in
productivity growth.
Policymakers could significantly contribute toraising productivity in EMDEs by encouraging
firms to upgrade to more high-value-added andtechnology-intensive subsectors (Cusolito andMaloney 2018; Syverson 2011). In addition,
improving the business environment fostering
capital market development, and encouraging FDI
could contribute to reducing cross-country
sectoral productivity dispersion. Action is alsoneeded to help reduce the vulnerability to adverse
productivity shocks, such as financial crises,
disasters, and conflict (Cerra and Saxena 2008,2017; Ray and Esteban 2017).
Social safety nets play a key role in mitigating theadverse effects of new technologies that may
initially be disruptive to employment. Policies that
improve social insurance for unemployment areneeded in the formal and informal sectors. Policies
that incentivize adult learning, particularly forhigh-order cognitive skills that complement newtechnologies, could help reintegrate displaced
workers into the labor force (Andrews, Avitabile,and Gatti 2019; World Bank 2018d). Measuresthat help close the gender gap and improve female
labor force participation would also contribute toraising growth and productivity (Ianchovichina
and Leipziger 2019). Overall, a reform packagethat combines filling investment needs, boosting
human capital, and improving the adoption of
new technologies could lift productivity growth byjust over half of a percentage point over 10 years(Figure 1.26.C). By bolstering productivity, thesepolicies will support poverty alleviation (Figure
1.26.D).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 1

41

TABLE 1.2 Emerging market and developing economies1
Commodity exporters2

Commodity importers3

Albania*

Lao PDR

Afghanistan

Pakistan

Algeria*

Liberia

Antigua and Barbuda

Palau

Angola*Madagascar

Bahamas, The

PanamaArgentina

Malawi

Bangladesh

Philippines

Armenia

Malaysia*

Barbados

PolandAzerbaijan*

Mali

BelarusRomania

Bahrain*

Mauritania

BhutanSamoa

BelizeMongolia

Bosnia and Herzegovina

SerbiaBenin

MoroccoBulgaria

Seychelles

Bolivia*

Mozambique

Cabo Verde

Solomon Islands

Botswana

Myanmar*Cambodia

Sri Lanka

BrazilNamibia

China

St. Kitts and Nevis

Burkina Faso

Nicaragua

Comoros

St. Lucia

BurundiNiger

Croatia

St. Vincent and the Grenadines

Cameroon*

Nigeria*

Djibouti

Thailand

Chad*

Oman*

Dominica

Tonga

Chile

Papua New Guinea

Dominican Republic

Tunisia

Colombia*

Paraguay

Egypt

TurkeyCongo, Dem. Rep.

Peru

El Salvador

TuvaluCongo, Rep.*

Qatar*

Eritrea

Vanuatu

Costa Rica

Russia*Eswatini

VietnamCôte d’Ivoire

RwandaFiji

Ecuador*

Saudi Arabia*

Georgia

Equatorial Guinea*

SenegalGrenadaEthiopia

Sierra Leone

Haiti

Gabon*South Africa

Hungary

Gambia, The

Sudan*

India

Ghana*

Suriname

Jamaica

GuatemalaTajikistan

JordanGuineaTanzania

Kiribati

Guinea-Bissau

Timor-Leste*

LebanonGuyanaTogo

LesothoHonduras

Turkmenistan*

Maldives

Indonesia*

UgandaMarshall Islands

Iran*

Ukraine

Mauritius

Iraq*

United Arab Emirates*

MexicoKazakhstan*

Uruguay

Micronesia, Fed. Sts.

Kenya

Uzbekistan

Moldova, Rep.

KosovoWest Bank and Gaza

Montenegro

Kuwait*ZambiaNepal

Kyrgyz Republic

Zimbabwe

North Macedonia* Energy exporters.
1. Emerging market and developing economies (EMDEs) include all those that are not classified as advanced economies and for which a forecast is published for this report. Dependent
territories are excluded. Advanced economies include Australia; Austria; Belgium; Canada; Cyprus; the Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Hong Kong
SAR, China; Iceland; Ireland; Israel; Italy; Japan; the Republic of Korea; Latvia; Lithuania; Luxembourg; Malta; Netherlands; New Zealand; Norway; Portugal; Singapore; the Slovak
Republic; Slovenia; Spain; Sweden; Switzerland; the United Kingdom; and the United States.
2. An economy is defined as commodity exporter when, on average in 2012-14, either (i) total commodities exports accounted for 30 percent or more of total goods exports or (ii) exports ofany single commodity accounted for 20 percent or more of total goods exports. Economies for which these thresholds were met as a result of re-exports were excluded. When data were
not available, judgment was used. This taxonomy results in the classification of some well-diversified economies as importers, even if they are exporters of certain commodities (e.g.,
Mexico).
3. Commodity importers are all EMDEs that are not classified as commodity exporters.

42

CHAPTER 1

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SPECIAL FOCUS 1
Price Controls:
Good Intentions, Bad OutcomesG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

S P EC IAL FO CU S 1

The use of price controls is widespread across emerging markets and developing economies (EMDEs), including
for food and key imported and exported commodities. While sometimes used as a tool for social policy, pricecontrols can dampen investment and growth, worsen poverty outcomes, cause countries to incur heavy fiscal
burdens, and complicate the effective conduct of monetary policy. Replacing price controls with expanded andbetter-targeted social safety nets, coupled with reforms to encourage competition and a sound regulatoryenvironment, can be both pro-poor and pro-growth. Such reforms need to be carefully communicated andsequenced to ensure political and social acceptance. Where they exist, price control regimes should be transparent
and supported by well-capitalized stabilization funds or national hedging strategies to ensure fiscal
sustainability.

Introduction•

How prevalent are price controls in EMDEs?

Price distortions are defined as instances “whenprices and production are higher or lower than thelevel that would usually exist in a competitivemarket” (WTO 2019). One source of such
distortions are price controls.1 Price controls canbe imposed in a variety of ways. They may involve
price ceilings, or price floors, imposed on selected
goods and services by the authorities.2•

What challenges do they impose for growthand development and government policies?

In emerging market and developing economies(EMDEs), price controls on goods are often
imposed to serve social and economic objectives.
They may be part of government efforts to protect
vulnerable consumers, by addressing market
failures or subsidizing the cost of essential goods.
Or they may be intended to maintain the incomesof producers, as part of a price-support program.
Alternatively, they can serve the purpose of pricesmoothing, especially for key commodities subject
to high volatility in international markets. Thiscan lower uncertainty about households’ real
incomes and firms’ production costs.
This special focus seeks to answer two questions.
Note: This Special Focus was prepared by Justin-Damien
Guénette.
1 Price controls have a long history with well documented
examples stretching back to Revolutionary France (Morton 2001). In
the 20th century, these policies were used extensively in several
western countries during the Second World War, culminating withwidespread controls in the United States and the United Kingdom in
the 1970s (Coyne and Coyne 2015). Price controls were alsoubiquitous in communist countries with planned economies, such as
Poland (Tarr 1994). Generalized price controls fell out of favor in the
1980s, as inflation declined, and governments pursued deregulation.
However, controlled pricing for certain goods and services, including
rent and pharmaceuticals, remain in use to this day (Morton 2001).
2 Government management of prices can also occur as a byproduct of other policies. For instance, preferential exchange rates forcertain goods and the imposition of non-tariff barriers can all push
prices away from that which would prevail in a competitive market.

Contribution. The research adds to the literature
on price controls in two ways. First, it presents
findings from a new data set. Whereas earlier work
is confined to advanced economies or selected
emerging markets, this study covers an almost
complete set of EMDEs.3 Second, it reviews pricecontrols on a wider range of goods.4

Use of price controls
Widespread price controls in EMDEs. Pricecontrols are widely employed across advanced
economies and EMDEs. They tend to be morepervasive in EMDEs than in advanced economies

3 The data set extracts the list of products subject to pricecontrols from the latest available Trade Policy Reviews for each
EMDE member country of the World Trade Organization. This listof products is compiled using existing legislation and additional
material provided by country authorities. The data set provides arough view of the prevalence of price control measures acrosscountries, but does not include any information on the extent ofthese controls.
4 The micro-founded theory of price controls was developed inpart to examine the case of commodity producers in developing
countries (Stiglitz and Newbery 1979; Newbery and Stiglitz 1982).
More recently, for EMDEs, price controls for petroleum products
have been studied extensively, while those on food products havereceived less attention (Verme and Araar 2017; Kojima 2013;
Devarajan 2013; Murphy et al. 2019; Shi and Sun 2017; Clements,
Jung and Gupta 2007; Ghosh and Whalley 2004). The WorldBank’s Energy Sector Management Assistant Program (ESMAP) has
conducted in-depth studies of subsidy reforms for energy markets
across EMDEs (ESMAP 2019; Ore et al. 2018). The use of pricecontrols for pharmaceutical products, wages and rent has been widely
studied in advanced economies (e.g., Coyne and Coyne 2015;
Nguyen et al. 1994). Studies for individual EMDEs include China,
Indonesia and several MENA countries (Shi and Sun 2017;
Clements, Jung and Gupta 2007; Verme and Araar 2017). The
OECD-WBG Product Market Regulation indicators provide
summary statistics on price controls for a limited set of EMDEs.

51

52

S P EC IAL FO CU S 1(OECD and World Bank 2018). And among
EMDEs, they are more prevalent in LICs (Figure
SF1.1). In EMDEs that have become middleincome countries (MICs) since 2001, pricecontrols are somewhat less common than in the
average EMDE, especially in goods other thanenergy, food, and construction materials.5•

Energy. Virtually all EMDEs impose pricecontrols on energy products, including
electricity and petroleum products such as
liquified petroleum gas and gasoline.

•

Food. Price controls are frequently applied tobasic foodstuffs. This practice is morewidespread in LICs than in other EMDEs:
virtually all LICs impose price controls onsome food items, compared with threequarters of other EMDEs. Products often
subject to price controls include water, sugar,
and rice.6 Since food expenditures represent
nearly 60 percent of the consumption basket
in LICs, compared with 42 percent in other
EMDEs, a larger portion of the LICs basket istypically subject to price controls (Laborde,
Lakatos, and Martin 2019). Virtually all LICs
and other EMDEs impose price controls on
petroleum products.

•

Construction materials. Nearly 20 percent of
LICs impose price controls on construction
materials. These include cement, reinforcing
bars, and metal sheets. Beyond LICs, controls
on construction materials are most common
in the Middle East and North Africa (MNA)
and Sub-Saharan Africa (SSA).

5 The set of LICs in 2001 that are now MICs includes Angola,
Armenia, Azerbaijan, Bangladesh, Bhutan, Côte d’Ivoire, Cameroon,
Republic of Congo, Comoros, Georgia, Ghana, Indonesia, India,
Kenya, Kyrgyz Republic, Cambodia, People’s Democratic Republicof Lao, Lesotho, Republic of Moldova, Myanmar, Mongolia,
Mauritania, Nigeria, Nicaragua, Pakistan, Sudan, Senegal, Solomon
Islands, São Tomé and Príncipe, Turkmenistan, Ukraine,
Uzbekistan, Vietnam, Zambia and Zimbabwe.
6 Almost all LICs, including Ethiopia, Mali, Niger, Guinea and
Rwanda impose some form of price controls on petroleum products.
As for food products, LICs such as Burkina Faso and the Democratic
Republic of Congo impose price controls on sugar. Chad, Haiti andGuinea-Bissau impose controls on rice, and Benin, Ethiopia andNiger impose controls on bread. Burkina Faso imposes controls on
cement, reinforcing bars and metal sheets. In addition to goods, pricecontrols are also often imposed on public transportation services suchas bus, train, and ship fares.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Price controls on traded goods. EMDEs,
including LICs, apply price controls on export andimport goods.7 Governments often impose
controls on the domestic prices of imports tomaintain real incomes of domestic consumers,
hold down costs to producers, or smooth domestic
price volatility.
•

Energy imports. In LICs, about 67 percent ofenergy imports—about 6 percentage points
more than the average for other EMDEs—are
potentially subject to domestic price controls(Figure SF1.2).8•

Food imports. In both LICs and other
EMDEs, only a small share of food andbeverages imports are potentially subject tocontrols.

•

Construction material imports. The largest
difference between LICs and other EMDEs
lies in the share of construction-related
imports that are potentially subject to pricecontrols: in LICs, they amount to one-quarterof imported construction materials, comparedwith almost none in other EMDEs.

Commodity exports. LICs often impose pricecontrols on exportable commodities. This may
involve a monopoly marketing agency, whichpurchases from domestic producers at a fixedprice, and resells to foreign purchasers at the world
price. This arrangement implicitly taxes producers
when the resale price exceeds the purchase price(Ghosh and Whalley 2004) or subsidizes
producers when the resale price falls below thepurchase price. About 25 percent of EMDEs that
rely heavily (with more than 10 percent of goods
exports) on a single export commodity group
impose price controls on it. For example, Burundi
imposes controls on the price of coffee while
Benin imposes controls on cashew nuts.

7 Unregulated prices depend on the world price, transport costs,
local monopoly power or other hurdles to the movement of goods,
and harvest conditions (Aksoy and Ng 2010).
8 Data on price controls on tradable goods combines theinformation on controlled prices from the World Trade
Organization’s Trade Policies Reviews with 4-digit HS trade values
from the World Bank’s World Integrated Trade Solutions database.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Price controls on financial services. While notcovered in the price control data set, the financial
sector is also often a target of price controls.
Around 60 EMDEs have imposed ceilings oninterest rates. These measures are often motivatedby a desire to provide targeted support to strategic
industries or to shield consumers from financial
exploitation. For example, in the case of Zambia,
controls were implemented from 2012 to 2015
to reduce the perceived risk of over indebtedness
and broaden access to credit (Maimbo andGallegos 2014).
Decline in price controls. Starting in the 1980s,
several EMDEs reduced the scope of pricecontrols, opting instead to strengthen theircompetition policies and regulation (WTO 20002019). In some cases, the liberalization of prices
was supported and encouraged by policy lending
programs and debt relief efforts in highly indebted
poor countries (HIPC). The removal of controls
often become more feasible following an easing of
the conditions that led to their imposition. For
example, after 2011, as food prices declined fromcyclical highs, some countries eliminated controls.
EMDEs such as Mexico, Rwanda, and Côte
d’Ivoire took advantage of the sharp decline in oil
prices in 2014-16 to reduce petroleum subsidies(Baffes et al. 2018; Stocker et al. 2015).
Reforms in MENA. Under pressure from social
tensions during the Arab Spring, some countries
in the region introduced or tightened food pricecontrols in 2011 (Ianchovichina, Loening andWood 2014). Conversely, however, high oil prices
and fiscal pressures encouraged a few MENA
countries, including the Arab Republic of Egypt,
Morocco, and Tunisia, to reform price controls
and related subsidies on energy between 2010 and2014 (Verme and Araar 2017).9 The reforms wereassociated with improvements in the ease of doing
business. Within two years of the reform,
enterprises in all three countries reported easieraccess to electricity (Figure SF1.2.D). The

9 Djibouti, Egypt, Jordan, Libya, Morocco, Tunisia, and Yemen
designed and implemented subsidy reform programs. These cases
contrast with some other countries in the region, where social
tensions during the Arab Spring, caused an increased use of foodprice controls in 2011 (Ianchovichina, Loening, and Wood 2014).

S P EC IAL FO CU S 1

53

FIGURE SF1.1 Price controls
LICs use price controls more extensively than other EMDEs, especially forenergy products such as petroleum and electricity, and basic foodstuffs
such as cereal products and sugar. A large portion of the LICs
consumption basket is subject to price controls given the elevated share offood in the consumption bundle. Across EMDEs more broadly, pricecontrols are most prevalent in MENA and Sub-Saharan Africa and least
prevalent in South Asia.
A. Economies with price controls

B. Goods most frequently subject
to Price Controls in LICs

C. Share of food in total consumption
expenditure

D. Economies with price controls
by sub-region

Source: World Bank; World Trade Organization.
Note: EMDEs = emerging markets and developing economies; LICs = low-income countries.
A. B. D. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy
Review publication.
A. C. D. Unweighted averages.
A. Sample includes 21 low-income countries, 23 LICs turned middle-income countries (MICs) since2001, and 56 other EMDEs.
B. Sample includes 21 low-income countries.
C. Sample includes 23 low-income countries and 67 other EMDEs.
D. Sample includes 21 low-income countries and 79 other EMDEs. EAP = East Asia and Pacific,
ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East andNorth Africa, SAR = South Asia, SSA = Sub-Saharan Africa.
Click here to download data and charts.

programs, however, differed substantially in theirscope, and speed of implementation. They also
varied with respect to compensatory transfers to
disadvantaged population groups. Morocco
reduced the fiscal burden of petroleum subsidies,while at the same time avoiding severe adverse
consequences for poverty and inequality. Egypt,
however, took a sequential, gradual, approach toreform especially for products such as liquified
petroleum gas (LPG), which account for adisproportionately large expense for the poor.

54

S P EC IAL FO CU S 1

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

measures for the poor, including expanding
food subsidies (Verme and Araar 2017).
Moreover, the government used a share of theproceeds from the reforms to increase
expenditures on health care and education
provision (ESMAP 2017a). However,attempts to communicate to the affectedpublic that they might eventually benefit fromthe diversion of energy subsidies to more
equitable uses failed, largely because thecountry does not have the social security net
to implement an effective system of cash
compensation (Verme and Araar 2017).

FIGURE SF1.2 Price controls on imported and exported
goods
The shares of imports and exports potentially covered by price controls arehigher in LICs than in other EMDEs. Reforms of price controls on energy
products in Egypt, Morocco and Tunisia were associated withimprovements in an index of the ease of getting electricity within two yearsof energy price reforms.
A. Share of total imports subject toprice controls

C. Share of countries with price controls on export goods

B. Share of 2-digit HS category
imports subject to price controls

Morocco. Starting in 2013, the government
first transitioned to price indexation for
petroleum products, and gradually moved to
fully liberalize most energy products. In
August 2014, prices of household utilities
jumped as part of a multiyear effort toliberalize electricity prices. The reforms were
implemented without triggering social unrest
despite the absence of cash transfers tohouseholds. The fiscal savings from thereform were instead used to fund otherreforms.

•

Tunisia. The fiscal cost of Tunisia’s energy
subsidies had risen to unsustainable levels (7percent of GDP in 2013), and in response thegovernment
gradually
reducedthem
beginning in late 2012 in tandem withreforms to social benefits. Petroleum andelectricity prices were increased over 2012-13
and an automatic price formula wasintroduced for gasoline in 2014. In 2016, thegovernment agreed to further reduce subsidies
as part of a reform program supported by IMF
lending. Energy prices were increased several
times since then, with the goal of fully
eliminating energy subsidies by 2022. Overthe years, measures were implemented tocushion the impact of reforms on vulnerable
households, including expanded social
housing and higher income tax deductions.

D. Ease of getting electricity

Source: World Bank; World Trade Organization.
Note: EMDEs = emerging markets and developing economies; LICs = low-income countries.
A.-C. 2017 data. Listed price control policies are retrieved from the latest (2003-19) country Trade
Policy Review publication.
A. B. Sample includes 12 low-income countries and 63 other EMDEs.
B. Share of 4-digit Harmonized System (HS) category subject to controlled prices in high-levelgroupings of 2-digit HS categories. Construction materials aggregate includes HS68 and HS73,
Energy aggregate includes HS27, Food and Beverage aggregate includes HS01 to HS22. Other
aggregate includes all other imports.
C. Countries that rely heavily on a single export defined as a country in which exports of one or more
4-digit HS category represents 10 percent or more of its total exports in 2017. Chart shows the shareof all LICs and other EMDEs that relying heavily on a single export whose price is subject to pricecontrols. Sample includes 12 low-income countries and 61 other EMDEs.
D. Chart shows the World Bank’s index for ease of getting electricity in year before (t-1) and the twoyears after (t+1, t+2) energy subsidy reform (World Bank 2019b). Time t=0 refers to 2014 for Egypt
and Morocco and 2012 for Tunisia.
Click here to download data and charts.

••

Egypt. In July 2014, comprehensive reforms tofuel and electricity prices resulted in asignificant rise in gasoline, natural gas, diesel,and electricity prices which contributed to a
spurt of headline inflation. Initial priceadjustments were followed by stepwise gradualincreases to fully eliminate energy subsidies
over a five-year period. While the initial priceincreases themselves are estimated to haveraised the poverty rate and inequality, thegovernment has put in place some mitigating

Reforms in other regions. In Ukraine in 2015-16,
the government raised the price of natural gas,
which had been heavily subsidized for decades.
These reforms were coupled with a strong public
communication campaign highlighting social

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

assistance mechanisms targeted to cushion theimpact on low-income households. The reforms
were successful in allowing public utilities toachieve cost recovery, with the targeted support
measures estimated to have reduced the poverty
rate (ESMAP 2017b). In India starting in 2012,
the government reformed its subsidy regime forliquified petroleum gas (LPG). LPG subsidies tohouseholds encouraged the formation of black
markets where subsidized LPG distributed tohouseholds was diverted to the commercial sector.
The government gradually increased the price of
LPG for households while implementing a largescale targeted cash transfer mechanism. The
program successfully eliminated distortions in the
LPG market, with limited adverse consequences
for the poor, and the fiscal savings obtained fromthe reduction in subsidies fully offset the costs ofthe targeted cash transfer (ESMAP 2016).

S P EC IAL FO CU S 1

they can discourage foreign investment inthose sectors by increasing the country risk
premium facing global firms (Sabal 2005). In
the opposite case, where the controlled price isabove that required for a competitive return toinvestment, its maintenance requires barriers
to entry or costly government stockpiling ofexcess supply (a common occurrence withprice support schemes in agriculture). Pricesupport controls can depress competition andsustain high producer margins (e.g., Rwanda’s
transportation sector; Teravaninthorn andRaballand 2009).
•

Lower productivity. Price control regimes may
tilt the allocation of resources towards thesubsidized sector. In LICs, this is often mostvisible in the agricultural sector where output
price controls have been complemented by
input (especially fertilizer) subsidies. Yet, suchpolicies can end up reducing productivity, andworsening income inequality (Goyal and Nash2017). They may lead to inefficient use of
subsidized inputs (Jayne, Mason, Burke andAriga 2016). They can also adversely affectincentives to adopt productivity-raising newtechnologies. Empirical evidence suggests thatmarket-oriented structural reforms, including
the reduction of price controls and theirrelated subsidies, are strongly associated withimproved firm-level productivity in EMDEs(Kouame and Tapsoba 2018). Conversely, inthe case of petroleum products in the Middle
East and North Africa, high subsidies that
underpin price controls appear to be
associated with lower per capita output
growth (Mundaca 2017).

•

Increased informality. Price controls that
distort consumption towards price-controlled
goods, can cause chronic shortages of thesegoods, the formation of parallel markets withhigher prices, and substitution towards lowerquality alternatives (Weitzman 1991; Pateland Villar 2016; Fengler 2012; Winkler
2015). Similarly, producers of price-controlled
goods may turn to black markets which haveelevated transaction costs and lack basic
regulation (Murphy, Pierru and Smeers2019). In addition, the situation encourages

Challenges of price controls
While they may be introduced with the best
intentions to improve social outcomes, pricecontrols
often
undermine
growthanddevelopment, impose fiscal burdens and canweaken the effectiveness of monetary policy. At
least in part, this is because price controls cause ashift in consumption towards the subsidized good,
and away from other non-subsidized goods.
Moreover, when there are trend increases ininternational prices, or when they interact withbarriers to entry, price control measures frequently
morph into distortive subsidy regimes. Importantsocial, fiscal and environmental costs are likely tofollow, as well as adverse consequences forinvestment and employment, and productivity
growth.
Growth challenges. The use of price controls canhave adverse consequences for growth for several
reasons:•

Stifled competition and reduced investment.
Price ceilings can depress producer margins
and discourage domestic investment andentrepreneurial activity, as in Zimbabwe’s
transportation sector (Newfarmer and Pierola2015). If margins depend on subsidies to local
businesses to compensate for price controls,

55

56

S P EC IAL FO CU S 1

production to shift to firms in the informal
sector, which avoid regulation (De Soto 2000;
World Bank 2019a).
••

Distorted financial markets. Price controls in
the financial sector, such as ceilings on interest
rates can distort financial markets (Maimbo
and Gallegos 2014). These measures reduce
the supply of credit to safer borrowers andsmall and medium-sized enterprises, increase
the level of non-performing loans, reduce
competition and innovation in lending
markets, and increase informal lending.
Moreover, they can exacerbate inequality bylimiting the poor’s access to lending.
Increased vulnerability to climate change. Pricecontrols and subsidies on energy products mayheighten vulnerability to climate change andinhibit the transition to a climate-resilient,
low-carbon economy.

Social policy and political economy challenges.
The use of price controls combined with large
subsidies is an inefficient tool for redistributing
domestic income (Devarajan 2013; Coyne andCoyne 2015). These policies tend to beinequitable, as wealthier segments of the
population, usually urban consumers, benefit
disproportionately given their greater consumption of the price-controlled good compared torural consumers and producers. For example,
subsidies and below-market prices for gasoline andliquid natural gas have proven highly regressive,with only a small share of the subsidy benefiting
the poorest segments of the population (Baffes etal. 2015; IEG 2008; Coady et al. 2006).
Fiscal challenges. Price controls impose an explicit
or implicit set of taxes and subsidies that varies
over time, and their enforcement may require
additional regulations to constrain consumption
and production. Typically, a system of pricecontrols on goods ends up as a growing burden on
either the fiscal budget and public debt or the
profitability of producers (Alleyne 2013; WorldBank 2014a). Potential or implicit fiscal costs
from price controls can be particularly high in
LICs due to their more widespread use of thesepolicies. Even in EMDEs, subsidies for products

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

subject to price controls, such as petroleum, canbe a large portion of government expenditures, insome cases exceeding 10 percent of GDP (Algeria,
Iran; World Bank 2014b).
Monetary policy challenges. In all advanced
economies, and in many EMDEs, monetary policy
has played a major role in reducing inflation to alow, stable rate, often in the context of an explicit
inflation-targeting regime. The key has been atransparent strategy aimed at the medium andlonger term. This has largely stabilized longer-run
expectations of inflation, in line with central bankobjectives. In these circumstances, the one-off
impact on the inflation rate of the removal of pricecontrols can be handled with the help of careful
communication from policymakers as to thestrategy they will employ to get inflation back on
track. In LICs, however, the monetary policy
challenges go deeper. First, the wider use of pricecontrols complicates the choice of inflation target
by weakening the usefulness of the overall CPI as ameasure of underlying inflation pressures (Pateland Villar 2016).10 Second, it can raise inflation
because the authorities tend to respondasymmetrically when faced with cost increases, as
is often the case in response to food price shocks(De Mello 2008; Ianchovichina, Loening andWood 2012). Third, it can increase the stickinessof the inflation process as changes in controlled
prices often involve a lengthy regulatory process(Springer de Freitas and Bugarin 2007). Fourth,
one-off changes in controlled prices can havepersistent effects on inflation in LICs, where
inflation expectations are less well anchored (Ha,Kose, and Ohnsorge 2019a; BIS 2003). Lastly,
price controls in the financial sector, including
ceilings on interest rates can reduce the ability ofmonetary policy to affect financial conditions.
Price controls in times of hyperinflation. The useof price controls has often coincided withhistorical episodes of hyperinflation. In Brazil inthe 1980s, for example, the use of price controls

10 In addition, volatility in headline CPI inflation is amplified by
the high proportion of food in the LIC consumer basket. Food prices
are liable to frequent large fluctuations from variations in local
harvests, and in international supply and demand.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

has proved ineffective at addressing hyperinflationin Brazil (Cardoso 1991). More recently, in thecase of Zimbabwe, widespread shortages of goods
in part due to excessively accommodative
monetary policy were accompanied by extensive
price controls (Munoz 2006; Coomer andGstraunthaler 2011). Similarly, high inflation in
República Bolivariana de Venezuela wasaccompanied by highly restrictive price controls(Vera 2017; Contreras and Guarata 2013).
Collateral damage from foreign price controls.
LICs are also more vulnerable to the collateral
damage from other countries’ price controls onfood and energy, because of the high share of foodand energy in their consumption baskets andtrade. Policies by individual countries to containthe effects of spikes in global commodity process
in their local markets have been shown to have
had the perverse effect of raising global prices(Laborde, Lakatos, and Martin 2019). Export
restrictions in major commodity producers
exacerbate global shortages, thus contributing tohigher prices on the international market. In thecase of the 2007-08 surge in food prices, amajority of EMDEs put in place policies toinsulate domestic markets from the rise ininternational prices (World Bank 2009).

Policy implications
Price controls have been used to mitigate theimpact of commodity price volatility on the mostvulnerable members of society. For instance, theuse of temporary stabilization funds, as introducedin Chile and Peru, or national hedging strategies,
as introduced in Mexico, have been used toprotect domestic consumers and firms from spikes
in the prices of basic commodities on international
markets (Kojima 2013; Ma and Valencia 2018).
However, most governments have had difficulty
designing frameworks that deliver lasting benefits.
Over time, price stabilization policies often result
in costly and distortionary subsidies, posing
important challenges to growth, development, andmacroeconomic policy suggesting that other policy
instruments may be more effective in achievingsocial protection objectives.

S P EC IAL FO CU S 1

Comprehensive reforms of price control policies
and related subsidies. Replacing price controls
with expanded and better-targeted social safety
nets, coupled with structural reforms, can be bothpro-poor and pro-growth. Indeed, policies tolower subsidies that underpin price controls
appear to be associated with higher per capita
output growth, in part because savings generated
by lower subsidies can fund productivityenhancing education and infrastructure (Mundaca2017). The removal of price controls needs to becoupled with targeted support for those segmentsof the population that might be adversely affected(World Bank 2014a).11 In India, for example, theremoval of price controls was accompanied bytargeted cash transfers and in Brazil by targeted
assistance to low-income households for energy
conservation (Deichmann and Zhang 2013).The
different prongs of reforms, however, need to becarefully sequenced and communicated.
Enhanced competition. Improving the competitive environment can be a more effective meansof lowering costs to consumers and producers thanthe use of price controls. Carefully-designed andproperly enforced antitrust laws and consumer
protection legislation, are essential components ofinstitutional frameworks that support market
mechanisms. A sound legal and regulatory
framework favoring competitive markets provides
a more effective response to many of the problems
that price controls attempt to address (Kovasic
1995). For example, the removal of price controls
and barriers to entry in the transportation sector
significantly increased competition and loweredtransportation costs in Rwanda (Teravaninthorn
and Raballand 2009). Even in the case where
incumbent firms maintained outsized market
shares, the presence of competition, and thepotential for new entrants, significantly lowered
their markups (World Bank 2006).

11 Despite the regressive nature of price controls and subsidies,poor households spend a higher share of their income on products
subject to price controls and are liable to suffer distressful real income
losses when price restrictions are lifted (World Bank 2014a).

57

58

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CHAPTER 2REGIONALOUTLOOKS

Growth in the East Asia and Pacific (EAP) region is projected to slow from an estimated 5.8 percent in 2019
to 5.7 percent in 2020 and moderate further to 5.6 percent in 2021-22. Easier financing conditions and fiscal
policy support will partly mitigate the lingering impact of trade tensions amid domestic challenges. Despite therecent slowdown, EAP remains the region with the fastest labor productivity growth. Nevertheless, productivity
levels remain below the EMDE average in most EAP economies. In China, growth is expected to slow
gradually, from an estimated 6.1 percent in 2019, to 5.9 percent in 2020, and to 5.7 percent by 2022. In therest of the region, growth is expected to recover slightly to 4.9 percent in 2020 and firm further to 5 percent in
2021-22. The balance of risks to the outlook has improved, but is still tilted to the downside. Downside risks
include a sharp slowdown in global trade due to a re-escalation of trade tensions; a sharper-than-expected
slowdown in major economies; and a sudden reversal of capital flows due to an abrupt deterioration in
financing conditions, investor sentiment, or geopolitical relations. An upside risk to the forecast is that the recent
trade agreement between China and the United States leads to a sustained reduction in trade uncertainty,
resulting in a stronger-than-expected recovery of regional investment and trade.

Recent developments
The East Asia and Pacific (EAP) region has been
experiencing a continued cooling of domestic
demand in China alongside sizable external
headwinds (Figure 2.1.1). Global demand has
weakened, and trade policy uncertainty related to
trade disputes between China and the UnitedStates was elevated prior to the recent bilateralagreement. In addition, trade tensions between
Japan and the Republic of Korea, a maturing
electronics cycle, and disruptions caused by rapid
shifts in technological and emission standards,
have also weighed on regional manufacturing
activity and trade (World Bank 2019a, 2019b).
The global trade slowdown and heightened trade
policy uncertainty have affected regional growththrough three main channels: weaker total exports;
disruptions in cross-border supply chains; anddeclining private investment amid low business
confidence (China, Indonesia, Malaysia, Thailand,
the Philippines). Regional export growth has
Note: This section was prepared by Ekaterine Vashakmadze.
Research assistance was provided by Juncheng Zhou and Yushu
Chen.

decelerated sharply from its 2017-18 peak.
Imports have also moderated, reflecting adrawdown of inventories and a slowdown ininvestment growth due to deteriorated businesssentiment amid delays in certain major public
infrastructure projects (China, Malaysia, Thailand,
the Philippines).
In China, weakening exports have compounded
the impact on GDP of the ongoing slowdown ofdomestic demand (Figure 2.1.2; World Bank
2019c). Regulatory tightening aimed at curbing
non-bank lending has contributed to further
cooling of domestic demand. Policy uncertainty
and higher tariffs on exports to the United States,
have dampened manufacturing activity, weighedon investor sentiment, and dented private
investment. Despite this, net exports have been
contributing to growth. Imports, especiallyintermediate goods imports, have contracted,
partly reflecting high base effect, drawdown of
inventories, disruptions in global and regional
supply chains, an onshoring of foreign
manufacturing operations, and a weaker renminbi.
The negative shock to exports and output fromtrade tensions with the United States has been
partly offset by currency depreciation, price

64

CHAPTER 2.1

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 2.1.1 EAP: Recent developments
Growth continues to slow in China and has moderated in the rest of theregion. Regional export growth has decelerated sharply from the 2017-18
peak. In the region excluding China, exports are showing incipient signs ofrecovery. Monetary policies have been eased across the region amid
subdued inflation. Net capital outflows from China have resumed in 2019.
In the rest of the region, capital flows have been essentially balanced.
Bond spreads have generally declined.
A. Growth, 2019

B. Export growth

C. Consumer price inflation

D. Nominal policy rates and change ininflation-adjusted policy rates

E. Net capital flows

F. EMBI spreads

Source: Haver Analytics; World Bank.
A. Aggregate growth rates are calculated using GDP weights at 2010 prices and market exchange
rates. Investment indicates fixed asset investment. Import and export data are volumes of goods andnon-factor services. Investment, export, and import data for East Asia and Pacific region excl. China
include Cambodia, Indonesia, Lao PDR, Malaysia, Mongolia, Philippines, Solomon Islands, Thailand,
Vanuatu, and Vietnam. GDP data for East Asian and Pacific Countries excl. China include Cambodia,
Fiji, Indonesia, Kiribati, Lao PDR, Malaysia, Marshall Islands, Micronesia Fed. Sets., Mongolia,
Myanmar, Palau, Papua New Guinea, Philippines, Samoa, Solomon Islands, Thailand, Timor-Leste,
Tonga, Tuvalu, Vanuatu, and Vietnam. Bars indicate 2019 which are estimates.
B. Export volumes. Data include only goods. 6-month moving average. Regional aggregate excludes
Cambodia, Fiji, Lao PDR, Mongolia, Myanmar, Solomon Islands, Papua New Guinea, Timor-Leste,
Vanuatu, and Vietnam due to data limitations. October-November export price deflators for China are
estimates. Last observation is November 2019 for China and October 2019 for EAP excl. China.
C. Average year-on-year consumer price inflation. Mid-point of inflation target for Indonesia,
Philippines, and Thailand. Inflation target for China and Vietnam. For Malaysia, the mid-point of Bank
Negara’s official forecast range of 0.7-1.7 percent in 2019. Last observation is November 2019.
D. Latest rate refers to Malaysia’s overnight policy rate, Indonesia’s 7-day reverse repo rate, China’s
loan prime rate, Thailand’s one-day repurchase rate, Vietnam’s discount rate and Philippines’
overnight reverse repo rate. Change refers to the difference in real interest rate between November
2019 and January 2019. Last observation is November 2019.
E. Net capital flows are estimates. Net capital inflows include net capital and financial accountbalance, errors and omissions. Last observation is 2019Q3.
F. J.P. Moran Emerging Market Bond Index (EMBI) spread. Last observation is December 2019.
Click here to download data and charts.

adjustments, some reshoring of production, andthe redirection of exports to other countries. As aresult, the current account surplus has widened.
In the rest of the region, some commodity
importers operating at or above capacity haveexperienced a cyclical moderation of activity(Cambodia, the Philippines, Thailand, Vietnam).
Weak export growth has added to the slowdown,
especially in the economies that are deeply
integrated into global and regional production
networks (Thailand, the Philippines). Although
Thailand and Vietnam have benefited somewhat
from the diversion of U.S. demand away from
China, the trade diversion only partially offset the
decline in their exports to Asia arising from theoverall negative impact of global trade tensions
and cooling global demand, including in China,
on the region.
In commodity exporters, which have only recently
recovered from the effects of the earlier fall incommodity prices, the pace and composition of
growth continues to reflect country-specificfactors. In larger and more diversified economies,
where past terms-of-trade shocks were less acuteand macroeconomic fundamentals are solid,
steady growth has continued at rates of around
4.5-5 percent per year (Indonesia, Malaysia). In
Indonesia, growth has been supported by private
consumption and a positive contribution from net
exports amid import compression. In Malaysia,
weak investment growth has been offset by robust
consumption growth supported by tight labormarkets.
The negative impact of slowing regional trade has
so far been partly mitigated by monetary and fiscal
policy support in major regional economies.
Monetary policy in many countries has become
more accommodative in response to slowing
activity amid subdued inflation (Malaysia, thePhilippines, Thailand, Vietnam). In China, thecentral bank has eased policy mainly by cuttingbank reserve requirements, including the 0.5
percentage point cut implemented in early January
2020. In Indonesia, continued capital inflows, astable exchange rate, and low inflation haveprovided the necessary space for Bank Indonesia
to continue policy easing. Several countries have

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

also provided fiscal support (China, Malaysia,
Thailand). Thailand announced a broad range ofstimulus measures, including a support package
for farmers, SMEs, and low-income households(World Bank 2019a). China has introduced
reductions in taxes and government fees, and ahigher limit for local government on-budget
borrowing. The consolidated fiscal and monetary
policy support package implemented is, however,significantly smaller than the one adopted in thewake of the global financial crisis, and somewhat
smaller than the one deployed in 2016.
Other factors—still-robust private consumption
across much of the region and importcompression—have dampened the impact of
weakening manufacturing activity and exports on
growth (Table 2.1.1). The recent de-escalation ofChina-U.S. trade tensions has buoyed asset prices
and business confidence, and contributed tosupportive external financing conditions. Bond
spreads have narrowed, and net capital inflows
have generally risen, despite sporadic episodes ofmarket pressures.

EAST ASIA AND PACIFIC

65

FIGURE 2.1.2 Recent developments, China
In China, growth has further decelerated amid continued cooling ofdomestic demand and heightened trade tensions. The negative shock toexports from higher tariffs on trade with the United States has been partly
offset by currency depreciation. Imports from the United States haveplummeted, while imports from other regions have also weakened. The
current account surplus has widened. The government has stepped up its
fiscal support measures, with a focus on tax- and non-tax-revenue cuts,and support for public investment spending through higher quotas for local
government bonds. The stock of debt has stabilized, reflecting the decline
of non-bank lending.
A. GDP growth

B. Export growth

C. Import growth

D. Balance of payments

E. General government debt andfiscal balance

F. GDP growth and total debt

OutlookAfter moderating from an estimated 5.8 percent in2019 to a projected 5.7 percent in 2020, regional
growth is expected to ease further to 5.6 percent in
2021-22 (Tables 2.1.1 and 2.1.2). This mainlyreflects a further moderate slowdown in China to
5.9 percent in 2020 amid continued domestic andexternal headwinds, including the lingering impactof trade tensions (Figure 2.1.3).
This outlook is predicated on no re-escalation of
trade tensions between China and the UnitedStates going forward and a gradual stabilization in
global trade. It also assumes that authorities in
China continue to implement monetary and fiscal
policies to offset the negative impact of weak
exports. The baseline projections embody aweakened global outlook relative to June, partly
reflecting a much weaker-than-expected outlook
for global trade, manufacturing, and investment.
Despite recent de-escalation in China-U.S.
bilateral trade tensions, heightened uncertainty
surrounding the external environment is likely to

Source: Haver Analytics; International Monetary Fund; National Bureau of Statistics of China; WorldBank.
A. Investment refers to gross capital formation, which includes change in inventories. Lastobservation is 2019Q3.
B.C. Data include only goods. Years cover January-November period. Last observation is November
2019. Asia includes both advanced and emerging Market and developing economies. ROW = alltrading partners excluding the United States and Asia. Last observation for Asia and for ROW is
October 2019. Export and import values. Data for total export growth in 2019 is -0.34 percent.
D. Net capital flows and change in reserves are estimates. Net capital inflows include net capital andfinancial account balance, errors and omissions. Last observation is 2019Q3.
E. Gross debt consists of all liabilities that require payment or payments of interest and/or principal by
the debtor to the creditor at a date or dates in the future. Other includes other net expenditure (incl.
social security and State-Owned Enterprise funds). Fiscal support measures are World Bank staff
estimates. General government gross debt in 2019 are estimates.
F. Total debt is defined as a sum of domestic and external debt. Aggregate growth rates arecalculated using GDP weights at 2010 prices and market exchange rates. Last observation for total
credit and GDP growth is 2019Q3.
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FIGURE 2.1.3 EAP: Outlook and risks
EAP growth is projected to gradually decline, mainly reflecting slower
growth in China. Growth in the rest of the region is expected to stabilize by
2020, with notable cross-country heterogeneity reflecting country specificconditions. The long-term investment outlook is for broad-based
deceleration. The region is characterized by deep global integration, whichmakes countries vulnerable to external trade or financial shocks.
A. GDP growth

B. Output and potential growth

C. Contribution to productivity growth

D. Ten-year ahead investment
forecasts

E. Export growth, 2013-18

F. Growth impact of 1 percentage
point slower growth in China, Japan,
or other G7 countries

Source: International Monetary Fund; Penn World Tables; The Consensus Forecasts; World Bank.
A. EAP excl. China = Cambodia, Indonesia, Lao PDR, Malaysia, Mongolia, Myanmar, Philippines,
Thailand, and Vietnam. Pacific Island excl. PNG includes Fiji, Kiribati, Marshall Islands, Micronesia,
Palau, Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu, and Vanuatu. 1990-2019 average for
EAP excl. China excludes Myanmar and 1990-2019 for Pacific Island excl. PNG excludes MarshallIslands, Micronesia, Palau, Timor-Leste, and Tuvalu. Aggregate growth rates are calculated using
GDP weights at 2010 prices and market exchange rates. Data in shaded areas are forecasts.
B. Potential growth estimates are from a multivariate filter model of WB (2018a). Aggregate growth
rates are calculated using GDP weights at 2010 prices and market exchange rates. Includes China,
Indonesia, Malaysia, Mongolia, the Philippines, and Thailand. Output growth in 2020 is a forecast.
C. Productivity defined as output per worker in 2010 U.S. dollars at 2010 prices and exchange rates.
D. 10-year-ahead forecasts surveyed in indicated year. Constant 2010 U.S. dollar investmentweighted averages. Sample includes China, Indonesia, Malaysia, the Philippines, and Thailand.
E. EA = East Asia. PI = Pacific Islands. EA1 = Brunei Darussalam, Cambodia, Malaysia, Mongolia,
Thailand, and Vietnam; EA2 = Indonesia, Lao PDR, Myanmar and Philippines. PI1 = Kiribati, MarshallIslands, Micronesia, Timor-Leste, Tonga, and Tuvalu; PI2 = Palau and Vanuatu; PI3 comprises Fiji,
Papua New Guinea, Samoa, and Solomon Islands.
F. Median cumulative responses after two years. Based on a Bayesian structural VAR model. Data
coverage is 1998Q1-2018Q2 and is shorter for some countries. The endogenous variables include, inthis Cholesky ordering: growth in G7 excluding Japan, EMBI, Japan’s growth, China’s growth, Korea’s
growth, and three variables for each shock-recipient country: real commodity price index, growth, andreal effective exchange rates. Global spillovers refer to spillovers from growth shocks in the G7
excluding Japan. The model includes a dummy that captures the global financial crisis of 2008-09.
Vertical lines represent the 33-66 percent confidence bands.
Click here to download data and charts.

persist, amid a fragile global outlook, compounding the trade weakness over the near term.
Regional growth excluding China is projected toslightly recover to 4.9 percent in 2020 and firm
further to 5 percent in 2021-22—toward its
potential—assuming
thatweaknessinmanufacturing and export activity does not spillover to consumption and services. Domestic
demand will continue to benefit from generally
supportive financing conditions, amid low
inflation and robust capital flows (Cambodia, thePhilippines, Thailand, Vietnam). Some countries
will benefit from large public infrastructure
projects coming onstream (Thailand, thePhilippines).
Growth in the economies that are deeply
integrated into global and regional production
networks is expected to moderate further in 202122, and to adjust a bit faster than expected towardpotential, reflecting capacity constraints andsubdued external demand. In particular, in
Malaysia, growth is expected to inch down to 4.5
percent in 2020-21, with weak export growth
partly offset by strong domestic demand,
underpinned by favorable financing conditions, arebound in investment, stable labor market
conditions, and low inflation.
In Indonesia, which depends less on exports thanother regional economies, growth is projected tofluctuate around 5 percent throughout the forecasthorizon. This forecast is predicated on a continued
support from private consumption, a pickup in
investment, solid growth of the working-age
population, and improving labor markets. In small
commodity exporters, growth is expected todecelerate, but remain strong (Mongolia), or
rebound in 2022 (Papua New Guinea), supported
by investment in infrastructure and mining.
While growth in the region is projected to remainrobust in the near term, underlying potential
growth is likely to continue to decline over thelong term (Chapter 3; World Bank 2018a,
2018b). The slowdown is expected to be broadbased, reflecting deteriorating demographic trends,
especially in China, Thailand, and Vietnam,
combined with a projected slowdown in capitalG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

accumulation and lower total factor productivity
in China as credit growth is reined in (Box 2.1.1.;
Chapter 3). Investment growth in the rest of theEAP region is also expected to be subdued andbelow historical averages, as the long-term
prospects for investment growth in the region
remain weak and have been persistently
downgraded since 2010.

Risks
The balance of risks has improved, but remains
tilted to the downside. Downside risks include a
sharp slowdown in global trade due to a reescalation of global trade tensions; a sharper-thanexpected slowdown in major economies; and asudden reversal of capital flows due to an abrupt
deterioration in global financing conditions,investor sentiment, or geopolitical relations.
A renewed spike in trade policy uncertainty could
cause a deterioration in confidence, investment,
and trade (Caldara et al. 2019; Freund et al.
2018). Failure by China and the United States toreach a long-term, comprehensive, and durable
agreement could lead to renewed trade tensions,with broad-ranging global and regional consequences.
An upside risk to the forecast is the possibility of asustained de-escalation of trade tensions between
China and the United States. The recent trade
agreement that reverses some tariff increases could
be the beginning of a constructive process leading
to a sustained reduction in policy uncertainty andtrade barriers. This could significantly improve
confidence and unlock pent-up demand forinvestment, bolstering growth.
In the baseline scenario, the impact of slower
global growth and external demand on the region
is offset by more supportive financing conditions
and stronger policy stimulus. However, a sharperthan-baseline deceleration of activity in large
economies—the Euro Area, China, or the UnitedStates—could have adverse repercussions acrossthe region through weaker demand for exports

EAST ASIA AND PACIFIC

and the disruption of global value chains, as wellas through financial, investment, commodity, andconfidence channels (Chapter 1; World Bank
2016a; World Bank 2019d).
Risks of a sharper-than-expected slowdown in
China stem from domestic challenges, as well asfrom the difficult external environment. The total
leverage of the economy—measured as the ratio of
total credit (general government and non-financial
private sector) to GDP—has surpassed 260
percent of GDP in 2019, although the share ofnon-bank lending continued to decline due toregulatory
tightening.
High
corporateindebtedness in sectors with weak profitability isof concern (World Bank 2019e). A sizable portionof recent support measures has taken the form of
expanding local government special bond quotas.
The growing debt burden on local authorities may
increase their vulnerability to shocks.
Most of the EAP region weathered thedeterioration of external conditions in 2019 well,relying on exchange rate flexibility and monetaryand fiscal stimulus. A further deterioration would
test the resilience of the region’s economies. Even
though most large countries have generally soundeconomic fundamentals—track record of solid
growth, fast labor productivity growth, large
consumer bases, diversified economies, soundpolicy frameworks, and strong policy buffers—the
region remains vulnerable to risks related toabrupt changes in global financial conditions.
Many countries have pockets of vulnerabilities.
These include elevated debt (Lao People’s
Democratic Republic, Malaysia, Mongolia,
Vietnam); sizable fiscal deficits (Lao PDR,
Vietnam); or heavy reliance on volatile capital
flows (Cambodia, Indonesia). Renewed episodesof financial market stress could have pronounced
and widespread effects on countries with highindebtedness (Chapter 1). Vulnerabilities amongsome EAP countries could amplify the impact ofexternal shocks, such as a sudden stop in capital
flows or a rise in borrowing costs.

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TABLE 2.1.1 East Asia and Pacific forecast summary
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE EAP, GDP 1

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

6.5

6.3

5.8

5.7

5.6

5.6

-0.1

-0.2

-0.2

(Average including countries with full national accounts and balance of payments data only) 2
6.5

6.3

5.8

5.7

5.6

5.6

-0.1

-0.2

-0.2

GDP per capita (U.S. dollars)

5.8

5.6

5.2

5.1

5.1

5.1

-0.2

-0.2

-0.2

PPP GDP

6.4

6.3

5.8

5.7

5.6

5.6

-0.1

-0.2

-0.2

Private consumption

6.1

8.4

6.8

6.9

6.6

6.6

-0.2

-0.1

-0.4

Public consumption

8.9

8.7

7.7

7.6

7.5

7.5

0.1

0.1

0.1

Fixed investment

4.7

5.1

4.1

4.6

4.7

4.6

-1.0

-0.5

-0.2

Exports, GNFS 3

9.5

5.0

1.4

1.3

2.0

2.4

-1.9

-2.6

-2.3

Imports, GNFS 3

8.4

8.4

-0.3

2.0

2.5

2.9

-5.0

-3.0

-3.2

Net exports, contribution to growth

0.4

-0.9

0.5

-0.2

-0.1

-0.1

0.9

0.2

0.4

EMDE EAP, GDP 2

Memo items: GDP
East Asia excluding China

5.4

5.2

4.8

4.9

5.0

5.0

-0.3

-0.3

-0.2

China

6.8

6.6

6.1

5.9

5.8

5.7

-0.1

-0.2

-0.2

Indonesia

5.1

5.2

5.0

5.1

5.2

5.2

-0.2

-0.2

-0.1

Thailand

4.0

4.1

2.5

2.7

2.8

2.9

-1.0

-0.9

-0.9

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates. Excludes Democratic People’s Republic of Korea and dependent territories.
2. Sub-region aggregate excludes Democratic People’s Republic of Korea, dependent territories, Fiji, Kiribati, the Marshall Islands, the Federated States of Micronesia, Myanmar, Nauru,
Palau, Papua New Guinea, Samoa, Timor-Leste, Tonga, and Tuvalu, for which data limitations prevent the forecasting of GDP components.
3. Exports and imports of goods and non-factor services (GNFS).
Click here to download data.

TABLE 2.1.2 East Asia and Pacific country forecasts1
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

Cambodia

7.0

7.5

7.0

6.8

6.8

6.8

0.0

-0.1

2021f
0.0

China

6.8

6.6

6.1

5.9

5.8

5.7

-0.1

-0.2

-0.2

Fiji

5.2

4.2

1.0

1.7

2.9

3.0

-2.4

-1.6

-0.4

Indonesia

5.1

5.2

5.0

5.1

5.2

5.2

-0.2

-0.2

-0.1

Lao PDR

6.9

6.3

5.2

5.8

5.7

5.6

-1.4

-0.9

-0.9

Malaysia

5.7

4.7

4.6

4.5

4.5

4.5

0.0

-0.1

-0.1

Mongolia

5.3

7.2

5.7

5.5

5.2

5.5

-1.5

-1.4

-1.0

Myanmar

6.8

6.5

6.6

6.7

6.8

6.8

0.1

0.1

0.0

Papua New Guinea

3.5

-0.8

5.6

2.9

2.9

3.0

0.0

-0.2

-0.6

Philippines

6.7

6.2

5.8

6.1

6.2

6.2

-0.6

-0.4

-0.3

Solomon Islands

3.0

3.5

2.9

2.8

2.8

2.7

0.0

0.0

0.1

Thailand

4.0

4.1

2.5

2.7

2.8

2.9

-1.0

-0.9

-0.9

Timor-Leste

-3.5

-1.1

4.2

4.6

4.9

5.0

0.3

0.0

-0.1

Vietnam

6.8

7.1

6.8

6.5

6.5

6.4

0.2

0.0

0.0

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here may
differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

EAST ASIA AND PACIFIC

BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers
East Asia and Pacific (EAP) remains the region with the fastest productivity growth, averaging 6.3 percent a year in 2013-18,
notwithstanding the second-steepest post-crisis slowdown among emerging market and developing economy (EMDE) regions.
Nevertheless, productivity levels remain below the EMDE average in most EAP economies. While factor reallocation toward moreproductive sectors, high investment, and trade integration with product upgrading have promoted above-average productivity
growth, most of these drivers are expected to become less favorable in the future. A comprehensive set of reforms to liberalize
services sectors, improve corporate management, level the playing field for private firms, enhance human capital, facilitate urban
development, and foster innovation is needed to reverse the recent productivity growth slowdown.

Introduction
Growth of labor productivity, defined as output (GDP)per worker, averaged 6.3 percent a year in the East Asia
and Pacific (EAP) region in 2013-18 (Figure 2.1.1.1).1
While this pace remained the fastest among emerging
market and developing economy (EMDE) regions, it wasalmost 3 percentage points below EAP’s pre-crisis (200308) average after the second-steepest post-crisis decline inlabor productivity growth among EMDE regions. The
post-crisis slowdown in productivity growth has beenbroad-based, affecting 60 percent of EMDEs in EAP.
At 12 percent of the advanced-economy average in 201318, average productivity in EAP remains below the EMDE
average.2 Labor productivity levels in EAP are morehomogeneous than in other EMDE regions. Similarly,
productivity growth is more homogeneous across EAP thanacross other EMDE regions, possibly reflecting particularly
close regional integration, including through regional
supply chains.
This box builds on a considerable literature that examines
productivity growth in EAP. Earlier studies havedocumented the recent productivity growth slowdown in
EAP using country-level and firm-level data.3 Others haveidentified education, innovation, market efficiency,
institutions, and physical infrastructure as the main driversof productivity improvements in EAP (Kim and Loayza2019). Another set of studies has empirically documented

Note: This section was prepared by Ekaterine Vashakmadze, building
upon analysis in Chapter 3. Research assistance was provided by
Juncheng Zhou and Shijie Shi.
1 Unless otherwise specified, productivity is definedas labor
productivity, that is, output per worker.
2 EAP averages are heavily influenced by China, which accounts for 80
percent of EAP output in 2013-18. That said, even the medianproductivity level in EAP is below that of the median EMDE region.
3 For studies using country-level data, see APO (2018); IMF (2006),(2017); World Bank (2018a), and World Bank (2019a). For studies
using firm-level data, see Di Mauro et al. (2018); de Nicola, Kehayova,
and Nguyen (2018); OECD (2016); and World Bank and DRCSC(2019).

how product and labor market reforms have increased
output and productivity.4
Against this backdrop and drawing on these studies, thisbox compares productivity developments in EAP withother EMDE regions. In particular, it discusses thefollowing questions:•

How has productivity evolved in the region?

•

What factors have been associated with productivity
growth in the region?

•

What policy options are available to boost regional
productivity growth?

This box considers labor productivity, defined as real GDP
per worker (at 2010 prices and market exchange rates).
The data are available for sixteen countries: Cambodia,
China, Fiji, Indonesia, Lao People’s Democratic Republic,
Malaysia, Mongolia, Myanmar, Papua New Guinea, thePhilippines, Samoa, the Solomon Islands, Thailand,
Tonga, Vanuatu, and Vietnam.

Evolution of regional productivity
Rapid productivity growth. Labor productivity growth in
EAP averaged 6.4 percent a year between the early 1980s
and 2018—the highest growth rate of all EMDE regions,mainly reflecting rapid growth in China. EAP laborproductivity growth rose from 4.3 percent a year in the
1980s to 6.3 percent a year in the 1990s, and peaked at
8.9 percent a year in 2003-08 (Figure 2.1.1.2). Since theglobal financial crisis, EAP productivity growth has slowed
to 6.3 percent a year on average during 2013-18. This
post-crisis slowdown is also accounted for largely by
China, in particular its policy-guided move towards moresustainable growth after a period of exceptionally rapid
expansion of fixed investment and exports; in the region’s

4 See Adler et al. (2017); Bouis, Duval and Eugster (2016); Chen
(2002); Nicoletti and Scarpetta (2005); Timmer and Szirmai (2000).

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BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
FIGURE 2.1.1.1 Productivity in EAPcompared with other country groups
EAP has remained the region with the fastest
productivity growth, at 6.3 percent a year in 2013-18,
notwithstandingthesecond-steepest
post-crisis
slowdown among EMDE regions. Nevertheless,productivity levels remain below the EMDE average inmost EAP economies.
A. Average annual growth in EMDE regions

B. Productivity levels in 2013-18 and annual convergence
rates in EMDE regions

Source: Penn World Table; The Conference Board; World Bank (World
Development Indicators).
Note: Unless otherwise specified, productivity refers to labor productivity,
defined as output per worker. Sample comprises 35 advanced economies
and 127 EMDEs, of which 16 are in East Asia and the Pacific (EAP), 21 are
in in Eastern Europe and Central Asia (ECA), 25 are in in Latin America andthe Caribbean (LAC), 14 are in Middle East and North Africa (MNA), 7 are in
South Asia (SAR), and 44 are in Sub-Saharan Africa (SSA).
A. Blue bars denote range across GDP-weighted averages for 6 EMDE
regions. Yellow lines denote simple average of the 6 EMDE regional
averages. Red dots denote simple average of 16 EMDEs in EAP.
B. Rate of convergence calculated as the difference in productivity growth
rates with the average advanced economy (AE) divided by the log differencein productivity levels with the average advanced economy. Regional rate ofconvergence is the GDP-weighted average of EMDE members of eachregion. "Level" of productivity refers to the GDP weighted average of regional
productivity as a share of the average advanced economy during 2013-2018.
Click here to download data and charts.

other major economies, productivity growth has beenbroadly stable. Around two-thirds of EAP economies in
2013-18 were still experiencing labor productivity growth
above their long-run average.
Within-region heterogeneity. While productivity growth
exceeded the EMDE average during 2013-18 in most EAP
economies (the exceptions being some Pacific Islands),
there was some cross-country heterogeneity. Productivity
growth was particularly fast in China, followed by several
large Association of Southeast Asian Nations (ASEAN)
economies, including Vietnam. These countries wereamong the ten percent of EMDE economies with thefastest productivity growth in the period. They benefited
from improvements in human capital, and trade openness,
technology transfer and adaptation, high investment rates,
and an industrial base that was rapidly becoming moresophisticated (Andrews et al. 2015). Productivity growth
was slowest among EAP economies in some PacificIslands, including Solomon Islands, partly reflecting
political tensions.
Low productivity levels. Notwithstanding rapid
productivity growth, average productivity levels in EAP(12 percent of the advanced-economy average in 2013-18),
including China, remained below the EMDE average(which is close to 20 percent of the advanced-economy
average; APO 2018; Di Mauro et al. 2018). Malaysia, theEAP economy with the highest productivity level (25percent of the advanced-economy average), has benefited
from several decades of sustained high growth rates
reflecting its diversified production and export base andsound macroeconomic policies (Munoz et al. 2016).
Labor productivity convergence. Whereas convergence of
productivity toward advanced-economy levels in mostother EMDE regions has slowed since the financial crisis,
it has remained robust in EAP reflecting macroeconomic
stability, strong fundamentals, still high investment rates,and diversified and competitive production bases in theregion’s major economies (Chapter 3). Assuming recent
productivity growth can be sustained, at least 50 percent of
economies in the region are on course to halve theirproductivity gap relative to advanced-economy averages
over the next 40 years. History shows how successful
productivity convergence by such economies as Singapore
and the Republic of Korea, which were reclassified asadvanced economies in the 1990s, required high andsustained productivity growth differentials relative toestablished advanced economies over several decades(Chapter 3).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

EAST ASIA AND PACIFIC

BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
FIGURE 2.1.1.2 Evolution of productivity in EAP
To a larger extent than in the average EMDE, the post-crisis slowdown in EAP’s productivity growth has reflected slowing
total factor productivity growth, especially in China. In EAP, slowing TFP growth accounted for two-thirds of the post-crisis
slowdown in labor productivity growth, compared to about half in the average EMDE. Notwithstanding rapid productivity
growth, average productivity levels in EAP—12 percent of the advanced-economy average—remain below the EMDE
average.
A. Annual productivity growth in EAP

B. Share of economies with productivity
growth in 2013-18 below long-run andpre-crisis averages

C. Contributions to annual productivity
growthD. Contributions to annual productivity
growthE. Productivity levels relative toadvanced-economy average

F. Annual labor force growth

Source: Barro and Lee (2015); Haver Analytics; International Monetary Fund; Penn World Tables; United Nations; Wittgenstein Centre for Demography and Global
Human Capital; World Bank (World Development Indicators).
Note: Unless otherwise specified, productivity refers to labor productivity, defined as output per worker in 2010 U.S. dollars at market exchange rates.
A. Average growth rates calculated using 2010 U.S. dollars at market exchange rates.
B. Share of countries for which productivity growth average over 2013-18 is lower compared to a long-run (1992-2018) and pre-crisis (2003-08) average. Yellow line
denotes 50-percent line.
C.D. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Samples comprise 92 EMDEs and 16 EAP economies.
E. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Samples comprise 35 advanced economies, 127 EMDEs and 16
EAP economies.
F. Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor force data retrieved in September 2019.
Click here to download data and charts.

Sources of productivity growth. Productivity growth canbe decomposed into its sources: factor accumulation(human or physical capital) and increases in the efficiency
of factor use (total factor productivity, or TFP). In EAP,
slowing TFP growth accounted for two-thirds of the postcrisis slowdown in labor productivity growth, compared toabout half in the average EMDE. This followed a decadeof surging TFP growth in EAP, as China’s World Trade
Organization accession in 2001 was followed by rapid
trade integration, large foreign direct investment (FDI)
inflows into the region, and rapid technological adaptation(Mason and Shetty 2019; Tuan, Ng, and Zhao 2009; Xu,
Xinpeng and Sheng 2012). These reforms wereaccompanied by improvements in macroeconomic
policies, strengthening institutions, and higher investment
in infrastructure and human capital in several countries(China, Indonesia, Malaysia, the Philippines, Vietnam).
The post-crisis slowdown in the region’s TFP growth
partly reflects a moderation in the pace of global
integration (Ruta, Constantinescu, and Mattoo 2017).
Weaker investment accounted for another one-third of theslowdown in labor productivity growth in EAP, as

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BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
investment booms before the global financial crisis and in
its immediate wake subsided, especially in response topolicy guided moderation in China (Kose and Ohnsorge2019).

the Philippines (World Bank 2018c). In Vietnam,
intersectoral reallocation has continued to account for
approximately half of labor productivity growth, with nosign of deceleration (World Bank and MPIV 2016).

Heterogeneity in productivity growth. EAP’s high average
productivity growth masks some divergence between
China and the rest of EAP. Whereas TFP growth andcapital deepening slowed in China between 2003-08 and2013-18 amid a policy-guided investment slowdown, they
accelerated in the rest of EAP and especially in someASEAN countries (the Philippines and Vietnam) reflecting
significant FDI inflows and high rates of investment
spending. The decline in China’s TFP growth has been
attributed not only to the slowdown in investment growth,with its associated embodied technical progress, but also tofading gains from global trade integration and institutional
reforms.5

Productivity growth in the manufacturing sector has beena major driving force behind overall productivity growth
in most EAP countries (APO 2018; Figure 2.1.1.3). Since
2000s, the contribution of services to productivity growth
has increased, albeit from a low base, as innovations in this
sector took hold.6 For example, e-commerce has
accelerated sharply in China, with e-commerce firms
having 30 percent higher productivity, as well as being
more export-oriented than other firms (IMF 2019).
Recent advances in information and communication
technology have bolstered productivity growth inwholesale and retail trade, hotels, and restaurants;
transport, storage, and communications; and finance, realestate, and business activities. It is likely that the growth invalue-added generated by intangible services isunderestimated to the extent they are incorporated in the
production of manufactured goods (ADB 2019).

Sources of productivity growth
Productivity growth through sectoral reallocation. Strong
pre-crisis productivity growth in EAP was supported bypolicies that encouraged resource reallocation from low- tohigh-productivity sectors, as well as within-sector upgrades(IMF 2006). Following the crisis, however, and as in other
EMDE regions, gains from factor reallocation toward
more productive sectors slowed sharply, as the pace ofurbanization slowed (in most cases well before reaching
Organisation for
Economic
Co-operation
andDevelopment—average levels) and overcapacity in China
weighed on the efficiency of investment. During 2013-15,
sectoral reallocation is estimated to have accounted forunder one-fifth of EAP productivity growth, less than halfof its share during 2003-08 (two-fifths; Figure 2.1.1.3).

In contrast to most other EMDE regions, within-sector
productivity growth accelerated in many EAP economies
in the post-crisis period. China was an exception: there,
within-sector productivity growth slowed amid increased
overcapacity, declining firm dynamism, and increasing
financial constraints, including as a result of rising leverage(IMF 2018a). This is notwithstanding considerable inhouse research and development, and technology transfers
both domestically and from abroad (Hu, Jefferson, andJinchang 2005).

In East Asia, structural transformation, in the form of the
movement of people and capital from agriculture to
manufacturing and services, has been a key driver of
productivity growth as countries have risen from low- to
middle-income status. Once countries have reachedmiddle-income levels, within-sector productivity gains
have become a more important driver of productivity
growth and cross-sectoral shifts less important (de Nicola,
Kehayova and Nguyen, 2018; Mason and Shetty, 2019).
However, there has been considerable heterogeneity acrossthe region in this respect: thus in recent years sectoral
reallocation has stalled in Thailand, proceeded slowly in
Malaysia, and continued apace in Indonesia, Vietnam, andDrivers of productivity. Fundamental drivers of
productivity have improved more rapidly in EAP than in
the average EMDE (Figure 2.1.1.3). In general,
productivity in economies with favorable initial conditions
have grown by up to 0.8 percentage point per year fasterthan other economies (Chapter 3), which partly explains
faster productivity growth in countries with strong humancapital, including China, Malaysia, and Vietnam.
Compared to many other EMDEs, productivity growth in
EAP economies have benefited from high investment(IMF 2006; World Bank 2019b). Other factors
contributing to relatively high productivity growth in the
EAP region include trade integration, including throughglobal supply chains; foreign investment, which supported
rapid technology adoption from abroad; and progress

5 See World Bank and DRCSC (2014); World Bank (2019a); Baldwin(2013), and Subramanian and Kessler (2013).

6 See APO (2018); ADB (2019); Cirera and Maloney (2017); andKinda (2019).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

EAST ASIA AND PACIFIC

BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
FIGURE 2.1.1.3 Factors underlying productivity growth in EAP
Factor reallocation toward more productive sectors, high investment, trade integration with product upgrading, and rapid
innovation have all contributed to above-EMDE-average productivity growth in EAP. Productivity growth in the manufacturingsector has been a major driving force behind overall productivity growth in most EAP countries. Fundamental drivers of
productivity have improved more rapidly in EAP than in the average EMDE.
A. Contributions to annual productivity
growthB. Annual sectoral productivity growth

C. Contributions to annual productivity
growthD. Composition of sectoral value-added

E. Drivers index

F. Level of drivers across regions, 2017

Source: APO productivity database; Expanded African Sector Database; Groningen Growth Development Center Database; Haver Analytics; ILOSTAT; OECD STAN;
United Nations; World KLEMS.
A.B. Productivity refers to labor productivity, defined as output per worker. Medians of county-specific contributions. Sample comprises 9 EAP economies and 46 EMDEs.
A. Within-sector contribution shows the contribution to overall productivity growth of initial real value added-weighted sectoral productivity growth; between-sector
contribution shows the contribution of intersectoral changes in employment shares.
C. Median of the country groups. Sample comprises 9 EAP economies.
D. Values are calculated using 2010 U.S. dollars at 2010 market exchange rates.
E. For each country, index is a weighted average—weighted by the normalized coefficients shown in Annex 3.5—of the normalized value of each driver of productivity.
Drivers include the ICRG rule of law index, patents per capita, share of non-tropical area, investment in percent of GDP, ratio of female average years of education tomale average years, share of population in urban area, Economic Complexity Index, years of schooling, and share of working-age population, and inflation. See Annex
3.5 for details. Regional and EMDE indexes are GDP-weighted averages. Samples comprise 7 economies in EAP.
F. Unweighted average levels of drivers, normalized as average of AEs as 100 and standard deviation of EMDEs as 10. Orange diamond represents average within EAP
economies in 2017. Blue bar represents range of the average drivers for six regions in 2017. Variables corresponding to the concepts are follows: Education = years ofeducation, Urbanization = share of population living in urban area, Investment = share of investment to GDP, Institution = Government Effectiveness, Econ.
Complexity = Economic complexity index+, Geography = share of land area which are outside of tropical region, Gender Equality = Share of the year of schooling forfemale to male, Demography=share of population under 14, Innovation=Log patent per capita, Trade = Export + Import/GDP.
Click here to download data and charts.

toward more complex products with higher value-added(World Bank 2019d).7 Macroeconomic stability has
7 EAP is characterized by an above-average share of larger andexporting firms (Chapter 3). In EAP, 35 percent of firms are large(compared with 25 percent in the average EMDE) and 16 percent offirms are exporters (compared with 12 percent in the average EMDE).
More productive firms tend to self-select into exporting firms which havehigher productivity, as they are exposed to frontier knowledge and bestencouraged investment, while trade and investment
openness and above-EMDE-average research anddevelopment have supported innovation (Kim and Loayza2019).
managerial practices that help them make better decisions regarding
investment, input selection, and production process (HallwardDriemeier, Iarossi, and Sokoloff 2002).

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BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
FIGURE 2.1.1.4 Prospects for productivity growth in EAP
Being less able to rely on export growth than in the past, EAP countries need to unleash domestic sources of productivity
growth. Priority areas include reforms to enhance human capital, address informality, foster innovation, and facilitate urban
development. In addition, achieving long-term sustainable development calls for debt overhangs to be addressed andexcessive leverage to be avoided.
A. Contribution of export growth toannual GDP growth

B. Human capital index and annual
productivity growth

C. Informal economies

D. Research and development
expenditure

E. Urbanization

F. Debt and labor productivity

Source: Elgin et al. (forthcoming); Haver Analytics; World Bank (World Development Indicators).
A.B.F. Productivity refers to labor productivity, defined as output per worker.
A. Growth of volume of exports of goods and non-factor services.
B. The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a
future worker of a child born today relative to the benchmark of a child with full health care and complete education. HCI data are for 2017. Labor productivity growth data
are for 2018.
C. Blue bars show the share of informal output in total output based on the Dynamic General Equilibrium (DGE) model. The diamonds show the share of informal
employment in total employment.
D. Data are not available for all featured economies.
E. Urbanization levels denote share of urban population in total population.
F. Total debt comprises bank credit to households, non-financial corporations, and general government debt (broad definition).
Click here to download data and charts.

That said, the factors supporting post-crisis productivity
growth have differed somewhat across EAP economies.
Growth of the drivers most strongly associated withproductivity growth, including labor force growth andinvestment, has slowed in EAP since 2008. Investment
growth in many EAP economies has slowed, led by apolicy-led moderation of investment rates to reduce credit
expansion. In addition, earlier favorable demographic
trends in China, Thailand, and Vietnam have waned as
populations have started to age. Other factors that had

previously helped to spur EAP productivity growth havealso deteriorated since the crisis. For example, the trend
toward broadening production to a more diverse range of
products at more upstream stages of the value chain slowed
partly because of a stagnation in global value chains after2008 (World Bank 2019b).
Prospects for productivity growth. Productivity gaps arestill substantial between advanced economies and EAP
countries, suggesting potential for further significant

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

EAST ASIA AND PACIFIC

BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
productivity gains. However, although EAP productivity
growth remains solid relative to long-run historical rates, it
is likely to soften further over the near future, as trends ina number of fundamental drivers of productivity becomeless favorable. Thus, trade and investment growth are
expected to continue to ease in an environment of
weakening global demand, heightened global policy
uncertainty, and a continued policy-guided slowdown ininvestment growth in China (Figure 2.1.1.4). Slowing
global trade growth may also lower incentives to innovate
or upgrade products and processes (World Bank 2019b).
Structural declines in working-age populations in major
economies will also weaken growth momentum (WorldBank 2016b, 2018a).

Policy implications
A comprehensive set of policy efforts can help countries in
the region improve their investment and productivity
growth and speed up their income convergence with theadvanced economies. These policies fall into four broad
categories: improving factors of production, including
through human capital development; encouraging
productivity at the firm level, including by leveling theplaying field for private relative to state-owned firms andimproving corporate governance; removing obstacles tobetween-sector reallocation, including through continued
urban development; and fostering a productivity-friendly
business environment. Specific policies within these four
broad categories depend on country specific circumstances(World Bank 2018b; Kim and Loayza 2017; Munoz et al.
2016).

Improving factors of production
Slowing capital deepening has contributed to the postcrisis productivity growth slowdown in several EAP
countries, while outside China the contribution of humancapital gains to productivity growth has stalled. To boost
productivity growth, policies are needed to improve public
investment, lift private investment, and improve humancapital.
Improve public investment. A wide range of policy efforts
are needed to improve the investment outlook, especiallyin countries with particularly large investment needs(Cambodia, Indonesia, Lao PDR, Myanmar; World Bank
2018a). Access to adequate infrastructure in EAP remains
fragmented, particularly in water and sanitation andtransport, and in several lower-middle-income economies(World Bank 2018a). In these countries, strengthening theefficiency of public investment management and fiscal

transparency could boost productive public investment(World Bank 2018b).
Remove obstacles to private investment. Private
investment could be spurred by higher FDI inflows that
could offer knowledge and technology transfers, deeper
regional trade integration and better institutional
environments (World Bank 2018b, 2019b). In China,
private investment could be lifted by improved market
access, increased competition, policies that provide a morelevel playing field relative to state-owned enterprises(SOEs), greater financial discipline, stronger intellectual
property rights, lower barriers to entry, and a gradual
opening of China’s financial system to international
investors (World Bank 2018a, 2018d; World Bank and
DRCSC 2019). Other major economies in the region,
including Indonesia, Malaysia, Thailand, and Vietnam,
could boost private investment by increasing private sector
participation in major infrastructure projects and bychanging their funding policies to provide more
opportunities for international and domestic private
investors.
Increase human capital. Children born in the EAP region
today will, at age 18, be only 53 percent as productive as
they could be if they benefited from best practices in
education and health (World Bank 2019b). Several EAP
economies have below-average educational attainment(Cambodia, Lao PDR). In general, reforms that augmenthuman capital, through initiatives to strengthen thequality and flexibility of education systems and improveeducation outcomes, are critical to achieving andsustaining high productivity growth.

Boosting firm productivity
While within-sector productivity growth has been resilient
in EAP, especially outside China, there is room forgenerating additional productivity gains. Factor
misallocation, although it has declined, remains sizable(World Bank 2019b). In the current weak external
environment that allows limited productivity gains
through knowledge and technology spillovers from trade,
this is likely to be a critical source of productivity gains forthe region. Policy measures can include levelling theplaying field for private and state-owned firms, improving
firm capabilities, streamlining regulations to encourage
informal enterprises to grow into more productive firms in
the formal economy, and fostering innovation.
Reduce market distortions and level the playing field for
private firms. A gradual transfer from public to private

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BOX 2.1.1 Labor productivity in East Asia and Pacific: Trends and drivers (continued)
firm ownership in many cases, and greater involvement ofinternational firms, as well as reforms to lower entry costs
and encourage fair competition, including in trade andinnovation, can help level the playing field for private
firms and state owned enterprises. Curbing preferential
lending agreements with state-owned enterprises andeasing the access of private firms to long-term funding can
improve the allocative efficiency of capital and raise
productivity. Greater product market competition would
spur innovation (Cusolito and Maloney 2018).
Encourage innovation. Effective policies to promote
innovation begin with strengthening managerial andorganizational practices (Cirera and Maloney 2017). In
addition, strengthening the effectiveness of research anddevelopment (R&D) spending and measures to raise
productivity in the services sectors are key (World Bank
2016c). Fiscal incentives for R&D are already in place insome EAP countries (China, Malaysia), but in many othercases R&D spending is small relative to GDP (Figure
2.1.1.4). Strengthening intellectual property rights regimes
while avoiding undue limitations on competition could
also encourage R&D, as could competition for research
grants. These reforms could be complemented by efforts
that facilitate moving up the value chain throughinnovation, especially in R&D-intensive sectors, andenabling new business processes, including throughdigitization, and higher energy efficiency.
Address informality. The share of informal output in the
EAP region is below the EMDE average while the share of
informal employment is above average (World Bank
2019f). Within the region, informality is higher in lowerincome countries. However, even higher-income
economies in EAP have urban informality (China,
Malaysia, Thailand). To address challenges associated withinformality higher-income countries can prioritize urban
planning along with the provision of essential social
protection to informal workers. Lower-income countries
can focus on policies that encourage investment andreduce costs of regulatory compliance.

Encouraging sectoral reallocation
Productivity gains from sectoral reallocation have slowed
in EAP. Policy measures to accelerate the process ofreallocation again include reforms to allow the services
sector to thrive and absorb labor and measures to sustain
rapid urbanization.
Liberalize service markets and shift out of agriculture. A
gradual liberalization of service sectors, including
education, health
care,
the
financial sector,
communications, transport, and utilities, could encourage
job creation in these sectors (Beverelli, Fiorini, andHoekman 2017). It could also boost manufacturing
productivity, as services sectors provide important inputs
into manufacturing.
Encourage urbanization. The reallocation of factors,
especially labor, from low-productivity agriculturalactivities to higher-productivity manufacturing andservices can accelerate the convergence of EAP to the
productivity frontier. Clarification of land ownershiprights and transferable social benefits could encourage suchlabor movement (Fuglie et al 2019). Urban planning canencourage a reallocation of labor towards more productive
sectors by improving access to jobs, affordable housing,public transportation, health care, education, and otherservices (World Bank 2015a). Road congestion, which is amajor problem in many large cities may discourage job
switching (World Bank 2018e, 2019f). Accelerated
productivity growth will also require improved
management of country and regional transportation,
telecommunications, and utility infrastructure in
metropolitan areas.

Creating a growth-friendly environment
Safeguard macroeconomic stability. Over the longer term,strong and sustained productivity gains require financial
stability (Chapter 3; Box 3.4). Elevated corporate debt,
especially in China, weighs on investment andproductivity in exposed corporations. Policy measures torein in financial risks are therefore critical.

Growth in Europe and Central Asia decelerated to an estimated 2 percent in 2019, reflecting a sharp slowdown
in Turkey as a result of acute financial market stress in 2018, as well as in the Russian Federation amid weak
demand and cuts in oil production. Regional growth is projected to strengthen in 2020, to 2.6 percent, as
activity recovers in Turkey and Russia, and to stabilize to 2.9 percent in 2021-22. Key external risks to theregional growth outlook include spillovers from weaker-than-expected activity in the Euro Area and escalationof global policy uncertainty. The region also remains vulnerable to disorderly commodity and financial market
developments. A comprehensive reform agenda is needed to boost productivity, increase investment in physicaland human capital, address continuing demographic pressures, and raise innovation.

Recent developments
Growth in Europe and Central Asia (ECA) isestimated to have decelerated markedly in 2019,to a three-year low of 2 percent (Table 2.2.1). The
weak regional performance predominantly reflects
slowdowns in the region’s two largest economies,
Russia and Turkey (Figure 2.2.1.A).
A sustained weakness in exports growth has
continued amid slowing manufacturing activity
and investment. Sluggish new export orders inrecent months suggest that export growth willcontinue to be weak in the near term, especially ineconomies with deep trade and financial linkages
to the Euro Area, such as those in Central Europe(Figure 2.2.1.B).
Headline inflation in ECA has eased, as theimpact from the value-added tax (VAT) hike in
Russia and earlier currency depreciation in Turkey
faded. This, combined with weakening growth
momentum, has allowed Russia and Turkey, as
well as other ECA economies, to pause or reverse
previous monetary policy tightening (Romania,
Ukraine; Figure 2.2.1.C). Inflation remains aboveor near target, however, limiting the scope for

Note: This section was prepared by Collette M. Wheeler. Research
assistance was provided by Vasiliki Papagianni and Julia Norfleet.

further policy rate cuts, with some economies
tightening policy to rein in inflation (Georgia,
Kazakhstan). Core inflation is also rising in some
economies, especially those with increasing wages
as a result of labor shortages and other capacity
constraints (Poland, Romania; Figure 2.2.1.D).
In Russia, softer-than-expected investment andtrade, together with a continuation ofinternational economic sanctions, resulted in a
growth slowdown to an estimated 1.2 percent.
Industrial activity also softened, as oil production
cuts agreed with OPEC took effect and pipelinerelated disruptions occurred. Retail sales volumes
weakened substantially following a VAT hike,while consumer confidence remained low. The
central bank reversed a previous tightening stance,
cutting the key policy rate five times since June.
In
Turkey,industrial
production
andmanufacturing data suggest that the economy
began to stabilize in late 2019, following thedisruptions from acute financial market pressuresin the previous year. Still, growth slowed sharply
for the year, falling 2.8 percentage points to nearnil. Elevated inflation and associated pressures onreal incomes, as well as rising unemployment,
dampened consumption. Investment contracted
deeply, to rates comparable with the global
financial crisis, partly reflecting lingering policy
uncertainty (Figure 2.2.1.E). Although the

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 2.2.1 ECA: Recent developments
Europe and Central Asia faced substantial headwinds in 2019 amid a
sharp slowdown in major economies, such as Turkey and Russia. Export
growth weakened significantly, particularly in Central Europe, which istightly connected to the Euro Area through value chains. Headline inflation
moderated in the region’s major economies, allowing for substantial policy
rate cuts to support growth. Capacity constraints and a slowing Euro Area
weighed on activity in Central Europe.
A. Contribution to regional GDP
growthB. Export volume growth,by subregion

C. Real interest rates and bondspreads in ECA

D. Core inflation and capacity
utilization in Central Europe

E. GDP and investment growth inTurkeyF. Industrial production and export
volume growth in Central Europe

Source: Consensus Economics; Haver Analytics; J.P. Morgan; Organisation for Economic Cooperation and Development; World Bank.
A.B. ECA = Europe and Central Asia. Aggregate growth rates calculated using GDP weights at 2010
prices and market exchange rates. Data for 2019 are estimated. Refer to Table 2.2.1 for further
details.
B. South Cauc. = South Caucasus.
C. Real interest rates calculated using the policy interest rate less the Consensus Economics
forecast for inflation. Bond spreads are from the J.P. Morgan Emerging Market Bond Index (EMBI).
Sample includes Hungary, Poland, Russia, Turkey, and Ukraine, due to data availability. Lastobservation is December 16, 2019 for the bond spread and November 2019 for the real interest rate.
D. Aggregates calculated using GDP weights at 2010 prices and market exchange rates. The sample
includes Hungary, Poland, and Romania. Last observation is 2019Q4 for capacity utilization and2019Q3 for core inflation.
E. Figure uses the annualized 4-quarter on 4-quarter average.
F. Dashed lines represent the 2000-18 average. Aggregate industrial production calculated using
production weights at 2010 prices and market exchange rates; aggregate export volume growth
calculated using GDP weights at 2010 prices and market exchange rates.
Click here to download data and charts.

contribution of net exports to growth was positive,
this was due in large part to import compression.
In the second half of 2019, the central banksharply reversed its policy stance by cutting thepolicy rate in half, to 12 percent, despite abovetarget inflation.
In Central Europe, the boost to private
consumption in early 2019 from rising real wages
and government transfers helped to sustain abovepotential growth. This impact dissipated by theend of the year, however, contributing to a
slowdown in growth to an estimated 4.2 percent,despite an investment-led construction sector
pickup in some economies (Hungary, Romania).
The slowdown in the Euro Area weighed onexports in some cases (Bulgaria, Romania; Figure
2.2.1.F).
In the Western Balkans, a deceleration in public
investment (Kosovo), manufacturing (Serbia), andexport growth (Albania, Serbia) contributed to amoderation in growth to an estimated 3.2 percent
in 2019. Temporary factors related to weather andenergy production dampened activity in Albania,
while strong import demand for public investment
projects led to negative contribution of net exports
in Montenegro. In Eastern Europe, industrial
production growth has softened, reflecting marked
weakness in manufacturing amid slowing export
growth, particularly in Belarus. Ukraine, however,benefited from a bumper crop harvest in the firsthalf of 2019.
Firming growth in the South Caucasus, to an
estimated 3.7 percent in 2019, was supported byprivate consumption, and on the supply side bystrong manufacturing growth, as well as by arecovery in mining production in Armenia.
Expanding natural gas production and steady
growth in non-energy sectors supported
Azerbaijan’s economy in the first half of 2019. In
Georgia, growth strengthened despite theimposition of travel restrictions by Russia. In
Central Asia, the cyclical expansion moderated,
yet growth was still robust at 4.5 percent in 2019.
In Kazakhstan, the largest subregional economy,
slowing exports from lower oil prices were offset
by fiscal expansion.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Outlook
Regional growth is expected to firm over theforecast horizon, to 2.6 percent in 2020 and 2.9
percent in 2021-22, on the assumptions that key
commodity prices and growth in the Euro Areastabilize, and that Turkey’s economy recovers
from earlier financial pressures and Russia firms
on the back of policy support (Figure 2.2.2.A).
Considerable variation across economies is
expected to continue. Economies in Central
Europe are anticipated to slow as fiscal policy
support wanes and demographic pressures persist,
while those in Central Asia are projected tocontinue growing at a robust pace, and more
rapidly than previously envisaged, on the back ofstructural reform progress (World Bank 2019g).
The baseline projection for regional growth also
assumes that trade tensions between the UnitedStates and China will not re-escalate; the United
Kingdom’s exit from the European Union will beorderly; and that fiscal and monetary policy avert
further financial market turbulence in Turkey as
the country moves past acute financial stress.
In Russia, growth is projected to firm moderately,
to 1.8 percent by 2021 (Table 2.2.2). Despite
OPEC and its partners recently announcing
deeper cuts until March 2020, oil production is
expected to remain stable in Russia due to an
exemption on gas condensates (IEA 2019).
National Projects—which are partly funded by the2019 VAT hike and include investment ininfrastructure and human capital—are expected tobuoy growth over the forecast horizon.
Nevertheless, private investment remains tepid in
the projection, due to policy uncertainty andslowing potential growth over the longer term as
demographic pressures increase, and as structuralproblems, such as the lack of competition,
accumulate.
Growth is projected to recover in Turkey, to 3
percent in 2020, as investment recovers from a
deep contraction in 2019. Gradual improvementin domestic demand is expected to support growthover the forecast horizon. This outlook assumes
that fiscal and monetary policy remain steady, thatE URO PE AND CE NTRAL AS IA

79

FIGURE 2.2.2 ECA: Outlook and risks
Growth in Europe and Central Asia is projected to firm to 2.6 percent in
2020, as activity recovers in Turkey and Russia, and to stabilize at 2.9
percent in 2021-22. Weaker-than-expected growth in the Euro Area or
China could dent activity in tightly connected subregions. Heightened
policy uncertainty in the broader region could impact portfolio flows to
ECA, while the future of funding options in Central Europe remains
uncertain. The ability to confront growth headwinds is reduced by limited
fiscal space.
A. Growth

B. Share of exports by destination,
2017

C. Turkey: Credit to firms andnon-performing loan ratio

D. Gross portfolio outflows in ECA
and Euro Area economic policy
uncertainty

E. European Union structural fundpayments to Central Europe, 2019

F. Gross government and external
debt, by subregion

Source: Baker, Bloom, and Davis (2016); Bank for International Settlements; European Commission;
Haver Analytics; Institute of International Finance; International Monetary Fund; World Bank.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
Shaded areas indicate forecasts. Data for 2019 are estimated. Yellow diamonds correspond toforecasts from the June 2019 Global Economic Prospects report.
B. The “Within region” data refer to within ECA for all stacked bars except the Euro Area, whichrefers to within the Euro Area. Shares are calculated from exports in millions of U.S. dollars.
C. Last observation is November 2019 for non-performing loans and 2019Q2 for credit tonon-financial corporations.
D. EPU = economic policy uncertainty. The Euro Area economic policy uncertainty is calculated by
Baker, Bloom, and Davis (2016), which is based on the frequency of words in domestic newspapers
mentioning economic policy uncertainty. Figure shows the 3-month moving average. Sample for
portfolio outflows includes Hungary, Poland, and Turkey, due to data availability. Last observation is
December 20, 2019 for portfolio outflows and November 2019 for economic policy uncertainty.
E. Note: Figure shows the cumulated payments to EMDEs in Central Europe for the 2014-20
program period. Data on the “Share of country allocation” reflect the amount of EU structural fundallocations paid out to each economy as a share of its total EU structural fund allocation as of
2019Q2, due to data availability.
F. ECA = Europe and Central Asia, South Cauc. = South Caucasus, Gov. = government,
Ext. = external. Aggregates calculated using the median. Sample includes 24 economies.
Click here to download data and charts.

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CHAPTER 2.2

remain steady, that the currency does not comeunder pressure, and that corporate debt
restructurings proceed smoothly.
Central Europe is forecast to sharply decelerateover the forecast horizon, to 3.4 percent GDP
growth in 2020 and 3 percent by 2022. Fiscal
support, and the resulting private consumption
boost, will begin to fade in some of the subregion’slargest economies by 2020-21, with limited fiscal
space available to fully offset potential adverse
spillovers from the Euro Area (Poland, Romania).
Shrinking working-age populations, partly
reflecting emigration to Western Europe in recent
years, limits growth prospects. Progress on
structural reforms is key to support private
investment growth over the medium term.
Growth in the subregion is highly dependent on
the continued absorption of EU structural funds,
with the current cycle expected to end in 2020.
Growth is projected to firm to 3.8 percent by
2021 in the Western Balkans, assuming politicalinstability and policy uncertainty remaincontained. Rising fiscal liabilities in the subregion,in some cases due to large public sector wage
increases, social transfers, or higher-than-expected
costs for infrastructure projects, could reduce
space for future countercyclical fiscal stimulus andweakenthebusinessclimate(Kosovo,
Montenegro, North Macedonia). Additionally,
recent earthquakes in the Western Balkans—primarily affecting Albania—took a heavy toll on
human life and physical infrastructure. The
outlook for the subregion remains challenging as
falling business confidence and heightened
uncertainty coincide with a worsening external
environment (World Bank 2019h).
The South Caucasus is estimated to grow 3.7
percent in 2019, and to decelerate to 3.1 percent
over the remaining forecast horizon. In
Azerbaijan, activity is expected to be dampened by
the effects of subdued oil prices and lingering
structural rigidities in the non-oil sector. Longerterm growth depends on continuation of domestic
reforms to enhance private sector development
and address fragilities emanating from thefinancial sector, as well as investment in human

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

capital to boost the quality of education andreduce skills mismatches.
Growth is expected to firm over the forecasthorizon in Eastern Europe and stabilize in Central
Asia, but growth in both subregions is subject toconsiderable policy uncertainty. These subregions
face a challenging external environment as growth
remains tepid in key trading partners, including
the Euro Area and Russia (for Eastern Europe)and China (for Central Asia). Ukraine, which is
the largest economy in Eastern Europe, recently
reached a preliminary agreement with the IMF for
a $5.5 billion program, which should help
advance structural reforms and foster growth overthe forecast horizon. In Central Asia, growth isexpected to slightly moderate this year following
agreed-upon production cuts by a non-OPEC
partner (Kazakhstan). Activity in Kazakhstan will
likely be dampened by the waning effect of earlier
fiscal stimulus, modest or slowing growth in key
trading partners (Russia, China), and low
productivity. In Eastern Europe and Central Asia,
the pace of growth depends on the successful
implementation of structural reforms to improvethe business environment, achieve debt
sustainability, and restructure state-owned
enterprises to improve competition (EBRD 2017;
Funke, Isakova, and Ivanyna 2017).

Risks
The regional outlook remains subject tosignificant downside risks, including slowing
growth in major trading partners, geopoliticalturbulence, heightened policy uncertainty,
exposure to disorderly financial market
developments, as well as weakening productivity
growth over the long run (Box 2.2.1). A sharperthan-expected slowdown in the Euro Area, ECA’s
most important trading partner, could generate
negative spillovers in economies with tightly
linked trade and financial ties (Figure 2.2.2.B;
Elekdag, Muir, and Wu 2015). Slowing growth inChina could be propagated through trade andcommodity price channels to Central Asia, as wellas metals exporters in the ECA region, which are
increasingly reliant on China as an export
destination. The region’s energy exporters—

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Azerbaijan, Kazakhstan, and Russia—remain
vulnerable to large swings in global commodity
prices, particularly when accompanied byheightened volatility (van Eyden et al. 2019).
In many economies in Central Europe, the policy
space to confront negative shocks is limited bypersistent budget deficits. Fiscal support hascontributed to these growing imbalances—increasing public sector wages, rising governmenttransfers, and low tax capacity, have widened fiscal
deficits, with the latter approaching or exceeding 3
percent of GDP—the upper limit of the EU
threshold, particularly in Romania. Across ECA,
public sector debt relative to GDP is higher thanprior to the global financial crisis, with the largest
increases observed in Eastern Europe and theSouth Caucasus. In Turkey, recent policies tosupport growth through credit expansion run therisk of worsened external imbalances (Figure
2.2.2.C).
Following military disagreements with the North
Atlantic Treaty Organization, Turkey faces a newround of U.S. economic sanctions. Renewed
involvement in conflicts in the Syrian Arab
Republic or Ukraine, could trigger additionalsanctions against large economies in the region.
More generally, a pervasive rise in policy
uncertainty could undermine business andinvestor sentiment (Figure 2.2.2.D). The reescalation of trade tensions, and a resulting
slowdown in global demand, could weaken
exports and commodity prices for the region,
presenting challenges to growth and fiscal
planning. Renewed trade tensions between the
United States and Europe, particularly withrespect to vehicle and auto part tariffs, could also
sideswipe the ECA region, especially foreconomies in Central Europe that are tightly
integrated into European value chains. Similarly, a
triggering of trade tensions between the UnitedStates and China could adversely affect some

E URO PE AND CE NTRAL AS IA

regional economies, particularly energy and metals
exporters. Additional sanctions on Russia could
have a negative impact on the region, particularlyin economies where domestic demand relies on
remittances (Central Asia, Eastern Europe, theSouth Caucasus).
Although policy uncertainty surrounding theUnited Kingdom’s exit from the European Union
has dissipated somewhat, the process remains
vulnerable to disruption until the end of thetransition period, currently scheduled for the endof the year (Bank of England 2018; H.M.
Government 2018). The future program of EU
structural funds after 2020 must also be
determined, with the potential redirection of EU
funds to advanced economies in Southern Europe
limiting funding options for Central Europe. In
several countries, structural fund payments
represented 5 percent or more of GDP over thelast program period from 2014-20 (Figure
2.2.2.E). Historically, when the absorption of EU
funding was low, activity also decelerated
substantially, as was the case in Poland in 2016.
An unexpected tightening of global financing
conditions could generate financial market
pressures in ECA, renewing capital outflows andcurrency volatility, particularly in economies withlarge external financing needs (Chapter 1; WorldBank 2019f; EBRD 2019a). Many regional
economies have relied on short-term capital
inflows to finance large current account deficits.
Low foreign-currency reserves leave these
economies all the more vulnerable to capital flightand constrain the capacity of central banks tobuffer the impact of negative external shocks. A
fall in incomes in the region’s largest economies
would dent remittance inflows to Eastern Europe
and Central Asia (World Bank 2016d). On thedomestic front, increased public spending and low
tax capacity have contributed to historically highpublic debt levels, which limit fiscal policy space(Figure 2.2.2.F).

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TABLE 2.2.1 Europe and Central Asia forecast summary
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE ECA, GDP1
EMDE ECA, GDP excl. Turkey

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

4.1

3.2

2.0

2.6

2.9

2.9

0.4

-0.1

0.0

3.0

3.3

2.6

2.5

2.6

2.6

0.2

-0.1

0.0

(Average including countries with full national accounts and balance of payments data only)2
EMDE ECA, GDP2

4.1

3.1

1.8

2.5

2.8

2.8

0.4

-0.1

-0.1

GDP per capita (U.S. dollars)

3.6

2.7

1.5

2.3

2.6

2.7

0.4

-0.1

-0.1

PPP GDP

4.0

3.1

1.9

2.6

2.8

2.8

0.4

0.0

-0.1

Private consumption

5.0

2.6

1.5

2.6

2.6

2.6

0.2

0.0

-0.1

Public consumption

3.4

2.0

1.6

1.7

1.8

1.8

0.0

0.1

0.0

Fixed investment

6.7

3.1

-0.6

4.3

4.2

4.2

0.5

1.0

0.6

3

7.3

5.6

2.8

2.3

3.4

3.4

-1.2

-2.0

-0.6

Imports, GNFS3

11.5

3.2

1.3

4.1

4.9

5.0

-1.7

-1.4

-0.9

Net exports, contribution to growth

-0.8

1.0

0.6

-0.5

-0.3

-0.4

0.1

-0.4

0.1

Commodity exporters4

2.1

2.6

1.9

2.1

2.3

2.3

0.1

-0.1

0.0

Commodity importers

6.1

3.7

2.1

3.1

3.5

3.4

0.7

0.0

0.0

5.1

4.7

4.2

3.4

3.1

3.0

0.5

0.1

0.0

2.6

4.0

3.2

3.6

3.8

3.8

-0.3

-0.2

-0.1

2.6

3.3

2.8

2.9

3.1

3.1

0.4

0.2

0.1

Exports, GNFS

Memo items: GDP

Central Europe6
Western Balkans

7

Eastern Europe8
South Caucasus

5

1.7

2.6

3.7

3.1

3.1

3.1

0.0

-0.8

-1.1

Central Asia10

4.6

4.7

4.5

4.4

4.6

4.5

0.3

0.4

0.5

Russia

1.6

2.3

1.2

1.6

1.8

1.8

0.0

-0.2

0.0

Turkey

7.5

2.8

0.0

3.0

4.0

4.0

1.0

0.0

0.0

Poland

4.9

5.1

4.3

3.6

3.3

3.1

0.3

0.0

0.0

9

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates.
2. Aggregates presented here exclude Bosnia and Herzegovina, Kazakhstan, Kosovo, Montenegro, Serbia, Tajikistan, and Turkmenistan, for which data limitations prevent the forecasting of
GDP components.
3. Exports and imports of goods and non-factor services (GNFS).
4. Includes Albania, Armenia, Azerbaijan, Kazakhstan, the Kyrgyz Republic, Kosovo, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
5. Includes Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Georgia, Hungary, Moldova, Montenegro, North Macedonia, Poland, Romania, Serbia, and Turkey.
6. Includes Bulgaria, Croatia, Hungary, Poland, and Romania.
7. Includes Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia.
8. Includes Belarus, Moldova, and Ukraine.
9. Includes Armenia, Azerbaijan, and Georgia.
10. Includes Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

83

E URO PE AND CE NTRAL AS IA

TABLE 2.2.2 Europe and Central Asia country forecasts1

Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

Albania

3.8

4.1

2.9

3.4

3.6

3.5

-0.8

-0.3

-0.2

Armenia

7.5

5.2

6.9

5.1

5.2

5.2

2.7

0.2

0.0

Azerbaijan

-0.3

1.4

2.5

2.3

2.1

2.1

-0.8

-1.2

-1.6

Belarus

2.5

3.0

1.0

0.9

0.5

0.5

-0.8

-0.4

-0.7

Bosnia and Herzegovina2

3.2

3.6

3.1

3.4

3.9

3.9

-0.3

-0.5

-0.1

Bulgaria

3.5

3.1

3.6

3.0

3.1

3.1

0.6

0.2

0.3

Croatia

3.1

2.7

2.9

2.6

2.4

2.4

0.4

0.1

0.0

Georgia

4.8

4.8

5.2

4.3

4.5

4.5

0.6

-0.5

-0.5

Hungary

4.3

5.1

4.9

3.0

2.6

2.6

1.1

0.2

0.0

Kazakhstan

4.1

4.1

4.0

3.7

3.9

3.7

0.5

0.5

0.7

Kosovo

4.2

3.8

4.0

4.2

4.1

4.0

-0.4

-0.3

-0.4

Kyrgyz Republic

4.7

3.5

4.2

4.0

4.0

4.2

-0.1

0.0

-0.1

Moldova

4.7

4.0

3.6

3.6

3.8

3.8

0.2

0.0

0.0

Montenegro

4.7

5.1

3.0

3.1

2.8

3.2

0.1

0.7

0.5

North Macedonia

0.2

2.9

3.1

3.2

3.3

3.1

0.2

0.0

-0.3

Poland

4.9

5.1

4.3

3.6

3.3

3.1

0.3

0.0

0.0

Romania

7.1

4.0

3.9

3.4

3.1

3.1

0.3

0.1

0.0

Russia

1.6

2.3

1.2

1.6

1.8

1.8

0.0

-0.2

0.0

Serbia

2.0

4.4

3.3

3.9

4.0

4.0

-0.2

-0.1

0.0

Tajikistan

7.1

7.3

6.2

5.5

5.0

5.0

0.2

-0.5

-1.0

Turkey

7.5

2.8

0.0

3.0

4.0

4.0

1.0

0.0

0.0

Turkmenistan

6.5

6.2

5.0

5.2

5.5

5.5

-0.6

0.1

0.6

Ukraine

2.5

3.3

3.6

3.7

4.2

4.2

0.9

0.3

0.4

Uzbekistan

4.5

5.1

5.5

5.7

6.0

6.0

0.2

0.2

0.0

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here may
differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates, unless indicated otherwise.
2. GDP growth rate at constant prices is based on production approach.
Click here to download data.

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers
Productivity growth in Europe and Central Asia (ECA) has fallen from an above-EMDE-average pre-crisis rate of 5.5 percent toa below-EMDE-average post-crisis rate of 1.6 percent—the steepest decline of any EMDE region. There has been wide
heterogeneity within the region, however, with productivity growth near zero since 2013 in the Western Balkans and above 2.5
percent in Central Europe. In the Western Balkans and the Russian Federation, investment weakness has weighed on productivity
growth. The productivity slowdown in ECA has predominantly reflected weaker within-sector productivity growth, with aparticularly sharp decline in the growth of services productivity, and weaker total factor productivity growth in Eastern Europe,
the South Caucasus, and the Western Balkans. Sectoral reallocation has also slowed in the post-crisis period, reflecting headwindsthat have limited the ability of firms with higher productivity to continue to absorb additional labor from less productive sectors.
A comprehensive reform agenda is needed to boost investment in physical and human capital, address continuing demographicpressures, and raise innovation. Such reforms are also needed to improve business climates and governance, reduce the role of thestate in the economy, and promote the diversification of commodity-dependent economies.

Introduction
Productivity growth in the Europe and Central Asia
(ECA) region has fallen from an above-EMDE-average
pre-crisis (2003-08) rate of 5.5 percent to a below-averagepost-crisis (2013-18) rate of 1.6 percent—the steepest
decline of any EMDE region (Figure 2.2.1.1).
Productivity levels in ECA in 2018 were one-half above
the EMDE average, but only 30 percent of the advancedeconomy average. The sharp post-crisis slowdown in
productivity growth has significantly reduced the pace of
ECA’s convergence with advanced economies.
Within the ECA region, there is wide heterogeneity acrosseconomies. Productivity growth in Central Europe has
been solid in the post-crisis period, at 2.6 percent, while ithas been near zero in Russia and the Western Balkans. The
region’s agricultural commodity exporters, most of which
are in Central Asia (excluding Kazakhstan) and Eastern
Europe, have ECA’s lowest productivity levels, at 3 and 10
percent of the advanced-economy average, respectively. In
contrast, Poland and Turkey have productivity levels over
35 percent of the advanced-economy average, reflecting
their integration into global value chains and roles asregional financial centers. Central Europe, whose
economies are members of the European Union (EU), is
deeply embedded in Western European supply chains andhas the highest productivity of the ECA subregions, at 34
percent of the advanced-economy average.
Against this backdrop, this box addresses the following
questions.
••

What have been the factors associated withproductivity growth in the region?

•

What policy options are available to boost regional
productivity growth?

For the purposes of this box, productivity, unless otherwise
indicated, refers to labor productivity, defined as real GDP(at 2010 prices and market exchange rates) per
worker. The data refer to a sample of 21 ECA economies:
Kosovo, Turkmenistan, and Uzbekistan are excluded insome analysis due to limited data availability.1

Evolution of regional productivity
Sharp post-crisis productivity growth slowdown. In thesteepest post-crisis decline of any EMDE region, average
productivity growth in ECA fell to 1.6 percent in 201318, below the EMDE average, from the above-average rateof 5.5 percent in 2003-08. This slowdown was
broad-based across the region, affecting nearly all
economies, with post-crisis productivity growth below
longer-term (1992-2018) averages in roughly two-thirds ofthe region’s economies (Figure 2.2.1.2).
Within-region heterogeneity. There has been wide
heterogeneity within the region. The productivity growth
slowdown was particularly steep in the South Caucasus
and Russia, as well as in the Western Balkans, the latter ofwhich was hit by the Euro Area crisis of 2010-12 amid
already elevated unemployment rates. In contrast, thedeceleration was milder in Central Europe, which is better

How has productivity growth evolved in the ECA
region?

Note: This box was prepared by Collette M. Wheeler, building upon
analysis in Chapter 3. Research assistance was provided by Vasiliki
Papagianni and Shijie Shi.

1 Central Europe includes Bulgaria, Croatia, Hungary, Poland, andRomania. Western Balkans includes Albania, Bosnia and Herzegovina,
Kosovo, Montenegro, North Macedonia, and Serbia. Eastern Europe
includes Belarus, Moldova, and Ukraine. South Caucasus includes Armenia, Azerbaijan, and Georgia. Central Asia includes Kazakhstan, theKyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

E URO PE AND CE NTRAL AS IA

BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
FIGURE 2.2.1.1 Productivity in ECA
compared with other regions
Productivity growth in Europe and Central Asia (ECA)
has fallen from an above-EMDE-average pre-crisis rateof 5.5 percent to a below-EMDE-average post-crisis rateof 1.6 percent—the steepest decline of any EMDE
region. Convergence toward advanced economies
slowed in the post-crisis period, after having been thefastest among EMDE regions in the pre-crisis period.
Productivity levels in ECA, while above the EMDE
average, are still one-third of those in advanced
economies.
A. Average annual productivity growth in EMDE regions

integrated into global supply chains, and Central Asia,
which has growing economic ties with China.
•

South Caucasus. The post-crisis decline in productivity
growth was most pronounced, at 14 percentage
points, in the South Caucasus. After reaching doubledigit annual productivity growth pre-crisis, thesubregion suffered several post-crisis shocks, including
conflict (Georgia), bouts of violence (Armenia), and aplunge in commodity prices (Armenia, Azerbaijan,
Georgia).

•

Western Balkans. Productivity growth also markedly
declined in the Western Balkans (by 5.4 percentage
points), where the Euro Area crisis disrupted financial
intermediation, including foreign bank retrenchment,
and progress on structural reforms stalled. Since 2013,
productivity growth in this subregion has been near
zero.

•

Russia. Amid international sanctions and the 2014-16
oil price collapse, Russia’s productivity growth was,on average, near zero in 2013-18—a sharp decline
from 6.0 percent in 2003-08.

•

Turkey. Productivity growth more than halved relative
to its pre-crisis average, slowing to 2.1 percent in2013-18, as the economy faced political andeconomic shocks.

•

Central Europe. Annual productivity growth slowedby just over 1 percentage point, from 3.7 percent to
2.6 percent, in Central Europe in the post-crisis
period, in tandem with the modest slowdown in theEuro Area. This partly reflects the close integration ofthis subregion with Western European supply chains.
Notwithstanding anemic Euro Area growth since theglobal financial crisis, Central Europe achieved thesecond highest productivity growth of any ECA
subregion in 2013-18, after only Central Asia. This,in part, reflected buoyed investment, which was
supported by the absorption of EU structural funds.

•

Eastern Europe. Annual productivity growth inEastern Europe slowed by about 4.5 percentage points
from pre-crisis rates, to 2.2 percent during 2013-18.
Productivity growth averaged only 1.4 percent in2013-16, which reflected the dual shocks of conflictin Ukraine and a commodity price plunge, but picked
up in the next two years.

•

Central Asia. Central Asia insulated itself somewhat
from the impact of the oil price slump of 2014-16

B. Productivity levels and convergence in EMDE regions

Source: Penn World Table; The Conference Board; World Development
Indicators, World Bank.
Note: ECA = Europe and Central Asia, EMDE = emerging market anddeveloping economies. Aggregate regional growth rates calculated using
GDP weights at 2010 prices and market exchange rates. Unless otherwise
specified, productivity refers to labor productivity, defined as output per
worker. Sample includes 127 EMDEs, of which 21 are ECA economies.
A. Blue bars denote the range across six (GDP-weighted) averages for
EMDE regions. Yellow bars denote the simple average of the six EMDE
regional averages.
B. Rate of convergence calculated as the difference in productivity growth
rates with the average advanced economy divided by the log difference inproductivity levels with the average advanced economy. Regional rate ofconvergence is the GDP-weighted average of EMDE economies of eachregion. “Level” of productivity refers to the GDP-weighted average of regional
productivity as a share of the average advanced economy during 2013-18.
Advanced-economy sample includes 35 advanced economies.
Click here to download data and charts.

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
FIGURE 2.2.1.2 Evolution of productivity in ECA
The post-crisis slowdown in productivity growth has affected nearly all the economies in ECA. There is wide heterogeneity
within the region, however, with productivity growth near-zero since 2013 in Russia and the Western Balkans but above 2.5
percent in Central Asia and Central Europe. The post-crisis productivity growth slowdown has reflected a sharp deceleration
in total factor productivity growth in Eastern Europe, the South Caucasus, and the Western Balkans but investment weakness
in Russia and Central Europe.
A. Productivity growth in ECA

B. Productivity growth in Central Asia,
South Caucasus, and Western Balkans

C. Productivity levels relative toadvanced-economy average, 2018

D. Share of economies with productivity
growth below long-run and pre-crisis
averages, 2013-18

E. Contribution to productivity growth

F. Contribution to productivity growth, by
ECA subregion

Source: Barro and Lee (2015); Haver Analytics; International Monetary Fund; Penn World Table; United Nations; Wittgenstein Centre for Demography and Global Human
Capital; World Development Indicators, World Bank.
Note: CA=Central Asia, CE=Central Europe, KAZ=Kazakhstan, SC=South Caucasus, WBK=Western Balkans. Unless otherwise specified, productivity refers to laborproductivity, defined as output per worker.
A.-F. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
C. Figure shows 2018 subregional productivity levels as a share of 2018 advanced-economy weighted average. Sample includes 35 advanced economies and 21 ECA
economies.
D. Figure shows the share of economies for which average productivity growth in 2013-18 was lower than a long-run (1992-2018) and the pre-crisis (2003-08) average.
Sample includes 127 EMDEs, of which 21 are ECA economies.
E.F. Productivity defined as output per worker in 2010 U.S. dollars. Samples are unbalanced due to data availability, and include up to 21 ECA economies and 92
EMDEs.
Click here to download data and charts.

and recession in Russia during 2015-16 by pivoting
its exports toward China. By 2018, China had
become the second largest export market for Central
Asia after the Euro Area, accounting for 20 percent ofexports. As a result, the subregion’s productivity
growth slowed mildly in comparison to the rest of theregion, by 2.3 percentage points to 3.4 percent in2013-18—the fastest productivity growth in ECA in
the period.

High productivity levels relative to EMDEs, but withwide range. Partly as a result of rapid productivity growthin 2003-08, the average productivity level in ECA in
2013-18 was 30 percent of the advanced-economy
average—roughly one-half above the EMDE average. The
ECA average, however, masks wide divergences across
ECA subregions, from 3 and 10 percent of the advancedeconomy average in predominantly agriculturalcommodity-exporting Central Asia (excluding Kazakhstan)G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

E URO PE AND CE NTRAL AS IA

BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
and Eastern Europe, respectively, to 34 percent of theadvanced-economy average in Central Europe, which is
deeply embedded into Euro Area supply chains and which
has benefited from the absorption of EU structural funds.
Poland and Turkey had productivity levels above 35
percent of the advanced-economy average, partly reflecting
their openness to trade and positions as regional financial
centers (World Bank 2014; World Bank 2019i). Since theglobal financial crisis, the pace of convergence to advanced-economy productivity levels in the ECA region as a whole
has slowed sharply, to average less than 1 percent per year
over 2013-18—one-fifth of its rate in 2003-08.

facing notable challenges with corporate over-indebtedness
and market concentration (EBRD 2018a). In both Turkey
and Central Asia, the sources of the productivity
deceleration were broad-based, reflecting a slowdown inphysical capital deepening and human capital
improvements, as well as in TFP growth, particularly in
Kazakhstan. In Central Asia, Eastern Europe, and theWestern Balkans, reform momentum has also slowed, withmany of these economies falling short of completing thetransition to competitive and inclusive markets.

Sources of productivity growth. Labor productivity
growth can be decomposed into its sources: Factor
accumulation (human or physical capital) and advances inthe efficiency of factor use (total factor productivity, or
TFP). Two-thirds of the post-crisis slowdown in
productivity growth in ECA is estimated to have been due
to slowing capital accumulation—partly reflecting weak
investment amid lower foreign direct investment (FDI)
inflows and declining commodity prices—and one-third
to slowing TFP growth, compared with about equal
contributions of these sources in the average EMDE.

Post-crisis slowdown across all sectors. Pre-crisis
productivity growth in ECA was mostly driven by shifts ofresources from agriculture and industry to higherproductivity services sectors, partly as a result of continued
reforms to address resource misallocation inherited from
central planning (World Bank 2008). The post-crisis
period, however, was marked by weakness of growth acrossall sectors as a slowdown in manufacturing, exacerbated bydwindling global trade growth and a collapse incommodity prices, spilled over to services (Figure 2.2.1.3;
Orlic, Hashi, and Hisarciklilar 2018). In contrast to the
EMDE average, the contribution of services to
productivity growth in 2013-15 was negative in ECA,
likely reflecting, in part, spillovers from the Euro Area debt
crisis and the continued migration of skilled labor toWestern Europe.

In Russia and Central Europe, particularly Bulgaria andRomania, weakening capital services deepening accounted
for most (three-quarters) of the slowdown in productivity
growth in the post-crisis period. In Russia, internationalsanctions, combined with the 2014-16 oil price plunge,
deterred investment, which was further dampened by the
weak business environment (Russell 2018). Although EU
structural funds have buoyed overall investment in Central
Europe, they have not fully offset weakness in machineryand equipment investment, which has been due partly to
reduced commercial credit supply (Gradzewicz et al. 2018;
Levenko, Oja, and Staehr 2019).
In contrast, reduced TFP growth has been the main source(accounting for three-quarters) of the productivity growth
slowdown in Eastern Europe, the Western Balkans, andthe South Caucasus. This has partly reflected pockets ofconflict and violence (Armenia, Georgia, Ukraine).
However, private and public investment has also been
weak in the post-crisis period, contributing to reduced
TFP growth. As a result of weak investment, thesesubregions face large infrastructure gaps, particularly intransport and telecommunications networks, which limits
the capacity to promote regional integration and, forenergy exporters, diversification (IMF 2014). Obstacles toprivate sector development also constrain TFP in thesesubregions, with certain economies in the Western Balkans

Sources of regional productivity growth

Sectoral reallocation as a source of productivity growth in
ECA. Resource reallocation toward more productive
sectors accounted for almost half of ECA’s productivity
growth in the 1990s, as output of the region’s services
sectors increased by nearly 15 percentage points of GDP(World Bank 2008; Arnold, Javorcik, and Mattoo 2011;
World Bank 2015b). In contrast, the surge in productivity
growth of 2003-08 mostly reflected within-sector growth,
as firms in Central Europe became integrated into EuroArea supply chains, technology transfer accelerated, andthe services sectors became more liberalized.2 After the
global financial crisis, however, within-sector productivity

2 As economies in Central Europe initiated the process to join theEuropean Union, structural policies that boosted competition andfacilitated integration with global value chains helped spur within-sector
growth, particularly within services. Thus, liberalization of services sectors
is likely to have increased the average productivity of incumbent firms
and facilitated the entry of new and more innovative firms. Please refer toBartelsman and Scarpetta (2007); Brown and Earle (2007); Georgiev,
Nagy-Mohacsi, and Plekhanov (2017); Shepotylo and Vakhitov (2015);
and World Bank (2008) for further detail.

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
FIGURE 2.2.1.3 Factors supporting productivity growth in ECAWithin-sector productivity growth—the main driver of pre-crisis productivity growth in ECA—fell sharply in the post-crisis
period, and productivity gains from sectoral reallocation halved as economies moved to services sectors with relatively low
productivity levels. The deceleration of productivity reflected slower improvements in a broad range of its fundamental
drivers.
A. Contribution to productivity growth

B. Sectoral productivity levels

C. Sectoral contribution to productivity
growthD. Sectoral composition of GDP

E. Share of EMDEs with a post-crisis
slowdown in growth of underlying driversof productivity

F. Drivers of productivity, 2017

Source: APO productivity database; Expanded African Sector Database; Groningen Growth Development Center Database; Haver Analytics; International Country Risk
Guide; ILOSTAT; Observatory of Economic Complexity; Organisation of Economic Co-operation and Development STAN; Penn World Table; United Nations; World
Bank; World KLEMS.
Note: Unless otherwise specified, productivity refers to labor productivity, defined as output per worker.
A.-D. The sample includes 6 ECA economies and 46 EMDEs.
A.D. Aggregates calculated using GDP weights at 2010 prices and market exchange rates.
A. Growth “within sector” shows the contribution to aggregate productivity growth of each sector holding employment shares fixed. The “between sector” effect shows thecontribution arising from changes in sectoral employment shares.
B. Figure shows the median of country groups.
C. Figure shows median values. “Manufacturing” includes manufacturing, mining and utilities; “Services” includes financial and business services, government andpersonal services, trade services, and transport services.
D. Figure shows the share of total value added within each sector. “Manufacturing” includes mining and utilities; “Finance” includes business services.
E. Post-crisis slowdown defined as a decline in the growth of each variable during 2008-17 compared to growth in the pre-crisis period, defined as 1998-2007. The blue
bars represent share of 21 economies in Europe and Central Asia economies where improvements in each driver of productivity were lower during 2008-17 than in the
pre-crisis period 1998-2007 or changes in 2008-17 were below zero. Orange diamond is the corresponding values for EMDE countries. Variables corresponding to eachconcept and their sample sizes are: Institutions=government effectiveness (20 ECAs; 126 EMDEs), Innovation=patents per capita (15 ECAs; 43 EMDEs),
Investment=investment to GDP ratio (21 ECAs; 109 EMDEs), Income equality=(-1)*Gini (21 ECAs; 121 EMDEs), Urbanization=urban population percentage (21 ECAs;
127 EMDEs), Complexity = Hidalgo and Hausmann (2009)'s Economic Complexity Index (17 ECAs; 79 EMDEs), Education=years of schooling (17 ECAs; 103 EMDEs),
Demography=share of working-age population (21 ECAs; 127 EMDEs), Gender equality= female average years of education divided by male average years (17 ECAs;
102 EMDEs). Green horizontal line indicates 50 percent.
F. Figure shows the unweighted average levels of drivers normalized as an average of AEs as 100 and standard deviation of 10. Blue bars represent average within
Europe and Central Asia economies in 2017. Orange whiskers represent the range of the average drivers for six regions in 2017. Variables corresponding to theconcepts are as follows: Education = years of education, Urbanization = share of population living in urban area, Investment = share of investment to GDP, Institution=
rules of law, Complexity=Economic complexity index, Geography=share of land area which are not in tropical region, Gender equality= Share of the year of schooling forfemale to male, Demography=share of population under 14, Innovation=Log patent per capita, Trade=Export+Import/GDP, Price stability=(-1)*log inflation rate. Sample
includes 21 ECA economies.
Click here to download data and charts.

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
growth collapsed, falling to near zero in 2013-15. Thismay have reflected falling investment in physical capital,
particularly in commodity exporters amid the collapse ofcommodity prices, as well as stalling structural reforms to
improve business environments (EBRD 2018b; Georgiev,
Nagy-Mohacsi, and Plekhanov 2017).
Between-sector shifts in resources to productivity growth
also declined in ECA after the crisis: In 2013-15 it was half
its pre-crisis average. The fall may partly have reflected ashift out of agriculture into lower-productivity sectors post-crisis (trade services) than pre-crisis (manufacturing), suchas was the case in Kazakhstan (World Bank 2019j). In
Romania, reallocation towards more efficient firms wasmore important within the manufacturing sector, as othersectors, including services, were less exposed to foreigncompetition (Iootty, Pena, and De Rosa 2019). More
broadly, spillovers from the Euro Area debt crisis, slowing
global trade growth, and the oil price plunge dampened
growth in sectors with higher levels of productivity—
including in finance, manufacturing, and mining—
limiting their ability to continue to absorb additional laborfrom other sectors with lower productivity (ILO 2017).
Indeed, unemployment grew and labor participation fell in
the region, particularly in the Western Balkans and South
Caucasus.
Widely varying sectoral productivity levels across ECA.
Although most sectors in ECA have productivity levels
above EMDE averages, aggregate regional numbers masksignificant variations. For example, although productivity
has improved in most sectors, agricultural productivity has
declined in the economies of Central Asia since theirtransition to market economies in the 1990s amid
disruptions to markets and trade (Gharleghi and Popov2018). While there are a few exceptions in Central Asia
where productivity in the production of specificcommodities has improved—mainly grains in Uzbekistan
and oil seeds in Kazakhstan—reallocating labor and capitalfrom less competitive agricultural subsectors to moreproductive sectors continues to have the potential to boost
economy-wide productivity.
More broadly in ECA, cross-sectoral productivity
differentials continue to imply scope for further overall
productivity gains from resource reallocation. In sectors
such as agriculture, mining and utilities, ECA’s
productivity lags about 50-70 percent behind advancedeconomy averages, and in mining and utilities it lags
behind even EMDE averages. On average in ECA,
productivity in agriculture (which accounts for 18 percent
of GDP) is about one-third of productivity in other low-

skilled sectors such as construction or trade and less than
one-sixth of productivity in high-skilled services such as
finance, which accounts for 9 percent of GDP.
Reform momentum
Two waves of reform spurred pre-crisis productivity
growth in ECA. In the first wave, in the 1990s, the regiontransitioned toward market from centrally planned
economies. In the early 2000s, the second wave of reforms
in ECA was associated with the initiation by countries inCentral Europe and the Western Balkans of their EU
accession process.
First wave. In the wake of the collapse of the Soviet Union
in the early 1990s, productivity plunged as transition
economies fell into deep recessions caused by the ruptureof trade and financial links with the Soviet Union, theemigration of skilled labor, and armed conflict in parts of
the region. Central planning was dismantled and replacedby more market-based approaches (Falcetti, Lysenko, andSanfey 2006). ECA economies were opened up tointernational trade and capital markets, prices and interest
rates were liberalized, and state-owned enterprises wereprivatized to a degree (Georgiev, Nagy-Mohacsi, andPlekhanov 2017). These reforms helped boost productivity
growth in the mid-1990s, particularly in Central Asia andthe South Caucasus (World Bank 2018f).
Second wave. In the early 2000s, accession to the EU by
the countries of Central Europe accelerated theirinternational economic integration, and drove institutional
improvements, further privatization, and deepening of
their capital markets (Bruszt and Campos 2016). FDI andprivate investment surged as reforms were anchoredexternally, with many ECA economies rapidly becoming
integrated into global value chains with Western Europe,
accelerating the adoption of new technologies andpractices (Aiyar et al. 2013, EBRD 2014). The growinginternational integration of financial and banking systems
helped deepen capital markets, particularly in Central
Europe. It was subsequently accompanied by a credit
boom (de Haas and van Lelyveld 2006).3
Post-crisis reform momentum. Post-crisis, ECA has faced
multiple headwinds, including the legacy of the global

3 The rise in foreign currency borrowing by households and firms,
however, left sectors exposed to external vulnerabilities, such as capitalflow reversals, and deepened the recession following the global financial
crisis as economies faced a credit crunch and a period of deleveraging(Zettelmeyer et al. 2010; de Haas et al. 2015).

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
financial crisis, the collapse of oil prices in 2014-16,
heightened geopolitical tensions, and internationalsanctions on Russia. Meanwhile, reform momentum has
slowed, with parts of the region witnessing reform
reversals, leaving a need for substantial reform progress,
especially in Central Asia and Eastern Europe—which are
not anchored to an EU accession process—and theWestern Balkans.4 Many of the commodity exporters in
the region also suffer from structural constraints, including
a lack of export diversification, large state presence infirms, unfavorable business environments, and weakinternational competitiveness (Azerbaijan, Kazakhstan,
Russia, Ukraine; EBRD 2017; Funke, Isakova, andIvanyna 2017).
Post-crisis slowdown in drivers of productivity. There has
been a broad-based slowdown in the growth of most key
drivers of labor productivity in ECA in the post-crisis
period. Demographic pressures have been intensifying,particularly in the past decade, in nearly all ECA
economies. Growth in working-age populations in theregion has long lagged the average for EMDEs as a resultof significant migration to western European countries inthe EU and to Russia and sharp declines in fertility rates.
Additionally, more than three quarters of the economies in
ECA have experienced post-crisis slowdowns in investment
rates, reflecting adverse shifts in investor sentiment amid
conflicts and financial pressures in the region, as well as
weak external economic growth, including in the EuroArea, and adverse shocks to Russia. Low innovation
rates—which partly stem from weak competitiveness,inadequate control of corruption, and a high presence ofstate-owned enterprises—also continue to dampen thebusiness environment and hinder investment in the region,
particularly in the absence of progress with other reforms(EBRD 2018a; EBRD 2019b).
Other factors affecting productivity in the region
Natural resource extraction. Standard productivity growthdecompositions fold the extraction of natural capital into
total factor productivity growth and, to a lesser extent,
physical capital growth (Brandt, Schreyer, and Zipperer
2017; Calderón and Cantu 2019). An economy’s natural

4 A reversal of structural reforms remains a key risk in these regions,and the pace of growth will depend partly on the successful
implementation of structural reforms to enhance the businessenvironment, achieve debt sustainability, and restructure state-owned
enterprises to improve competition. Please refer to EBRD (2013); Lehne,
Mo, and Plekhanov (2014); Georgiev, Nagy-Mohacsi, and Plekhanov(2017); Rovo (2019); and World Bank (2019g) for further detail.

capital consists of its natural resources such as oil, metals,
and agricultural land, and is particularly relevant to ECA
given the presence of large commodity exporters. During
the pre-crisis commodity price boom and theaccompanying boom in resource exploration anddevelopment, the increased extraction of natural capital
lifted productivity growth in ECA (Khan et al. 2016). The
rate of natural capital extraction declined in some
economies following the boom and as commodity prices
fell, dampening TFP growth.
Urbanization. Urbanization tends to be associated withproductivity gains because it encourages more rapid
dissemination of knowledge and technologies andfacilitates the reallocation of resources from lower tohigher-productivity sectors: such reallocation can beconstrained by limited urban development, as has been thecase in the Kyrgyz Republic (World Bank 2018g). The factthat ECA’s population density is lower than in other
EMDE regions with similar GDP per capita levels (such as
Latin America and Caribbean) indicates scope for further
productivity gains from urbanization. Yet, relative to therest of the world, economies in ECA—particularly those inCentral Europe, the Western Balkans, and Eastern
Europe—have recently experienced declines in urban, as
well as total, populations amid decades of belowreplacement fertility and net emigration (World Bank
2017a).

Policy options
Across nearly all ECA economies, productivity growth has
slowed since the financial crisis. To reinvigorate
productivity growth, a four-pronged, comprehensive
policy approach is needed to improve the provision andquality of factors of production, boost firm productivity,
promote productivity-enhancing sectoral reallocation, andestablish a more growth-friendly business environment.
Some of these policies offer the prospect of relatively shortterm productivity gains, such as changes in state-owned
enterprise ownership and improvements in the investment
climate, while others are more likely to lay the foundation
for longer-term productivity gains, such as efforts toimprove human capital or adjust migration policies.
Within these broad categories, specific policy priorities
need to be tailored to country-specific circumstances,
especially given the region’s wide heterogeneity.
Improving factors of production
In ECA, roughly two-thirds of the post-crisis slowdown in
productivity growth has reflected slower physical capitalG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
FIGURE 2.2.1.4 Drivers of productivity growth in ECA
Investment growth across the region has fallen in the post-crisis period, reflecting external headwinds—including acommodity price plunge—and idiosyncratic factors—including conflict in pockets of the region and financial pressures inlarge economies. The workforce is continuing to age, and the working-age population share is declining. Learning gaps are
sizable in parts of the region, the control of corruption indicator and business climates remain weak, and the role of the state
has remained large.
A. Actual and Consensus forecasts forinvestment growth

B. Learning gaps, 2017

C. Share of regional GDP accounted forby economies with growing working-age
populations

D. Doing Business indicators

E. Assessment of transition to acompetitive market economy, 2019

F. Control of corruption, 2017

Source: Consensus Economics; European Bank for Reconstruction and Development; Kraay (2018); United Nations; World Bank.
Note: Unless otherwise specified, productivity refers to labor productivity, defined as output per worker.
A. Blue bars denote actual investment growth, where investment is measured as gross fixed capital formation. Actual growth aggregate calculated using GDP weights at2010 prices and market exchange rates. Consensus forecasts aggregate calculated as a simple average of surveys for periods indicated based on data availability.
Unbalanced sample includes 8 ECA economies, due to data availability.
B. CE = Central Europe, CA = Central Asia, EE = Eastern Europe, SC = South Caucasus, and WBK = Western Balkans. The learning gap is the difference between
expected years of schooling and learning-adjusted years of schooling, as in Kraay (2018). The sample includes 21 ECA EMDEs.
C. The working-age population is defined as people aged 15-64. Unbalanced sample including 23 ECA economies.
D. AEs=advanced economies. Figure shows the median ECA value for 2010 and 2019, and the median advanced economy for 2019. The full names of the Doing
Business reform areas given on the x-axis are: Making it easier to start a business, making it easier to deal with construction permits, making it easier to get electricity,
making it easier to register property, making it easier to get credit, making it easier to protect minority investors, making it easier to pay taxes, making it easier to tradeacross borders, making it easier to enforce contracts, and making it easier to resolve insolvency. Sample includes 33 advanced economies and 22 ECA economies.
E. Figure shows the distance to the frontier for achieving a full transition to a competitive market economy, as measured by EBRD (2019b). Economies with higher index
levels are closer to the frontier, where scores range from 1 to 10, with 10 denoting the synthetic frontier. The sample includes 24 ECA economies.
F. CE = Central Europe, CA = Central Asia, EE = Eastern Europe, SC = South Caucasus, and WBK = Western Balkans. The indicator reflects perceptions of the extent towhich public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests, asmeasured by the World Governance Indicators. Sample includes 23 ECA economies and 150 EMDEs.
Click here to download data and charts.

accumulation, with investment weakness particularly
notable in Central Europe, Russia, and, more recently,
Turkey, which together account for over 85 percent of
ECA’s GDP. In some parts of the region, infrastructure
investment gaps are sizable. There is thus need for arenewed push to close infrastructure gaps as well as toboost private investment. Meanwhile, human capital

accumulation has contributed less to productivity growthin ECA than in other EMDEs, suggesting there is also aneed to improve this source of productivity growth.
Addressing investment and infrastructure gaps.
Investment growth has fallen sharply in ECA in the postcrisis period, reflecting a commodity price plunge as well

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
as weakening external economic growth and investor
sentiment, amid conflict, international sanctions, andfinancial pressures (Figure 2.2.1.4). Across the region,
reforms to boost private sector development, transition tocompetitive and inclusive markets, and regional
integration are needed to attract private investment andcapital flows, particularly to economies outside the EUthat lack access to financing sources, such as EU structural
funds (EBRD 2018a; World Bank 2019g).
In certain subregions, particularly Central Asia, removing
key bottlenecks to private sector development, such as
inadequate infrastructure, is especially important tosupport productivity growth. In some pockets of theregion, improved connectivity could accelerate theabsorption of technology and speed convergence withadvanced economies (Gould 2018). Infrastructure needs
remain large in ECA, particularly in transport andelectricity. Unreliable electricity supply hinders activity inparts of the region: while the percentage of firms
experiencing electrical outages is lower in ECA than in any
other EMDE region, related losses for affected firms inCentral Asia can exceed 9 percent of annual sales (Blimpo
and Cosgrove-Davies 2019; IMF 2019a). In surveyed
manufacturing firms in Uzbekistan, for instance, smallerfirms report more interruptions of electricity, gas, andwater supply, than larger firms, as well as a lack of territoryor high lease rates on land as impediments to expanding
output production (Trushin, E. 2018). Appropriate land
use planning and urbanization policies can substantiallyreduce the cost of meeting transport needs while
minimizing carbon footprints (ITF 2018; Rozenberg andFay 2019).
Raising human capital. In a few economies in ECA,
particularly in Central Asia, inadequate investment in
human capital has left parts of the workforce poorly
equipped with the skills required for the future, andunprepared for rapid technological change (Flabbi and
Gatti 2018). Boosting human capital investment—
including through education and health—could help
remove bottlenecks to productivity growth. How
education systems adapt to evolving skill needs will be a
key determinant of the productivity and distributional
effects of technological change (Barro and Lee 2015).
In several economies in ECA, educational attainment andthe acquisition of needed skills have been lower than
expected given the level of school enrollment and theaverage years of schooling (Altinok, Angrist, and Patrinos
2018). The learning gap (the difference between years
spent in schools and educational assessment outcomes) is

wider than the global average in most Western Balkan
economies—particularly in Kosovo and North
Macedonia—as well as in a few economies in Eastern
Europe (Moldova), Central Asia (the Kyrgyz Republic),
and the South Caucasus (Georgia). While most economies
in Central Europe have smaller gaps than ECA as a whole,
Romania is an exception. Turkey also has a larger gap than
ECA, in addition to low education attainment in the workforce, large gender gaps in education, and an inadequacy ofskills, which is often cited as a constraint for doing
business and a bottleneck to innovation in the country(World Bank 2019d). Some economies in ECA with large
learning gaps, including Georgia, have taken measures toreform the education sector and its funding (Kraay 2018).
Although ECA has the lowest rate of extreme poverty of all
EMDE regions, the share of school-age children notenrolled in school is higher than in both East Asia andPacific and Latin America and Caribbean (World Bank
2018h). Economies in the Western Balkans (Albania,
Montenegro, Serbia), Eastern Europe (Moldova), Central
Asia (the Kyrgyz Republic, Uzbekistan), and Turkey haveelevated out-of-school rates relative to the ECA average forsecondary education (UNICEF 2019). The diversity ofsituations in ECA—with human capital investment quite
high in some economies but lagging in others (such as inCentral Asia)—indicates a need for policies to be tailoredto countries’ specific needs (Kraay 2018). Education policy
and training programs can also be redesigned to adapt theskills of aging populations to changing needs and newtechnologies (Hallward-Driemeier and Nayyar 2018;
World Bank 2018a).
Counteracting unfavorable demographic trends. In ECA,
the workforce is continuing to age, and the working-age
population share is declining, with many young and skilledworkers having emigrated. These developments are likely
to weaken productivity growth and highlight the need for
education to help workers adapt to new job requirements
and technologies (Aiyar, Ebeke, and Shao 2016).
Generating stronger productivity growth will requiremeasures to mitigate the decline in skilled workforces.
Implementing more flexible immigration policies could
help relieve skilled labor shortages by attracting skilled
foreign workers in an orderly way (Delogu, Docquier, andMachado 2014; World Bank 2019g).
Boosting firm productivity
Within-sector productivity gains stalled in ECA in 201315, consistent with slowing reallocation of resources
between firms and slowing productivity growth within

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
firms. This highlights the need to boost firm productivity
in the region, including by completing the transition tocompetitive and inclusive markets, which could strengthen
the environment for private investment and innovation(World Bank 2019a). Policy options include measures tolevel the playing field for private and state-owned firms
and expanding access to finance to a wider range of firms.
Leveling the playing field. State-owned enterprises tend to
be less efficient than those in the private sector (WorldBank 1995). In Eastern Europe and Central Asia, and tosome extent Russia, the state’s presence in the economy
remains large, with state ownership accounting for morethan 10 percent of firms surveyed in some cases, and with
ECA ranking second overall among EMDE regions, after
Sub-Saharan Africa (World Bank 2019k). In Ukraine,
firms with at least partial state presence account forroughly 20 percent of total turnover by firms and over 25
percent of firms’ assets (Balabushko et al. 2018). Stateowned enterprises also have a large presence in Moldova,
accounting for one-third of GDP (World Bank 2019l).
Restructuring or privatizing state-owned enterprises
therefore still presents an opportunity to raise economywide productivity in several countries across the region, if
it is accompanied by effective regulation andimprovements in management, corporate governance, andthe business environment (Brown, Earle, and Telegdy
2006; EBRD 2019b; Funke, Isakova, and Ivanyna 2017).
Additionally, there are a number of economies, including
in Eastern Europe, where price controls remain in place forparticular goods, tending to constrain competition andlower productivity.
Financial market development and financial inclusion.
Small and medium-sized enterprises (SMEs) have thelargest potential for productivity catch-up with advanced
economies. Their growth continues to be hindered bymany factors, including insufficient access to finance andregulatory barriers (Ayyagari, Demirgüç-Kunt, andMaksimovic 2017; Cusolito, Safadi, and Taglioni 2017;
Wang 2016). The largest gaps in financial inclusion for
SMEs in ECA are in Central Asia and the South Caucasus(excluding Georgia), where access to financial services isnearly as limited as in the Middle East and North Africa,
South Asia, and Sub-Saharan Africa (IMF 2019b).
Policies that promote more widespread adoption of digitaltechnologies, including in the delivery of financial andpublic sector services, could bolster financial inclusion andboost productivity by helping spread innovation andimproving private sector and government efficiency(Baldwin 2019). In economies with large informal sectors,

more widespread adoption of these technologies could also
help expand tax bases through the fiscalization of informal
sector transactions (World Bank 2019a). Increasing SMEs’
access to finance could help these firms increase theiraverage size and reduce their reliance on retained earnings
to fund investment, which in turn would support job
creation (Ayyagari, Demirgüç-Kunt, and Maksimovic
2017; Ayyagari et al. 2016).
Encouraging sectoral reallocation
Between-sector productivity gains from sectoral
reallocation have slowed in ECA since the global financial
crisis. Also, some ECA economies remain undiversified:renewed efforts to diversify commodity-based economies
could generate new opportunities for labor to move toward
more productive employment.
Diversifying economies. Energy-exporting economies,
including those in ECA, are characterized by generally low
levels of economic diversification, in terms of both exports
and fiscal revenue (Grigoli, Herman, and Swiston 2017).5
Energy sector production tends to be capital-intensive,with relatively high labor productivity (Aslam et al. 2016;
Danforth, Medas, and Salins 2016; Stocker et al. 2018).
Productivity growth, however, has been more tepid in
ECA’s energy-exporting countries than in the region
overall, with post-crisis (2013-18) growth at 1.2 percent
versus 1.6 percent, reflecting weaker TFP growth.
Diversification therefore presents an opportunity to boost
TFP and productivity growth, as well as macroeconomic
stability (Brenton, Newfarmer, and Walkenhorst 2009;
Papageorgiou and Spatafora 2012). Diversification of
resource-based economies can be promoted by reforms
that increase capital and skill accumulation, innovation,and reduce transaction costs.6
Enhancing a growth-friendly environment
Several ECA economies have severe institutional
weaknesses that continue to erode incentives forinnovation and investment. Addressing these weaknesses
requires reforms to improve governance and businessclimates.

5 On the budget front, Russia has made strides in anchoring fiscal
policy by implementing a fiscal rule that targets a primary balance of zeroat the benchmark oil price of $40 per barrel. Any excess fiscal reserves
that are generated from higher oil prices are saved in the National
Welfare Fund.
6 Please refer to Beck (2018); Gylfason (2018); Lederman and Maloney(2007); Hesse (2008); and IMF (2016a) for further detail.

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BOX 2.2.1 Labor productivity in Europe and Central Asia: Trends and drivers (continued)
Growth-friendly governance. Over the long term,
institutional quality is one of the most important
determinants of productivity growth (Chapter 3). In ECA,
productivity catch-up to advanced economies wasparticularly pronounced in Central Europe during the precrisis period, reflecting the anchoring of structural andinstitutional reforms to the EU accession process(Rodríguez-Pose and Ketterer 2019). Overall, however, theregion continues to face governance challenges with nearly
75 percent of ECA EMDEs falling below the global
average for the control of corruption, including almost allof the economies of Central Europe, Eastern Europe, andthe South Caucasus (Kaufmann, Kraay, and Mastruzzi2010). Because progress in confronting perceived andactual corruption has been slow, continuing efforts havereinforced the perception that the control of corruption ishigher than in other EMDEs (Transparency International2019).
Structural reforms to improve governance can lead tosizable productivity gains, particularly in countries that are
farthest from best practices (Chapter 3; Acemoglu,
Johnson, and Robinson 2005; Cusolito and Maloney2018). Major governance and business reforms in EMDEs
have in the past been associated with higher growth rates
in output, total factor productivity, and investment(Hodge et al. 2011; Divanbeigi and Ramalho 2015; WorldBank 2018a). The detrimental effects of corruption on
firm productivity can be exacerbated by excess or complex
regulation (Amin and Ulku 2019). Anticorruption
campaigns, as well as reductions in the number ofregulations and tax complexity, have helped some
economies tackle corruption (IMF 2019c).
Growth-friendly business climates. Lack of exposure tointernational competition—including from non-tariffbarriers and complex trade rules—as well as restrictive
product market and services regulation, remain structuralbottlenecks in the region, hindering the ability to attract
foreign direct investment and private investment in some
economies (Kazakhstan, Russia, Ukraine; World Bank
2016b; Shepotylo and Vakhitov 2015). Over the past
decade, several ECA economies made significant strides in
improving their business environments. As a result, inseveral countries in Central Europe, the Western Balkans,and the South Caucasus, business environment indexes
have recently approached the levels in advanced EU
economies (World Bank 2018a).
Notwithstanding these improvements, business climates in
Eastern Europe and Central Asia lag the ECA average,
with the latter trailing the EMDE average in access toelectricity and the ease of trading across borders (WorldBank 2019j). For example, in Ukraine, the largest
economy in Eastern Europe, the average worker takes one
year to produce the same amount that the average worker
in Germany produces in 17 days (World Bank 2019m). At
current growth trends, Ukraine is unlikely to converge to
Poland’s per capita income, despite having had similar
income levels in 1990; this partly reflects Ukraine’s
relatively low ratio of capital stock to GDP. Removing
market distortions and improving resource allocation
could triple manufacturing productivity and help improve
prospects in Ukraine (Ryzhenkov 2016). The Western
Balkans also struggle to attract FDI notwithstandingreforms to improve business climates and further
integration into regional and global markets (Jirasavetakul
and Rahman 2018; World Bank 2019h; World Bank
2019n). Although Turkey has high productivity levels, itlags well behind the ECA average for resolving insolvency,
which could dampen overall productivity as less productive
firms are more likely to remain in the market (World Bank
2019d). To address this, Turkey has recently introduced amore streamlined procedure that focuses on business
continuation instead of liquidation.

Growth in Latin American and the Caribbean slowed markedly in 2019, to an estimated 0.8 percent, heldback by idiosyncratic factors in large economies, headwinds from slowing global trade, and social unrest in
several countries. As activity in Brazil gathers pace amid improving investment conditions, policy uncertainty in
Mexico fades, and the recession in Argentina eases after bouts of severe market stress, regional growth is projected
to rise to 1.8 percent in 2020 and about 2.4 percent in 2021. This recovery will not be sufficient to reverse thegrowing per capita income gap with advanced economies in some LAC economies. Moreover, the regional
outlook is subject to significant downside risks, including from market volatility and adverse market responses to
weak fiscal conditions; deeper-than-expected spillovers from slowdowns in Argentina, China, and the UnitedStates; heightened social unrest; and disruptions from natural disasters and severe weather.

Recent developments
Growth in Latin America and the Caribbean(LAC) decelerated markedly in 2019, to an
estimated 0.8 percent. The slowdown was broadbased across economies and sectors. All three ofthe largest economies in the region—Brazil,
Mexico, and Argentina—grew significantly lessthan projected in June. Brazil experienced a largerthan-expected impact from a major mining
accident, as well as slowing exports to China, andrelatively sluggish improvements in labor market
conditions. Growth in Mexico was hindered by anuncertain investment climate, tight monetary
policy, and public spending cuts, while
Argentina’s economy was held back by the effects
of renewed financial stress. In Colombia, however,growth accelerated as private consumption andinvestment picked in the context ofaccommodative monetary policy and fiscal
incentives to support investment.
The regional growth slowdown was generally moreacute in industrial sectors than in services (Figure
2.3.1.A). Industrial activity was stagnant orcontracting in five of the region’s six largest
economies in the first half of 2019 (Argentina,
Note: This section was prepared by Dana Vorisek. Research
assistance was provided by Vanessa Arellano Banoni. The regional
aggregate statistics presented in this section do not include Venezuela.

Brazil, Chile, Mexico, Peru), though conditions inmost countries improved later in the year. Within
the industrial sector, mining activity contracted
sharply following an iron ore mining dam disasterin Brazil, continued oil production declines in
Mexico, and temporary mining disruptions inChile, while policy uncertainty contributed to a
sharp contraction in mining and construction
activity (Figure 2.3.1.B).
Sluggish investment and private consumption heldback regional growth in 2019. Investment
contracted as policy uncertainty lingered, investor
sentiment worsened, and governments retrenched.
In Mexico, the government cancelled public
infrastructure projects. Several other countries cut
public spending (Argentina, Ecuador, Haiti,
Panama, Paraguay).
Regional export growth has slowed along with
global trade activity. Yet export trends among thelarge regional economies are not uniform. Bilateral
tariff hikes between China and the United Statesgave an initial boost to Brazil’s soybean exports,
which has since faded as demand slowed and thesoybean price differential between Brazil and therest of the world narrowed. Exports from Brazil,
Chile, and Peru to China—the largest export
destination of all three countries—plateaued or
slowed in the second half of 2019 (Figure
2.3.1.C). However, exports from Mexico, 80

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FIGURE 2.3.1 LAC: Recent developments
Services sector growth has slowed in LAC, mirroring a much stronger
slowdown in the industrial sector. Within the industrial sector, mining
production contracted sharply in 2019. Economies highly reliant on China
as a trade destination have seen their exports plateau or fall after tariff
hikes between China and the United States, while Mexico’s exports to theUnited States have continued to grow. A sharp recession in Argentina has
impacted Bolivia, Brazil, and Paraguay through trade and remittancechannels. With output gaps becoming more negative, and inflation at thelow end of target ranges, monetary policy is easing in numerous countries.

A. Services and industrial sector
growthB. Industrial production growth,by subsector

C. Exports to China and the UnitedStatesD. Exports to and remittances from
Argentina

E. Regional output gap

F. Inflation

Source: Central Bank of Bolivia; Central Bank of Costa Rica; Central Bank of the Dominican Republic; Central Bank of Guatemala; Central Bank of Paraguay; Central Bank of Uruguay; Haver Analytics; International Monetary Fund (World Economic Outlook); World Bank.
A. Lines show GDP-weighted averages of Argentina, Brazil, Chile, Colombia, Mexico, and Peru (90percent of regional GDP). Last observation is 2019Q3.
B. Lines show industrial production-weighted averages of Brazil, Chile, Colombia, Mexico, and Peru.
Last observation is September 2019.
C. Index based on exports (in value) from Brazil, Chile and Peru to China, and from Mexico to theUnited States. Gray area begins when China and the United States began to increase bilateral tariffs,
in July 2018. Last observation is October 2019.
D. Bars show average year-on-year growth of monthly flows during the indicated period. Bars for2019 are constructed using monthly data through November (exports) and September (remittances).
E. Includes data for Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican
Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay,
Peru, and Uruguay. Shaded areas around center line indicate confidence intervals. Last observationis 2019Q2.
F. Blue boxes show central inflation targets; vertical lines show target bands.
Click here to download data and charts.

percent of which go to the United States,
continued to grow. There is evidence that Mexico
has benefitted from trade diversion to the UnitedStates as a result of the U.S.-China trade conflict(UNCTAD 2019; World Bank 2019o). In thefirst half of 2019, the electrical machinery,transport equipment, and agriculture and foodsectors benefited the most, in value terms.
Late 2019 was marked by the emergence of social
tensions in Bolivia, Chile, and Ecuador, related toeconomic policy decisions and elections. The
events contributed to a downgrade of estimatedgrowth in all of these countries. The recent
developments follow similar events in Haiti andNicaragua, which contributed to deteriorating
economic conditions in both countries. Colombia,
as well, experienced protests in late 2019.
Economic and social conditions in Venezuela
continue to be dire. The population isexperiencing frequent electricity outages and water
shortages; widespread scarcity of basic goods; anda sharp rise in preventable diseases, malnutrition,and mortality rates. More than 4.7 million people
have left. Several countries have imposed entry
restrictions on Venezuelans as the provision ofservices to migrants becomes more fiscallyburdensome and social tensions rise. However,these restrictions are not expected to halt outward
migration from Venezuela, and migration may
have growing policy implications elsewhere. In
Colombia, for instance, the fiscal council has
allowed additional spending related to migrants.
In the medium to long term, host countries could
benefit. The Colombian government estimatesthat the net impact of migration on growth, after
accounting for the fiscal cost, will be 0.1-0.5
percentage point between 2018 and 2021 relative
to a no-migration scenario, primarily via theconsumption channel (Colombia Departamento
Nacional de Planeación 2018).
In Argentina, following a sharp currency
depreciation in the wake of the primary election
results in August, the government implemented
capital controls and imposed a maturity extension
on part of its short-term debt. The bouts offinancial stress in Argentina since early 2018 haveaffected neighboring countries through trade(lower exports to Argentina from Brazil andG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Paraguay, in particular), remittances (sharply
lower from Argentina to Bolivia and Paraguay),
and tourism (downturn in spending and arrivals
by Argentines in Uruguay; Figure 2.3.1.D).
With a small number of exceptions (Argentina,
Ecuador, Venezuela), bond yields in the region
have been broadly stable in recent months. Somecurrencies have depreciated against the U.S. dollar(Argentina, Brazil, Chile, Colombia, Paraguay,
Uruguay). Capital inflows to the region, whichcome predominantly from the United States andthe Euro Area, have slowed.
The output gap in nearly all economies has
become steadily more negative, after the regional
output gap nearly closed in late 2018 (Figure
2.3.1.E). With growth and inflation expectations
broadly moderating and inflation at the low end oftarget ranges among most inflation-targeting
countries, a growing number of central banks areeasing monetary policy (Figure 2.3.1.F). Policy
interest rates in Brazil, Chile, Costa Rica, theDominican Republic, Jamaica, Mexico, Paraguay,
and Peru were lowered in the second half of 2019,
in most cases multiple times.
Several major policy developments have occurredin the region. Following revisions to the previously
negotiated United-States-Mexico-Canada Agreement, among others on labor standard
enforcement mechanisms, the agreement moved
closer to ratification by the United States with thepassage by the House of Representatives in
December. Mexico has ratified the agreement. In
Brazil, a long-awaited pension reform was passed
by Congress in October. Policymakers have begun
to work on tax reform, the next key item on thereform agenda. Mercosur (Argentina, Brazil,
Paraguay, and Uruguay) reached a trade
agreement with the European Union in June thatwas two decades in the making. The agreement
will now need to be ratified and implemented at
the country level.

Outlook
Although growth projections have been revised
down since June, an expected easing of domestic
constraints in the three largest economies, together
with a continued growth acceleration in

LATIN AME RIC A AN D THE C ARIBBE AN

Colombia, is still envisioned to boost the outlook
for the region (Figure 2.3.2.A). Regional growth isforecast to increase to 1.8 percent in 2020, and to
2.4 percent in 2021 (Tables 2.3.1 and 2.3.2).
Under this projection, growth is not expected to
exceed the average during the past three decades.
Moreover, the projected performance will not be
sufficient to reverse the widening per capita
income relative to advanced economies since 2014
in some countries in the region (Figure 2.3.2.B).
Metals and agriculture prices are projected to beflat due to weak global demand, providing little
incremental support for exporters of thesecommodities. Likewise, lower global demand foroil, together with expanding production in the
United States, will put downward pressure on oil
prices in the short term.
Fiscal space is limited or absent in most of theregion, leaving little capacity to pursue
expansionary spending to support growth. One
exception is Chile, which is planning a fiscal
stimulus that will boost public investment andsupport small and medium enterprises.
Policymakers in some other large economies(Brazil, Mexico) remain committed to spending
restraint over the forecast horizon despite sluggish
growth, in order to improve medium-term fiscal
sustainability and retain investor confidence. In
Argentina, continued fiscal consolidation will be anecessary component of the budget strategy.
In the baseline outlook, growth in the largest
economies will pick up and domestic demand—inparticular, investment—will strengthen. Thisoutlook is also contingent on an acceleration ofexports, after weakness in 2019 (Figure 2.3.2.C).
In Brazil, a boost to investor confidence following
progress on major reforms, a moderate easing oflending conditions, and a gradual improvement inlabor market conditions are slated to support a
pickup in investment and private consumption,
helping push growth to 2 percent in 2020 and 2.5
percent in 2021. Investment in Mexico is also
expected to pick up as investor sentiment
improves and the private sector is more involved
in infrastructure projects, while easing monetary
policy will provide modest support to private
consumption. Growth is forecast to rise to a still

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FIGURE 2.3.2 LAC: Outlook and risks
Growth in LAC is projected to firm during the forecast horizon, supported
predominantly by an easing of domestic constraints in the three largest
economies in the region. The recovery will be subdued, however, and will
not be sufficient to offset a growing per capita income gap with advanced
economies in some LAC economies. Faster regional growth is contingent
on an upturn in investment, which has been repeatedly downgraded
during the past year, and on a pickup in weak export growth. Weak fiscal
positions are a risk for financial stability and growth in the region, whilehigh levels of inequality could spark further social unrest and result in
economic disruptions.
A. Growth

C. New export orders

E. Fiscal balance and government
debt

B. Per capita income in LAC relative
to advanced economies

D. Investment growth forecast

F. Income inequality

Source: Haver Analytics; International Monetary Fund (World Economic Outlook); World Bank.
(PovcalNet).
B. Per capita GDP is calculated as the sum of GDP in the countries in the indicated groups divided
by the sum of the population in the same country groups.
C. Last observation is November 2019.
D. Lines show GDP-weighted averages of all LAC economies (20 in total) for which expenditurecomponents of GDP are available.
E. Sample includes 32 countries.
F. Bottom 40 percent refers to the bottom four deciles of consumption or income share, middle 50
percent to the middle five deciles, and top 10 percent to the top decile. Data are for latest available
year from 2010 to 2017. EMDEs = emerging market and developing economies. AEs = advanced
economies. LAC, EMDE, and AE bars show simple averages of 19, 111, and 31 countries,
respectively.
Click here to download data and charts.

subdued 1.2 percent in 2020 and 1.8 percent in
2021.
In the near-term projection, investment andconsumption in Argentina will continue
contracting, though at a slower pace, while importcompression will recede. Ultimately, the economy
is expected to experience three years ofcontraction, and growth to revert to a positive rate
only in 2021.
In Colombia, investment is expected to accelerate
as planned infrastructure projects are carried out.
Favorable financing conditions are envisioned tosupport domestic demand more broadly. These
factors will support a rise in growth to 3.6 percent
in 2020 and about 3.9 percent in 2021-22.
Growth in Chile is projected to recover after
interruptions from social unrest in late 2019, to
2.5 percent in 2020 and 3.0 percent in 2021. This
assumes a higher volume of copper exports aftermine disruptions in 2019, improved private sector
sentiment as business sector reforms are rolled out,
and a boost from fiscal stimulus.
Aggregate growth in Central America is projected
to firm over the forecast horizon. Easing credit
conditions (especially in Costa Rica) and an
unwinding of temporary setbacks to construction
and infrastructure projects (Panama) will help
boost output. The subregion is also expected tobenefit from trade and business environment
reforms in recent years, including an expandedcustoms union between Guatemala andHonduras.
Growth in the Caribbean is forecast to accelerate
in the near term, predominantly due to major
offshore oil production developments in Guyana,
while growth in the largest Caribbean economy,
the Dominican Republic, is projected to be stable
at about 5 percent as the tourism sector stabilizes
following disruptions in 2019 linked to healthconcerns.

Risks
LAC continues to face predominantly downside
risks to growth. External risks, particularly thoselinked to trade and finance, are elevated. A further

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

growth slowdown in China, should the bilateral
U.S.-China trade dispute reescalate, could expose
LAC to additional negative spillovers throughtrade, commodity price, and confidence channels.
This risk is particularly acute for countries highly
reliant on China as an export destination (Brazil,
Chile, Peru, and Uruguay). Likewise, sluggish
U.S. growth could be a greater-than-expected
hindrance for Mexico and other countries reliant
on the United States. Continued weak export
growth would contribute to a rise in already-large
current account deficits in some countries(Bolivia, Colombia, El Salvador, Honduras, andPanama, among others). The financing of large
external imbalances could become morechallenging should countries experience severe
currency pressures or an unexpected rise inborrowing costs.
Adverse market responses to domestic market
conditions within the region, including weak fiscal
profiles, could dent capital inflows andinvestment. Investment growth is projected tofirm in the baseline outlook but has been
repeatedly
buffetedbyunanticipateddevelopments during the past year and could
continue to be hindered by policy uncertainty(Figures 2.3.2.D).

LATIN AME RIC A AN D THE C ARIBBE AN

more politically challenging to implement
structural reforms, which are key to boosting
longstanding low productivity (Box 2.3.1).
Social tensions in several countries in late 2019
could become more widespread, with negative
economic repercussions. Discontent about lack ofopportunities is a significant underlying risk forsocial stability, and ultimately for growth, in theregion. Although inequality in many LAC
countries has fallen in recent years, due in largepart to gains at the low end of the income
distribution, it remains high relative to otherregions (World Bank 2016e, 2020; Messina andSilva 2019). The share of income going to thebottom 40 percent of households in LAC
economies is lower, on average, than in all
EMDEs and in advanced economies, while theshare going to the top 10 percent is higher, at 35
percent, versus 25 percent in advanced economies
and 31 percent in EMDEs (Figure 2.3.2.F).

Adverse intraregional spillovers from the market
volatility and sharp recession in Argentina could
take a further toll if restoring the country’s
economic stability takes longer than expected.
Negative repercussions are especially a risk for
Bolivia, Paraguay, and Uruguay. Moreover, forArgentina, another bout of severe financial market
stress could further inhibit debt sustainability andset back an already protracted economic recovery.

Disruptions related to natural disasters, including
the heightened frequency, duration, and force ofclimate events, are a persistent and growing
downside risk for a host of LAC economies. The
human and economic toll of Hurricane Dorian in
The Bahamas in September 2019 is the latest
example of the Caribbean’s vulnerability tohurricanes, and illustrates the devastatingconsequences of natural disasters in the region. In
Brazil, the large-scale fires in the Amazon
rainforest last year have had widespreadenvironmental consequences. They have alsopresented a policy risk, in that the authorities’sluggish response has complicated the politicaltask of completing the EU-Mercosur trade
agreement.

Should easing monetary policy be insufficient tocounter weak growth, commitments to public
spending prudence in the region could comeunder pressure. Though a return to an
expansionary fiscal stance would provide a
temporary boost to growth, it could have negative
consequences for financial stability and fiscal
sustainability. Fiscal positions are already on track
to deteriorate somewhat during the forecasthorizon as a result of sustained budget deficits(Figure 2.3.2.E). Weak growth could make it

The EU-Mercosur agreement has the potential tosignificantly boost the depth of global tradeintegration in LAC if it passes in its current form,
especially as the region has long been less open to
trade than most other EMDE regions (WorldBank 2019p). Completion of the agreement in its
current form is an upside risk for the outlook.
Deeper trade linkages and participation in global
value chains have the potential to stimulate
productivity through increased investment anddeeper participation in global value chains.

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TABLE 2.3.1 Latin America and the Caribbean forecast summary
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE LAC, GDP1

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

1.9

1.7

0.8

1.8

2.4

2.6

-0.9

-0.8

-0.3

(Average including countries with full national accounts and balance of payments data only)2
EMDE LAC, GDP2

1.9

1.7

0.8

1.8

2.4

2.6

-0.9

-0.7

-0.3

GDP per capita (U.S. dollars)

0.7

0.6

-0.3

0.8

1.5

1.8

-1.1

-0.8

-0.2

PPP GDP

2.0

1.7

0.8

1.8

2.5

2.7

-1.0

-0.8

-0.2

Private consumption

2.7

2.1

1.1

2.1

2.7

2.9

-0.7

-1.1

-0.2

Public consumption

0.7

0.9

-0.3

0.9

1.0

1.2

-0.1

-0.3

-0.2

Fixed investment

-0.2

2.1

-0.3

2.6

4.0

3.6

-1.6

-0.5

-0.4

Exports, GNFS3

3.8

4.2

1.1

2.8

3.1

3.5

-3.0

-0.9

-0.7

Imports, GNFS

6.3

5.5

0.4

3.3

3.8

4.0

-2.6

-1.4

-0.8

-0.5

-0.3

0.1

-0.1

-0.2

-0.1

0.1

0.1

0.0

South America4

1.6

1.4

0.9

1.9

2.6

2.7

-0.7

-0.7

-0.1

Central America5

3.8

2.7

2.5

3.0

3.3

3.4

-0.6

-0.4

-0.3

Caribbean6

3.3

5.0

3.8

5.6

3.9

4.3

0.0

1.1

-0.5

Brazil

1.3

1.3

1.1

2.0

2.5

2.4

-0.4

-0.5

0.2

Mexico

2.1

2.1

0.0

1.2

1.8

2.3

-1.7

-0.8

-0.6

Argentina

2.7

-2.5

-3.1

-1.3

1.4

2.3

-1.9

-3.5

-1.8

3

Net exports, contribution to growthMemo items: GDP

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time. The World Bank has ceased producing a growth forecast for Venezuela and has removed Venezuela from all growth aggregates in which it was previously included.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates.
2. Aggregate includes all countries in Table 2.3.2 except Dominica, Grenada, Guyana, Haiti, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, and Suriname.
3. Exports and imports of goods and non-factor services (GNFS).
4. Includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay.
5. Includes Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama.
6. Includes Antigua and Barbuda, The Bahamas, Barbados, Belize, Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the
Grenadines, and Suriname.
Click here to download data.

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101

LATIN AME RIC A AN D THE C ARIBBE AN

TABLE 2.3.2 Latin America and the Caribbean country forecasts1
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

Argentina

2.7

-2.5

-3.1

-1.3

1.4

2.3

-1.9

-3.5

-1.8

Belize

1.9

2.1

2.7

2.1

1.8

1.8

0.4

0.0

-0.1

Bolivia

4.2

4.2

2.2

3.0

3.2

3.4

-1.8

-0.6

-0.2

Brazil

1.3

1.3

1.1

2.0

2.5

2.4

-0.4

-0.5

0.2

Chile

1.3

4.0

1.3

2.5

3.0

3.0

-2.2

-0.6

0.0

Colombia

1.4

2.6

3.3

3.6

3.9

3.9

-0.2

-0.1

0.2

Costa Rica

3.4

2.6

2.0

2.5

3.0

3.2

-1.0

-0.6

-0.4

Dominican Republic

4.7

7.0

5.3

5.0

5.0

5.0

0.1

0.0

0.0

Ecuador

2.4

1.4

-0.3

0.2

0.8

1.2

-0.3

-0.2

0.0

El Salvador

2.3

2.5

2.4

2.5

2.5

2.5

-0.2

0.0

0.1

Grenada

4.4

4.2

3.5

2.9

2.9

3.2

-0.4

-0.8

-0.8

Guatemala

2.8

3.1

3.4

3.0

3.2

3.2

0.1

0.3

0.2

Guyana

2.1

4.1

4.5

86.7

10.5

14.6

-0.1

53.2

-12.4

Haiti2

1.2

1.5

-0.9

-1.4

-0.5

1.4

-1.3

-3.0

-1.8

Honduras

4.8

3.7

3.3

3.5

3.5

3.5

-0.3

-0.3

-0.4

Jamaica

1.0

1.9

1.0

1.1

1.2

2.0

-0.6

-0.6

-0.7

Mexico

2.1

2.1

0.0

1.2

1.8

2.3

-1.7

-0.8

-0.6

Nicaragua

4.7

-3.8

-5.0

-0.5

0.6

1.0

0.0

-1.6

-0.7

Panama

5.6

3.7

3.5

4.2

4.6

4.8

-1.5

-1.2

-0.6

Paraguay

5.0

3.7

0.7

3.1

3.9

3.8

-2.6

-0.9

-0.1

Peru

2.5

4.0

2.6

3.2

3.5

3.6

-1.2

-0.7

-0.5

St. Lucia

2.6

0.9

1.8

3.2

3.0

2.4

-1.6

-0.3

0.6

St. Vincent and the Grenadines

1.0

2.2

2.3

2.3

2.3

2.3

0.2

0.0

0.0

Suriname

1.8

2.6

2.2

2.5

2.1

2.1

0.2

0.4

0.0

Uruguay

2.6

1.6

0.5

2.5

3.5

3.2

-1.0

0.2

1.0

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here
may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates.
2. GDP is based on fiscal year, which runs from October to September of next year.
Click here to download data.

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers
Labor productivity growth in Latin America and the Caribbean (LAC) slowed to near zero during 2013-18, among the lowest of
the six emerging market and developing economy (EMDE) regions. This rate is well below the 1.7 percent average during the precrisis period (2003-08) and a return to the average during the preceding four decades. In two-fifths of LAC economies,
productivity growth was negative during 2013-18. Sluggish productivity growth during 2013-18 mainly reflects negative totalfactor productivity (TFP) growth in some large LAC economies, as the commodity price slump and intensifying market distortions
allowed unproductive firms to continue operating. Despite anemic productivity growth, the level of productivity remains higher
than the EMDE average, albeit still less than one-quarter of the level in advanced economies. Many countries in the region would
benefit from reforms to improve competition and innovation, deepen trade linkages, improve the quality of education, reducelabor market inefficiencies, strengthen institutional quality, and increase the volume and efficiency of infrastructure investment.

Introduction•

For decades, productivity growth in Latin America and the
Caribbean (LAC) has been anemic (Fernández-Arias andRodríguez-Apolinar 2016). After a brief pre-crisis burst,
productivity growth fizzled out again after the global
financial crisis. Relative to a pre-crisis (2003-08) average of
1.7 percent, productivity growth in the region dropped to
0.4 percent during 2013-18—a slowdown broadly in linewith the emerging market and developing economy(EMDE) average but from lower starting rates (Figure
2.3.1.1.A). Although the level of productivity in LAC isstill higher than in most other EMDE regions, sluggish
productivity growth in the post-crisis period has slowed
the region’s progress toward the level of productivity inadvanced economies (Figure 2.3.1.1.B). The productivity
slowdown during 2013-18 was broad based, affecting three-fifths of LAC countries (Figure 2.3.1.1.C).

This Box defines productivity as labor productivity,
represented by real GDP per person employed (at 2010
prices and exchange rates). This definition deviates fromsome previous work on productivity in the region, which
focused on total factor productivity (TFP). Labor
productivity data used in this Box are available for nine
EMDEs in South America (Argentina, Bolivia, Brazil,
Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay),seven EMDEs in North and Central America (Costa Rica,
El Salvador, Guatemala, Honduras, Mexico, Nicaragua,
and Panama), and nine EMDEs in the Caribbean(Barbados, Belize, the Dominican Republic, Guyana,
Haiti, Jamaica, St. Lucia, St. Vincent and the Grenadines,
and Suriname). Data availability further restricts the
sample in the two decomposition exercises below.

Within LAC, productivity growth has been heterogeneous
across the three geographical subregions. South America,
which was hard hit by the 2011-16 commodity price slide,
political uncertainty, and challenging macroeconomic
conditions in the largest economies, had the lowest
productivity growth during 2013-18, at an average of just
0.1 percent per year, and the Caribbean had the highest, at
2.5 percent. Productivity growth in the Mexico andCentral America subregion was 1.2 percent during 201318, higher than in 2003-08, although this occurred in thecontext of weak long-term productivity growth in Mexico.
Against this backdrop, this Box addresses the following
questions:•

How has productivity growth evolved in the region?

•

What factors have been associated with productivity
growth in the region?

Note: This box was prepared by Dana Vorisek, building upon analysis
in Chapter 3. Research assistance was provided by Vanessa Arellano
Banoni and Shijie Shi.

What policy options
productivity growth?

areavailable

to

boost

Evolution of regional productivity
Post-crisis productivity growth slowdown to near zero.
Like other EMDE regions, productivity growth in LAC
has slowed since the global financial crisis. At 0.4 percent
during 2013-18, the post-crisis average returned
productivity growth to its long-term average of near zero
(0.3 percent; Figure 2.3.1.1.D). This rate is well below
average post-crisis productivity growth in EMDEs (2.6
percent). Negative productivity growth occurred 9 of 25
countries, nearly all of which are in South America and the
Caribbean, in 2013-18. In most cases, productivity growth
was also lower than both the pre-crisis and long-term
averages, as major economies in the region struggled withpoor business climates, political tensions, regulatoryburdens, and plunging commodity prices. Over the courseof the past four decades, troughs in productivity growth
have broadly coincided with major adverse economic
events, including a series of severe debt crises in the 1980s
that spawned the region’s “lost decade,” the global
financial crisis, and periodic commodity price slumps. A
brief pre-crisis burst in productivity growth to 1.7 percent

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
FIGURE 2.3.1.1 Evolution of labor productivity growth in LAC
Productivity growth in LAC fell from 1.7 percent in 2003-08 to 0.4 percent in 2013-18. The level of productivity in LAC is still
higher than that in other EMDE regions, yet sluggish productivity growth in the post-crisis period has resulted in the region’s
losing ground in converging toward the level of productivity in advanced economies. Despite weak aggregate productivity
growth in the region, some countries, including Bolivia, Costa Rica, the Dominican Republic, and Paraguay, achieved
productivity growth in line with the EMDE average during 2013-18.
A. Productivity growth

B. Level of productivity and rate ofconvergence

C. Share of economies with 2013-18
productivity growth below previous
averages

D. Productivity growth

E. Productivity growth, by country

F. Productivity levels, 2018

Source: Conference Board; Penn World Tables; World Bank (World Development Indicators).
A.-E. Productivity is defined as labor productivity (real GDP per person employed). Country group aggregates for a given year are calculated using constant 2010 U.S.
dollar GDP weights. Data for multiyear spans shows simple averages of the annual data. Sample includes 25 LAC countries and 127 EMDEs.
A. Blue bars show the range of average productivity across the six EMDE regions: East Asia Pacific (EAP), Europe and Central Asia (ECA), Latin America and the
Caribbean (LAC), the Middle East and North Africa (MENA), South Asia (SAR), and Sub-Saharan Africa (SSA). Orange dashes show the average of the six regional
aggregates.
B. Rate of convergence is calculated as the difference in productivity growth rates over the log difference in productivity levels between LAC and advanced economies(AEs). Blue bars and orange dashes show the range and average of the six EMDE regional aggregates. “Level” of productivity refers to the GDP-weighted average ofregional productivity as a share of the average advanced economy during 2013-18.
C. Orange line represents a 50 percent threshold.
D. Dotted lines show 1981-2018 averages.
E. Data for multiyear spans shows simple averages of the annual data. DOM = the Dominican Republic, PRY = Paraguay, BOL = Bolivia, CRI = Costa Rica, PAN =Panama, PER = Peru, COL = Colombia, GTM = Guatemala, URY = Uruguay, MEX = Mexico, SLV = El Salvador, NIC = Nicaragua, CHL = Chile, HND = Honduras,
BRA = Brazil, HTI = Haiti, JAM = Jamaica, BRB = Barbados, ARG = Argentina, ECU = Ecuador, and SUR = Suriname.
F. Productivity is measured in 2010 U.S. dollars. Country group aggregates are calculated using 2010 U.S. dollar GDP weights. Sample includes 25 LAC economies(9 in South America, 7 in Mexico and Central America, and 9 in the Caribbean) and 127 EMDEs.
Click here to download data and charts.

during 2003-08 comprised LAC’s second-longest period of
positive productivity growth since 1980.
Within-region heterogeneity of labor productivity
growth. Notwithstanding weak labor productivity growthat the aggregate level in LAC during 2013-18, there wasconsiderable heterogeneity across countries. Bolivia, Costa
Rica, the Dominican Republic, and Paraguay featured thehighest labor productivity growth in the region, measuring
well above pre-crisis and long-term averages (Figure
2.3.1.1.E). The improvement in the Dominican Republicreflects greater contribution from capital deepening andhigher TFP growth; this arose from increased foreign
direct investment (FDI) inflows, which were encouragedby the reforms that opened most sectors to foreign

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
investment and by tax incentives for foreign investment(World Bank 2018i). Bolivia and Paraguay benefited from
population migration from rural to urban areas, whichcoincided with a shrinking share of agriculture as a share of
employment (IMF 2016b; World Bank 2018j). In Costa
Rica, the rise in productivity growth was broad-based
across sectors, supported by continued policy reforms andpositive spillovers from FDI inflows (OECD 2018a). In
four of the six LAC economies with the highest
productivity growth during 2013-18 (Bolivia, theDominican Republic, Panama, and Peru), productivity
growth benefited from the steepest declines in the share of
informal activity in the region during the decade to 2016
(World Bank 2019f).
High productivity levels relative to EMDEs but slowing
convergence with advanced economies. Despite low
productivity growth in the region over an extended period,
the level of productivity in LAC (22 percent of theadvanced-economy average) is above the EMDE average(19 percent of the advanced-economy average; Figure
2.3.1.1.F). High productivity levels in LAC are a legacy ofthe mid-20th century. Since the 1980s, labor productivity
in LAC relative to the level in advanced economies has
fallen (Ferreira, de Abreu Pessôa, and Veloso 2013;
Fernández-Arias and Rodríguez-Apolinar 2016). The precrisis rise in productivity growth halted this divergence
only briefly. This is in stark contrast to the narrowing
labor productivity gap between the broader group of
EMDEs and advanced economies since the 1990s.

Sources of regional productivity growth
Labor productivity can be decomposed into threesources—human capital accumulation, physical capital
accumulation, and TFP, or the efficiency with which laborand capital are used during production. The post-crisis
productivity growth slowdown predominantly reflected areturn to negative TFP growth rates, as had prevailed in
LAC during the 1990s (Figure 2.3.1.2.A; Busso, Madrigal,
Pagés 2013). However, the post-crisis (2013-18) average
disguises a steep slowdown in investment growth during
2016-18, as Brazil struggled to exit a deep recession, theeffects of the commodity price slump rippled through theregion’s many commodity-reliant economies, andnumerous economies experienced bouts of policy
uncertainty.
•

South America. The post-crisis labor productivity
slowdown was most pronounced in South America.
which was deeply impacted by the commodity priceslump and country-specific constraints in large

economies (Figure 2.3.1.2.B). TFP growth in SouthAmerica was continually negative during 2013-18, inpart reflecting growing directed credit in Brazil (Dutz
2018; Calice, Ribiero, and Byskov 2018). It also
reflected intensifying economic distortions (such as
trade restrictions and price controls) in Argentina
during the early part of the period, which allowed
unproductive firms to survive.
•

Mexico and Central America. In Mexico and Central
America, the early impacts of the global financial crisis
in 2007 and 2008 weighed on TFP in Mexico during
2003-08. Although post-crisis TFP growth was
subdued, and capital deepening weakened during thisperiod in the context of the repeated bouts of policy
uncertainty, the removal of the crisis effects in Mexico
allowed higher productivity growth in the subregion
during 2013-18.

•

The Caribbean. In the Caribbean, TFP growth
accelerated during the post-crisis period, largely
reflecting capital deepening in the largest economy inthe subregion, the Dominican Republic.

Post-crisis productivity growth slowdown across sectors.
As in the average EMDE, manufacturing made the largest
sectoral contribution to productivity growth in LAC
during the 1990s and the pre-crisis period. Relative to thepre-crisis period, the post-crisis period in LAC was marked
by a broad-based slowdown in productivity growth acrosssectors, particularly in manufacturing, trade, and finance.
Stalling within-sector labor productivity growth. For
countries with available sectoral data, the within-sector
contribution to productivity growth has historically been
greater than the between-sector contribution from labor
reallocation from low-productivity to higher-productivity
sectors (Figure 2.3.1.3.A). This is consistent with otherstudies of the region (Brown et al. 2016; Diao, McMillan,
and Rodrik 2017). During the 1990s, a substantial part oflabor productivity growth was due to within-sector growth
as LAC countries liberalized trade policy in the second halfof the 1980s and the early 1990s (Rodrik 2016a). The
1990s and early 2000s were a period of significant change
in LAC’s manufacturing industry. Faced with increasing
foreign competition as the result of globalization, domestic
manufacturing firms implemented more efficient processes
that required less labor, and uncompetitive firms ceased
operating. As workers were displaced from manufacturing,
they shifted toward lower-productivity services andinformal activities (Pagés-Serra 2010; McMillan, Rodrik,
and Verduzco-Gallo 2014). In Argentina and Brazil, two

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)of the largest economies in the region, labor shifted in the
1990s from manufacturing into less-productive nontradable sectors, such as personal services and wholesale
and retail trade, limiting between-sector productivity
growth. Of the six LAC countries with available sectoral
data, only Costa Rica and Mexico have consistently
experienced positive between-sector productivity growth,and even in those cases the within-sector contribution has
been smaller than the between-sector contribution.
As the manufacturing sector in LAC transformed during
the 1990s and early 2000s, the agricultural sector became
more productive relative to other sectors, with a shrinking
share of agricultural employment accounting for a stableshare of output between 1995 and 2008 (Figures 2.3.1.3.B
and 2.3.1.3.C). The government sector, however, becameless productive, accounting for a growing share of
employment and the same share of output.
Since 2013, between-sector productivity gains have stalled
in several large economies (Argentina, Brazil, Colombia).
Within-sector productivity growth has collapsed to nearzero as multiple structural constraints (e.g., inefficient
provision of credit in Brazil and trade restrictions and pricecontrols in Argentina) were compounded by an inability toadjust to adverse events, including unfavorable policy
choices, a commodity price collapse, and financial stressepisodes.

FIGURE 2.3.1.2 Sources of productivity
growth in LAC
Sluggish productivity growth in LAC during the postcrisis period predominantly reflected a negative
contribution from total factor productivity (TFP). The TFP
contraction was especially pronounced in SouthAmerica. In recent years, capital deepening has made aslowing contribution to productivity growth.

A. Contributions to productivity growth

B. Contributions to productivity growth, by subregion

Sectoral productivity levels in LAC relative to EMDEs. In
most sectors, and particularly in mining, productivity
levels in LAC are higher than the EMDE average.
However, LAC lags notably in trade and finance.
Removing productivity barriers in these sectors would
benefit aggregate regional productivity.
Key drivers of productivity. LAC has long lagged other
EMDE regions in several key drivers of productivity—
investment, innovation, and trade—and performs only
about average in other drivers (Figure 2.3.1.4.A). Overtime, the drivers of productivity in LAC have improved
but the improvement has not kept pace with that in
EMDEs (Figure 2.3.1.4.B). Cyclical factors, such as weak
investment in large economies in the region and gyrationsin global commodity price trends, are also linked to weak
productivity growth in LAC. Investment growth has
weakened substantially in the post-crisis period (Figure
2.3.1.4.C).
Limited innovation and technology adoption. Innovation,achieved through dedicating resources to research anddevelopment (R&D) or introducing new processes orSource: Barro and Lee (2015); International Monetary Fund; Penn World
Tables; United Nations (Human Development Reports), Wittgenstein Centre
for Demography and Global Human Capital; World Bank.
A-B. Country groups aggregated using constant 2010 U.S. dollar GDP
weights.
A. Samples include 25 LAC economies and 92 EMDEs.
B. Samples include 9 economies in South America, 7 economies in Mexico
and Central America, and 9 economies in the Caribbean.
Click here to download data and charts.

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
FIGURE 2.3.1.3 Sectoral productivity in LAC
Within-sector productivity growth, the main driver of productivity growth in LAC during the pre-crisis period, was much lower
during the post-crisis period in several large economies, while between-sector productivity growth slowed in all economies
with available sectoral data.
A. Within-sector and between-sector
contributions to productivity growth

B. Composition of employment, by sector

C. Composition of GDP, by sector

Source: Groningen Growth Development Center database, Haver Analytics, ILOSTAT, OECD STAN, United Nations, World KLEMS, World Bank.
A. The within-sector productivity contribution shows the initial real value added-weighted productivity growth; the between-sector contribution measures the productivity
growth from a cross-sectoral shift of employment. ARG = Argentina, BRA = Brazil, CHL = Chile, COL = Colombia, CRI = Costa Rica, and MEX = Mexico.
B.C. “Other industry” includes construction, mining, and utilities; “finance” includes business services; “government” includes personal services. Samples include 6 LAC
economies (Argentina, Brazil, Chile, Colombia, Costa Rica, and Mexico) and 46 EMDEs.
Click here to download data and charts.

products, has been a key driver of labor and firm
productivity in LAC (Crespi and Zuniga 2011; Grazzi andJung 2016). Likewise, adoption of new technologies can
reduce information costs and facilitate market access,thereby increasing productivity and expanding output inthe region (Dutz, Almeida, and Packard 2018). LAC ismissing key opportunities to raise productivity throughthese channels. R&D expenditure as a share of GDP is low
in LAC relative to that in comparator EMDEs, as is thelikelihood of firms in LAC introducing productinnovations (Lederman et al. 2014; Figure 2.3.1.4.D).
Weak trade linkages. In three large economies in theregion (Argentina, Chile, Mexico), deeper participation in
global value chains is associated with positive effects onfirm productivity (Montalbano, Nenci, and Pietrobelli2018). Yet nearly all LAC economies trade less (as a shareof their GDP) than EMDEs overall, and global value chain
participation is lower than in the East Asia and Pacificregion and in Europe and Central Asia (Figure 2.3.1.4.E).
Even the LAC countries most integrated in global value
chains (Chile, Costa Rica, and Mexico) are not among themost integrated EMDEs (OECD 2018b). The
opportunity for regional productivity gains through tradeis further hindered by the structure of intra- andextraregional trade relationships. Although LAC countries
are party to numerous trade agreements, there is little

harmonization of rules of origin and non-tariff measuresacross agreements, and there is no region-wide trade
agreement. These characteristics result in fragmentation of
trading priorities and, together with weak diversification of
traded goods in many countries, limit the development of
intraregional global value chains. Rules of origin imposed
under preferential trade agreements in the region are
estimated to negate more than 15 percent of the positive
trade effect of the agreements, while the costs of non-tariffmeasures imposed by LAC countries are estimated toequate to a 15 percent tariff for intermediate goods(Cadestin, Gourdon, and Kowalski 2016).
Poor-quality education and labor market constraints. At amedian of 9.2 years in 2018, the duration of schooling in
LAC compares favorably with 7.7 years in the average
EMDE. In addition, the gap between the median years ofschooling in LAC and advanced economies narrowed
during the past decade, from 3.5 years in 2008 to 2.9 years
in 2018. However, learning outcomes in LAC fall short oftheir potential, as indicated by international standardized
test results and high dropout rates at the tertiary level(World Bank 2017b). Moreover, in most LAC countries,
education outcomes are highly correlated withsocioeconomic conditions, a scenario reinforced bypersistently elevated income inequality (World Bank
2018k). Ultimately, skills deficiencies and mismatches andG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
FIGURE 2.3.1.4 Drivers of labor productivity growth in LAC
Multiple structural constraints contribute to low productivity growth in LAC. The region performs particularly poorly relative toother EMDE regions in measures of investment, innovation, and trade. In other drivers, LAC is a mediocre performer relative
to other regions. The drivers of productivity growth have become more supportive over time but at a slower pace than the
EMDE average.
A. Drivers of productivity growth, 2017

B. Index of productivity drivers

C. Investment growth

D. R&D spending

E. Trade

F. Firms indicating inadequately
educated workers as their biggest
obstacle

Source: Freedom House; Haver Analytics; International Country Risk Guide; Organisation for Economic Co-operation and Development; Observatory of Economic
Complexity; Penn World Tables; United Nations Educational, Scientific, and Cultural Organization (Institute for Statistics); United Nations Population Prospects; World
Integrated Trade Solution; World Bank (Doing Business, Enterprise Surveys, and Global Financial Development Database).
A. Unweighted average levels of drivers, normalized as average of AEs (index =100) and standard deviation of EMDEs as 10. Blue bars represent average of LAC
economies in 2017. Orange whiskers represent range of averages for the six EMDE regions in 2017. Variables are defined as: Education=years of education,
Urbanization=share of population living in urban areas, Investment=share of investment to GDP, Institutions=government effectiveness, Economic complexity=Economic
Complexity Index of Hidalgo and Hausmann (2009), Gender equality=share of years of schooling for females to males, Demography=share of population under age 14,
Innovation=log patents per capita, and Trade=(exports+imports)/GDP. Samples include 17-31 LAC economies, depending on the driver, and 63-150 EMDEs.
B. For each country, index is a weighted average—weighted by the normalized coefficients shown in Annex 3.3—of the normalized value of each driver of productivity.
Drivers include the ICRG rule of law index, patents per capita, non-tropical share of land area, investment in percent of GDP, ratio of female average years of education tomale average years, and share of population in urban area, Economic Complexity Index, years of schooling, working-age share of population, and inflation. Regional and
EMDE indexes are GDP-weighted averages for single years and simple averages for time periods. Samples includes 17 LAC economies and 77 EMDEs.
C. Bars show investment-weighted averages. Last observation is 2019Q3. Data for Mexico for Q3 is estimated.
D. Sample includes 16 economies for LAC and 94 for EMDEs.
E. Bars show 2015-17 average of exports plus imports as a share of GDP. BRA = Brazil, ARG = Argentina, COL = Colombia, URY = Uruguay, DOM = the Dominican
Republic, ECU = Ecuador, PAN = Panama, PER = Peru, GTM = Guatemala, JAM = Jamaica, CRI = Costa Rica, CHL = Chile, BOL = Bolivia, PRY = Paraguay,
SLV = El Salvador, HND = Honduras, MEX = Mexico, NIC = Nicaragua. Sample includes 96 EMDEs.
F. Sample includes 30 LAC economies and 113 EMDEs.
Click here to download data and charts.

low-quality education have negative implications for laborproductivity and the functioning of labor markets. The
incidence of youth who are neither in school nor working
is high (de Hoyos, Rogers, and Székely 2016). An
estimated half of firms are unable to find local workers
with the skills they need, and consequently turn to foreignlabor (OECD 2018b). Firm-level survey data for 2013-18

indicate that 7 percent of firms in LAC perceive an
inadequately educated workforce as their biggest obstacle,
more than double the share in all EMDEs (Figure
2.3.1.4.F). The poor functioning of labor markets due toskills deficiencies are compounded by longstanding
regulatory rigidities that prevent efficient allocation andmobility of workers (Kaplan 2009).

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
High informality. The informal sector averages slightly
more than one-third of GDP, higher than in all other
EMDE regions except Sub-Saharan Africa (World Bank
2019f). In LAC, high informality has been associated withlower aggregate and firm-level productivity (Loayza,
Servén, and Sugawara 2010; de Paula and Sheinkman
2011; Chong, Galdo, and Saavedra 2008). In Paraguay,
informal firms are not only less productive than formal
firms, but have negative spillovers on formal firms’
productivity (Vargas 2015).

Policy options
A range of options, targeted to country experiences, can be
pursued to boost productivity in LAC and put the region
on a path toward closing the productivity gap withadvanced economies. Productivity in the region stands tobenefit most from policy reforms to boost TFP, rather
than to improve factors of production.
Improving factors of production
Increase the volume and efficiency of infrastructure
investment. Relative to the pre-crisis period, capital
deepening has been the main source of productivity
growth in large parts of the region during the post-crisis
period. However, it has slowed sharply in the past three
years, and large infrastructure gaps remain. Access to waterand electricity in LAC is high relative to all EMDEs;
however, the region underperforms in transportation andsanitation (Fay et al. 2017). To address this, transportation
development is underway in several countries. Colombia,
for instance, is implementing 4G, a major public roadinfrastructure program. In addition, across the region,
there is significant capacity to reduce infrastructure gaps by
improvinginfrastructure
spending
efficiency—inparticular, through improvements at the appraisal andevaluation stages of public investment projects and in
public procurement systems.
Boosting firm productivity
Pursue well-targeted competition and innovation
policies. Reducing barriers to entry for firms and therigidity of labor regulations, on which LAC performs
poorly compared to other EMDE regions and which
encourages informal operation, is critical for promoting
entrepreneurship and productivity. In Peru, for example,
the elimination of subnational barriers to entry is found tohave boosted firm productivity (Schiffbauer and Sampi2019). Boosting low R&D spending and low technologyrelated innovations can also improve financial inclusion

through development of secure digital payment systems
and fintech regulatory frameworks (World Bank 2017c).
Improving the speed of uptake of new technologies in
LAC, where firms adopt new technologies with asignificant lag relative to the United States, would also
boost productivity (Eden and Nguyen 2016).
Deepen trade linkages and reduce trade barriers. Traderelationships can boost productivity by facilitatingknowledge exchange and innovation for the participating
firms (Bown et al. 2017). Significant productivity gains
could be made by reducing barriers to trade in LAC. The
landmark European Union-Mercosur trade agreement,
finalized by negotiators in June 2019 but not yet ratified,holds significant promise for decreasing trade barriers anddeepening trade flows between Latin America and Europe.
In addition, there have been some recent efforts to reduce
trade barriers within the region; for instance, the Pacific
Alliance eliminated tariffs among its members (Chile,
Colombia, Mexico, and Peru) in May 2016.
Boost quality of education and implement labor market
reforms. With the working-age share of the population in
the region now at a peak and on track to begin the longterm downward trajectory that East Asia and Pacific andEurope and Central Asia have already begun, thecontribution of additional labor to productivity growth in
LAC will fall in the years ahead. Advancing human capitalthrough education and skills development will become
increasingly important. For many countries in the region,
including Brazil, adapting labor markets to shifting
economic opportunities in the strongly integrated global
economy will require revision of dated labor market
regulation (Dutz 2018). For firms, additional use of onthe-job training is an important element of boosting the
productivity of their workers, especially in the context of
rapidly changing technologies. Implementing programs
that engage youth who are neither working nor studying isa critical policy concern in the region (Almeida andPackard 2018). Skills training programs such as Jovenes en
Acción in Colombia and ProJoven in Peru have had
positive impacts on employment and productivity among
the target populations and could be replicated elsewhere(Attanasio et al. 2015; Diaz and Rosas 2016).
Apprenticeship programs, which have been successful inseveral advanced economies, could also be explored.
Reducing labor market rigidities (such as restrictions on
use of term contracts, restrictions on working hours, use ofminimum wages above market equilibrium, andimposition of high costs and penalties for redundancy) canboost productivity.

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BOX 2.3.1 Labor productivity in Latin America and the Caribbean: Trends and drivers (continued)
Encouraging sectoral reallocation
Given that within-sector productivity gains in several large
economies in LAC have stalled since the global financial
crisis, countries in the region should rekindle efforts toimplement policies that reallocate capital and labor
towards more productive firms within the sectors. Policies
could aim to strengthen competition, including throughtrade, and reform labor markets to facilitate the movement
and productivity of labor. At the same time, thelongstanding weakness in the region’s between-sector
productivity growth in the region calls for policies that
reduce misallocation of capital and labor toward sectors
with low productivity. In particular, with limited
opportunity for further industrialization, LAC countries
should target lack of competition in services industries,
including transport, finance, trade, and information andcommunications technology, and ensure that workers havesufficiently strong skills to thrive in occupations beingtransformed by technology (Araujo, Vostroknutova, andWacker 2017; World Bank forthcoming).
Creating a business-friendly environment
Implement supportive governance and business climate
reforms. Institutional quality is a key driver of

productivity over the long term. For instance, fair contract
enforcement, straightforward and transparent legal
processes, and contained political risk have all been shownto support productivity gains (Acemoglu et al. 2019;Rodrik 1999; Rodrik, Subramanian, and Trebbi 2004).
Relative to other regions, however, LAC is a mediocre
performer on measures of governance (Figure 2.3.1.4.A).
Moreover, the region’s performance has deteriorated
during the post-crisis period in measures of government
effectiveness, control of corruption, and regulatory quality(Kaufmann, Kraay, and Mastruzzi 2010). Especially when
the burden of regulation is high, as it tends to be in LAC,
corruption is detrimental for productivity (Amin and Ulku2019). On measures of doing business, no country in LAC
is among the top 50 performers in the world (World Bank
2020). Business environment reforms can also help reduce
the size of the informal sector, where productivity is lower
than in the formal sector. The process of institutional
reforms could be spearheaded through productivity
commissions such as those created in Chile, Colombia,
and Mexico. Colombia, for example, is implementing aseries of structural reforms as part of its Productive
Development Policy 2016-2025.

109

Regional growth in the Middle East and North Africa decelerated to an estimated 0.1 percent in 2019.
Geopolitical and policy constraints on oil sector production slowed growth in oil-exporting economies, despite
support from public spending. Growth in oil importers remained stable, as reform progress and resilient tourism
activity were offset by structural and external headwinds. Regional growth is projected to pick up to 2.4 percent
in 2020 and to about 2.8 percent in 2021-22, as infrastructure investment and business climate reforms
proceed. Risks are tilted firmly to the downside—geopolitical tensions, escalation of armed conflicts,
slower-than-expected pace of reforms, or weaker-than-expected growth in key trading partners could heavily
constrain activity. Sustained proliferation of these risks could also hamper long-term productivity prospects.

Recent developments
Growth in the Middle East and North Africa(MENA) slowed to an estimated 0.1 percent in
2019, down from 0.8 percent the previous year
(Table 2.4.1; Figure 2.4.1.A).1 The slowdown
largely reflected the sharp growth contraction in
the Islamic Republic of Iran, following thetightening of U.S. sanctions, geopolitical tensions
in the Strait of Hormuz, and diplomatic setbacks.
Weakened global growth weighed on demand foroil and other exports, further hindering activity inthe region generally (Figure 2.4.1.B).
Public spending has been robust in some oil
exporters, including those in the Gulf
Cooperation Council (GCC). Non-oil activity has
also shown supportive signs (Figure 2.4.1.C).
However, these developments were insufficient tooffset weak activity in the oil sector. In addition

Note: This section was prepared by Lei Sandy Ye. Research
assistance was provided by Vanessa Arellano Banoni.
1 The World Bank’s Middle East and North Africa aggregate
includes 16 economies and is grouped into three subregions. Bahrain,
Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates
comprise the Gulf Cooperation Council (GCC); all are oil exporters.
Other oil exporters in the region are Algeria, the Islamic Republic of
Iran, and Iraq. Oil importers in the region are Djibouti, the Arab
Republic of Egypt, Jordan, Lebanon, Morocco, Tunisia, and WestBank and Gaza. Syrian Arab Republic, the Republic of Yemen, andLibya are excluded from regional growth aggregates due to data
limitations.

to less supportive global demand, commitments
to the oil production-cut agreement of theOrganization of the Petroleum ExportingCountries and other signatory countries (OPEC+)
and regional geopolitical events further
constrained the oil sector.
Among oil importers, growth has been morestable. In Egypt, the subregion’s largest economy,
net exports as well as investment, partly supported
by more accommodative monetary stance,
continued to support growth. The maturity of its
external debt has also shifted towards long-term
instruments (Figure 2.4.1.D). Favorable tourism
activity continues to support growth in oil
importers, such as Morocco and Tunisia.
However, agricultural production has become lessfavorable and weighed on activity in Morocco.
Export growth potential in oil importers wasweighed by weakened global demand, includingfrom the Euro Area.
Inflation in the region generally eased. In GCC
economies in 2019, it registered less than 1
percent on average (Figure 2.4.1.E). Inflation in
Egypt subsided substantially in the second half ofthe year, allowing the central bank to cut interest
rates three times since August. In smaller oil
importers (e.g., Jordan), inflation has also
moderated generally. In Iran, however, inflationrose sharply to more than 50 percent in mid-2019,
partly reflecting the earlier depreciation of the rial

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CHAPTER 2.4

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE 2.4.1 MENA: Recent developments
Growth in the MENA region fell in 2019, for the third consecutive year, to an
estimated 0.1 percent. In the large oil-exporting economies, oil production
cuts, weak global economic momentum, and U.S. sanctions on Iranweighed on activity, despite signs of non-oil activity improvement. Activity
among oil importers was supported by improved conditions in Egypt.
Inflation rose sharply in Iran, while remaining generally low elsewhere.
Easier financing conditions in advanced economies have supported
international capital raising.

A. Growth

B. Global oil demand growth andpricesC. Composite PMI

D. Egypt: Inflation, policy rate, andexternal debt maturity

in the parallel market, although inflation has
subsided in late 2019 to below 30 percent.
Financial sector conditions in MENA have been
supportive to activity. Banking systems in the
GCC economies remain broadly resilient, withcapital adequacy ratios generally sound and nonperforming loan ratios contained. Benign global
financing conditions associated with more
accommodative advanced economies’ monetary
policy have supported equity flows in the region
and encouraged investor risk appetite in the large
economies (e.g., GCC and Egypt). In the GCC,
new bonds were issued in international capital
markets in both the corporate and sovereign
sectors, and bank credit growth has shown
improvement (Figure 2.4.1.F). However, access tofinance elsewhere remains a major obstacle toinvestment, especially for small and medium-sizedenterprises (SMEs; Ghassibe, Appendino, andMahmoudi 2019).

Outlook

E. Inflation

F. International debt securities
outstanding

Source: Bank for International Settlements; Haver Analytics; International Energy Agency; International Monetary Fund; World Bank.
A. Weighted average growth rates of real GDP. Gray shaded area denotes forecasts.
B. Left panel denotes year-on-year growth of period average global oil demand in millions of barrels aday. Right panel denotes average oil price during the periods shown. Oil price denotes average ofBrent, Dubai, and WTI. 2019H2 denotes latest data as of Dec 19. 2020 denote World Bank forecast
for oil price and IEA forecast for global oil demand. 2019 global oil demand data are estimates.
C. Above 50 denotes expansion; below 50 contraction. 2019Q4 denotes average of October andNovember.
D. Averages over the period denoted. Policy rate refers to the overnight lending rate. Inflation refers
to CPI inflation. 2019Q4 data for inflation and policy rate denote average of October and November.
E. CPI inflation (year-on-year monthly rate). Last observation is November 2019 for Iran and oil
importers and October for the GCC. GCC include 6 economies. Oil importers include 4 economies.
F. Includes 5 GCC and 6 non-GCC economies. Sum of international debt securities outstanding.
“Corporates” include non-financial and financial corporations.
Click here to download data and charts.

Growth in the region is projected to accelerate in2020 to 2.4 percent, supported by higher
investment, promoted by both infrastructure
initiatives and stronger business climates. The
forecasted stabilization in Iran assumes that the
impact of sanctions tapers somewhat (Table
2.4.2). Regional growth is expected to remain
stable over 2021-22, at about 2.8 percent.
Continued reform efforts and strengthening
domestic demand in key economies should
provide support to activity. Despite the projected
growth acceleration, long-standing challenges,
such as high unemployment rates among youth
and women and high poverty rates in somecountries, will remain. In particular, for economies
affected by fragility, conflict, and violence, armed
conflicts imposed further setbacks to poverty via
lower provision of public services and social safety
nets. More sustained growth will be needed toresolve these challenges.
Among oil exporters, growth is expected to pickup to 2 percent in 2020. Infrastructure
investment, along with an improved regulatoryenvironment backed by business climate reforms,
are expected to support activity in the GCC

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

(World Bank 2019q, Figure 2.4.2.A). Iran’s
economy is expected to stagnate at a lower base, as
the initial intensive impact of sanctions on oil
production and exports is assumed to taper
somewhat. Algeria’s growth is expected to pick up
modestly, as policy uncertainty abates somewhat
and investment improves. Investment associatedwith reconstruction and fiscal easing is expected tosupport Iraq’s growth. Facilities and capacity
expansion in oil and gas sectors is also expected tosupport activity in many oil exporters. Over the
medium term, growth in GCC economies is
expected to remain steady, underpinned byplanned diversification programs, longer-term
infrastructure programs, and measures to easeforeign investment restrictions.
Growth in oil importers is expected to rise slightly
in 2020, to 4.4 percent, led by improvements inlarger economies. Growth in oil importers is
contingent upon the materialization of reform
plans and no escalation of political risks. Tourism,
aided by government promotion initiatives andimproved security, is expected to continue
supporting activity in Egypt, Morocco, andTunisia. However, for smaller oil importers,banking sector fragility and high public debt are
significant constraints on growth (Figure 2.4.2.B).
Moreover, the sustainability of debt or external
position in these economies often depends on thematerialization of expected multilateral andbilateral financing flows or on the strength ofsovereign credit; and are vulnerable to sudden
shifts in market confidence. Modest growth insmaller oil importers weighs further on the highbudgetary financing pressures of these economies
and the sustainability of their high debt.
Medium-term growth prospects for the MENA
region are contingent on an attenuation of armed
conflicts, and on limiting their regional spillovers.
Structural reforms, such as those to provide
stronger fiscal management and to enhance theinvestment climate, are underway in many GCC
and non-GCC economies. New financial reforms,
such as investment law and stronger minority
investor protection in Egypt; the relaxation offoreign investment restrictions across 13 sectors
and in SME licensing in the United ArabEmirates; and a new secured transactions law inMID D LE E AS T AN D NO RTH AFRIC A

113

FIGURE 2.4.2 MENA: Outlook and risks
Stronger momentum in the non-oil sector in the GCC, aided by businessclimate reforms, is expected to support activity. Oil importers’ growthprospects are also supported by policy reforms but are challenged by highdebt levels and structural issues. Geopolitical risks are acute and haveprolonged the refugee crisis in fragile areas. Political instability hampers
reform progress and poses a major constraint to productivity. Lower-thanexpected growth in the Euro Area would constrain external demand for theregion, especially oil importers.
A. Improvement in business climate:2018-19

B. Public debt in MENA

C. Labor market competitivenessD. Syrian refugees’ intention to return

E. Political instability as biggest
obstacle to firm operations

F. Euro Area growth forecasts

Source: Bank for International Settlements,; Haver Analytics; International Monetary Fund; United
Nations; World Bank; World Economic Forum.
A. Includes 6 GCC and 9 non-GCC economies. Unweighted average of each economy’s change inDistance to Frontier Score in the denoted measures between 2018-19 (2020 DoingBusiness edition).
B. Unweighted averages. 2019 data are estimates.
C. Index of labor market competitiveness based on the Global Competitiveness Index. Index
constructed based on data on labor market entry/exit, wage flexibility and skills match. Unweighted
averages. AE denotes advanced economies. Based on 2019 data edition.
D. Based on United Nation’s Annual Surveys on Syrians’ Refugees’ Perceptions and Intentions toReturn to Syria. Survey respondents include Syrian refugees in Egypt, Iraq, Lebanon, and Jordan. Xaxis denotes two questions to survey respondents on whether they “hope to return to Syria one day”
and whether they “intend to return to Syria in the next 12 months (“Yes”, “No”, “Do not know”). 2018
data denote survey conducted between Nov 2018 and Feb 2019.
E. Percent of firms citing political instability as biggest obstacle to business operations, based on theWorld Bank’s Enterprise Surveys data. Unweighted averages across economies. Data for latest
available year across 9 MENA economies.
F. Legend denotes month-year for which World Bank forecast is published. Columns denote thegrowth forecast year.
Click here to download data and charts.

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CHAPTER 2.4

Jordan have been adopted. They are expected tohelp relieve financial constraints in the corporate
sector, support investor confidence, and raise
foreign direct investment (FDI). Structuralreforms of this nature could help raise thehistoricallyweak
long-term
productivity
performance in these countries (Arezki et al.
2019a,b; Youssef et al. 2019; Box 2.4.1).
Nonetheless, the scope for improvements in many
areas remains large – for instance, limited churn offirms, barriers to competition, and labor market
inefficiencies hinder MENA firms’ ability togenerate private sector jobs (Figure 2.4.2C; Arezki
et al. 2019a).

Risks
Risks are firmly tilted to the downside. These
include the long-standing risks from geopoliticalconflicts, political uncertainty, and volatility in oil
prices as well as more recent risks associated with
reescalation of global trade tensions.
Geopolitical risks have increased substantially.
Syria and surrounding countries remain filledwith high uncertainties and diverse intra– andinterregional developments. Armed conflicts in
Syria have held back refugees’ short-term intention
to return, despite greater desire to ultimately
resettle in their home country (Figure 2.4.2.D). In
Yemen, the near-term prospects remain highly
uncertain due to the active conflict, now in its
fifth year. Yemen’s socioeconomic outlook
depends critically on a cessation of hostilities and arenewed political vision for the country. An
escalation of U.S.-Iran tensions would posedifficulties for other regional economies as well as
Iran itself.
Political uncertainty also clouds MENA’s growthprospects, particularly in non-GCC economies.
While political impasse and some previously
delayed reforms have been partly resolved, policy
uncertainty in Algeria remains significant.
Reconstruction in Iraq had already experienced
some delays, and a lack of political consensus on
economic reforms continues to challenge the

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

government (Mansour, Maseeh, and Celiku2019). Such uncertainties and delays could hinder
productivity and private sector development –survey evidence shows that political instability is
by far a bigger obstacle to firm operations in
MENA than any other EMDE regions (Figure
2.4.2.E; World Bank 2016f).
Geopolitical factors related to U.S.-Iran tensions,as well as the recent attack on Saudi Aramco’s oil
facilities, have raised volatility in oil prices. This
volatility may rise further. A sharp rise in oil pricevolatility may complicate or stall fiscal adjustments
in both oil exporters and importers. It could also
set back investment programs in oil exporters andcause difficulties for subsidy reforms in oil
importers by increasing the uncertainty associatedwith future revenue and income streams.
Renewed escalation of global trade tensions may
further weaken growth prospects in advanced
economies and several large EMDEs. This may
translate into further setback to growth in theEuro Area, to which the MENA region andespecially the Maghreb region have significant
trade exposure (Figure 2.4.2.F). Oil importers are
subject to risks from the GCC, a significant sourceof remittances and FDI flows. Global tradetensions may also affect the MENA region
through the oil price channel (IEA 2019). Sharp
oil price declines via weaker global oil demand
would significantly affect activity in MENA oil
exporters.
Volatility in external financing conditions could
destabilize MENA’s financial markets. For
example, higher uncertainty about the path ofadvanced economy’s monetary easing stance could
present a downside risk to capital flows to GCC
economies, which have low debt levels relative tooil importers but rising exposure to international
financial markets. Moreover, it could raise theirdifficulties in financing contingent liabilities inpublic spending projects through large bondissuances. For oil importers, volatility in global
interest rates could raise the debt service costs of
their high levels of public debt.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

115

MID D LE E AS T AN D NO RTH AFRIC ATABLE 2.4.1 Middle East and North Africa forecast summary
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE MENA, GDP1

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

1.1

0.8

0.1

2.4

2.7

2.8

-1.2

-0.8

0.0

(Average including countries with full national accounts and balance of payments data only)

2

EMDE MENA, GDP2

1.4

0.9

-0.4

2.3

2.7

2.8

-1.5

-0.6

0.0

GDP per capita (U.S. dollars)

-0.4

-0.8

-2.0

0.7

1.2

1.4

-1.6

-0.8

-0.2

PPP GDP

1.7

0.9

-0.4

2.4

2.9

2.9

-1.5

-0.6

0.1

Private consumption

2.6

0.7

1.4

1.9

2.2

2.2

0.1

0.0

0.1

Public consumption

4.9

2.9

0.4

2.1

2.3

2.4

-1.0

0.7

1.3

Fixed investment

1.7

0.2

2.4

5.2

5.7

6.1

-2.0

-0.5

-0.7

Exports, GNFS3

4.5

2.4

-1.7

3.0

3.6

3.7

-2.1

-0.9

0.0

7.7

-2.0

1.1

3.4

4.0

4.0

-0.8

0.1

0.2

-0.5

2.0

-1.3

0.3

0.4

0.4

-0.8

-0.5

0.0

Imports, GNFS

3

Net exports, contribution to growthMemo items: GDP
Oil exporters4

0.6

0.1

-0.8

2.0

2.3

2.3

-1.5

-0.9

0.1

GCC countries5

-0.3

2.0

0.8

2.2

2.6

2.7

-1.3

-1.0

-0.1

Saudi Arabia

-0.7

2.4

0.4

1.9

2.2

2.4

-1.3

-1.2

-0.1

Iran

3.8

-4.9

-8.7

0.0

1.0

1.0

-4.2

-0.9

0.0

Oil importers6

3.8

3.9

4.0

4.4

4.6

4.6

-0.1

-0.1

-0.1

Egypt

4.8

5.5

5.7

5.9

6.0

6.0

0.0

0.0

0.0

4.2

5.3

5.6

5.8

6.0

6.0

0.1

0.0

0.0

Fiscal year basis7

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates. Excludes Libya, Syria, and Yemen due to data limitations.
2. Aggregate includes all countries in notes 4 and 6 except Djibouti, Iraq, Qatar, and West Bank and Gaza, for which data limitations prevent the forecasting of GDP components.
3. Exports and imports of goods and non-factor services (GNFS).
4. Oil exporters include Algeria, Bahrain, Iran, Iraq, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.
5. The Gulf Cooperation Council (GCC) includes Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.
6. Oil importers include Djibouti, Egypt, Jordan, Lebanon, Morocco, Tunisia, and West Bank and Gaza.
7. The fiscal year runs from July 1 to June 30 in Egypt; the column labeled 2018 reflects the fiscal year ended June 30, 2018.
Click here to download data.

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TABLE 2.4.2 Middle East and North Africa economy forecasts1

Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

Algeria

1.3

1.4

1.3

1.9

2.2

2.2

-0.6

0.2

0.8

Bahrain

3.8

2.2

2.0

2.1

2.4

2.4

0.0

-0.1

-0.4

Djibouti

5.1

5.5

7.2

7.5

8.0

8.4

0.2

0.0

0.0

Egypt

4.8

5.5

5.7

5.9

6.0

6.0

0.0

0.0

0.0

4.2

5.3

5.6

5.8

6.0

6.0

0.1

0.0

0.0

Iran

3.8

-4.9

-8.7

0.0

1.0

1.0

-4.2

-0.9

0.0

Iraq

-2.5

-0.6

4.8

5.1

2.7

2.5

2.0

-3.0

0.4

Jordan

2.1

1.9

2.0

2.2

2.4

2.5

-0.2

-0.2

-0.2

Kuwait

-3.5

1.2

0.4

2.2

2.0

2.0

-1.2

-0.8

-0.9

Fiscal year basis

2

Lebanon

0.6

0.2

-0.2

0.3

0.4

0.5

-1.1

-1.0

-1.1

Morocco

4.2

3.0

2.7

3.5

3.6

3.8

-0.2

0.0

0.0

Oman

0.3

1.8

0.0

3.7

4.3

4.3

-1.2

-2.3

1.5

Qatar

1.6

1.5

0.5

1.5

3.2

3.2

-2.5

-1.7

-0.2

Saudi Arabia

-0.7

2.4

0.4

1.9

2.2

2.4

-1.3

-1.2

-0.1

Tunisia

1.8

2.5

1.6

2.2

2.6

2.6

-1.1

-1.0

-0.9

United Arab Emirates

0.5

1.7

1.8

2.6

3.0

3.0

-0.8

-0.4

-0.2

West Bank and Gaza

3.1

0.9

0.5

2.5

2.6

2.7

0.0

1.5

1.0

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here
may differ from those contained in other Bank documents, even if basic assessments of economies’ prospects do not significantly differ at any given moment in time.
1. GDP at market prices and expenditure components are measured in 2010 prices and market exchange rates. Excludes Libya, Syria, and Yemen due to data limitations.
2. The fiscal year runs from July 1 to June 30 in Egypt; the column labeled 2018 reflects the fiscal year ended June 30, 2018.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

MID D LE E AS T AN D NO RTH AFRIC A

BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers
Labor productivity growth in the Middle East and North Africa (MENA) has been the weakest among emerging market anddeveloping economy (EMDE) regions, both pre-crisis and post-crisis. It averaged 0.3 percent between 2013-18, although withwide heterogeneity. Weak productivity growth had widened the productivity gap between advanced economies and MENAEMDEs. Large public sectors, underdeveloped private sectors, and lack of economic diversification hold back productivity growth,
although recent reform initiatives in many countries in the region are promising.

Introduction
Labor productivity growth in the Middle East and North
Africa (MENA) has been the weakest among emerging
market and developing economy (EMDE) regions,averaging 0.3 percent during 2013-18 (Figure 2.4.1.1).
There is wide heterogeneity across the region in
productivity growth, but on average, the productivity gap
between MENA EMDEs and advanced economies has
widened. In energy exporters, labor productivity growthhas been severely constrained by weak investment, while inenergy importers, it has stagnated below the EMDE
average rate. Moreover, the continuing importance ofcommodity exports in many economies means that they
have not experienced the diversification or expansion ofother sectors that helped drive high productivity growth inregions like East Asia and the Pacific.
Against this backdrop, this box addresses the following
questions for the MENA region:•

How has productivity growth evolved?

•

What factors have been associated with productivity
growth?

•What policy options
productivity growth?

areavailable

to

boost

Unless otherwise noted, discussion of productivity in thisbox refers to labor productivity, measured as output per
worker. The primary sample under which regional laborproductivity trends are discussed is based on 14 MENA
economies: Algeria, Bahrain, Egypt, Iran, Iraq, Jordan,
Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia,
Tunisia, and the United Arab Emirates.

Evolution of regional productivity
Low labor productivity growth. From already weak precrisis rates (1.3 percent during 2003-08), laborproductivity growth in MENA decelerated further, toNote: This box was prepared by Lei Sandy Ye, building upon analysis
in Chapter 3. Research assistance was provided by Vanessa Arellano
Banoni and Shijie Shi.

about 0.3 percent during 2013-18. This slowdown
affected about half of EMDEs in the region, especiallyenergy exporters (Figure 2.4.1.2). Weak post-crisis
productivity growth in the region continues a longstanding trend that featured productivity growth below the
EMDE average for the past two decades.
Within-region heterogeneity. Productivity trends in the
MENA region differ considerably by country. Amongenergy exporters, productivity growth averaged about 0
percent in 2013-18 amid a 50 percent oil price collapse
from its mid-2014 peak. The oil price collapse also did not
greatly benefit energy importers in the region –productivity growth remained flat at about 1.5 percent
during both 2003-08 and 2013-18, well below the EMDE
average.
Wide dispersion in labor productivity levels. At nearly
half of advanced-economy productivity, MENA has thehighest productivity level of any EMDE region. However,productivity levels in MENA differ widely within region,
with substantially higher levels in the Gulf Cooperation
Council (GCC) economies than in energy importers. This
disparity reflects the variation in natural resource
endowments between lower-middle-income energy
importers such as Egypt, Morocco, and Tunisia, and highincome energy exporters such as Saudi Arabia and United
Arab Emirates. MENA’s convergence towards advanced
economy productivity levels has decelerated further fromthe 2003-08 to 2013-18 periods due to weak productivity
growth.
Sources of labor productivity growth. In the two decades
prior to the oil price collapse of 2014-16, laborproductivity growth in the region was primarily supported
by capital deepening, driven by capital investment byenergy exporters (IMF 2012, 2015; Malik and Masood2018). In an alternative labor productivity decomposition
that also incorporates natural resources (Brandt, Schreyer
and Zipperer 2017), natural resource activity appears todrive MENA productivity growth significantly. Its averagecontribution to productivity growth shrank from about
1.2 percentage points during 2003-08 to 0.2 percentage
point during 2013-14, the last year for which natural
resources data are available (Figure 2.4.1.2).

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BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
FIGURE 2.4.1.1 Productivity in MENA inregional comparison
Labor productivity growth in the Middle East and North
Africa (MENA) has been the weakest among emerging
market and developing economy (EMDE) regions, bothpre-crisis and post-crisis. It averaged 0.3 percent
between 2013-18. Despite high average productivity
level relative to other EMDE regions, weak productivity
growth has recently widened its productivity gap withadvanced economies.
A. Average productivity growth

B. Productivity levels and convergence

Source: Penn World Table; The Conference Board; World Bank.
Note: Productivity is defined as labor productivity (real GDP per person
employed). Sample includes 35 advanced economies and 127 EMDEs: 16 in
East Asia and the Pacific (EAP), 21 in Eastern Europe and Central Asia
(ECA), 25 in Latin America and the Caribbean (LAC), 14 in Middle East andNorth Africa (MNA), 7 in South Asia (SAR), and 44 in
Sub-Saharan Africa (SSA). The 14 MNA economies in the sample are
Algeria, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman,
Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates.
A. Aggregate growth rates in 2010 U.S. dollars at 2010 prices and exchange
rates.
B. Rate of convergence is calculated as the difference in productivity growth
rates over the log difference in productivity levels between MNA andadvanced economies (AE). Blue bars and orange dashes show the range
and average of the six EMDE regional aggregates. “Level” of productivity
refers to the GDP weighted average of regional productivity as a share of theaverage advanced economy during 2013-2018.
Click here to download data and charts.

The commodity sector is capital-intensive. As a result, oilprices and capital expenditures are closely linked in the
MENA region (IMF 2018b; Albino-War et al. 2014).
Foreign direct investment is also highly undiversified andheavily concentrated in the commodity sector (WorldBank 2003). After the global financial crisis, investment
growth in the region slowed sharply. Among energy
exporters, this slower growth has been attributed to tightfinancial constraints associated with lower oil prices.
Among energy importers, the legacies of the Arab Spring
movements led many economies to increase investment on
defense at the expense of infrastructure and otherproductivity-enhancing projects and initiatives (Baffes etal. 2015; Ianchovichina 2017).
Pre-crisis capital deepening was partly offset bycontractionary total factor productivity (TFP) growth, theweakness of which has been widely documented for theregion over the past three decades.1 The inverse
relationship between capital accumulation and TFP
growth suggests inefficient investment, and may be
attributed to two factors. First, predominantly public
investment combined with the large economic role ofstate-owned enterprises crowds out private investment andjob creation. Second, fiscal policy tends to be procyclical—
just like public investment—as countries often pursue
expansionary fiscal policy during oil price booms (Abdih etal. 2010). During periods of high capital investment andoil price booms, technology-enhancing-oriented reform
momentum tends to be weaker, weighing on TFP growth.
Negative TFP growth in MENA before the global
financial crisis stands in sharp contrast to the robust precrisis TFP growth in the broader group of EMDEs. TFP
growth started to pick up as oil prices bottomed out in
2016, although it remained low at 1 percent on averageduring 2016-18.2
Heterogeneity in sources of labor productivity growth.
While labor productivity growth in the MENA region as awhole has long been anemic and continues to be weak,
there has been wide divergence within the region in its

1 Weak or negative TFP growth is found to be a prevalent feature in
the MENA region during the past three decades. For regional andcountry-specific studies that highlight TFP growth in MENA, see Baier,
Dwyer, and Tamura (2006); Bisat, El-Erjan, and T. Helbling (1997);
Callen et al. (2014); IMF (2012); Keller and Nabli (2002); Malik and
Masood (2018); World Bank (2017d); and Yousef (2004).
2 TFP growth can also be affected by non-technology factors, such as
capital and labor utilization. Hence, TFP growth estimates may over- or
understate the true change in the influence of technology on productivity(Dieppe, Kindberg-Hanlon, and Kiliç Çelik, forthcoming).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
FIGURE 2.4.1.2 Evolution of labor productivity growth in MENA
The post-crisis productivity growth slowdown was concentrated in energy exporters and affected about half of the region’s
economies. During 2013-18, average productivity growth was around zero percent in energy exporters and about 1.5
percent (still below the EMDE average) in energy importers. Productivity growth has been largely driven by declining capitalstock amid weak TFP growth, especially in energy exporters. Productivity levels in energy exporters are much higher than inenergy importers. The contribution of natural resources to productivity growth fell significantly from the 2003-08 to 2013-18
periods.
A. Productivity growth in MENA

B. Share of economies with productivity
growth below long-run and pre-crisis
averages

C. Productivity relative to advanced
economies

D. Contributions to regional productivity
growthE. Contributions to productivity growth

F. Role of natural resources

Source: Barro and Lee (2015); Haver Analytics; International Monetary Fund; Penn World Tables; United Nations (Human Development Reports), Wittgenstein Centre
for Demography and Global Human Capital; World Bank.
Note: Productivity is defined as labor productivity (real GDP per person employed). Aggregate growth rates calculated using GDP weights at 2010 prices and market
exchange rates.
A-C. The sample includes 14 MNA economies: Algeria, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and theUnited Arab Emirates. Includes 127 EMDEs.
A. Dashed lines indicate the average long-term labor productivity growth (1981-2018).
B. Share of countries for which productivity growth average over 2013-18 is lower compared to a long-run (1992-2018) and pre-crisis (2003-08) average.
D.E. MNA Sample in decomposition is the same as in A but excludes Algeria and UAE due to data availability. Includes 92 EMDEs in D.
F. The sample includes 10 MNA economies with available data on natural resources capital: Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi
Arabia, and Tunisia.
Click here to download data and charts.

driving forces. For energy exporters, productivity growth
decelerated markedly from 2003-08 to the post-crisis
period of 2013-18 due to sharply declining investment
activity. For energy importers, productivity growth
improved modestly from a weak base, largely due to therecovery from negative average TFP growth rates during
2003-08 to slightly above zero percent during 2013-18.

Sources of regional labor productivity growthHigh barriers to factor reallocation. Factor reallocation
toward more productive activity has played only a limited
role in driving productivity growth in MENA. This muted
influence has reflected high barriers to entry anddistortions such as the lack of competitive markets (Arezki

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BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
FIGURE 2.4.1.3 Factors supporting productivity growth in MENA
Productivity levels relative to advanced economies are the highest in MENA for the capital-intensive industrial sector, while
employment is concentrated in the services sector. Evidence for Egypt and Morocco suggests that productivity growth inNorth Africa has been limited to within-sector productivity gains.
A. Sectoral productivity

B. Sectoral employment

C. Within versus between sector
contribution to productivity growth

Source: Groningen Growth Development Center Database; Haver Analytics; International Labour Organization; Penn World Tables; World Bank.
Note: Productivity is defined as labor productivity (real GDP or value-added per person employed).
A.B. Medians across economies in each sector. Includes 12 MENA economies. Panel A based on 2017 data. AE average denotes weighted average across advanced
economies.
C. The within-sector productivity contribution shows the initial real value added-weighted productivity growth; the between-sector contribution measures the productivity
growth from a cross-sectoral shift of employment. Based on nine-sector decomposition.
Click here to download data and charts.

et al. 2019a). Small exporting firms are hesitant to scale up
their operations and benefit little from global value chain
integration (World Bank 2016f). For the North Africa
region, evidence from Egypt and Morocco suggests thatwithin-sector productivity gains were the main source of
productivity growth for their economies (Figure 2.4.1.3).
In Saudi Arabia, employment appears to have moved
towards sectors with relatively low productivity in the past
(Fayad and Rasmussen 2012). These trends imply
distortions in the economy exist that prevent more
efficient reallocation of resources across sectors. High
capital intensity of the commodity sector accounted forhigh average productivity levels in MENA, and scope for
productivity improvement in the private sector remains
large. Moreover, the majority of employment isconcentrated in the services sector, reflecting an
exceptionally high proportion of the workforce (about
one-fifth) employed in the public sector (Tamirisa andDuenwald 2018).
Other drivers of labor productivity growth. Weakproductivity in the MENA region has been associated with
underdevelopment of the private sector, overreliance on
the public sector, and lack of economic diversification(Devarajan and Mottaghi 2015).

•

Large public sector. On average, about one-fifth of theregion’s workforce is employed in the public sector,
and public-private sector wage gaps are among thehighest in the world (Purfield et al. 2018; Tamirisa
and Duenwald 2018). The education system istargeted towards government employment, with few
high-quality private sector jobs (World Bank 2018l).
These dynamics hold back the adoption of technology
from abroad (Mitra et al. 2016; Raggl 2015;
Samargandi 2018). In the Gulf Cooperation Council,
weak productivity growth has been associated withlow mobility of high-skilled foreign workers (Callen etal. 2014).

•

Restrictive business climate. Poor governance quality,
large informal sectors, and cumbersome tax policy andadministration hampered the reallocation of resourcesfrom low-productivity to higher-productivity firms(Nabli 2007; World Bank 2016f). Non-GCC
economies in MENA rank especially low in the WorldBank’s Worldwide Governance Indicators, such as
regulatory quality and government effectiveness.
Private firms often face challenges in access to finance;
yet, providing access to formal finance is associatedwith labor productivity growth being 2 percentage
points higher in MENA firms (Blancher et al. 2019).

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MID D LE E AS T AN D NO RTH AFRIC A

BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
FIGURE 2.4.1.4 Policy challenges
Multipronged and sustainable reforms that improve governance and boost private sector development are crucial in MENA.
Reforms could lift the potential of its young population and relieve constraints to firm productivity, such as access to finance.

A. Access to finance as an obstacle toproductivity

B. Youth unemployed or not in education

C. Governance: Non-GCC economies

Source: World Bank.
A. Percent of firms citing access to finance as a major obstacle to firm operations. Based on World Bank’s Enterprise Surveys. Latest available survey year for eacheconomy denoted. Non-GCC MENA denotes average of all economies shown in the figure.
B. Share of youth not in education, employment, or training, as a percent of youth population. UAE stands for United Arab Emirates. Latest available data since 2015.
C. Includes 10 non-GCC economies. Unweighted averages. Based on 2018 data (or latest available year). Index, based on Worldwide Governance Indicators, ranges
from -2.5 to 2.5. A lower index denotes worse rating.
Click here to download data and charts.

•

Anemic private sector. Firm productivity in MENA has
been restricted by low firm turnover and creation.
Only six limited liability companies were createdannually for every 10,000 working-age people in
MENA during 2009-12—considerably less than inother EMDEs (Schiffbauer et al. 2015).

•

Lack of diversification. Trade openness and export
diversification in MENA remain low among EMDE
regions. This lack of diversification is partly the resultof exchange rate misalignments associated with highreliance on extractive industries or low technologicalcontent of exports (Benhassine et al. 2009). In thelarge EMDEs of the region, low export diversification
has been found to hinder productivity growth.3
Research and development, as measured by the
number of patent applications per capita, has been
above the EMDE average. However, it remains well
below advanced-economy averages and has held backproductivity growth and diversification (Samargandi
2018, Rahmati and Pilehvari 2017).

Recent reforms. A number of large economies in theregion have adopted reform plans in the past five years that
3 See IMF (2013, 2015); Morsey, Levy, and Sanchez (2014);
Samargandi (2018).

may have begun to support productivity growth. In the
GCC, a series of plans include measures to improve
productivity and diversify away from the energy sector.
Efforts to boost small and medium-sized enterprise (SME)
growth and encourage private-sector development include
the establishment of an SME agency in Saudi Arabia and
SME delicensing in the United Arab Emirates. Amongenergy importers, measures to improve the business andprivate sector climate have been enacted in Egypt,
Morocco, and Tunisia (World Bank 2019r). Initial market
responses to these developments suggest that efficiency
gains have been generated. For instance, Saudi Arabia wasincluded in the MSCI Emerging Markets Index recently,
and many GCC economies established policies to relax
foreign investment restrictions (e.g., UAE’s relaxation of
restriction in 13 sectors in 2019). These changes have been
associated with foreign investment inflows, which in
EMDEs often catalyze productivity-enhancing private
investment (Henry 2007). These policies have also made iteasier to raise international capital, which has alreadyhelped finance fiscal and balance-of-payments needs in
MENA (IMF 2019d). Egypt’s macroeconomic reforms
since 2016 include the liberalization of the exchange rate,business climate reforms, and energy subsidy reforms.
These reforms have been positively perceived by investors
and may have raised the country’s export and investment
prospects (Youssef et al. 2019).

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BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
Prospects for labor productivity growth. Recent broadbased reform commitments across the region arepromising for labor productivity growth. However, manyreforms are subject to high risk of delays in
implementation, especially in non-GCC economies where
political fragmentation and budget irresolution havefrequently held back multiyear reform plans. In some nonGCC economies, recent protests related to social tensions
and political developments underscore the fragility
associated with reform progress. Armed conflicts ineconomies like Yemen continue to challenge the peace thatthese economies need in order to work toward higher
productivity.

Raise human capital. The contribution of human capitalto labor productivity growth has been modest in the past
two decades, amounting to only about half a percentage
point. The region’s human capital challenge is to improveeducational access for youth and women, improve theconnection between educational attainment and private
sector jobs, and to shift its bias in educational training
away from the public sector (World Bank 2018l). These
measures would help the productivity potential of its large
youth population. More educational programs to improve
the skills match between workers and employers canenhance the quality of jobs in MENA (Gatti et al. 2013).
Boost firm productivity

Policy options
Concerted and multipronged efforts are required toreliably raise productivity growth. Policies need to be
directed at raising the quality of human capital andboosting private sector investment, increasing firm
productivity, removing obstacles to sectoral reallocation,and creating business-friendly environments. Within thesebroad themes, specific policies need to be tailored to a
country’s specific circumstances.4
The effectiveness of reform in practice is contingent on thehealth of each economy and the timing of political events(Alesina, et al. 2019). Under some circumstances, atargeted approach that leverages synergies may be
warranted. Deep institutional reforms to raise market
contestability, for example, may also bring a variety ofcollateral benefits like higher technological progress(Arezki et al. 2019a). Similarly, well-designed deploymentof FinTech could help garner broad-based support for
institutional reforms (World Bank 2019r).
Improving factors of production
Boosting private investment. While capital deepening has
been a main driver of productivity growth in MENA, it
has been primarily supported by large public spending (forexample, in the commodity sector in the GCC; IMF
2018b). This suggests large scope to boost private
investment. A wide range of reforms is needed toencourage private investment, including expanding accessto finance, improving business climates and governance,
reducing the wage premium of government employment,
and leveling the playing field with state-controlled
enterprises (Arezki, et al. 2019a).
4 Higher labor productivity gains in the region could in turn help
reduce external imbalances in the region (Arezki et al. 2019b).

Disincentives for innovation and factor reallocation
between firms discourages labor productivity in MENA.
Improve access to finance. Access to finance is a large
obstacle for firms in MENA, particularly for non-GCC
economies, as lack of financing hinders their ability toinvest and innovate (Figure 2.4.1.4). Better access tocredit, supported by broader credit bureau coverage andstronger insolvency resolution regimes, appears to yieldsizable benefits to productivity growth in MENA
(Ghassibe, Appendino, and Mahmoudi 2019). New
insolvency resolution laws adopted in Djibouti, Egypt,
Saudi Arabia, and Jordan are promising for facilitatingdebt resolution between creditors and debtors. New
minority investor protection regulation in Egypt helps
improve corporate governance and investor confidence byrequiring shareholder approval in issuing new shares.
Address informality. Informality, although low by average
EMDE standards, presents a challenge to businesses in
non-GCC economies. Competition from the informal
sector is a major obstacle for formal sector businesses inseveral large economies (Morocco, Tunisia), and a higher
share of informal workers in SMEs is associated with lower
wages and more limited export potential (Elbadawi and
Loayza 2008). Aligning tax systems to international bestpractices (e.g., harmonized electronic filing systems in
Morocco) and reducing regulatory hurdles for firms canhelp attract informal firms to more productive formal
activity while raising revenue collection.
Encouraging efficient resource reallocation
Reallocation towards more productive private sector
activities has made limited contributions to productivity
growth in MENA. In energy exporters, policies toencourage diversification of exports and output can

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MID D LE E AS T AN D NO RTH AFRIC A

BOX 2.4.1 Labor productivity in the Middle East and North Africa: Trends and drivers (continued)
generate new opportunities for labor to move into moreproductive private sector opportunities. In energy
importers, such as Egypt and Morocco, expanding
exporters’ global market reach and improving the qualityof exports could help improve productivity (World Bank
2016f).
Diversification through trade. Reforms in investment,
trade, and tariff policies will help MENA economies moveup the export value chain and encourage greater productvariety, which currently lags behind international
benchmarks. Regional integration efforts (e.g., Compact
with Africa) could provide an avenue to promote
diversification and raise productivity.
Diversification from commodity dependence. For energy
exporters, including the GCC, stronger fiscal management
could help promote diversification by broadening therevenue base (Diop and Marotta 2012; World Bank
2019q). For energy importers, options for diversification
may include investment in renewable energies via publicprivate partnerships (e.g., Egypt; Vagliasindi 2013), or
initiatives to boost the private services sector (e.g., tourism
initiatives in oil importers). Efforts to expand the reach offirms to the global market can also help boost productivity
growth (World Bank 2016f).
Creating a growth friendly environment
Improve business climates. Business climate reforms, suchas the reduction of regulatory hurdles to start businesses or
the removal of particularly distortionary taxes, can help
boost private investment and productivity. They can also
provide firms easier access to critical inputs, such as
improved electricity supply. They can support productivity
through better allocation of resources (e.g., more efficienttaxation systems) and stronger entrepreneurship activities(e.g., lower cost to start a business). In MENA, reforms
that move an economy one unit higher in the Global
Competitiveness Index have been estimated to raise
productivity growth significantly (Mitra et al. 2016).
Many MENA economies have adopted broad-based
business climate reforms recently, including improved
electricity connection in Bahrain, enhanced electronic tax
filing in Jordan, and easier property registration in Kuwait.
Improve governance. Governance quality in MENA,
especially non-GCC economies, lags behind other EMDEs
and has exhibited little improvement over the past decade(Figure 2.4.1.4). Weak governance has discouraged private
sector activity and investment (Nabli 2007). Governancereforms, such as streamlining public service delivery andstrengthening legal frameworks in areas like procurement
laws can increase productivity growth by encouraging
more efficient allocation of resources. They can also
increase investment prospects through improved investor
confidence. Reforms for state-owned enterprises intelecom industries can also enhance productivity via higher
efficiency (Arezki et al. 2019b).
Improve gender equality. Women comprise only about
one-fifth of the labor force in MENA. Bridging the gender
gap in a number of areas, including workforce
development and access to digital and financial services, isespecially relevant for MENA. Closing these gaps can raise
productivity
growththroughmorevibrant
entrepreneurship and private sector participation.
Legislation to reduce economic discrimination against
women in Tunisia is an example of recent reform in this
area.

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Growth in South Asia is estimated to have decelerated to 4.9 percent in 2019, reflecting a sharper-thanexpected and broad-based weakening in domestic demand. In India, activity was constrained by insufficientcredit availability, as well as by subdued private consumption. Regional growth is expected to pick up gradually,
to 6 percent in 2022, on the assumption of a modest rebound in domestic demand. While growth in
Bangladesh is projected to remain above 7 percent through the forecast horizon, growth in Pakistan is projectedto languish at 3 percent or less through 2020 as macroeconomic stabilization efforts weigh on activity. Growth
in India is projected to decelerate to 5 percent in FY2019/20 amid enduring financial sector issues. Policy
measures such as enhancing foreign direct investment inflows and competitiveness, promoting access to financefor small enterprises, and improving infrastructure can deliver productivity gains and lift growth in the region.
Key risks to the outlook include a sharper-than-expected slowdown in major economies, a reescalation ofregional geopolitical tensions, and a setback in reforms to address impaired balance sheets in the financial andcorporate sectors.

Recent developments
South Asia’s growth is estimated to havedecelerated to 4.9 percent in 2019, substantiallyweaker than 7.1 percent in the previous year
(Figure 2.5.1.A). The deceleration was
pronounced in the two largest economies, India
and Pakistan. Weak confidence, liquidity issues in
the financial sector (India), and monetary
tightening (Pakistan) caused a sharp slowdown in
fixed investment and a considerable softening inprivate consumption. Export and import growth
for the region as a whole moderated, in line with acontinued slowdown in global trade and industrial
activity (World Bank 2019s). Business confidence
was hampered by subdued consumer demand in
India and security challenges in Sri Lanka.

most pronounced in the manufacturing andagriculture sectors, whereas government-related
services subsectors received significant support
from public spending. GDP growth decelerated to
5 percent and 4.5 percent (y/y) in the April-June
andJuly-September
quartersof2019,
respectively—the lowest readings since 2013.
Sharp slowdowns in household consumption andinvestment offset the rise in government spending(Figure 2.5.1.B). High-frequency data suggest that
activity continued to be weak for the rest of 2019
(Figure 2.5.1.C).

Demand faltered amid credit tightening, reflecting
structurally high non-performing assets (e.g.,
Bangladesh, India, Pakistan), liquidity shortages in
the non-bank financial sector in India, andtightening policies in Pakistan. In India, activity
slowed substantially in 2019, with the deceleration

In Pakistan, growth decelerated to an estimated
3.3 percent in FY2018/19, reflecting a broadbased weakening in domestic demand. Significant
depreciation of the Pakistani rupee (the nominal
effective exchange rate depreciated about 20
percent over the past year) resulted in inflationarypressures (SBP 2019). Monetary policy tightening
in response to elevated inflation restricted access tocredit. The government retrenched, curtailing
public investment, to deal with large twin deficits
and low international reserves.

Note: This section was prepared by Temel Taskin. Research
assistance was provided by Jankeesh Sandhu.

Bangladesh, the third-largest economy in theregion, fared better than India and Pakistan, withgrowth officially estimated at 8.1 percent in

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FIGURE 2.5.1 SAR: Recent developments
Regional growth is estimated to have decelerated to 4.9 percent in 2019. In
India, the combination of funding issues in non-banking financial
companies (NBFC) and uncertainty weighed on growth. Industrial
production points to continuing weakness in activity. While regional exports
softened in aggregate, Bangladesh’s export growth accelerated, partly
reflecting trade diversion amid trade tensions between major economies.
Monetary policy was broadly accommodative amid weak activity andsubdued inflation. Current account deficits narrowed with weakening
imports.
A. Growth

B. Private consumption andinvestment in India

C. Industrial production growth

D. Bangladesh: Goods exports growth

E. Inflation

F. Monetary policy rates

Source: Bangladesh Bureau of Export Promotion; Haver Analytics; World Bank.
A. SAR = South Asia region. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP
weights. Data for 2019 are estimates.
B. Last observation is 2019Q3.
C. Last observation is October 2019.
D. Exports data are merchandise exports and in current U.S. dollars.
E. Bangladesh, India, and Pakistan data reflects fiscal years. 2019 data reflects November for
Bangladesh, India, Pakistan, and Sri Lanka.
F. Data represent monetary policy rates of Reserve Bank of India, State Bank of Pakistan,
Bangladesh Bank, and Central Bank of Sri Lanka. Last observation is December 2019.
Click here to download data and charts.

FY2018/19. A moderation in domestic demand
was more than offset by a pickup in exports, partly
as a result of trade diversion following bilateral
tariff increases between China and the UnitedStates. Bangladesh’s exports showed signs ofsoftening in recent months, after a substantial
increase in exports to major trade partners in thelast fiscal year (Figure 2.5.1.D).
Growth in Sri Lanka continued to soften in 2019,
to an estimated 2.7 percent, as tourist arrivals
collapsed following terror attacks in April (WorldBank 2019t). The Central Bank of Sri Lanka eased
its policy stance with cuts in interest rates andreserve requirements in response to subdued
economic activity. In Afghanistan, growthrecovered to an estimated 2.5 percent in 2019,benefiting from a pickup in agriculture thanks tobenign weather conditions. However, politicaluncertainty and security challenges weighed on
the manufacturing and services sectors.
Growth in Nepal is estimated at 7.1 percent in
FY2018/19, the third consecutive year of over 6
percent growth. Activity was underpinned by solid
remittance inflows, buoyant tourist arrivals, andgood monsoons. In Bhutan, activity remained
subdued as underlying drivers—hydropower andtourism—have not picked up significantly in
FY2018/19, resulting in 3.9 percent GDP growth.
While tourist arrivals increased, tourism receipts
declined reflecting lower average spending by
tourists. Growth in Maldives moderated, despite
an increase in tourism, amid softening
construction activity, which partly reflected thecompletion of infrastructure projects and delays in
the implementation of new ones. Accordingly,
activity is estimated to have expanded by 5.2
percent in 2019.
Inflation has been mostly stable in the region on
the back of weak domestic demand and broadly
stable currency markets, with the notable
exception of Pakistan (Figure 2.5.1.E). Central
banks in other major economies were able to cut
policy rates several times amid negative output
gaps and persistently below-target inflation (India,
Sri Lanka; Figure 2.5.1.F).
Progress in fiscal consolidation has broadly
weakened. Pakistan’s budget deficit rose more

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

sharply than expected. Contributing factors were ashortfall in revenue collection, combined with asizable increase in interest payments. In Sri Lanka,
weaker-than-expected tax revenues and increased
public spending resulted in a widening budget
deficit. Current account deficits have generally
narrowed over the past year, on the back ofsoftening imports in the region.

SOUTH ASIA

127

FIGURE 2.5.2 SAR: Outlook and risks
Growth is projected to increase gradually, reflecting a modest rebound indomestic demand. The regional outlook for 2020 has deteriorated recently,
and risks are tilted to the downside. Financial sector weakness will likely
weigh on activity unless balance sheet vulnerabilities are addressed.
NBFCs represent a significant share of total loans, and their linkages withthe banking sector imply broad-based contagion risks in India. Lack ofprogress in reforms to improve tax collection could exacerbate fiscal
deficits.
A. SAR: Growth contributions

B. SAR: Growth forecasts

C. Non-performing assets

D. India: Non-bank financial systemassets, 2018

E. Fiscal balances

F. Current account balances

Outlook
Growth in South Asia is projected to gradually
pick up over the forecast period, from 4.9 percent
in 2019 to 6 percent in 2022 (Figure 2.5.2.A;
Tables 2.5.1, 2.5.2). This projection assumes amodest rebound in domestic demand. The weak
global trade outlook will continue to weigh onregional export growth in the near term. Regional
economic activity is expected to benefit frompolicy accommodation (India, Sri Lanka),
improvement in business confidence and support
from infrastructure investments (Afghanistan,
Bangladesh, Pakistan).
In India, weakness in credit from non-bankfinancial companies is expected to linger.
Although a gradual growth recovery is expected in
the second half of the fiscal year, the challenges
faced by the economy over the first half should
contribute to a third consecutive year of slowing
growth in FY2019/20 (April 2019-March 2020).
Thereafter, growth is expected to gradually
recover, to 6.1 percent in FY2021/22. This
forecast is predicated on the monetary policy
stance remaining accommodative. It also assumes
that stimulative fiscal and structural measuresalready taken—including corporate tax cuts,
income transfers to farmers, spending on ruraldevelopment, support measures to the automobile
industry, and further liberalization of foreign
direct investment (FDI)—will begin to pay-off.
The scope for more proactive support from fiscal
and monetary policies is limited, as inflation has
recently crossed the midpoint of the target range,
and weaker-than-expected tax revenues are being
accompanied by increased public spending.
Macroeconomic adjustment in Pakistan, including
a continuation of tight monetary policy and fiscal
consolidation, is expected to continue. Growth isSource: Haver Analytics; Consensus Economics; Reserve Bank of India; World Bank.
A. SAR= South Asia region. Aggregate growth rates are calculated using constant 2010 U.S. dollar
GDP-weights. Data for 2019 are estimates. Shaded areas are forecasts.
B. Blue bars represent World Bank forecasts. Last observation is December 2019.
C. Last observation is 2019Q2 for Afghanistan, Bhutan, India, Pakistan, and Maldives, and 2019Q1
for Sri Lanka. Bangladesh observation is for 2018.
D. Data obtained from RBI (2019) and represent December 2018.
E. Shaded areas indicate forecasts. Data for 2019 are estimates. The data refer to fiscal years ofcountries except for Sri Lanka, as described in Table 2.5.1.
F. Data for 2019 are estimates. The data refer to fiscal years of countries except for Sri Lanka, as
described in Table 2.5.1.
Click here to download data and charts.

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projected to bottom-out at 2.4 percent in
FY2019/20 (July 2019-June 2020). Thereafter, as
macroeconomic conditions improve and structuralreforms support investment, growth is projected tosteadily advance, reaching 3.9 percent by
FY2021/22.

both within region and across major
destinations—is much larger (World Bank
2019v). Greater participation in global andregional value chains would lift growth, convey
positive productivity and technology spillovers,
and narrow current account deficits in the region.

Growth in Bangladesh is projected to remain
above 7 percent throughout the forecast horizon.
A solid macroeconomic framework, politicalstability, implementation of planned public
infrastructure projects, and ongoing reforms to
improve the business environment underlie thisprojection (World Bank 2019u).

Risks

Sri Lanka’s growth is projected to advance to 3.3
percent in 2020. The acceleration afterwards will
be supported by recovering investment andexports, as long as the security challenges andpolitical uncertainty of 2019 dissipate. Growth isseen to stabilize around 3.7 percent over the rest of
the forecast horizon, in line with potential growth.
In Afghanistan, activity is expected to continue
accelerating, assuming a stable political transition
after elections and a subsequent improvement inbusiness confidence. Nepal’s economy is projected
to grow at about 6.5 percent through 2022,
supported by strong services and construction
sector activities, amid buoyant tourist arrivals andrising public spending. Growth in Bhutan andMaldives will continue to be underpinned by
tourism and infrastructure projects, over theforecast horizon averaging 6.5 percent and 5.6
percent, respectively.
Over the medium term, regional growth isexpected to rise toward potential. Trends inurbanization, progress in human capital
accumulation, and demographic developments
will support potential growth and productivity.
Policy measures such as enhancing FDI inflows
and competitiveness, promoting access to financefor small enterprises, and improving infrastructure
can deliver productivity gains in the region(World Bank 2019d; Kochhar et al. 2006; Box
2.5.1). South Asia’s participation in international
trade remains substantially below that of otherregions. While both imports and exports as a shareof GDP in South Asian countries are below levelsof comparable economies, the gap in exports—

South Asia’s growth outlook has deteriorated
considerably over the past six months. Private
consumption and investment weakened sharply
amid challenges in the financial sector, whichhampered confidence (Figure 2.5.2.B). Risks to
the growth outlook remain tilted to the downsideand relate primarily to financial sector
vulnerabilities, geopolitical tensions, and lack ofprogress on reforms. Although recent tensions
between India and Pakistan have abated, areescalation would damage confidence and weighon investment in the region.
Non-performing assets in the financial sector
remain high amid weakening regional growth(Figure 2.5.2.C). Further deterioration of balance
sheets of banks and corporates would threaten thefunding of productive investments (Behera andSharma 2019). Failure to close the infrastructure
gaps would hold back output and employment(World Bank 2020). Announced initiatives, suchas the recapitalization and consolidation of public
sector banks and measures to foster FDI inflows,
are expected to support activity. Insufficient
progress in implementing these reforms would set
back growth in the region.
The non-bank financial system in India remains
vulnerable to stress. A major idiosyncratic default
could trigger a broader liquidity shortage in the
sector, as it did over the past year (RBI 2019).
Non-banks represent a significant share of totalloans, and their linkages with the banking sector
imply that contagion risks are material (Figure
2.5.2.D).
Lack of progress in reforms to improve tax
collection could result in more acute revenue
shortfalls (Bangladesh, Sri Lanka) and put furtherpressure on elevated fiscal deficits (Pakistan; Figure
2.5.2.E). This could have negative consequences
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SOUTH ASIA

projected growth, as well as for the fiscal space
available to respond to a future cyclical downturn.
With respect to external risks, a sharper-thanexpected slowdown in major external markets
such as the United States and the Euro Area,
would affect South Asia through trade, financial,and confidence channels, especially for countries

with strong trade links to these economies(Chapter 1). For countries with elevated debt
levels and large current account deficits (Figure
2.5.2.F; Pakistan, Sri Lanka), an unexpected
tightening in global financing conditions could
sharply raise borrowing costs and lead to stops incapital inflows (Sengupta and Gupta 2015).

TABLE 2.5.1 South Asia forecast summary
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE South Asia, GDP

1, 2

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

6.7

7.1

4.9

5.5

5.9

6.0

-2.0

-1.5

-1.2

(Average including countries with full national accounts and balance of payments data only)3
EMDE South Asia, GDP3

6.7

7.1

4.9

5.5

5.9

6.0

-2.0

-1.5

-1.2

GDP per capita (U.S. dollars)

5.5

5.9

3.7

4.3

4.8

4.8

-2.0

-1.5

-1.1

PPP GDP

6.7

7.1

4.9

5.5

5.9

5.9

-2.0

-1.5

-1.2

Private consumption

6.7

7.9

4.4

5.8

6.3

6.8

-2.6

-1.1

-0.7

Public consumption

12.4

10.3

10.2

7.8

7.6

7.6

2.6

0.9

0.5

Fixed investment

8.5

10.3

4.0

6.4

6.5

6.5

-4.3

-1.4

-1.4

Exports, GNFS4

5.5

10.3

4.9

5.2

5.9

6.0

-0.5

0.0

0.4

Imports, GNFS4

15.5

15.1

3.4

4.8

6.1

6.2

-2.8

-1.0

0.0

Net exports, contribution to growth

-2.7

-1.9

0.1

-0.3

-0.5

-0.5

0.8

0.3

0.1

16/17

17/18

18/19e19/20f

20/21f

21/22f

18/19e19/20f

20/21fSouth Asia excluding India

5.8

6.0

5.6

4.8

4.7

5.1

0.1

0.0

-0.3

India

8.2

7.2

6.8

5.0

5.8

6.1

-0.4

-2.5

-1.7

Pakistan (factor cost)

5.2

5.5

3.3

2.4

3.0

3.9

-0.1

-0.3

-1.0

Bangladesh

7.3

7.9

8.1

7.2

7.3

7.3

0.8

-0.2

0.0

Memo items: GDP2

Source: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates.
2. National income and product account data refer to fiscal years (FY) for the South Asian countries, while aggregates are presented in calendar year (CY) terms. The fiscal year runs
from July 1 through June 30 in Bangladesh, Bhutan, and Pakistan, from July 16 through July 15 in Nepal, and April 1 through March 31 in India.
3. Subregion aggregate excludes Afghanistan, Bhutan, and Maldives, for which data limitations prevent the forecasting of GDP components.
4. Exports and imports of goods and non-factor services (GNFS).
Click here to download data.

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TABLE 2.5.2 South Asia country forecasts
Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)
2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

Calendar year basis 1
Afghanistan

2.7

1.8

2.5

3.0

3.5

3.5

0.1

-0.2

-0.1

Maldives

6.9

6.7

5.2

5.5

5.6

5.6

-0.5

0.3

0.3

Sri Lanka

3.4

3.2

2.7

3.3

3.7

3.7

-0.8

-0.3

0.0

Fiscal year basis1

16/17

17/18

18/19e19/20f

20/21f

21/22f

18/19e19/20f

20/21fBangladesh

7.3

7.9

8.1

7.2

7.3

7.3

0.8

-0.2

0.0

Bhutan

6.3

3.8

3.9

5.6

7.6

6.2

-1.5

0.2

2.4

India

8.2

7.2

6.8

5.0

5.8

6.1

-0.4

-2.5

-1.7

Nepal

8.2

6.7

7.1

6.4

6.5

6.6

0.0

0.0

0.0

Pakistan (factor cost)

5.2

5.5

3.3

2.4

3.0

3.9

-0.1

-0.3

-1.0

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here may
differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
1. Historical data is reported on a market price basis. National income and product account data refer to fiscal years (FY) for the South Asian countries with the exception of Afghanistan,
Maldives, and Sri Lanka, which report in calendar year. The fiscal year runs from July 1 through June 30 in Bangladesh, Bhutan, and Pakistan, from July 16 through July 15 in Nepal, andApril 1 through March 31 in India.
Click here to download data.

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SOUTH ASIA

BOX 2.5.1 Labor productivity in South Asia: Trends and drivers
In contrast to other emerging market and developing (EMDE) regions, labor productivity growth in South Asia (SAR) has slowed
only mildly since the global financial crisis. In 2013-18, SAR productivity growth remained the second fastest (after East Asia andPacific) among EMDE regions, at 5.3 percent a year. Rapid growth has helped reduce the region’s wide productivity gap with theadvanced-economy average. But the level of productivity in SAR remains the lowest among EMDE regions, in part reflecting
widespread informal economic activity and struggling manufacturing sectors. Low human capital, poor business environments,
inefficient resource allocation, and limited exposure to foreign firms and foreign investment weigh on productivity. Opening up
SAR economies by enhancing foreign direct investment inflows and participation in global and regional value chains could
support technology and information transfer to the region. Promoting access to finance and improving infrastructure could unlock
growth bottlenecks for firms and lift productivity in the region.

Introduction
In contrast to other emerging market and developing(EMDE) regions, productivity growth in South Asia
(SAR) slowed only mildly after the global financial crisis
from pre-crisis rates, to 5.3 percent a year during 2013-18(Figure 2.5.1.1.A). This followed a steady rise from anemic
rates in the mid-1980s when heavily state-directedeconomic policy strategies dampened investment andinnovation. As a result, the region’s convergence towards
advanced-economy productivity levels was the secondfastest over 2013-18 (after East Asia and the Pacific).
Despite this, the region had the lowest average
productivity level of any EMDE region, at 5 percent of theadvanced-economy average in the post-crisis period(Figure 2.5.1.1.B).
Against this backdrop, this box will discuss the following
questions about the evolution of productivity growth in
the SAR region:•

How has productivity evolved in the region?

•

What have been the factors associated withproductivity growth?

•

What policy options
productivity growth?

because of limited data availability: India and Sri Lanka forgrowth accounting decompositions, and India, Pakistan,and Sri Lanka for sectoral analysis using nine-sector data.

Evolution of regional productivity
Robust productivity growth. Productivity growth in SAR
remained robust, at 5.3 percent a year, in 2013-18, only
narrowly below the pre-crisis average of 6.4 percent in
2003-08 (Figure 2.5.1.1.C). In the post-crisis period, aslight moderation in India’s productivity growth, andlarger declines in the smaller economies of Afghanistan,
Bhutan and Sri Lanka, were partially offset by pickups in
Bangladesh and Pakistan (Figure 2.5.1.1.D). The region’s
resilience reflected three main elements: (1) SAR’s limited
exposure to external headwinds, (2) continued rapid
urbanization, and (3) an improving business environmentthat supported productivity gains from the continuing
shift away from agriculture toward more productive
services sectors (World Bank 2016a; APO 2018). As aresult, in the post-crisis period, the share of economies
with productivity growth below long-run and pre-crisis
averages weas lower than in other EMDEs (Figure
2.5.1.1.E).

boost

•

This box defines productivity as labor productivity,
measured as real GDP per worker at constant (2010) local
currency prices. Cross-country comparisons of laborproductivity levels use average 2010 market exchange rates.
Data for labor productivity at the national level, as well as
for the three main production sectors (agriculture,
manufacturing and mining, and services) are available for
all EMDEs in SAR: Afghanistan, Bangladesh, Bhutan,
India, Maldives, Nepal, Pakistan, and Sri Lanka. However,
the analysis in some cases uses only limited samples

In India, disruptions to economic activity due to cash
shortages in 2016 and transitional costs related to theintroduction of the new Goods and Services Tax(GST) system in 2017 contributed to a slowing ofproductivity growth to 5.6 percent a year during 2013-18, from the 2003-08 average of 7.1 percent a year.
Nevertheless, India’s post-crisis productivity growth
remained in the highest decile among EMDEs. It was
supported by investments in the energy and transport
sectors, improvements in the ease of doing business,and ongoing structural reforms.

•

In Pakistan, annual productivity growth picked up
from a pre-crisis average of 2.4 percent to 3.1 percent
during 2013-18, slightly below the EMDE average of
3.4 percent. During the post-crisis period,
productivity growth benefited from strong foreign

areavailabletoNote: This section was prepared by Temel Taskin, building upon
analysis in Chapter 3. Research assistance was provided by Jankeesh
Sandhu and Shijie Shi.

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BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.1 Evolution of productivity growth in SAR
On average labor productivity expanded by 5.3 percent a year over the last ten years, significantly higher than the EMDE
average. The catch-up to advanced economies starts from a low base, as productivity levels in SAR are just one-quarter oflevels in the average EMDE. The increasing trend in productivity growth was broad-based in larger economies of the region.
However, there is significant dispersion in the level of productivity across the region.
A. Productivity growth

B. Productivity gap and convergence

C. Productivity growth in SAR and
EMDEs

D. SAR: Productivity growth distribution

E. Share of economies with productivity
growth below long-run and pre-crisis
averages, 2013-18

F. Relative productivity levels in SAR

Source: Haver Analytics; Penn World Tables; World Bank.
Note: SAR = South Asia region. EMDE = emerging and developing economy. AE = advanced economy. Productivity refers to labor productivity unless otherwise
indicated. Sample includes 127 EMDEs and 7 SAR economies unless otherwise indicated.
A.B. Range indicates interquartile range of country-level productivity distribution. Rate of convergence calculated as the difference in productivity growth rates with theaverage advanced economy divided by the log difference in productivity levels with the average advanced economy.
C.E.F. Aggregate growth rates calculated using U.S. dollar GDP weights at 2010 prices and exchange rates.
D. The year brackets refer to the average growth within the corresponding periods.
Click here to download data and charts.

direct investment (FDI) inflows and infrastructure
projects which supported private sector activity.
••

In Bangladesh, post-crisis productivity growth
benefited from improved macroeconomic andpolitical stability which supported both public andprivate fixed investment. As a result, productivity
growth in Bangladesh was robust during 2013-18 at
5.1 percent, slightly above the pre-crisis average of 4.7
percent and in the top decile of EMDEs.
Productivity growth in the rest of the region either
stalled or declined in the post-crisis episode in linewith the global trend (Chapter 3). The factors behindthe slowdown included natural disasters, macroeconomic and political instability, and weaker growthof global trade and manufacturing activity.
SAR’s robust productivity growth through the 2000s is instark contrast to its weakness during the 1980s and 1990s,
even though in those decades also it was mostly stronger
than in other EMDEs. In the 1980s, India’s state-directedeconomy generated minimal productivity growth as heavy
regulation and widespread corruption (the “license raj”)
stifled manufacturing, investment, and technology
adoption. In the wake of India’s 1991 balance of payments

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SOUTH ASIA

BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
crisis, major reforms reduced restrictions on product andfactor markets and allowed more trade, catalyzing a surge
in productivity growth (Rodrik and Subramanian 2004;
Virmani and Hashim 2011). In Pakistan, productivity
growth was limited by macroeconomic instability (LopezCalix et al. 2012; Amjad and Awais 2016).

trade in recent years has weighed further on investment aswell as exports. The slowdown of investment growth wasfrom high pre-crisis rates that were fueled partly by large
foreign direct investment inflows after financial
liberalization reforms in the 1990s (Fujimori and Sato
2015; Park 2010).

Low productivity levels. Despite its strong growth over the
past three decades, labor productivity in SAR in 2013-18
was still only 5 percent of the advanced economy average,
the lowest among EMDE regions and significantly below
the EMDE average, which was around 20 percent of theadvanced-economy average. In contrast to other EMDE
regions, however, the pace of convergence has picked up
since the global financial crisis. At the recent rate ofconvergence, about half of economies in South Asia would
halve their productivity gap with advanced economies overthe next 40 years.

Sources of regional productivity growthWithin-region dispersion in productivity levels.
Productivity differences across countries are very large in
SAR. Afghanistan and Nepal have the lowest productivity
levels, at around 7 percent of the EMDE average, partly
reflecting political instability, including prolonged armed
conflict in Afghanistan, and natural disasters. Bhutan,
Maldives, and Sri Lanka have higher productivity levels, inthe range of 32-85 percent of the EMDE average,
reflecting the benefit of relatively large service sectors, inparticular tourism activity. Productivity levels in the threelargest economies of SAR—India, Bangladesh, andPakistan—are lower, ranging between 14 and 27 percentof the EMDE average, reflecting their relatively large
informal sectors, low urbanization rates, and weakfinancial development (Figure 2.5.1.1.F).
Slowing contribution from capital deepening. Labor
productivity growth can be decomposed into contributions
from increases in other factors of production (human andphysical capital) and advances in the effectiveness of theiruse (total factor productivity, TFP). Estimates for thisdecomposition are available for India and Sri Lanka. In
these economies, a slowdown in investment growthaccounted for all of the post-crisis slowdown in
productivity growth, and thus for more than the averagecontribution of investment to productivity slowdowns in
all EMDEs. The contributions to labor productivity
growth of total factor productivity growth and humancapital growth remained the same as in the pre-crisis
period (Figure 2.5.1.2.A). The weakening of investment
growth in part reflected the economic disruptions in Indiaaround the currency exchange of 2016 and theintroduction of the GST in 2017. Slower growth of global

The slight moderation in SAR’s post-crisis productivity
growth was accounted for mainly by India, and mainly by
weaker growth in the industrial sector, as in other EMDEs.
The median productivity level of the industrial sector in
SAR is just slightly more than one half of the EMDE
median (Figure 2.5.1.2.B). Poor manufacturing
productivity in part reflects limited integration into
international trade networks and global value chains,which has constrained the region’s interaction with moreproductive foreign firms and reduced opportunities tobenefit from technology transfer from other countries(Figure 2.5.1.2.C). This said, post-crisis productivity
growth in this sector remained higher than the EMDE
average reflecting improvements in the businessenvironment as well as ongoing public investment intransportation and energy infrastructure.
Growing productivity gains from between-sector
reallocation. Factor reallocation from low- productivity tohigh-productivity sectors and firms has historically not
been an important source of productivity gains in SAR(World Bank 2017e; Mallick 2017; Doughtery et al. 2009;
Goretti, Kihara, and Salgado 2019). However, since theglobal financial crisis, between-sector reallocationaccounted for more than one-third of productivity growthin 2013-15, up from one-tenth in 2003-08. Meanwhile,
within-sector productivity growth slowed sharply, by morethan one-third from pre-crisis rates (Figure 2.5.1.2.D).
Most of the post-crisis productivity gains from sectoral
reallocation reflected a shift from agriculture, which
accounted for about 10 percent of SAR GDP in 2015 but
almost half of employment, into services, which accounted
for about half of GDP but roughly one-third of
employment (Figure 2.5.1.2.E). Agriculture, the region’s
lowest-productivity sector (with average productivity 22
percent of the advanced-economy average), has roughly
one-tenth the productivity of financial services (68 percentof the advanced-economy average) which is the region’s
most productive sector. In the post-crisis episode, thecontribution of services sectors to economy-wide
productivity in SAR has declined while that of agriculture
has increased, as in other EMDEs (Figure 2.5.1.2.F).

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BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.2 Sectoral productivity and employment in SAR
The gains in productivity of the region are mostly accounted for by improvements in TFP growth and capital deepening.
Productivity levels in industry and services sectors are much higher relative to the agriculture sector, and have grownsignificantly over the past three decades. Progress in within-sector productivity growth has played a much larger role inSouth Asia relative to other EMDEs. The share of employment in trade and financial services increased over time as workers
have shifted away from low-productivity agricultural production to these sectors.

A. Productivity decomposition

B. Sectoral productivity trends in SAR

C. Sectoral productivity levels, 2015

D. Within- and between-sector
contributions to productivity growth

E. Sectoral employment shares

F. Sectoral contribution to productivity

Sources: APO productivity database; Expanded African Sector; Groningen Growth Development Center database; ILOSTAT; OECD STAN; United Nations; WorldKLEMS.
Note: SAR = South Asia region, EMDE = emerging and developing economy. Productivity refers to labor productivity unless otherwise indicated.
A. SAR sample includes India and Sri Lanka. EMDE sample includes 92 countries.
B. The year brackets refer to the average growth within the corresponding periods. SAR sample includes Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal,
Pakistan, and Sri Lanka. EMDE sample includes 127 EMDEs.
C.-F. EMDE sample includes 46 countries. SAR sample includes 3 countries: India, Pakistan, Sri Lanka.
D. Growth within sector shows the contribution of initial real value added-weighted productivity growth rate of each sector, holding employment shares fixed, and ‘between
sector’ effect shows the contribution arising from changes in sectoral employment shares. Median of the county-specific contributions.
E.-F. “Other” includes transport services and government services. “Manufacturing” includes mining and utilities; “Finance” includes business services.
Click here to download data and charts.

Other drivers of productivity. In SAR, the contributionsof most of the long-run drivers of productivity to
productivity growth have remained low compared to other
EMDEs and advanced economies despite substantial
progress since the early 1990s in a range of these variables(Figure 2.5.1.3.A). Measures of gender equality and trade
openness are below other EMDE regions, as demonstrated
by very low female participation rates and weak integration
with global value chains. In the post-crisis period, the paceof improvement in several of the long-run determinants ofproductivity slowed, including average years of schooling,labor force participation, investment, urbanization andeconomic complexity. Nonetheless, improvements in thesedrivers did continue. Despite a slowdown in the post-crisis
episode, investment continued to contribute toproductivity growth more than in other EMDEs andadvanced economies (Figure 2.5.1.3.B). By contrast,
limited global integration, weakness in control of

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SOUTH ASIA

BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.3 Drivers of productivity growth in SAR
Many of the drivers remain at the low end of the EMDE regional range suggesting scope for further improvements. While
investment consistently supports economic activity, research and development lag significantly behind other regions.
A. Level of drivers of productivity in SAR

B. Investment

C. Research and development, 2017

Source: Haver Analytics; United Nations; World Bank.
Note: EMDE = emerging and developing economy. AE = advanced economy. SAR = South Asia region.
A. Unweighted average levels of drivers normalized as average of AEs as 100 and standard deviation is 10. Blue bars represent average within SAR economies in 2018.
Orange lines represent range of the average drivers for six regions in 2018. Variables corresponding to the concepts are as follows: Education = years of education,
Urbanization = share of population living in urban area, Investment = share of investment to GDP, Institution = WGI Government Effectiveness Index, Econ. Complexity =Economic complexity index, Geography=share of land area which are not in tropical region, Gender equality=female average years of education minus male average
years, Demography = share of population under 14, Innovation = Log patent per capita, Trade = (Export+Import)/GDP, Price stability = (-1)*log inflation rate. Numbers ofcountries are 7 for SAR. See Annex 3.3 for details.
B. Investment growth: growth in gross fixed capital formation; investment share: change in gross fixed capital formation as a share of GDP.
C. R&D exp: research and development expenditures. Aggregates are calculated using constant 2010 U.S. dollar GDP weights.
Click here to download data and charts.

corruption, low research and development activity, andpervasive informality continued to weigh on SAR’s
productivity growth (Figure 2.5.1.3.C).
•

Limited global integration. Export-oriented firms
have been more productive than non-exporters in
SAR (Figure 2.5.1.4.A). However, SAR’s largest
economies are less open to trade than the average
EMDE or advanced economy (Figure 2.5.1.4.B).
Similarly, while FDI inflows have grown, they remain
below the EMDE average (Figure 2.5.1.4.C). SAR’s
limited contacts with more productive foreign firms
reduce the potential for technology and informationtransfer (Figure 2.5.1.4.D; Maiti 2019; Fujimori and
Sato 2015; Topalova and Khandelwal 2011).

•

Lack of supporting infrastructure. Many firms cite
infrastructure gaps as important obstacles to theirbusiness activities. Firms that cited infrastructure
obstacles were found to be less productive in Pakistan
and Bangladesh (Grainger and Zhang 2017;
Fernandes 2008). The environment has also been lesssupportive in terms of access to finance (Figure
2.5.1.4.E), with state-owned banks dominatingbanking system assets (e.g. roughly 70 percent inIndia) and their balance sheets encumbered byelevated nonperforming loan ratios (usually around 10
percent).

•

Firm characteristics. Heavy regulatory restrictions
have deterred firm growth and prevented firms frombecoming more productive, including throughproductivity-improving investment (Cirera andCusolito 2019; Kanwar and Sperlich 2019).
Complicated tax systems, labor regulations, andlicensing requirements have been factors containing
the productivity of smaller firms (Figure 2.5.1.4.F).
Such factors have encouraged widespread informality,with the informal sector accounting for roughly onethird of GDP and self-employment accounting for 70
percent of total employment in SAR (World Bank
2019f). The potential for productivity gains in SARfrom resource reallocation from less productive tomore productive firms has been estimated to be large(Lall, Shalizi and Deichmann 2003).1

1 For example, equalizing the efficiency of capital and labor allocation
across firms to the level of United States could have increased TFP in
India as much as 50 percent in the 1990s (Hsieh and Klenow 2009).
Similarly, a one-standard deviation decrease in the misallocation of landand buildings in India was estimated to have improved labor productivity
by 25 percent between 1989 and 2010 (Duranton et al. 2015). Direct
and indirect contribution of services to the total value added of
manufacturing sector varies between 33 percent and 50 percent as of2017 in South Asia (Mercer-Blackman and Ablaza 2018).

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BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.4 Policy options in SAR
Low trade openness remains a major constraint for productivity growth in SAR. Continued urbanization in the region canbring agglomeration benefits and enhance productivity if it is accompanied with doing-business reforms given that firms inlarger cities tend to be more productive. Low FDI inflows to SAR, compared to other EMDEs, hold back positive spillovers
from productive foreign firms. Given their low productivity, state banks weigh on financial sector productivity. Small firms face
more severe obstacles to access to finance and their TFP is lower than large firms in South Asia.

A. Export status, location scale, and TFP
in SAR

B. Trade openness

C. FDI inflows

D. Ownership status and TFP in SAR

E. Access to financeF. Firm size and TFP in SAR

Source: World Bank.
Note: Firm-level TFP is computed using a Cobb-Douglas production function, assuming elasticities of output with respect to inputs are the same across countries in agiven income group. See Chapter 3 Appendix 3.3 for a detailed description of calculation and sample coverage. SAR = South Asia region. EMDE = emerging anddeveloping economy. AE = advanced economy.
A.D.E.F. Calculations are based on World Bank Enterprise Surveys. TFPR = Log Total Factor Productivity based on Revenues. TFPVA = Log Total Factor Productivity
based on Value Added. The bars represent estimated coefficients of dummy variables for “exporter”, “located in a city with population larger than 1 million”, “foreign
owner”, and “public enterprise” in a regression where dependent variable is log TFP and independent variables are the aforementioned dummy variable (large, exporter,etc.), country dummy variables, and year dummy variables. Survey weights are used in all calculations. Sample includes 15,248 firms in 109 EMDEs, including 20 LICs,
for the period 2007-17.
B. Trade openness index is described as the ratio of imports and exports to GDP. Aggregates are calculated using constant 2010 U.S. dollar GDP weights. Sample
includes 155 EMDEs and 35 AEs.
C. FDI = Foreign direct investment. Aggregates are calculated using constant 2010 U.S. dollar GDP weights. Sample includes 155 EMDEs and 35 AEs.
E. The vertical axis shows the percentage of responses which indicate “access to finance” as a moderate/major/very severe obstacle.
Click here to download data and charts.

•

Weak human capital. SAR has lagged most EMDE
regions in educational enrolment and attainment, as
well as in mortality indicators. In addition, poor
operations and human resource management quality
has reduced the productivity of firms (Bloom et al.
2012).

•

Gender gaps. South Asia’s female labor force
participation rate is far below comparable economies,and progress in this area is mixed across the region(Goretti, Kihara, and Salgado 2019). Gender gaps inworkforce participation, education, and financial
inclusion restrain the region’s long-term growthpotential (Khera 2018 ).
Robust productivity outlook. Looking ahead, the fact thatmany of the drivers of productivity have remained at thelow end of the EMDE range indicates scope for substantial

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SOUTH ASIA

BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.5 Productivity prospects in SAR
Rising working-age population shares, educational attainment and life expectancy will improve human capital. Increasing
urbanization, accompanied by sectoral reallocation, could support productivity in the region. On the other hand, the region ishighly vulnerable to natural disasters, environmental deterioration and climate change risks.

A. School enrollment projections

B. Urbanization projections

C. Damage from natural disasters

Source: Centre for Research on the Epidemiology of Disasters; United Nations; World Bank.
Note: SAR = South Asia Region. EMDE = Emerging and Developing Economy.
A.-C. Aggregates are calculated using constant 2010 U.S. dollar GDP weights.
A. Last observation is 2018.
B. SAR sample includes 8 South Asian countries. EMDE sample includes 159 countries. Last projection year is 2050.
C. Simple average of aggregate regional damages per year.
Click here to download data and charts.

improvements. Increasing rates of school enrolment would
lift human capital and improve productivity (Figure
2.5.1.5.A). Low urbanization rates compared to other
EMDEs limit the benefits from agglomeration in SAR in
the near term, but longer-term trends may be expected toraise the contribution of urbanization to productivity
growth (Figure 2.5.1.5.B). Recent reforms, such as the
new GST system in India and the Inland Revenue Act in
Sri Lanka are expected to broaden the tax base and makeresources available for human capital and infrastructure
investments (World Bank 2018m). Business climates have
improved significantly in recent years, as shown for
example by shortening approval times for trademarks andpatents, lowering restrictions on foreign direct investment,
and accelerating investment in energy and transport
infrastructure (World Bank 2017f). On the other hand,
the region is highly vulnerable to natural disasters, andenvironmental deterioration and climate change risks
weigh on the productivity growth outlook (Figure
2.5.1.5.C). An improved productivity outlook will require
the resolution of financial sector issues to unlock credit forinvestment along with further improvements in the ease of
doing business.
The working-age share of the population is expected toincrease in SAR until 2045, providing a larger and more
prolonged demographic dividend than in most otherregions. Against the backdrop of improving human capital,and continued urbanization, this increase in the labor force
is expected to contribute to productivity growth in theyears ahead (Annex 3.3).

Policy options
Many drivers of productivity are still much lower in SAR
than in advanced economies and other EMDE regions,indicating significant room for policy reforms that reduce
obstacles to faster productivity growth. Such policies need
to be directed at improving the quality as well as quantityof human and physical capital, increasing firm
productivity, encouraging efficient sectoral reallocation,and creating business-friendly environments.

Improving factors of production
Support physical capital accumulation, especiallyinfrastructure investment. The post-crisis slowdown in
SAR productivity growth mostly reflected weaker capital
accumulation. Many firms cite infrastructure gaps asimportant obstacles to their business activities (Figure
2.5.1.6.A). Moreover, firms facing infrastructure obstacles
have been found to be less productive than others inPakistan and Bangladesh (Grainger and Zhang 2017;
Fernandes 2008). Improved infrastructure in the energy

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BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
and transportation sectors, as well as technology-oriented
capital accumulation, can promote productivity growthand boost international competitiveness (Calderón, MoralBenito, and Serven 2015).
Strengthen investment in human capital. While theregion has benefited from raising life expectancy, reducing
mortality, and expanding access to education over the past
three decades, there is still significant room for further
human capital development (Figure 2.5.1.6.B). With theincreasing working-age share of the population in theregion, delivering strong output growth and improvements
in human capital will be key to progress in productivity
growth (Goretti, Kihara, and Salgado 2019). A better
educated and healthier workforce can have better andmore stable jobs and be more productive (World Bank
2018a). Policies to expand school attendance and support
nutrition programs for early childhood development canboost educational outcomes in SAR (Beteille 2019; Torlese
and Raju 2018; World Bank 2018n).
Tackle gender gaps. Addressing constraints on economic
opportunities for women can provide significant gains inlong-term growth (Khera 2018). Key policies such asincreasing access to childcare, improving financial
inclusion, and ensuring public safety and sanitation canpromote gender equality and boost productivity in SAR(Sharafudheen 2017; World Bank 2016g).

Enhancing firm productivity
Increase the region’s integration into the global economy.
SAR’s participation in international trade remains
substantially less than that of other regions (Gould, Tan,and Emamgholi 2013). While both imports and exports in
SAR, relative to GDP, are lower than in comparable
economies, the gap in exports—both within and outside
the region—is much larger than that in imports (WorldBank 2019v). The empirical evidence on positive
productivity spillovers from international trade and FDI
inflows indicates that measures to foster FDI andparticipation in global and regional value chains can lift
productivity in SAR. SAR may benefit from shifting FDI
flows in the context of recent shifts in global
manufacturing activity.
Bangladesh’s apparel sector benefited substantially fromtailored policies during the 1990s and 2000s, which liftedbarriers to international trade and investment andenhanced participation in global value chains. Theinteraction with foreign firms lifted productivity of local
suppliers through the demand for inputs with higher

standards and quality. Similarly, Bangladesh’s duty-freeaccess to the European Union (EU) from 2001 boosted
knitwear exports to the EU between 2000 and 2004,
enhanced the productivity of producers, and helped them
expand to other export markets (World Bank 2019d).
Improve corporate management practices. Lack of
information and training on best management practices
seems to limit progress in productivity at the firm level.
Governments can help improve the quality ofmanagement in the region by organizing training
programs and workshops to disseminate information on
best management practices. In India, for example,
productivity in firms that provided management training
increased by 17 percent in the first year of the intervention(Bloom et al. 2013). The low number of patents granted
and the limited number of staff engaged in research anddevelopment in South Asian firms have also been in part
attributed to limited management capacity (Cirera andMaloney 2017). Policies that ensure property rights andcreate technology hubs can increase firm participation in
product innovation and expand their business in foreign
markets (Cirera and Maloney 2017).
Address informality. Self-employment accounts foraround 70 percent of employment in SAR (Figure
2.5.1.6.C). The level of output informality (DGE andMIMIC) and some obstacles related to business operations
are comparable to other EMDEs (Figure 2.5.1.6.D). This
sector is associated with lower productivity and weaker
access to finance, a barrier to productive investment and aconstraint on firms. Encouraging participation in global
value chains and enhancing a business-friendly regulatory
and tax environment can promote resource reallocationfrom less productive informal activities to more productive
formal ones in SAR (Artuc et al. 2019; Amin, Ohnsorge,and Okou 2019).
With sizable rural populations employed informally inagriculture and large shares of self-employment in theworkforce, productivity in the region could benefit
significantly from improvements in the productivity of theinformal sector. Policies to promote such improvements
could include efforts to by improve labor force skills andenhance the functioning of agricultural markets (Goretti,
Kihara, and Salgado 2019).

Promoting efficient sectoral reallocation of
resources
Promote productivity-enhancing sectoral reallocation andimprovements in within-sector allocation of resources.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

SOUTH ASIA

BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
FIGURE 2.5.1.6 Constraints to productivity growth in SAR
Many firms experience obstacles in their operations due to infrastructure gaps and political instability. The region is behind
other EMDEs in terms of some doing business indicators, as well as human capital development, limiting opportunities toimprove productivity. Financial development is also weaker compared to other EMDEs, which is reflected in low credit to
GDP ratios. Many of these obstacles to doing business contribute to the high levels of informality in the region.

A. Biggest obstacles in SAR

B. Human capital

C. Informality

D. Obstacles related to regulations

E. Financial development

F. Doing business, distance to frontier

Source: Elgin et al. (2012); United Nations; World Bank.
Note: SAR = South Asia region. EMDE = emerging and developing economy. AE = advanced economy.
A. Calculations are based on World Bank Enterprise Surveys. Survey weights are used in calculations. Left section represents the responses to “How much of an
obstacle?” question in World Bank Enterprise Survey. The vertical axis shows the percentage of responses which indicate moderate/major/very severe obstacle. Right
section represents the responses to “What is the biggest obstacle affecting the operations of this establishment?” question. Vertical axis shows the percentage of
responses. Others include: Access to land, business licensing and permits, corruption, courts, crime/theft/disorder, customs and trade regulations, inadequately educatedworkforce, labor regulations, practices of competitors in the informal sector, tax administration, tax rates.
B. HCI = Human Capital Index. Range reflects the minimum and maximum of the distribution across countries. Higher values of the index reflect better human capitaldevelopment. See World Bank (2018a) for details of the methodology. Aggregates are calculated using U.S. dollar GDP weights at 2010 prices and exchange rates.
C. DGE = dynamic general equilibrium model. MIMIC = multiple indicators multiple causes model. Both DGE and MIMIC estimates measure the informal output in percent
of official GDP.
D. Calculations are based on World Bank Enterprise Surveys and represents the responses to “How much of an obstacle?” question. The vertical axis shows thepercentage of responses which indicate moderate/major/very severe obstacle.
F. SAR sample includes 8 South Asian countries. EMDE sample includes 159 countries. The orange whiskers indicate interquartile range of EMDEs.
Click here to download data and charts.

SAR has received a welcome boost to productivity fromintersectoral reallocation of resources since the global
financial crisis. A policy challenge will be to maintain thismomentum. The productivity gains from sectoral
reallocation from agriculture to more productive sectors
can be increased if accompanied by improved local services
and urban planning (Ellis and Roberts 2016; World Bank
2019s). Such policies should be complemented bymeasures to increase the productivity of the agriculture
sector (Cusolito and Maloney 2018).

The contribution of within-sector productivity growth has
weakened substantially since the global financial crisis.
This calls for a renewed effort to promote the reallocationof capital and labor to more productive firms within
sectors. By one estimate, such interfirm reallocation could
unlock 40-60 percent productivity gains in India (Hsieh
and Klenow 2009). Productivity-enhancing interfirm
reallocation could be encouraged by policies to foster
competition and by reducing regulatory burdens that
discourage firm growth (Duranton et al. 2016).

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BOX 2.5.1 Labor productivity in South Asia: Trends and drivers (continued)
Encourage intersectoral linkages. Intersectoral linkages
play an important role in improving productivity throughvalue chains in South Asia. For instance, progress in
information and communication technologies provides
positive productivity spillovers to broader services sectors(Krishna et al. 2016). Reducing barriers to trade andencouraging intersectoral and regional linkages can lift
productivity through technology spillovers. For example,
in India, Bangladesh, and Sri Lanka the creation of special
economic zones has helped expand exports and productdiversification (Aggarwal, Hoppe, and Walkenhorst 2019).

Creating a growth-friendly environment
Unlock access to finance. Infrastructure spending inrecent years has eased supply-side bottlenecks in SAR.
However, poor access to finance remains a hindrance forthe region, particularly given the weaknesses on corporate
and financial sector balance sheets. Weak access to financeconstrains small and medium-sized firms—especiallywomen-owned businesses—and holds back firm-levelproductivity gains in India (Figure 2.5.1.6.E; World Bank
2013a; Schiantarelli and Srivastava 1997).
Improve the ease of doing business. Despite
improvements in recent years, SAR is still among the leastbusiness-friendly EMDE regions, reflected in distance-tofrontier scores in doing business statistics (2.5.1.6.F).
India’s economic reforms during the early 1990s enhanced
openness and eased regulatory burdens in the services
sector, and these were followed by a significant expansion
in domestic and foreign investment. In India also, theentry of foreign service providers was associated with morecompetitive business services, which supported
productivity gains in the manufacturing sector (Arnold etal. 2016).
Ensure macroeconomic and political stability. Economic
and financial crises have proven to hold back productivity
in the region, as observed after the global financial crisis
and in economic downturns in India and Pakistan in the
1990s. Political instability seems to be a more severe
obstacle to the operations of South Asian firms than inother EMDE regions (World Bank 2013b, 2013c).
Strengthening economic policy institutions, improving
monetary and fiscal policy frameworks, and enhancing
financial regulation and supervision can help to provide astable macroeconomic framework for firms, reduce
uncertainty, and boost productivity.

Growth in Sub-Saharan Africa moderated to a slower-than-expected 2.4 percent in 2019. Activity wasdampened by softening external demand, heightened global policy uncertainty, and falling commodity prices.
Domestic fragilities in several countries further constrained activity. Growth is projected to firm to 2.9 percent
in 2020 and strengthen to 3.2 percent in 2021-22—notably weaker than previous projections. The growth
pickup is predicated on improving investor confidence in some large economies, a strengthening cyclical recovery
among industrial commodity exporters along with a pickup in oil production, and robust growth among several
exporters of agricultural commodities. Nonetheless, these growth rates will be insufficient to make significant
progress in reducing poverty in many countries in the region, highlighting the need for lasting improvements inlabor productivity to bolster growth over the medium term. Downside risks to the outlook include a sharperthan-expected deceleration in major trading partners; increased investor risk aversion and capital outflows
triggered by elevated debt burdens; and growing insecurity.

Recent developments
The feeble economic recovery in Sub-SaharanAfrica has lost momentum, with growth in 2019
estimated to have edged down to 2.4 percent,from 2.6 percent in 2018. This was a weaker pacethan anticipated in June (Figure 2.6.1.A).
Intensifying global headwinds such as decelerating
activity in major trading partners, elevated policy
uncertainty, and falling commodity prices, havebeen compounded by domestic fragilities inseveral countries.
In Angola, Nigeria, and South Africa—the threelargest economies in the region—growth was
subdued in 2019, remaining well below historical
averages and contracting for a fifth consecutive
year on a per capita basis. Activity in Nigeria waslackluster, as both macroeconomic policy and thebusiness environment remain unconducive tostrong domestic demand. Growth in 2019 isestimated to have remained broadly unchanged at
2 percent, as the agricultural sector continued tounderperform due to lingering insurgency in the
Northeast and farmers-herdsmen clashes, while
Note: This section was prepared by Rudi Steinbach. Research
assistance was provided by Jankeesh Sandhu.

unreliable
electricity
supplyconstrained
manufacturing activity. Some of this weakness
was, however, offset by increased oil production.
In South Africa, growth remained anemic in 2019as it fell to an estimated 0.4 percent. Weak growth
momentum has reflected an array of overlapping
constraints. These include persistent policy
uncertainty, constrained fiscal space, subdued
business confidence, infrastructure bottlenecks—
especially in electricity supply—and weakening
external demand, particularly from the Euro Area
and China. In addition, financial stresses at the
public energy utility have worsened thegovernment budget balance and raised debt
sustainability concerns, weighing further onsentiment (Figure 2.6.1.B).
Activity in Angola is estimated to have contracted
by 0.7 percent in 2019, as oil output declined forthe fourth consecutive year due to lower yields
from aging fields and postponed investment in
new capacity. Nonetheless, growth in the non-oil
sector strengthened further as several key reforms
continued to improve the business environment.
In Sudan, the fourth largest economy in theregion, political instability, alongside an ongoingcurrency crisis, has caused activity to contract

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FIGURE 2.6.1. SSA: Recent developments
The recovery in Sub-Saharan Africa has stalled, as intensifying global
headwinds have compounded domestic weakness in several economies.
In South Africa, power cuts and financial stress constrained growth andworsened fiscal deficits. More broadly, lower commodity prices areweighing on activity in commodity exporters and contributing todeteriorating current account balances. Inflation has been mostly
subdued—helped in part by lower oil prices. Persistent budget deficits
have partly reflected weaker commodity revenues and growing interest
burdens.

A. Growth

B. South Africa budget deficit andsupport for Eskom

C. Commodity price changes

D. Current account balances

E. Inflation, annual rate

F. Fiscal balances

Source: Haver Analytics; National Treasury, Republic of South Africa; World Economic Outlook,
International Monetary Fund; World Bank; World Bank Pink Sheet; Zimbabwe National Statistics.
Note: “Industrial-commodity exporters” represents oil and metal exporting countries. “Other SSA”
includes agricultural commodity exporting and commodity importing countries.
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
“Industrial-commodity exporters” excludes Angola, Nigeria, and South Africa.
B. Eskom is the South African public energy utility. Years represent fiscal years; for example, the year2017 is the 2017/18 fiscal year.
C. Bars represent the percentage change in the November 2019 monthly price relative to January
2018. “High” and “Low” represent the respective peaks and troughs of price changes, in percent,since January 2018.
D. Unweighted averages of country groupings.
E. AGO = Angola, GHA = Ghana, ZAF = South Africa, ZMB = Zambia, ETH = Ethiopia,
ZWE = Zimbabwe. 2019Q4 reflects the average of October and November.
F. Unweighted averages.
Click here to download data and charts.

sharply. However, the formation of a three-year
interim government to oversee the country’s
transition to democracy helped improve stability
in the second half of last year.
Beyond the large economies, growth deteriorated
in several industrial commodity exporters in 2019as weaker prices and softer demand dampened
activity in extractives sectors (Democratic
Republic of Congo, Liberia, Namibia; Figure
2.6.1.C). In contrast, growth accelerated in somecountries as investments in new oil and mining
capacity boosted activity (Ghana, Guinea,
Mauritania).
Among exporters of agricultural commodities,
growth rates have been more robust,notwithstanding some mild slowdowns. Estimatesfor 2019 indicate that growth averaged in excess of
5 percent, as sustained public investment ininfrastructure continued to support activity (Togo,
Uganda). Yet, growth softened in some othercountries as decelerating external demand andlower commodity prices constrained export
revenues (Madagascar, Rwanda). In others,
agricultural production suffered from severe
drought (Senegal, Zimbabwe), or late rains(Kenya). Zimbabwe also suffered a sharp rise ininflation that continued to squeeze real incomes,
resulting in a large contraction in economic
activity, estimated at 7.5 percent. Activity has
been further constrained by persistent shortages offood, fuel, electricity, and foreign exchange.
Current account deficits are estimated to havewidened, on average, across the region (Figure
2.6.1.D). In several countries, capital imports
relatedtolarge
infrastructure
projects
underpinned deficits (Mauritania, Mozambique,
Niger, Uganda). In others, weaker export
performances, due to softening external demandand lower commodity prices, were responsible forlarger external balances (Angola, Chad, Republicof Congo). In some countries, current accountbalances improved as a result of importcompression due to weak domestic demand(Namibia, South Africa, Zambia). In others,
infrastructure improvements and reforms inexport-oriented industries led to increased exports
and an improved trade balance (Burkina Faso,
Côte d’Ivoire). Current account financing was

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

more challenging for most of 2019, as growing
concerns over global growth prospects andheightened trade tensions weighed on investor
sentiment and capital inflows. Eurobond issuances
during the first ten months of 2019 were down by
one-third compared to the same period in 2018.
Inflation continued to moderate in most of theregion last year, partly reflecting lower oil prices aswell as earlier monetary policy tightening in somecountries (Figure 2.6.1.E). This allowed
authorities in several countries to adopt more
accommodative monetary policy stances (Angola,
Botswana, Kenya, Mauritius, Mozambique,
Namibia, South Africa). In some countries,
however, inflation accelerated amid rising foodprices (Ethiopia, Zambia, Zimbabwe) andexchange rate pressures (Zambia, Zimbabwe).
Large and persistent budget deficits have reflected
growing interest burdens, as well as weaker
commodity revenues among industrial-commodity
exporters and sustained public investment among
exporters of agricultural commodities (Figure
2.6.1.F). In several countries, budget balances
have improved due to a combination of fiscal
discipline, more efficient domestic resource
mobilization, tax administration reforms, andreforms of energy subsidies (Benin, Cabo Verde,
Cote d’Ivoire, Gabon, Mali, Sierra Leone).

Outlook
Growth in the region is expected to firm to 2.9
percent in 2020, and accelerate further to an
average of 3.2 percent in 2021-22 (Figure
2.6.2.A). The pickup assumes that investor
confidence improves in some large economies,
that energy bottlenecks ease, that a pickup in oil
production contributes to a cyclical recovery
among industrial commodity exporters, and thatrobust growth continues among exporters ofagricultural commodities. However, the forecast
for 2020-22 is 0.4 percentage point lower thanpreviously projected, reflecting weaker demand
from key trading partners, lower commodity
prices, and adverse domestic developments inseveral countries.
On a per capita basis, the outlook translates into
Sub-Saharan Africa growth of 0.3 percent in 2020,

SUB-SAHARAN AFRICA

firming to an average of 0.7 percent in 2021-22.
In the projection, per capita incomes rise by morethan 4 percent per year in several countries that,together, account for one-tenth of the region’spoor (e.g., Côte d’Ivoire, Ethiopia, Rwanda,
Senegal). However, per capita incomes contract
among some of the largest economies that accountfor one-third of the region’s poor (Angola,
Nigeria, Sudan). Projected per capita growth forthe region is insufficient to yield significant
progress in poverty alleviation. Lasting
improvements in labor productivity are needed tobolster growth over the medium term (Box 2.6.1;
Figure 2.6.2.B).
Growth in Nigeria is expected to remain subdued.
The macroeconomic framework—characterized bymultiple exchange rates, foreign exchange
restrictions, high persistent inflation, and a central
bank targeting manifold objectives—does notprovide a firm anchor for confidence. Growing
uncertainty about the direction of government
policies is expected to further dampen the outlook.
Growth is projected to remain broadly unchanged,
rising only to an average of 2.1 percent in 202022. This is weaker than previous projections,reflecting softer external demand, lower oil
prices, and a slower-than-previously-expected
improvement in oil production in view of the lackof much-needed reforms.
In South Africa, growth is expected to firm to 0.9
percent in 2020, before strengthening to an
average of 1.4 percent in 2021-22. This assumes
that the new administration’s structural reform
agenda gathers pace, that policy uncertainty
wanes, and that investment—both public andprivate—gradually recovers. The outlook is,
however, markedly weaker than previous
projections. Increasingly binding infrastructure
constraints—notably in electricity supply—are
expected to inhibit domestic growth, while export
momentum will be hindered by weak external
demand.
Growth in Angola is projected to rise to 1.5
percent in 2020 and to average 2.7 percent in
2021-22. This projection assumes that ongoing
structural reforms—supported by prudent
monetary policy and fiscal consolidation—provide
greater macroeconomic stability, continue to

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FIGURE 2.6.2 SSA: Outlook and risks
Growth in the region is projected to firm somewhat as investor confidence
in some of the large economies improves and oil production in major oil
exporters picks up, while activity among exporters of agriculturalcommodities remains solid. Per capita growth, however, will remain below
1 percent. Several downside risks could materialize, including slower-thanexpected growth in major trading partners, episodes of financial stressgiven rising debt vulnerabilities, and disruptions to activity amid increased
displacement of populations and growing climate risks.

A. GDP growth

B. GDP growth per capita

C. Cumulative revisions to 2020
growth in key trading partners

D. Government debt in SSA

E. Internally displaced populations
and countries requiring external
assistance for foodF. Extreme weather events in SSA

Source: The Emergency Events Database; Université Catholique de Louvain; United Nations Foodand Agricultural Organization; United Nations High Commissioner for Refugees (UNHCR); World
Bank; World Economic Outlook, International Monetary Fund.
A.-B. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange
rates. “Industrial-commodity exporters” excludes Angola, Nigeria, and South Africa.
C. Cumulative revisions to the 2020 growth forecasts since the June 2018 Global Economic
Prospects report.
D. “2020 SSA median” reflects the median of 47 countries.
E. “Food insecurity” reflects countries in SSA requiring external assistance for food. These countries
are expected to lack the resources to deal with reported critical problems of food insecurity. The
sample includes countries with a lack of food availability, widespread lack of access to food, orsevere but localized problems. “Displaced population” reflects only internally displaced populations(IDPs) who are protected or assisted by UNHCR. These are also not necessarily representative of
the entire IDP population in a given country.
F. Data reflect annual averages of extreme weather events in SSA as of October 31, 2019.
Click here to download data and charts.

improve the business environment and bolster
private investment. In particular, recently
announced oil-sector reforms are expected tosupport a recovery in oil production.
Elsewhere in the region, growth is forecast to
strengthen, stabilizing just below 5 percent in
2021-22. In the West African Economic andMonetary Union (WAEMU), growth is expected
to average 6.7 percent. Among the region’sexporters of agricultural commodities, sustained
strong public infrastructure spending, combinedwith increased private sector activity (Madagascar,
Rwanda, Uganda), or continued reforms to raise
the productivity and competitiveness of exportoriented sectors (Burkina Faso, Côte d’Ivoire), willcontinue to support output. In Kenya, growth isexpected to remain solid, but soften somewhat asaccommodative monetary policy does not fully
offset the impact of a fiscal tightening.
In contrast, the ongoing cyclical recovery among
oil and metals exporters will be more sluggish,
reflecting weaker external demand and softer
commodity prices. In some countries, growth isprojected to moderate somewhat over the forecast,in part due to slowing resource production(Democratic Republic of Congo, Ghana). Activity
in Ghana—the region’s fifth largest economy—isexpected to soften from the 7 percent growth of2019 partly due to slowing oil production as
much-needed maintenance on various oil fields iscarried out to ensure their long-term viability.
Longer-term growth prospects will, however, besupported by the improved strength of thefinancial sector following much-needed reforms
implemented during 2018-19. Despite the global
headwinds, investments in new oil and mining
capacity are expected to support faster growth inseveral oil and metals exporters (Botswana,
Cameroon, Chad, Guinea, Mozambique,
Namibia). In Sudan, the business climate is
expected to improve if tensions continue theirrecent easing during the 3-year political transition.

Risks
The balance of risks for Sub-Saharan Africa isfirmly to the downside. A sharper-than-expected
deceleration in major trading partners such as

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

China, the Euro Area, or the United States, would
substantially lower export revenues andinvestment. Together these economies account for
40 percent of the region’s goods exports and onethird of FDI inflows, and their growth prospects
continue to be downgraded (Figure 2.6.2.C).
China, in particular, accounts for one-half ofglobal metals demand and one-quarter of global
oil demand (World Bank 2018o). A faster-thanexpected slowdown in China would cause a sharp
fall in commodity prices and, given Sub-SaharanAfrica’s heavy reliance on extractive sectors forexport and fiscal revenues, weigh heavily on
regional activity.
Government debt in the region is expected toreach 62 percent of GDP, on average, in 2020, up
from its trough of 39 percent of GDP in 2011.
This broad-based rise in government debt has led
to sharp increases in interest burdens, crowding
out non-interest expenditure and raising concerns
about debt sustainability. Countries with elevateddebt burdens are susceptible to sudden increases ininvestor risk aversion (Angola, Ghana,
Mozambique, Namibia, South Africa, Zambia;
Figure 2.6.2.D). This can lead to sizable currency
depreciations, capital outflows, and increases inborrowing costs as risk premia rise sharply. Where
debt is largely denominated in foreign currency,
sharp currency depreciations would makeservicing debt more challenging.
Ballooning debt burdens of state-owned
enterprises represent substantial contingent
liability risks in several countries (Ethiopia,
Ghana,South
Africa,The
Gambia);materialization of these risks could damage
already-fragile fiscal outlooks (Bachmair and

SUB-SAHARAN AFRICA

Bogoev 2018; Bova et al. 2016). In addition toraising fiscal sustainability concerns, economic
activity can be directly affected by potential
disruptions at state-owned enterprises, particularly
if they provide essentials such as electricity. Somecountries are, however, implementing reforms to
improve the functioning of state-owned
enterprises and to alleviate their government’s
exposure to contingent liabilities (Ethiopia,
Ghana, The Gambia).
Insecurity,
conflicts,
andinsurgencies—
particularly in the Sahel—would weigh on
economic activity and food security in several
economies (Burkina Faso, Chad, Ethiopia, Mali,
Niger, Nigeria), if they were to intensify further or
spread geographically (Figure 2.6.2.E; FAO 2019;
UNHCR 2019). Moreover, the large populations
that are forcibly displaced by these conflicts clusterin areas that often become a source of further
instability, with poverty rates being worse than in
their places of origin (Beegle and Christiaensen
2019).
Extreme weather events are becoming more
frequent as the climate changes, posing asignificant downside risk to activity due to thedisproportionate role played by agriculture inmany economies in the region (Figure 2.6.2.F).
The devastation caused by the tropical cyclones
that hit low-income countries in East andSouthern Africa in 2019 bear testimony to this, as
do persistent drought conditions, particularly in
the Sahel and Southern Africa. As droughts
continue to suppress agricultural output, they
increase food insecurity, raise food price inflation,
exacerbate poverty levels, and often contribute toforced displacement of populations (IPCC 2019).

145

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CHAPTER 2.6

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TABLE 2.6.1 Sub-Saharan Africa forecast summary

Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

EMDE SSA, GDP1

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

2.7

2.6

2.4

2.9

3.1

3.3

-0.5

-0.4

-0.4

(Average including countries with full national accounts and balance of payments data only)

2

2.7

2.6

2.5

2.9

3.1

3.3

-0.4

-0.4

-0.4

GDP per capita (U.S. dollars)

-0.1

-0.1

-0.2

0.3

0.5

0.7

-0.4

-0.4

-0.4

PPP GDP

2.9

2.8

2.7

3.1

3.3

3.5

-0.4

-0.4

-0.4

Private consumption

2.5

2.4

2.1

2.6

2.8

2.8

-0.1

-0.1

0.0

Public consumption

1.2

2.5

2.7

2.4

2.5

2.6

0.1

-0.1

-0.2

Fixed investment

4.5

6.2

3.1

3.1

4.1

5.5

-2.8

-3.0

-2.6

Exports, GNFS

4

6.1

3.1

1.6

1.5

2.6

3.0

-0.7

-1.6

-0.4

Imports, GNFS4

1.0

5.9

2.4

2.5

3.1

3.6

-0.6

-0.9

-0.6

Net exports, contribution to growth

1.5

-0.7

-0.2

-0.3

-0.1

-0.1

0.0

-0.2

0.1

4.8

4.4

4.1

4.6

4.7

4.8

-0.5

-0.3

-0.3

EMDE SSA, GDP2,3

Memo items: GDP
SSA excluding Nigeria, South Africa,and Angola
Oil exporters5

1.5

1.4

1.7

2.3

2.3

2.4

-0.4

-0.2

-0.3

CFA countries6

3.5

4.2

4.6

5.1

5.2

5.2

-0.4

0.1

0.1

CEMAC

0.0

1.4

2.3

3.3

3.4

2.9

-0.8

0.2

0.1

WAEMU

6.6

6.6

6.4

6.4

6.5

6.9

-0.2

-0.1

0.0

1.0

1.2

1.1

1.5

1.8

2.0

-0.5

-0.5

-0.4

Nigeria

0.8

1.9

2.0

2.1

2.1

2.1

-0.1

-0.1

-0.3

South Africa

1.4

0.8

0.4

0.9

1.3

1.5

-0.7

-0.6

-0.4

-0.1

-1.2

-0.7

1.5

2.4

3.0

-1.7

-1.4

-0.4

SSA3

AngolaSource: World Bank.
Note: e = estimate; f = forecast. EMDE = emerging market and developing economies. World Bank forecasts are frequently updated based on new information and changing (global)
circumstances. Consequently, projections presented here may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not differ at any
given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates. Excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan.
2. Subregion aggregate excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan, for which data limitations prevent the forecasting of GDP components.
3. Subregion growth rates may differ from the most recent edition of Africa's Pulse (https://www.worldbank.org/en/region/afr/publication/africas-pulse) due to data revisions and theinclusion of the Central African Republic and São Tomé and Principe in the subregion aggregate of that publication.
4. Exports and imports of goods and non-factor services (GNFS).
5. Includes Angola, Cameroon, Chad, Republic of Congo, Gabon, Ghana, Nigeria, and Sudan.
6. Includes Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Republic of Congo, Côte d’Ivoire, Equatorial Guinea, Gabon, Mali, Niger, Senegal, and Togo.
Click here to download data.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

TABLE 2.6.2 Sub-Saharan Africa country forecasts1

Percentage point differencesfrom June 2019 projections(Real GDP growth at market prices in percent, unless indicated otherwise)

Angola147

SUB-SAHARAN AFRICA

2017

2018

2019e

2020f

2021f

2022f

2019e

2020f

2021f

-0.1

-1.2

-0.7

1.5

2.4

3.0

-1.7

-1.4

-0.4

Benin

5.8

6.7

6.4

6.7

6.7

6.7

-0.1

0.2

0.2

Botswana

2.9

4.5

4.0

4.1

4.2

4.2

-0.2

0.2

0.2

Burkina Faso

6.3

6.8

6.0

6.0

6.0

6.0

0.0

0.0

0.0

Burundi

0.5

1.6

1.8

2.0

2.1

2.2

0.0

-0.1

0.1

Cabo Verde

3.7

5.1

5.0

5.0

5.0

5.0

0.6

0.4

0.3

Cameroon

3.5

4.1

4.0

4.2

4.3

4.5

-0.2

-0.2

-0.3
0.0

Chad

-3.0

2.6

3.0

5.5

4.8

4.8

-0.4

-0.1

Comoros

3.8

3.4

1.7

4.8

3.7

3.6

-1.4

1.6

0.5

Congo, Dem. Rep.

3.7

5.8

4.3

3.9

3.4

3.6

-1.6

-2.6

-3.4

Congo, Rep.

-1.8

1.6

2.2

4.6

1.9

2.4

-3.2

3.1

0.0

Côte d’Ivoire

7.7

7.4

7.3

7.0

7.1

7.1

-0.1

-0.3

-0.2
2.8

Equatorial Guinea

-4.7

-6.1

-4.3

-2.3

1.0

-4.8

-2.1

-0.4

Eswatini

2.0

2.4

1.3

2.6

2.5

2.5

0.2

1.0

0.8

Ethiopia2

10.0

7.9

9.0

6.3

6.4

7.1

1.1

-1.9

-1.8

Gabon

0.5

0.8

2.9

3.0

3.2

3.3

0.1

-0.7

-0.7

Gambia, The

4.8

6.6

6.0

6.3

5.8

5.5

0.6

1.1

0.8

Ghana

8.1

6.3

7.0

6.8

5.2

4.6

-0.6

-0.2

-0.6

Guinea

10.0

5.8

5.9

6.0

6.0

6.0

0.0

0.0

0.0

Guinea-Bissau

5.9

3.8

4.6

4.9

5.0

5.0

0.3

0.1

-0.5

Kenya

4.9

6.3

5.8

6.0

5.8

5.8

0.1

0.1

-0.2

Lesotho

-0.4

1.5

2.6

0.7

2.1

2.8

1.1

0.3

-2.0

Liberia

2.5

1.2

-1.4

1.4

3.4

4.2

-1.8

-0.2

2.1

Madagascar

4.3

5.1

4.7

5.3

4.4

5.0

-0.5

0.0

-0.7
0.1

Malawi

4.0

3.5

4.4

4.8

5.2

5.3

-0.1

0.1

Mali

5.3

4.7

5.0

5.0

4.9

4.9

0.0

0.1

0.1

Mauritania

3.0

3.6

6.4

5.7

5.8

8.7

-0.3

-0.1

-0.2
0.5

Mauritius

3.8

3.8

3.9

3.9

4.0

4.0

0.0

0.0

Mozambique

3.7

3.4

2.0

3.7

4.2

4.4

0.0

0.2

0.0

Namibia

-0.9

-0.1

-0.5

0.9

1.7

1.9

-1.4

-0.6

-0.2

Niger

4.9

6.5

6.3

6.0

5.6

11.9

-0.2

0.0

0.0

Nigeria

0.8

1.9

2.0

2.1

2.1

2.1

-0.1

-0.1

-0.3

Rwanda

6.1

8.6

8.5

8.1

8.0

8.0

0.7

0.1

0.5

Senegal

7.1

6.8

6.3

6.8

7.0

7.0

-0.5

-0.2

0.0

Seychelles

4.3

4.1

3.5

3.3

3.3

3.4

0.1

0.3

0.1

Sierra Leone

3.8

3.5

4.8

4.9

4.9

5.0

-0.6

-0.5

-0.3

South Africa

1.4

0.8

0.4

0.9

1.3

1.5

-0.7

-0.6

-0.4

Sudan

4.3

-2.3

-2.6

-1.4

-0.6

0.2

-0.7

-0.1

0.2

Tanzania

6.8

5.4

5.6

5.8

6.1

6.2

0.2

0.1

0.0

Togo

4.4

4.9

5.3

5.5

5.5

5.5

0.3

0.3

0.4

Uganda2

3.9

5.9

6.1

6.5

5.9

6.0

0.0

0.0

0.1

Zambia

4.1

3.1

1.8

2.6

2.6

4.0

-0.7

-0.2

-0.2

Zimbabwe

4.7

3.5

-7.5

2.7

2.5

2.8

-4.4

-0.8

-2.4

Source: World Bank.
Note: e = estimate; f = forecast. World Bank forecasts are frequently updated based on new information and changing (global) circumstances. Consequently, projections presented here
may differ from those contained in other Bank documents, even if basic assessments of countries’ prospects do not significantly differ at any given moment in time.
1. GDP and expenditure components are measured in 2010 prices and market exchange rates. Excludes Central African Republic, São Tomé and Príncipe, Somalia, and South Sudan.
2. Fiscal-year based numbers.
Click here to download data.

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers
Since 2013, Sub-Saharan Africa has experienced a broad-based slowdown in labor productivity growth. Productivity growth has
all but stalled amid falling commodity prices, weakening external demand, and growing domestic fragilities. In the decade prior to
the global financial crisis, productivity growth benefited from strengthening institutions, stronger investment, infrastructuredevelopment, improving human capital, and better macroeconomic policy frameworks, but the pace of improvement has
stagnated. Productivity in the region is still only one-half of that in EMDEs and roughly one-tenth of that in advanced economies.
Ambitious policy efforts will be needed to generate the productivity growth required for per capita incomes in Sub-Saharan Africa
to reach those of its EMDE peers, let alone those of advanced economies. To stimulate labor productivity growth, the region needs
to implement policies that boost agricultural productivity, increase resilience to climate change, broaden economic diversification,and continue human capital development.

Introduction
In one of the steepest declines of any emerging market anddeveloping economy (EMDE) region, labor productivity
growth has slowed sharply in Sub-Saharan Africa (SSA)
since the global financial crisis, from about 2.9 percent
during the pre-crisis period of 2003-2008 to 0.5 percent
during 2013-18 (Figure 2.6.1.1.A). The slowdown wasparticularly sharp among industrial commodity
exporters—exporters of oil and metals account for roughly
80 percent of the region’s GDP—whereas productivity
growth continued to accelerate among several agriculturalcommodity exporters.1 This deceleration returns
productivity growth to near its 1990s average (-0.4
percent) and ends a period of solid growth of 2-3 percent
throughout the pre-crisis period, when it was supported bya favorable external environment, strengthening
institutions, improving human capital, and bettermacroeconomic policy frameworks.
SSA’s productivity levels are low, at around one-half of the
EMDE average and 11 percent of the advanced-economy
average in 2018 (Figure 2.6.1.1.B). However, if a few high-productivity countries are excluded, SSA’s productivity
levels are far lower, at a mere 3 percent of the advancedeconomy average. At near-nil productivity growth, SSA’s
productivity levels have now started to further diverge
from advanced-economy averages. Among EMDE regions,only the Middle East and North Africa has a slower paceof convergence, but starting from productivity levels that
average about four times those of Sub-Saharan Africa.
Absent major policy efforts to lift productivity growth, its

Note: This box was prepared by Rudi Steinbach, with contributions
from Sinem Kilic Celik, and builds upon analysis in Chapter 3. Research
assistance was provided by Jankeesh Sandhu and Shijie Shi.
1 An economy is defined as a commodity exporter when, on averagein 2012-14, either (1) total commodities exports accounted for 30
percent or more of total goods exports or (2) exports of any single
commodity accounted for 20 percent or more of total goods exports.
Economies for which these thresholds are met as a result of reexports are
excluded. Commodity importers are economies not classified ascommodity exporters.

stagnation offers dim prospects for the nearly 60 percent of
the global extreme poor that currently reside in SSA.
Against this backdrop, this box addresses the following
questions:1.

How has productivity evolved in the region?

2.

What are the factors associated with productivity
growth in the region?

3.

What policy options
productivity growth?

areavailable

to

boost

This box defines productivity as labor productivity,
represented by real GDP per person employed (at 2010
prices and exchange rates). Growth in labor productivity isdecomposed into the contributions made by changes inthe standard factor inputs (human and physical capital per
worker) and the effective use of these inputs, as captured
by total factor productivity, assuming a Cobb-Douglas
production function. Cross-country comparisons of laborproductivity use market exchange rates in 2010 to convert
national currency units into U.S. dollars. Data are
available for 44 EMDEs in SSA, of which 21 are oil or
metals exporters, 19 are exporters of agriculturalcommodities, and 5 are commodity importers.2

Evolution of regional productivity
Robust pre-crisis productivity growth. Productivity
growth in SSA started improving in the mid-1990s, as theregion recovered from some of the adverse factors that had
weighed heavily on activity in the 1980s and early 1990s.3
Prior to the crisis, productivity growth rose sharply, to 2.9
2 One country, Chad, is classified as both an oil and an agriculturalcommodity exporter.
3 Adverse developments in the 1980s and early 1990s included amultitude of sovereign debt, banking, and currency crises, debt overhang,
low commodity prices, weak investment, and severe conflicts and politicalinstability in several countries (Calderón and Boreux 2016; Reinhart andRogoff 2009; Straus 2012).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

SUB-SAHARAN AFRICA

BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
percent, on average during 2003-08. Growth was
supported by a favorable external environment, including acommodity price boom between 2001-11 that fueled an
inflow of foreign capital and unprecedented investment
and benefited many of the region’s low-income countries(Figure 2.6.1.2.A; Khan et al. 2016; Steinbach 2019;
World Bank 2019a). Faster productivity growth was also
supported by improvements in education, health care,infrastructure, financial access, and trade openness(Calderón and Servén 2010; Cole and Neumayer 2006;
Shiferaw et al. 2015; World Bank 2018k, 2019z).
In the 2000s, productivity growth in the region’s industrial
commodity-exporting countries picked up sooner andmore sharply than in agricultural commodity exporters
and commodity importers. In addition to the higher
export revenues brought about by rising commodity
prices, oil and metal exporting countries benefited fromsubstantial investments in commodity production andexploration (Khan et al. 2016; Schodde 2013). The
productivity growth pick-up in industrial-commodity
exporters was also driven by country-specificdevelopments. In South Africa—the region’s largest metal
exporter—productivity growth accelerated sharply after
the country’s transition to democracy in 1994, thanks inpart to improving policy frameworks, increased trade
openness and foreign capital inflows (Arora 2005; Du
Plessis and Smit 2007). By the mid-2000s, the more than
20 percent decline in productivity during the final decadeof Apartheid had been fully reversed.

FIGURE 2.6.1.1 Productivity in SSA inregional comparison
Productivity growth in Sub-Saharan Africa (SSA) rose
sharply in the pre-crisis period, reflecting a favorable
external environment and improvements in key drivers of
productivity. Stronger productivity growth also allowed alarge productivity gap between advanced economies
and SSA EMDEs to narrow slightly over this period.
Since then, productivity growth in the region has slowed
sharply. At near-zero productivity growth, the region’s
productivity levels have, on average, diverged fromadvanced economy levels during the post-crisis period.
A. Productivity growth

B. Productivity gap and convergence

Stalling post-crisis productivity. Since the global financial
crisis, productivity growth has fallen sharply in SSA, tonear-nil (0.5 percent) on average during the post-crisis
period (2013-18). Productivity growth slowed in a broad
range of economies, with post-crisis productivity growthfalling below its pre-crisis average in over 60 percent ofcountries. Oil- and metal-exporting countries experienced
the steepest slowdowns amid the commodity price slumpof 2014-16, as productivity growth fell to 0 percent in the
post-crisis period, from 3.2 percent growth pre-crisis.
Post-crisis productivity growth in agricultural commodityexporters and commodity importers was more resilient,particularly among the former for whom it strengthened to
2.3 percent. Despite the sharp fall in agriculturalcommodity prices during the commodity price slump—albeit less severe than the drop in industrial commodity
prices—sustained productivity growth was supported by
improving macroeconomic policy frameworks, investment
in infrastructure, and continuous efforts to improvebusiness environments. Doing Business rankings improved

Source: Penn World Table; The Conference Board; World Development
Indicators, World Bank.
Note: Unless specified otherwise, productivity is defined as labor productivity,(real GDP per person employed).
A. Sample includes range and simple average for the 127 EMDEs and simple
average for 44 Sub-Saharan Africa countries.
B. Sample includes 35 advanced economies (AE) and 127 EMDEs. Rate ofconvergence is calculated as the difference in productivity growth rates overthe log difference in productivity levels between SSA and advanced
economies. Blue bars and orange dashes show the range and average of thesix EMDE regional aggregates. “Level” of productivity refers to the GDPweighted average of regional productivity as a share of the averageadvanced economy during 2013-2018.
Click here to download data and charts.

149

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CHAPTER 2.6

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
FIGURE 2.6.1.2 Evolution of labor productivity growth in SSA
The sharp slowdown in SSA’s productivity growth relative to the pre-crisis period is concentrated among exporters ofindustrial commodities, in part reflecting the commodity-price slump of 2014-16. Excluding five high-productivity countries,
productivity levels in the region are, on average, 3 percent that of advanced economies. Rapid productivity growth between
the 1990s and 2008 reflected improvements in human capital, the deepening of physical capital, as well as a rise in totalfactor productivity (TFP). Following the commodity price slump, TFP slowed sharply among industrial-commodity exporters.
Among exporters of agricultural commodities, capital deepening has reflected continued investment in infrastructure. TFP
has contracted in recent years, mostly among industrial-commodity exporters. However, the fall in TFP was likely less severe
when the contribution from slowing extraction of natural capital is accounted for.
A. SSA and EMDE labor productivity
growthB. Productivity relative to advanced
economies

C. Contributions to productivity growth

D. Contributions to productivity growth,by export composition

E. Contributions to productivity growthin Nigeria

F. Contribution to productivity growth, by
natural capital

Source: Penn World Table; Wealth Accounting, World Bank.
Note: Unless specified otherwise, productivity is defined as labor productivity (real GDP per person employed).
A. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Dashed lines indicate average long-term labor productivity growth(1981-2018 for SSA; 1990-2018 for EMDEs excl. China). Samples include 44 Sub-Saharan African economies and 126 EMDEs. “Other SSA” includes agriculture
exporters and commodity importers.
B. GDP-weighted averages calculated using GDP weights at 2010 prices and market exchange rates. Sample includes 127 EMDEs and 44 Sub-Saharan African
economies. “SSA high productivity” includes Equatorial Guinea, Gabon, Mauritius, Seychelles, and South Africa.
C.-F. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Samples include 26 Sub-Saharan African economies and 92
EMDEs.
D. “Industrial-commodity exporters” includes metals and oil exporters. “Other SSA” includes agricultural commodity exporters and commodity importers.
F. For comparability, the sample for both the natural and standard decomposition includes 22 countries.
Click here to download data and charts.

by three positions in the median agricultural commodityexporter between the pre- and post-crisis periods,
compared to a median deterioration of seven positions
among industrial commodity exporters. Several countryspecific reasons also helped lift productivity among
agricultural commodity exporters. In Rwanda,
productivity growth was boosted by continued reforms tostrengthen institutions and governance, upgrade
infrastructure, increase access to education, and improvethe business environment, to attract private investment(World Bank 2019w). In 2018, the country led SSA in its
ease of doing business, ranking 29th globally. In Côte
d’Ivoire, a return to stability following the end of decadelong civil strife in 2011 has since enabled a sharp rise inproductivity, amid increased public investment, recovering
foreign direct investment (FDI) inflows, an improving
business environment and rising export activity (Klapper,
Richmond, and Tran 2013; World Bank 2015c).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

SUB-SAHARAN AFRICA

BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
Low productivity levels. Productivity in SSA is thesecond-lowest of all EMDE regions, after South Asia.
However, if the five most productive economies are
excluded (Equatorial Guinea, Gabon, Mauritius,
Seychelles, South Africa), SSA has the lowest productivityof all EMDE regions, at 3 percent of the advancedeconomy average in 2018 (Figure 2.6.1.2.B). Higher
productivity levels in these five economies—at 24 percentof the advanced-economy average—is roughly one-quarter
above the EMDE average. It exceeds productivity in other
SSA economies, in part due to significant oil wealth(Equatorial Guinea, Gabon), dominant tourism sectors inisland states (Mauritius, Seychelles), and a considerably
higher capital stock combined with mineral wealth (South
Africa). The post-crisis slowdown in productivity growthhas dimmed prospects for SSA’s continued convergencewith advanced economies and other EMDEs. If recent
rates of productivity growth persist, less than 5 percent of
economies in SSA are on course to halve their productivity
gap with advanced economies over the next 40 years.
Post-crisis total factor productivity decline. The postcrisis slowdown in SSA’s productivity growth reflected lesseffective use of factor inputs, as captured by total factor
productivity (TFP; Figure 2.6.1.2.C).4 TFP growth, which
accounted for the majority (three-fifths) of productivity
growth pre-crisis, plunged from 1.4 percent pre-crisis to-0.9 percent post-crisis in the sharpest deterioration of any
EMDE region. Rapid pre-crisis TFP growth, especially inindustrial commodity exporters, reflected heavy resource
investment and exploration during the commodity boom,
large FDI inflows, communication infrastructure
improvements (including the increased use of mobile
phones), expanded access to finance, and better businessclimates (Figure 2.6.1.2.D; Aker and Mbiti 2010;
Goedhuys, Janz, and Mohnen 2008; Keefer and Knack
2007; Wamboye, Tochkov, and Sergi 2015). The sharp
post-crisis decline in TFP was most pronounced inindustrial commodity exporters, following the commodity
price collapse of 2014-16 and the accompanying collapse
4 The standard productivity growth decomposition does not explicitly
account for the contribution of natural capital as a factor of production.
As a result, the TFP estimates produced here are potentially biased as they
implicitly include the productivity contribution from natural capital.
From a longer-term perspective, World Bank (2019z) finds that thesignificant difference between productivity in SSA and that of the
productivity frontier (United States) largely reflected weak factor
accumulation between 1960 and the 1990s, as the index of human capitalin SSA relative to that of the United States declined sharply from 1960 to1980, while the relative accumulation of physical capital remained
subdued. In contrast, from 2000, the gap in efficiency (or TFP) became
the major contributor to difference in productivity between SSA and thefrontier. This TFP gap widened further from 2010 onwards.

in investment, FDI inflows, and exports, compounded bysomewhat weaker business environments.5 In Liberia andSierra Leone, the post-crisis fall in TFP was exacerbated by
the devastating Ebola outbreak of 2014-16 (World Bank
2019x).
In contrast, TFP has remained resilient, or evenstrengthened, among some exporters of agriculturalcommodities and commodity importers (Côte d’Ivoire,
Kenya, Mauritius, Togo). Agricultural commodity prices
fell less steeply, on average, than industrial commodity
prices during the 2011-16 commodity price slump, andbeneficial terms of trade supported activity among
commodity importers. Faster TFP growth in these
economies was also underpinned by sustained public
investment in infrastructure, continued efforts to improvebusiness environments, and more robust macroeconomic
policy frameworks.
Post-crisis acceleration of capital deepening. The
contraction in TFP growth offset the post-crisis boost to
productivity growth generated from capital deepening.
Labor productivity in agricultural commodity exporters
benefited from heavy public investment.6 In Nigeria,
investment was fueled by large FDI inflows into theenergy, banking, manufacturing, and telecommunications
sectors (although investment slowed sharply after 2014 asoil prices collapsed; Figure 2.6.1.2.E; World Bank 2019y).
In contrast, investment has fallen sharply in otherindustrial commodity exporters in SSA—by 7 percentage
points of GDP in the median economy—following the
2014-16 commodity price slump, compounding thealready slowing TFP growth.
Impact of natural resource extraction on productivity
measurement. Natural capital accounts for an economy’s
natural resources, such as oil, metals, and agricultural land,
and is particularly relevant given SSA’s commodity
reliance. Standard productivity decompositions fold theextraction of natural capital into total factor productivity
and, to a lesser extent, physical capital, biasing theirestimated contributions to productivity growth (Brandt,
Schreyer and Zipperer 2017; Calderón and Cantu 2019;
World Bank 2019z). During the pre-crisis commodity

5 TFP declines have been most severe in oil-exporting Angola,
Nigeria, and Chad, as well as in metal-exporting countries such as
Botswana, Mozambique, Sierra Leone and South Africa.
6 Greater fiscal space, partly due to the Multilateral Debt Relief
Initiative (MDRI) and Heavily-Indebted Poor Countries (HIPC)
initiative, supported increased investment in infrastructure and humancapital which resulted in an 18-percentage-point rise in average secondary
school enrollment rates from 33 percent in 2000 to 51 percent in 2014.

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
FIGURE 2.6.1.3 Sectoral productivity growth in SSA
The sectoral reallocation of labor in Sub-Saharan Africa has been an important driver of regional productivity growth;
however, its contribution has dwindled more recently. Agriculture in SSA has the lowest productivity, while productivity ishighest in mining and finance. Low aggregate productivity in the region is partly explained by the agricultural sector’ssignificant contribution to value added, combined with the disproportionate share of employment devoted to the sector.
A. Within-sector and structuralcontributions to productivity growth

B. Sectoral productivity, 2015

C. Employment by sector

Source: APO productivity database; de Vries, Timmer, and de Vries 2013; Expanded Africa Sector Database; Groningen Growth Development Center database; Haver
Analytics; ILOSTAT; Mensah and Szirmai (2018); Mensah et al. (2018); OECD STAN; United Nations; World Bank; World KLEMS.
Note: Unless specified otherwise, productivity is defined as labor productivity (real GDP per person employed).
A. Growth within sector shows the contribution of initial real value added-weighted productivity growth rate of each sector and ‘between sector’ effect shows thecontribution arising from changes in sectoral employment shares. Median of the county-specific contributions. Sample includes 19 Sub-Saharan African economies and
46 EMDEs.
B. Figure shows the median of country groups. The sample includes 19 SSA economies and 46 EMDEs.
C. Sample includes 19 SSA countries and 46 EMDEs.
Click here to download data and charts.

price boom and the accompanying boom in resource
exploration and development, the increased extraction ofnatural capital lifted productivity growth in SSA (Figure
2.6.1.2.F; Khan et al. 2016). However, as the boom ended
and commodity prices began to fall, natural capital
extraction declined accordingly, and its contribution
detracted from overall productivity growth. Data fornatural capital is available until 2014, the year thecommodity price slide intensified, but well before prices
reached their early-2016 troughs. Even during these earlyyears (2013-14), it appears that the post-crisis fall in TFP
was likely less severe than the standard decomposition
suggests, as the decline in natural capital potentiallyaccounted for a large share of the slowdown in TFP
growth from pre-crisis years.7

Sources of regional productivity growth
Productivity growth through sectoral reallocation. The
7 Direct comparisons between the standard decomposition and that
including natural capital are complicated by the smaller country sample
in the natural capital decomposition, as it includes 22 countries (72percent of SSA GDP) compared to 26 countries (83 percent of SSA
GDP) in the standard decomposition. Furthermore, the decline innatural capital may capture a lower valuation of the stock of natural
capital.

post-crisis slowdown in productivity growth from precrisis rates
reflects slowing gains brought by thereallocation of labor from low-productivity sectors (mostly
agriculture) to higher-productivity sectors. In contrast,
within-sector productivity growth has continued apace(Figure 2.6.1.3.A).8
Productivity has differed widely across sectors in SSA(Figure 2.6.1.3.B). Productivity in agriculture—the least
productive sector that employs more than half of theworkforce and accounts for 18 percent of GDP—isbetween 4 and 7 percent of the productivity in mining andfinance, the two most productive sectors at the nine-sector
level (Figure 2.6.1.3.C).9 Relative to the wider EMDE
sample, agricultural productivity in SSA is about threetimes lower, on average. Low agricultural productivity in
SSA reflects the prevalence of subsistence farming, suboptimal crop selection, poor land quality amid unfavorable
climates, limited uptake of modern technologies andproduction methods to improve yields, and small farmsizes (Adamopoulos and Restuccia 2014, 2018; Caselli
2005; Sinha and Xi 2018). Moreover, the use of price
8 Sectoral productivity data are available for only about half the SSA
economies with data for aggregate productivity.
9 The sample includes 19 SSA economies at the nine-sector level.

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
controls—a widespread practice across particularly lowincome countries in the region—often distort theallocation of resources and inputs in agricultural sectors
and weigh further on productivity by adversely affecting
incentives to invest in human capital or adopt modern
technologies and production methods (Special Focus 1;
Chen 2017; Chen and Restuccia 2018; World Bank
2019z). The agricultural sector’s significant contribution
to value added, combined with the disproportionate shareof employment devoted to the sector, helps explain SSA’s
low aggregate productivity relative to other EMDE
regions.
Pre-crisis, sectoral reallocation accounted for more thanhalf of aggregate productivity growth as labor moved fromagriculture to services sectors and, to a lesser extent,
manufacturing (Chapter 3; Enache, Ghani, and O’Connell
2016; Haile 2018; Rodrik 2016b). This process was
facilitated by rapid urbanization as the urban share of
population rose by 5 percentage points, to 39 percent,between 2000 and 2010. Since the crisis, however, thesectoral reallocation of labor to more productive sectors
has slowed. As growth in commodity-exporting economies
fell sharply during the commodity price slump of 2014-16,
construction stalled, consumption eased, and credit
contracted. Real-income losses in industrial sectors spilledover to weaker demand in the broader economy. As aresult, services sectors were no longer able to absorb asmuch labor as they did pre-crisis.
Other drivers of productivity growth. Rapid
improvements in the key drivers of productivity during thepre-crisis period supported productivity growth until the
global financial crisis; however, the pace of improvement
has since lost momentum. Productivity drivers withparticularly prominent slowdowns in improvements
include innovation, gender equality, education, health,trade openness, institutional quality, and investment(Figure 2.6.1.4.A and 2.6.1.4.B). Moreover, SSA
continues to lag well behind other EMDEs in most driversof productivity (Figure 2.6.1.4.C).
Institutional quality and the business environment.
Although various aspects of governance and institutional
quality improved in the region from the late 1990s into
the pre-crisis period, this progress has mostly stalled, andeven deteriorated in some instances. On average, businessclimates have also regressed during the post-crisis period;
today, almost two-thirds of SSA countries still rank in thelowest quartile of countries by business climates, and onehalf do so for poor governance (Figure 2.6.1.4.D). Poorbusiness climates and governance, as well as distortions

caused by price controls, have not only constrained
productivity by distorting the efficient allocation of
resources, but have also deterred private sector investment(Cirera, Fattal Jaef, and Maemir 2017; World Bank
2019z).
Integration with the global economy. Between the mid1990s and 2008, the region’s openness to trade—that is,
the sum of imports and exports relative to the size of theeconomy—rose 16 percentage points to 81 percent ofGDP, helping to boost productivity. However, alongsidefalling commodity prices and slowing external demand,
particularly from China and the Euro Area (the region’s
two largest trading partners), trade integration has partiallyunwound in the post-crisis period, with openness falling to74 percent of GDP by 2017. The region’s heavy
dependence on commodity extraction sectors manifests ina smaller share of exporting firms compared to the EMDE
average (Figure 2.6.1.4.E). Although the share of foreignowned firms—which are generally more productive thantheir domestically owned counterparts—is high, such firms
tend to cluster in extractives sectors with limited links toother sectors (Figure 2.6.1.4.F; Liu and Steenbergen 2019;
World Bank 2018p). SSA’s participation in global value
chains is mostly limited to exports of raw agriculturalcommodities and natural resources used as inputs in othercountries’ exports (World Bank 2019d). Greater
manufacturing sector participation in international tradeand global value chains has been constrained by thesector’s relative lack of international competitiveness, inpart due to high productivity-adjusted labor costs (Gelb et
al. 2017) and an array of non-tariff barriers, including theregion’s disadvantageous geography (Christ and Ferrantino
2011; Raballand et al. 2012).
Prospects for productivity growth slowdown. Althoughwide sectoral productivity differentials offer ample
productivity growth potential through sectoral reallocation
away from the agriculture sector, headwinds to
productivity growth are substantial and expected to persist.
•

Weather-related
shocks.
Given
agriculture’s
prominence in economic activity in SSA, climatechange presents severe challenges to productivity
growth prospects in agricultural sectors as mean
temperatures continue to rise and extreme weather
events occur more frequently (IPCC 2014; Steinbach
2019; World Bank 2019a, 2019f).

•

Constraints to public investment. Government
indebtedness in SSA has increased sharply since 2013,
rising by 20 percentage points, on average, to 60

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
FIGURE 2.6.1.4 Drivers of productivity growth in SSA
Despite significant improvements, key productivity drivers remain significantly below those of advanced economies and
EMDEs. Moreover, their pace of improvement has slowed in recent years. On average, business environments in SubSaharan Africa are more challenging than in other countries. While the region boasts the largest share of higher-productivity
foreign-owned firms, its firms export less than their counterparts in other EMDEs.
A. Index of productivity growth drivers

B. Share of SSA economies with slower
improvements in drivers 2013-18 relative
to 2003-08

C. Levels of drivers across regions, 2018

D. Obstacles to doing business

E. Share of exporting firms

F. Ownership status

Source: Penn World Table; United Nations (2015); World Bank (Enterprise Surveys, Wealth Accounting, and World Development Indicators).
Note: Unless specified otherwise, productivity is defined as labor productivity (real GDP per person employed).
A. For each country, index is a weighted average (the normalized coefficients appearing in Annex 3.3) of the normalized value of each driver of productivity. Drivers
include the International Country Risk Guide rule of law index, patents per capita, share of non-tropical area, investment as a percent of GDP, ratio of female average
years of education to male average years, share of population in urban areas, Economic Complexity Index, years of schooling, share of working-age population, andinflation. See Chapter 3 (Annex 3.3) for details. Regional and EMDE indexes are GDP-weighted averages. Samples include 54 EMDEs and 11 economies in SSA.
B. Blue bars represent share of 48 economies in Sub-Saharan African economies where improvements in each driver of productivity were lower during 2008-17 than in
the pre-crisis period 1998-2007, or changes in 2008-17 were below zero. Orange diamond is the corresponding values for 152 EMDE countries. Variables are definedas: Institutions = Government effectiveness; Innovation = patents per capita; Investment = investment to GDP ratio; Income equality = (-1) * Gini; Urbanization = urban
population percentage; Economic complexity = Hidalgo and Hausmann (2009)'s Economic Complexity Index; Education = years of schooling; Demography = share ofworking-age population; and Gender equality = female average years of education divided by male average years. Samples include 26-48 SSA economies, depending on
the driver, and 98-151 EMDEs.
C. Unweighted average levels of drivers, normalized as average of advanced economies as 100. Blue bar represents average within SSA. Orange lines represent rangeof the average drivers for six regions in 2017. Variables corresponding to the concepts are follows: Education = years of education; Urbanization = share of population
living in urban area; Investment = share of investment to GDP; Institution = Government Effectiveness; Economic Complexity = Economic Complexity Index+; Geography= share of land area which are outside of tropical region; Gender Equality = Share of the year of schooling for female to male; Demography = share of population under
14; Innovation = Log patent per capita; Trade = Exports + Imports/GDP; and Price stability = (-1)* inflation rate.
D. Unweighted averages. Variables corresponding to the concepts are follows: Corruption = percent of firms identifying corruption as a major constraint; Electricity =Percent of firms identifying electricity as a major constraint; Financial access = percent of firms identifying access to finance as a major constraint; Informal sector
competition = percent of firms identifying practices of competitors in the informal sector as a major constraint; Tax system is the average of tax rates (percent of firms
identifying tax rates as a major constraint) and tax administration (percent of firms identifying tax administration as a major constraint); Trade regulations = percent of firms
identifying customs and trade regulations as a major constraint; Crime = percent of firms identifying crime, theft and disorder as a major constraint.
E. Share of exporting firms. Firms classified as high, medium, and low export more than 75 percent, between 50 and 75, and up to 25 percent of their sales, respectively.
F. Share of firms with foreign ownership.
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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
percent of GDP in 2019. Reduced fiscal space could
weigh on future productivity growth as it will likely
constrain investment in productivity-enhancing
infrastructure, health, and education as well as
research and development. It can also make countries
more vulnerable to financial crises (Box 3.4).
••

Commodity-reliance. Growth prospects for commodity
sectors that could encourage capital deepening aredim. Long-term commodity demand growth isexpected to moderate as growth in China—the largest
source of commodity demand—slows and shifts
toward less resource-intensive sectors (World Bank
2018o).
High informality. High informality in the region—around 40 percent of official GDP and 90 percent of
total employment—may inhibit faster aggregate
productivity growth, as productivity among informal
firms are only one-seventh of that in their formal
counterparts (La Porta and Shleifer 2014; WorldBank 2019f). In addition, much-needed productivityenhancing government spending is constrained
because informal firms do not pay taxes.

Policy options
Coordinated policy efforts are required to achieve stronger
productivity growth, notable reductions in extreme
poverty, and a narrowing of the significant income gap
with the rest of the world. There are four strands of policy
options that emerge from the findings of this box.
Improving factors of production
Boosting human capital and leveraging demographic
dividends. Improving human capital has been an
important source of productivity growth in SSA.
Continued investment and increased spending on healthcare, including greater provision of treatment for highly
prevalent conditions such as malaria and HIV/AIDS,
could raise productivity of the labor force and life
expectancy in general (Figure 2.6.1.5.A; Asiki et al. 2016;
Barofsky, Anekwe and Chase 2015; Ferreira, Pessôa and
Dos Santos 2011). Increased life expectancy due toimproved health care also generates incentives to invest in
education (Cervellati and Sunde 2011). In Ethiopia, a
rapid decline in fertility rates between 1995-2015, risingincomes, and falling poverty rates reflected an approachcombining improvements in education and health, familyplanning, and increased economic opportunity (WorldBank 2019aa). Harnessing the region’s potential
demographic dividend from declining fertility rates andfalling dependency ratios requires policies that support
female empowerment, including education, health care,and greater labor market access for women (Figure
2.6.1.5.B; Bloom, Kuhn and Prettner 2017; Groth and
May 2017; Kalemli-Ozcan 2003). As the ratio of theyoung dependent population to the working-age
population declines in SSA, resources could be freed up to
invest in the health and education of the young, boosting
the productivity of the future labor force and spurring per
capita growth (Ashraf, Weil and Wilde 2013).
Narrowing the gender gap. Despite some improvements,
gender gaps remain large in SSA (World Bank 2012).
Although the gender gap in labor force participation has
been narrowing, on average, significant gaps in earnings ofwomen relative to men persist. This reflects genderdisparity in secondary and tertiary education, differing
occupations, and greater time devoted by women tohousework and childcare (World Bank 2019aa).
Moreover, improvements in the ratio of average years ofeducation of females to males have been slowing in the
post-crisis period. This is reflected by lower productivity offemales in agriculture, as well as female entrepreneurs—
crops tended by women yield one-third less per hectare
than those of men, and a similar margin applies to profits
earned by female entrepreneurs (Figure 2.6.1.5.C;
O’Sullivan et al. 2014; Campos et al. 2019). Policies toempower women and boost their productivity include
those promoting skills building beyond traditional training
programs, such as a greater focus on developing anentrepreneurial mindset; this approach has been found to
lift sales and profits in Togo (Campos et al. 2017, WorldBank 2019aa). Other policies include relieving capitalconstraints faced by females due to lower asset holdings
offering limited collateral; and addressing social norms that
constrain women’s economic opportunities and earnings,
such as perceptions about the type of work that is suitable
to men or women.
Closing infrastructure gaps. Although capital deepening
has continued apace among the region’s agriculturalcommodity exporters and commodity importers, it has
slowed considerably among most industrial commodity
exporters, and severe infrastructure deficiencies remain
throughout the region. Meeting the infrastructure-related
Sustainable Development Goals in 2030 will require
additional investment spending between 2015-30 ofroughly 7 percent of GDP per year in SSA (excluding
maintenance spending)—the highest of all EMDE regions(Figure 2.6.1.5.D; Rozenberg and Fay 2019). Stronger
productivity growth—through both capital-deepening
investment and improved TFP—is contingent on

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
FIGURE 2.6.1.5 Prospects for productivity growth in SSA
Continued improvements in health care could raise life expectancy and the overall productivity of the labor force, as
increased life expectancy also generates incentives to invest in education. Sub-Saharan Africa could harness a significant
demographic dividend, as falling fertility rates lead to a lower dependency ratio. Owing to limited access to resources andtraining, crops tended by women yield one-third less per hectare than those of men; a similar margin applies to profits
earned by female entrepreneurs. To meet the SDGs by 2030 will require investment spending of about 7 percent of GDP peryear. Reducing trade costs in SSA will help accelerate regional and global integration. Conflicts have been rising in theregion, particularly acts of violence against civilians.
A. Human capital development

B. Dependency ratios and fertility rates

C. Shortfalls in profits and agricultural
output of females relative to males

D. Infrastructure spending needs

E. Import and export compliance costs

F. Conflict events

Source: Armed Conflict Location and Event Data Project database; Campos et al. (2019); O’Sullivan et al. (2014); Rozenberg and Fay (2019); World Bank Doing
Business 2020; United Nations.
Note: Unless specified otherwise, productivity is defined as labor productivity (real GDP per person employed).
A. Unweighted averages. “Mortality rate” refers to under-five mortality.
B. The dependency ratio is calculated as the ratio of the population at ages 0–14 plus the population aged 65+ to the population at ages 15–64.
C. Bars for “Entrepreneur profits” show the extent to which profits for male-owned firms exceed those of female-owned firms using data from impact evaluations. Bars for“Agricultural output per hectare” show the extent to which agricultural output per hectare on male-managed plots exceeds that of female-managed plots. Entrepreneur
profits in Ghana reflect the average of both the Grants for Micro-Enterprises Survey and the Tailoring Survey; Entrepreneur profits in Nigeria reflect the average of boththe Growth and Employment Survey and the Business Plan Competition Survey. Agricultural output per hectare accounts for differences in plot size and geographic
factors. Agricultural output in Nigeria reflects a simple average of gaps for northern Nigeria (46 percent) and southern Nigeria (17 percent).
D.E. Bars show average annual spending needs during 2015-30. Estimates are generated using policy assumptions that cap investment needs at 4.5 percent of LMICs’
GDP per year. SSA=Sub-Saharan Africa, SAR=South Asia, MNA=Middle East and North Africa, EAP=East Asia and Pacific, LAC=Latin America and the Caribbean.
E. Unweighted averages. Sample includes 156 EMDEs and 47 SSA economies. EMDE average excludes SSA.
F. Sample includes 30 SSA economies. Last observation is November 9, 2019.
Click here to download data and charts.

infrastructure deficiencies being addressed. Access toelectricity is a critical obstacle to achieving development
goals in SSA, and reforms to improve access in asustainable manner need to strike a balance between
affordable provision for consumers, particularly the poor,and cost recovery for utilities (Blimpo and CosgroveDavies 2019; Vorisek and Yu (forthcoming). In addition
to closing infrastructure gaps, improvements to theresilience of existing infrastructure are needed to limit
frequent disruptions, particularly in power, water andsanitation, transport, and telecommunications (WorldBank 2019ab). To ensure public investment is efficient inboosting growth and productivity, it should be supported
by adequate public investment management frameworks
that encompass strong cash management and procurement
processes.

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
Boosting firm productivity
Boosting productivity in agriculture. Given the large
share of activity and employment accounted for by
agriculture, measures to raise agricultural productivity at
the farm level—especially in staple crops—can yieldsignificant development gains (Beegle and Christiaensen
2019). These include ensuring secure land tenures, better
access to markets and finance, better crop choices, more
effective and increased use of fertilizers, improved
irrigation, diffusion and adoption of new technologies, aswell as targeted trainings to help small farmers reap the
benefits of cutting-edge knowledge and practices specificto the area and product (Chen 2017; Fuglie et al. 2019;
Sinha and Xi 2018; World Bank 2019aa). For example,
text messages providing advice and reminders to sugarcane
farmers in Kenya helped boost fertilizer use and crop yields(Casaburi et al 2014; Fuglie et al. 2019). Ensuring genderequality in access to resources could further boost
agricultural productivity; giving women in Malawi and
Ghana the same access to fertilizers and other inputs asmen could boost maize yields by one-sixth (World Bank
2012). Gains from faster productivity growth inagriculture will free up workers to transition to other,more productive, sectors.

digital technologies—more so than other regions (Choi,
Dutz, and Usman 2019; Hjort and Poulsen 2019). SSA’s
comparatively low levels of human capital and high degreeof informality are ideally suited for the adoption anddevelopment of productivity-enhancing, low-skill-biased
digital technologies in the agriculture, manufacturing andservices sectors. In some countries, the use of digitaltechnologies has been found to boost firm productivity byfacilitating process and product innovation (Democratic
Republic of Congo, Tanzania; Cirera, Lage, and Sabetti2016). Digital technologies can also help in banking theunbanked and transform lending in SSA. Kenya’s mobile
money service, M-Pesa, boosted the financial savings offemale-headed households and enabled women to moveout of agriculture into more productive sectors (Suri andJack 2016). Digital loans offered through mobile money
platforms are also growing in popularity and may grant
financial inclusion to individuals without credit scores or
sufficient collateral, as digital loan providers use alternative
credit scores based on telecommunications data (Cook and
McKay 2015; Francis, Blumenstock, and Robinson 2017;
World Bank 2019aa). However, the use of digital credit
has so far been largely concentrated in urban areas, at shortmaturities, and not as investment loans by the rural poor
(Björkegren and Grissen 2018).

Addressing informality. Although informality is higher in
SSA than in other EMDE regions, informal firms often
brim with potential—more formal firms in SSA started as
informal firms, and this period of transition is found to be
shorter than in other EMDEs (World Bank 2019f).
Policies to unlock informal firms’ potential include
upgrading skills of workers, ensuring better access to
inputs and resources like financial services, transport andcommunications connectivity, health services, land andproperty rights, and product markets (Oosthuizen et al.
2016). Removing barriers to enter the formal sector canfurther accelerate the transition out of informality:
lowering registration costs by half could double the shareof formal enterprises through formalization of informal
firms and new entrants (Nguimkeu 2015; World Bank
2019aa). Regulatory and institutional reforms to build
public trust can strengthen incentives for firms to operate
formally. Policies aimed directly at the youth can bolster
the prospects of the future workforce and help alleviate
youth unemployment. In Rwanda, entrepreneurship has
been introduced as a secondary school subject to help
prepare the youth to be successful entrepreneurs or tocompete in the formal labor market (Choi, Dutz, andUsman 2019).

Accelerating trade openness and global integration. The
African Continental Free Trade Area (AfCFTA) has thepotential to boost regional trade and bolster firm
productivity by facilitating investment, international
competitiveness, the transfer of technology and new
innovations, and participation in regional and global value
chains (Berg and Krueger 2003; Calderon and Cantú
2019; Del Prete, Giovannetti, and Marvasi 2017; Laget etal. 2018; World Bank 2019d). To maximize the potential
productivity gains from the free trade area, infrastructureneeds to be expanded—particularly transport networks—
and business climates improved. In addition, gains from
AfCFTA depend on the implementation of tradefacilitation measures and addressing of significant nontariff barriers to trade—trade costs in SSA, such as borderand documentary compliance costs, are roughly one-halfhigher than those of other EMDE regions (Figure
2.6.1.5.E; World Bank 2019d). Currently, most regional
trade in SSA takes place among countries within existingregional economic communities, as high tariffs and nontariff barriers limit trade between countries of different
groupings.

Leveraging digital technologies. Firm productivity in SSA
could also benefit significantly from the proliferation of

Enabling factor mobility. Productivity gains from sectoral

Encouraging sectoral reallocation

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BOX 2.6.1 Labor productivity in Sub-Saharan Africa: Trends and drivers (continued)
reallocation of labor in the region—a major driver of precrisis productivity growth—can be reignited by policies
aimed at reducing the barriers to factor mobility. Thesebarriers include low human capital of the labor force, weak
infrastructure (such as inadequate transport systems inurban areas), low access to finance, and disadvantageous
trade policies. In Nigeria, tariff structures have been shown
to reduce incentives for sectoral reallocation to higherproductivity sectors, as the tariffs systematically boosted
profitability of the least productive sectors but not that ofhigher-productivity sectors (World Bank 2017g).
Diversification. Countries with highly diversified
economic activity across a broad range of sectors tend tohave higher productivity levels (Chapter 3). SSA, however,remains heavily dependent on extractives sectors,
particularly for export and fiscal revenues, with the latter
dependence often a cause of procyclical fiscal policies.
Policy measures aimed at broadening the production basetoward a wider and more complex array of export goods,
across a range of manufacturing and services sectors, will
enable greater participation in value chains and help
insulate economic activity from the destabilizing effects oflarge international commodity price swings. In Côte
d’Ivoire—the world’s largest supplier of cocoa beans—diversification along the cocoa value chain through the
expansion of domestic grinding and processing facilities
has allowed the country to also produce a diverse array ofvalue-added cocoa products and to overtake theNetherlands as the world’s leading cocoa-processing
country (World Bank 2016h). AfCFTA could contributeto economic diversification if it leads to the establishmentof regional value chains. However, successful economic
diversification requires several supporting measures,
including improved human capital, better infrastructure,stronger governance, and deeper financial markets withincreased access to credit (Fosu and Abass 2019).
Creating a growth-friendly environment
Protection from climate change. Some of the adverse
effects of climate change can be mitigated throughappropriate land-use planning and investment in climatesmart infrastructure (Collier, Conway and Venables 2008;
World Bank 2019a). Effective social protection policies,
possibly financed with energy taxes or the removal of fuelsubsidies, could provide resources to support livelihoods
during extreme events (Hallegatte et al 2015). Climate
adaptation policies can be strengthened by building

capacity in policy implementation, boosting access toadaptation financing, and raising public awareness ofclimate change (Adenle et al. 2017; World Bank 2019ac).
Stability. SSA has historically witnessed many conflicts,
particularly between the 1970s and early 2000s, that not
only took heavy human tolls, but also shook the stability of
the affected countries by weakening institutions andseverely damaging or destroying infrastructure. Conflicts in
Burundi, the Democratic Republic of Congo, Liberia,
Rwanda, and Sierra Leone inflicted losses of human life
equivalent to between 1 and 10 percent of their
populations (Steinbach 2019; World Bank 2019a). Morerecently, rising incidence of conflict—particularly acts ofviolence against civilians—has increasingly weighed on
activity in several countries and forcibly displaced large
populations (Figure 2.6.1.5.F). Efforts to achieve lasting
peace can strengthen economic activity and boost
productivity through stronger investment and increased
TFP (Chen, Loayza, and Reynal-Querol 2008).
Strengthening institutional quality and businessenvironments. Business environments stand to benefit
from improved infrastructure; limited access to reliable
electricity and poor transport infrastructure are often citedas key constraints to business in SSA. In addition, highnon-infrastructure-related costs, such as high prices totransport goods within countries and across borders, tend
to exacerbate the burden of weak infrastructure. In many
instances, high road-transport costs reflect excessive market
power of trucking companies. Competition-enhancing
deregulation can help alleviate this business constraint andboost productivity. For example, in landlocked Rwanda,
deregulation in the transport sector led to an abrupt fall intransport costs (Barrett et al. 2017) Business environment
deficiencies can further be addressed by increasing access tofinance, simplifying tax systems, reducing regulatoryburdens and compliance requirements, improving judicial
systems to address corruption and strengthen enforcement,
and liberalizing labor and product markets (Bah and Fang
2015; World Bank 2019f). Strengthening institutional
quality by improving judicial systems can help address
corruption—a leading obstacle to doing business—andstrengthen contract enforcement. Such structural reforms
can bolster firm productivity (Kouamé and Tapsoba
2018). Reforms aimed at improving the businessenvironment can also help lower the size of the informal
sector, which tends to have lower productivity than theformal economy.

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Low for How Much Longer?
Inflation in Low-Income Countries

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S P EC IAL FO CU S 2Inflation in low-income countries (LICs) has declined sharply to a median of 3 percent in mid-2019 from apeak of 25 percent in 1994. The drop has been supported by the move to more flexible exchange rate regimes,
greater central bank independence, and a generally more benign external environment since the 1990s.
However, low LIC inflation cannot be taken for granted amid mounting fiscal pressures and the risk of
exchange rate shocks. To maintain low and stable inflation, monetary and fiscal policy frameworks need to bestrengthened and supported by efforts to replace price controls with more efficient policies.

Introduction
The number of low-income countries (LICs) has
more than halved since 2001. As of 2019, 31
countries are classified as “low income” according
to the World Bank definition, down from 64 in
2001, following the graduation of 35 mostly
metals-exporting and transition economies to
middle-income status.1 Today, LICs are
predominantly agriculture-based, small, andfragile, and they tend to have weak institutions(World Bank 2015). All but six are in SubSaharan Africa.
LICs have made large strides in price stabilization
over the past five decades, with sharp declines in
inflation levels and volatility (Figure SF2.1). Thatsaid, the level and volatility of inflation in LICs
has remained higher than in advanced economies
and other emerging market and developing
economies (EMDEs) over the past two decades(Ha, Ivanova et al. 2019a). Reasons include
monetary policy challenges that arise in LICs dueto their volatile economies, pervasive use ofadministered pricing, conflicts among central
bank policy objectives, weaknesses in monetary
policy transmission, and limited analytical
capacity at central banks. The disinflation in
today’s LICs was also considerably lesspronounced than in the (larger number of)
EMDEs that were classified as LICs in 2000 but
have since achieved middle-income status.
Low inflation has typically been associated withmore stable output and employment, higher
output growth and investment, and falling poverty
rates. Low and stable inflation makes relative priceNote: This Special Focus was prepared by Jongrim Ha andFranziska Ohnsorge.
1 In addition, there are two countries (South Sudan and Syrian
Arab Republic) that are newly grouped as LICs in 2019.

changes more apparent, provides confidence forlong-term savers and investors, protects thepurchasing power of household income andwealth, and enhances financial stability (Easterly
2019; Ha, Kose, and Ohnsorge 2019a).2 Bycontrast, economies that have experienced highinflation have suffered significantly lower economic growth (Kremer, Bick, and Nautz 2013).
Low and stable inflation is especially important for
LICs, where a large number of the world’s poor
reside. Those most at risk are the “near poor”—
those living on incomes just above $1.90 per day,the World Bank’s threshold for extreme poverty.
(The very poorest households hold few nominal
assets or incomes that would be affected byinflation.) Poorer households—which are moreprevalent in LICs than in the EMDEs—may
suffer greater welfare losses from inflation thanwealthier households because they are less able toprotect the real value of their income and assets
from the impact of inflation (Ha, Ivanova et al.
2019b). An erosion of their real incomes andassets through inflation could tip these households
into extreme poverty.3 In addition, by stabilizing
output fluctuations that disproportionally hurt thepoor, the adoption of a credible monetary policy
regime that maintains low and stable inflation
may help reduce poverty and inequality (Romerand Romer 1999).

2 Several policy outcomes have improved considerably since the1990s, including lower inflation, smaller black market premiums,
and lesser currency overvaluation (Easterly 2019).
3 Although the evidence of a positive correlation between
inflation and inequality or poverty is mixed at the aggregate level, thelinks are better established at the household level (Ha, Ivanova et al.
2019b). For example, single-country studies on EMDEs, such as
India (Datt and Ravallion 1998), the Philippines (Blejer and
Guerrero 1990), and Brazil (Ferreira and Litchfield 2001), find thathigher inflation is associated with a lower share of income held by thepoor or higher inequality. Using panel data of 24 developed and 66
developing countries over 1990–2014, Siami-Namini and Hudson(2019) similarly find bi-directional Granger causality between
inflation and income inequality in both groups.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

FIGURE SF2.1 Inflation in low-income countries andpoverty
Inflation and inflation volatility in LICs have declined since 1970, broadly inline with other EMDEs. The decline has been broad-based acrosscountries, as well as across components of inflation. Those that havegrown to middle-income status have had faster declines in inflation. The
remaining LICs feature higher poverty than EMDEs. Those just above theextreme poor level are at risk of being tipped back into poverty when
inflation erodes the real value of their assets and incomes.

A. Inflation

Against this backdrop, this Special Focus delves
into the characteristics of LIC inflation, quantifies
its drivers, and examines related monetary policy
challenges. Specifically, it discusses the following
questions:•

How has inflation evolved in LICs?

•

What factors have supported
developments in LICs?

•

What policy challenges do LIC central banks
face in managing inflation?

B. Number of LICs by inflation bracket

inflation

Evolution of inflation

C. Inflation

E. Inflation in former and current LICs

D. Inflation volatility

F. Poverty

Source: Haver Analytics; International Monetary Fund; World Bank.
Note: Data for 26 low-income countries and 99 other EMDEs. Inflation refers to year-on-year inflation.
EMDEs = emerging market and developing economies; LICs = low-income countries.
A. Blue lines are cross-country medians of inflation; dashed lines indicate the interquartile range
across 26 LICs. 2019 inflation rates are based on year-on-year inflation during the first half of 2019 in
19 LICs.
B. Number of LICs in which inflation was in the bracket indicated. Data for 2019 are not yet available
for some LICs and was not included.
C.D. Cross-country medians of inflation (C) or standard deviations of inflation (D). The differences
across sample periods are all statistically significant.
E. Median inflation across countries. “LICs turned MICs” indicates 33 countries classified as lowincome countries in 2000 but classified as middle-income countries as of 2019. “Current LICs”
indicates 29 low-income countries as of 2019.
F. Median share of population in extreme poverty (living on less than $1.90 per day) and near-poverty(living on $1.90-$3.20 per day) in 27 LICs and 109 other EMDEs.
Click here to download data and charts.

Among LICs, median inflation has fallen by twothirds since 1970, to 3.2 percent in mid-2019—broadly in line with inflation developments inother EMDEs). The inflation decline has beenbroad-based across countries as well as inflation
components (e.g., food, energy). As a result, thewide heterogeneity of inflation among LICs in the
1990s has narrowed sharply.
1970s to 1990s. Median inflation among LICs
was 9-10 percent over this period. Althoughbroadly in line with inflation in other EMDEs,
LIC inflation underwent several spikes (up to 25
percent), especially in the early 1990s, amid
currency crises. In half the years between 1970
and 2000, the majority of LICs had double-digit
inflation.
Post-2000. Median inflation in LICs has fallen
rapidly—to 3.2 percent in mid-2019 from a peakof 25.2 percent in 1994 (Figure SF2.1.A). Thisdecline was broad-based and narrowed some of thewide heterogeneity in inflation among LICs. In
one-third of LICs, inflation in mid-2019 was lessthan one-third of its level in 1970. In an evenlarger number (63 percent) of LICs, inflation in
mid-2019 was less than one-third of its 1994 level.
By 2008, hyperinflation episodes in LICs
(inflation in excess of 1,000 percent) had also
subsided.4 In mid-2019, inflation was in the single
4 In the 1990s Democratic Republic of Congo and Tajikistan
experienced inflation over 1,000 percent.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

digits in more than three-quarters of LICs,
compared with less than one-fifth in 1994 (Figure
SF2.1.B).
Since 1970, core, food price, and energy priceinflation have also declined, as has inflation
volatility (Figures SF2.1.C and SF2.1.D).

S P EC IAL FO CU S 2

181

FIGURE SF2.2 Factors supporting falling inflation in
LICs
The decline in LIC inflation has been supported by the move to more
flexible exchange rate regimes, greater central bank independence, lower
government debt, and a more benign external environment.

A. Central bank transparency index

B. Number of LICs, by exchange rate
regimeC. Government debt

D. Financial and trade openness

E. Inflation, by country characteristics

F. Exchange rate volatility

Inflation in non-LIC EMDEs. Although the
inflation decline in LICs has been broadly in linewith developments in other EMDEs, its levelremains well above its counterparts.5 Disinflation
in today’s LICs has also fallen short of that among(the larger number of) EMDEs that used to be
LICs in 2000 but that have since achieved middleincome status, even though these countries started
with lower levels of inflation (Figure SF2.1.E).

Factors supporting inflation
developments
Since 2000, improvements in LIC policies and abenign global macroeconomic environment havesupported the decline in LIC inflation. That said,
policy frameworks in the median LIC remain
generally weaker than those in other EMDEs.
Improved policies. The adoption of more resilientmonetary, exchange rate, and fiscal policy
frameworks has facilitated more effective control
of inflation (Hammond, Kanbur, and Prasad
2009; Taylor 2014). Inflation has tended to belower in LICs with higher degrees of central bankindependence and transparency, lower central
bank head turnover, and lower public debt ratios(Easterly 2019; Ha, Kose, and Ohnsorge 2019a).
Since 1970, monetary policy frameworks havestrengthened in LICs. For example, the index ofcentral bank transparency by Dincer, Eichengreen,
and Geraats (2019) (available for 9 LICs) doubled
between 1998, when the series starts, and 2015,
when the series ends (Figure SF2.2.A). In 1970, all
but three LICs had pegged exchange rates whereas,in 2019, less than half (14 of 29 LICs with

5 For instance, inflation remains in double-digits in Ethiopia,
mainly due to recent currency depreciation and surging food prices
after road disruptions and a drought.

Source: Dincer, Eichengreen, and Geraats (2019); Dreher, Stum, and De Haan (2010); Haver
Analytics; International Monetary Fund (IMF); Shambaugh (2004); World Bank.
Note: Data for 28 low-income countries and 96 other EMDEs. EMDEs = emerging markets anddeveloping economies; GDP = gross domestic product; LICs = low-income countries.
A.C. Unweighted averages.
A. Central bank transparency index as defined in Dincer, Eichengreen, and Geraats (2019). Data for
9 LICs and 83 other EMDEs.
B. Exchange rate regime as defined in Shambaugh (2004).
C. Data for 2019 are based on IMF (2019).
D. Median trade openness (measured by trade-to-GDP ratio) and financial openness (international
asset and liabilities to GDP) across countries.
E. Median year-on-year inflation in LICs during 1998-2018, by country characteristics. “High” indicates
above-median financial openness, central bank transparency, and turn-over rate of central bankgovernors. “Low” indicates below-median financial openness, central bank transparency, andturn-over rate.
F. Exchange rate volatility is the cross-country average of the standard deviation of nominal effective
appreciation in 28 low-income countries during each time period.
Click here to download data and charts.

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S P EC IAL FO CU S 2available data) did (Figure SF2.2.B).6 In addition,
fiscal pressures on monetary policy also appear tohave eased. In part as a result of debt relief
initiatives, government debt has declined from apeak of 121 percent of GDP in 2000, on average,
to 53 percent of GDP in 2019— broadly in linewith the average non-LIC EMDE (Figure
SF2.2.C).7
More benign external environment. LIC
economies, on average, have become more open to
trade and finance since the 1970s, although theyremain less open than other EMDEs (Figure
SF2.2.D; IMF 2011a). Higher capital accountopenness, in particular, has been associated withlower inflation, whereas there appears to be little
difference between LICs that have been highly
open to trade and those that have not (Figure
SF2.2.E). Despite a growing number of LICs
switching to floating exchange rate regimes,
exchange rates have been considerably more stablesince 1998 than in the preceding two decades(Figure SF2.2.F). This has helped lower LIC
inflation volatility and inflation.
Global inflation cycle. LICs are now more
integrated into the global economy. As a result,LIC
inflation has
become increasingly
synchronized with the global inflation cycle. What
was once a negligible contribution to LIC
inflation, global inflation’s impact on domestic
inflation has become sizeable, especially since2000 (Ha, Kose, and Ohnsorge 2019b; Parker2018).8 Over the past decade, the global

6 Several Sub-Saharan African LICs (as well as some recent lowand middle-income countries) belong to monetary unions (e.g., theWest African Economic and Monetary Union, and the Central
African Economic and Monetary Community). Many of these LICs
have also experienced low levels of inflation over the recent decades(Ha, Kose and Ohnsorge 2019a).
7 In addition, the relationship between fiscal position andinflation appears to be non-linear: in a low-inflation environment,
fiscal deficits tend to be less inflationary (Catão and Terrones 2005;
Lin and Chu 2013). As a result, the current low-inflation
environment may help further mute the pressures from fiscal
dominance on inflation in LICs.
8 Using a dynamic factor model for 99 countries (including 16
LICs), Ha, Kose, and Ohnsorge (2019b) find that the contributionof global inflation factor to domestic inflation variation increased to17 percent in 2001-17 from a 3-4 percent in 1970s to 1990s. Parker(2018) similarly finds that global inflation accounted for around aquarter of inflation variation in LICs over 2001-2012, compared toits contribution (10-20 percent) in the earlier periods.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

disinflation around the global financial crisis andoil price plunges in 2014-16 may have added
downward pressure to inflation in LICs.

Monetary policy challenges
The level and volatility of inflation in LICs haveremained higher than in advanced economies andother EMDEs over the past two decades. Thisdifference may reflect monetary policy challenges
particular to LICs arising from higher economic
volatility and pervasive use of administeredpricing, conflicts among central bank policy
objectives, weaknesses in monetary policy
transmission, and limited analytical capacity at
central banks (Ha, Ivanova, et al. 2019a).
Volatile economies. Policymakers in LICs must
contend with greater economic volatility thantheir counterparts in other countries. This in partreflects the greater frequency of supply shocks andthe poorer anchoring of inflation expectations thatallow exchange rate fluctuations to spill over into
inflation.
•

Supply shocks. LIC economies are particularly
vulnerable to supply shocks, especiallyweather-related ones. Agriculture sectors tend
to be large, poor transport links prevent risk
sharing, and food comprises a large share ofhousehold consumption (Bleaney andFrancisco 2018; Cachia 2014). As a result,
rainfall appears to have the most pronounced
effect on economic growth in EMDEs in SubSaharan Africa (Barrios, Bertinelli, and Strobl2010).

•

Exchange rate volatility. Exchange rates in
LICs tend to be more volatile than those inother EMDEs, in part reflecting their greater
frequency of supply shocks. With inflation
expectations poorly anchored, exchange ratepass-through also tends to be higher in LICs
than in other EMDEs (Ha, Ivanova et al.
2019a).

Conflicts among policy objectives. LICs
frequently have multiple monetary policy
objectives, with inflation being only one among
several. This in part reflects challenges inG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

formulating an appropriate numerical inflation
target for LICs. The threshold at which inflation
has clear negative effects on output is significantly
higher for EMDEs than for advanced economies
and varies widely depending on country
characteristics (Khan and Senhadji 2001). A
survey of low- and lower-middle-income countries
that listed price stability as a central bankobjective, found that most countries did not havea numerical inflation target, and those that hadsuch a target simply tended to align it with thebank’s inflation forecast (IMF 2015). LICs central
banks are thus likely to have a broader set ofobjectives; the exchange rate is more likely to be a
separate and important policy objective (Berg andMiao 2010; Rodrik 2018).9 Other objectives may
include supporting activity or fiscal sustainability.
•

Conflicts between inflation and output
objectives. To lower inflation after a history ofhigh inflation, the central bank must be
willing to tolerate weak activity perhaps for an
extended period. A commitment to lowering
inflation from a history of high inflation will
require the central bank to be willing totolerate weak activity perhaps for an extended
period (Kasa 2001; Gemayel, Jahan, and Peter2011). However, frequent supply shocks in
LIC, for example from the effects of weather
events on agricultural production, may raise
inflation while depressing output (Frankel2011).10 Stabilizing inflation in response tosuch supply shocks may thus require failing tomaintain output (Adam 2011; Bashar 2011;
Nguyen et al. 2017).

•

Conflicts between inflation and exchange rate
objectives. In LICs (as in some other EMDEs)

9 Using a heterogeneous structural vector autoregressive model for
105 countries, Ha, Ivanova et al. (2019a) find that core inflation in
LICs with a floating exchange rate regime is less robust in the face of
external shocks than in countries that fixed exchange rates. In
advanced economies and other EMDEs, shocks to global core
inflation account for a much larger fraction of the variance ofdomestic core inflation in fixed regimes than in floating regimes.
10 For instance, a poor harvest will tend to increase inflation in
the short term while depressing economic activity. Supply shocks
thus push inflation and output growth in opposite directions, giving
rise to a conflict between monetary policy’s primary objective of
stabilizing prices and its secondary objectives of supporting growthand maintaining a narrow output gap.

S P EC IAL FO CU S 2

the exchange rate may be an important policy
objective (Buffie et al. 2004; IMF 2015;
Mishkin and Savastano 2001; Taylor 2001). A
declared strategy of stabilizing the exchange
rate against currencies of trading partners witha track record of low and stable inflation may
well be compatible with achieving domestic
price stability and the limited international
financial integration of many LICs may still
afford some room for active monetary policy(Ostry, Ghosh, and Chamon 2012).
However, when currency exposures are high,exchange rate pressures may prevent central
banks from acting to preserve low and stableinflation.
•

Conflicts between inflation and fiscal objectives.
For LIC governments with weak revenueraising capabilities and an absence of wellfunctioning capital markets, inflation may
become an important source of financing
fiscal deficits (Baldacci, Hillman, and Kojo
2004). The presence of large fiscal deficits orhigh government debt in LICs can cause fiscal
policy to rely on accommodative monetary
policy to ensure fiscal sustainability (Baldini
and Poplawski-Ribeiro 2011; Weidmann2013). In almost every year between 1992 and2002, two-thirds of LICs had higher debt-toGDP ratios than the one-third of non-LIC
EMDEs with the highest debt levels. In half
the years between 1995 and 2017, the medianfiscal deficit in LICs was above that in nonLIC EMDEs. Weak institutions (Bleaney,
Morozumi, and Mumuni 2016) and politicalinstability (Aisen and Veiga 2006) may
reinforce the negative association between
budget deficits and price stability. Central
banks in LICs are therefore more likely to faceconflicts between price stability and pressures
to maintain low interest rates or provide
outright fiscal financing (Mas 1995; Prasad2010).

Widespread price controls. Price controls—
typically imposed to protect vulnerable groups—
are more common in LICs than in other EMDEs(Special Focus 1). The most frequently used pricecontrols in LICs are on basic food stuffs andpetroleum. Since food expenditures represent

183

184

S P EC IAL FO CU S 2nearly 60 percent of the consumption basket in
LICs, compared with 42 percent in other EMDEs,
a significant portion of the basket is therefore
subject to administered pricing (Laborde, Lakatos
and Martin 2019).11 Price controls cantemporarily contribute to price stabilization in
LICs, especially for key commodities subject toperceived excessive volatility in international
markets.12 However, this poses monetary policy
challenges, as well as fiscal and growth challenges
that can heighten conflicts between monetary
policy objectives.
Weaknesses in the instruments and transmission
mechanism of monetary policy. In advanced
economies and many EMDEs, the key monetary
policy instrument is a short-term interest rate,
most often an interbank rate. An advancedeconomy central bank can guide the interbank rate
through bank reserves and standing facilities. In
LICs, however, interbank markets are typicallyabsent, as are liquid secondary markets ingovernment securities, which the central bankcould seek to influence through open-market
operations (Mishra, Montiel, and Spilimbergo2012). The government securities market in LICs
tends to be a primary market in whichcounterparties are commercial banks that buy andhold government securities. Thus, the central
bank often conducts monetary policy by directlylending to and borrowing from the commercial
banking system. However, even the bank lending
channel can be impaired in LICs.
•

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

in LICs are half the share of GDP of other
EMDEs (Figure SF2.3.A). Only one-third ofadults have a bank account in LICs, comparedwith 57 percent in other EMDEs (Figure
SF2.3.B). As a result, the financial system has
only weak links to overall economic activity.
Around 80 percent of investment in LICs isfinanced internally and three-quarters of firms
do not tap banks to finance investment(Figures SF2.3.C and SF2.3.D).
•

Weak institutions. The institutional and legal
environment in LICs—including property
rights, accounting and disclosure standards,
and contract enforcement—tends to be weak
(Beck, Demirgüç-Kunt, and Levine 2004).
This makes financial intermediation fromprivate savers to private borrowers costly andrisky, inducing banks to limit this activity andto hold safer government securities.

•

Preponderance of large firms. Productive
activity in LICs is often characterized by a few
large, well-established firms and many verysmall, opaque, and often unstable ones. The
marginal cost of bank lending to large firms
tends to be lower than that of extending credit
to small firms. As a result, the volume oflending to large firms may be very insensitive
to fluctuations in bank funding costs inducedby monetary policy (Mishra and Montiel
2013; Mishra et al. 2014).

•

Widespread informality. The informal sector
accounts for about almost two-fifths of GDP
and 90 percent of employment in the average
LIC, in part reflecting large agricultural
sectors and a high share of unskilled workers(World Bank 2019). Firms in the informal
sector have limited access to credit from thebanking sector and capital markets, and thus
have limited interactions with the formal
financial sector. This dampens monetary
policy transmission through the formal
financial system.

•

Other factors. In addition, the strength ofmonetary transmission in LICs has provendifficult to estimate because of data
limitations (Li et al. 2016). What empiricalevidence has been estimated suggests that theLimited financial inclusion. LICs tend to havelarge informal sectors but small formal
financial sectors (World Bank 2019). Broadmoney and domestic credit by financial sector

11 In addition, LICs suffer collateral damage from other countries’
administered prices on food and energy because of the high share offood and energy in LIC consumption baskets and trade. Volatility inglobal food and energy commodity prices is amplified when othercountries respond to rising global commodity prices by imposing
price and other controls to suppress prices in local markets (Laborde,
Lakatos, and Martin 2019). The resulting higher volatility of importprices in LICs complicates central banks’ efforts to maintain low andstable inflation.
12 Median food and headline inflation were lower in the 52
EMDEs with food price controls than in the 23 EMDEs without
such controls. However, the relative price distortions introduced byhighly restricted food price controls have been associated with highinflation in LICs (Special Focus 1).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

transmission is weak for several reasons: credit
and other financial markets tend to beshallow; contract enforceability is limited;
information asymmetries are pervasive; andmany LICs retain elements of financial
repression in the form of interest rate
controls.13 For example, while changes inpolicy rates tended to be transmitted almost
one-for-one into retail bank lending rates inadvanced economies, pass-through in EMDEs
was only in the range of 30-45 percent (Abuka
et al. 2015; Saborowski and Weber 2013).
Shortcomings in the analytical capacity of central
banks. Because monetary policy affects the
economy with lags, an important component ofany monetary policy regime is the ability of thecentral bank to accurately forecast its target
variables on the assumption of unchanged policies
as well as to assess the effects of policy changes on
those variables. Few LIC central banks have thestructural models with proven track records
required for such forecasts (IMF 2015). This
reflects in part lack of relevant historical data,
insufficient knowledge about the macroeconomic
structure of the economies concerned, rapid
structural change in the economy, and shortages ofresearch expertise (Gemayel, Jahan, and Peter
2011; IMF 2015).
Complications introduced by globalization.
Globalization is likely to alter the monetarytransmission mechanism in complicated ways
(Abuka et. al. 2015; Montiel and Pedroni 2018).
It increases the economy’s exposure to external
shocks, in the form of exogenous changes in theforeign-currency prices of traded goods,
remittance flows, and capital flows. It may also
alter the trade-offs between different central bankobjectives.

Policy options going
forwardGoing forward, the achievements of low and stableinflation in many LICs cannot be taken for
granted. If the external environment turns less
13 For details, see Mishra, Montiel, and Spilimbergo (2012); IMF(2015); and Mishra and Montiel (2013).

S P EC IAL FO CU S 2

185

FIGURE SF2.3 Monetary policy challenges in LICs
Financial systems are small and have narrow reach in LICs, and this limits
monetary policy transmission through the financial sector. Broad moneyand domestic credit by the financial sector in LICs are half the share of
GDP than in other EMDEs. Only a third of adults have bank accounts in
LICs, compared with 60 percent in other EMDEs. Around 80 percent ofinvestment in LICs is financed internally, while less than 20 percent isfinanced by the banking sector.

A. Broad money and domestic credit

B. Fraction of adults with bankaccounts

C. Share of investment that isinternally financed

D. Share of firms that approach banks
to finance investment

Source: Enterprise Survey; Global Findex Database; World Development Indicators.
Note: EMDEs = emerging markets and developing economies; LICs = low-income countries.
Unweighted averages across countries.
A. Broad monetary and domestic credit provided by financial sector (both percent of GDP) in 2017,
based on 20 LICs and 110 other EMDEs.
B. Proportion of adults (age over 15) holding a bank account in 2017. Survey based on 23 LICs and86 other EMDEs.
C. Proportion of investment financed internally. Enterprise survey based on 15 LICs and 47 other
EMDEs.
D. Proportion of firms using banks to finance investments. Enterprise survey based on 15 LICs and47 other EMDEs.
Click here to download data and charts.

benign or fiscal pressures mount, the ability ofcentral banks in LICs to maintain low inflation
may be tested. Since 2013, government debt has
risen rapidly, by almost 15 percentage points of
GDP in the median LIC; about half of LIC debt
is external and, hence, predominantly foreigncurrency-denominated (World Bank 2019). This
increases LIC governments’ vulnerability tofinancial market disruptions that raise borrowing
costs. Mounting fiscal pressures could heighten
tensions between the multiple objectives of LIC
central banks. Separately, because of poorly
anchored inflation expectations, exchange rate

186

S P EC IAL FO CU S 2

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

that reduce the economy’s vulnerability toshocks, strengthen automatic fiscal stabilizers,
increase the flexibility and effectiveness of
discretionary fiscal policy, and increase theflexibility of labor markets. Institutionalchanges could include entrusting responsibility for financial stability to a separate
supervisory and regulatory authority,
associated with a well-capitalized deposit
insurance agency.

depreciations following financial market stresscould raise LIC inflation. Broader policy efforts
aimed at strengthening fiscal and monetary policy
frameworks, and improving debt management, aretherefore required in LICs to safeguard low andstable inflation.
Many of the monetary policy challenges facing
LICs are related to their level of economic andfinancial development. Addressing these challenges
requires a broader development process andincludes: the development of financial markets toprovide the central bank with more effective
policy instruments; the improvement of systems
compiling economic statistics; and capacity
development in central banks and economic
ministries, including strengthening economic
expertise.
•Strengthening central bank independence.
Central bank independence has increased
among LICs since the early 1990s, partly as ameans to allow central banks to give primacy
to price stability over other objectives andenhance their credibility (Dincer andEichengreen 2014; Garriga 2016). However,central bank independence of lower- andmiddle-income countries remains less than inother EMDEs and advanced economies, andde jure independence does not necessarilytranslate into de facto independence (IMF2019).

•

Clarifying priorities in central banks’ objectives.
A transparent prioritization of central bankobjectives in the event of conflicts between
different objectives could help central banks
achieve their primary targets. Other policy
options could be developed to help achieving
central banks’ secondary objectives—
including for output or financial stability—ofmonetary policy. Such policies could include
the judicious use of budgetary policy when
there is fiscal space, and structural reforms•

Expanding central bank tools. The central bankcould develop or strengthen instruments
separate from monetary policy to address its
objective of financial stability, including
capital flow management measures andmacroprudential policies.

•

Considering best suitable nominal anchors formonetary policy. Although inflation targeting,with its usual focus on the CPI, has been themost popular among advanced economies andlarger EMDEs, other EMDEs and LICs could
consider alternative nominal anchors formonetary policy that best suit their economic
structures. For example, countries that
produce commodities that are subject tovolatile global commodity prices, and haveprocyclical access to global capital markets,
could target export prices or producer prices.
These targets may stabilize output better than
CPI targeting in the presence of frequentterms of trade or financial shocks (Frankel2011).

•

Building and maintaining central bankcredibility. The central bank could strengthen
its efforts to convince the public of the
primacy it gives to the low-inflation objective(Mishkin 1997). Declaration of a specificinflation target could serve this purpose, but
this strategy may not yet suit LICs with weak
and uncertain monetary transmission, data
deficiencies, and limited analytical capacity.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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Laborde, D., C. Lakatos, and W. Martin. 2019.
“Poverty Impact of Food Price Shocks and Policies.”
Policy Research Working Paper 8724. World Bank,
Washington, DC.
Li, B. G., S. A. O’Connell, C. Adam, A. Berg, and P.
Montiel. 2016. “VAR Meets DSGE: Uncovering theMonetary Transmission Mechanism in Low-Income
Countries.” IMF Working Paper 16/90, International
Monetary Fund, Washington, DC.
Lin, H.-Y., and H.-P. Chu. 2013. “Are Fiscal Deficits
Inflationary?” Journal of International Money and
Finance 32 (C): 214-33.

Prasad, E. S. 2010. “Financial Sector Regulation and
Reforms in Emerging Markets: An Overview.” NBER
Working Paper 16428, National Bureau of Economic
Research, Cambridge, MA.
Rodrik, D. 2018. “The Real Exchange Rate andEconomic Growth.” Brookings Papers on Economic
Activity (Fall): 365-439.
Saborowski, C., and S. Weber. 2013. “Assessing theDeterminants of Interest Rate Transmission throughConditional Impulse Response Functions.” IMF
Working Paper 13/23, International Monetary Fund,
Washington, DC.

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S P EC IAL FO CU S 2

Shambaugh, J. C. 2004. “The Effect of Fixed Exchange
Rates on Monetary Policy.” Quarterly Journal of
Economics 119 (1): 301-52.

South Africa and the Challenge of a Changing
Mandate, South African Reserve Bank, Pretoria,
December 19.

Siami-Namini, S., and D. Hudson. 2019, “Inflation
and Income Inequality in Developed and Developing
Countries.” Journal of Economic Studies 46 (3): 611632.

Weidmann, J. 2013. “Who Calls the Shots? The
Problem of Fiscal Dominance.” Speech at the BdF-BBk
Macroeconomics and Finance Conference, Deutsche
Bundesbank Eurosystem, Paris.

Taylor, J. 2001. “The Roles of Exchange Rates in
Monetary-Policy Rules.” American Economic Review 91(2): 263-67.

World Bank. 2015. Global Economic Prospects: Having
Fiscal Space and Using It. January. Washington, DC:
World Bank.

———. 2014. “Inflation Targeting in Emerging
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Skies. January. Washington, DC: World Bank.

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CHAPTER 3

FADING PROMISE
How to Rekindle Productivity Growth

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CHAPTER 3

A broad-based slowdown in labor productivity growth has been underway since the global financial crisis. In
emerging market and developing economies (EMDEs), the slowdown has reflected weakness in investment andmoderating efficiency gains as well as dwindling resource reallocation between sectors. The pace of improvements
in key drivers of labor productivity—including education, urbanization, and institutions—has slowed or
stagnated since the global financial crisis and is expected to remain subdued. To rekindle productivity growth, acomprehensive approach is necessary: facilitating investment in physical, intangible, and human capital;
encouraging reallocation of resources towards more productive sectors; fostering firm capabilities to reinvigorate
technology adoption and innovation; and promoting a growth-friendly macroeconomic and institutional
environment. Specific policy priorities will depend on individual country circumstances.

Introduction
Productivity growth is the primary source of
lasting income growth, which in turn is the main
driver of poverty reduction. Most cross-country
differences in income per capita have been
attributed to differences in productivity (Figure3.1).1 Whereas the one-quarter of emerging
market and developing economies (EMDEs) withthe fastest productivity growth have reduced theirextreme poverty rates by an average of more than
1 percentage point per year since 1981, poverty
rates rose in EMDEs with productivity growth in
the lowest quartile.
The broad-based slowdown in labor productivity
growth over the past decade has raised concerns
about progress in achieving development goals. In
EMDEs, the slowdown puts at risk hard-won
gains in productivity catch-up to advanced
economies prior to the 2007-09 global financial
crisis. Labor productivity gaps with advanced
economies remain substantial, with workers in the
average EMDE producing less than one-fifth ofthe output of those in advanced economies.
Against this backdrop, this chapter presents acomprehensive examination of the evolution of
productivity, the correlates of productivity
improvements, and policy options to rekindle
productivity growth. Specifically, the chapter
addresses the following questions:•

How has productivity growth evolved over thelast four decades?

Note: This chapter was prepared by Alistair Dieppe and Gene
Kindberg-Hanlon, with contributions from Atsushi Kawamoto,
Sinem Kilic Celik, Hideaki Matsuoka, Yoki Okawa, and CedricOkou. Research assistance was provided by Khamal Clayton, Aygul
Evdokimova, Awais Khuhro, Xinyue Wang, and Heqing Zhao.
1 See for details Caselli (2005) and Hall and Jones (1999).

•

How has the pace of productivity convergencechanged?

•What are the underlying factors associatedwith productivity growth?

•

What policy options are available to boost
productivity growth?

Contribution and framework. The chapter makes
several contributions to the literature and policy
debate on labor productivity. The framework of
the analysis in this chapter is as follows:•

EMDE focus. Thus far, the literature has
focused on trends in subsets of countries suchas advanced economies, OECD countries or
specific regions.2 The chapter is the first to
provide both an overarching global and indepth EMDE view of productivity trends
alongside detailed regional analysis. To
achieve this, it utilizes a comprehensive
dataset of multiple measures of productivity
growth for up to 29 advanced economies and
74 EMDEs during 1981-2018.

•

Multiple approaches. The chapter synthesizes
findings from empirical exercises using
macroeconomic, sectoral, and firm-level dataon productivity. Previous studies havetypically analyzed productivity using data for
only one of these three dimensions.3 This

2 For details, see Fernald (2012), Adler et al. (2017), OECD
(2015), ADB (2017), Dabla-Norris et al. (2015), Cusolito andMaloney (2018), World Bank (2018a).
3 For macroeconomic analysis, see Adler et al. (2017) and Kim
and Loayza (2019). For sectoral analysis, see McMillan, Rodrik, andVerduzco-Gallo (2014); and McMillan, Rodrik, and Sepulveda(2017). For firm-level analysis, see Cirera and Maloney (2017);
Cusolito and Maloney (2018); and Fuglie et al. (2019).

193

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 20202016 and since then has remained low, at 1.9
percent in 2018. The post-crisis slowdown has
been broad-based, affecting nearly 70 percent
of advanced economies and EMDEs and over
80 percent of the global extreme poor and has
affected all EMDE regions (Figure 3.2). In
advanced economies, the slowdown continues
a trend that has been underway since the late
1990s. In EMDEs, which have a history ofrecurring multi-year productivity growthsurges and setbacks, the productivity growth
slowdown from peak (6.6 percent in 2007) totrough (3.2 percent in 2015) has been thesteepest, longest, and broadest yet. Commodity-exporting EMDEs—which accountfor almost two-thirds of EMDEs—have been
the worst affected.6

FIGURE 3.1 Labor productivity, per capita income andpoverty reduction
Cross-country differences in labor productivity explain most of the variation
in income per capita. Poverty declined by more than 1 percentage point on
average per year in the one-quarter of EMDEs with the highest productivity
growth during 1981-2015, while poverty rose in EMDEs with the lowest
productivity growth.
A. Labor productivity and per capita
incomeB. Annual change in the poverty ratein EMDEs, by productivity growth

Source: PovcalNet; World Bank.
Note: Sample includes 29 advanced economies and 74 EMDEs.
A. Income per capita and output per worker measured in US dollars at 2010 prices and exchange
rates.
B. Unweighted averages using annual data during 1981-2015. Fastest-growing EMDEs are those in
the top quartile by productivity growth; slowest-growing EMDEs are those in the bottom quartile oflabor productivity growth. Poverty rate defined as the share of the population living on less than $1.90
a day (2011 PPP).
Click here to download data and charts.

•

Large labor productivity gaps, slow convergencein EMDEs. Average output per worker in
EMDEs is less than one-fifth of that in the
average advanced economy, and just 2 percent
in LICs. Although EMDE productivity
convergence improved ahead of the global
financial crisis, it is now progressing at rates
that would require over a century to halve thecurrent productivity gap with the averageadvanced economy. However, the pace of
convergence differs across regions: more thanhalf of EMDEs in East Asia and Pacific (EAP)are on course to halve their productivity gap
in less than 40 years, while fewer than 20
percent of economies in the Middle East andNorth Africa (MNA), Latin America and the
Caribbean (LAC), and Sub-Saharan Africa(SSA) will likely achieve the same reduction
over this timeframe.

•

Accounting for the slowdown. Slower capital
deepening has accounted for the lion’s shareof the post-crisis (2013-18) slowdown in
productivity growth in advanced economies
from pre-crisis averages (2003-08). In
EMDEs, subdued investment and slowing
total factor productivity (TFP) growth havechapter combines these approaches andincludes a thorough review of the literature ineach area.
•

Comprehensive assessment of correlates of
productivity growth. The chapter reviews alarge body of literature on the correlates of
productivity growth. It undertakes an
empirical exercise that expands upon previous
work, whose data typically use either a shorter
sample or a narrower set of correlates.4 The
chapter also quantifies the damage that
financial crises inflict on productivity growth.5

Main findings. The following findings emerge
from the chapter.
•

Broad-based post-crisis decline in laborproductivity growth. Global labor productivity
growth slowed from its pre-crisis peak of 2.7
percent in 2007 to a trough of 1.5 percent in

4 Durlauf, Kourtellos, and Tan (2008); Kim and Loayza (2019);
Adler et al. (2017).
5 This complements earlier work documenting damage fromfinancial crises to the level of potential output (Cerra and Saxena2008) and to potential growth (Furceri and Mourougane 2012a).

6 In commodity-exporting EMDEs, productivity growth slowed by
4.1 percentage points between 2007 and 2015 to around 0,
compared with 3.5 percentage points in commodity-importing
EMDEs.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020accounted, in approximately equal measure,for the post-crisis productivity growth
slowdown. About one-half of the slowdown in
EMDEs reflects fading gains from thereallocation of resources towards moreproductive sectors. Reallocation previously
drove more than one-third of pre-crisis
productivity growth in EMDEs, and threequarters in LICs.
••

Challenging prospects for labor productivity
growth. Since the global financial crisis,
improvements in many key correlates of
productivity growth in EMDEs have slowed
or gone into reverse. Working-age population
growth has slowed, educational attainment
has stabilized, and the pace of expansion into
more diverse and complex forms of
production has lost momentum as the growthof global value chains stalled. At the firmlevel, EMDE firms that are large and exportoriented are closest to the productivity
frontier, suggesting that continued global
trade weakness and slower global production
integration could be particularly damaging to
productivity growth in EMDEs. In addition,
the global financial crisis dented productivity
growth and momentum has yet to be rebuilt.
Policy priorities. The broad-based nature of thelabor productivity growth slowdown can beaddressed with a comprehensive set ofpolicies. Policies can lift labor productivity
economy-wide by stimulating private andpublic investment, and improving humancapital; fostering firm productivity, including
by upgrading workforce skills; exposing firms
to trade and foreign investment; facilitating
the reallocation of resources towards moreproductive and a more diversified set of
sectors; and creating a generally growthfriendly macroeconomic and institutional
environment.

Concepts. Throughout this chapter, productivity
is defined as output (GDP) per input of a unit oflabor. To ensure as large and comparable a sample
as possible over time and across countries, thischapter uses the number of people employedrather than the number of hours worked as the

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195

FIGURE 3.2 Global productivity developments
A broad-based slowdown in productivity growth has been underway,
affecting the majority of advanced economies and EMDEs. In EMDEs,
productivity growth slowed from its most recent peak of 6.6 percent in2007 to 3.2 percent in 2015, the steepest, longest, and broadest slowdown
in 40 years. Productivity levels in EMDEs are less than 20 percent of theadvanced-economy average, and just 2 percent in LICs. The productivity
slowdown has coincided with lower gains from sectoral reallocation and aslowdown in improvements in many drivers of productivity growth.
A. Global, advanced-economy,and EMDE productivity growth

B. Share of economies and global
poor with 2013-18 productivity growth
below historical averages

C. Magnitude and extent of multi-year
productivity slowdowns andrecoveries

D. EMDE productivity levels, 2013-18

E. Within and between sector
contributions to productivity growth

F. Share of EMDEs with a post-crisis
slowdown in the growth of underlying
drivers of productivity

Source: World Bank (full sources in subsequent figures).
Note: Productivity is defined as output per worker. Unless otherwise indicated, data are from asample of 29 advanced economies (AEs) and 74 emerging market and developing economies(EMDEs). Aggregates are GDP-weighted at constant 2010 prices and exchange rates.
B. Percent of economies, or share of global extreme poor (population living on less than $1.90 perday), with productivity growth in 2013-18 below pre-crisis (2003-08) or long-term (1981-2018) average
productivity growth. Grey line indicates 50 percent.
C. “Magnitude of slowdown” is the cumulative decline in EMDE productivity growth from the peak of
the episode to the trough for episodes lasting more than two years. “Magnitude of rebound” is the
cumulative increase in EMDE productivity growth from the trough (end) of the episode to three years
later. “Affected EMDEs” is the share of EMDEs that experienced a slowdown.
D. Blue bars show unweighted average output per worker during 2013-18 relative to the advancedeconomy average. Whiskers indicate interquartile range relative to the advanced-economy average.
E. Sample includes 80 economies, including 46 EMDEs (of which 8 are LICs), using data for 19952015. Growth “within sector” shows the contribution to aggregate productivity growth of each sector
holding employment shares fixed. The ‘between sector’ effect shows the contribution arising fromchanges in sectoral employment shares.
F. Post-crisis slowdown defined as the share of economies where improvements in each underlying
driver of productivity during 2008-2017 was less than zero or the pace of improvement during the precrisis period 1998-2007. Variables definitions in Chart 3.9.A.
Click here to download data and charts.

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measure of labor input.7 A second measure, totalfactor productivity (TFP), is also featured in thechapter. TFP measures the efficiency with which
factor inputs are combined and is often used toproxy technological progress (Annex 3.2).

Evolution of laborproductivity growth
Since 2007, a broad-based slowdown in laborproductivity growth has been underway that hasreached the majority of advanced economies and
EMDEs. For EMDEs, this has partly reversed a precrisis productivity growth surge, althoughproductivity growth remains above the very weakrates of the 1980s and 1990s. Some low-income
countries have escaped the productivity growth
slowdown but productivity growth has regressed insome fragile and conflict-afflicted low-income
countries.
Global productivity. From its peak in 2007,
global productivity growth has slowed by 0.8
percentage point, to 1.9 percent in 2018. The
post-crisis (2013-18) average of 1.8 percent was
0.5 percentage point below the pre-crisis (200308) average and slightly below the long-term(1981-2018) average (Figure 3.3). This post-crisis
slowdown from pre-crisis averages was broadbased, affecting two-thirds of economies, bothadvanced economies and EMDEs. Those
economies with slower post-crisis productivity
growth than during the pre-crisis period accountfor over 80 percent of global GDP and theextreme poor.
Advanced economies. The post-crisis slowdown inadvanced-economy productivity growth continues
a trend that has been underway since the late
1990s, following a brief resurgence from an evenlonger-running negative trend. The slowdown has
been attributed to a declining contribution frominformation and communication technology(ICT) intensive sectors in the United States, andslow adoption of ICT technologies, and restrictive
7 Number of people engaged includes employees and selfemployed. Alternative measures might better capture labor input but
have insufficient coverage for EMDEs (Annex 3.1). In countries withlarge informal sectors, both employment and output may be subject
to sizable measurement error (World Bank 2019a, Annex 3.1).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

product market regulations in parts of Europe.8
During the global financial crisis, productivity
growth in advanced economies plunged and neverrecovered to pre-crisis levels. At 0.8 percent on
average during 2013-18, it was one-half its longterm average and 0.4 percentage points below its
pre-crisis average. This slowdown relative to longrun averages affected nearly 90 percent ofadvanced economies.
EMDEs. Productivity growth in EMDEs has
slowed sharply from its 2007 peak of 6.6 percent
to a low of 3.2 percent in 2015 and, since then,has inched up to 3.6 percent in 2018. The postcrisis slowdown from pre-crisis averages affectednearly 70 percent of EMDEs and, in around halfof EMDEs, productivity growth has now fallen
below its long-term (1981-2018) average. The
slowdown has been particularly pronounced in
China, where a policy-guided decline in public
investment growth has been underway for several
years, and in commodity exporters, which havebeen hit hard by the commodity price plunge of
2014-16. Weak post-crisis productivity growthfollows on the heels of a major productivity surge
during 2003-08 when EMDE productivity
growth more than doubled from 1990s averages,
in part reflecting a strong cyclical rebound fromthe 1997-98 Asian financial crisis.
Since 1980, EMDE productivity growth has gone
through three multi-year surges and setbacks in
productivity
growth.
Previous
multi-year
slowdowns—in 1986-1990 and 1995-1998—
preceded global recessions (1991) or global
slowdowns and EMDE crises (1998). However,the slowdown since 2007 has been the mostprolonged, steepest and broadest-based yet.9 In
8 For a summary of the effects of the ICT slowdown on U.S.
productivity in the 2000s, see Duval, Hong, and Timmer (2017),
Jorgenson, Ho, and Stiroh (2008), and Fernald (2012). In Europe,
the trend decline in productivity has been ascribed to sectoral
misallocation due to cheap credit in southern Europe (Gopinath et al.
2017), a failure to adopt ICT and associated technology to the same
extent as the United States (van Ark, O’Mahony, and Timmer 2008),
and restrictive product market regulations (Haltiwanger, Scarpetta,
and Schweiger 2014).
9 The most recent slowdown in productivity growth has lasted
eight years—compared with the four years of 1986-90 and the three
years of 1995-98—and, from peak to trough, has been around 50
percent steeper than the slowdowns in the late 1980s and the late
1990s. It has reached 64 percent of EMDEs, slightly more than theslowdown in the 1990s (59 percent) and 1980s (57 percent).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

contrast to previous episodes, the current
productivity slowdown has yet to be marked by astrong rebound.
EMDE productivity growth remains slightly
above its average in the 1980s and 1990s, which
was well below the pre-crisis surge in productivity
growth. In commodity importers, average
productivity growth in 2013-18 has remained
more than twice its 1980s average and one-third
above its 1990s average. However, in commodityexporting EMDEs, the post-crisis commodity
price plunge has returned productivity growth tojust 0.6 percent, rates which are weak but still
above the growth rates of the 1980s.
LICs. On average, LIC productivity growth has
fallen only modestly to 2.4 percent during 201318, substantially above the negative rates of the
1980s and early 1990s. However, productivity
growth has again slowed sharply or turned
negative in some fragile and conflict-afflictedstates (Burundi, Mozambique).
Regions. Productivity growth decelerated in all
EMDE regions during 2013-18 from their precrisis (2003-08) averages (Box 3.1). This
slowdown occurred amid heightened debt levels
which increase the probability of financial crises
and crowd out productive investments. The mostpronounced slowdown (by 3.8 percentage points
to 1.5 percent in 2013-18) occurred in Europe
and Central Asia (ECA), where the global
financial crisis and subsequent Euro Area debt
crisis caused severe economic disruptions.
Productivity growth has also fallen steeply in Latin
America and the Caribbean (LAC), the Middle
East and North Africa (MNA), and Sub-SaharanAfrica (SSA), to near zero. Productivity growthdeclined substantially in East Asia and Pacific(EAP) and more modestly in South Asia (SAR)
from pre-crisis levels, but it continued to berobust, remaining above 5 percent in both regions.
Missed opportunities. The steep productivity
growth slowdown since the global financial crisis
implies considerable output losses relative to a
counterfactual of productivity growth continuing
at its pre-crisis trend. Output per worker inadvanced economies would be 5 percent higher
today had productivity growth continued at its

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197

FIGURE 3.3 Evolution of global productivity growth
In EMDEs, productivity growth has declined from pre-crisis levels, although
it remains strong relative to longer-run averages in half of EMDEs. At 0.6
percent, EMDE commodity exporters have had the weakest average
productivity growth since 2013. Productivity growth in EMDE commodity
importers and LICs has been more resilient.
A. Global, advanced-economy, and
EMDE productivity growth

B. EMDE productivity growth

C. Economies with 2013-18
productivity growth below historical
averages

D. EMDE average productivity growth,pre- and post-crisis

E. Productivity growth in EMDE
regions

F. Cumulative productivity losses
relative to 2003-08 trend

Source: Penn World Table; The Conference Board; World Bank, World Development Indicators.
Note: Productivity is defined as output per worker. Data are from a balanced sample between 19812018 and includes 29 advanced economies (AEs), and 74 emerging market and developing
economies (EMDEs) including 11 low-income countries (LICs), as of 2019 World Bank classifications,
52 commodity exporters and 22 commodity importers. GDP-weighted (at constant 2010 prices andexchange rates) aggregates.
A.B. GDP weighted averages (at 2010 prices and exchange rates).
C. Share of economies for which average productivity growth during 2013-18 was lower than thelong-run (1981-2018) average or the pre-crisis (2003-2008) average.
E. GDP-weighted productivity growth for 8 EMDEs in East Asia and the Pacific (EAP), 10 EMDEs in
Eastern Europe and Central Asia (ECA), 18 EMDES in Latin America and the Caribbean (LAC), 10
EMDEs in Middle East and North Africa (MNA), 2 EMDEs in South Asia (SAR), and 26 EMDEs in
Sub-Saharan Africa (SSA).
F. Percent fall in productivity level by 2018 relative to a counterfactual scenario where productivity
continued to grow at its 2003-08 average growth rate from 2009 onwards.
Click here to download data and charts.

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BOX 3.1 EMDE regional labor productivity trends and bottlenecks
The post-crisis slowdown in productivity growth was particularly severe in East Asia and Pacific, Europe and Central Asia, and
Sub-Saharan Africa amid slowing investment growth, financial market disruptions, and a post-crisis commodity price slide.
Meanwhile, productivity growth in Latin America and the Caribbean and the Middle East and North Africa—the slowest even
before the global financial crisis—has fallen to near-zero as investment collapsed amid political uncertainty, episodes of financial
stress in major economies, and falling commodity prices. As a result, the pace of catch-up to advanced-economy productivity levels
has slowed in most regions since the global financial crisis and, in some regions, productivity is even falling further behind. In
almost all regions, productivity gains from the reallocation of labor from low-productivity to higher-productivity sectors haveslowed sharply. To boost productivity, policies are needed to address key obstacles to productivity growth. Some of these obstacles
are shared across EMDE regions, including resource-reliant economies, widespread informality, shortcomings in education, andweak governance, and some are region-specific bottlenecks.

Introduction
Although common across all EMDE regions, the postcrisis productivity growth slowdown has differed markedly
in severity. Generally, it was more pronounced in more
open EMDE regions that are closely integrated into
advanced-economy supply chains. Meanwhile, in regionswith a large number of commodity exporters, productivity
growth has fallen close to zero. As a result, to varying
degrees, the catch-up to advanced-economy productivity
levels has slowed since the global financial crisis and, insome regions, productivity is even falling further behind.
Policy priorities to reignite productivity growth differ
across regions.
This box draws out differences in regional productivity
trends and policy priorities (summarizing Boxes 2.1-2.6).1
Specifically, it addresses the following questions:•

How has the evolution of productivity varied acrossregions?

•

What factors were
productivity growth?

associatedwithstronger

For the purposes of this box, productivity is defined aslabor productivity—that is, real GDP per worker (at 2010
prices and exchange rates).

Evolution of productivity
Post-crisis labor productivity growth slowdown. An
exceptional pre-crisis surge in productivity growth wasbroad-based across regions, with productivity in more than
50 percent of economies in each region except The Middle

Note: This box was prepared by Gene Kindberg-Hanlon with research
assistance from Shijie Shi.
1 To be as representative of each region as possible, this box uses a
broader sample than the main text in Chapter 3, resulting in a shortertime horizon under consideration. This box and the regional boxes covera sample containing 127 EMDE economies, compared to 74 in the maintext.

East and North Africa (MENA) growing faster than theadvanced economy average (Rodrik 2011; Roy, Kessler
and Subramanian 2016; Figure 3.1.1). Since the global
financial crisis (2013-18), however, productivity growth
has slowed from pre-crisis (2003-08) rates in all EMDE
regions.
The slowdown was particularly steep in East Asia and thePacific (EAP), especially in China, as well as in Europe andCentral Asia (ECA) and Sub-Saharan Africa (SSA). In
these regions, investment growth has declined sharply
from pre-crisis levels amid a policy-guided public
investment slowdown in China (EAP), financial system
disruptions associated with the Euro Area crisis (ECA),
and the commodity price collapse of 2014-16 (ECA, SSA).
However, in all three regions, there were important
exceptions to the sharp slowdown. In EAP, the slowdown
was concentrated in China while productivity growthcontinued to be robust in other major EAP economies,
especially some ASEAN economies (the Philippines andVietnam), as FDI and investment growth remained robust(Box 2.1). In ECA, the slowdown was muted inagricultural economies in Central Asia that shifted theireconomic ties towards China and in Central European
economies that continued to integrate into Western
European supply chains and benefited from investment
financed by European Union structural funds. In SSA,
productivity growth accelerated in agricultural commodity
exporters.
The slowdown was mildest in South Asia (SAR), in part
because the region is the least open EMDE region to
global trade and finance, continued to urbanize rapidly,
and, as a predominantly commodity-importing region,
benefited from the commodity price slide. In MENA, theslowdown was mild since limited links to global financial
markets insulated commodity-importing economies fromglobal financial stress.
Post-crisis productivity growth across regions.
Productivity growth in Latin America and the Caribbean

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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BOX 3.1 EMDE regional labor productivity trends and bottlenecks (continued)
FIGURE 3.1.1 Evolution of regional labor productivity
The post-crisis slowdown in labor productivity growth was particularly severe in EAP, ECA and SSA as these regions
struggled with slowing investment growth, financial market disruptions, and weaker commodity prices. In EAP and ECA, theslowdown in productivity growth has reflected both a slower pace of capital deepening and weaker TFP growth. In MENA
and SAR, TFP has continued growing or stabilized after earlier contractions (MENA).
A. Labor productivity growth in EMDE
regions

B. Share of economies growing fasterthan the average advanced economy

C. Annual rate of productivity
convergence, 2003-08 and 2013-18

D. Regional average productivity differentials, GDP-weighted, 2018

E. Contributions to regional productivity
growth; EAP, ECA, LAC

F. Contributions to regional productivity
growth: MNA, SAR, SSA

Source: International Monetary Fund; Penn World Table; The Conference Board; World Bank, World Development Indicators.
A.B.C.D. Productivity refers to output per worker at 2010 prices and exchange rates. Sample includes 35 advanced economies (AE) and 16 EMDEs in East Asia and thePacific (EAP), 21 EMDEs in Eastern Europe and Central Asia (ECA), 25 EMDES in Latin America and the Caribbean (LAC), 14 EMDEs in Middle East and North Africa(MNA), 7 EMDEs in South Asia (SAR), and 44 EMDEs in Sub-Saharan Africa (SSA).
A. GDP-weighted average labor productivity growth.
B. Share of economies with faster productivity growth than the advanced-economy average in each period.
C. Rate of convergence calculated as the difference in productivity growth rates with the average advanced economy divided by the log difference in productivity levels
with the average advanced economy. Regional rate of convergence is the GDP-weighted average of EMDE members of each region.
D. Whiskers show the range within the region as a percent of the advanced economy average while bars show the GDP-weighted average level of productivity relative toadvanced economies. Productivity reflects output per worker measured in US dollars at 2010 prices and exchange rates.
E.F Aggregates calculated using GDP weights at 2010 prices and exchange rates. The sample includes 92 emerging market and developing economies (EMDEs),
including 8 East Asia and Pacific, 21 Europe and Central Asia, 19 Latin America and the Caribbean, 12 Middle East and North Africa, 2 South Asia, and 30 Sub-SaharanAfrica economies.
Click here to download data and charts.

(LAC), MNA, and SSA—even before the crisis, theslowest—has fallen to near zero as investment collapsed
amid political uncertainty, episodes of financial stress inmajor economies, and falling commodity prices (Box 2.3).
As a result, productivity growth in the majority of EMDEs
in LAC, MNA, and SSA now lags that in advanced
economies and, on average in these regions, productivity
levels are diverging from those in advanced economies. In
contrast, productivity growth continues above 5 percent in

EAP and SAR, where investment growth is still higher
than in other EMDE regions (EAP, SAR) or the shift
towards more productive sectors has accelerated (SAR). In
these two regions, productivity continues to converge
towards advanced-economy levels at approximately the
pre-crisis pace.
Regional dispersion of productivity. On average,
productivity in EMDEs was just 19 percent of the

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BOX 3.1 EMDE regional labor productivity trends and bottlenecks (continued)
advanced-economy average in 2018.2 Among EMDE
regions, average labor productivity is highest in the MNA(45 percent of the advanced-economy average), LAC andECA (about 22-30 percent, respectively) and lowest in
SAR (6 percent) and SSA (11 percent). However, theseregional averages disguise wide dispersion within someregions, especially MNA, ECA, and SSA. In some Gulf
Cooperation Council (GCC) countries in MNA, forexample, productivity is near advanced-economy averages
whereas in heavily agricultural economies, such as the Arab
Republic of Egypt and Morocco, it amounted to 10
percent of the advanced-economy average (Box 2.4).
Similarly, close trade integration with Western Europe
and, increasingly, China and major reforms since thecollapse of the Soviet Union have helped raise average
productivity levels in ECA to the second-highest among
EMDE regions (30 percent). However, there is wide
heterogeneity, with Poland producing around 38 percentof the advanced economy average worker, while some
agricultural economies in Central Asia produce just 3
percent (Box 2.2). In SSA, LICs produce about 2 percentof the advanced economy average whereas oil exporters
such as Gabon produce 33 percent (Box 2.6). In contrast,
closely integrated EAP has a narrower range of
productivity levels (2-25 percent of the advanced-economy
average).

In MNA and SAR, in contrast, TFP continued growing at
the pre-crisis pace (SAR) or stabilized after earlier
contractions (MNA), even as capital deepening slowed
sharply (SAR) or reversed (MNA). In MNA, the oil pricecollapse of 2014-16 weighed heavily on investment in oil
exporters and political tensions discouraged investment incommodity importers. However, macroeconomic andstructural reform efforts helped stem pre-crisis contractions
in TFP. In SAR, persistent post-crisis investment
weakness—in part due to disruptive policy changes andtapering growth of FDI inflows—was offset byproductivity-enhancing sectoral reallocation, as labor
moved out of agriculture into more productive sectors
amid rapid urbanization (Box 2.5).

Capital deepening versus total factor productivity
growth. Productivity growth can be decomposed into theuse of factor inputs (human or physical capital) or theeffectiveness of their use (total factor productivity, or TFP,
Figure 3.1.1). In EAP and ECA, the post-crisis slowdown
in productivity growth has reflected both a slower pace ofcapital deepening and weaker TFP growth, albeit tovarying degrees. Two-fifths of the slowdown in EAP
reflected slowing capital deepening, the remainder slowing
TFP growth. In EAP, a policy-guided move towards moresustainable growth in China and trade weakness weighedon investment and capital deepening. In ECA, most (twothirds) of the productivity growth slowdown reflected acollapse in investment growth as conflict erupted in partsof the region, sanctions were imposed on the RussianFederation, political and economic shocks unfolded in
Turkey, financial systems transformed after the Euro Area
debt crisis, and the commodity price collapse hit
commodity exporters (Arteta and Kasyanenko 2019).

A wide range of factors have weighed on productivity
growth since the global financial crisis, but their relativerole has differed across regions. In all regions other thanSAR, productivity gains from the reallocation away fromlow-productivity (usually agriculture) sectors to higherproductivity sectors have slowed (Enache, Ghani, andO’Connell 2016). In addition, the pre-crisis pace ofimprovements in various aspects of the supporting
environment for productivity growth has slowed.
Productivity levels in all regions remain less than half of
those in advanced economies, providing significant scopefor faster productivity growth. However, significant
bottlenecks to productivity convergence remain, many ofwhich differ across regions.

2 In this section, GDP-weighted averages of productivity are used tocompare productivity levels across economies—in the main text, simple
averages are used.

Conversely, in SSA and LAC, TFP contracted. In major
LAC economies, continued post-crisis credit extension or
intensifying economic distortions (such as trade
restrictions and price controls) allowed unproductive firms
to survive to a greater extent than pre-crisis. In SSA, thecontraction in TFP was partly offset by accelerating capital
deepening as a number of countries invested heavily inpublic infrastructure, typically financed by debt.

Regional sources of productivity growth andbottlenecks

Sectoral reallocation
Declining gains from sectoral reallocation. In all regions
except MNA, switching employment from lowproductivity sectors to sectors with above-average
productivity levels supported productivity growth during
2003-08, especially in EAP, ECA, and SSA (Figure 3.1.2).
In SSA, it accounted for more than half of growth in the
median economy during 2003-2008 (Diao, McMillan,
and Rodrik 2017).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 3

BOX 3.1 EMDE regional productivity trends and bottlenecks (continued)
FIGURE 3.1.2 Sectoral contributions to regional productivity growth
Since the global financial crisis productivity gains from sectoral reallocation have faded across all regions (with the exceptionof SAR). In SAR and SSA, around half of employment is in the agricultural sector, which only accounts for around 20 percent
of output, reflecting low productivity in this sector. The wide dispersion of sectoral productivity levels within regions
demonstrates the importance of introducing measures to reduce misallocation and boost productivity in the weakest sectors.
A. Within and between sector
contributions to regional productivity
growth: EAP, ECA, LAC

B. Within and between sector contributions to regional productivity growth:
MNA, SAR, SSA

C. Composition of employment by sector,
2015

D. Composition of value-added by sector,
2015

E. Sectoral contribution to aggregate
productivity growth, 2013-15

F. Sectoral productivity levels dispersion
within regions, 2015

Source: APO productivity database; Expanded African Sector Database; Groningen Growth Development Center Database; Haver Analytics; ILOSTAT; OECD STAN;
United Nations; World KLEMS.
Note: Sample includes 46 EMDEs, of which 8 are LICs and 9 East Asia and Pacific, 6 Europe and Central Asia, 6 Latin America and the Caribbean, 3 Middle East andNorth Africa, 3 South Asia, and 19 Sub - Saharan African economies.
A.B. Median contribution for each region. Growth within sector shows the contribution of initial real value added-weighted productivity growth rate of each sector and‘between sector’ effect shows the contribution arising from changes in sectoral employment shares.
E. Median contribution to productivity growth.
F. Range of (regional averages of) sector-specific productivity levels relative to advanced-economy average productivity for the same sector in 2015, valued at 2011
purchasing power adjusted exchange rates. The range for MNA excludes sectoral productivity for mining which exceeds 1000 percent of the advanced-economy average.
Click here to download data and charts.

Since the global financial crisis, however, productivity
gains from sectoral reallocation have faded across all
regions (with the exception of SAR). In commodity-reliant
regions such as LAC, MNA, and SSA, this in part reflected
lower absorption of labor by services and construction
sectors as real income losses in resource sectors spilled over
into weaker demand. In EAP, it reflected slowing labor
reallocation as overcapacity was gradually being unwound.
In ECA, high-productivity manufacturing, financial, andmining sectors suffered during the Euro Area debt crisisand the post-crisis commodity price collapse. Meanwhile,in SAR, the move of labor out of low-productivity
agriculture into more productive sectors accelerated as
rapid urbanization continued and strong consumption
growth fueled employment in higher-productivity tradeservices.
Looking ahead, further sectoral reallocation continues tohave a high potential to lift productivity growth in SSA
and SAR, where low-productivity agriculture accounts foraround 50 percent of employment and 20 percent of
output. Substantial gaps in productivity between sectors

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BOX 3.1 EMDE regional productivity trends and bottlenecks (continued)
remain, offering the potential for further aggregate
productivity gains from resource reallocation between
sectors.
Bottlenecks to productivity growth

Several bottlenecks to higher productivity are shared, tovarying degrees, by multiple EMDE regions. These
include commodity-reliance, widespread informality, poor
education, and weak governance. Other bottlenecks aremostly region-specific.

Reliance on commodity exports. In LAC, MNA, and
SSA, commodities account for over 20 percent of exports
on average. In ECA, they account for 30 percent ofexports, largely due to Russia, where around 60 percent ofexports are (mostly energy) commodities. Economies thatare highly reliant on a narrow range of commodity exports
can also suffer from misallocation and procyclical trends
for productivity growth (Frankel 2010). Conversely,
producing across a broad range of sectors can insulate
economies from external shocks, and can facilitate
knowledge transfer to strengthen productivity (Kraay,
Soloaga, and Tybout 2002; Schor 2004). In EAP, forexample, high pre-crisis productivity growth was spurredby rapid integration into global supply chains andattraction of FOi which enabled a substantial increase in
the range and sophistication of production in the region(Wei and Liu 2006).
Weak governance and institutions. In most EMDE
regions, governance and business climates are less businessfriendly than in advanced economies. The largest distances
to the frontier (the most business-friendly climates) are in
SSA, SAR, and LAC, but also in pockets of ECA (Central
Asia and Eastern Europe) and MNA (North Africa). In all
regions, a large majority of EMO Es fall below the global
average for tackling corruption. Poor institutions havebeen associated with weak firm productivity and inefficient
government investment in productivity-augmenting
infrastructure (Cirera, Fattal-Jaef, and Maemir 2019). In
EAP, poor corporate governance in some sectors
contributes to resource misallocation and weighs on
productivity.
Informality. Informality is pervasive in EMDEs, although
there are large differences in the productivity of informal
sectors across regions. Informal firms are less productive
than those in the formal sector and, by competing on morefavorable terms, can deter investment and erode theproductivity of formal firms (Amin, Ohnsorge, and Okou
2019). In all regions except MNA, the informal sector

accounts for 25-40 percent of official GDP (22 percent of
GDP in MNA); however, reflecting heterogeneity inproductivity levels, informal employment (measured as
self-employment) varies widely from 22 percent (MENA)
to 62 percent (SSA) of total employment (World Bank
2019a).

Limited human capital. Higher-skilled and bettereducated labor forces tend to adopt new technologies,
including new ICT and manufacturing technologies, more
readily and more effectively (World Bank 2019c). In EAPand ECA, expected years of schooling for children are now
within one year of advanced economies on average, but
SAR and SSA lag more than 3 years behind the advancedeconomy average (Figure 3.1.3). Even where years ofschooling are on par with advanced economies, education
can be ineffective where learning outcomes are poor
(World Bank 2018a). In learning-adjusted terms, which
controls for the quality of education in addition to years of
attainment, SAR and SSA lag substantially (six or morelearning-adjusted years) behind advanced economies.
Region-specific factors. In each region, some challenges toimproving or sustaining productivity growth are notable:•

In EAP, the region faces challenges in sustaining
productivity growth as rapid trade integration, whichspurred productivity growth in the 2000s, fades. Withmaturing supply chains and weak global trade, thepriority has shifted towards improving the allocationand efficiency of investment, including in a wider
range of sectors (World Bank and DRCSC 2019).

•

In ECA, reform momentum has stalled in many
economies since the global financial crisis. This
follows on the heels of a period of rapid progress in
the 1990s and 2000s in the transition to market-based
economies and, in Central Europe, in the accession to
the European Union (Georgiev, Nagy-Mohacsi, andPlekhanov 2018). Restrictive product market andservices regulations now hinder competition and deter
foreign investment.

•

In MNA, the government accounts for a large share of
employment relative to other regions. About one-fifthof the workforce is employed in the public sector.
This is in part driven by a sizable wage premium for
public-sector workers and a bias in the education
system toward training for public sector employment.
The non-GCC private sector is anemic, with lower
firm turnover than in other EMDE regions.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

CHAPTER 3

BOX 3.1 EMDE regional productivity trends and bottlenecks (continued)
FIGURE 3.1.3 Potential bottlenecks to productivity growth
Several bottlenecks to higher productivity are shared, to varying degrees, by EMDE regions. These include undiversified
economies, weak governance, widespread informality, poor learning outcomes, and low trade and financial openness.
A. Share of commodities in total exports,
2013-2018

B. Government effectiveness, 2013-2018

C. Informal economy, 2016

D. Educational attainment, 2017

E. Trade and financial openness,
2013-2018

F. Business climates, 2020

Source: United Nations; World Bank, Doing Business, Human Capital Project, World Development Indicators, Worldwide Governance Indicators.
A. Exports of metals, agricultural and energy products in percent of total exports. GDP-weighted average for each region. Average during 2013-2018.
B. WGI index defined as capturing perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures,the quality of policy formation and implementation, and the credibility of the government’s commitment to such policies. Bars show interquartile range.
C. Average informal output (DGE-based estimates, percent of official GDP) and employment estimate (self-employment, percent of total employment) in each region.
Based on World Bank (2019a).
D. Expected years of schooling and learning-adjusted years of schooling from the World Bank’s Human Capital Project. Learning-adjusted years of schooling uses
harmonized cross-country test scores to adjust the average years of schooling.
E. Unweighted average of trade (exports plus imports) in percent of GDP and net foreign direct investment inflows in percent of GDP.
F. Unweighted average distance to frontier measure of the ease of doing business score from the 2020 Doing Business Indicators. A higher value indicates a businessclimate that is closer to best practices. Bars show range.
Click here to download data and charts.

•

In LAC, productivity could be boosted by policies toimprove innovation and competition. Greater tradeintegration and more welcoming environments for
FDI could lift productivity growth throughknowledge and technology transfers.

•

In SAR, productivity has been held back by belowaverage international trade integration and FDI,
which limits technology and knowledge spillovers,
and restricted access to finance from a banking systemthat is heavily state-dominated.

•

In SSA, low productivity reflects the presence of large
agricultural sectors, including widespread subsistence
agriculture. A policy priority is therefore to lift
productivity in the agricultural sector. In addition,
SSA economies tend to be involved in supply chains
only at early stages of production, producing primary
products, and have few exporting firms.

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average pace ahead of the crisis (2003-2008).
Losses relative to the exceptionally high rate ofproductivity growth in EMDEs ahead of the crisis
are closer to 14 percent, and higher still at 19
percent for EMDE commodity exporters.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

differences) drive two-thirds of global inequality(World Bank 2018c).
•

Commodity importers and exporters. Relative
productivity levels are slightly higher incommodity-importing EMDEs on average(19 percent of advanced-economy productivity) than in commodity-exporting EMDEs(17 percent) and, lower in non-oil exporters(10 percent) than in oil exporters (28 percent)(Chapter 2 boxes).

•

LICs. In LICs, productivity is just 2 percent of
the advanced-economy average, having madenegligible progress in narrowing this gap sincethe 1990s (World Bank 2019b).

•

Regions. Productivity is lowest on average in
SSA and SAR (8 and 7 percent of theadvanced-economy average respectively).
Within SSA, which hosts most LICs andmostly non-oil commodity exporters,
productivity is even lower in many economies,
falling to just 2 percent of the advanced
economy average in the bottom quartile of theregion (Box 3.1). It is highest in MNA (36percent of the advanced-economy average),which hosts several high-income oil exporters,and ECA (19 percent of the advancedeconomy average), parts of which are closely
integrated with EU supply chains and EU
labor markets. Throughout the 2000s, pre- as
well as post-crisis, the gap with advanced
economies has closed fastest in EAP and SAR
but continued to widen in parts of LAC,
MNA, and SSA.

Labor productivity
convergence
EMDE productivity levels are less than one-fifth of
the advanced-economy average, falling to just 2
percent in LICs. In some large EMDEs, such as
China and India, productivity is growing
substantially faster than in advanced economies,
resulting in productivity catch-up. However, average
EMDE productivity growth is just half a percentage
point faster than in advanced economies, requiring
more than a century to halve productivity gaps.
Faster productivity growth occurs in countries
with lower initial productivity levels whencontrolling for factors such as the level of humancapital and institutional quality (Durlauf,
Johnson, and Temple 2005; Johnson andPapageorgiou 2018). At 3.6 percent in 2018,
productivity growth in EMDEs remained morethan four times as high as in the average advanced
economy (0.8 percent). However, this aggregate
growth rate is dominated by China and India, thelargest EMDEs by output and population, where
productivity growth is above five percent. Many
EMDEs are growing at a substantially slower pacethan China and India: on average, EMDE
productivity is growing by just 0.5 percentage
point faster than in advanced economies.
Productivity gaps. Despite some narrowing of the
productivity gap in 60 percent of EMDEs sincethe 1990s, output per worker in EMDEs remains
less than one-fifth that of the average advanced
economy (Figure 3.4).10 This productivity
differential accounts for a considerable proportionof global income inequality since global per capita
income differences (reflecting mainly productivity

10 This productivity gap is measured using output per worker in2010 U.S. dollars at market exchange rates. When measured at
Purchasing Power Parity (PPP) adjusted U.S. dollars, the gap toadvanced economies is smaller, with EMDE productivity around one-third of the advanced economy average (World Bank 2018a).

Pace of productivity convergence. Productivity
convergence between low and high-productivity
economies became broad-based in the late 1990s,
with little evidence for convergence prior to this(Patel, Sandefur, and Subramanian 2018; Figure3.4).11 While the presence of convergence during
the 2000s is reassuring, its pace is disappointing.
At current productivity growth rates, productivity
gaps to advanced-economy average productivity
11 The speed of productivity convergence can be formally assessedusing a “β convergence” test, where productivity growth is regressedon the initial level of productivity (Barro 1991; Barro and Sala-iMartin 1992).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020are narrowing by 0.3 percent per year on
average—requiring more than a century just to
close half of the gap. But the pace of convergence
differs across regions. At current rates of
productivity growth, less than 20 percent of
economies in LAC, MNA or SSA—but at least 50
percent of those in EAP and SAR—are on course
to halve their productivity gap over the next 40
years.

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205

FIGURE 3.4 Distribution of productivity levels andconvergence progress
On average, productivity in EMDEs is less than one-fifth of the advancedeconomy average, and in LICs it is just 2 percent. EMDE productivity gaps
with the advanced-economy average widened during the 1970s-1990s but
narrowed from 2000 onwards. However, the implied pace of convergenceis low—even at the peak of EMDE growth, the productivity gap would havetaken over a century to halve.
A. EMDE productivity levels, 2013-18
simple average

B. Simple average of productivity
relative to advanced economies byregion, 2013-18

C. Share of EMDEs with narrowing
productivity gap to advanced
economies

D. EMDE productivity levels since the1990s, GDP-weighted average

E. Estimated annual decline inproductivity gap

F. Share of economies, by years tohalve the productivity gap withadvanced economies

Sources of post-crisis
slowdown in laborproductivity growth
Aggregate labor productivity growth can be
decomposed into its sources: into factor inputs and theefficiency of their use, or into sectors. These
decompositions suggest that the post-crisis productivity
growth slowdown in EMDEs, in approximately
equal measure, reflected weak investment and aslowdown in total factor productivity growth, as well
as fading gains from factor reallocation towards moreproductive sectors.
Decomposition into factor inputs
Approach. In the first step, productivity growth isdecomposed into contributions from individual
factor inputs (capital and human capital) and theeffectiveness of their use (total factor productivity,
or TFP, growth), assuming a Cobb-Douglas
production function (Annex 3.2). Capital
deepening directly increases labor productivity,
while human capital improvements (e.g. educationand training) enhances the quality of labor input
and therefore the resulting output produced. TFP
measures the efficiency with which all factors are
employed, and is often considered a proxy for thetechnology behind the production process.12 TFP
growth can also be affected by non-technology

12 The decomposition above is an accounting framework that does
not control for dynamic interactions between TFP and investment
growth. However, there is evidence that weak underlying TFP andinvestment growth reinforce each other, which could have amplified
the post-crisis productivity slowdown. Weaker rates of investment
reduce TFP growth by reducing the incorporation of newtechnologies into the production process (Adler et al. 2017; Hulten
1992). Conversely, slower technological change reduces the expected
return on capital and, hence, the incentives to invest.

Source: Penn World Table; The Conference Board; World Bank, World Development Indicators.
Note: Productivity defined as output per worker in U.S. dollars (at 2010 prices and exchange rates).
Based on 29 advanced economies and 74 EMDEs, which include 22 commodity-importing EMDEs
and 52 commodity-exporting EMDEs.
A. Blue bars indicate unweighted average output per worker during 2013-18 relative to the advancedeconomy average. Whiskers indicate interquartile range relative to the advanced-economy average.
B. Unweighted average productivity during 2013-18 relative to average advanced economy by region(2013-18). Includes 29 advanced economies and 74 EMDEs = 8 EMDEs in East Asia and the Pacific(EAP), 10 EMDEs in Eastern Europe and Central Asia (ECA), 18 EMDES in Latin America and the
Caribbean (LAC), 10 EMDEs in Middle East and North Africa (MNA), 2 EMDEs in South Asia (SAR),
and 26 EMDEs in Sub-Saharan Africa (SSA).
C. Share of EMDEs with faster productivity growth than the advanced-economy average.
D. GDP-weighted (at 2010 prices and exchange rates) averages.
E. Line shows the implied annual rate of decline of the productivity gap based on a regression of laborproductivity growth on initial productivity. Shaded area indicates 90 percent confidence intervals.
Estimation performed over 10-year rolling windows in the specification

logΔyt c βyt-10 εt where y is output per worker. Coefficient converted to the average annual
decline in the productivity gap following Sala-i-Martin (1992).
F. The proportion of EMDEs in each region that will close half of the productivity gap with the averageadvanced economy in each bracket of years based on average growth during 2013-18 relative toaverage advanced economy growth and the outstanding productivity gap over the same period.
Click here to download data and charts.

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factors, such as changing levels of capital and labor
utilization—therefore estimates may over orunderstate the true change in the influence oftechnology on productivity. Efforts to control for
utilization have found that while some of the precrisis surge in productivity in EMDEs was ademand-driven phenomenon of increased
utilization, a large proportion of the subsequent
slowdown was structural, reflecting factors otherthan fading demand after the global financial crisis(Dieppe, Kiliç Çelik, and Kindberg-Hanlon,
Forthcoming).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020(2003-08) relative to the 1980s and 1990s
and has now recovered modestly.15•

EMDEs. The post-crisis slowdown in EMDE
productivity growth from pre-crisis averages
reflected, in approximately equal measure,investment weakness and slowing TFP
growth. In commodity-exporters, the contribution of capital accumulation faded almost
entirely, after having accounted for about
half of productivity growth pre-crisis. Thiswas compounded by contracting TFP growth,which had accounted for most of theremainder of pre-crisis productivity growth.
Investment stalled or contracted in commodity exporters during the commodity
prices collapse of 2011-16 (Aslam et al. 2016;
World Bank 2017). TFP growth has also
been weak historically, contributing little tocatch-up growth (De Gregorio 2018). In
commodity-importers, especially China, capital
deepening accounted for much of the
productivity gains over the past four decades.
This momentum has slowed since the global
financial crisis reflecting diminishing growthprospects, heightened uncertainty, and weak
FDI inflows. In the early 2000s, TFP wasboosted by earlier reforms that allowed greater
FDI inflows in the 1990s and WTO accession
in 2001 which unleashed a productivity boomin China and its trading partners, while adecade of service-sector oriented reforms
boosted productivity in India (Bosworth andCollins 2008; He and Zhang 2010; Tuan,Ng, and Zhao 2009).

•

LICs. In LICs, heavy public infrastructure
investment and business climate improvements have supported post-crisis output andproductivity growth (World Bank 2019c).
This followed on the heels of a decade ofheavy investment into mines and oil fields

Factors inputs versus the effectiveness of theiruse. Globally, the post-crisis (2013-18) slowdown
in labor productivity growth from pre-crisis(2003-08) averages amounted to half of apercentage point, the majority of which was a
result of a slowdown in capital accumulation(both public and private; World Bank 2019b). In
advanced economies, the slowdown in TFP
growth was a minor source of the post-crisis
decline in labor productivity growth, due to a
structural slowdown prior to the crisis.13 In
EMDEs, however, it accounted for about one-halfof the slowdown in labor productivity growth.
•

Advanced economies. Investment weakness
accounted for virtually all of the post-crisis
slowdown in productivity growth from precrisis averages in advanced economies (Figure3.5). From 2008, investment growth slowed
sharply in response to weak and highly
uncertain growth prospects, heightened policy
uncertainty, and credit constraints in theaftermath of the global financial crisis.14
Investment contracted by an average of 6
percent per year between 2008-09. While theinvestment share of GDP has recovered close
to pre-crisis levels, it has been accompanied bystrong rates of employment growth, such thatthe growth of capital per worker has remained
subdued (ECB 2017). TFP growth had
already declined in the pre-crisis period

13 This finding is in line with previous studies of the United Statesand other advanced economies (Adler et al. 2017; Fernald et al.
2017).
14 See for details Duval, Hong, and Timmer (2017) and Ollivaud,
Guillemette, and Turner (2016).

15 Much of the recent discussion of advanced economy TFP
growth has focused on the slowdown in the United States, where
TFP has weakened further since the crisis following a surge from themid-1990 to 2000s (Fernald et al. 2017; Cowen 2011; Gordon
2018). In contrast, average TFP growth was low in the pre-crisis
period in major European economies such as Germany and France(0.1-0.4), and even negative in Italy and Spain, such that the postcrisis TFP slowdown is much less pronounced for advanced
economies in aggregate.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

amid surging pre-crisis commodity prices. As
a result, continued post-crisis strength inproductivity growth reflected increased capital
accumulation. Modest improvements inhuman capital partly offset increasingly
negative TFP growth in these economies. A
continued concentration in the agriculturaland extractives sectors has led to low
technological progress, with additional
negative shocks from conflict and from highlevels of debt in the 1980s and 1990s also
contributing to frequently negative TFP
growth (Claessens et al. 1997; IMF 2014).
•

EMDE
regions. Capital accumulationaccounted for virtually all of the post-crisis
slowdown in productivity growth in MNA,
where oil-exporting EMDEs suffered stalled
or contracting investment amid the oil pricecollapse of 2014-16 (Stocker et al. 2018). It
also accounted for most of the slowdown in
ECA, whose banking systems were hard-hit by
the Euro Area crisis and the subsequent retreat
from the region of EU-headquartered banks(Arteta and Kasyanenko 2019). In EAP, adeliberate policy-guided public investment
slowdown in China is underway and slower
capital accumulation accounted for about
two-fifths of the slowdown in post-crisis
productivity growth. In SSA, which hosts
most LICs, and in LAC, the slowdown was
entirely driven by declining TFP growth. In
contrast to other EMDE regions, TFP growthstrengthened in MNA, from negative precrisis rates amid heavy resource investment,
and in SAR, which was little-affected by the
disruptions of the global financial crisis.

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207

FIGURE 3.5 Decomposition of productivity growth
Almost three-quarters of the post-crisis slowdown in global productivity
growth from pre-crisis averages—and virtually all in advanced
economies—reflected a slowdown in capital accumulation. The post-crisis
slowdown in EMDE productivity growth from pre-crisis averages reflected,
in approximately equal measure, investment weakness and slowing TFP
growth. In LICs, strong investment has supported post-crisis output andproductivity growth.
A. Contributions to productivity
growth in advanced economies

B. Contributions to productivity
growth in EMDEs

C. EMDE commodity exporter andimporter productivity contributions

D. Contributions to productivity
growth in LICs

E. Contributions to regional
productivity growth: EAP, ECA, LAC

F. Contributions to regional
productivity growth: MNA, SAR, SSA

Decomposition into sectors
Approach. Higher aggregate productivity growthin EMDEs in the pre-crisis period was associatedwith a reallocation of resources towards moreproductive sectors in addition to productivity
growth within sectors (Diao, McMillan, andRodrik 2017). More recently, pre-crisis gains
from such reallocation appear to have faded. Thisis illustrated in a decomposition of economy-widelabor productivity growth into within- andbetween-sector productivity growth for 80
economies, including 38 EMDEs, of which 7

Source: Barro and Lee (2015); International Monetary Fund; Penn World Tables; The Conference
Board; United Nations; Wittgenstein Centre for Demography and Global Human Capital; World Bank,World Development Indicators.
Note: Productivity defined as output per worker. Aggregate growth rates calculated using constant2010 US dollar weights. 52 commodity exporters, 22 EMDE commodity importers, 8 East Asia andPacific, 10 Europe and Central Asia, 18 Latin America and the Caribbean, 10 Middle East and North
Africa, 2 South Asia, and 26 Sub - Saharan Africa economies. GDP weights. The sample includes 29
advanced economies, and 74 emerging market and developing economies including 11 low-income
countries.
Click here to download data and charts.

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are LICs, for nine sectors during 1995-2015(Box 3.2).
Wide differentials in sectoral productivity. Labor
productivity varies widely across sectors, being
lowest by far in agriculture and highest in mining,financial and business services, and utilities. In
EMDEs, labor productivity in mining andfinancial and business services, which are often
foreign-owned, is thirty to forty times the level ofproductivity in the agriculture sector, which isoften characterized by smallholder farms (Figure
3.6; Lowder, Skoet, and Raney 2016). In
advanced economies, this differential isconsiderably narrower (three times). As a result,agricultural productivity in EMDEs lags far
behind that in advanced economies—in the
average EMDE, agricultural productivity is lessthan one-fifth that in the average advancedeconomy. In contrast, services sectors such astransport or financial and business services are
small in EMDEs, accounting for 22 percent ofvalue-added in total, but feature productivity thatis two-fifths to one-half of advanced-economy
productivity on average.
Fading gains from factor reallocation in EMDEs.
In EMDEs, about one-half of the post-crisis(2013-15) slowdown in productivity growth frompre-crisis (2003-08) averages reflected fading gains
from resource reallocation towards moreproductive sectors. In the 1990s and pre-crisis,such resource reallocation had accounted for morethan one-third of average labor productivity
growth, in line with earlier findings (Diao,
McMillan, and Rodrik 2017). Productivity gains
from such a reallocation were particularly large in
Sub-Saharan Africa, where they accounted for overhalf of productivity growth during 2003-2008,
amid a large fall in the share of agricultural
employment.
Post-crisis, the contribution of reallocation to
productivity growth fell to less than one-quarteron average in EMDEs. To some degree as
countries reach middle-to high income, sectoral
reallocation tends to become a less importantdriver of productivity growth (de Nicola,
Kehayova, and Nguyen 2018; Mason and Shetty2019). In addition, technology and knowledge

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

spillovers between sectors may also be diminishing(Foerster et al. 2019). However, productivity gaps
between sectors in EMDEs remain sizeable. In
contrast to other regions, productivity gains fromreallocation continue to be sizable in SAR,
accounting for one-half of post-crisis productivity
growth, as agricultural employment moves into
industrial sectors.
Challenges for within-sector productivity
growth. Within-sector productivity gains also
decelerated post-crisis, in EMDEs as well as
advanced economies. The post-crisis slowdown
may reflect the challenges faced by the mostproductive firms (large, export-oriented ones)
amid post-crisis trade and investment weakness(Box 3.3). In many EMDEs, an additional
challenge may arise from the sheer size of theinformal sector (World Bank 2019a). The laborproductivity of informal firms is, on average, only
one-quarter of the productivity of formal firms.
Informal firms are less able than formal firms toreap the productivity gains from economies ofscale (size), accumulated experience (age),
agglomeration benefits (location), and best
managerial practices (Fajnzylber, Maloney, andMontes-Rojas 2011). Moreover, aggressivecompetition from informal firms can erode theproductivity of exposed formal firms by about 24
percent relative to those formal firms that do not
face informal competition (Loayza 2016; WorldBank 2019a). A more conducive business climate,and economic development more broadly, canalleviate some of the corrosive productivity effects
of informal competition on formal firms.
Fading gains from reallocation away fromagriculture in LICs. In LICs, agriculture accounts
for 31 percent of GDP, on average, but
agricultural productivity is low (Cusolito andMaloney 2018). As a result, a reallocation of
employment, especially from agriculture, tohigher-productivity sectors accounted for almost
two-thirds of LIC productivity growth prior to theglobal financial crisis (Box 3.2). Since then,however, this engine of LIC productivity growthappears to have stalled. In part, this is due to acollapse in global industrial commodity prices,which have discouraged further growth in
employment in the mining and extraction sector,

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BOX 3.2 Sectoral sources of productivity growth
Labor reallocation towards higher-productivity sectors has historically accounted for about one-third of aggregate productivity
growth in EMDEs. This mechanism has, however, weakened since the global financial crisis. Fading productivity gains fromlabor reallocation have accounted for about one-half of the post-crisis productivity slowdown in EMDEs. In commodity-exporting
EMDE regions, deindustrialization contributed to the slowdown.

Introduction
Factor reallocation towards higher-productivity sectors haslong been recognized as one of the most powerful driversof aggregate productivity growth (Baumol 1967).1 It has
been identified as an important driver of productivity
growth in economies as diverse as Sub-Saharan Africa,
China and Vietnam (Cusolito and Maloney 2018; deVries, de Vries and Timmer 2015; Fuglie et al. 2019).
Especially in East Asia, the move out of agriculture intohigher-productivity industry and services has been creditedwith rapid productivity growth (Helble, Long, and Le2019).
In part as a result of several decades of sectoral reallocation
away from agriculture, agriculture now accounts for only
10 percent of EMDE value-added—one-quarter less than
two decades earlier and less than one-third the share ofindustrial production (Figure 3.2.1). LICs are an exception
where agriculture still accounts for one-third of valueadded, more than industry, and accounts for over 60 ofemployment.
Meanwhile, services sectors have grown rapidly over the
past two decades. They now account for about one-half ofvalue-added in EMDEs as well as LICs, compared withthree-quarters of value-added in advanced economies.
Services sectors have also been the main source of postcrisis productivity growth, accounting for almost twothirds of productivity growth in the average EMDE
(compared with one-fifth accounted for by industry) andmore than three-quarters in the average LIC.
Services describe a highly heterogeneous set of activities.
Whereas industry mostly consists of manufacturing (64percent in the average EMDE), services include in almost
equal measure trade services, transport services, financial
and business services, and government and personal
services. These service subsectors vary widely in their skilland capital-intensity as well as their productivity.

Note: This box was prepared by Alistair Dieppe and Hideaki
Matsuoka.
1 Throughout this box, productivity refers to labor productivity,
defined as value added per employed worker.

Against this backdrop, this box examines the sources of the
post-crisis slowdown in productivity growth from a
sectoral angle. Specifically, it addresses the following
questions.
•

What are the main features of sectoral productivity?

•

What was the role of sectoral reallocation in the postcrisis productivity growth slowdown?

Much of the earlier literature on sectoral productivity has
focused on three sectors (agriculture, manufacturing, andservices) with only a limited number of cross-country
studies including more sectors.2 There is evidence that thefindings of reallocation are sensitive to the level ofaggregation (de Vries et al. 2012; Üngör 2017). To explore
these issues, this box draws on a comprehensive dataset for
80 countries and 9 sectors over 1995-2015.

Features of sectoral productivity
Wide productivity differentials across sectors.
Productivity differs widely across sectors, offering large
potential for productivity gains by factor reallocation
across sectors (Figure 3.2.3). In the average EMDE,
productivity in the most productive sector—mining,which accounts for 4 percent of value-added—is twelve
times that in the least productive sector—agriculture,
which accounts for 10 percent of value-added.3 In the
average LIC, the range is even larger: productivity in themost productive sector—financial and business services,
accounting for 13 percent of value-added—is twenty-twotimes that in the least productive sector—agriculture,
which accounts for almost one-third of value-added

2 Diao, McMillan, and Rodrik (2017) and McMillan, Rodrik, andVerduzco-Gallo (2014) employ 38 and 39 countries; Martins (2019) use
7 sectors and 169 countries, and International Monetary Fund (2018)use 10 sectors and 62 countries. Further disaggregation using micro panel
data (such as by Hicks et al. 2017) would help to ensure differences inmarginal product are accounted for.
3 The high productivity extractive sectors offer few opportunities for
sectoral reallocation and are intrinsically limited by the size of the resource, and market power. It should be noted that refining and processing of extractives can sometimes be classified as manufacturing inresource rich countries.

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BOX 3.2 Sectoral sources of productivity growth (continued)
FIGURE 3.2.1 Agriculture, industry and services
In part as a result of a several decades of sectoral reallocation away from agriculture, agriculture now accounts for only 10
percent of EMDE value-added—one-quarter less than two decades earlier and less than one-third the share of industrial
production. LICs are an exception; agriculture still accounts for one-third of value-added in these economies, more thanindustry. Meanwhile, services sectors—which include a highly heterogeneous set of activities—have grown rapidly over the
past two decades, accounting for about half of post-crisis productivity growth.
A. Composition of value-added

B. Composition of employment

C. Contributions to productivity growth

Source: APO productivity database, Expanded African Sector Database, Groningen Growth Development Center Database, Haver Analytics, ILOSTAT, OECD STAN,
United Nations, World KLEMS.
Note: Based on sample of 80 countries.
A.B. Share of agricultural, industry and services in value added. Industry includes mining, manufacturing, utilities, and construction. Services include trade services,transport services, financial and business services, government and personal services. Black horizontal line indicates 50 percent.
Click here to download data and charts.

(Figure 3.2.2).4 Since the 1990s, the productivity
dispersion within the manufacturing and service sectors,
has narrowed. Similar differentials, between the mostproductive sector (financial and business services) and theleast productive sector (agriculture), in advanced
economies are considerably narrower.
Wide sectoral productivity differentials across countries.
Productivity in all sectors is lower in EMDEs than inadvanced economies, and lower again in LICs. The gap
between EMDE and advanced-economy productivity isparticularly wide (almost 80 percent) in agriculture, whichtends to be characterized by smallholder ownership andfamily farms in EMDEs (Lowder, Skoet, and Raney
2016). This reflects in part slow technology adoption in
the agriculture sector in some of the poorest EMDEs. In
mining, which tends to be dominated globally by a few
large companies, the productivity gap is considerably
narrower (just over 20 percent).
Sectoral productivity growth. Productivity growth in thevarious subsectors of services varied widely, from negative
4 As agricultural workers often do not work full time in agriculture, thesectoral gap is diminished if productivity is measured per hours instead of
per worker (McCullough 2017). However, even after accounting forhours and human capital per worker, a large sectoral gap remains formany of countries (Gollin, Lagakos, and Waugh 2014).

(pre-crisis) or near zero (post-crisis) in mining to thehighest sectoral growth rates (4.8 percent) in transport
services in EMDEs in 2003-08 (Duernecker, Herrendorf,
and Valentinyi 2017).5 The post-crisis (2013-15)
slowdown in manufacturing productivity growth was thelargest among all nine sectors, nearly 2 percentage points
below the pre-crisis average (2003-08).
In advanced economies, the post-crisis productivity growth
slowdown was broad-based across almost all sectors (except
construction). More than one-half of the post-crisis (201315) slowdown in productivity growth from pre-crisis rates(2003-08) in the average EMDE originated in the
manufacturing sector. The slowdown in agricultural
productivity growth had only a limited aggregate effect in
EMDEs due to its relatively small share in the economy.
In contrast, EMDE productivity growth picked up after

5 Two waves of service sector growth have been identified in the literature: a first wave in countries with relatively lower income levels and asecond wave in countries with higher income levels. The first wave appears to be made up primarily of traditional (personal) services, the second wave of modern (financial, communication, computer, technical,
legal, advertising and business) services that are receptive to the application of information technologies and tradable across borders(Eichengreen and Gupta 2013). Moreover, there is evidence of the second wave also occurring in lower income countries after 1990 which aredemocracies, and have high trade and financial openness.

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BOX 3.2 Sectoral sources of productivity growth (continued)
FIGURE 3.2.2 Sectoral labor productivity
Productivity differs widely across sectors and subsectors, especially in EMDEs and even more so in LICs. Productivity in all
sectors is lower in EMDEs than in advanced economies, and lower again in LICs. The gap to advanced-economy
productivity is particularly wide in agriculture, and narrow in mining. Industry was the main source of pre-crisis productivity
growth; its slowdown accounted for more than half the post-crisis slowdown in aggregate productivity in EMDEs.
A. Sectoral productivity relative towithin-group average productivity

B. Sectoral productivity relative to theadvanced-economy median

C. Sectoral productivity growth inadvanced economies

D. Sectoral productivity growth in EMDEs

E. Contributions to productivity growth

F. Contributions to productivity growth
slowdown between 2003-08 and 2013-15

Source: APO productivity database, Expanded African Sector Database, Groningen Growth Development Center Database, Haver Analytics, ILOSTAT, OECD STAN,
United Nations, World KLEMS.
Note: Based on samples of 80 countries. Median of the county-specific productivity level, or growth rate.
A. Bar charts range from the minimum to the maximum sector productivity gap.
B. Sectoral productivities compared at PPP exchange rates.
E.F. “Industry” includes mining, manufacturing, utilities, and construction; “Finance” includes business services; “Government” includes personal services.
Click here to download data and charts.

the global financial crisis in construction, utilities andmining.

Role of sectoral reallocation
Framework. The productivity differentials between sectors
offer the potential for productivity gains from labor
reallocation towards higher-productivity sectors, inaddition to within-sector productivity gains (Figure
3.2.3).6 This is captured in a shift-share analysis that
6 However, Fuglie et al. (2019) point out that different factor shares invalue added would result in a gap of average labor productivity even if thefactor allocation is efficient. A gap in average productivity is not sufficientevidence of misallocation because labor productivity can be equalized at
the margin.

decompose aggregate labor productivity into within-sector
and between-sector components (Wong 2006, PadillaPérez and Villarreal 2017). Within-sector productivity
growth captures changes in aggregate labor productivity
growth due to productivity improvements within sectors.
This may reflect improvements in human capital,
investments in physical capital, or the reallocation of
resources from the least to the most productive firms
within each sector. Between-sector productivity growth isdriven by the change in employment share and the
productivity differential. It reflects both the reallocation of
resources to sectors with higher productivity levels (static
sectoral effect), and the reallocation of employment
towards sectors with higher productivity growth (dynamic

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BOX 3.2 Sectoral sources of productivity growth (continued)
FIGURE 3.2.3 Between- and within-sector sources to productivity growth
While productivity growth in advanced economies has predominantly originated within sectors, between-sector gains haveaccounted for a sizable portion of EMDE productivity growth, and its post-crisis slowdown. In EMDEs, the between-sector
productivity gains have involved shifts out of agriculture into higher-productivity sectors that have differed over time.
A. Sectoral productivity relative tocountry productivity

B. Contributions to productivity growth

C. Contributions to within-sector productivity growth

D. Contributions to between-sector
productivity growth

E. ECA, LAC, MNA: Composition of
employment

F. ECA, LAC, MNA: Contributions to
productivity growth

Source: APO productivity database, Expanded African Sector Database, Groningen Growth Development Center Database, Haver Analytics, ILOSTAT, OECD STAN,
United Nations, World KLEMS.
B-D. Growth within sector shows the contribution of initial real value-added weighted productivity growth rate and structural change effect give the contribution arisingfrom changes in the change in employment share. Median of the county-specific contributions. Based on samples of 80 countries. “Manuf.” includes mining andutilities; “Finance” includes business services; “Government” includes personal services.
Click here to download data and charts.

sectoral effect). Underlying drivers of such between-sector
productivity growth include changes in household’s
preferences and changes in relative sectoral productivity, inpart as a result of diverging evolutions of labor quality(Lagakos and Waugh 2013).7

7 Improvements in agricultural productivity can significantly reduce
agriculture’s share of employment, contributing to between-sector
productivity growth (Gollin, Parente, and Rogerson 2007). The role ofagriculture in structural change depends on economic integration within
the domestic economy and with global markets (Barrett et al. 2017).

Decomposition of aggregate productivity growth. While
productivity growth in advanced-economies has
predominantly originated within sectors, between-sector
gains have accounted for one-third of EMDE productivity
growth since the 1990s. In part as a result of narrowing
cross-sector productivity differentials and, in some regions,labor movements into lower-productivity sectors, fading
sectoral reallocation has accounted for about one-half ofthe post-crisis slowdown in EMDE productivity growth.
The between-sector EMDE productivity gains have
involved shifts out of agriculture into higher-productivity
sectors that have differed over time.

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BOX 3.2 Sectoral sources of productivity growth (continued)•

Advanced economies. Productivity growth in advanced
economies, where sectoral productivity differentials
tend to be narrower than in EMDEs, has been almost
entirely driven by within-sector productivity growthsince the 1990s. Within-sector productivity growthhas dwindled to 0.6 percent during 2013-15—lessthan half its 1990s average (Figure 3.2.3). The
predominant structural change has been thereallocation of resources from manufacturing to thefinancial and business services sector, two sectors withcomparable levels of productivity.

•

EMDEs. In contrast, between-sector productivity
gains in EMDEs boosted productivity growth precrisis (2003-08) by 1.1 percentage points. Post-crisis,
this contribution fell to 0.5 percentage points,
accounting for about one-half of the slowdown in
EMDE productivity growth. Between-sector productivity gains have mainly reflected a move out ofagriculture and manufacturing into services. In LICs,
between-sector gains accounted for almost half ofpost-crisis productivity growth, down from almost
three-quarters of pre-crisis productivity growth.8
Whereas pre-crisis between-sector productivity gains
in LICs mainly reflected a shift out of agriculture into
manufacturing, their main post-crisis source was ashift out of agriculture into services such as tradeservices and finance and business services that havebenefited from information and computing
technologies (Eichengreen and Gupta 2013).

Leapfrogging. Over the two decades until the global
financial crisis, one-third of the EMDE employment thatleft agriculture moved into industrial sectors(predominantly manufacturing and construction) andanother one-third into trade services. The share of
agricultural employment in EMDEs declined by 9.4
percentage points between 1995 and 2008 while the sharesof industry and trade services rose by 2.5 and 3.0
percentage points, respectively. Although trade services
and construction typically have below-average productivity
and manufacturing productivity is near the EMDE
average, the employment shift out of extremely low-

8 This is consistent with Diao, McMillan, and Rodrik (2017) and, for
Sub-Saharan Africa, McMillan, Rodrik, and Verduzco-Gallo (2014).

productivity agriculture generate aggregate productivity
gains. In LICs, a somewhat larger portion (almost half) of
the 10 percentage point decline in the share of agricultural
employment was absorbed by trade services and only justover one-third by industry. The phenomenon of
employment shifting out of agriculture into services has
been dubbed “leapfrogging” in the context of concerns
about premature deindustrialization (Rodrik 2016).
Looking ahead, productivity gains arising from low-skilledlabor shifting out of agriculture into manufacturing orservices may diminish if robotization and artificial
intelligence discourage this movement.
Deindustrialization. In three regions—Europe andCentral Asia (ECA), Latin America and the Caribbean(LAC), the Middle East and North Africa (MNA)—themanufacturing sector’s (as well as agriculture’s) share of
employment has shrunk since the crisis, continuing a precrisis trend.9 Employment has largely shifted into
construction (MNA), finance (ECA, LAC) and tradeservices (ECA, MNA). Since some of these sectors,
especially construction and trade services, have lower
productivity than manufacturing, this has resulted in a
sharply lower contribution (ECA) or even negative
contribution (LAC, MNA) of between-sector sources of
productivity growth (Rodrik 2016). In LAC, for example,
trade liberalization in the 1990s led to cheaper
manufacturing imports and a contraction in employment
in the uncompetitive manufacturing sector. Much of thislabor was absorbed in construction and trade services thatwere buoyed by pre-crisis commodity boom (Gollin,
Jedwab, and Vollrath 2015).

Conclusion
Large sectoral productivity differentials in EMDEs and
LICs offer the potential of additional productivity gains
when labor moves towards higher-productivity sectors.
Such between-sector productivity gains have contributed
importantly to productivity growth in EMDEs and LICs
since the 1990s. However, since the global financial crisis,
these gains appear to have faded.

9 To some degree this could reflect an outsourcing of parts of the
manufacturing sector to the service sector.

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FIGURE 3.6 Sectoral productivity developments
Productivity varies widely across sectors, with agricultural productivity in
EMDEs lagging both advanced economies and other sectors in EMDEs.
Fading gains from resource reallocation towards more productive sectors
have accounted for about half of the post-crisis slowdown in productivity
growth. Within-sector productivity growth has also slowed.
A. Sectoral productivity relative tocountry average

C. Composition of value-added

B. Sectoral productivity in EMDEs
relative to advanced-economy levels

D. Contribution to aggregate
productivity growth

many of these and some other correlates of
productivity growth have slowed or gone into reverse.
These include investment weakness; a slower pace ofurbanization; maturing gains from macroeconomic
stability and global integration; and diminishing
improvements or stagnation in educational attainment, gender equality, and governance.
A large number of variables have been proposed as
possible drivers of productivity (Annex 3.3).16
These drivers can be grouped into three categories:
the quality and quantity of factors of production
and the effectiveness of their use, such as capital,
education, and innovation; the supporting
economic environment, such as institutions andsocial conditions; and the degree of market
development, such as trade integration andfinancial market development. This section
presents the correlations of productivity growthwith initial conditions for these drivers and, in asecond step, discusses the evolution of thesedrivers.
Correlation between productivity growthand its drivers

Source: APO productivity database, Expanded African Sector Database, Groningen Growth
Development Center Database, Haver Analytics, ILOSTAT, OECD STAN, United Nations, WorldKLEMS.
Note: Sample includes 80 economies (including 46 EMDEs, of which 8 are LICs). “Manuf.” includes
mining and utilities; “Finance” includes business services; “Government” includes personal services.
A. Deviation of sectoral productivity level from country-specific average productivity.
B. Grey horizontal line indicates 50 percent.
C. Share of total value added.
D. Growth “within sector” shows the contribution to aggregate productivity growth of each sector
holding employment shares fixed. The ‘between sector’ effect shows the contribution arising fromchanges in sectoral employment shares. Median of the country-specific contributions.
Click here to download data and charts.

which have above-average productivity levels in
LICs. Despite having high productivity levels, themining and extraction sectors often offer limited
scope for expanding employment outside ofcommodity
booms,
andtherefore
few
opportunities for sustainable sectoral reallocation.

Long-run drivers of
productivity growth
During the pre-crisis productivity surge in EMDEs,
growth was highest in those economies with morefavorable institutional environments, more developed
product and factor markets, and higher or higherquality factor inputs. Subsequently, improvements in

Methodology. The contributions of potential
drivers of productivity growth are estimated in across-section regression to identify the main initialcountry features associated with subsequently
higher long-term productivity growth (1960-2018
and 1995-2018) for 59 countries, including 38
EMDEs. Key correlates of productivity growth are
selected from a pool of 29 variables by Bayesian
techniques to systematically exclude variables thathave poor explanatory power for productivity
growth and overlapping variables which reflect thesame underlying driver (Annex 3.3).
Key initial conditions for higher productivity
growth. Productivity in economies with favorable
starting conditions in the 1960s grew significantly
faster than other economies annually. A better
educated workforce (proxied by years of schooling)andstrongerinstitutions(proxiedbyimprovements in the rule of law), greater

16 See Durlauf, Johnson, and Temple (2005) and Kim and Loayza(2019).

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BOX 3.3 Patterns of total factor productivity: A firm perspective
There is substantial variation in firm-level total factor
productivity (TFP) across industries and across regions. Weakfirm productivity in emerging market and developing
economies (EMDEs) partly reflects the divergence between a
few highly productive firms and a large number of firms that
operate far from the productivity frontier. The difference
between frontier and laggard firms is, on average, larger in
EMDEs than in advanced economies. Among EMDE firms,
large firms tend to be more productive than small firms.
Firms in technology-intensive industries, mainly located in
East Asia and Pacific (EAP), Europe and Central Asia
(ECA), and South Asia (SAR), tend to be more productive
than firms in more traditional sectors. Measures to promote
exports and improve business climates can help close theobserved TFP gap.

Introduction
Firm-level productivity in emerging markets anddeveloping economies (EMDEs) has been low relative toadvanced economies, and growth has lost momentum overthe past decade. This has diminished prospects among
many EMDEs to catch up with the advanced economies(Andrews, Criscuolo, and Gal 2016; Cusolito andMaloney 2018).
Numerous factors have been identified as underlying the
low firm-level productivity observed in EMDEs: weak
institutions and pervasive informality, slow technology
innovation and adoption, subdued investment and poor
quality infrastructure, low human capital and poor firmmanagement practices, protectionist trade policies andweak economic integration (Cusolito and Maloney 2018;
World Bank 2019d, 2019e).1 Moreover, outdated
technologies, lagging innovation, misallocation of labor toinefficient sectors, and market rigidities weigh on
productivity and contribute to dispersion in total factor
productivity(TFP)
acrosscountries(Araujo,
Vostroknutova, and Wacker 2017; Bahar 2018; Syverson2011). In some EMDEs, low participation in global value
chains, or lack of openness to foreign direct investment
and migration, has resulted in missed opportunities for a
productivity boost through the transfer of innovative
processes and managerial capabilities (Goldberg et al.
2010; World Bank 2019d).
This box undertakes a cross-sectional study to analyze
firm-level TFP patterns, and maps these to firm
Note: This box was prepared by Cedric Okou.
1 Many studies focus on labor productivity, which depends on both
TFP and capital per worker–also known as capital deepening.

characteristics in EMDEs to address the following
questions:•

How does firm-level TFP vary across EMDE sectors
and regions?

•

What firm characteristics account for the dispersion in
TFP?

TFP variation across sectors and regions
Productivity varies across firms, within sectors, and acrossregions (Goñi and Maloney 2017). By focusing on TFP,
differences due to capital deepening or other factor inputs
can be abstracted from. This allows to identify where
TFP dispersion and gaps are the largest, and where steps
are needed to improve productivity. Firm-level TFP data
are obtained from surveys conducted by the World Bank
from 2007 to 2017 (Cusolito et al. 2018). The database of
survey results contains TFP for 15,181 manufacturingfirms in 108 EMDEs, including 20 low-income countries(LICs). A cross-sectional analysis of the firm-level TFP
database is undertaken, which complements longitudinalstudies that use micro-level panel data, but with a smaller
country coverage.2 Two measures of TFP are constructed:
output and value-added revenue TFP measures. The
latter is obtained by subtracting the value of intermediate
inputs (materials, electricity, etc.) from output before
computing TFP. TFP measurement challenges are
discussed in Annex 3.5.
TFP across sectors. Differences in firm-level TFP acrosssectors have been frequently emphasized in the literature.3
On average, firms in technology-intensive industries havehigher TFP than those in other sectors (Figure 3.3.1.A).
Technology-intensive industries, denoted by TINT,
include computing and electrical machinery, precisionequipment, electronics, information, and communication
sectors (as in Fernald 2015). One explanation for thisobservation is that firms operating in a technologyintensive industry rely more on research and development(R&D) and network linkages than physical assets, and as

2 This analysis does not explore the time series dimension because
World Bank’s firm output and input data used to construct TFP
estimates were collected at different time in different countries. For
example, these firm surveys were conducted in 2007 in South Africa andin 2017 in Ecuador. Moreover, the number of surveyed firms in many
countries is small, which does not allow to conduct robust within andcross-country comparisons.
3 See for example, Bartelsman and Doms (2000) and Levchenko andZhang (2016).

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BOX 3.3 Patterns of total factor productivity: A firm perspective (continued)
FIGURE 3.3.1 Firm TFP and distance-to-frontier in EMDEs by industry
Firms in technology-intensive industry (TINT) have higher average TFP. These technology-intensive firms are also more
tightly clustered around their industry-specific frontier than firms in other sectors.
A. TFP estimates, by industry

B. Distance-to-frontier and average
output TFP, by industry

C. Distance-to-frontier and averagevalue-added TFP, by industry

Source: World Bank Enterprise Surveys, World Bank.
Note: Firm-level TFP is computed using a Cobb-Douglas production function for each industry, assuming that elasticities of output with respect to inputs are the sameacross countries in a given income group. The distance-to-frontier of TFP is computed within each industry, excluding the top 2.5 percent of firms. For each sector, thelocation shows the average and the size of the marker (circle) is proportional to one standard deviation of distance to frontier of TFP. Averages and standard deviations
are computed using survey weights. Sample includes 15,181 firms in 108 EMDEs, including 20 LICs, for the period 2007-17. Firms operate in 15 industries:
APPA = apparel, CHEM = chemicals, FABM = fabricated metals, FOOD = food, FURN = furniture, LEAT = leather, MACH = non-electrical machinery, META = metals,
MINE = non-metallic minerals, MOTO = motor vehicles, PAPE = paper, RUBB = rubber, TEXT = textiles, TINT=technology-intensive, WOOD = wood. The
technology-intensive industry (TINT) includes firms in computing and electrical machinery, precision equipment, electronics, information, and communication sectors.
A. In the manufacture of paper (PAPE) industry, the value-added TFP is positive and much higher than the corresponding (negative) output TFP due to a relatively highelasticity of output with respect to intermediate inputs.
B. C. Distance-to-frontier of firm-level TFP (minus) and TFP (log), by industry. The right-hand-side y-axis represent the frontier.
Click here to download data and charts.

such can reap the benefits of technology to boost
productivity (Chevalier, Lecat, and Oulton 2012).
Distance to TFP frontier across sectors. TFP dispersion
may signal rigidities in the generation, transfer andacquisition of technology across firms in a sector. To assess
within-sector productivity dispersion, a firm’s distance toan industry-specific TFP frontier is computed.4 Firms inbasic manufacturing industries, such as non-electricalmachinery (MACH), textiles (TEXT), leather (LEAT),
and basic metals (META), are not only on average lessproductive than firms in other sectors, but also relatively
far from their industry-specific frontiers (Figure 3.3.1.B
and 3.3.1.C). By contrast, firms in technology-intensive
industries (TINT) are more tightly clustered around theirindustry-specific frontiers and are more productive.5
TFP across regions. Across regions, firms in East Asia andPacific (EAP) are, on average, more productive than those
4 For a given firm i, the distance to an industry-specific TFP frontier(97.5th quantile) is computed as DTFi = TFP0.975 - TFPi≤0.975. The top 2.5
percent firm-level TFP values are dropped to minimize the impact of
extreme values. Results are robust to alternative 1 and 5 percent cutoffs of
top firm TFP values.
5 This finding is broadly in line with the evidence in HallwardDriemeier and Nayyar (2017).

in other regions (Figure 3.3.2.A). EAP also has the highest
proportion of large size firms and firms exporting morethan half of their sales (Figure 3.3.2.C and Figure
3.3.2.D). Most firms in technology-intensive industries arelocated in EAP, Europe and Central Asia (ECA), andSouth Asia (SAR) (Figure 3.3.2.B; regional boxes in
Chapter 2). Perceptions of corruption and licensing as
obstacles for firm operation seem to correlate negatively
with total factor productivity (Figure 3.3.2.E-F).
Robustness of TFP dispersion. Substantial TFP dispersion
may signal misallocation of factor inputs or rigidities in thegeneration, transfer, and acquisition of technology acrossfirms (Hsieh and Klenow 2009). However, commonlyused dispersion metrics can also reflect mismeasurements,
quality differences, adjustment costs, markups, andinvestment risks, among other factors. Recent evidenceshows that half of the dispersion is unrelated tomisallocation, and driven rather by markups andtechnology wedges (Cusolito and Maloney 2018). Thus,
dispersion results should be interpreted with caution.
Nonetheless, the variation in distance to frontier intechnology-intensive industries is less than one-fifth of thatin basic manufacturing industries (leather, metals,
machinery), suggesting that firms in technology-intensive
industries are much closer to their sector-specific frontier.

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BOX 3.3 Patterns of total factor productivity: A firm perspective (continued)
FIGURE 3.3.2 Firm TFP by regions
Firms in EAP are more productive than those located in other EMDE regions. EAP also has the highest share of large-size
firms and those exporting more than half of their sales. Most firms in technology-intensive industry (TINT) are located in EAP,
ECA, and SAR. Perceptions of corruption and licensing as obstacles for firm operation correlate negatively with total factor
productivity (TFP).
A. Firm-level TFP, by region

B. Percentage of firms in each region,by industry

C. Firm size, by region

D. Exporting firms, by region

E. Perception of corruption, by region

F. Perception of licensing obstacles,
by region

Source: World Bank Enterprise Surveys, World Bank.
Note: Firm-level TFP is computed using a Cobb-Douglas production function for each industry, assuming elasticities of output with respect to inputs are the same acrosscountries in a given income group. Unweighted regional averages are computed. Sample includes 15,181 firms in 108 EMDEs, including 20 LICs, for the period 2007-17.
EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and
SSA = Sub-Saharan Africa.
A. Solid lines are averages of output TFP (log) for EMDEs (orange) and LICs (red). EMDEs = emerging markets and developing economies, LICs = low-income countries.
B. Bars show in each industry the percentage of firms in each region, by industry. Firms operate in 15 industries: APPA = apparel, CHEM = chemicals, FABM = fabricatedmetals, FOOD = food, FURN = furniture, LEAT = leather, MACH = non-electrical machinery, META = metals, MINE = non-metallic minerals, MOTO = motor vehicles,
PAPE = paper, RUBB = rubber, TEXT = textiles, TINT = technology-intensive, WOOD = wood. The technology-intensive industry (TINT) includes firms in computing andelectrical machinery, precision equipment, electronics, information, and communication sectors.
C. Firm size in terms of number of employees.
D. Share of exporting firms. High, medium, and low exports firms export more than 75 percent, between 50 and 75, and up to 25 percent of their sales, respectively.
E. Share of firms that perceive corruption as an obstacle for their operations.
F. Share of firms that perceive licensing and permits as an obstacle for their operations.
Click here to download data and charts.

Firm characteristics associated with higher
TFP growth

EMDEs: within-firm upgrading and spillovers, regulatoryenvironment, and managerial ability.

Heterogeneous characteristics related to entering,incumbent, and exiting firms can explain the observed
patterns of TFP dispersion (Bartelsman and Doms 2000).
A large and expanding literature points to three broad
categories of correlates of sectoral TFP dispersion in

Within-firm upgrading and technology spillovers.
Controlling for both size and exports, firms in thetechnology-intensive industry are on average much closer
to the TFP frontier than firms in traditional industries
such as non-electric machinery, food, and non-metallic

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BOX 3.3 Patterns of total factor productivity: A firm perspective (continued)
FIGURE 3.3.3 Distance-to-frontier of TFP, firm characteristics, and regulations
The average firm in the technology-intensive industry (TINT) is significantly closer to the frontier than the average firm in nonelectric machinery (MACH), food (FOOD), and non-metallic minerals (MINE) industries, after controlling for firms’ size andexports. As firms grow by number of employees and increase their ratios of exports to total sales, they move closer to the
TFP frontier. A conducive business environment can enhance firm-level TFP. Improvements in business freedom and control
of corruption are correlated with a reduction in the distance-to-frontier of TFP.
A. Distance to TFP frontier differential
between traditional industries and thetechnology-intensive industry

B. Distance to TFP frontier differential
between firms in lowest and highest
quartile of firm size and exports

C. Distance to TFP frontier differential
between firms in lowest and highest
quartile of business environment

Source: World Bank Enterprise Surveys, World Bank.
Note: Firm-level TFP is computed using a Cobb-Douglas production function for each industry, assuming that elasticities of output with respect to inputs are the sameacross countries in a given income group. The distance-to-frontier (DTF) of TFP is computed within each sector, excluding the top 2.5 percent of firms. Sample includes
15,181 firms in 108 EMDEs, including 20 LICs, for the period 2007-17.
A. Distance-to-frontier of TFP differential between traditional industries, such as manufacturing of non-electric machinery (MACH), food (FOOD), and non-metallic
minerals (MINE), and the technology-intensive (TINT) industry, controlling for firm characteristics (firm size and exports). Based on OLS regressions of the DTF of TFP(dependent variable) on industry dummies, controlling for firm characteristics and using the technology-intensive industry (TINT) as the base category as per Annex 3.5.
B. Distance to TFP frontier differential between the median firm in the lowest quartile and highest quartile of firms in terms of firm size (number of workers) and exports(share of exports in total sales). Based on OLS regressions of the DTF of TFP (dependent variable) on industry dummies, controlling for firm characteristics and using thetechnology-intensive industry (TINT) as the base category (Annex 3.5). A positive DTF differential implies that firms in the lowest quartile in terms of size and exports arefar from the frontier relative to firms in the highest quartile. The lowest quartile of exports is zero, as more than half of firms have no exports.
C. Distance to TFP frontier differential between the median firm in the lowest quartile and highest quartile of firms in terms of business freedom and control of corruption
index, controlling for firm characteristics. Based on OLS regressions of the DTF of TFP (dependent variable) on industry dummies and business environment quality,
controlling for firm characteristics and using a technology-intensive industry (TINT) as the base category as per equation 3. A positive DTF differential implies that firms in
the lowest quartile in terms of business freedom and control of corruption are far from the frontier relative to firms in the highest quartile.
Click here to download data and charts.

minerals industries (Figure 3.3.3.A). Knowledge,
experience, R&D, and information technology can raise
TFP through improvements in product quality andproduction process upgrading within firms.6 Firms with alarge number of employees are significantly closer to the
TFP frontier, as larger firms can invest more in R&D andbring together a richer set of ideas. On average, theproductivity of a firm in the highest quartile of size isabout 12 and 22 percent closer to output and value-added
TFP frontiers relative to a firm in the lowest quartile ofsize (Figure 3.3.3.B). Moreover, technology in frontierfirms can have positive spillovers for productivity in otherfirms through agglomeration linkages and cross-borderflows of goods, capital and people. Firms can reap
agglomeration benefits by emulating the best production

6

See Brynjolfsson and Hitt (1995) and Goldberg et al. (2010).

practices and organization structures of “nearby” highly
productive firms (Dercon et al. 2004; Syverson 2011).
Knowledge is also transferred through contacts with otherfirms, courtesy of trade, foreign direct investment andmigration (De Loecker 2007). Firms with a high share ofexports are significantly closer to the TFP frontier. A firmin the top quartile of exports, measured as a share ofexports in total sales, is about 4 and 6 percent closer to
output and value-added TFP frontiers relative to a firm in
the lowest quartile of exports (Figure 3.3.3.B). Enabling
effective innovation policies appears critical to boosting
innovation gains (Cirera and Maloney 2017).
Regulatory environment. Institutions reflect political andlegal forces that shape social and economic environments.
Regulations and policies affect firms’ productivity throughincentives to acquire human capital, physical capital, andtechnology (Bartelsman and Doms 2000). Firm
productivity tends to drop in poorly-regulated markets,

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BOX 3.3 Patterns of total factor productivity: A firm perspective (continued)
due to adverse incentives and the lack of creative
destruction (Goldberg et al. 2010). In contrast,
improvements in the business environment are associatedwith lower distance to TFP frontier, even after controllingfor firm characteristics. Conducive regulatory practices—reflected in highest quartile values of business freedom
index—may entail up to 9 percent reduction in the
distance-to-frontier of TFP relative firms in the lowest
quartile. Similarly, high quality governance—proxied by
the top quartile estimates of control of corruption index—
is associated with up to 12 percent drop in the distance to
TFP frontier relative to firms in the bottom quartile(Figure 3.3.3.C).
Managerial ability. TFP also reflects how efficiently
productive factors—labor, capital, and intermediate
inputs—are assembled. Through their talents or thequality of their practices, managers coordinate theintegration of factor inputs in the production process.
Management and organizational styles may vary acrossfirms due to competition, location, ownership, and tradeties. Intervention-led improvements in management

practices can raise productivity by more than 10 percent(Van Reenen 2011). A policy shift that is more focused onenhancing firm managerial capabilities can, therefore,
strengthen production synergies and bolster TFP gains(Cusolito and Maloney 2018).

Conclusion
The dispersion of firm-level TFP within and acrossindustries in emerging markets and developing economies(EMDEs) is associated with various firm characteristics.
TFP dispersion correlates negatively with firm size, partly
because large firms can invest more in R&D to innovate.
Exports also facilitate the transfer and adoption of newtechnologies, and therefore, can help close the gap between
laggards and frontier firms. Moreover, a conducive
business climate characterized by a greater freedom inentrepreneurship and less corruption can support TFP
improvements. Undertaking policies to support R&D andinnovation, promote exports, combat corruption, increase
the ease of doing business, and enhance firm managerial
capabilities, appears critical to boosting productivity.

innovation (proxied by higher per capita patents),
stronger investment (as a share of GDP), higher
levels of urbanization (proxied by population
density), price stability, and a diverse andsophisticated economic structure (proxied by the
economic complexity index of Hidalgo andHausmann 2009), are all significantly associatedwith higher productivity growth (Figure 3.7).17

manufacturing and service sectors (Box 3.2). The
level of education and investment also produces
larger impacts on productivity in EMDEs in thelong-runestimation,highlighting
theirimportance at lower levels of productivity. Since
1995, the relationship between labor productivity
and the economic complexity of tradable goods
has strengthened in EMDEs.

Differences between EMDEs and advanced
economies. The estimated impact of improved
levels of each driver of productivity growthdepends on the stage of development andtherefore differs between EMDEs and advanced
economies. The extent of urbanization has a larger
impact on productivity growth in EMDEs than inadvanced economies, reflecting higher returns to
the reallocation of workers away from rural
agricultural production to higher productivity

Evolution of the drivers of productivity

17 These are largely consistent with existing studies which tend tohave shorter time spans and smaller cross-sections (Durlauf,
Kourtellos, and Tan 2008; Kim and Loayza 2019).

Pre-crisis improvements. There were substantial
gains in many of the underlying drivers of
productivity growth in the pre-crisis period,
growing faster in EMDEs than advanced
economies (Figure 3.8). The selected drivers canbe aggregated to an index based on the size of
their estimated impacts on productivity—
demographics, economic complexity, the numberof patents filed, and price stability are allconsidered to be key determinants of productivity
growth over this period by the econometricmodel. Cumulatively over 1995-2008, productivity in the one-quarter of EMDEs with the most

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FIGURE 3.7 Impact of drivers on productivity growth
Productivity in economies with favorable initial conditions grew by up to 0.8
percentage point per year faster than other economies. The scale of theseeffects varies over time and between EMDEs and advanced economies. In
1960, the importance of innovation and economic complexity was lower in
EMDEs. Demography and economic complexity have become increasingly
important determinants of EMDE productivity growth in recent decades.

A. Effects of initial level of drivers on
productivity growth, 1960-2018

B. Effects of initial level of drivers for
EMDEs on productivity growth, 19602018 vs. 1995-2018

Source: World Bank.
A. B. Estimated marginal contribution to annual long-term productivity growth if the driver improves
from the 25th to the 75th percentiles. Sample includes 59 economies, 36 of which are EMDEs.
Groups which are not significant in both 1960-2018 and 1995-2018 (Finance, Income equality, andhealth) are excluded from the chart. Variables corresponding to each concept are: Institutions = ICRG
rule of law index, Geography=share of non-tropical area, Innovation=patents per capita,
Investment=investment to GDP ratio, Income equality=(-1)*Gini coefficient, Urbanization=urban
population (% total), Econ. complexity = Economic Complexity Index of Hidalgo and Hausmann(2009), Education=years of schooling, Demography=share of working-age population, Genderequality= female average years of education minus male average years. See Annex 3.3 for details.
B. Marginal contribution of demography for 1995-2018 is 0.83.
Click here to download data and charts.

favorable initial conditions grew by nearly 15
percent more than productivity in those with theleast favorable initial conditions. Among LICs, thedifferential between the two groups was evenlarger (53 percent). LICs were better able tobenefit from catch-up growth in the presence offavorable initial conditions.
Post-crisis slowdown in improvements. The paceof growth of the drivers most strongly associatedwith productivity growth has slowed in EMDEs
since 2008, consistent with the slowdown in
productivity growth over this period (Figure 3.9).
Investment growth in EMDEs slowed, reflecting
weak activity and spillovers from advanced
economies, weaker growth of commodity demand,and political uncertainty. In addition, earlier
favorable demographic trends in many EMDEs
have waned as the population ages. From 2018 to2030 the working-age share of the population is
expected to decline by 3 percentage points inadvanced economies and 2.5 percentage points in

EMDEs. For educational attainment, growth has
been three times higher than in advanced
economies. Nonetheless, as countries catch up (asmeasured by average years of schooling), thepotential for further growth has slowed.18
Other factors that had helped spur EMDE
productivity growth also have deteriorated since
the crisis. For example, the trend toward
broadening production to a more diverse range of
products at more upstream stages of the value
chain slowed partly because the expansion of
global value chains stagnated after 2008 (WorldBank 2019d). In addition, improvements ininequality and measures of institutional quality
have also stagnated or declined in many countries.
Finally, gains in price stability, which hadsignificantly improved operating environments forfirms in the 1990s, slowed (Ha, Kose, andOhnsorge 2019).

Prospects for productivity
growthThe post-crisis weakness in several fundamental
drivers of productivity growth is expected to persist or
deepen. The weak outlook for the drivers can be
improved though a concerted reform effort.
Weakening investment. The post-crisis period has
been characterized by pronounced investment
weakness reflecting adverse terms-of-trade shocks
for commodity exporters, slowing foreign direct
investment inflows for commodity importers,
spillovers from advanced-economy growth
weakness, heightened policy uncertainty, andprivate debt burdens (World Bank 2017). The
legacy of weak investment since the crisis anddiminishing long-term outlook for investment
growth raises concerns about future productivity
growth (World Bank 2019b). Moreover, subdued
investment growth, especially in R&D-dependent
sectors, can hinder technological progress and
TFP growth through weaker capital-embodied
technological change (Adler et al. 2017).

18 While the gap in average years of education with advanced
economies has declined, substantial gaps in the quality of education
remain (World Bank 2018b).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Slower growth at the technology frontier. There
has been a broad-based slowdown in both laborproductivity and TFP growth in advanced
economies since the early 2000s with limited signsof an impending upturn. To the extent that thisreflects slowing productivity growth inmultinationals and the origins of foreign direct
investment—two major channels for knowledge
and technology spillovers to EMDEs—this is
likely to weigh on EMDE productivity, too(Wooster and Diebel 2010). However, there are
mixed views on the prospects of groundbreaking
technological progress that could return growth tohistorical norms, and also spillovers to EMDEs.
On the one hand, the impact on productivity
growth of new innovations compared to 20thcentury innovations seems to be reduced (Fernald
2015; Gordon 2016). On the other hand, recently
introduced new digital technologies and those on
the horizon such as artificial intelligence andinnovations in IT sectors may begin to feedthrough to measured productivity (Cusolito andMaloney 2018).
Fewer opportunities for technology transfer.
Substantial productivity gaps to the frontier are
still present in EMDEs, providing opportunities
for rapid productivity growth. However, routes totechnology transfer are narrowing. The expansionof global value chains has come to a halt in the
post-crisis period after rapid expansion in the precrisis period (World Bank 2019d). Rising
implementation of protectionist measures risks
further compounding the weakness in global value
chains and trade. Moreover, firms in EMDEs maylack the necessary capabilities to adopt newtechnologies without sustained improvements inhuman capital such as enhancements in
educational quality and management abilities
despite the progress in education attainments(Cirera and Maloney 2017).
A more challenging environment for structuraltransformation. As highlighted in Box 3.2, thecontribution to productivity growth from the
manufacturing sector has been in decline andpresents fewer opportunities for EMDE
productivity growth. Secular trends, such as adeclining employment share in the manufacturingsector in some economies and risks fromautomation will make manufacturing-led

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221

FIGURE 3.8 Pre-crisis developments in productivity
drivers and productivity growth
All drivers of productivity growth in EMDEs, except for innovation, genderequality and institutions, improved more than in advanced economies
during the pre-crisis period, helping to narrow the productivity gap withadvanced economies. There was a strong link between drivers andproductivity growth—those economies with better initial conditions in the
1990s grew at faster rates subsequently. The benefits of improving drivers
are larger for LICs.

A. Share of EMDEs with faster
improvements in drivers relative toadvanced economies, 1995-2008

B. Quartiles of productivity drivers
and average EMDE productivity
growth, 1995-2008

Source: Barro and Lee (2015); International Monetary Fund; Observatory of Economic Complexity;
United Nations; World Bank, World Development Indicators.
A. Share of EMDE countries whose improvement in drivers are larger than average changes foradvanced economies. Variables corresponding to each concept are (sample in parentheses):Institutions (74) = WGI Rule of Law Index, Innovation (30) = patents per capita, Investment (72)=
investment to GDP ratio, Income equality (72) = (-1)*Gini coefficient, Urbanization (74) = Urban
population (% total), Econ complexity (56) defined as Economic Complexity Index of Hidalgo andHausmann (2009), Education (69) = years of schooling, Demography (74) = share of working age
population, Gender equality (28) = Ratio of female to male labor market participation.
B. Average level of productivity growth and “index of drivers” in each quartile over 1995-2008. “Indexof drivers” created by weighting normalized levels of each potential driver in chart A by its estimatedimpact on productivity growth (Figure 3.7; Annex 3.3).
Click here to download data and charts.

development increasingly challenging in the future(Hallward-Driemeier and Nayyar 2017; Sinha
2016). Furthermore, gains from faster productivity
growth in the agricultural sector, freeing up
workers to transition to other sectors, havedeclined.
Rising debt risk in EMDEs. Amid record-high
EMDE debt, a wide range of adverse shocks could
precipitate a financial crisis in EMDEs, which
could do severe damage to productivity (Box 3.4).
Since 2010, total debt in EMDEs has risenmarkedly by 54 percentage points, to 168 percent
of GDP in 2018, with private debt growing fasterthan public debt, reaching 120 percent of GDP in2018 (Chapter 4). Low productivity growth andrising sovereign debt burdens may even reinforce
one another (Posen and Zettelmeyer 2019).
Climate change. Over the longer-term, climatechange will likely increase the challenges toimproving productivity in the agricultural sector,

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FIGURE 3.9 Post-crisis slowdown of the drivers of
productivity growth
In EMDEs, improvements in a broad range of productivity drivers slowed
after 2008. Investment growth slowed to one-third of its pre-crisis rate in
EMDEs. Working-age population shares are expected to contract in the
coming years. And the growth of educational attainment has also slowed
as EMDEs reduce the gap with advanced economies.

A. Share of EMDEs with a post-crisis
slowdown in the growth of underlying
drivers of productivity

B. Average investment growth

structures more readily (Maestas, Mullen, andPowell 2016). The working-age share of the
population rose by 13 percentage points of the
population during 1995-2008 in MENA, thefastest-growing region, and 8 percentage points in
EAP, the second-fastest growing. In the coming
years, EMDE populations are set to age. In EAPand ECA, the working-age share of the population
is expected to decline by 3-4 percentage points ofthe population by 2030, while, in LAC, MENA,
SAR, and SSA it will stagnate.

Policy implications

C. Average annual growth ineducational attainment

D. Change in working-age shareof the population

Source: Barro and Lee (2015); International Monetary Fund; Observatory of Economic Complexity;
Penn World Table; IMF World Economic Outlook; United Nations; World Bank, World Development
Indicators; Wittgenstein Centre for Demography and Global Human Capital.
A Post-crisis slowdown defined as the share of economies where improvements in each underlying
driver of productivity during 2008-2017 was less than zero or the pace of improvement during thepre-crisis period 1998-2007. Variables corresponding to each concept are (sample in parentheses):
Investment (69)=investment to GDP ratio, Demography (74)=share of working-age population,
Innovation (33)=patents per capita, Gender equality(32)= Share of female labor market participation
rate to male, Urbanization (74)=Urban population (% total), Institutions (74)= WGI Rule of Law Index,
Income equality (72)=(-1)*Gini coefficient, Education (72)=years of schooling, ECI (55) defined as
Economic Complexity Index of Hidalgo and Hausmann (2009). Price stability excluded due todemand-side influences on inflation following the global financial crisis.
B. GDP-weighted average annual investment (gross fixed capital formation) growth.
C. GDP-weighted change (at 2010 prices and exchange rates) in average years of education.
D. Changes in the working-age share of the population (aged 15-64).
Click here to download data and charts.

with large falls in crop yields expected as global
temperatures rise (Fuglie et al. 2019). Agriculture
currently accounts for 30 percent of GDP in LICs,
compared to just 9 percent in non-LIC EMDEs.
In addition, EMDEs in several regions are heavily
reliant on agriculture: around half of employment
is in the agricultural sector in SAR and SSA.
Less favorable demographics. Younger populations and larger working-age population tend to
adopt new technologies, skills, and organizational

Concerns about prospects for productivity growth in
EMDEs call for a renewed emphasis on structural
policies that can unlock productivity gains, but
undertaking the right structural policies ischallenging. Drawing on the findings in this chapter,four strands of policy options emerge.
The results suggest that a four-pronged policy
approach can lift productivity. First, policies canraise labor productivity economy-wide bystimulating private and public investment andimproving human capital. Second, policies canfoster firm productivity by exposing firms to tradeand foreign investment and strengthening humancapital, and upgrading workforce skills includingthat of firm managers. Third, policies can facilitate
the reallocation of resources towards moreproductive sectors and a more diversified set of
sectors. Finally, to be effective, these policies need
to be set in the context of a growth-friendly
macroeconomic and institutional environment(Cirera and Maloney 2017).
Within these four broad strands, specific priorities
depend on country characteristics. For example,
countries with large unmet investment needs may
want to prioritize expanding fiscal resources toachieve more and better public investment.
Countries with anemic private investment may
want to prioritize business climate andinstitutional reforms, reduce support for stateowned enterprises, and broadening access tofinance to allow private sector investment toflourish. Countries with predominantly lowskilled workers may want to improve health andeducation for workers and managers alike.
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BOX 3.4 Debt, financial crises, and productivity
High debt levels increase the probability of financial crises and weigh heavily on productivity growth through a wide range ofchannels. During debt accumulation episodes associated with financial crises, cumulative productivity gains three years into theepisode are 2 percentage points lower than in episodes without crises. Financial crises are accompanied by large and protracted
declines in productivity: five years after the financial crisis, productivity is 6.5 percent lower than it would have been without acrisis.

Introduction
Productivity growth is vulnerable to a range of adverse
shocks including those associated with financial crises,
especially in the context of rapid debt accumulation(Chapter 4). Following the global financial crisis andsubsequent global recession of 2007-09, a broad range ofcountries experienced a rapid accumulation of debt
together with a significant slowdown of productivity
growth. Debt accumulation raises both long-term andshort-term risks to productivity growth. In the long-term,
it can lead to misallocation of resources towards low
productivity projects, worsen investment prospects, weighon competitiveness, and curb technological transfers
embodied in investment.1 In the short-term, debt
accumulation also increases the probability of financial
crises that sharply raise borrowing cost, worsen balance
sheets and depress productivity growth, which can last
over an extended period.2
Against this backdrop, this box discusses the linkages
between productivity and financial crises as well as rapid
debt accumulation. Specifically, it addresses the following
two questions:•Through which
productivity?

channels

does

debt

affect•What is the empirical link between financial crises
and productivity?

Channels of transmission
Elevated debt levels can affect productivity growth via
several channels. These include misallocation of resources,
policy uncertainty and debt overhangs that weigh on
productivity-enhancing investment, and a higher probability of financial crises.
Misallocation of resources. If used to fund productive
investments with high rates of return, debt can have
Note: This box was prepared by Alistair Dieppe, Sinem Kilic Celik,
and Cedric Okou.
1 Blanchard and Wolfers (2000); Bulow and Rogoff (1989).
2 See Aguiar and Gopinath (2006), Arteta and Hale (2008), Blanchard,
Cerutti, and Summers (2015), Cerra and Saxena (2008, 2017), Furceri
and Mourougane (2012a), Jordà, Schularick, and Taylor (2013), andReinhart and Rogoff (2009, 2010).

positive effects on productivity and growth (Reinhart andRogoff 2010; Poirson, Pattillo, and Ricci 2004). However,debt accumulation can impede productivity byencouraging a misallocation of resources towards projects
that yield short-term returns at the expense of long-term
returns or offer low risk at the expense of high returns(Poirson, Pattillo, and Ricci 2002; Checherita-Westphal
and Rother 2012). These short-term projects can include
those that rely heavily on returns from asset priceappreciation on expectations of rapid future growth(Claessens and Kose 2017, 2018).
Debt overhangs. Rapid debt accumulation can lead to
debt overhangs whose debt service crowds out productive
investment.3 At the firm level, a large outstanding debt
stock can weigh on investment and, hence, theproductivity growth that technology embedded in thisinvestment can generate. At the government level, debt
service on high debt may crowd out other productivityenhancing spending, including for education, health orinfrastructure.
Policy uncertainty. Especially high government debt
increases uncertainty about growth prospects. For
investors, large projected government debt service costcreates policy uncertainty because they may eventuallycompel governments to introduce distortionary taxation(including on future investment returns), curtail growthenhancing spending, or delay reforms that may support
innovation and productivity (IMF 2018). Such
uncertainty lowers incentives to invest in productivityenhancing technologies (Krugman 1988).
Higher probability of financial crises. Higher debt
increases the probability of financial crises. These tend tobe associated with severe short-run productivity losses andlasting productivity weaknesses. Financial crises includedebt, banking, and currency crises.
•

Sovereign debt crises. Higher government debt may
encourage governments to shift towards lower-cost

3 Debt overhang can occur in the presence of high levels of debt, as
potential investors hold back new investments because they faceheightened uncertainty about tax rates on future investment returns,
given the government’s large projected revenue needs to service theoutstanding debt.

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BOX 3.4 Debt, financial crises, and productivity (continued)
FIGURE 3.4.1 Productivity in debt accumulation episodes and financial crises
About 40 percent of all episodes of debt accumulation are associated with financial crises. During those episodes,
productivity gains are significantly lower than during other episodes. Specifically, a financial (banking, currency and debt)
crisis is accompanied on average by a 6.5 percent cumulative decline in the level of labor productivity after 5 years, and thenegative effect is protracted, exceeding 7 percent at an 8 year-horizon.
A. Total debt accumulation episodesaround crises

B. Cumulative productivity gains during
episodes of rapid debt accumulation

C. Impact of financial crises on EMDE
productivity and output levels

Source: World Bank.
A. Share of total (government and private) debt accumulation episodes that were associated with financial (banking, currency, debt) crises.
B. * and ** indicates 10 and 5 percent significance level for the difference between productivity growth during the median total debt accumulation associated with crises
and the median total debt accumulation episode not associated with crises.
C. Bars show the average loss in labor productivity and output levels in EMDEs, expressed in percent, at impact, 1, 2, … and 8 years after a financial crisis (Laeven andValencia 2018). Financial crises include banking, currency and debt crises. Whiskers represent 90 percent confidence intervals. The estimation is based on local
projection method (Jordà 2005), which includes control variables (country fixed effects, lagged shocks, forward bias correction terms, and lagged TFP growth) and bias
correction (Teulings and Zubanov 2014) for forward values of the crisis dummy between time t and t+h-1.
Click here to download data and charts.

but higher-risk debt issuance such as at shorter
maturities or in foreign currency (Kalemli-Özcan,
Laeven, and Moreno 2018). This heightens theprobability that financial market stress precipitates asovereign debt crisis that sharply raises investor risk
premia and borrowing cost.4 These tend to coincidewith severe economic disruption just as sovereign debt
distress prevents governments from supporting
activity with counter-cyclical fiscal policy (Reinhart
and Rogoff 2010). This depresses public and private
investment and restricts other productivity-enhancing
public spending.
Banking and currency crises. Other types of financial
crises, including systemic banking crises and currency
crises, can also do lasting damage to productivity(Cerra and Saxena 2017; Oulton and Sebastiá-Barriel2017). The disruptions in financial intermediation
during banking crises curb the funding ofproductivity-enhancing technologies and typicallytrigger recessions (De Ridder 2017). In thesubsequent protracted weakness, elevated long-term•

4

Aguiar and Gopinath (2006); Arellano (2008); Sandri (2015).

unemployment erodes human capital.5 Because of
their shorter duration, currency crises are typically lessharmful to productivity. However, combined banking
and currency crises can be particularly damaging for
economic activity and productivity.

Empirical link between financial crises andproductivity
Productivity gains during rapid debt accumulation
episodes. Long-term productivity gains during rapid debt
accumulation episodes have been considerably lower whenthese debt accumulation episodes were associated withfinancial crises. As in Chapter 4, rapid debt accumulation
episodes are defined as an expansion from trough to peakof total debt-to-GDP ratios by more than one standard
deviation, with troughs and peaks identified using theHarding and Pagan (2002) algorithm. This yields 190
episodes, of which almost half were associated withfinancial crises—identified as in (Laeven and Valencia2018) —at some point during the episode.

5 See Blanchard and Wolfers (2000) and Furceri and Mourougane(2012b).

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BOX 3.4 Debt, financial crises, and productivity (continued)
In a debt accumulation episode accompanied by a crisis,
median productivity three years into the episode was 3
percent higher than at the beginning of the episode. Thisis statistically significantly less than during a debt
accumulation episode that was not associated with a crisis(5 percent). The difference may reflect the severe shortterm damage to productivity driven by financial crises.
Two years later (five years into the episode), productivity
differences between the two types of episodes were nolonger statistically significant.
Impact of financial crises on productivity. The
productivity losses associated with financial crises are
estimated in a local projections model of productivity
levels in financial crises episodes. These episodes are
identified as in (Laeven and Valencia 2018). There are 299
financial crisis episodes for which labor productivity
estimates are available. 72 percent of these episodes
occurred in 71 middle- or high-income EMDEs and 10
percent in 13 low-income countries.
Financial crises are accompanied by large and lastingproductivity losses. Immediately after the onset of a debt
crisis, labor productivity declines on average by
about 2.2 percent and then falls by a cumulative 6.5

expose their private sectors to foreign knowledge
and technologies through greater trade and foreign
direct investment (Boxes 2.1-2.6).
Policy interactions can lead to unintended
consequences. For instance, trade liberalization
reforms can increase the exposure of private sector
firms to foreign knowledge and frontier
technologies, and boost productivity. However,trade liberalization can also be associated withgreater informality in the short-run if labormarkets are not flexible, thus counteracting
policies that aim at facilitating the reallocation of
resources towards more productive sectors (Bosch,Goni, and Maloney 2007; World Bank 2019a).
Therefore, these potential interactions should be
accounted for when designing a policy mix for acountry.

percent at the end of five years (Figure 3.4.1). The effectpersists into the eighth year. This is consistent with earlier
studies that document protracted effects of financial crises
on productivity growth (Obstfeld 1996; Morris and Shin
1998; Barro 2001).6

Conclusion
Financial crises weigh heavily on productivity growththrough a wide range of channels. During debt
accumulation episodes associated with financial crises,
cumulative productivity gains three years into the episodeare 2 percentage points lower than in episodes withoutcrises. Financial crises are accompanied by large andprotracted productivity losses—following an initial drop of
2.2 percent, productivity falls by a cumulative 6.5 percent
five years after the onset of the crisis. In this context, therapid post-crisis build-up of debt in EMDEs increases
vulnerability to financial crises and represents an important
downside risk to productivity growth (Chapter 4).

6 The damage to output and productivity does not differ statisticallysignificantly over the first eight years following the crisis.

slowdown in labor productivity growth. Elsewhere(SSA, SAR), sizable infrastructure deficits restrictfirms’ ability to improve productivity. Better
physical capital and infrastructure—transport,
power, telecommunications—can reinforce acountry’s competitiveness and boost its productivity (Calderón, Moral-Benito, and Servén 2015).
A key challenge is to prioritize investments toreconcile large development needs with funding
constraints and to improve public investment
management. Low– and middle-income countries
will need to spend between 4.5 to 8.2 percent of
GDP on new infrastructure annually to 2030 inorder to meet infrastructure-related Sustainable
Development Goals (Rozenberg and Fay 2019).19
Where fiscal space exists, governments shouldfund infrastructure spending in areas likely togenerate high-returns. SSA is estimated to have the

Improving factors of production
Meet infrastructure investment needs. In several
regions (ECA, MNA, SAR), weaker rates of capital
deepening accounted for most of the post-crisis

19 SDG targets for universal access to safely managed water,
sanitation, and hygiene services, improved irrigation infrastructure to
improve food supplies, universal access to electricity and improved
transport infrastructure.

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FIGURE 3.10 EMDE infrastructure and education gaps
Infrastructure needs to meet the Sustainable Development Goals arehighest in SSA. While education gaps, measured as years of schooling,
are closing in many regions, they remain large in SAR and SSA. The gaps
to advanced economy levels are even larger after adjusting for educational
quality.
A. Infrastructure gaps

B. Years of education and learningadjusted years of education (2017)

Source: Rozenberg and Fay (2019); World Bank, Human Capital Project.
A. Investment and maintenance needs based on the Sustainable Development Goals as set out inRozenberg and Fay (2019) including both new investment and maintenance of existing capital stock.
Infrastructure investment includes investment in electricity, transport, water supply and sanitation,
flood protection, and irrigation. Preferred is defined as the infrastructure “pathway [that] limits
stranded assets, has a relatively high per capita consumption due to electric mobility, and invests
mostly in renewable energy and storage.”
B. GDP-weighted expected years schooling and learning-adjusted years of schooling from the WorldBank’s Human Capital Project. Leaning-adjusted years of schooling use harmonized cross-country
test scores to adjust average years of schooling.
Click here to download data and charts.

FIGURE 3.11 Developments in Fintech and Govtech
Economies with the largest “unbanked” populations have also seen the
biggest increases in fintech innovations to payment systems and otherfinancial services. The rise of fintech has been largest in SSA. Thesesystems are critical to improving access to finance to make productivityenhancing investments. EMDE government transparency still lags
advanced economies. New ICT can facilitate the rapid dissemination of
information within and outside of government to monitor performance andservice shortfalls.
A. Access to banking services andmobile money accounts

B. Information openness: national
government data availability

Source: GSM Association (GSMA), Open Knowledge Foundation, World Bank.
A. Mobile money accounts based on a sample of 16 EMDEs, excluding China, in East Asia and thePacific (EAP), 7 EMDEs in Eastern Europe and Central Asia (ECA), 18 EMDES in Latin America andthe Caribbean (LAC), 9 EMDEs in Middle East and North Africa (MNA), 7 EMDEs in South Asia
(SAR), and 40 EMDEs in Sub-Saharan Africa (SSA).
Bank accounts, defined as depositors at commercial banks, based on a sample of 22 EMDEs,
excluding China, in East Asia and the Pacific (EAP), 24 EMDEs in Eastern Europe and Central Asia
(ECA), 32 EMDES in Latin America and the Caribbean (LAC), 19 EMDEs in Middle East and North
Africa (MNA), 8 EMDEs in South Asia (SAR), and 48 EMDEs in Sub-Saharan Africa (SSA).
B. Global Open Data Index is a proxy for the availability of open national government data at large.
GDP weighted average. 2016/7 data. It based on a sample of 27 Advanced economies, 14 EMDEs in
Eastern Europe and Central Asia (ECA), 6 EMDEs in East Asia and the Pacific (EAP), 25 EMDES in
Latin America and the Caribbean (LAC), 2 EMDEs in Middle East and North Africa (MNA), 6 EMDEs
in South Asia (SAR), and 12 EMDEs in Sub-Saharan Africa (SSA).
Click here to download data and charts.

highest infrastructure deficit required to meet theSDGs (Figure 3.10). Poor infrastructure, such as
power supply problems, have been found to lower
manufacturing TFP in Bangladesh and reduce
export diversification in lower-income EMDEs(Osakwe and Kilolo 2018). A range ofinfrastructure investments in the road andtelecommunications networks in South Africa
were found to have positive effects on
manufacturing TFP (Bogetic and Fedderke 2009).
Remove private sector investment constraints.
Removing business environment constraints, laborand product market inefficiencies, and improving
corporate governance should be prioritized (WorldBank 2019a). In addition, credit constraints canalso hold back investment, with many EMDEs
lacking developed capital markets and financial
products for much of the population (Sahay et al.
2015). Weak access to finance is a key constraint
to small and medium firms in SAR–especially for
women-owned businesses—and holds back firmlevel productivity gains in India (Box 2.5). Efforts
are needed to encourage the use of fintech
products in regions where access to traditionalbanking products and sources of finance is low,while addressing associated risks of thesetechnologies, such as financial crime andcybersecurity risks (Figure 3.11; IMF and WorldBank 2019). Investing an additional 4.5 percent of
GDP annually in infrastructure in EMDEs would
lift long-run productivity growth by 0.3
percentage point (Figure 3.12).
Raise human capital. Better-educated andhealthier workers hold better-paying jobs, havemore stable careers, and are more productive.
Moreover, a better educated and healthierworkforce is more capable of advanced technology
adoption (Bils and Klenow 2000). Educational
gaps with advanced economies are largest in SAR
and SSA, where expected years of schooling is 3
and 5 years lower than in advanced economies,
respectively. This gap increases to 6 and 7 years
when adjusting for quality, suggesting that
educational reforms should be a priority in theseregions (Figure 3.10). In addition, tailoredinterventions at early ages are important. These
can include measures to expand school attendance,
provide student grants, support nutrition

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

programs for early childhood development,
upgrade teachers’ training, foster teacher
accountability and incentivize performance, which
can boost educational outcomes. Conditional cashtransfer programs can have persistent effects
on educational attainment and the quality of
employment (Kugler and Rojas 2018).
Transitioning to lower fertility rates can reduce
dependency rates and free up resources to invest in
education and health—Botswana and Ethiopia
have experienced rapid declines in fertility rates inrecent decades, alongside large falls in poverty
rates (World Bank 2019f). By increasing
educational attainment at the same rate as its
fastest 10-year cumulative increase ending between
2000-2008, EMDEs could raise long-run
productivity growth by about 0.1 percentage point(Figure 3.12).
Another key component of human capital ishealth. Although life expectancy at birth in
EMDEs has increased to 70 years on average as of2017, this is still about 10 years below averageadvanced-economy levels (81 years). Improvements in access to clean water, the provision ofadequate sanitation, health care, training, andperformance-based payments to health service
providers can yield substantial rewards on thewell-being of the population and lift productivity(World Bank 2012, 2018b).
Boosting firm productivity
Foster firm capabilities. The structural slowdown
in TFP growth in EMDEs suggests a need toreinvigorate technology adoption and innovation.
Interventions to ease international and domestic
knowledge diffusion and boost firm absorptive
capacities will buttress innovative activities (DeVisscher, Eberhardt, and Everaert 2018). On-thejob training and targeted educational reforms canupdate skills to complement current and newly
introduced technologies, many of which require
higher cognitive skills and tertiary education levels
compared to previous technologies. Firm
management capabilities have been shown to be
key in generating high-quality R&D andtechnology adoption. In India, firms provided
with training on management practices saw
productivity rise by 17 percent—a key factor for
improving management quality has been

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227

FIGURE 3.12 Productivity growth: reform scenario
A reform package that combines filling investment needs, boosting humancapital, and improving the adoption of new technologies could lift
productivity by just over half of a percentage point over 10
years. Replicating the success of China and Vietnam in shifting out ofagriculture towards manufacturing and trade services could provide asignificant boost to productivity growth in low-income economies.

A. EMDE productivity reform scenario

B. Sectoral reallocation scenario

Source: World Bank
Note: GDP-weighted average. EMDEs = emerging markets and developing economies.
A. The reform scenario assumes: (1) Fill investment needs: the investment share of GDP increases
by 4.5 percentage points as in the Rozenberg and Fay (2019) “preferred” infrastructure scenario. The
increase is phased in over 10 years (2) Boost human capital: average years of education increases ineach EMDE at its fastest cumulative 10-year pace ending during 2000-08; (3) Reinvigorate
technology adoption: economic complexity (Hidalgo & Hausmann 2009) increases at the same pace
as its fastest 10-year rate of increase ending during 2000-08.
B. The sectoral reallocation scenario assumes the sectoral reallocation reform replicates the
successful transformation of China and Vietnam during 2003-2008. The share of employment in the
agriculture sector falls by 15 percent and is reallocated to the manufacturing and trade services
sectors over a 5 year period.
Click here to download data and charts.

participation in global value chains to boost
knowledge diffusion on management practices(Bloom et. al. 2013; Cirera and Maloney 2017).
However, private firms may be reluctant toundertake costly investments in R&D to open
foreign markets if competitors can free-ride.
Policies that ensure property rights and promote
public-private partnerships to create technology
extension centers in sectoral clusters can increase
firm participation in global value chains, and lift
productivity (Cirera and Maloney 2017).20
Firm-level analysis suggests that to benefit fromtechnology spillovers EMDEs need to foster tradeand financial integration (Box 3.3). Reducing
trade restrictions, alongside increasing levels ofhuman capital, increase export diversification andreduce reliance on commodity exports (Giri,
Quayyum, and Yin 2019). Efforts to improve
trade openness can include regional trade
agreements, such as the African Continental Free
20 Technology extension centers generate and transfer new foreignand domestic technologies, tailored to a country’s specific needs, tolocal users.

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Trade Area which includes economies in MENA
and SSA. In India, reforms in the 1990s to boost
foreign (and domestic) competition in the service
sector also had large positive spillovers to
manufacturing productivity (Arnold et al. 2016).
Bangladeshi
garment
exporters
increased
productivity after gaining tariff-free access to EU
markets in 2001, which also boosted productivity
in domestically-focused firms (World Bank
2019d). In China, firms’ participation in foreign
supply chains and FDI complemented
domestically-led research and development,
spurring homegrown innovation (Hu, Jefferson,
and Jinchang 2005). Enhancing technology
adoption in EMDEs—returning economic
complexity growth to its fastest pace during the
EMDE growth and trade surge during 20002008—could increase productivity growth by 0.2
percentage point annually (Figure 3.12).
Address informality. The informal sector isassociated with lower average productivity levels
and accounts for around 70 percent of
employment in EMDEs, with particularly highconcentrations in SSA and SAR (World Bank
2019a). In Paraguay, informal firms have beenfound to be not only less productive than formal
firms, but to have negative spillovers on formal
firms’ productivity (Vargas 2015). Reducing thescope for rent-seeking bureaucratic processes that
obstruct formalization, improving the fairness ofregulation, and enhancing the even-handedness ofregulatory and tax enforcement have been
associated with a more efficient reallocation of
input factors from less productive informal
activities to more productive formal ones (Aminand Islam 2015; Amin, Ohnsorge, and Okou
2019). Beyond formalization, pro-productivity
and skill-upgrading interventions could be more
focused on informal small-scale firms andunskilled workers (Nguimkeu and Okou 2019).
Encouraging sectoral reallocation
Support sectoral reallocation and diversification.
Sectoral reallocation is an important engine ofproductivity growth (Box 3.2). The largest gains
in productivity occur at low levels of income asworkers shift away from the agricultural sector,
with lower benefits in middle-income EMDEs.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Furthermore, middle-income countries tend to behighly diversified across a broad range of bothmanufacturing and service sectors, although at
high levels of development diversification tends toslow and there is a re-specialization (Imbs andWacziarg 2003).
Sectoral diversification is of vital importance toeconomies with a high reliance on commodity
extraction, who have usually experienced thelowest levels of productivity growth globally(Bahar and Santos 2018).21 Commodity exporting
economies in LAC, MENA, and SSA have hadhighly procyclical investment and low average
TFP growth during the past three decades. The
benefits of diversification include greatermacroeconomic stability as well as higher average
rates of productivity growth. Economies that havesuccessfully reduced their reliance on oil exports,such as Malaysia, Mexico, and Indonesia, initiallyexpanded to complementary industries, such as
natural-resource processing and manufacturing, or
expanded to labor-intensive manufacturing, before
expanding to more complex manufacturing orservices sectors. In addition, these economies
established free trade zones, used tax incentives,and established industrial clusters to promote FDI(Cherif and Hasanov 2016).
Seek opportunities in services, boost lagging
sectors. Many high value-added service sectors
provide opportunities for rapid productivity catchup growth (Box 3.2; Hallward-Driemeier andNayyar 2017). High-productivity service sectors
such as finance, ICT, accounting and legal services
are likely to become increasingly tradable due totechnological advances, but require an enhanced
education, including at the tertiary level due totheir skill-intensive nature. In LICs, notwithstanding rapid pre-crisis productivity gains,productivity levels in the agricultural sector remainless than 10 percent of the average advanced
economy. SSA hosts the largest number of LICs
and may stand to benefit most from reallocation
away from agriculture. Yet, LICs in SSA have so
21 EMDE commodity-exporters have historically experienced a“crowding-out” effect on other faster growth industries during
periods of high commodity prices, which has hindered them fromclosing the productivity gap with advanced economies.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

far shifted away from agriculture towards
industrial sectors at a slower pace than LICs in
Asia (Box 2.6).
Agricultural productivity can be improved throughtargeted measures to increase infrastructure inthese regions, ensure secure land tenures, andpromote access to finance. Productivity led growthin agriculture could free-up input factors. In
Vietnam,successfulreforms
includedstrengthening of land property rights and relaxed
restrictions on external and internal trade of
agriculture goods. This could facilitate thereallocation of resources from agriculture to moreproductive sectors such as manufacturing andservices, and boost overall productivity (Fuglie etal. 2019). If EMDEs replicated thesuccessful
2003-08 sectoral reallocation of China andVietnam from the agriculture sector to
manufacturing and trade services, this would lift
productivity growth by 0.3 percentage points.
Given sizeable differences in sectoral productivity,
LICs would particularly benefit, with a boost ofover 1.5 percentage points (Figure 3.12).
Address market failures. Government efforts topromote specific sectors should first identify
market failures that have prevented sectoral
reallocation. In addition, the complexity and scaleof interventions to foster new industries need to bebalanced against government and institutional
capacity to manage risks such as political captureby special interests (Maloney and Nayyar 2018).
In addition, distortions that prevent the efficient
allocation of resources to productive sectors andfirms should be removed. Productivity in firms in
India and China may be 30-60 percent lower due
to misallocation of capital and labor across sectors
which may be driven by market distortions (Hsieh
and Klenow 2009). Where firm entry is costly—
whether due to high levels of regulation or
regulations that favor state-owned firms—
regulations can be streamlined, access to financeexpanded, implicit subsidies reduced, andcorporate governance standard improved. In
regions with high energy subsidies (LAC, MNA),
lowering these subsidies can also reduce themisallocation of resources into low-productivity
and inefficient energy-intensive sectors.

CHAPTER 3

Creating a growth-friendly environment
Strengthen
institutions
andgovernment
efficiency. Over the long term, institutional
quality is one of the most important determinantsof productivity growth (Figure 3.7). Productivity
gains have been shown to stem from faircompetition, even-handed contract enforcement,
simplified and transparent legal processes, andcontained political risk (Acemoglu et al. 2019).
Governments can promote productivity growth bylowering transaction costs, increasing trust in
institutions and facilitating long-term contracts(Leipziger and Thomas 1993). Major governancereform spurts are associated with faster TFP andinvestment growth (Figure 3.13).22 Other
measures to improve the business environment,such as product market and trade reforms or
cutting red tape, may boost productivity by morein the presence of good governance (IMF 2019).
New
information
andcommunications
technologies (“Govtech”) can provide one channel
through which governments can facilitate therapid dissemination of information within andoutside of government to monitor performanceand service shortfalls and improve transparency(Figure 3.11; World Bank 2018d).
Safeguard
macroeconomic
stability.
Ashighlighted in Box 3.4, episodes of rapid debt
accumulation and other triggers for financial crises
have historically had scarring effects on
productivity. Total EMDE debt has risen by 54
percentage points since 2010 and currently stands
at 168 percent of GDP, exposing many EMDEs tothe risk of financial instability (Chapter 4). Even
excluding China, where corporate debt has soared
post-crisis, total EMDE debt has risen to a nearrecord 107 percent of GDP in 2018. Private sector
debt vulnerabilities can be contained withmacroprudential
policiesandsupervisory
monitoring of risks. Where sovereign debt
vulnerabilities exist, including those fromcontingent private-sector liabilities, establishingfiscal rules can increase confidence in thesustainability of debt, lengthening the maturity of

22 These spurts are defined as those that improve at least one offour Worldwide Governance Indicators (government effectiveness,
control of corruption, rule of law, and regulatory quality) by at least 2
standard deviations over two years.

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FIGURE 3.13 Effect of governance reform spurts
Governance reform spurts have been associated with increased potential
TFP and investment growth. Setbacks, where perceptions of the quality of
governance decline sharply, are associated with slowing investment and
TFP growth.
A. Average change in potential TFP
growth around World GovernanceIndicators reforms

B. Average change in investment
growth around World GovernanceIndicators reforms

Source: World Bank.
Note: TFP growth refers to potential TFP growth, as estimated in World Bank 2018eA.B. Simple averages of potential TFP (A) and investment (B) growth during reform spurts andsetbacks (minus simple average potential TFP and investment growth outside such episodes) for all
countries (“Global”) or for EMDEs only (“EMDE”) using World Governance Indicators. Based on an
event study of 305 statistically significant reform events—defined as two-standard-error changes in
one of four World Governance Indicators—for 136 EMDEs and 36 advanced economies. Data arefrom 1996-2018.
Click here to download data and charts.

existing debt can ease near-term financing hurdles,
and improving the quality of spending towards
high-return infrastructure investment can yield
growth improvements.
Improve gender equality. Improvements ingender equality, in particular by narrowing
differentials in education and labor force
participation, can drive sustained improvements in
productivity growth by enhancing the humancapital available for production. Women currently
comprise only about one-fifth of the labor force in
MNA and one-quarter of the labor force in SAR.
In SSA, where female employment rates are high,female entrepreneurs tend to have lower profits
and access to capital. Gender inequality can beaddressed by ensuring equal legal rights, targetedtraining programs, relieving capital and financing
constraints for women, and addressing social
norms that constrain women’s economic
opportunities. Policies to empower women andboost their productivity include building skills
beyond those taught in traditional training
programs, such as a greater focus on developing anentrepreneurial mindset—this approach has beenfound to lift sales and profits in Togo (WorldBank 2019f). In the analysis of the underlying
drivers of productivity, economies with the lowest
gap between female and male educational
attainment grew by an average of 0.2 percentage
point faster each year than those with the highest
differential when controlling for othercharacteristics of the economy (Figure 3.7).

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CHAPTER 3

EMDE economies (Fajnzylber, Maloney, andMontes-Rojas 2011).1

ANNEX 3.1 Challenges of
Productivity Measurement
There are two primary ways of measuring
productivity: labor productivity and total factor
productivity (TFP). The former is defined by the
total output produced by a unit of labor, the lattermeasures the efficiency with which factor inputs
are combined. TFP can also be interpreted as thetechnology embedded in the production process,but may also incorporate wider factors such asorganizational characteristics. This annex reviews
the different techniques and challenges of thesedifferent productivity measures and explains how
they are tackled in this study.

•

Intensity of labor input. The number of people
involved in the production process does nottake into account various work-arrangementsthat vary the intensity of labor input (Katzand Krueger 2016; Brandolini and Viviano2018). The intensity of labor input is, forexample, better captured by hours worked but
these data are not available for many
countries.

•

Quality of labor input. The effectiveness oflabor input may be affected by the level of
education, training, and health of workers.
These aspects of human capital can beaddressed by estimating the years of schoolingfor education and the number of expected
years of life for health. However, the qualityof formal education and health, and theamount of on-the-job training is difficult tomeasure consistently in a panel setting.

Labor productivity. One of the common
approaches is measuring labor productivity as
output per worker by taking the number of
employees as the unit of labor input. Its advantage
is in its wide availability across countries. Its
disadvantage rests in the failure to account for thequality and intensity of labor input.
•

Comprehensiveness. Having high ratios ofinformality in EMDEs makes it challenging toappropriately measure productivity. Whileboth output and employment might bemismeasured due to non-registration, manynational statistics offices estimate the size ofthe informal sector and adjust their GDP
estimates accordingly (SNA 1993, 2008;
UNECE 2008; Charmes 2012). The
difficulty in estimating the scale of informal
output and lack of consistency in approachallows scope for productivity mismeasurement. Labor input is intended to capture
all of those involved in the production
process. Thus, total employment figures
include self-employment, which accounts fora large proportion of informal employment in
EMDEs (World Bank 2019a). However,some self-employment does not involve theinformal sector, while the scale of additional
employment in the informal sector is also
subject to uncertainty—therefore, difficulties
in both the measurement of informal output
and employment contribute to uncertainty
around the productivity level, particularly in

Total factor productivity. One of the mostcommonly used measures of technologicalenhancement is total factor productivity growth.
The standard growth accounting approach is oneof the most common methodologies in theliterature to estimate TFP. It is appealing due toits simple nature and its ease of interpretation.
Being estimated as residual, it depends on theassumed functional form and any measurement
error for factor inputs. In the context of theUnited States, this has triggered a debate about
the extent to which TFP growth adequatelyreflects new technologies.
•

Functional form. TFP is defined as “a shift in
the production function”, in contrast tobiased technological change. Its calculation
assumes the existence of a well-behaved andstable production function which also
accurately describes the technology in use(Baqaee and Farhi 2018). One of the

1 The direction of the bias depends on how national statistics
offices adjust their employment and official GDP to cover the
informal sector, which may vary across countries (UNECE 2008).

231

•

•

Labor productivity = Yt/Lt = At(Kt /Lt )(1-α)HtαΔLPt = (1 - α)Δkt + αΔht + Δat
Kt

kt = log ( Lt )ht = log Ht

1-α

α

at

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Capital services (Kt). Data on capital services arefrom the Penn World Table 9.1 (PWT) (Feenstra,
Inklaar, and Timmer 2015). In contrast to
previous versions of PWT, this edition utilizes
capital services as a measure of capital inputs
instead of capital stocks (Inklaar, Woltjer, andGallardo 2019).
Human capital (Ht). The human capital index
from the Penn World Table 9.1 is usedthroughout the sample. This measure uses average
years of schooling of the working-age populationin combination with an estimate of the global
returns to education.
Labor share estimates. The output-labor elasticity(α), proxied by the labor income share, is also
derived from the PWT 9.1 database. It isestimated using the labor compensation to output
ratio, including adjustments to take account of
mixed-income and wages from self-employment.
Labor shares are allowed to vary across countries
in this chapter’s decompositions. This analysis
uses constant labor shares over time, defined as thelong-term average of labor share data from PWT
9.1, although it varies across countries.

ANNEX 3.3 Drivers of
productivity1
Productivity improvements are key for spurring
sustained economic growth and social progress inthe presence of limited quantity and quality offactor inputs—labor inputs, physical capital, andnatural resources (Easterly and Levine 2001;
Caselli 2005). Drawing from growth theories, theempirical literature has identified many potential
drivers of productivity growth.2 These can beclassified into three broad categories: inputs ofproduction, such as innovation, physical capitaland labor;

1 This annex was prepared by Alistair Dieppe, Atsushi Kawamoto,
Yoki Okawa, and Cedric Okou.
2 As some concepts overlap there could be alternative classifications
which focus on other concepts such as competition, geography, andsocial fragmentation.

CHAPTER 3•

supporting environments, such as institutions,policies and social conditions; and•market development, such as trade integrationand financial deepening.

This annex reviews the theoretical and empiricalliterature that establishes linkages between each of
the most commonly identified drivers andproductivity growth and assesses differences across
EMDE regions as well as over time.
Inputs of production
Innovation. Technical innovations create betterways to produce goods, deliver services, andimprove within-sector productivity of firms.
Despite large productivity gaps in EMDEs relative
to advanced economies, most EMDEs invest
much less in formal research and development(R&D) than advanced economies (Goñi andMaloney 2017). The number of patents per
capita—one indicator of the pace of innovation—is particularly low in Latin America and the
Caribbean (LAC), South Asia (SAR), and SubSaharan Africa (SSA; Annex Figure 3.3.1).
Nonetheless, gradual improvements in process or
product quality have been reported across all
income levels (Goñi and Maloney 2017). New
patents tend to be more productivity-enhancing incountries with ample supply of highly educatedand skilled labor force, while gradualimprovements in productivity can be achieved
even with low human capital levels (World Bank
2018e).
Physical capital. Labor productivity can beboosted by capital accumulation, underpinned byinvestment and matched with adequate absorptive
capacity (Eberhardt and Presbitero 2015). In
particular, investments in infrastructure, includingtransport, water and sanitation, power, andtelecommunications
cancomplement
technological progress and lift productivity.3
Infrastructure needs in EMDEs remain large.
Achieving infrastructure-related SDGs in low- andmiddle-income countries will require an average

3 See, for example, Aschauer (1989); Servén (2015); and Martins(2019).

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ANNEX FIGURE 3.3.1 Productivity drivers in 2017, byregion•Education. As a labor force becomes better
trained and more highly skilled, it has agreater propensity to contribute totechnological advancements and to benefit
from positive innovation. Countries with
better-educated working-age populations tend
to have higher productivity (Barro and Lee2015). This could reflect workforces in
EMDEs moving jobs from sectors requiring
limited skills, such as agriculture, to sectors
requiring greater skill levels, such as
manufacturing and services (Box 3.2). Despite
significant catch-up over the past five decades,
the gap in average years of schooling between
EMDEs (8.6 years) and advanced economies(12.3 years) remains sizeable. There is a
substantial dispersion among EMDE regions.
For instance, Europe and Central Asia (ECA)
has the highest years of schooling among
EMDEs, just one year short of the advancedeconomy average. By contrast, SSA and SAR
has low years of schooling, less than half of
the advanced-economy average (Annex Figure
3.3.1).

•

Health. Healthy workers can work more
efficiently and learn faster; they are also morecommitted to improving their skills and are
better equipped to innovate (World Bank
2018e). Better health complements educationin reinforcing the supply of good-quality
labor, in turn raising human capital, attracting
investment, and improving productivity.

•

Demographic trends. Workforce aging is often
negatively associated with productivity growth(Aiyar, Ebeke and Shao 2016; Aksoy et al.
2019).4 New technologies can disrupt thevalue of existing human capital, as senior andunskilled workers may need retraining. The
strength of this mechanism may depend on
the economic structure of the country, as
productivity benefits more from experience insome occupations and from innovation inothers. This effect is particularly pronounced
in advanced economies, where the workingage share of the population shrank

All EMDE regions fall short of advanced-economy conditions in important
productivity drivers, including innovation, human capital, institutions,macroeconomic stability, and trade openness. There is considerable
variation across regions: SSA and LAC tend to rank low in many of these
dimensions whereas EAP and ECA tend to rank highly.
A. Patents per capita

B. Years of schooling

C. Rule of Law

D. Inflation

E. Trade openness

F. Economic complexity

Source: Observatory of Economic Complexity; United Nations; World Bank, Worldwide GovernanceIndicators.
Note: Data for 2017. Unweighted averages. Trade openness is the sum of exports and imports inpercent of GDP. Samples include 22-33 countries in advanced economies, 6-23 countries in EAP,
4-24 countries in ECA, 17-31 countries in LAC, 7-16 countries in MNA, 3-8 countries in SAR, and10-48 countries in SSA,
Click here to download data and charts.

yearly investment of 4 to 8 percent of GDP
during 2015-30 (Rozenberg and Fay 2019;
Vorisek and Yu, forthcoming).
Labor. The productivity of labor can be improvedin several ways. A better-educated or healthierwork-force can adjust more easily to productivityenhancing changes.

4 The Solow model suggests a decline of the working age
population could increase the capital per worker and positively affectlabor productivity.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020by 3 percentage points between 2008 to 2018.
In the decades ahead, EMDEs are projected tofollow the same path. Between 2018 and
2030, the working-age population share is
expected to decline by 3 percentage points
for advanced economies and 2.5 percentage
points for EMDEs. In East Asia and Pacific(EAP) and ECA, the working-age population
share has already begun to decline, whereas
SSA continues to benefit from rising workingage population shares. Realizing the potentialof a youthful population requires investing in
education and accelerating job creation.
Supporting environment
Institutions. Institutions are the entities thatshape human interactions within a society (North
1990). Institutions come in many forms—rule oflaw, barriers to firm creation and operation, andsystem of government, to name a few. Better
quality institutions are associated with fairer
competition and higher productivity (Easterly andLevine 2003; Levchenko 2007). Increased
competition is found to support innovation andraise productivity through improvements inmanagement and product quality (Van Reenen2011). Acemoglu et al. (2019) find that thetransition to democracy raises productivity by 20
percent in the subsequent 25 years, but the results
vary across studies and some have not uncovered
an effect (Ruiz Pozuelo, Slipowitz, and Vuletin2016). Productivity improvements depend on acountry’s distance to the technology frontier(Prati, Onorato, and Papageorgiou 2013). Thereremains a large gap between the quality of
institutions, proxied by the government
effectiveness index, between all EMDE regions
and advanced economies, and the gap hasremained almost unchanged over the past twenty
years (Annex Figure 3.3.1).
Price stability. Price stability in part reflects theabsence of major distortions and uncertainty inthe macroeconomic environment (Rodrik,
Subramanian, and Trebbi 2004b). Priceinstability, which can be reflected by highinflation or a large difference between the black
market and official exchange rates, may hinder
investment, lead to sizeable capital outflows, andare negatively correlate with productivity and

CHAPTER 3

economic growth (Gramacy, Malone, and Horst
2014). Price stability in EMDEs, proxied byinflation, has substantially improved over time,and currently stands at about 4 percent (except in
SSA), down from 18 percent in 1990 (Annex
Figure 3.3.1). Nevertheless, in many EMDEs,
monetary and fiscal policy frameworks still lag
behind best practices (Koh and Yu 2019).
Income equality. Income inequality has been
explored as a potential underlying driver of low
productivity growth. However, the literature isagnostic about the impact of inequality on
productivity and economic growth (Herzer andVollmer 2012; Alvaredo et al. 2018). The elusive
empirical link may be due to the u-shaped
relationship between income equality and thestage of development: the adverse effects ofincome inequality tends to be high for low-income
and high-income countries, but not high in
middle-income countries (Banerjee and Duflo2003). Income inequality has fallen in some
EMDE regions, such as LAC. Yet, it remains
much higher in EMDEs than in advanced
economies. As of 2017, inequality measured by
the Gini index, was 41 for EMDEs, compared to
33 for advanced economies.5
Gender equality. Large gaps between women andmen in measures of education, health, and accessto economic opportunities can lower productivity.
Better income-earning opportunities for women
can increase human and physical capital
investment through higher household income andhigher returns for building women’s humancapital (Klasen and Santos Silva 2018). It may also
lower fertility and, hence, help provide each childwith better education and health care. An
increasing share of women in the labor force, withfair pay and equal job opportunity, can also bebeneficial for productivity growth, as it brings aricher collection of perspectives to the decisionmaking and production process (Gallen 2018). By
contrast, the exclusion of all women frommanagerial positions can reduce income per capita
by 12 percent (Cuberes and Teignier 2012, 2014).
The gap between EMDEs and advanced
5 The Gini index is a measure of the distribution of income acrossincome percentiles, presented on a scale of 0 to 100, where 100 is themost unequal.

235

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CHAPTER 3

economies for the latter of these indicators has
declined during the last five decades.
Market development
Trade. Trade can significantly improve
productivity growth (World Bank 2019d)
although some studies find only a weakrelationship between trade and productivity(Rodrik, Subramanian, and Trebbi 2004).
Imports of machinery or high-technology goods
can directly improve productivity at the firm,
sector, and country level. Lower tariffs canincrease imports, facilitate knowledge transfers,
and strengthen firm-level productivity (Kraay,
Soloaga, and Tybout 2002). Exporting firms tend
to have higher productivity than non-exporting
ones. The high productivity of exporting firms canbe explained by self-selection in some cases(Clerides, Lach, and Tybout 1998). However,evidence from Kenya and the Republic of Korea
suggests that exports can increase productivity
after controlling for self-selection (Graner andIsaksson 2009). Learning-by-exporting effects on
productivity depend on the income level ofimporters or exporters. The learning effect is large
when the exporter and importer have similar
productivity levels or importer’s human capital ishigh (Graner and Isaksson 2009; Keller 2004;
Blalock and Gertler 2004; Aw, Chung, andRoberts 1998). ECA and EAP are the EMDE
regions that are most open to trade whereas SAR isthe least open (Annex Figure 3.3.1).
Foreign direct investment. Investment fromabroad can bring advanced technology, improved
organizational structure, and good management
practices from frontier technology economies,
boosting productivity in host economies where itis lagging (Griffith, Redding, and Simpson 2003).
Cross-border capital flows have a positive effect on
productivity, especially those with a high level ofdevelopment and high-quality institutions.
However, this positive relationship is weaker for
EMDEs (Keller and Yeaple 2009). In developing
countries, the cost of subsidies offered to firms to
attract foreign investments can exceed the positiveeffect of FDI on productivity (Haskel, Pereira, andSlaughter 2007).
Economic complexity. Economic complexity is

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

measured as a composite indicator that compares
each country’s sectoral export shares with thesector’s share in world trade. The economic
complexity is higher if the country exports more“complex” goods such as X-ray appliances, which
can be exported from only a few other economies(Hausmann et al. 2014). Greater export
complexity has been associated with higher laborproductivity through its association with thediversification and sophistication of a country’s
economic structure (Hausmann and Hidalgo
2010). EMDEs largely lag behind advanced
economies in terms of economic complexity(Annex Figure A3.3.1)
Urbanization. Urbanization can facilitate
agglomeration benefits such as knowledge
spillovers, and improved skills matching within
the labor force. Densely populated areas bringpeople and firms closer together, making it easier
to share ideas, exchange information, invent newtechnologies, design new projects, engage in newpartnerships, and start new businesses (Abel, Dey,and Gabe 2012). These agglomeration benefits
can in turn lift productivity.
Finance. Well-developed financial markets canimprove the efficiency of capital allocation,
facilitate technology spillovers and help firms takeadvantage of productivity-enhancing investments(Fisman and Love 2003; Levine 1997). Financial
development and integration are associated withproductivity growth (Aghion, Howitt, and MayerFoulkes 2005). Financial markets allow firms todiversify investment risk, increase liquidity, andstimulate entrepreneurship and productivity.
Estimating impacts of drivers on productivity
growthMethodology. A cross-section analysis isundertaken where the dependent variable is thelong-run growth of productivity during 19602018 and separately over 1995-2018. In addition
to the initial level of log productivity (y0), other
regressors (X0)—discussed in the literature andmeasured at the beginning of the period—are
included:yT,j - yo,j βyo,j Xo,jγ εj,where εj is a disturbance term, and j denotes a

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

specific economy.6 The set of coefficients γ
capture how each covariate (X0,j) drive
productivity dynamics over the long-run. The
wide range of potential drivers associated withproductivity growth leads to a large range ofpotential model specifications (Fernández, Ley,and Steel 2001; Durlauf, Kourtellos, and Tan
2008, Durlauf, Johnson, and Temple 2005). In
order to reduce the bias stemming from an ad-hoc
selection and omission of variables, a BayesianModel Averaging (BMA) approach is applied,which considers different subsets of potential
variables
andevaluates
their
inclusion
probabilistically. Nonetheless, the estimation
results can be unstable in the presence of strong
collinearity, as many variables can essentiallyrepresent the same concepts (Ghosh and Ghattas
2015). Therefore, based on existing literature andgrowth theories, variables that represent common
concepts are grouped together. The posterior
distributions of the coefficients obtained from the
BMA procedure are then aggregated to the grouplevel.7
Impacts. The estimation is undertaken for 59
countries, including 36 EMDEs.8 It shows that
better educated workforce, stronger institutions,
greater innovation, stronger investment, higher
levels of urbanization, price stability and a diverseand sophisticated economic structure are all
significantly associated with higher productivity
growth (Figure 3.7). Furthermore, the estimatedimpact depends on the stage of development andhas changed over the more recent period. The
estimated coefficients can be interpreted as the
hypothetical coefficient of each theoretical driverof productivity growth. Using these coefficients an

6 Most candidate variables can be viewed as outcomes of
productivity, in addition to drivers of productivity, which constrains
the interpretation of causal claims from the regressions. To counterthe reverse causality issue, the variables used in the analysis are levels
in 1960 (or 1995), based on the assumption that serial correlation in1960 and average growth for the next 58 years is small.
7 Parametric estimations cannot exclude the possibility of omitted
variable bias. Panel estimation focusing on more recent periods can
reduce this issue by the inclusion of country fixed effects and a wider
range of potential variables, but usually rely on the constant country
effects assumption and can suffer from serial correlation and othertypes of biases.
8 Variables related to theories in the existing literature are chosen
where data exists before 1970 for a large sample of economies.
Observations which are not available in the particular year was
substituted by the observations in the closest year available.

CHAPTER 3

237

ANNEX FIGURE 3.3.2 Productivity changes in
productivity drivers, by region
Productivity drivers—here captured in a composite index—have improved
considerably in EMDEs since the 1980s. However, in several regions,
including EAP, ECA, LAC and SAR, the pace of improvement appears tohave stalled since the global financial crisis.
A. East Asia and Pacific

B. Europe and Central Asia

C. Latin America and the Caribbean

D. Middle East and North Africa

E. South Asia

F. Sub-Saharan Africa

Source: World Bank
Note: For each country, index is a weighted average—weighted by the normalized coefficients shownin Figure 3.7—of the normalized value of each driver of productivity. Drivers include the ICRG rule oflaw index, patents per capita, share of non-tropical area, investment in percent of GDP, ratio of
female average years of education to male average years, share of population in urban areas,
Economic Complexity Index, years of schooling, and share of working-age population. Regional and
EMDE indices are GDP-weighted averages. Samples include 7 economies in EAP, 8 economies in
ECA, 18 economies in LAC, 6 economies in MNA, 4 economies in SAR, and 11 economies in SSA.
Click here to download data and charts.

aggregate index of drivers of productivity growthis formed. It shows it grew rapidly on average in
EMDEs in the pre-crisis period supporting
productivity growth (Annex Figure 3.3.2).
However, since the global financial crisis,
improvements in the drivers have begun to level
off as the pace of improvement has slowed,
particularly in several EMDE regions (EAP, ECA,
LAC, and SAR) amid a productivity growth
slowdown.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

ANNEX TABLE 3.3.1 Variables included in the regressions and sourcesGroup

Variable

SourceFinancial development

Ratio of domestic credit to GDP

World Development Indicators (WDI)

Investment

Ratio of gross fixed capital formation to GDP

WDI

Education

Years of schooling

Barro & Lee, UN

Human capital

UNDP

Years of tertiary schooling

Barro & Lee, UN

Years of primary and secondary schooling

Barro & Lee, UN

Economic Complexity Index plus

Economic observatory(Exports + Imports)/GDP

WDI

Patents per capita

WDI

Patents per capita * years of tertiary schooling

WDI

Equality

100 - Gini coefficient

UNU wider database

Institutions

Political Rights Index

Freedom House

Civil Rights Index

Freedom House

Rule of Law Index

International Country Risk Guide, PRS

Ratio of government consumption to GDP

WDI and various other sources

Share of population in urban areas

WDI

Population density

WDI

Economic Complexity
Innovation

Urban
HealthDemography
GenderGeography

Stability

Survival rate after 5 years per 1000 births = 1000-Infant mortality rate WDI
Life expectancy at birth

WDI

Share of population aged 15-64

WDI

Share of population aged below 15

WDI

Ratio of years of schooling of female to male

Barro & Lee, UN

Ratio of years of primary schooling of female to male

Barro & Lee, UN

Ratio of labor participation rate of female to male

WDI

Dummy for landlocked countries

WDI

Share of land which is in tropical regions

WDI

EMDE energy exporter dummyWorld Bank(-1) * CPI Inflation Rate

WDI

Black market exchange rate relative to the official rate

WDI

Note: Sources and list of variables included in the Bayesian selection model. Variables selected with the highest probability of inclusion for each category are in bold.

GLOBAL ECONOMIC PROSPECTS I JANUARY 2020

ANNEX 3.4 Data andmethodology for sectoral
productivity
Data. The database consists of sectoral andaggregate labor productivity statistics for 80
countries, and nine sectors covering the period up
to 2015. Compared with the literature using ninesector data, it employs a large and diverse sample
of countries. The database combine data from theOECD STAN database, World KLEMS (EU,LAC and Russia), the Groningen Growth
Development Center (GGDC) database (de Vries,
de Vries and Timmer 2015), and the Expanded
Africa Sector Database (EASD, Mensah andSzirmai 2018) for value added data andemployment. The APO Productivity Database,
UN data, ILOSTAT and National sources areused for supplementary purposes. Following
McMillan, Rodrik, and Verduzco-Gallo (2014),
local currency value added is converted to U.S.
dollars using 2011 PPP exchange rate obtained
from Penn World Table for the international
comparison of productivity levels. 1

CHAPTER 3

239

Shift-share analysis. Following (Wong 2006) and(Padilla-Perez and Villarreal 2017), this chapter
employs a shift-share-analysis which decomposes
aggregate labor productivity into the growthwithin a sector and shifts between sectors:

k Y  y 
k y  y k
 y j  y j
y
j
j
j
j
=  
s j
 +   s j +   

y
Y
y
y
y
j =1
j =1
j =1  y  y j
 j 
 j 

Intra-Sectoral
EffectStatic Sectoral
EffectDynamic Sectoral
Effect Shift

Structural Change Effectwhere y is aggregate labor productivity, Y; is laborproductivity of sector j, Y; is initial value added of
sector j, s; is employment share of sector j.
Structural changes are driven by the change in
employment share. They are further decomposed
into those which are due to the reallocation ofsources to sectors which higher productivity levels(static sectoral effect), and those due toreallocation
towardsectors
withhigherproductivity growth (dynamic sectoral effect).

1 Van Biesebroeck (2009) builds an expenditure-based sectorspecific PPP in OECD countries, using detailed price data.

ANNEX TABLE 3.4.1 Sectoral classifications
I.

1. Agriculture

Agriculture, forestry and fishing

2. Mining

Mining and quarrying

3. Manufacturing

Manufacturing

4. Utilities

Electricity, gas, steam and air conditioning supply

5. Construction

Construction

6. Trade services

Wholesale and retail trade; repair of motor vehicles and motorcycles; Accommodation and food service activities

7. Transport services

Transportation and storage; Information and communication

8. Financial and Business services

Financial and insurance activities; Real estate activities; Professional, scientific and technical activities;
Administrative and support service activities

9. Government and Personal
services

Public administration and defence; compulsory social security; Education; Human helath and social work activities;
Arts, entertainment and recreation; Other service activities; Activities of households as employers; undifferentiatedgoods-and services-producing activities of households for own use; Activities of extraterritorial organizations andbodies.

240

GLOBAL ECONOMIC PROSPECTS I JANUARY 2020

CHAPTER 3

ANN EX 3.5 Methodology forBox 3.3

ANNEX 3.6 Local projection
methodology for Box 3.4

Measurement challenges. Revenue-based TFP(TFPR) measures conflate physical productivity
and price effects (Foster et al. 2008; Andrews,
Criscuolo, and Gal 2016). These price effects cansubstantially distort TFPR estimates in noncompetitive markets or when output prices andinputs choice are correlated. For instance, a highproductivity firm with market power can lower
output prices to increase its market share. In thiscase, TPFR estimates can be low even though thefirm is highly productive. Producer prices, if
available, can be used to deflate firm-level sales
and obtain physical TFP (TFPQ) estimates(Cusolito and Maloney 2018; Van Beveren 2012).
Moreover, specifying a single production function
for a firm using multiple production technologies
is restrictive and can bias TFP estimates (Bernard,
Redding, and Schott 2010; Goldberg et al. 2010).
Disaggregated product-level data, if available, canbe used to construct product-level TFP and help
account for the richness in production mix.

The computation of crises impacts follows the
local projection (LP) method Qorda 2005). The
dependent variable is the cumulative change in
output or productivity levels between horizons t-1
and t+1 , measured as the natural logarithms
yt,j ). The baseline model is given by

Methodology. The fitted specification isργ

where DTFt is the distance-to-frontier of TFP forfirm i in industry g, 0 stands for the constantterm, ref = TINT is the reference industry, andcoefficients ρg are interpreted relatively to thereference group. X;1 is firm i's jth characteristic
such as GDP per capita (in 2009 U.S. dollars per
worker), size (number of employees), exports (as aproportion of total sales), and business climate(control of corruption, business freedom). The
error term is denoted by V ;.

yt + h , j − yt −1, j =  ( h ), j +  ( h ),t +  ( h ) Et , j +  s =1  l ( h )t , s Et − s , j +
P

 
h −1
s =1

Et + h − s , j +  s =1  ( h ), s yt − s , j + u( h )t , j .
pf ( h ), s

(1)

Where h = 0, 1,2, .. . , 8 is the horizon, α(h),j andτ(h),t are country j and time fixed effects, and u(h)t,j is an error term. The coefficient of interest β(h)captures the dynamic multiplier effect (impulse
response) of the dependent variable with respect tothe event dummy variable Et,j . The number of lagsfor each variable is denoted by p and set to 1 forthe estimation. The specification controls for (i)country and time specific trends, (ii) lagged event
dates, (iii) future values of the event dummy
between time t and t+h-1 to correct for possible
forward bias (Teulings and Zubanov 2014), and(iv) past changes Δyt-s,j . Additional controls forcountry-specific interactions and non-linear effects
may also be included.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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THE FOURTH WAVE
Rapid Debt Buildup

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CHAPTER 4

The global economy has experienced four waves of debt accumulation over the past fifty years. The first three
ended with financial crises in many emerging market and developing economies. During the current wave,
which started in 2010, the increase in debt in these economies has already been larger, faster, and more broadbased than in any of the previous three waves. Current low interest rates —which markets expect to be sustained
into the medium term—appear to mitigate some of the risks associated with high debt. However, emerging
market and developing economies are also confronted by weak growth prospects, mounting vulnerabilities, andelevated global risks. A menu of policy options is available to reduce the likelihood of the current debt wave
ending in crises and, if crises were to take place, to alleviate their impact.

Introduction
Waves of rapid debt accumulation have been arecurrent feature of the global economy over the
past fifty years, in both advanced economies andemerging market and developing economies(EMDEs). Since the 2008-09 global financial
crisis, another wave has been building, with global
debt reaching an all-time high of about 230
percent of global GDP in 2018 (Figure 4.1).
Total EMDE debt reached almost 170 percent of
GDP in 2018 ($55 trillion), an increase of 54
percentage points of GDP since 2010. Although
China accounted for the bulk of this increase—inpart due to its sheer size—the debt-buildup was
broad-based: In about 80 percent of EMDEs total
debt was higher in 2018 than in 2010. Following
a steep fall during 2000-10, debt has also risen in
low-income countries (LICs), reaching 67 percent
of GDP (around $270 billion) in 2018, up from48 percent of GDP (around $140 billion) in2010.
In contrast, in advanced economies, total (public
and private) debt has remained steady near therecord levels reached in the early aftermath of the
global financial crisis, at 264 percent of GDP in2018 ($130 trillion). While government debt has
risen to a high of 104 percent of GDP ($50
trillion), private sector debt has fallen slightly
amid deleveraging in some sectors.
The current environment of low interest rates,
combined with subpar global growth, has
Note: This chapter was prepared by a team led by M. Ayhan
Kose, Peter Nagle, Franziska Ohnsorge, and Naotaka Sugawara, with
contributions from Jongrim Ha, Alain Kabundi, Sergiy Kasyanenko,
Wee Chian Koh, Franz Ulrich Ruch, Lei (Sandy) Ye, and Shu Yu. It
is based on Kose et al. 2019. Vanessa Banoni, Julia Norfleet, Jankeesh
Sandhu, Shijie Shi, and Jinxin Wu provided research assistance.

triggered a lively debate about the benefits andrisks of further government debt accumulation tofinance increased spending. It is generally agreed
that public borrowing can be beneficial,
particularly in EMDEs with large development
challenges, if it is used to finance growthenhancing investments, such as infrastructure,
health care, and education. Debt accumulation
can also be appropriate temporarily as part ofcounter-cyclical fiscal policy, to boost demand andactivity in economic downturns.
However, high debt carries significant risks for
EMDEs, as it makes them more vulnerable toexternal shocks. The rollover of existing debt canbecome increasingly difficult during periods offinancial stress, potentially leading to a crisis. Highgovernment debt levels can also limit the size andeffectiveness of fiscal stimulus during downturns,
and can dampen longer term growth by weighing
on productivity-enhancing private investment.
EMDEs have been navigating dangerous waters as
the current debt wave has coincided with a decadeof repeated growth disappointments, and they are
now confronted by weaker growth prospects in a
fragile global economy (Kose and Ohnsorge2019). In addition to their rapid debt buildup
during the current wave, these economies haveaccumulated other vulnerabilities, such as growing
fiscal and current account deficits, and acompositional shift toward short-term external
debt, which could amplify the impact of shocks.
Thus, despite current exceptionally low realinterest rates, including at long maturities, thelatest wave of debt accumulation could follow thehistorical pattern and eventually culminate infinancial crises in EMDEs. A sudden global shock,such as a sharp rise in interest rates or a spike inrisk premia, could lead to financial stress in more

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FIGURE 4.1 Evolution of debt
Global debt has trended up since 1970, reaching around 230 percent of
GDP in 2018. Debt has risen particularly rapidly in EMDEs, reaching apeak of about 170 percent of GDP in 2018. Much of the increase since2010 has occurred in the private sector, particularly in China. Debt in lowincome countries has started to rise after a prolonged period of decline
following debt relief measures in the late 1990s and 2000s. Advancedeconomy debt has been broadly flat since the global financial crisis, withincreased government debt more than offsetting a mild deleveraging in theprivate sector.
A. Global debt

C. Debt in LICs

B. Debt in EMDEs

D. Debt in advanced economies

Source: International Monetary Fund; World Bank.
Note: Averages computed with current U.S. dollar GDP as weight and shown as a 3-year moving
average. Vertical lines in gray are for years 1970, 1990, 2002, and 2010.
B. Dashed lines refer to EMDEs excluding China.
Click here to download data and charts.

vulnerable economies. Indeed, these risks were
illustrated by the recent experiences of Argentina
and Turkey, which witnessed sudden episodes of
sharply rising borrowing costs and severe growth
slowdowns in 2018.
Among LICs, the rapid increase in debt and theshift from concessional toward financial market
and non-Paris Club creditors have raised concerns
about debt transparency and debt collateralization.
Elevated debt in major EMDEs, including China,
could amplify the impact of adverse events andtrigger a growth slowdown, posing risks to global
and EMDE growth.
Against this backdrop, this chapter compares thecurrent wave of debt buildup to previous episodes
and considers the policy implications. The chapter

employs a wide range of approaches, including
event studies, econometric models, country casestudies, and a detailed review of historicalepisodes. Specifically, it examines the following
questions.
•

How have previous waves of debt in EMDEs
evolved?

•How does the current wave of debt
accumulation compare to earlier waves?

•

What are the macroeconomic implications ofrapid debt accumulation?

•

What are the lessons and policy implications
for EMDEs?

Contributions. An extensive literature has studied
various aspects of debt accumulation, especially inthe context of government and private debt crises.
This chapter adds to this literature in fivedimensions. First, the chapter provides the first indepth analysis of the similarities and differencesamong four distinct waves of broad-based debt
accumulation in EMDEs since 1970.1 Each wave
contains episodes that have been widely examinedin the literature but they have rarely been put into
a common framework. Examining debt buildups
as waves allows a richer analysis by considering theinteraction of global drivers with country-specificconditions. Earlier work has taken on a longer
historical perspective and focused mainly on debt
developments in advanced economies, typically
based on case studies. Second, in contrast to earlier
studies, the chapter puts the ongoing (fourth)wave of broad-based debt accumulation in
EMDEs into historical perspective.2 Third, the
1 Previous studies have examined the impact of mounting
government debt in advanced economies (BIS 2015; Cecchetti,
Mohanty, and Zampolli 2011; Erhardt and Presbitero 2015;
Eichengreen et al. 2019; Mbaye, Moreno-Badia, and Chae 2018a;
OECD 2017; Panizza and Presbitero 2014; Reinhart, Reinhart, andRogoff 2012). For EMDEs, previous studies have often analyzed
certain periods of debt distress, or crises in individual countries. For
example, contagion from the Asian crisis has been examined by Baig
and Goldfajn (1999); Chiodo and Owyang (2002); Claessens andForbes (2013); Glick and Rose (1999); Kaminsky and Reinhart(2000, 2001); Kawai, Newfarmer, and Schmukler (2005); Moreno,
Pasadilla, and Remolona (1998); and Sachs, Cooper, and Bosworth(1998).
2 The recent debt accumulation, without the historical context,
have been discussed in IMF (2019a, 2016a) and World Bank (2015,2016a, 2017a).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

chapter undertakes the first comprehensive
empirical analysis of a large number of individual
episodes of rapid government and private debt
accumulation in 100 EMDEs since 1970. The
separate analysis of individual episodes offers key
insights into the macroeconomic consequences, atthe country level, of debt accumulation. Earlier
work has examined developments in government
and private debt markets separately, or focused ona smaller group of (mostly advanced) economies
or regions.3 Fourth, the chapter identifies the mostfrequent triggers of crises and the country-levelvulnerabilities that contribute to or exacerbate
crises. Fifth, armed with insights from an extensive
analysis of the global and national waves of debt
accumulation and the empirical linkages between
elevated debt and financial crises, as well as theearlier literature, the study distills lessons andpresents a rich menu of policy options that canhelp EMDEs boost resilience to future crises.
The chapter documents the following findings.
Three previous waves. Prior to the current wave,
EMDEs experienced three waves of broad-based
and rapid debt buildup. The first (1970-89) was
focused in Latin America and the Caribbean(LAC) and Sub-Saharan Africa (SSA), the second(1990-2001) in East Asia and Pacific (EAP) andsome other EMDEs in Europe and Central Asia
(ECA) and LAC, and the third (2002-09) was
chiefly in ECA. The fourth wave (2010 onwards),
in contrast, has covered all EMDE regions.
Similarities and differences among previous
waves. All debt waves began during prolonged
periods of very low real interest rates, and were
often facilitated by changes in financial markets
that contributed to rapid borrowing. The threeearlier waves all ended with widespread financial
crises and coincided with global recessions (1982,
1991, and 2009) or downturns (1998, 2001).
Crises were usually followed by reforms designed
3 Government debt crises have been discussed in Kindleberger
and Aliber (2011); Reinhart, Reinhart, and Rogoff (2012); Reinhart
and Rogoff (2010, 2011); and World Bank (2019a). Credit booms
have been examined in Dell’Arricia et al. (2014, 2016); Elekdag and
Wu (2013); Jordà, Schularick, and Taylor (2011); Mendoza andTerrones (2008, 2012); Ohnsorge and Yu (2016); and Tornell andWestermann (2005).

CHAPTER 4

to lower external vulnerabilities and strengthen
policy frameworks. These similarities notwithstanding, the financial instruments used forborrowing have shifted over time as new
instruments or financial actors emerged. The
nature of EMDE borrowers in international
financial markets has also changed, with theprivate sector accounting for a growing share ofborrowing through the first three waves.
Another global wave of debt underway. The debt
buildup in EMDEs in the fourth wave, whichstarted in 2010, has already been larger, faster andbroader-based than in any of the previous waves.
The annual increase in EMDE debt since 2010
has been larger, by some margin, than during thefirst three waves. Whereas previous waves werelargely regional in nature, the fourth wave was
global, with total debt rising in more than 70
percent of EMDEs in all regions and rising by atleast 20 percentage points of GDP in more than
one-third of EMDEs. In the fourth wave, mostnational episodes of debt accumulation combined
government and private debt accumulation, in
contrast to the previous three waves which had agreater focus a single sector.
Debt buildups often associated with crises. Since
1970, there have been about 520 national episodesof rapid debt accumulation in 100 EMDEs.
Around half of these episodes were accompanied
by a financial crisis, with sizeable economic costs.
Crises during rapid government debt buildups
featured larger output losses than crises during
rapid private debt buildups.
Debt accumulation as shock amplifier. Whilefinancial crises during rapid debt accumulation
episodes were often triggered by external shocks,
such as sudden increases in global interest rates,
domestic vulnerabilities often increased thelikelihood of crises and amplified their adverse
impact. Most countries where crises erupted
suffered from unsustainable combinations of
inadequate fiscal, monetary, or regulatory
frameworks. Crises were more likely, or the
economic distress they caused was more severe, incountries with higher external debt—especiallyshort-term—and lower levels of international
reserves.

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Policy implications. While there is no magicbullet of a policy prescription to ensure that thecurrent debt wave proceeds smoothly, theexperience of past waves of debt points to thecritical role of policy choices in determining theoutcomes of these episodes. Sound debt
management and debt transparency can help
reduceborrowingcosts,
enhancedebt
sustainability, and dampen fiscal risks. Strong
monetary, exchange rate, and fiscal policy
frameworks can safeguard EMDEs’ resilience in a
fragile global economic environment. Robust
regulatory and supervisory regimes, which are also
well coordinated between home and hostsupervisors of foreign banks, can help containfinancial market risks and encourage prudent
lending to the private sector. Good corporate
governance can help ensure that debt is used forthe most productive purposes.

Evolution of past waves
of debt
The buildup of EMDE debt since 1970 has notbeen linear. At different points in time, different
countries, and regions, have undergone periods ofrapid debt accumulation (Figure 4.2). These haveoften been followed by crises, and periods ofdeleveraging. This section examines “waves” ofbroad-based debt accumulation in EMDEs, andconsiders their similarities and differences. It
identifies four waves of debt since 1970, of which
the fourth is still ongoing.
Identification of the four waves
The dating of the four waves meets some basic
criteria.
•

The first wave begins in 1970.4 Data
limitations prevent more detailed analysis of
the period prior to 1970.

•

The end of a wave is broadly defined as theyear in which the total debt-to-GDP ratio in
the affected region or country group peaks

4 1970 is also used as the starting year by Laeven and Valencia(2018) in their database of financial crises.

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

and is followed by two consecutive years of
decline.
•

The dating of the end of waves is consistent
with the approximate timing of policies toresolve the financial crises that theyengendered. In 1989, for example, Mexico
issued the first Brady bonds, marking the
beginning of resolution of the Latin American
debt crisis. In 1998-2001, a series of IMF
programs led to debt resolution after the East
Asian and Russian financial crises. In 2009,
governments implemented a large-scale,internationally coordinated policy stimulus tocombat the adverse effects of the global
financial crisis.

Features of the first three waves
This identification yields three historical waves ofglobal debt accumulation and one ongoing. The
first wave runs from 1970 to 1989, the second
from 1990 to 2001, the third from 2002-09, andthe fourth since 2010.
First wave
The first wave spanned the 1970s-80s, withborrowing
primarily
accounted
forbygovernments in LAC and low-income countries in
SSA (Kose et al. 2019). The combination of low
interest rates and a rapidly growing syndicatedloan market encouraged EMDE governments toborrow heavily (Gadanecz 2004).
LAC. The debt buildup was greatest in LAC,
which accounted for over half of all debt flows to
EMDEs in 1973-81 (Bertola and Ocampo 2012;
Devlin 1990). As part of a strategy of importsubstitution industrialization, countries relied on
external debt to finance infrastructure andinvestment in heavy industries (Baer 1972; Bruton
1998; Diaz-Alejandro, Krugman, and Sachs
1984). Many LAC economies borrowed frominternational banks via new syndicated loan
markets, which provided a way to recycle dollardenominated oil revenues from oil-exporters toimporters (Altunbaş, Gadanecz, and Kara 2006).
Vulnerabilities mounted, as widening current
account and fiscal deficits were financed by

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

external debt, and inflation rose, while pegged
exchange rate regimes were backed by low levels ofreserves. The late 1970s and early 1980s saw aseries of global shocks, including an oil price spikeand U.S. monetary policy tightening that
accompanied a global recession. The crisis began
in 1982 with Mexico announcing that it would
not be able to service its debt, and spread rapidly
to other LAC and SSA countries. The U.S.
administration’s Brady plan eventually provided
comprehensive debt relief in 1989 (Cline 1995;
Unal, Demirgüç-Kunt, and Leung 1993). The
debt crisis resulted in a “lost decade” in LAC, with
GDP per capita not recovering its pre-crisis level
until 1993, after having grown by 50 percent
during 1970-1980 (Loayza, Fajnzylber, andCalderón 2005).
SSA. Many low-income countries (LICs),
especially in SSA, borrowed heavily in the 1970s
and 1980s from official creditors (Daseking andPowell 1999). Debt was typically used to financedomestic-focused industry (Greene 1989). Amidrising global interest rates and deteriorating termsof trade, several countries suffered debt crises in
the 1980s (Dornbusch, Branson, and Cline 1985).
In response, the World Bank and IMF provided
financial support for adjustment programs, while
the Paris Club creditors agreed to “flowrescheduling,” under which debt principal andinterest payments were delayed. While thesepolicies helped with liquidity issues, they led to asteady increase in debt (Dicks 1991).
While growth in LICs was robust in the 1970s, it
was persistently weak in the subsequent two
decades with income per capita falling during
1980-99 amid rapid population growth.
Eventually, the World Bank and IMF, along withother multilaterals and bilateral creditors,
announced the “Heavily Indebted Poor
Countries” (HIPC) initiative in 1996, which was
followed by the Multilateral Debt Relief Initiative(MDRI) in 2005 (IMF 2006; World Bank and
IMF 2017a).
Second wave
The second wave ran from 1990 until the early
2000s as financial and capital market liberalization
enabled banks and corporates in EAP and

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257

FIGURE 4.2 Debt in EMDEs
The region and sector of debt accumulation has varied substantially overthe four EMDE waves (1970-1989, 1990-2001, 2002-09, and since 2010).
A. Total debt

B. External debt

C. Government debt

D. Private debt

E. Government debt in EMDE regions,excluding China

F. Private debt in EMDE regions,excluding China

Source: International Monetary Fund; World Bank.
A. B. Light blue and yellow lines exclude China.
C.D. Averages computed with current U.S. dollar GDP as weight and shown as a 3-year moving
average. Dashed lines for EAP refer to EAP excluding China. Lines for ECA start in 1995 due tosmaller sample size prior to that year. Vertical lines in gray are for years 1970, 1990, 2002, and 2010.
E.F. GDP-weighted averages. EAP = East Asia and Pacific, ECA = Europe and Central Asia;
LAC = Latin America and Caribbean; MNA = Middle East and North Africa; SAR = South Asia;
SSA = Sub-Saharan Africa.
Click here to download data and charts.

governments in ECA to borrow heavily; it ended
with crises in these regions in 1997-2001.
EAP. The EAP region registered one of the fastest
increases in private debt in the 1990s. Poor bankregulation and supervision, together with implicit
government guarantees for banks and corporates,
encouraged risk taking by the domestic financial
sector and allowed already highly leveraged

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corporates to borrow heavily (Kose et al. 2019).
Countries also suffered from poor corporate
governance, a prominent presence of state-owned
enterprises (e.g., Thailand), weak business climates(e.g., Indonesia), and heavy investment in nontradeable sectors such as commercial real estate(e.g., Thailand; Krugman 2000). Rising private
debt, particularly short-term debt, left several EAP
countries (Indonesia, Malaysia, Philippines, andThailand) vulnerable to a reversal in capital flows.
In early 1997, capital inflows to Thailand began totaper off amid investor concerns about external
debt
sustainability.
Despitegovernment
intervention in early 1997, Thailand was forced toabandon its currency peg in July 1997. Financial
markets quickly turned on countries with similar
vulnerabilities, and Indonesia, Korea, Malaysia,
and the Philippines experienced large capitaloutflows which resulted in substantial pressure on
their currencies (Corsetti, Pesenti, and Roubini
1998; Kawai, Newfarmer, and Schmukler 2005).
Corporates were unable to service their debt,
resulting in large loan losses for banks andtriggering banking crises. Governments created“bad banks” to absorb non-performing loans ofcommercial lenders, recapitalized banks, andimproved corporate debt restructuring regimes(Mishkin 1999). Prior to the crisis, the sharp risein borrowing among EAP countries was
accompanied by rapid GDP growth but, during
the crisis, GDP and investment growthplummeted.
LAC and ECA. The late 1990s saw crises occur insome other major EMDEs, notably Russia,
Argentina, and Turkey. These countries
experienced sovereign debt crises when a broadbased loss of investor confidence triggered capitaloutflows and forced governments to abandon
currency pegs. A notable exception was Brazil,
which suffered a currency crisis in 1999, but
avoided a banking and sovereign debt crisis. The
authorities dampened exchange rate depreciation,
but at considerable fiscal cost. The earlier “Tequila
crisis” in 1995 also falls into the second wave,
when Mexico accepted assistance from the IMF
and others to stem a currency crisis but avoided afull sovereign debt crisis (Laeven and Valencia
2018; Kose et al. 2019).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Third wave
The third wave was a runup in private sector
borrowing in ECA from U.S. and EUheadquartered “mega-banks” after regulatory
easing and amid initially accommodative
monetary policy in advanced economies (Cetorelli
and Goldberg 2011). While the buildup of debt in
the third wave primarily occurred in advanced
economies, the emerging mega-banks fueled asteep increase in direct cross-border lending on theinterbank market, lending through subsidiaries,
and investment in EMDE debt markets(Balakrishnan et al. 2011).
This wave ended when the global financial crisis
disrupted bank financing in 2008-09 and tippedseveral ECA economies into deep recessions(Aslund 2010). The crisis in ECA was short-lived,in part due to IMF and EU support (Berglof et al.
2009). In contrast to the ECA region (andadvanced economies), most EMDEs proved
resilient to the global financial crisis, in part
because they had limited exposures to the actualglobal shocks at the time (Kose and Prasad 2010).
Many EMDEs also improved debt management,
supporting a reduction in currency, interest andmaturity risks (Anderson, Silva, and VelandiaRubiano 2010).
Similarities and differences between waves
The first three waves of broad-based debt
accumulation featured several similarities (Box4.1). At the beginning of each wave, the initialdebt buildup was associated with low or falling
global interest rates and major changes in financial
markets, often in response to deregulation. The
first three waves eventually witnessed severe andwidespread financial crises in EMDEs with severe
macroeconomic consequences, usually triggered byexternal shocks and amplified by domestic
vulnerabilities. Financial crises were typicallyfollowed by reforms in affected countries to lower
external vulnerabilities and strengthen policy
frameworks.
There were also noticeable differences between thethree waves. The sectors and regions that were themost active borrowers, and the financial
instruments involved changed over the course of

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

the three waves: borrowing shifted from thegovernment sector to the private sector, while thetype of debt moved from syndicated loan markets
in the first wave, to government bond markets andinternational private sector borrowing in thesecond wave, to cross-border and foreign-ownedbank lending in the third wave. In all three waves,
financial crises resulted in substantial economic
damage, but their severity varied between waves
and across regions. The waves also varied in termsof the speed of resolution, with sovereign debt
crises typically taking longer to resolve, and having
much larger negative macroeconomic impacts thanprivate debt crises.

The current wave of debt
in historical context
Since 2010, another wave of debt accumulation
has been building. The buildup has been global,
but especially fast in EMDEs (Box 4.2, Figure4.3). As a result, total debt in EMDEs has risen toalmost 170 percent of GDP, on average, in
2018—a record high—from 114 percent of GDP
in 2010 (Kose et al. 2019). China, where
corporate debt has soared post-crisis, accounted forthe bulk of this buildup—partly due to its sheer
size—but the buildup was broad-based. Excluding
China, total EMDE debt has risen to a near-record
107 percent of GDP in 2018. The debt-to-GDP
ratio has risen in all EMDE regions with the
exception of SAR, where it has been broadly flat,and in almost 80 percent of EMDEs, with morethan one-third seeing increases of at least 20
percentage points of GDP.
The current, fourth, wave of debt accumulation
bears many similarities to the previous waves. But
there are also important differences. Among theseis its sheer magnitude: it is the largest, fastest andmost broad-based wave of debt accumulation yet.
Similarities with the previous three waves
The fourth wave shares a number of features withearlier waves: a changing global financial
landscape, mounting vulnerabilities, and concerns
about inefficient use of borrowed funds.
Financial landscape. As in the previous threewaves, the current wave has seen changes in

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259

FIGURE 4.3 The fourth wave: Debt accumulation
Since the global financial crisis, another wave of debt accumulation has
been underway. The fourth wave has been especially rapid in EMDEs, andhas seen government debt increasing in tandem with mounting private
sector debt. The share of debt accounted for by bonds has continued torise, and large EMDEs have seen a sharp increase in domestically issued
bonds.
A. Total debt

B. Government debt

C. EMDE external debt, by borrower
and type of instrument

D. Change in EMDE bond issuance,
2010-16, by sector and domicile

Source: International Monetary Fund; World Bank.
C. “Public-official” includes “private other” which is chiefly accounted for by export guarantee
agencies.
D. Chart shows the change in debt securities (in percentage points of GDP) between 2010 and 2016
(last observation). Other EMDEs includes 8 countries. Data for India are unavailable.
Click here to download data and charts.

financial markets, and very low interest rates (as aresult of accommodative monetary policy
following the global financial crisis). Financial
systems in EMDEs have deepened and become
more complex (Didier and Schmukler 2014).
Both corporate and sovereign borrowers haveincreasingly accessed capital markets, in someregions following the retrenchment of large
international banks. Over the past decade, morethan 20 EMDEs have accessed international
capital markets for the first time. In SSA,
Eurobond issuance has grown, with several
countries tapping the Eurobond market for the
first time.
Domestic debt has also become increasingly
important, with a rising share of local currencydenominated bonds (Essl et al. 2019; Kose and

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BOX 4.1 Similarities and differences between the previous three waves
In each of the first three waves of broad-based debt accumulation, the initial runup in debt was facilitated by changes infinancial markets, and low real interest rates in major advanced economies. These waves witnessed severe financial crises in
EMDEs, usually triggered by external shocks and amplified by domestic vulnerabilities. They typically led to policy reforms inaffected countries to lower external vulnerabilities and strengthen monetary and fiscal policy frameworks. The three waves
differed in the composition of borrowers; the financial instruments involved; the speed of crisis resolution; and theirmacroeconomic impact.

Introduction
Since 1970, there have been four waves of EMDE debt
accumulation, of which the fourth one is still underway(see Kose et al. 2019 for a detailed discussion of each ofthese waves). The first wave spanned the 1970-80s, with arapid accumulation of debt by governments in LAC and
SSA which led to a series of defaults in the early 1980s,
and ended with debt relief and restructuring occurring in
the late 1980s-90s (LAC), and 1990s-2000s (SSA). The
second wave ran from 1990 until the early 2000s as
financial and capital market liberalization enabled banks
and corporates in East Asia and the Pacific (EAP) andgovernments in Europe and Central Asia (ECA) to borrowheavily; it ended with a series of crises in these regions in
1997-2001. The third wave was a runup in private sector
borrowing in ECA from U.S. and EU-headquartered“mega-banks” after regulatory easing; this wave ended
when the global financial crisis disrupted bank financing in
2008-09 and tipped especially ECA countries into deep
(albeit short-lived) recessions.
This box synthesizes the main features of the three waves
that have by now concluded. In particular, it addresses thefollowing questions in detail.
•

What were their similarities?

•

What were their differences?

Similarities
The first three waves of broad-based debt accumulation
featured several similarities. All of the waves had common
drivers, including changes in financial markets and low
interest rates. The waves also typically ended in crises with
substantial macroeconomic impacts, which led to policychanges. In part as a result of these policy changes,
countries weathered subsequent crises better.
Beginning of the waves: Low global interest rates, changes
in financial landscape
The initial debt buildup in each wave was associated with
Note: This box was prepared by Peter Nagle.

low or falling global interest rates, and major changes infinancial markets, often in response to deregulation. These
enabled previously credit constrained borrowers to accessinternational financial markets and accumulate debt.
Shortcomings in domestic policy frameworks often
contributed to rapid debt buildups, and exacerbated theseverity of crises.
Low or falling global interest rates. The beginning of eachof the three waves was associated with low, or falling,
global real interest rates, which encouraged borrowing(Figure 4.1.1). In the first wave, the U.S. real policy rate
averaged around 0.6 percent over 1970-79, with several
years of negative real interest rates. During the second
wave, the U.S. real policy rate declined from a high of 5
percent in 1989 to a low of 0.5 percent in 1993, as theFederal Reserve cut policy rates in response to the global
recession in 1991. Similarly, the U.S. real policy rate fell
into negative territory at the beginning of the third wave
following the 2001 recession in the United States.
New financial instruments. The emergence of the
syndicated loan market in the 1970s set the stage for the
first wave. The introduction of Brady bonds in the 1990s
spurred the development of sovereign bond markets that
underpinned sovereign borrowing in the second wave,
while capital account liberalization in many EMDEs in the
1990s, especially in EAP, facilitated private sector
borrowing. The third wave in the 2000s largely consistedof cross-border flows via international banks in advanced
economies after deregulation in the United States and theEU.
Economic upturns. The beginnings of the first and second
waves coincided with recoveries from global recessions(1975, 1991, 2009) and the beginning of the third wave
with the recovery from the global slowdown of 2001 (Koseand Terrones 2015).
During the waves: Borrower country policies
Borrower country policies often encouraged rapid debt
accumulation, or exacerbated the risks associated with it.
Fixed exchange rate regimes and weak prudential
frameworks encouraged risk taking; weak fiscal frameworks
encouraged unfunded government spending; andG LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

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BOX 4.1 Similarities and differences between the previous three waves (continued)
FIGURE 4.1.1 Comparison of previous waves
The start of each wave generally coincided with a period of low, or falling, interest rates. The end of waves was also
associated with a sharp slowdown in capital inflows, which restarted as new waves got underway. Debt episodes that
ended in banking crises typically resulted in large increases in government debt.
A. U.S. interest rates

B. Capital flows to EMDEs

C. Government debt during past banking
crises

Source: Bloomberg; International Monetary Fund; World Bank.
A. Start of a wave defined as the first three years of the wave. Crisis defined as the year before, and year of, widespread crises. For the first wave, these are 1970-72,
and 1981-82. For the second wave, these are 1990-92, and 1996-97. For the third wave, these are 2002-04, and 2008-09. For the final wave, the start is 2010-12, andthe “latest” is the final two years of the sample, 2017-18. Real interest rates are calculated as the difference of nominal interest rates and the GDP deflator.
B. Net capital inflows to EMDEs, in percent of GDP. The start of each wave is the first year, the peak is the peak capital inflow before the start of crises in the wave, andthe trough is the lowest point after the crisis year. For the first wave, these dates are 1970, 1978, and 1988 respectively. For the second wave, they are 1990, 1995, and2000. For the third wave, they are 2002, 2007, and 2009. The fourth wave begins in 2010 and the latest data are for 2018.
C. “Before” and “after” denote, respectively, one year before and after the onset of banking crisis, as shown by numbers below the corresponding country names, takenfrom Laeven and Valencia (2018). Indonesia refers to central government debt only.
Click here to download data and charts.

government spending priorities or weak prudential
supervision directed funding to inefficient uses.
Fixed exchange rate regimes. During the first and second
waves, especially, fixed or managed exchange rates in LAC,
EAP and ECA encouraged capital inflows by leading
lenders and borrowers to underestimate exchange raterisks. With interest rates on foreign currency loans below
those for domestic currency loans and the fixed exchange
rate interpreted as an implicit guarantee of foreignexchange claims, borrowers readily took on foreign
currency-debt and domestic banks offered dollarized or
euro-ized accounts on a large scale to local clients(Impavido, Rudolph, and Ruggerone 2013; Magud,
Reinhart, and Rogoff 2011).
Weak prudential frameworks. Structural changes infinancial markets were typically not accompanied byappropriate reforms to prudential or supervisory
frameworks, allowing excessive risk-taking. In the second
wave, for example, rapid liberalization of capital markets
encouraged EAP banks to borrow heavily frominternational markets (Furman et al. 1998). In the third
wave, the risks posed by growing cross-border lending andmacro-financial linkages were underappreciated byfinancial supervisors (Briault et al. 2018; Claessens andKose 2018).

Weak fiscal frameworks. In episodes of rapid government
debt accumulation, in LAC and SSA in the first wave andin ECA in the second wave, many countries ran persistentfiscal deficits financed with external debt.
Inefficient use of debt. While debt flows were often used
to finance productive investment, in some cases debt wasused for domestic-facing investments, such as importsubstitution industrialization that eroded competitivenessin LAC in the first wave or construction and property
booms that did not raise export revenues in EAP and ECA
in the second and third waves. Weak corporate
governance, including inadequate oversight of projects andinvestment decisions as well as declining profitability, also
led to inefficient investment in several EAP countries(Capulong et al. 2000).
End of waves: Financial crises
Rapid debt accumulation initially supported growth but
was often associated with financial crises.
Triggers. Financial crises have often been triggered byshocks that raised investor risk aversion, risk premiums
and borrowing costs, followed by a sudden stop of capital
flows, or by growth slowdowns that eroded debt
sustainability (Frankel and Rose 1996; Easterly 2002;
Kaminsky and Reinhart 2000; Summers 2001). In the first

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BOX 4.1 Similarities and differences between the previous three waves (continued)
wave, around the global recession of 1982, deterioratingrisk sentiment restricted access to new borrowing in LAC
and SSA. In the second wave, capital flows to EMDEs
stalled or reversed in the global slowdown of 1998, amid aloss of investor confidence following the East Asian andRussian crises (Kaminsky 2008; Kaminsky and Reinhart
2001). In the third wave, banking system liquidity dried
up in the 2008 global financial crisis, interrupting crossborder lending in ECA. Domestic political events also
contributed to some crises, for example in Turkey andArgentina in the third wave (Ozatay and Sak 2002).
Types of financial crises. Many crises began with sharp
depreciations and capital outflows, which wereoccasionally the precursor to sovereign debt crises. Large
depreciations increased debt service on dollar-denominateddebt and led to surges in inflation. Sudden stops orreversals in capital flows complicated debt rollover. In all
three waves, countries that slid into crises had sizable
vulnerabilities, such as large external, short-term foreign
currency-denominated or variable-rate debt; low reserves;pegged exchange rates; and weak monetary, fiscal, andprudential frameworks.
Macroeconomic impact. Debt buildup in the first threewaves was often followed by crises or stagnation, especiallywhen the debt buildup was predominantly driven bysovereign debt. Currency depreciations were often large,
especially during the first and second wave, and triggered
sharp spikes in inflation and deteriorating debt-to-GDP
ratios when debt was denominated in dollars. That said,
there were considerable differences in the severity ofmacroeconomic outcomes between the waves, as discussed
below.
Fiscal impact. Financial crises were often fiscally costly. In
the first wave, defaulting governments in LAC lost capital
market access for many years. In the second and third
waves, governments had to support ailing banks inrecognition of implicit guarantees for financial systems.1
90 percent of banking crises have required bankrestructuring, and roughly 60 percent have led to thenationalization of one or more banks.
Policy responses. In all waves, the countries suffering crises
For a global sample, the average cost of government intervention in
the financial sector during crises in 1990-2014 amounted to 9.7 percent
of GDP, with a maximum of 55 percent of GDP (IMF 2016a). The
average cost of government intervention in public sector enterprises
during 1990–2014 amounted to about 3 percent of GDP and the average
cost of the realization of contingent liabilities from public-private
partnerships was 1.2 percent of GDP (Bova et al. 2016).
1

implemented policies that helped build resilience to futurefinancial stress. In the first and second waves, LAC andEAP governments took measures to increase reserves andlimit future buildups of external debt. Many moved
towards inflation targeting and flexible exchange rates. In
the second and third waves, EAP and ECA governments
eventually strengthened bank supervision, corporatebankruptcy laws and fiscal frameworks. However, progresshas varied across countries, with some remaining more
vulnerable to shocks than others.

Differences across the waves
The three waves differed in the most active borrowing
sector and their regional focus; the financial instruments
involved; the speed of resolution of crises; and theirmacroeconomic impact.
Borrowing sector and region
In the first wave, borrowing was primarily accounted forby the public sector in LAC and SSA (Figure 4.1.2).2 In
these two regions, governments ran persistent fiscal deficits
which were used to fund current expenditure in somecountries, as well as investment. In the second wave, boththe private sector (EAP) and the public sector (ECA, LAC)
played a role. In the third wave—with fewer countries
with large debt runups than in the previous two waves—the private sector in ECA was the primary source ofborrowing. Sovereign debt levels in most EMDEs were
either muted or falling in the third wave. Governments in
EAP (second wave) and ECA (third wave) typically had
sound fiscal positions in the run-up to crises. As a result of
these shifts, the share of the public sector in externalborrowing fell from a high of 95 percent in 1989 to 53
percent in 2018.
Financial instruments and debt resolution
Financial instruments. The source of credit in each wave
also evolved. In the first wave, sovereigns borrowed fromthe official sector through bilateral lending andmultilateral loans, as well as from commercial banks via
the syndicated loan market (lending from commercial
banks accounted for around one-third of total external
2 The first and third waves were global in the sense of total EMDE
debt rising whereas the second wave had a narrower regional focus.
During the first wave, EMDE government debt rose sharply; similarly,
during the third wave, EMDE private debt rose sharply, driving up
EMDE total debt (Figure 4.1). In contrast, during the second wave,
EMDE government debt declined while EMDE private debt, resulting ina limited overall increase in total EMDE debt over the course of thesecond wave.

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CHAPTER 4

BOX 4.1 Similarities and differences between the previous three waves (continued)
FIGURE 4.1.2 Changes in debt by sector and region
Whereas earlier waves were concentrated in a few regions, the debt buildup in the fourth wave has been broad-based. Like
the third wave, private and government sectors accounted almost equally for external borrowing.
A. Change in government EMDE debt,
by region

B. Change in private EMDE debt,
by region

C. Composition of external debt in
EMDEs

Source: World Bank.
A.B. EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and Caribbean; MNA = Middle East and North Africa; SAR = South Asia;
SSA = Sub-Saharan Africa.
C. Long-term external debt only.
Click here to download data and charts.

public debt in EMDEs by 1980-81). The introduction of
Brady bonds in the 1990s spurred the development ofsovereign bond markets, and in the 2000s, local bondmarkets deepened, allowing governments to obtain longterm finance, including from foreign investors. In the ECA
region, the private sector accessed cross-border lending by
European banks, whose subsidiaries and branches werebased in ECA countries but headquartered in advanced
economies. As a result, there has been a shift frominternational debt to domestic debt, and a move toward
debt securities, including local currency bonds.

Turkey and Argentina, which required IMF
assistance. Restructuring after Argentina’s 2001 debt
default was not completed until many years later.4•

Faster private debt resolution. In the second wave,
private sector debt in EAP was resolved quite quickly,
with speedy support from the public sector throughbank recapitalization and other support schemes,
often with IMF assistance. Non-financial corporate
debt
resolution,
particularly among larger
conglomerates, was much slower than for the financial
sector, and non-performing loans remained elevatedfor several years after the crisis (Kawai 2002). In thethird wave, globally accommodative policies; IMF
assistance; the European Bank Coordination(“Vienna”) Initiative in 2009; and other banking
system support together helped stem currency andbanking crises.

•

New resolution mechanisms. At the start of the firstwave, there was little consideration for borrowers’
ability to service their debt. Over time, creditors
moved toward acceptance of some debt reduction.
This paved the way for the conversion of syndicatedDebt resolution: speed, scope, and mechanisms. The
speed of resolution largely depended on whether the
debtors were in the public or private sector. The difficultyof debt restructuring led to gradual progress in debt
resolution and restructuring mechanisms.
Slow government debt restructuring. In the first wave,
the resolution of widespread sovereign debt defaults in
LAC and SSA was slow, given Paris Club countries’
concerns about advanced economy bank solvency andthe lack of a well-defined restructuring mechanism(Callaghy 2002).3 In the second wave, debt resolution
was again prolonged for sovereign debt crises in•Borensztein and Panizza (2009) find that the reputational andeconomic cost of sovereign debt defaults is significant although shortlived, in part because crises precede defaults and defaults tend to happen
at the trough of the recession.
3

4 Argentina arranged a first restructuring of its debt in 2005, which was
accepted by about three-quarters of bond holders (Hornbeck 2013). A
second restructuring was agreed in 2010, which two-thirds of theremaining bondholders accepted. 7 percent of bondholders were
“holdout” creditors, who eventually reached a settlement in 2016.

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BOX 4.1 Similarities and differences between the previous three waves (continued)
FIGURE 4.1.3 GDP per capita in EMDEs during the four waves
In the first wave of debt, countries in LAC and SSA saw prolonged stagnation in per capita growth after debt crises erupted.
In the second wave, rapid growth in EAP was interrupted by the Asian financial crisis in 1998 but growth soon recovered. In
the third wave, growth in ECA was robust throughout the period but fell in the final year when the crisis hit.
A. First wave

B. Second wave

C. Third wave

Source: World Bank.
Note: Data are per capita GDP level (at 2010 prices and exchange rates) in each region at the pre-crisis peak and the end of the wave in each region, indexed to thestart of the wave. For LAC and SSA in the first wave, the peak was in 1980; in EAP and ECA in the second wave it was in 1997; and in ECA in the third wave it was in2008. The orange diamonds in Figures A-C show the average for all EMDEs excluding the highlighted regions in each chart, for the corresponding years. EAP = East
Asia and Pacific, ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; SSA = Sub-Saharan Africa.
Click here to download data and charts.

loans to Brady bonds, and later the HIPC and MDRI
debt relief initiatives for official debt in low-income
countries. Collective action clauses (CACs) were later
introduced to facilitate sovereign debt restructuringwith multiple bondholders (Eichengreen, Kletzer, andMody 2003). For private debt, the Insolvency andCreditor Rights Standard developed best practices fornational insolvency systems (Leroy and Grandolini2016). There has been a substantial improvement in
insolvency protections over the course of the threewaves (World Bank 2019a).
Macroeconomic impact
During the first three waves, financial crises did substantial
economic damage, but the severity varied between the
waves, and across regions.
Output cost. In the first wave, LAC suffered a lost decadeof stagnant per-capita incomes following the 1982 crisis(Figure 4.1.3). Per capita incomes in SSA fared evenworse, with GDP per capita declining for many years.
Sovereign debt crises in Turkey and Russia during thesecond wave also generated severe output losses. In
contrast, in the second wave, EAP countries withpredominantly private debt buildups experienced only atemporary slowdown from the East Asia crisis. In the third
wave, ECA countries with predominantly private debt
buildups saw large but short-lived declines in output.
Currency depreciations. Depreciations were substantiallylarger and more common in the first and second waves,when exchange rates were mostly fixed or crawling pegs,
and often had to be abandoned in the face of speculative
attacks. By the third wave, more countries had flexible
exchange rates, reducing the likelihood of substantial
overvaluations to begin with.
Inflation. Inflation following crises rose more in the firstwave, and to a lesser extent, in the second. In part, this wasdue to larger depreciations in these waves. It also reflected
subsequent improvements in monetary frameworks—amove toward inflation-targeting and independent central
banks that helped anchor inflation expectations (Ha, Kose,
and Ohnsorge 2019).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

Ohnsorge 2019; Turner 2002).5 Especially in thelargest EMDEs, domestic bond issuance has risen
rapidly. Foreign portfolio investors are also
becoming more active in local bond markets,
accounting for a growing share of local currencydenominated sovereign bonds.
The current wave has also seen a significant
increase in nonbank financial intermediation in
EMDEs. These nonbank financial institutions
have expanded rapidly in a number of EMDEs,
particularly large economies.
Vulnerabilities. Over the course of the fourthwave, vulnerabilities have once again grown (Ruch2019). Since 2010, EMDE total external debt has
risen to 26 percent of GDP on average in 2018,reflecting sizable and persistent current accountdeficits. In 2018, 55 percent of EMDEs had
weaker current account balances than in 2010; 76
percent ran current account deficits (comparedwith 69 percent in 2010); and 44 percent had
current account deficits in excess of 5 percent of
GDP (Figure 4.4). The number of countries withfiscal deficits has also risen.
In addition, both government and private debt
have shifted toward riskier forms in many
EMDEs, with a rise in the share of debt that isheld by non-residents (for governments), isdenominated in foreign currency (for corporates)
and is on non-concessional terms. A greater shareof corporate debt than before the global financial
crisis is held by firms with riskier financial profiles,
as supportive financing conditions have allowed
firms to issue more debt with weaker credit quality(Beltran and Collins 2018; Feyen et al. 2017; IMF
2015a). EMDE financial markets are now more
tightly integrated into the global financial system,
which could in some circumstances facilitate thecontagion of global financial shocks both toforeign currency and, to a lesser extent, local
currency debt markets.

5 However, such a switch may bring other risks, as countries
switching from external to domestic debt could be trading a currency
mismatch for a maturity mismatch (Panizza 2008; Broner, Lorenzoni
and Schmukler 2013). Nominal interest rates on domestic debt tend
to be higher than on external debt (IMF 2015a).

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FIGURE 4.4 The fourth wave: Vulnerabilities and use ofborrowed funds
The fourth wave has seen growing vulnerabilities in EMDEs, with a rise inboth domestic and external debt as countries have run persistent current
account and fiscal deficits. The composition of debt has shifted, with agreater share held by non-residents and a rise in non-concessional debt.
Public investment has fallen sharply in EMDEs, suggesting that rising debt
is being used for current spending, rather than growth-enhancing
investment, despite a fall in interest payments.

A. EMDEs with current accountdeficits

B. EMDEs with fiscal deficits

C. Average maturity andnon-concessional debt in EMDEs

D. Non-resident share of government
debt, foreign currency share ofcorporate debt

E. Public expenditures in EMDEs

F. Cumulative change in house prices,
selected country groups

Source: Bank for International Settlements; International Monetary Fund; OECD; World Bank.
C. Median of 65 EMDEs for maturity and 122 EMDEs for non-concessional debt
D. Non-resident share of government debt is average for 45 EMDEs, with a smaller sample size forearlier years. Foreign currency share of corporate debt of average for 21 EMDEs.
F. Chart shows the cumulative percentage increase in house prices over the course of a wave, prior
to the crisis. The range covers 1990-97 for EAP, 2001-2008 for ECA, and 2010-18 for EMDEs. EAP
contains three countries, ECA contains 5, and EMDEs contains 31 countries. Orange diamonds
denoted the median, and blue bars the interquartile range of country groups.
Click here to download data and charts.

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BOX 4.2 The fourth wave
Since 2010, another wave of debt accumulation has been building and total debt in EMDEs has reached almost 170 percent ofGDP, on average, in 2018—a record high—from 114 percent of GDP in 2010. This increase was accompanied by shifts
toward borrowing from non-traditional creditors and financial institutions, as well as capital markets. As with previous waves,the fourth wave has seen mounting vulnerabilities for EMDEs.
The fourth wave of debt buildup among EMDEs began in
2010. It was broad-based across EMDE regions andborrowing sectors. The debt buildup has beenaccompanied by a decade of anemic growth in EMDEs(Kose and Ohnsorge 2019). Changes in advancedeconomy financial sectors also propelled shifts in creditors
to EMDE governments and corporates. This box examines
the fourth wave by addressing the following questions.
•

How did debt evolve in the fourth wave?

•

Which factors have contributed to debt accumulation
during the fourth wave?

period government debt-to-GDP has risen in threequarters of EMDEs and by at least 10 percentage
points in almost 60 percent of them. Government
debt saw a marked increase among commodityexporting countries in the aftermath of thecommodity price plunge in 2014 (particularly oil
prices), as fiscal deficits surged amid declining revenue
and large fiscal stimulus (World Bank 2018c).
•

Evolution of debt
Broad-based public and private debt buildup. Since 2010,
another wave of debt accumulation has been underway.
The buildup has been especially fast in EMDEs, withgovernment debt increasing in tandem with mounting
private sector debt. As a result, total debt in EMDEs has
risen to almost 170 percent of GDP, on average, in
2018—a record high—from 114 percent of GDP in 2010
(Kose et al. 2019). The debt-to-GDP ratio has risen in all
EMDE regions with the exception of SAR, where it has
been broadly flat, and in 80 percent of EMDEs, with morethan one-third seeing an increase of at least 20 percentage
points of GDP.1 Excluding China, where corporate debt
has soared post-crisis, total EMDE debt has risen to anear-record 107 percent of GDP in 2018. The pace ofincrease in EMDE debt excluding China has slowed since2016, with a modest decrease in private sector debt
offsetting a small increase in government debt. However,this masks substantial variation between regions, with large
increases in debt-to-GDP ratios in SSA and LAC anddeclines in MNA and ECA.
•

Government debt. Since 2010, EMDE government
debt has risen, on average, by 12 percentage points of
GDP to 50 percent of GDP at end-2018. Over thisNote: This box was prepared by Peter Nagle.
1 Total debt has risen particularly rapidly in Argentina, Cambodia,
Chile, and China. Turkey stands out as having the third fastest increasein private sector debt after Cambodia and China. Among low-income
countries, Mozambique, The Gambia, and Togo and have seen thelargest increases in debt.

Private debt. The private sector has also rapidly
accumulated debt since the global financial crisis,
particularly in China. About two-fifths of EMDEs
witnessed private sector credit booms in at least one
year during 2011-18 (Ohnsorge and Yu 2016; WorldBank 2016a).2 The rise in debt in China has been
focused in a few sectors, notably the real estate,mining, and construction sectors, and among stateowned enterprises.

Shifts to riskier debt. Both government and private debt
have shifted toward riskier funding sources in many
EMDEs, making these countries more vulnerable to a
deterioration in global investor sentiment (Figure 4.2.1).
•

Government debt. The increase in government debt
has been accompanied by a growing share of
non-resident investors (to 43 percent in 2018) and anincreasing reliance on non-concessional terms.
Sovereign ratings have also been downgraded formany EMDEs since 2010. This also increases thefragility of EMDE banks where there is some evidence
that exposures to sovereigns have increased (Feyen
and Zuccardi 2019).

•

Private debt. On average, across EMDEs withavailable data, foreign currency-denominatedcorporate debt has risen from 19 percent of GDP in2010 to 26 percent of GDP in 2018, although its
share of total corporate debt remained around 40
percent over this period (IIF 2019b). By end-2018,
one third of these EMDEs had foreign currency
denominated corporate debt above 20 percent of
GDP. In addition, a greater share of corporate debt

2 About half of all credit booms are followed by at least a mild
deleveraging within three years (Ohnsorge and Shu 2016).

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BOX 4.2 The fourth wave (continued)
FIGURE 4.2.1 The fourth wave: Debt developments
Low-income countries have seen a sharp increase in borrowing from non-Paris club bilateral sovereign lending and nonconcessional lending. As EU- and U.S.-headquartered banks have downsized their EMDE operations, cross-border banklending to EMDEs shifted to EMDE-headquartered banks. EMDE corporate and sovereign borrowers have increasingly
turned to capital markets to raise new debt.

A. Share of non-concessional debt in
LICS

B. Creditor composition of LIC external
public debt

C. Pan-regional banks

D. Global assets of 10 largest G-SIBs bybank domicile

E. Debt securities outstanding

F. Claims on the official sector

Source: Bank for International Settlements; Claessens and van Horen 2014; International Monetary Fund; World Bank.
A. Dashed blue lines denote the interquartile range, while solid blue line is the median. Includes 30 low-income countries and excludes Somalia, South Sudan, and Syria
due to data restrictions.
B. GDP-weighted average across 32 low-income countries. Bilateral includes public and publicly guaranteed (PPG) loans from governments and their agencies(including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Multilateral includes PPG loans and credits from the World
Bank, regional development banks, and other multilateral and intergovernmental agencies. It excludes loans from funds administered by an international organization on
behalf of a single donor government. Private include PPG bonds that are either publicly issued or privately placed; PPG debt from commercial bank loans from privatebanks and other private financial institutions; as well as export and supplier credits.
C. GFC = global financial crisis. Based on annual bank statements; before the GFC = 2008 or 2009 depending on data availability; after GFC = 2018 or latest data
available.
D. Based on the Financial Stability Board 2018 list of global systemically important banks (G-SIBs).
E. Sample includes Argentina, Brazil, Colombia, India, Indonesia, Malaysia, Mexico, Philippines, Russia, South Africa,
Thailand and Turkey.
F. BIS estimates of the claims by foreign banks on official sector: sample includes Argentina, Brazil, Chile, Colombia, Hungary, India, Indonesia, Israel, Malaysia, Mexico,
Poland, Russia, Thailand, Turkey, Republic of Korea, and South Africa
Click here to download data and charts.

than before the global financial crisis has been owed
by firms with riskier financial profiles, as supportive
financing conditions have allowed firms to issue moredebt with weaker credit quality (Beltran and Collins
2018; Feyen et al. 2017).
LIC government debt. In LICs, debt has also shifted
toward non-concessional, non-Paris Club bilateralcreditors, notably China, as well as commercial creditors
over the past decade (World Bank 2018b; World Bankand IMF 2018a). In 2016, non-Paris Club debt accounted
for more than a fifth of the median LIC’s external debt,
and about 13 percent of their public debt, raising concerns
about debt transparency as well as debt collateralization(Essl et al. 2019).

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BOX 4.2 The fourth wave (continued)
Estimates of current public debt levels in LICs also sufferfrom limited debt transparency, including issues related tocontingent liabilities, state-owned enterprise debt and PPPtransactions, and the assets held by LIC governments.
These data limitations are especially acute for debt owed tocommercial and non-Paris Club creditors. Poor data
coverage can give rise to unexpected sudden increases indebt, for example when the debt of loss-making SOEs
migrates onto the books of the central government. For
example, in Mozambique and the Republic of Congo, therevelation of unreported debt led to large upward revisions
to official debt figures, which resulted in debt distress(IMF 2018a). Only a third of the 59 countries eligible forInternational Development Association borrowing reportprivate sector external debt statistics (World Bank and
IMF 2018b).

European banks withdrew.3 LAC was an exception, with agrowing role of domestic banks, rather than of banks basedin other countries in the region, as domestic banks
acquired assets from exiting foreign lenders. The regional
expansion of EMDE banks has yet to reach the scale ofpre-crisis cross-border activity of lenders from theadvanced economies.

Changes in the composition of creditors. Since the global
financial crisis, borrowing by EMDEs has shifted toward
capital markets and regional banks, and away from global
banks. Bond issuance has allowed firms to access financewhen bank credit supply tightened or at different terms
from bank loans (Cortina, Didier, and Schmukler 2016).
The role of regional EMDE banks has also grown as large
international banks have retrenched from EMDEs in theaftermath of the global financial crisis (BIS 2018; Feyen
and Gonzalez de Mazo 2013). As large international banks
retrenched, cross-border bank lending to EMDEs shiftedto EMDE-headquartered banks, which greatly expanded
their regional presence, most notably in SSA (Cerutti andZhou 2017, 2018; IMF 2015b; World Bank 2018c).

Contributing factors to debt accumulation

Chinese banks accounted for two-thirds of EMDE-toEMDE lending between 2013 and 2017 and for most of
the doubling in cross-border claims on SSA economies inthe same period, to over 10 percent of GDP on average(Cerutti, Koch, and Pradham 2018; Dollar 2016). Other
EMDE banks have also increased their presence in
EMDEs within their respective regions. A notable
exception has been the Middle East and North Africa
region, where declining current account surpluses resulting
from weaker oil revenues have reduced the region’s ability
to recirculate savings from high-income oil exporters tolower-income EMDEs with persistent current accountdeficits (World Bank 2019b).
In SSA, banks headquartered in Togo, Nigeria and South
Africa have expanded rapidly to other EMDEs in theregion (Arizala et al. 2018). In ECA, Russian banks
initially expanded post-crisis within the region, as Western

Finally, the domestic institutional investor base hascontinued to grow in EMDEs, offering the prospect of apotentially stabilizing pool of domestic savings. Assets ofpension funds and insurance companies had risen to 46
percent of GDP by end-2016, on average, in EMDEs.
Such assets remain equivalent to only about half of theassets of the bank and non-bank financial system (WorldBank 2019c).4

Evolving financial instruments. The latest wave has been
associated with a growing importance of domestic debt,
while external debt grew more slowly than in the mostaffected regions during previous waves. The fourth wave
has seen rising demand for EMDE bonds frominternational investors such as asset managers (Shin 2014).
Domestic bond issuance has risen sharply, particularly inlarge EMDEs, while exceptionally long-term (50- and100-year) international bonds have been issued by some
EMDEs, including Mexico in 2010, and Argentina in2017. Over the past decade, more than 20 EMDEs
accessed international capital markets for the first time.
New frontier market bond indices, such as J.P. Morgan’s
NEXGEM launched in 2011 or MSCI’s Frontier Market
Index launched in 2007, have facilitated international
capital market access and broadened the investor base forcountries which thus far only had intermittent capital
market access.

3 For example, example, Russia’s largest lender, Sberbank, acquired
Volksbanken’s VBI Eastern European operations in 2012.
4 Data on assets of pension funds and insurance companies are only
available for 22 EMDEs. Foreign institutional investors’ role in EMDE
financial markets has also grown but in some sectors remains small. For
example, in just under 1000 infrastructure projects since 2011, the shareof institutional investors has more than tripled but still accounts for only
0.7 percent of the average project value (World Bank 2018a). Someinstitutional investors in EMDEs have been shown to behave
procyclically, leaving EMDE financial markets during times of stressrather than acting as stabilizing investors with deep pockets (Raddatz and
Schmukler 2012).

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BOX 4.2 The fourth wave (continued)
The share of corporate debt financed by debt securities on
average rose from 16 percent to 25 percent of total lending
between end-2007 and end-2018. This included issuanceon both international and domestic debt markets. The
volume of international debt securities issued by EMDEs
increased more than three times between 2007 and 2018.
Domestic debt issuance excluding China increased from33 percent of GDP in 2007 to 47 percent of GDP in
2018.
EMDE sovereign borrowers are also relying more heavily
on capital markets. From 2007 to 2017, debt securities
issued by EMDE governments increased by 4.4 percentage
points of GDP on average, to 22 percent of GDP. In SSA
Eurobond issuance has grown, with several countries
tapping this market for the first time. Sovereign debt
issuance has grown particularly rapidly in domestic bondmarkets, especially in EAP (G20 IFAWG 2018). In some
EMDEs, the share of nonresident investors in local
currency sovereign bond holdings exceeds 30 percent,which makes these economies more vulnerable to sudden
shifts in investor confidence (G20 IFAWG 2018).
New financing vehicles such as infrastructure bonds andgreen finance bonds have stimulated lending to specific
EMDE sectors where banks used to be the primary sourceof funding (FSB 2018a; McKinsey Global Institute2018).5 However, infrastructure financing, in general, hasdeclined in EMDEs following the sharp reduction incross-border lending and stricter post-crisis regulations in
the financial sector (G20 2013; Kose and Ohnsorge2019).6
Very low interest rates, weak growth. Interest rates havebeen at very low levels throughout the fourth wave as aresult of unconventional monetary policy among central
banks, including negative policy rates and quantitativeeasing. This has encouraged an aggressive search for yield,
large capital flows to EMDEs, and a sharp fall in bondspreads. Around one quarter of sovereign and corporate
bonds in advanced economies—and some foreign-

5 In advanced economies, financial instruments that were widely used
before the crisis have regained popularity. Especially in the United States,
leveraged loan issuances—the majority of which are now covenant-litewith lesser protections for creditors, and which are predominantly held in
Collateralized Loan Obligations (CLOs) and loan funds—have risen
again above elevated pre-crisis levels. Concerns have been raised whether
CLO prices are fully aligned with risks (Domanski 2018; FSB 2019).
6 Grants and concessional loans are the primary source ofinfrastructure finance in LICs, with bank lending providing acomplementary source of funding only in a small number of countries(Gurara et al. 2017).

currency bonds issued by Poland, and Hungary—currently
trade at negative yields.7 Spreads on emerging market debt
both for corporate and sovereign bonds reached all-timelows in 2017, boosting borrowing. Average spreads on
corporate bond issuance have fallen for all EMDEs,
including LICs. Spreads have also fallen for lower rated
corporate bonds.
An additional reason for rapid debt accumulation has beena sharp slowdown in growth over the course of the fourthwave that eroded EMDE fiscal positions and resulted inadditional borrowing to maintain current spending levels.
Government debt levels in commodity exporters surged
following the collapse in commodity prices, particularly
after the oil price plunge in 2014, driving much of theincrease in EMDE debt (excluding China) in the second
half of the current wave (World Bank 2018a).
Growing non-bank financial intermediation. The current
wave has also seen a significant increase in shadow banking
activities in EMDEs. Shadow banking refers to non-bankfinancial intermediation that takes place outside of theregulated financial system and may provide credit to riskier
borrowers who often lack access to bank credit. Shadowbanking systems, which were small before the global
recession, have expanded rapidly in a number of EMDEs,
particularly in large economies such as China and India(IMF 2014). In these two countries, assets of non-bankfinancial institutions now represent over a third of total
financial system assets. In China alone, this share has morethan doubled over the last decade, and the size andcomplexity of its non-bank financial sector is becoming
comparable to those of advanced economies (Ehlers et al.
2018).
A decade of lighter regulation of non-banks than banks,
combined with rapid growth, has increased maturity
mismatches and credit risks in non-banks (IMF 2018c).
Financial stress in non-banks may quickly propagate to therest of the financial system, owing to its
interconnectedness with banks (FSB 2017, 2018b, 2019;
Pozsar et al. 2013). This has been illustrated by a recent
shift toward stricter regulations and supervision of nonbanks in China and a default of one of the largest nonbank lenders in India, which have already created tighter
financial conditions for the private sector in thoseeconomies (IMF 2019b).

7 In the two EMDEs with negative yielding sovereign bond issuances,
government, household and corporate debt have risen only marginally (atmost 7 percentage points of GDP) over the past decade.

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FIGURE 4.5 Comparison of features of fourth wave andearlier waves: Debt

private debt has financed residential construction,
which does not yield export earnings.

The fourth wave has seen the largest and fastest increase in debt-to-GDP
ratios among EMDEs. It has also been the most broad-based increase in
debt across regions and borrowing sectors.

Differences from the previous three waves

A. Change in total debt

B. Annual average change in total
debt

C. Share of economies with increasein government debt, by region

D. Share of economies with increasein private debt, by region

Source: . International Monetary Fund; World Bank.
Note: First wave: 1970-89; second wave: 1990-2001; third wave: 2002-09; fourth wave: 2010
onwards.
A. Change in total debt-to-GDP ratio over the source of each wave.
B. Average annual change calculated as total increase in debt-to-GDP ratio over the duration of thewave, divided by the number of years in a wave.
C.D. Sample includes 142 EMDEs. Data show the share of economies where the debt-to-GDP ratio
increased over the duration of the wave. Regions are excluded if country-level data are available forless than one-third of the full region.
Click here to download data and charts.

Use of debt. In the current wave of debt, there
have been signs that government debt is being
used for “less efficient” spending rather than on
productive investment in physical or humancapital that could boost potential growth in
EMDEs. Public investment in EMDEs fell froman average of 2.1 percent of GDP in 2002-09, to
0.9 percent in 2010-18 (IMF 2019c). Among
commodity exporters, declining tax revenues
following the commodity price plunge in 2014-16
widened fiscal deficits and raised debt despite
lower investment (World Bank 2018a).
Meanwhile, house prices have risen sharply insome EMDEs, suggesting that some of the rise in

The fourth wave has featured the largest, fastest
and most broad-based debt accumulation in
EMDEs yet. In contrast to earlier waves,
government debt has risen in tandem withmounting private sector debt. Compared to thefirst and third waves—when advanced-economy
debt accumulation outpaced EMDE debt
accumulation—the fourth wave has beenaccompanied by near-stable advanced-economy
debt-to-GDP ratios. However, some otherdevelopments have been more reassuring. During
the latest wave, there have been reforms that havemade the international financial system more
resilient and enlarged the global financial safety
net. Many EMDEs have improved theirmacroeconomic and prudential policy frameworks
over the past two decades.
Largest, fastest, and most broad-based wave yet.
Including or excluding China, the annual increasein EMDE debt since 2010 (almost 7 percentage
points of GDP, on average) has been larger, bysome margin, than during the first three waves(Figure 4.5). In contrast to previous waves, whichwere largely regional in nature, the fourth wave
was global. Total debt has risen in more than 70
percent of EMDEs in all regions—previous waves
saw higher rates of increase within specific regions,but not across all regions simultaneously. Morethan one-third of EMDEs have seen an increase in
debt of at least 20 percentage points of GDP.
Finally, the majority of debt accumulation
episodes have featured combined government andprivate debt buildups—in contrast to the previous
three waves when the majority of debt
accumulation episodes were either predominantly
government or predominantly private episodes.
Stronger policy frameworks. Many EMDEs learnt
the lessons from crises in the previous waves andadopted reforms designed to improve resilience.
These include greater exchange rate flexibility, andmore robust monetary policy frameworks andcentral bank transparency—since 1999, thenumber of EMDEs who have adopted inflation

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targeting has increased from 3 to 24 (Figure 4.6).
EMDEs have also made reforms to fiscal
frameworks, with the number of countries withfiscal rules rising from 12 in 1999 to 62 in 2018,
andsubstantial
improvements
in debt
management policies and tools (World Bank2013). Foreign exchange reserves to debt haverisen markedly across EMDE regions, although
they have fallen from the highs of 2009-10. More
EMDEs are using macroprudential tools,
particularly placing stricter limits on foreignexchange positions. Bankruptcy rights have also
been strengthened, but there is still considerable
room for improvement (Kose and Ohnsorge2019).
Financial regulatory reforms. Financial sector
reforms implemented since the global financial
crisis are also increasing resilience (BIS 2018). The
G20 global financial regulatory reform agenda has
implemented major financial reforms since theglobal financial crisis, including the international
adoption of the Basel III capital and liquidity
standards (FSB 2018c).
Global financial safety nets have been significantly
expanded, with resources available in countryspecific, regional and multilateral financial safety
nets tripling between 2007 and 2016, including
through the creation of regional financing
arrangements (RFAs), expanded IMF resources,
and increased international reserve holdings (IMF
2018c). 6
Stable debt in advanced economies. In contrast to
the first and third waves—when advancedeconomy debt accumulation outpaced EMDE
debt accumulation—the fourth wave of EMDE
debt accumulation was accompanied by nearstable advanced-economy debt-to-GDP ratios.
Advanced economies have also seen pronounced
private-sector deleveraging which reduced theshare of private debt in total debt during thefourth wave.

6 The global financial safety net consists of 1) self-insurance
against external shocks using foreign reserves or fiscal space atnational level, 2) bilateral are swap lines among countries, 3) regional
financing arrangements, and 4) the global financial backstop
provided by the IMF (Brueggemann et al. 2018).

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FIGURE 4.6 Comparison of fourth wave and earlier
waves: Policies and institutions
Many EMDEs learned lessons from crises in the previous waves andadopted policies to improve resilience. These include more robust
monetary and exchange rate policy frameworks, fiscal rules,
macroprudential tools, higher foreign exchange reserves relative toexternal debt, and improved bankruptcy processes.

A. EMDEs with inflation targeting
central banks

B. EMDEs with fiscal rules

C. Foreign reserves in EMDE regions

D. EMDEs with flexible exchange rates

E. Macroprudential policy in EMDEs

F. Bankruptcy rights protection in
EMDEs

Source: Cerutti, Claessens, and Laeven (2017); Dincer and Eichengreen (2014); Ha, Kose, andOhnsorge (2019); Huidrom et al. (2019); International Monetary Fund; Kose et al. (2017); World Bank.
A. Inflation targeting as classified in the International Monetary Fund’s Annual Report of Exchange
Arrangements and Exchange Restrictions.
B. An economy is considered to be implementing a fiscal rule if it has one or more fiscal rules on
expenditure, revenue, budget balance, or debt.
D. An economy is considered to have a flexible exchange rate if it is classified as “Floating” or “FreeFloating” in the International Monetary Fund’s Annual Report of Exchange Arrangements andExchange Restrictions.
E. Sample includes 123 EMDEs. Unweighted average of the Macroprudential Policy Index of Cerutti,
Claessens, and Laeven (2017). The Macroprudential Policy Index measures the number of tools usedby authorities and is based on a simple sum of up to 12 including, but not limited to, countercyclicalcapital buffers and loan-to-value ratios.
F. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms
that improve the business climate. EAP, ECA, LAC, MNA, SAR, and SSA include 22, 22, 32, 19, 8,
and 46 economies, respectively. Advanced economies include 36 economies. Based on World Bank
Doing Business reports for 2010, and 2019.
Click here to download data and charts.

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BOX 4.3 Debt and crises
This box conducts an econometric exercise to illustrate the extent to which debt accumulation can increase the likelihood of acrisis. A substantial rise in either government or private debt is associated with a significantly higher probability of a crisis
occurring in the following year. A combined increase in government and private debt had a particularly strong association withthe probability of a currency crisis in the next year. A high share of short-term debt, or large debt servicing costs, similarly raised
the likelihood of a crisis. Countries that experienced crises typically had major institutional shortcomings, including debt andfiscal mismanagement, inadequate banking regulation, poor corporate governance, and political uncertainty.
The event study suggests that episodes of debt
accumulation that were accompanied by crises oftenfeatured larger debt buildups than episodes without crises.
This box quantifies the effect of debt accumulation on thelikelihood of financial crises using an econometric analysis.
Specifically, it answers the following questions.
•

What factors have been found to correlate withfinancial crises?

•

What factors are associated with an increased
likelihood of crises?

•

What were the common features of crisis episodes?

Empirical literature
The econometric exercise here builds on an extensive
literature on early warning models.1 The first generation ofearly warning models, in the 1980s and 1990s, aimed at
predicting currency crises and largely focused on
macroeconomic and financial imbalances. Measures ofbalance sheet health became more prominent in suchmodels after the Asian financial crisis, especially in
predicting banking crises. A combination of government
solvency and liquidity indicators have also been used instudies of sovereign debt crises.

Debt accumulation and financial crises:
An econometric analysis
Econometric specification. In the baseline regression
specification, the probability of a financial crisis isestimated as a function of the pace of debt accumulation

Note: This box was prepared by Wee Chian Koh and Peter Nagle,
with contributions from Jongrim Ha, Alain Kabundi, Sergiy Kasyanenko,
Wee Chian Koh, Franz Ulrich Ruch, Lei (Sandy) Ye, and Shu Yu.
1 See Berg, Borensztein, and Patillo (2005); Chamon and Crowe(2012); Frankel and Saravelos (2012); Kaminsky, Lizondo, and Reinhart(1998) for extensive reviews of the literature on early warning models.
For models involving currency crises, see Eichengreen, Rose, andWyplosz (1995); Frankel and Rose (1996); and Kaminsky and Reinhart(2000). For models involving banking crises, see Borio and Lowe (2002);
Demirgüç-Kunt and Detragiache (1998); and Rose and Spiegel (2012).
For models involving debt crises, see Dawood, Horsewood, and Strobel(2017) and Manasse, Roubini, and Schimmelpfenning (2003).

and several control variables in a panel logit model withrandom effects for a sample of 139 EMDEs over 19702018 (Annex 4.2). All explanatory variables are lagged
because the focus is on pre-conditions that make crises
more likely. In addition, the use of lagged variables
attenuates potential endogeneity bias caused bycontemporaneousinteractions
between economic
fundamentals and crises. Regressions are estimatedseparately for sovereign debt crises, currency crises andbanking crises since these are likely to be associated withdifferent sectoral vulnerabilities.
The correlates of crises are drawn from a rich empiricalliterature on the determinants of financial crises, or of thevulnerabilities that worsen the impact of crises. This
literature has identified the following correlates of higher
crisis probabilities:•

Factors that increase rollover risk. These are particularly
relevant during periods of financial stress; the include
high short-term external debt and high or rapidly
growing total, government, or private debt; current
account deficits;•

Factors that restrict policy room to respond. These
include low international reserves; large fiscal andcurrent account deficits; and weak institutions.

•

Factors that suggest overvaluation of assets. These
indicate potential for large asset price corrections; theinclude exchange rate misalignment and credit or asset
price booms.

Of these potential correlates, the regression model
identifies several that are statistically significant and robust
correlates of the probability of financial crises.2 These
include external vulnerabilities (higher short-term debt,

2 Annex 4.1 lists the variables used in the baseline model and presents
a number of robustness tests; for example, for alternative model
specifications (random effects probit model) and twin crises. Twin crises
are defined as the simultaneous occurrence of any two types of financial
crises (sovereign debt, banking, or currency). Such episodes are usuallyassociated with much larger changes in typical leading indicators. The
correlates in the baseline model indeed have higher statistical significancein predicting twin crises than individual crises.

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BOX 4.3 Debt and crises (continued)
higher debt service, lower international reserves), adverse
shocks (higher U.S. interest rates, lower domestic output
growth), and faster debt accumulation—especially if trueof both government and private debt.3 These findings arebroadly consistent with the existing literature on leading
indicators of financial crises, particularly with regard to theimportant role of the composition of debt and pace of debt
accumulation.4 In addition, the regressions here suggestthat combined private and government debt buildups
significantly increase the probability of a currency crisis.
Debt accumulation. An increase in debt, either
government or private, was associated with significantly
higher probabilities of crises in the following year. For
example, an increase of 30 percentage points of GDP ingovernment debt over the previous year (equivalent to themedian buildup during a government debt accumulation
episode) increased the probability of entering a debt crisis
to 2.0 percent (from 1.4 percent) and that of entering acurrency crisis to 6.6 percent (from 4.1 percent). For
private debt, a 15 percentage points of GDP increase in
debt (equivalent to the median increase during a private
debt accumulation episode) doubled the probability ofentering a banking crisis, to 4.8 percent, or a currency
crisis, to 7.5 percent, in the following year—probabilities
that are considerably larger than those for a similarly-sizedbuildup in government debt.
Combined government and private debt increase.
Simultaneous increases in both government and private
debt amplified the probability of a currency crisis. Thus, a
15 percentage points of GDP increase together with a 30
percentage point of GDP increase in government debt
resulted in a 24 percent probability of entering a currency
crisis the next year—more than six times the probability
had debt remained stable (3.9 percent) and about onethird more than similarly-sized government or private debt
buildup separately.

averaged -1 percent. Contractions of this magnitude
increased the probability of entering a sovereign debt crisisin the subsequent year to 1.9 percent from 1.2 percent
outside crisis episodes. A 2-percentage point increase in
U.S. real interest rates—half the cumulative increaseduring a typical tightening phase of U.S. monetary
policy—increased the probability of entering a currency
crisis by one-half to 6.0 percent from 4.1 percent.
External vulnerabilities. A larger share of short-term debt
in external debt, greater debt service cost and lower reserve
cover were associated with significantly higher probabilities
of financial crises.
•

Short-term debt. Compared to the probability of asovereign debt crisis of 1.2 percent associated with ashare of short-term debt of 10 percent of external debt
(the average during non-crisis episodes), a 30 percent
share of short-term debt in external debt (Mexico’s
share before it plunged into a twin currency and debt
crisis in 1982) raised the probability of entering asovereign debt crisis in the following year to 2.0
percent.

•

Debt service. A 50 percent ratio of debt service toexports—Mexico’s average debt service burden in theearly 1980s—was associated with probabilities ofentering a sovereign debt crisis of 2.8 percent and abanking crisis of5.5 percent. This was more thandouble the probabilities associated with a 15 percent
debt service-to-export ratio in the average non-crisis
episode.

•Reserve coverage. The probability of a debt or banking
crisis exceeded 3 percent, and that of a currency crisis
5 percent, for a reserve coverage of 1 month ofimports (which was the case in Mexico in the early
1980s) compared to probabilities of 0.6-2.0 percent
for banking and debt crises, and 3.8 percent for
currency crises, when the reserve coverage amounted
to 4 months of imports (the average for non-crisis
episodes).

Adverse shocks. Compared to average growth outsidecrises (4 percent), growth in EMDE crisis episodes
3 The same variables remain statistically significant in a regressionthat combines sovereign debt and banking crises, but the change ingovernment debt becomes insignificant. This may reflect the fact thatbanking crises have been more than twice as common as sovereign debt
crises since 1970. Since almost all crises in the sample are associated withdebt accumulation episodes, dummy variables indicating the presence ofa private or government or combined (private and government) debt
accumulation episode are not statistically significant.
4 Relevant empirical regularities are discussed in, for example,
Manasse, Roubini, and Schimmelpfenning (2003) on sovereign debt
crises; Kauko (2014) on banking crises; and Kaminsky, Lizondo, andReinhart (1998) on currency crises.

Other vulnerabilities. Other vulnerabilities identified
tended to be more specific to certain types of crises orborrowing sectors.
•

Wholesale funding. Higher wholesale funding, proxiedby the ratio of credit to deposits, was associated with agreater probability of a banking crisis but appears tohave been largely unrelated to the probabilities ofsovereign debt and currency crises.

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BOX 4.3 Debt and crises (continued)••

Real exchange rate overvaluation. Real exchange rate
overvaluation was associated with a higher probabilityof a currency crisis but tended to be largely unrelated
to banking and sovereign debt crises (Dornbusch et al.
1995).
Concessional debt and FDI flows. A higher share ofconcessional debt, which consists of loans extended on
more generous terms than commercial ones, wasassociated with a lower probability of a sovereign debt
crisis but tended to be largely unrelated to banking
and currency crises. Larger FDI inflows, a more stableform of finance than portfolio inflows, were associatedwith a lower probability of a currency crisis.

Crisis probabilities: Small or large? In isolation, some ofthese probabilities may appear small. This is expected since
they are associated with individual indicators. However,the probabilities could cumulate rapidly when multiple
indicators deteriorate at the same time as has frequently
happened prior to financial crises. Indeed, as documented
in the previous chapters, in a typical financial crisis, anadverse shock is often compounded by elevated debt andmultiple other vulnerabilities.

Lessons from financial crisis episodes
The preceding section quantified how shocks andvulnerabilities have affected the likelihood of crises. In
addition, beyond measures that can be easily quantified,countries with financial crises during or after a debt
accumulation episode shared some structural andinstitutional weaknesses that made their economies more
prone to crises once an adverse shock hit. These structuraland institutional weaknesses are explored in this section in
a set of selected country case studies of financial crises.

Macroeconomic policies
Inefficient use of debt. In addition to financing importsubstitution policies, public debt was used in somecountries in the first wave to finance current government
spending and policies that led to overly expansionary
macroeconomic policies (Argentina, Brazil, Chile, Peru).
In other countries, rapid private-sector borrowing resulted
in debt-fueled domestic demand booms, including
property booms (Thailand, Ukraine) or inefficientmanufacturing investment (Korea).
Inadequate fiscal management. Many countries had severe
fiscal weaknesses. This included weak revenue collection(Argentina, Brazil, Indonesia, Russia), widespread tax
evasion (Argentina, Russia), public wage and pension
indexing (Argentina, Brazil, Mexico, Uruguay), monetaryfinancing of fiscal deficits (Argentina, Brazil), andsubstantial use of energy and food subsidies (Egypt,
Venezuela).
Risky composition of debt. Many of the crisis countries
borrowed in foreign currency. They struggled to meet debt
service obligations and faced steep jumps in debt ratios
following currency depreciations (Indonesia, Mexico,
Thailand). In Uruguay, for example, almost all public debt
was denominated in U.S. dollars in the mid-1990s. Several
countries relied on short-term borrowing and faced
rollover difficulties when investor sentiment deteriorated(Indonesia, Korea, Philippines, Russia in the late 1990s).
In Europe and Central Asia (ECA) in the 2000s, countries
borrowed cross-border from nonresident lenders and faceda credit crunch once liquidity conditions tightened for
global banks that were the source of this lending(Hungary, Kazakhstan in the late 2000s).

These case studies look into 43 crisis episodes in 34
EMDEs that have witnessed rapid government or private
debt accumulation episodes since 1970 (Annex 4.3). Mostof these cases (65 percent) involved overlapping private
and government debt accumulation episodes. Almost allcases (90 percent) involved twin crises, and 40 percent
involved triplet crises.5

Balance sheet mismatches. A substantial number ofcurrency and banking crises, and the majority ofconcurrent currency and banking crises, were associatedwith balance sheet mismatches (Indonesia, Malaysia,
Mexico, and Russia in the late 1990s). Sovereign debt
crises less frequently involved balance sheet mismatches,
except when banking supervision was weak (Indonesia,
Turkey in the 1990s).

5 The main references for these country case studies are provided in
Kose et al.(2019). For a discussion of some of these macroeconomic,
structural and institutional shortcomings see Balassa (1982); Kaufmann(1989); and Sachs (1985, 1989) on growth strategies and uses of debt;
Roubini and Wachtel (1999) on current account sustainability;
Daumont, Le Gall, and Leroux (2004) and Kawai, Newfarmer, andSchmukler (2005) on inadequate banking regulation; Brownbridge andKirkpatrick (2000) on balance sheet mismatch; and Capulong et. al.
(2000) for poor corporate governance.

Structural and institutional features
Poorly designed growth strategies. Many of the casestudies of crises in the 1970s and early 1980s showed
heavy state intervention through state-led industrialization,state-owned companies, and state-owned banks (Balassa
1982). Industrial policy in countries such as Argentina,

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BOX 4.3 Debt and crises (continued)
Brazil, and Venezuela focused on import substitution
industrialization, typically financed by external borrowing.
Lack of economic diversification. A number of the crisis
countries had undiversified economies, which increased
their vulnerability to terms of trade shocks. Several
countries in Latin America and the Caribbean (LAC) and
Sub-Saharan Africa (SSA), in particular, were heavily
dependent on both oil and non-oil commodity exports(Bolivia, Niger, Nigeria, Paraguay, Uruguay in the 1970s
and 1980s). When commodity prices fell in the 1980s, theprofitability of (often state-owned) corporates in the
resource sector, fiscal revenues, and export proceeds
collapsed, which triggered financial crises.
Inadequate banking regulation. Poor banking regulation
was a common feature in many case studies. Several SSA
countries experienced banking crises in the 1980s
primarily because of the failure of banks that were typicallystate-owned and subject to little oversight (Cameroon,
Kenya, Niger, and Tanzania). In EAP, financial
deregulation contributed to insufficient regulation andoversight of the financial sector in the second wave
(Indonesia, Korea, Malaysia, Philippines, and Thailand).
This resulted in growing weaknesses, including balance
sheet mismatches, and excessive risk taking by corporates(see below). In several countries in ECA during the 2000s,
cross-border lending was inadequately regulated bydomestic regulators (Hungary and Kazakhstan).

commodity prices for commodity exporting
economies (LAC and SSA in the 1980s, Russia in the
1990s), and contagion from both global crises (200709 global financial crisis) and regional crises (East
Asian financial and Russian crises in the 1990s),
which generated sudden withdrawals of capital
inflows.
•

Natural disasters and domestic shocks. Natural disasters
such as droughts were a major contributing factor tocrises in some countries, typically smaller, lessdiversified economies (e.g. Bangladesh in the 1970s,
Nepal in the 1980s, Zimbabwe in the 2000s).

•

Other domestic shocks. In a small number of countries,
crises were triggered, or exacerbated, by otherdomestic shocks. Typically, these were episodes ofpolitical turmoil (Turkey, Zimbabwe).

Crisis resolution. Many, though not all, crises wereresolved by policy programs of adjustment and structuralreform supported by financing from the IMF, World
Bank, and other multilateral bodies and partner countries.
•

IMF support. The vast majority of countries in thesecase studies adopted IMF-supported policy programs
to overcome their crises. The countries that did notuse IMF support typically had stronger fundamentals,
including lower public debt and larger international
reserves (e.g. Colombia, Kazakhstan, Malaysia).

Poor corporate governance. Among case studies of the
1980s and 1990s, poor corporate governance was acommon shortcoming, notably in some East Asian
countries (Indonesia, Korea, and Thailand). Along withpoor bank regulation, this led to inefficient corporate
investment, as banks lent to firms without rigorously
evaluating their creditworthiness.

•

Debt restructuring. Among the case studies ofsovereign debt crises, many ended with default andrestructuring of debt (e.g. Argentina, Mexico,
Nigeria). These cases were more common in the
1980s, 1990s, and early 2000s. Debt restructuring
was often prolonged and occurred well after the initial
sovereign debt crisis.

Political uncertainty. Many sovereign debt crises wereassociated with severe political uncertainty (Indonesia,
Philippines, Turkey, Venezuela).

•

Reforms. IMF support was conditional on theimplementation of macroeconomic and structuralreforms. For many EMDEs in LAC in the 1980s andin EAP in the late 1990s, crises were the trigger forpolicy changes to allow greater exchange rate
flexibility and strengthen monetary policy regimes.

Triggers and resolution of crises
Triggers. The case studies suggest that crises were usuallytriggered by external shocks, although in a small numberof countries domestic factors also played a role.
•

External macroeconomic shocks. The most common
trigger of crises was an external shock to the real
economy. These included a sudden rise in global
interest rates (LAC in the 1980s), a slowdown inglobal growth (ECA in the 2000s), a fall in

Conclusion
Crises are typically sparked by an adverse shock, such as anincrease in global interest rates or a growth slowdown,
whose impact is amplified and propagated via country
vulnerabilities such as high levels of debt, especially shortterm debt, and low international reserves. In line with theliterature, the econometric exercise conducted here

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BOX 4.3 Debt and crises (continued)
documents that a rapid rise in government or private debt
increases the probability of crises. A combined runup ingovernment and private debt—as has been the case during
the fourth global wave—increases the probability of acurrency crisis.
In several cases, crises revealed shortcomings that weremainly recognized ex post but had rarely been flagged
before these crises. Following these crises, research(described in academic studies and policy reports) shifted
its focus to these issues. For example, the Asian financial
crisis propelled the challenges of balance sheet mismatches
and weak corporate governance as well as the need forrobust bank supervision to the forefront of policy
discussions (Brownbridge and Kirkpatrick 2000; IMF
1999). The launch of the Financial System Assessment
Program in 1999 started systematic assessments offinancial sectors (IMF 2000). The 2007-09 global
financial crisis shifted an earlier consensus on the use ofcapital controls. Before 2008, capital controls were largely
considered ineffective and detrimental (Forbes 2004,
2007). After the global financial crisis, the literature shiftedto a guarded endorsement of capital controls isappropriately designed and implemented in the “right”
circumstances (Forbes, Fratzscher, and Straub 2015; IMF
2012, 2015b).

Rapid debt accumulation
episodesSpurts in debt buildups are common in EMDEs.
When they coincide in many EMDEs, they form
the global waves of debt discussed above. Thissection examines the implications of national rapid
debt accumulation episodes at the country level. It
uses an event study approach that compares rapid
debt accumulation episodes that coincided with afinancial crisis (which might be a currency,
banking, or sovereign debt crisis) with those thatescaped a crisis. Box 4.3 analyses the factors which
increase the likelihood of a financial crisis
occurring, including quantifying the impact of arise in debt.
Features of national rapid debt
accumulation episodes
Definition of episodes. An episode of rapid debt
accumulation is defined as a period during which
the government debt-to-GDP ratio or the private
sector debt-to-GDP ratio rises by more than one
standard deviation from a trough to its next peak.
This approach closely follows the dating of turning
points of business cycles but the key results arerobust to using a definition more closely alignedwith the literature on credit booms (Claessens,
Kose, and Terrones 2012; Mendoza and Terrones
2012; Annex 4.1). This approach results in 256

episodes of rapid government debt accumulation in
99 EMDEs since 1970, among a sample of 100
EMDEs with available data for 1970-2018. It also
yields 263 episodes of rapid private debt
accumulation in 100 EMDEs, out of a sample of
100 EMDEs with available data for 1970-2018.
Frequency of episodes. Debt accumulation
episodes have been common (Figure 4.7). EMDEs
in SAR, SSA, and LAC—the regions with thelargest number of episodes per country—had, on
average, about 3 government and 3 private debt
accumulation episodes since 1970. Most episodes
occurred in SSA (34 percent of all governmentand 33 percent of all private debt accumulation
episodes), in part reflecting the large number ofcountries in the region.
Duration. The average duration—the time
between trough and peak debt-to-GDP ratios—for both private and public episodes varied widely
but amounted to 7 years for the mediangovernment episodes and 8 years for the median
private episode. Most episodes had run theircourse in less than a decade. However, 21 percent
of government episodes and 29 percent of private
debt episodes lasted for more than a decade. Thelong duration of some of these episodes suggeststhat the debt buildup in part reflected financial
development.
Amplitude.

Although

again

with

wide

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

heterogeneity among the episodes, the government
debt buildup in the median government debt
accumulation episode (30 percentage points of
GDP from trough to peak) was double the private
debt buildup in the median private debt
accumulation episode (15 percentage points ofGDP). Variation in the amplitude of debt
accumulation episodes across countries wasparticularly wide for government debt
accumulation episodes. In one-quarter of such
episodes, the government debt buildup amounted
to more than 50 percentage points of GDP. Debt
accumulation on such a scale was rare for theprivate sector: in three-quarters of private debt
accumulation episodes, private debt rose by lessthan 30 percentage points of GDP.
Combined government and private debt
accumulation episodes. About 70 percent ofgovernment and private debt accumulation
episodes overlap. These overlapping, combined
government and private episodes, are statisticallysignificantly shorter and more pronounced thansolely-privateorsolely-government
debt
accumulation episodes (Annex Table 4.1.1).
Episodes coinciding with crises. Financial crises—
defined as in Laeven and Valencia (2018)—canoccur at any point during a debt accumulation
episode, and more than one type of crisis canoccur during an episode. Since 1970, based on all
episodes that have concluded, more than half ofgovernment debt accumulation episodes and 40
percent of private debt accumulation episodes
have been associated with crises (Figure 4.8).
Crises were particularly common during the firstand second waves. Most crises occurred wellbefore the end of the debt accumulation episode(Annex 4.1). Crises were equally common inlonger-lasting (such as those lasting a decade ormore) and shorter episodes (lasting less than adecade).
Macroeconomic outcomes during national
rapid debt accumulation episodes
The one-half of debt accumulation episodes thatwere associated with financial crises had
considerably weaker macroeconomic outcomesthan those that subsided without crises. The

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277

FIGURE 4.7 Episodes of rapid debt accumulation in
EMDEs
Episodes of rapid debt accumulation have been common among EMDEs,
in both the government and private sectors. In the average year between
1970 and 2018, three-quarters of EMDEs were in either a government or a
private debt accumulation episode or both. Since the early 2000s, thenumber of combined government and private debt accumulation episodes
has increased. During 1970-2018, the median debt accumulation episodelasted 7-8 years. During rapid debt accumulation episodes, government
debt typically rose (trough to peak) by 30 percentage points of GDP, andprivate debt by 15 percentage points of GDP.
A. EMDEs in rapid debt accumulation
episodesB. EMDEs in rapid debt accumulation
episodesC. Rapid government debt
accumulation episodes by region

D. Rapid private debt accumulation
episodes by region

E. Duration of rapid debt
accumulation episodes

F. Change in debt during rapid
accumulation episodes

Source: International Monetary Fund; World Bank.
A.-D. Share of EMDEs in the sample that are in rapid debt accumulation episodes.
Click here to download data and charts.

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FIGURE 4.8 Crises during rapid debt accumulation
episodes in EMDEs
About half of all episodes of government and private debt accumulation
during 1970-2018 were associated with financial crises. Different types ofcrises often occurred at the same time. The number of crises has fallen
since the first two waves of debt.
A. Government debt accumulation
episodes associated with crises

C. Crises in EMDEs

B. Private debt accumulation
episodes associated with crises

D. Crises during debt waves

Source: International Monetary Fund; World Bank.
Note: Episodes associated with crises are those which experienced financial crises (banking,currency, and debt crises, as in Laeven and Valencia 2018) during or within two years after the end of
episodes. For definition of episodes and sample, see Annex 4.1.
Click here to download data and charts.

macroeconomic implications have tended to beworse when rapid debt growth stemmed fromboth the government and the private sector.7
Government debt accumulation episodes.
Government debt accumulation episodes thatinvolved crises were typically associated withgreater debt buildups, weaker economic outcomes,
and higher vulnerabilities than non-crisis episodes(Figure 4.9). In the episodes associated withfinancial crises, the government debt buildup was
about 14 percentage points of GDP larger after
eight years than in non-crisis episodes. After eight
years, output and output per capita in episodes

with crises were around 10 percent lower than in
episodes without a crisis; investment was 22
percent lower; and consumption was 6 percent
lower. Several external indicators—international
reserves, external debt—deteriorated significantly
more in episodes associated with crisis than in non-crisis episodes as governments drew down reserves
in an effort to stem depreciation.
Private debt accumulation episodes. After eight
years, private debt accumulation episodes
associated with crises featured weaker output andper capita income (by about 6 percent);
consumption (by 8 percent); and investment (by15 percent). Private debt accumulation episodes
with crises also saw significantly more pronounced
deteriorations in external positions—international
reserves, external debt—than non-crisis episodes.
Similarities. Regardless of the borrowing sector,
rapid debt accumulation episodes with crises
featured considerably worse macroeconomic
outcomes and vulnerabilities than those notassociated with crises. Both types of debt
accumulation episodes associated with crises sawlarger falls in reserves and greater increases inexternal debt than non-crisis episodes. Fiscal andcurrent account deficits widened in both types of
episodes but more in government debt
accumulation episodes than in private debt
accumulation episodes.
Differences. Government debt accumulation
episodes associated with crises tended to be morecostly than private debt accumulation episodes
associated with crises, with much larger shortfalls
in output growth, especially in the early years afteracrisis.
Conversely,
government
debt
accumulation episodes associated with crises
featured much larger drops in investment thansimilar private debt accumulation episodes,
possibly reflecting greater disruptions to financing
conditions in crises during government debt
accumulation episodes.

What comes next?
7 Combined government and private debt accumulation episodes
were accompanied by significantly weaker investment andconsumption growth than solely-private episodes. Excluding episodes
associated with crises, combined episodes also featured slower overall
growth than solely private debt accumulation episodes.

The current wave, not yet a decade old, has alreadyincluded the euro area debt crisis and several
EMDE currency crises. Although EMDEs have

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

gone through periods of volatility during thecurrent wave of debt, they have not yet
experienced widespread financial crises. The key
question is whether the current wave of debt
accumulation will at some point end in financial
crises in many EMDEs, as all its predecessors
eventually did, or whether such crises will be
avoided perhaps because EMDEs have learned andapplied their lessons from the past.
Prolonged period of low interest rates. The
current environment of low interest rates andpersistently low inflation in advanced economies
alleviates some risks associated with the latest wave
of debt. Policy interest rates in many advanced
economies are near historical lows after major
central banks recently reverted to an easing stanceafter winding down tightening cycles in 2018
(Figure 4.10). Monetary policy in advanced
economies is likely to be accommodative for theforeseeable future as growth prospects andinflation expectations remain subdued. Interest
payments on government debt in EMDEs havefallen from an average of 2.6 percent of GDP in
2000-07, to 1.6 percent of GDP in 2010-18,
despite the increase in debt over that period. At
current nominal GDP growth and long-term
interest rates, debt appears to be on stable orfalling trajectories in almost half of EMDEs.
An easing of U.S. financial conditions, abellwether for global financial conditions, hastypically accompanied an increase in capital flows
to EMDEs (Feyen et al. 2015). But increased
borrowing can also raise vulnerability to a future
rebound in interest rates. Historically, rising global
interest rates have been a key trigger for financial
crises (Bulow et al. 1992; Bulow and Rogoff 1989;
Reinhart and Rogoff 2010, 2011). Hence, low or
falling global interest rates provide no sure
protection against financial crises for EMDEs.
Half of all crises during episodes of rapid debt
accumulation occurred in years when U.S. longterm (10-year) interest rates were falling and oneeighth of episodes occurred in years when U.S.
long-term real interest rates were below 1 percent(as they have been since 2016).
Weak growth prospects. In addition to interest
rates and fiscal positions, growth is another major
determinant of debt sustainability. An important

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279

FIGURE 4.9 Macroeconomic developments during debt
accumulation episodes
Rapid debt accumulation episodes associated with financial crises show
slower output, investment and consumption growth. Private debt
accumulation episodes associated with crises also had lower international
reserves and higher external debt than episodes without any crisis events.

A. Government episodes: Government
debt

B. Private episodes: Private debt

C. Government episodes: Output,
investment and consumption

D. Private episodes: Output,
investment and consumption

E. Government episodes: International
reserves and external debt

F. Private episodes: International
reserves and external debt

Source: Bruegel; International Monetary Fund; Laeven and Valencia (2018); World Bank.
Note: Median for episodes with data available for at least 8 years from the beginning of the episode.
Year “t” refers to the beginning of rapid government debt accumulation episodes. Episodes
associated with crises are those that experienced financial crises (banking, currency, and debt crises,
as in Laeven and Valencia (2018)) during or within two years after the end of episodes. “*”, “**”, and“***” denote that medians between episodes associated with crises and those with no crises are
statistically different at 10 percent, 5 percent, and 1 percent levels, respectively, based on Wilcoxon
rank-sum tests.
A.B. Government (A) or private (B) debt in percent of GDP two and eight years after the beginning of
the government debt accumulation episode (t).
C.D. Cumulative percent increase from t, based on real growth rates for output (GDP), output (GDP)per capita, investment, and consumption.
E.F. Series shown as percent of GDP.
Click here to download data and charts.

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FIGURE 4.10 Fourth wave: Opportunities and risks
The current environment of very low interest rates has alleviated immediaterisks associated with the latest accumulation of debt since long-term
interest rates are below growth in about half of EMDEs. However, whiledebt levels in advanced economies are on a sustainable path, debt levels
in almost half of EMDEs are on a rising path. Although current levels of
EMDE government or private debt are, on average, still below or nearthose in the median rapid debt accumulation episode, increases ingovernment or private debt since 2010 have already exceeded those of thetypical historical episode in about one-quarter of EMDEs.
A. Long-term interest rates

B. Share of economies with interest
rates below growth

C. Sustainability gaps

D. Countries with negative
sustainability gaps

E. Current levels of government debt
vs. previous rapid debt accumulation
episodesF. Current levels of private debt vs.
previous rapid debt accumulation
episodesSource: Bloomberg; Kose et al. (2017); World Bank.
A. Average long-term nominal government bond yields (with maturity of 10 years) computed withcurrent U.S. dollar GDP as a weight, based on up to 36 advanced economies and 84 EMDEs.
B. Share of countries where long-term nominal interest rates (represented by 10-year local currency
government bond yields) are below nominal GDP growth for 1990-2018 in up to 34 advanced
economies and 83 EMDEs.
C.D. A sustainability gap is defined as the difference between the actual primary balance and thedebt-stabilizing balance. Averages computed with current U.S. dollar GDP as weights, based on at
most 34 advanced economies and 83 EMDEs.
D. Share of economies in which sustainability gaps are negative (for example, debt is on a rising
trajectory, or fiscal positions are debt-increasing). Sample includes 34 advanced economies and 83
EMDEs.
E.F. Median levels of debt during debt accumulation episodes, as defined in Annex 4.1. t=0 indicatesthe peak of debt accumulation episodes that were completed before 2018. For current debt
accumulation, t=0 indicates 2018.
Click here to download data and charts.

reason for rapid debt accumulation has been the
sharp growth slowdown over the course of the
fourth wave. EMDE growth slowed after 2020 toa trough of 4.1 percent in 2016 before a modest
recovery took hold (Kose and Ohnsorge 2019).
Current trends in fundamental drivers of growthsuggest that it is likely to slow further over the
next decade, to a pace about 0.5 percentage point
lower than in 2013-17 (Ruch 2019; World Bank
2018a). For commodity-exporting EMDEs—
almost two-thirds of EMDEs—prospects will befurther dimmed by the expected slowdown incommodity demand growth as major commodityconsuming EMDEs slow and mature (WorldBank 2018b). The past decade has been marked byrepeated growth disappointments. If these persist
into the next decade, they could lead to growing
concerns about debt sustainability, even in a worldof low interest rates.
Vulnerability to external shocks. The previous
three waves highlight the risks associated with asharp buildup of debt. Financial crises typicallyoccurred when external shocks hit EMDEs withdomestic vulnerabilities. Many EMDEs have
improved their monetary and fiscal policy
frameworks over the past two decades. However,
elevated debt levels in the current wave of debt
accumulation have been accompanied by risingfiscal, corporate and external vulnerabilities. These
include lower international reserves and larger
shares of EMDEs with current account and fiscal
deficits.
There has been a significant change in thecomposition of debt in EMDEs. This shift could
generate new vulnerabilities. Increasing issuance of
foreign-currency-denominated corporate debt in
EMDEs has contributed to rising currency
exposures and heightened the risks of financial
distress in the corporate sector and the banking
system in the event of U.S. dollar appreciation.8 In
some EMDEs, the share of nonresident-heldbonds in local currency bond markets has grown
to more than 30 percent. In LICs, debt has been
increasingly financed by non-concessional sources.

8 This appreciation could be triggered, for example, by reversals
of capital flows to EMDEs on heightened global risk aversion.

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Shocks could have several sources:
•

•

•

•

Although it seems unlikely in the foreseeable
future, a return to monetary policy
normalization in advanced economies could
raise borrowing cost. It would be likely totrigger U.S. dollar appreciation and a turn ininvestor sentiment that would, especially,affected those EMDEs with large foreign
participation in local bond markets (Cerutti,
Claessens, and Ratnovski 2017; Ruch 2019).
A decade of tightening banking regulation has
been accompanied by the emergence of credit
risk and maturity mismatches in the non-bankfinancial system in advanced economies (Koseand Ohnsorge 2019). Financial stress in nonbank financial institutions could quickly
propagate to the rest of the financial system,
owing to the interconnectedness between
nonbanks and banks. Growing linkages
between non-bank financial systems inadvanced economies and EMDEs haveincreased both the likelihood and thepotential magnitude of spillovers from distressin advanced-economy nonbanks to EMDE
bond markets and broader financial systems.
Many commodity-exporting EMDEs rely
heavily on revenues from the resource sector
to find government expenditures and service
sovereign debt (Correa and Sapriza 2014). As
a result, commodity price shocks haveperiodically disrupted government financesand been a source of financial instability in
EMDEs, culminating in some cases insovereign debt default or other financial crises.
The large corporate debt buildup in China has
been primarily to domestic creditors. Its
counterpart in the financial system could
eventually reveal non-performing loans andresult in a growth slowdown in China.
Concerns remain that the rapid pace ofinvestment growth may have contributed toovercapacity in some industries (Yu and Shen
2019; Wang, Wan and Song 2018;
Maliszewski et al. 2016). Although it has
recently declined, high corporate leverage in
China, particularly that of state-owned
enterprises, has been associated with adeterioration of corporate financial performance, and many corporates are facing
deteriorating profitability (Molnar and Lu
2019; World Bank 2018b, 2019a). In view ofthe size of China’s economy, adverse spillovers
to other EMDEs would be likely (Ahmed etal. 2019; World Bank 2016b).
•

LICs have accumulated debt rapidly andincreasingly from non-concessional and lesstransparent sources (Essl et al. 2019). This
increases their vulnerability to financing
shocks and to the revelation of previously
undisclosed debt obligations (Bova et al.
2016; Horn, Reinhart, and Trebesch 2019;
Lee and Bachmair 2019).

•

For some EMDEs, risks related to climatechange are substantial. The experience ofseveral economies in LAC shows that debt
crises can be triggered by natural disasters(Rasmussen 2004). To the extent that natural
disasters are becoming more frequent andpersistent as a result of climate change, theypose a growing risk to debt sustainability invulnerable EMDEs. Furthermore, the move toa low-carbon economy could have a material
effect for energy-exporting EMDEs. A shift
away from the use of carbon-intensive fuels
could leave the assets of fossil fuel companies,
including state-owned companies, stranded byrules to curb climate change (Carney 2015).
This could have critical implications for debt
sustainability both at the firm and the country
level.

Vulnerability to domestic shocks. Elevated debt
increases an economy’s vulnerability to domestic
financing and political shocks even in anenvironment of benign global financing
conditions. Domestic financing shocks can trigger
sharp increases in borrowing cost. These may
include the sudden emergence of contingent
government liabilities, including in state-owned
enterprises or public-private partnerships. Policy
surprises or sudden bouts of policy uncertainty canalso fuel investor concerns about debt repayment,
causing a jump in borrowing cost.
Broader costs of debt accumulation. In addition
to restricting economies’ ability to weather shocks,

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high debt may also act as a constraint on growthof its own accord through three effects (Kose et al.
2019). First, high debt constrains governments’ability to respond to downturns. For example,
fiscal stimulus during the 2008-09 global financial
crisis was considerably smaller in countries withhigh government debt than in those with low
government debt (World Bank 2015).
As well as limiting the use of fiscal policy, highgovernment debt tends to render fiscal policy lesseffective (Huidrom et al. 2019). Second, high debt
service costs may crowd out growth-enhancing
public investment or social safety nets (Obstfeld
2013; Reinhart and Rogoff 2010; Romer andRomer 2018). Third, high debt could also create
uncertainty about macroeconomic and policy
prospects (IMF 2018a; Kumar and Woo 2010).
This can crowd out productivity-enhancing
private investment and weigh on output growth.

Seven lessons
The analysis of waves of global and national debt
accumulation episodes yields several importantlessons for EMDEs. Box 4.3 complements thelessons learned by considering 43 episodes of debt
accumulation followed by financial crises in 34
EMDEs, and examining the similarities anddifferences between these case studies.
Accumulate debt with care. Borrowing, when wellspent and sustainable, could support growth.
Waves of broad-based debt accumulation havetypically coincided with global upturns amid
accommodative monetary policy and financial
market development. However, about half of
rapid debt accumulation episodes at the country
level were associated with financial crises. Episodesof rapid government debt accumulation weremore likely than episodes of rapid private debt
accumulation to be associated with crisis, and werecostlier than rapid buildups of private debt.
Use debt efficiently. The present combination of
weak global growth and low interest rates makes
government debt accumulation an appealing
option for EMDEs to boost growth-friendly
spending (World Bank 2019d). However, it iscritical that the debt be used for productive

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

purposes to boost potential growth and exports, as
painfully learned from the experience of the firstwave. Crises were common in countries thatborrowedheavilytofinancestate-ledindustrialization or real estate markets (e.g.
Argentina and Brazil in the first wave, Thailand in
the second wave).
Maintain a resilient debt composition. A debt
composition tilted toward foreign currencydenominated, short-term, or nonresident-helddebt makes countries more vulnerable to shifts inmarket sentiment, currency depreciation, or spikes
in global interest rates and risk premia. Crises havebeen more likely when the share of short-term
debt was higher. The first and second waves
showed how a high share of foreign currencydenominated debt meant that currency
depreciations led to an increase in both debt
servicing costs and debt ratios.
Regulation and supervision matter. Inadequate
regulatory and supervisory regimes, including gaps
in coordination between home and hostsupervisors, can encourage excessively risky
lending and debt buildup. This was the case in the
Asian financial crisis during the second wave andin ECA countries during the third wave.
Conversely, a robust regulatory system, that is also
well coordinated between home and hostsupervisors of foreign banks, can temper the
incentive to take excessive risks resulting from thepublic safety net for the financial system (moral
hazard; Briault et al. 2018).
Beware of external shocks (especially when there
are domestic vulnerabilities). Crises typicallyoccurred when external shocks hit countries that
had substantial domestic vulnerabilities, including
a reliance on external and short-term debt inconjunction with a fixed exchange rate and low
levels of international reserves (Bordo, Meissner,and Stuckler 2010; Mishkin 1999). In contrast,
countries with higher international reserves weresignificantly more resilient to these types of shocks(Gourinchas and Obstfeld 2012). In addition toexternal shocks, domestic political shocks
contributed to crises by increasing policy
uncertainty and weakening investor sentiment.

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Private debt can rapidly turn into government
debt. Large private sector losses, including losses
threatening bank solvency, and the materializationof contingent liabilities, including those of stateowned enterprises, can lead governments toprovide substantial financial support (Mbaye,
Moreno-Badia, and Chae 2018b). This occurredin the EAP region in the second wave, and in ECA
in the third wave, with governments providing
substantial support to banks. While the provisionof government support can save the banking
system from collapse, it can also lead to a steep
jump in public debt which, in turn, can heighten
the fragility of banks with large sovereign
exposures (Bova et al. 2016; Claessens et al. 2014;
Feyen and Zuccardi 2019; World Bank 2015).
Fiscal space can shrink rapidly as a result eventhough fiscal deficits may have been moderate.
Develop effective mechanisms to recognize losses
and restructure debt. Having mechanisms in place
to promptly recognize and restructure debt can
improve the prospects for recovery from crisis,
particularly public debt crises (Kroszner 2003) orbanking crises (Rutledge et al. 2012). The
protracted resolution after the Latin American
crises of the 1970s and the SSA debt distress in the
1980s and 1990s were associated with a period ofvery low, or even negative, per capita income
growth. Growth only rebounded after the Brady
plan and the HIPC and MDRI debt initiatives
resolved debt distress and reduced debt overhangs.

Policy implications
Policy frameworks have improved in many
EMDEs since the first two waves of debt. These
improvements played a critical role in mitigating
the adverse impact of the global financial crisis onthese economies at the end of the third wave ofdebt accumulation. However, there is still
considerable scope for further improvement.
Specific policy priorities ultimately depend on
country circumstances but there are four broad
strands of policies that can help contain the risks
associated with the recent debt accumulation.
Policies for managing debt
Governments need to put in place mechanisms
and institutions that help them strike the proper

CHAPTER 4

balance between the benefits and costs ofadditional debt. These include sound debt
management, high debt transparency, andthorough monitoring of contingent liabilities.
While these policies mostly apply to borrowers,
creditors also need to implement measures tomitigate risks associated with excessive debt
accumulation.
Sound debt management can help reduceborrowing costs, enhance debt sustainability, anddampen fiscal risks.9 Debt managers are
increasingly adopting pro-active policies to build
buffers and make the composition of debt more
resilient, but further progress is needed (WorldBank 2013). Prudent debt management favors
debt contracted on terms that preserve
macroeconomic and financial resilience—
preferably at longer maturities, at fixed (andfavorable) interest rates, and in local currency. A
debt composition that is less vulnerable to market
disruptions reduces the likelihood that a decline inmarket sentiment, sharp depreciations, or interest
rate spikes erode debt sustainability. A welldeveloped and liquid domestic bond market can
reduce the need for foreign currency-denominatedlending and help ensure stability in government
financing (Arvai and Heenan 2008; World Bankand IMF 2001).
Transparency about balance sheets is a prerequisite for sound debt management. History
shows that public debt spikes can reflect therevelation of previously undisclosed liabilities suchas those revealed in Mozambique during thefourth wave (Jaramillo, Mulas-Granados, andJalles 2017; Weber 2012). Greater fiscal
transparency is associated with lower borrowing
costs, improvements in government effectivenessand lower government debt (Kemoe and Zhan
2018; Montes, Bastos, and de Oliveira 2019).
Improvements in data collection practices for LIC
debt would help policymakers undertake betterinformed borrowing decisions, and have been
associated with lower borrowing costs (Cady andPellechio 2006; World Bank and IMF 2018c).

9 Recognizing the need for better debt management, the WorldBank and IMF have developed guidelines, best practices, andframeworks to assist countries in implementing debt management
strategies (World Bank and IMF 2014).

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Principles and guidelines for debt transparency
have been created, both by international financial
institutions, including the IMF’s fiscal
transparency code, and by the private sector (IIF
2019a; IMF 2019d).
Monitoring and mitigation of contingent
liabilities are integral for sound public debt
management. Recent survey evidence suggests thata majority of public debt managers are monitoringrisks of contingent liabilities; only a minority,
however, use risk mitigation tools, such as reserve
accounts (40 percent of respondents) or risk
exposure limits on contingent liabilities (30percent of respondents; Lee and Bachmair 2019).
Creditors, including international financial
institutions, play an important role in mitigating
the risks associated with debt accumulation. For
example, while country authorities have theprimary responsibility to transparently report theirdebt data, international financial institutions
support transparency and sustainable lending
practices through several measures. The IMF and
the World Bank collect and disseminate debt
statistics that are used by a wide range ofstakeholders; produce published analyses of public
debt data via debt sustainability analyses (DSAs);
support countries’ efforts to produce mediumterm debt management strategies (MTDSs);
publish information on countries’ borrowing
capacity; and directly liaise with multilateral,bilateral, and private creditors. All of these efforts
provide important support to borrowers andlenders in their decision making.
Macroeconomic policies
Notwithstanding substantial improvements sincethe 1990s, macroeconomic policy frameworks canbe strengthened further in many EMDEs (Koseand Ohnsorge 2019). Monetary policy
frameworks and exchange rate regimes can bestrengthened to increase central bank credibility.
Fiscal frameworks can ensure that borrowing
remains within sustainable limits and borrowed
funds are used well.
Macroeconomic and exchange rate policy
frameworks. The benefits of stability-oriented andresilient monetary policy frameworks cannot be

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

overstated. During episodes of financial stress,
when EMDE currencies tend to depreciate
sharply, strong monetary policy frameworks will
be helpful not least because the exchange rate passthrough to inflation tends to be smaller incountries with more credible, transparent andindependent central banks; inflation-targeting
monetary policy regimes; and better-anchoredinflation expectations (Kose et al. 2019). With lesspass-through from depreciation to inflation,
central banks in EMDEs will have more scope tosupport activity. Flexible exchange rates canprovideaneffective
mechanismformacroeconomic adjustment and can help avoid
currency overvaluations and the buildup of large
currency mismatches on balance sheets—acommon precursor of crises. A flexible exchange
rate regime requires, however, that monetary
policy pursue a credible policy of inflation control
to provide an effective nominal anchor to theeconomy. Such a policy framework needs to becomplemented by strong macroeconomic andinstitutional arrangements.
Fiscal rules can help avoid fiscal slippages, ensurethat revenue windfalls during times of strong
growth are prudently managed, and manage andcontain risks from contingent liabilities (Cebotari
2008; Currie and Velandia 2002; Romer andRomer 2019; Ulgenturk 2017). Strong fiscal
frameworks have also been associated with lower
inflation and inflation volatility, supporting thecentral bank in delivering its mandate (Ha, Kose,
and Ohnsorge 2019). EMDEs have madeimportant strides in the adoption and design offiscal rules (Schaechter et al. 2012).10 However,fiscal rules may only be effective once a certain
degree of broader government effectiveness isachieved and sound budgetary institutions are inplace.11
10 Schaechter et al. (2012) create an overall fiscal rule index that
captures both the number and characteristics of fiscal rules inoperation in advanced economies and EMDEs and show how
EMDEs have played catch-up to advanced economies since 2000.
Ardanaz et al. (2019) find that well-designed fiscal rules can help
safeguard public investment during downturns.
11 Calderón and Nguyen (2016) estimate that fiscal and monetary
policy procyclicality is greater in countries with weak institutions.
Bergman and Hutchison (2015, 2018) show that fiscal rules are
effective only when government effectiveness exceeds a minimum
threshold. World Bank (2015) discusses the circumstances andfeatures that can make fiscal rules more effective.

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Alternatives to debt accumulation are available toexpand fiscal resources for priority spending.
Public spending can be reallocated to uses that are
more likely to boost future growth, including
education and health spending as well as climatesmart infrastructure investment to strengthen
economic resilience. Government revenue bases
can be broadened by removing special exemptions
and strengthening tax administration (Gaspar,
Ralyea, and Ture 2019; IMF 2019c; World Bank
2017b). Government can also take action to foster
private sector-led growth. Reform agendas toimprove business climates and institutions haveresulted in significant gains in investment andproductivity EMDEs (World Bank 2018a). In
turn, increased private sector growth expands therevenue base and, ultimately, strengthens
government revenues.
Financial sector policies
Robust financial sector regulation and supervision
can help prevent risks from building up. Financial
market deepening can help mobilize domestic
savings that may provide more stable sources offinancing than capital inflows.
Improved financial system regulation andsupervision, by acting on systemic exposures andensuring adequate capital buffers, can help prevent
risks from building up. Robust prudential
regulation and supervision can help pre-empt thebuildup of systemic financial weaknesses.
Macroprudential policies can help moderate
lending to households and corporates. The use ofliving wills for banks and robust bank bankruptcy
regimes can also help with the orderly winding
down of insolvent institutions, including throughthe bail-in of creditors. Credibility andpredictability of bank resolution can help prevent
spillovers from the failure of one financial
institution to others by reassuring creditors about
the continued functioning of the financial system
as a whole (Hoshi 2011).
Financial market deepening can help expand thepool of stable long-term domestic savings available
for domestic investment. This requires an enabling
environment of robust institutions, protection of
creditor rights, sound regulatory quality andmacroeconomic stability (Laeven 2014; Sahay et

CHAPTER 4

al. 2008). At the same time, however, excessively
rapid growth in financial markets can generate
financial stability risks. A careful balance between
measures to promote financial market deepening
and supervision and regulation is critical.
Strengthening institutions
Well-enforced frameworks for sound corporate
governance can help ensure that funds borrowedby private corporates are well used. Soundbankruptcy frameworks can help prevent debt
overhangs from weighing on investment for
prolonged periods.
The promotion of good corporate governance canmitigate risks arising from the corporate sector.
Stronger corporate governance can tilt firms’
financing towards equity rather than debt (Mande,
Park, and Son 2011); increase hedging of foreign
currency positions to protect against external
shocks (Lel 2012); and encourage more efficient
firm operation (Henry 2010). Other measures canalso help contain risks from corporate credit
growth, such as increased stress testing of listed
corporates’ balance sheets.
Effective bankruptcy and insolvency regimes canhelp in the resolution of private debt crises andhave benefits outside of crises (Leroy andGrandolini 2016). Several EMDEs have recently
reformed bankruptcy procedures, but in general,
EMDE bankruptcy protection laws lag
international best practices.12 Strengthening
bankruptcy protection can boost investment andfacilitate responsible corporate risk-taking, helping
to relieve the costs of debt overhang (World Bank
2014b). Well-functioning legal, regulatory, andinstitutional frameworks are crucial forcommercial banks and companies to resolve nonperforming loans and facilitate business exit andreorganization (Menezes 2014). A robust
insolvency regime can improve financial inclusion
and increase access to credit, by reducing the costof lending.

12 These include the introduction of a new bankruptcy law in
Egypt and strengthening of secured creditors’ rights in India.

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G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

ANNEX FIGURE 4.1.1 Country examples of debt
accumulation episodes
A. Turkey: Government debt

C. Philippines: Private debt

B. Mexico: Government debt

D. Malaysia: Private debt

Source: International Monetary Fund; World Bank.
Note: Blue line indicates debt outside debt accumulation episodes. A period of debt accumulation is
identified with the algorithm in Harding and Pagan (2002). When a change in debt-to-GDP ratios overan accumulation period is above the maximum of 10-year moving standard deviation of the ratios
during the period, it is considered as a rapid debt accumulation (shown as an orange area). When it isbelow the threshold, it is treated as a non-rapid accumulation (shown as a light blue area). If a crisis(i.e., banking, currency, or debt crisis) occurs during a rapid debt accumulation period or within two
years since the end of the period, it is regarded as an episode of rapid debt accumulation associatedwith a crisis (shown as a red line). An ongoing episode (e.g., the third orange area in Panel C) is alsoclassified as either rapid or non-rapid accumulation, based on the same methodology.
Click here to download data and charts.

ANNEX 4.1 Event study
methodology
Identifying episodes of rapid debt accumulation.
The identification of episodes of rapid
accumulation of government and private debt
proceeds in two steps. First, the Harding andPagan’s (2002) algorithm is used to identify thecyclical turning points in the debt-to-GDP ratios.
In particular, a debt cycle (from one peak debt-toGDP ratio to the next peak debt-to-GDP ratio) is
assumed to last at least five years with a minimum
two-year duration of the contraction phase (frompeak to trough) and the expansion (oraccumulation) phase (from trough to peak).1

This dating method is documented in Kose, Nagle, Ohnsorge,and Sugawara (2019).
1

Second, an expansion phase is labeled as a rapid
accumulation episode if an increase in debt-toGDP ratio (from trough to peak) exceeds the
maximum ten-year moving standard deviation(over the period t-9 to t) of the debt-to-GDP ratio
during the phase (Figure A4.1.1).
In scaling debt by GDP, this approach implicitly
focuses on the concept of debt burden, which
captures the ability of borrowers to repay theirdebt.2 An increase in the debt burden, as measured
by the debt-to-GDP ratio used here, could reflect
an output collapse, an exchange rate depreciation,or outright borrowing. Regardless of theseunderlying reasons, an increase in the debt burden
makes it more challenging to service debt andmakes the debt burden more likely to become asource of financial or economic stress.
In practice, output contractions are the source ofincreases in debt-to-GDP ratios only in a minorityof episodes identified here (one-third ofgovernment debt episodes and two-fifths ofprivate debt episodes). Currency crises are indeed
associated with larger debt buildups during thedebt accumulation episodes identified here, butthese currency crises typically happen well before(two years before) debt peaks and the increase in
debt during the year of the currency crisis only
accounts for one-tenth (private debt episodes) to
one-quarter (government debt episodes) of thetotal debt buildup during these debt accumulation
episodes associated with currency crises.
Phases at the beginning and end of data series arealso classified as either rapid or non-rapid
accumulation, if they are on the expansion
trajectory. While they are identified in the same
way as in the other cases, the beginning and end of
episodes are set when data availability of
government and private debt begins and ends.
An episode of rapid debt accumulation isassociated with a financial crisis if a crisis—banking, currency, or debt crisis—occurs during
the period of rapid debt accumulation or at least
within two years since the end of the episode. The
2 Debt buildup results from both demand and supply factors.
Regardless of which of these predominates, a high debt-to-GDP ratio
presents a vulnerability in the event of adverse shocks.

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287

ANNEX TABLE 4.1.1 Comparison of combined government and private debt accumulation episodes withsolely government or private debt accumulation episodes.

Duration (years)
Amplitude (percentage points)

Rapid accumulation with crises
Government
Private
Both
debt
debt
(combined)
7
8
3
42.6

13.1

35.3

Rapid accumulation without crises
PrivateBoth
Government debt
debt
(combined)
7
8
4
21.6

14.8

26.0
4.2

Growth (percent)

2.2

3.7

2.7

4.1

4.6

Per capita growth (percent)

0.1

1.9

0.9

2.0

2.5

2.0

Investment growth (percent)Private consumption growth(percent)
Reserves (percent of GDP)
Short-term external debt
(percent of GDP)

1.9

5.7

2.2

6.3

7.2

6.1

2.5

4.0

2.9

4.1

4.8

4.2

7.2

7.2

6.6

12.9

13.2

12.9

4.4

4.8

4.3

3.9

3.7

3.8

Note: Amplitude for "Both (combined)" is measured as an average of amplitudes of government debt and private debt during a combined part. Bold numbers indicate statistically significant
difference from combined episodes.

information on crisis years is obtained from
Laeven and Valencia (2018). The year coverage forcurrency crises is extended to 2018, by following
the methodology in Laeven and Valencia (2018)using data on end-of-year exchange rates vis-à-vis
U.S. dollars from the IMF. This association only
describes the timing or coincidence between rapid
accumulation of debt and financial crisis, andtherefore does not imply any causal link between
the two.
Sample. The sample includes data for 100
EMDEs for 1970-2018, while the identification ofdebt accumulation uses data prior to 1970 (seeKose et al. 2019 for details). Small states, as
defined by the World Bank, are excluded. This
results in 256 episodes of rapid government debt
accumulation and 263 episodes of rapid private
debt accumulation in a sample of 100 EMDEs
with available data for 1970-2018.

ANNEX 4.2 Regression
methodology
Discrete choice modelling. The most common
estimation methods used in the empiricalliterature on predicting crises are logit and probit
models. The baseline specification used in thisstudy is a panel logit model with random effects,
but for robustness purposes, a random effects
probit model and a fixed effects logit model arealso used. The Hausman test suggests that the
random effects model is appropriate for debt andbanking crises but not for currency crises.
However, even for currency crises, the coefficientestimates and their statistical significance remainsimilar in fixed effects and random effects models.
To exploit the time and cross-sectional
dimensions, a panel dataset of 139 EMDEs withannual data over the period 1970–2018 isconstructed. The details of the methodology aredescribed in Kose et al. (2019).
Selection of explanatory variables. The variables
are chosen from a close examination of theempirical findings from the early warning crisis
literature (see Chamon and Crowe 2012; Frankel
and Saravelos 2012; and Kaminsky, Lizondo, andReinhart 1998 for an extensive review). A large
number of variables is included (with various datatransformations, such as levels, growth rate,
percentage point change, deviation from trend)
that can be grouped into several categories:•

Debt profile: public and private debt (percent
of GDP); short-term debt and concessional
debt (in percent of total debt); debt service on
external debt (in percent of exports).

•

Capital account: international reserves (inmonths of imports), currency mismatch(foreign liabilities to foreign assets), net FDI
inflows (in percent of GDP).

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Current account: exchange rate overvaluation(percent deviation from Hodrick Prescottfiltered trend).

•

Foreign environment: U.S. interest rate
(deflated by GDP deflator, in percent).

•

Domestic environment: GDP growth (inpercent).

•

Banking sector: funding ratio (banking system
credit to deposits);

To attenuate potential endogeneity bias caused bycontemporaneous interaction between economic
fundamentals and crises, lagged values of the
explanatory variables are used, except for U.S.
interest rate. Robustness checks using alternativemodel specifications as well as results forprobabilities of twin and triplet crises are provided
in Kose et al. (2019).
Probability of crises. The probability of crises
occurring are evaluated at specific points ofinterest for illustration (while keeping all othervariables at their average values). For details, see
Kose et al. (2019).

G LO BAL EC O NO MIC P ROS P EC TS | J AN U ARY 2020

ANNEX 4.3 Case studies
The in-depth literature review of Box 4.3 covered
43 crisis case studies for 30 EMDEs since 1970.
While non-exhaustive, the case studies werechosen to: (i) be representative of debt
accumulation episodes over the past fifty years; (ii)include the large EMDEs in major regional debt
crises episodes; (iii) represent crises in low-income
countries; and (iv) a sufficiently comprehensive
literature to base an assessment on.
In the case of the in-depth literature review, thesearch covered all publicly available country
reports and flagship publications of international
financial institutions (Asian Development Bank,
African Development Bank, European Bank for
Reconstruction and Development, Inter-American
Development Bank, International Monetary
Fund, World Bank) and academic publications
published during 1970-2018. Publications were
found on the institutions’ websites and, especiallybefore 1997, in the EconLit database. The main
sources are detailed in Kose et al. (2019).

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297

STATISTICAL
APPENDIXS T A TI S T I C A L A P P E N D IX

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0301

Real GDP growth
Annual estimates and forecasts1(Percent change)
2017

2018

2021f

2022f

18Q2

18Q3

18Q4

19Q1 19Q2 19Q3e

World

3.2

3.0

2.4

2.5

2.6

2.7

3.1

2.8

2.5

2.4

2.3

..

Advanced economies

2.4

2.2

1.6

1.4

1.5

1.5

2.4

2.0

1.7

1.8

1.6

1.7

United States

2.4

2.9

2.3

1.8

1.7

1.7

3.2

3.1

2.5

2.7

2.3

2.1

Euro Area

2.5

1.9

1.1

1.0

1.3

1.3

2.2

1.6

1.2

1.4

1.2

1.2

Japan

1.9

0.8

1.1

0.7

0.6

0.4

1.0

-0.3

-0.3

0.8

0.8

1.9

4.5

4.3

3.5

4.1

4.3

4.4

4.5

4.3

4.0

3.6

3.6

..
5.7

Emerging market and developing economies
East Asia and Pacific2019e 2020f

Quarterly estimates2(Percent change, year-on-year)

6.5

6.3

5.8

5.7

5.6

5.6

6.4

6.2

6.1

6.1

5.9

Cambodia

7.0

7.5

7.0

6.8

6.8

6.8

..

..

..

..

..

..

China

6.8

6.6

6.1

5.9

5.8

5.7

6.7

6.5

6.4

6.4

6.2

6.0

Fiji

5.2

4.2

1.0

1.7

2.9

3.0

..

..

..

..

..

..

Indonesia

5.1

5.2

5.0

5.1

5.2

5.2

5.3

5.2

5.2

5.1

5.1

5.0

Lao PDR

6.9

6.3

5.2

5.8

5.7

5.6

..

..

..

..

..

..

Malaysia

5.7

4.7

4.6

4.5

4.5

4.5

4.5

4.4

4.7

4.5

4.9

4.4

Mongolia

5.3

7.2

5.7

5.5

5.2

5.5

5.3

4.5

9.1

8.6

6.2

4.2

Myanmar

6.8

6.5

6.6

6.7

6.8

6.8

..

..

..

..

..

..

Papua New Guinea

3.5

-0.8

5.6

2.9

2.9

3.0

..

..

..

..

..

..

Philippines

6.7

6.2

5.8

6.1

6.2

6.2

6.2

6.0

6.3

5.6

5.5

6.2

Solomon Islands

3.0

3.5

2.9

2.8

2.8

2.7

..

..

..

..

..

..

Thailand

4.0

4.1

2.5

2.7

2.8

2.9

4.7

3.2

3.6

2.8

2.3

2.4

Timor-Leste

-3.5

-1.1

4.2

4.6

4.9

5.0

..

..

..

..

..

..

Vietnam

6.8

7.1

6.8

6.5

6.5

6.4

6.7

6.8

7.3

6.8

6.7

7.3

4.1

3.2

2.0

2.6

2.9

2.9

3.9

3.0

1.8

1.1

1.3

..

3.8

4.1

2.9

3.4

3.6

3.5

4.3

4.7

3.3

2.4

2.3

..

Armenia

7.5

5.2

6.9

5.1

5.2

5.2

..

..

..

..

..

..

Azerbaijan

-0.3

1.4

2.5

2.3

2.1

2.1

..

..

..

..

..

..

Belarus

2.5

3.0

1.0

0.9

0.5

0.5

3.9

2.2

1.3

1.3

0.5

..

Bosnia and Herzegovina

3.2

3.6

3.1

3.4

3.9

3.9

3.9

3.1

3.9

2.8

2.6

..

Bulgaria

3.5

3.1

3.6

3.0

3.1

3.1

3.0

3.3

3.0

4.5

3.8

3.1

Croatia

3.1

2.7

2.9

2.6

2.4

2.4

3.2

3.0

2.2

4.1

2.4

2.9

Georgia

4.8

4.8

5.2

4.3

4.5

4.5

5.4

3.6

5.1

5.0

4.6

5.8

Hungary

4.3

5.1

4.9

3.0

2.6

2.6

5.0

5.3

5.3

5.3

4.9

5.0

Kazakhstan

4.1

4.1

4.0

3.7

3.9

3.7

4.3

3.9

4.1

3.8

4.4

..

Kosovo

4.2

3.8

4.0

4.2

4.1

4.0

..

..

..

..

..

..

Kyrgyz Republic

4.7

3.5

4.2

4.0

4.0

4.2

..

..

..

..

..

..

Moldova

4.7

4.0

3.6

3.6

3.8

3.8

5.3

3.3

3.8

4.4

5.8

4.3

Montenegro5

4.7

5.1

3.0

3.1

2.8

3.2

..

..

..

..

..

..

Europe and Central Asia
Albania

North Macedonia

0.2

2.9

3.1

3.2

3.3

3.1

1.7

2.4

6.2

3.9

3.4

3.6

Poland

4.9

5.1

4.3

3.6

3.3

3.1

5.4

5.5

4.4

4.6

4.1

4.1

Romania

7.1

4.0

3.9

3.4

3.1

3.1

3.8

4.0

4.2

5.0

4.4

3.0

Russia

1.6

2.3

1.2

1.6

1.8

1.8

2.2

2.2

2.7

0.5

0.9

1.7

Serbia

2.0

4.4

3.3

3.9

4.0

4.0

5.0

4.2

3.5

2.7

2.9

4.8

Tajikistan

7.1

7.3

6.2

5.5

5.0

5.0

..

..

..

..

..

..

Turkey

7.5

2.8

0.0

3.0

4.0

4.0

5.6

2.3

-2.8

-2.3

-1.6

0.9

Turkmenistan

6.5

6.2

5.0

5.2

5.5

5.5

..

..

..

..

..

..

Ukraine

2.5

3.3

3.6

3.7

4.2

4.2

3.8

2.8

3.5

2.5

4.6

4.1

Uzbekistan

4.5

5.1

5.5

5.7

6.0

6.0

..

..

..

..

..

..

302

S T A TI S T I C A L A P P E N D IX

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0Real GDP growth (continued)
Annual estimates and forecasts1(Percent change)

Quarterly estimates2(Percent change, year-on-year)

2017

2018

2019e

2020f

2021f

2022f

18Q2

18Q3

18Q4

19Q1

19Q2

19Q3e

1.9

1.7

0.8

1.8

2.4

2.6

1.8

1.6

1.1

0.6

0.7

..

Argentina

2.7

-2.5

-3.1

-1.3

1.4

2.3

-3.8

-3.6

-6.1

-5.8

0.0

-1.7

Belize

1.9

2.1

2.7

2.1

1.8

1.8

..

..

..

..

..

..

Bolivia

4.2

4.2

2.2

3.0

3.2

3.4

4.8

4.0

3.3

3.4

2.8

..

Brazil

1.3

1.3

1.1

2.0

2.5

2.4

1.1

1.5

1.2

0.6

1.1

1.2

Chile

1.3

4.0

1.3

2.5

3.0

3.0

5.3

2.6

3.6

1.5

1.9

3.3

Colombia

1.4

2.6

3.3

3.6

3.9

3.9

2.9

2.6

2.7

3.2

3.0

3.3

Costa Rica

3.4

2.6

2.0

2.5

3.0

3.2

3.8

2.6

1.3

2.2

0.6

..

Dominican Republic

4.7

7.0

5.3

5.0

5.0

5.0

7.3

7.6

6.3

5.6

3.7

..

Ecuador

2.4

1.4

-0.3

0.2

0.8

1.2

1.4

1.5

0.8

0.6

0.3

..

El Salvador

2.3

2.5

2.4

2.5

2.5

2.5

2.9

2.2

2.2

2.5

1.8

..

Grenada

4.4

4.2

3.5

2.9

2.9

3.2

..

..

..

..

..

..

Guatemala

2.8

3.1

3.4

3.0

3.2

3.2

3.6

3.6

3.5

3.1

3.5

..

Guyana

2.1

4.1

4.5

86.7

10.5

14.6

..

..

..

..

..

..

Haiti3

1.2

1.5

-0.9

-1.4

-0.5

1.4

..

..

..

..

..

..

Honduras

4.8

3.7

3.3

3.5

3.5

3.5

4.0

3.3

4.5

2.8

1.7

2.6

Jamaica2

1.0

1.9

1.0

1.1

1.2

2.0

2.2

1.9

2.0

1.8

1.3

..

Mexico

2.1

2.1

0.0

1.2

1.8

2.3

3.0

2.5

1.4

1.2

-0.9

-0.3

Nicaragua

4.7

-3.8

-5.0

-0.5

0.6

1.0

-5.2

-4.4

-7.7

..

..

..

Panama

5.6

3.7

3.5

4.2

4.6

4.8

3.0

3.3

4.2

3.1

2.9

2.7

Paraguay

5.0

3.7

0.7

3.1

3.9

3.8

6.9

1.6

1.0

-2.1

-3.0

..

Peru

2.5

4.0

2.6

3.2

3.5

3.6

5.4

2.5

4.8

2.4

1.2

3.0

St. Lucia

2.6

0.9

1.8

3.2

3.0

2.4

..

..

..

..

..

..

St. Vincent and the Grenadines

1.0

2.2

2.3

2.3

2.3

2.3

..

..

..

..

..

..

Suriname

1.8

2.6

2.2

2.5

2.1

2.1

..

..

..

..

..

..

Uruguay

2.6

1.6

0.5

2.5

3.5

3.2

2.2

1.8

0.6

-0.5

0.0

0.9

1.1

0.8

0.1

2.4

2.7

2.8

2.4

2.8

3.5

2.4

1.7

..

Algeria

1.3

1.4

1.3

1.9

2.2

2.2

..

..

..

..

..

..

Bahrain

3.8

2.2

2.0

2.1

2.4

2.4

2.1

1.9

5.3

2.6

0.8

..

Djibouti

5.1

5.5

7.2

7.5

8.0

8.4

..

..

..

..

..

..

Egypt3

4.2

5.3

5.6

5.8

6.0

6.0

5.4

5.3

5.5

5.6

5.7

5.6

Iran

3.8

-4.9

-8.7

0.0

1.0

1.0

2.5

..

..

..

..

..

Iraq

-2.5

-0.6

4.8

5.1

2.7

2.5

..

..

..

..

..

..

Jordan

2.1

1.9

2.0

2.2

2.4

2.5

..

..

..

..

..

..

Kuwait

-3.5

1.2

0.4

2.2

2.0

2.0

0.6

2.7

2.0

0.9

0.4

..

Lebanon

0.6

0.2

-0.2

0.3

0.4

0.5

..

..

..

..

..

..

Morocco

4.2

3.0

2.7

3.5

3.6

3.8

..

..

..

..

..

..

Oman

0.3

1.8

0.0

3.7

4.3

4.3

..

..

..

..

..

..

Qatar

1.6

1.5

0.5

1.5

3.2

3.2

1.9

1.5

0.5

0.8

-1.4

..

Saudi Arabia

-0.7

2.4

0.4

1.9

2.2

2.4

1.6

2.4

4.3

1.7

0.5

..

Tunisia

1.8

2.5

1.6

2.2

2.6

2.6

..

..

..

..

..

..

United Arab Emirates

0.5

1.7

1.8

2.6

3.0

3.0

..

..

..

..

..

..

West Bank and Gaza

3.1

0.9

0.5

2.5

2.6

2.7

..

..

..

..

..

..

Latin America and the Caribbean

Middle East and North Africa

S T A TI S T I C A L A P P E N D IX

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

303

Real GDP growth (continued)
Annual estimates and forecasts1(Percent change)

Quarterly estimates2(Percent change, year-on-year)

2017

2018

2019e

2020f

2021f

2022f

18Q2

18Q3

18Q4

19Q1

19Q2

19Q3e

6.7

7.1

4.9

5.5

5.9

6.0

7.8

6.9

6.4

5.8

4.9

4.5

Afghanistan

2.7

1.8

2.5

3.0

3.5

3.5

..

..

..

..

..

..

Bangladesh3,4

7.3

7.9

8.1

7.2

7.3

7.3

..

..

..

..

..

..

Bhutan3,4

6.3

3.8

3.9

5.6

7.6

6.2

..

..

..

..

..

..

India

7.2

6.8

5.0

5.8

6.1

6.1

8.0

7.0

6.6

5.8

5.0

4.5

Maldives

6.9

6.7

5.2

5.5

5.6

5.6

..

..

..

..

..

..

Nepal3,4

8.2

6.7

7.1

6.4

6.5

6.6

..

..

..

..

..

..

Pakistan3,4

5.2

5.5

3.3

2.4

3.0

3.9

..

..

..

..

..

..

Sri Lanka

3.4

3.2

2.7

3.3

3.7

3.7

3.9

3.5

1.8

3.7

1.5

2.7

2.7

2.6

2.4

2.9

3.1

3.3

2.0

2.7

2.8

2.2

2.5

..

Angola

-0.1

-1.2

-0.7

1.5

2.4

3.0

..

..

..

..

..

..

Benin

5.8

6.7

6.4

6.7

6.7

6.7

..

..

..

..

..

..

Botswana

2.9

4.5

4.0

4.1

4.2

4.2

5.3

4.1

4.2

4.3

3.1

3.1

Burkina Faso

6.3

6.8

6.0

6.0

6.0

6.0

..

..

..

..

..

..

Burundi

0.5

1.6

1.8

2.0

2.1

2.2

..

..

..

..

..

..

Cabo Verde

3.7

5.1

5.0

5.0

5.0

5.0

..

..

..

..

..

..

Cameroon

3.5

4.1

4.0

4.2

4.3

4.5

..

..

..

..

..

..

Chad

-3.0

2.6

3.0

5.5

4.8

4.8

..

..

..

..

..

..

Comoros

3.8

3.4

1.7

4.8

3.7

3.6

..

..

..

..

..

..

Congo, Dem. Rep.

3.7

5.8

4.3

3.9

3.4

3.6

..

..

..

..

..

..

Congo, Rep.

-1.8

1.6

2.2

4.6

1.9

2.4

..

..

..

..

..

..

South Asia

3,4

Sub-Saharan Africa

Côte d'Ivoire

7.7

7.4

7.3

7.0

7.1

7.1

..

..

..

..

..

..

Equatorial Guinea

-4.7

-6.1

-4.3

-2.3

1.0

-4.8

..

..

..

..

..

..

Eswatini

2.0

2.4

1.3

2.6

2.5

2.5

..

..

..

..

..

..

Ethiopia

10.0

7.9

9.0

6.3

6.4

7.1

..

..

..

..

..

..

Gabon

0.5

0.8

2.9

3.0

3.2

3.3

..

..

..

..

..

..

Gambia, The

4.8

6.6

6.0

6.3

5.8

5.5

..

..

..

..

..

..

Ghana

8.1

6.3

7.0

6.8

5.2

4.6

5.4

7.4

6.8

6.7

5.7

5.6

Guinea

10.0

5.8

5.9

6.0

6.0

6.0

..

..

..

..

..

..

Guinea-Bissau

5.9

3.8

4.6

4.9

5.0

5.0

..

..

..

..

..

..

Kenya

4.9

6.3

5.8

6.0

5.8

5.8

6.4

6.4

5.9

5.6

5.6

..

Lesotho

-0.4

1.5

2.6

0.7

2.1

2.8

0.2

-3.1

3.0

-1.4

1.2

..

Liberia

2.5

1.2

-1.4

1.4

3.4

4.2

..

..

..

..

..

..

Madagascar

4.3

5.1

4.7

5.3

4.4

5.0

..

..

..

..

..

..

Malawi

4.0

3.5

4.4

4.8

5.2

5.3

..

..

..

..

..

..

Mali

5.3

4.7

5.0

5.0

4.9

4.9

..

..

..

..

..

..

Mauritania

3.0

3.6

6.4

5.7

5.8

8.7

..

..

..

..

..

..

Mauritius

3.8

3.8

3.9

3.9

4.0

4.0

..

..

..

..

..

..

3

Mozambique

3.7

3.4

2.0

3.7

4.2

4.4

..

..

..

..

..

..

Namibia

-0.9

-0.1

-0.5

0.9

1.7

1.9

..

..

..

..

..

..

Niger

4.9

6.5

6.3

6.0

5.6

11.9

..

..

..

..

..

..

Nigeria

0.8

1.9

2.0

2.1

2.1

2.1

1.5

1.8

2.4

2.1

2.1

2.1

Rwanda

6.1

8.6

8.5

8.1

8.0

8.0

..

..

..

..

..

..

Senegal

7.1

6.8

6.3

6.8

7.0

7.0

..

..

..

..

..

..

Seychelles

4.3

4.1

3.5

3.3

3.3

3.4

..

..

..

..

..

..

Sierra Leone

3.8

3.5

4.8

4.9

4.9

5.0

..

..

..

..

..

..

304

S T A TI S T I C A L A P P E N D IX

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0Real GDP growth (continued)
Annual estimates and forecasts1(Percent change)
2017

2018

2019e

2020f

Quarterly estimates2(Percent change, year-on-year)

2021f

2022f

18Q2

18Q3

18Q4

19Q1

19Q2

19Q3e

Sub-Saharan Africa (continued)
South Africa

1.4

0.8

0.4

0.9

1.3

1.5

0.1

1.3

1.1

0.0

0.9

0.1

Sudan

4.3

-2.3

-2.6

-1.4

-0.6

0.2

..

..

..

..

..

..

Tanzania

6.8

5.4

5.6

5.8

6.1

6.2

6.1

7.1

7.1

6.5

7.2

..

Togo

4.4

4.9

5.3

5.5

5.5

5.5

..

..

..

..

..

..

Uganda3

3.9

5.9

6.1

6.5

5.9

6.0

5.0

6.5

6.8

5.6

5.4

..

Zambia

4.1

3.1

1.8

2.6

2.6

4.0

4.7

6.0

2.5

2.3

2.2

..

Zimbabwe

4.7

3.5

-7.5

2.7

2.5

2.8

..

..

..

..

..

..

World Bank and Haver Analytics.
Note: e = estimate; f = forecast.
1. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.
2. Quarterly estimates are based on non-seasonally-adjusted real GDP, except for advanced economies, as well as Ecuador and Poland. Data for Bosnia and Herzegovina are from theproduction approach. Quarterly data for Jamaica are gross value added.
Regional averages are calculated based on data from following countries.
East Asia and Pacific: China, Indonesia, Malaysia, Mongolia, Philippines, Thailand, and Vietnam.
Europe and Central Asia: Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Georgia, Hungary, Kazakhstan, North Macedonia, Poland, Romania, Russia, Serbia, Turkey, andUkraine.
Latin America and the Caribbean: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Nicaragua,
Panama, Paraguay, Peru, and Uruguay.
Middle East and North Africa: Bahrain, Egypt, Iran, Kuwait, Qatar, and Saudi Arabia.
South Asia: India and Sri Lanka.
Sub-Saharan Africa: Botswana, Ghana, Kenya, Nigeria, South Africa, Tanzania, Uganda, and Zambia.
3. Annual GDP is on fiscal year basis, as per reporting practice in the country.
4. GDP data for Pakistan are based on factor cost. For Bangladesh, Bhutan, Nepal, and Pakistan, the column labeled 2019 refers to FY2018/19. For India, the column labeled 2018 refers to
FY2018/19.
5. Quarterly data are preliminary.
Click here to download data.

S T A TI S T I C A L A P P E N D IX

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

Data and Forecast Conventions
The macroeconomic forecasts presented in thisreport are prepared by staff of the Prospects
Group of the Development Economics VicePresidency, in coordination with staff from theMacroeconomics, Trade, and Investment Global
Practice and from regional and country offices,and with input from regional Chief Economist
offices. They are the result of an iterative processthat incorporates data, macroeconometric models,and judgment.
Data. Data used to prepare country forecasts
come from a variety of sources. National Income
Accounts (NIA), Balance of Payments (BOP), andfiscal data are from Haver Analytics; the World
Development Indicators by the World Bank; theWorld Economic Outlook, Balance of Payments
Statistics, and International Financial Statistics by
the International Monetary Fund. Population
data and forecasts are from the United Nations
World Population Prospects. Country- andlending-group classifications are from the World
Bank. DECPG databases include commodity
prices, data on previous forecast vintages, and inhouse country classifications. Other internal
databases include high-frequency indicators suchas industrial production, consumer price indexes,
house prices, exchange rates, exports, imports, andstock market indexes, based on data from
Bloomberg, Haver Analytics, OECD Analytical
House Prices Indicators, IMF Balance of Pay-ments Statistics, and IMF International Financial
Statistics.
Aggregations. Aggregate growth for the world andall sub-groups of countries (such as regions andincome groups) is calculated as GDP-weighted
average (at 2010 prices) of country-specific growth
rates. Income groups are defined as in the WorldBank’s classification of country groups.
Forecast Process. The process starts with initialassumptions about advanced-economy growth andcommodity price forecasts. These are used asconditioning assumptions for the first set ofgrowth forecasts for EMDEs, which are produced
using macroeconometric models, accounting
frameworks to ensure national account identities
and global consistency, estimates of spillovers
from major economies, and high-frequency
indicators. These forecasts are then evaluated toensure consistency of treatment across similar
EMDEs. This is followed by extensive discussions
with World Bank country teams, who conduct
continuous macroeconomic monitoring anddialogue with country authorities and finalize
growth forecasts for EMDEs. The Prospects
Group prepares advanced-economy and commodity price forecasts. Throughout the forecastingprocess, staff use macro-econometric models thatallow the combination of judgement andconsistency with model-based insights.

305

306

S E L E CTE D TO P I C S

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

Global Economic Prospects: Selected Topics, 2015-20
Growth and Business Cycles
Informality
Growing in the shadow: Challenges of informality

January 2019, Chapter 3

Linkages between formal and informal sectors

January 2019, Box 3.1

Regional dimensions of informality: An overview

January 2019, Box 3.2

Casting a shadow: Productivity in formal and informal firms

January 2019, Box 3.3

Under the magnifying glass: How do policies affect informality?

January 2019, Box 3.4

East Asia and PacificJanuary 2019, Box 2.1.1

Europe and Central Asia

January 2019, Box 2.2.1

Latin America and the Caribbean

January 2019, Box 2.3.1

Middle East and North Africa

January 2019, Box 2.4.1

South Asia

January 2019, Box 2.5.1

Sub-Saharan Africa

January 2019, Box 2.6.1

Low for how much longer? Inflation in low-income countries
Currency depreciation, inflation, and central bank independence

January 2020, Special Focus 2
June 2019, Special Focus 1.2

The great disinflation

January 2019, Box 1.1

Growth in low-income countries: Evolution, prospects, and policies

June 2019, Special Focus 2.1

Long-term growth prospects: Downgraded no more?

June 2018, Box 1.1

Is the global economy turning the corner?

January 2018, Box 1.1

Building solid foundations: How to promote potential growth

January 2018, Chapter 3

What is potential growth?

January 2018, Box 3.1

Understanding the recent productivity slowdown: Facts and explanations

January 2018, Box 3.2

Moving together? Investment and potential output

January 2018, Box 3.3

The long shadow of contractions over potential output

January 2018, Box 3.4

Productivity and investment growth during reforms

January 2018, Box 3.5

East Asia and PacificJanuary 2018, Box 2.1.1

Europe and Central Asia

January 2018, Box 2.2.1

Latin America and the Caribbean

January 2018, Box 2.3.1

Middle East and North Africa

January 2018, Box 2.4.1

South Asia

January 2018, Box 2.5.1

Sub-Saharan Africa

January 2018, Box 2.6.1

Fading promise: How to rekindle productivity growth

January 2020, Chapter 3

EMDE regional productivity trends and bottlenecks

January 2020, Box 3.1

Sectoral sources of productivity growth

January 2020, Box 3.2

Patterns of total factor productivity: a firm perspective

January 2020, Box 3.3

Debt, financial crises, and productivity

January 2020, Box 3.4

Labor productivity in East Asia and Pacific: Trends and drivers

January 2020, Box 2.1.1

Labor productivity in Europe and Central Asia: Trends and drivers

January 2020, Box 2.2.1

Labor productivity in Latin America and the Caribbean: Trends and drivers

January 2020, Box 2.3.1

Labor productivity in Middle East and North Africa: Trends and drivers

January 2020, Box 2.4.1

Labor productivity in South Asia: Trends and drivers

January 2020, Box 2.5.1

Labor productivity in Sub-Saharan Africa: Trends and drivers

January 2020, Box 2.6.1

Inflation

Growth prospects

Global output gap
Potential growth

Productivity

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

S E L E CTE D TO P I C S

Global Economic Prospects: Selected Topics, 2015-20
Growth and Business Cycles
Investment slowdown
Investment: Subdued prospects, strong needs
Weak investment in uncertain times: Causes, implications and policy responses

June 2019, Special Focus 1.1

Investment-less credit booms

January 2017, Box 3.1

Implications of rising uncertainty for investment in EMDEs

January 2017, Box 3.2

Investment slowdown in China

January 2017, Box 3.3

Interactions between public and private investment

January 2017, Box 3.4

January 2017, Chapter 3

Investment slowdown (continued)
East Asia and PacificJanuary 2017, Box 2.1.1

Europe and Central Asia

January 2017, Box 2.2.1

Latin America and the Caribbean

January 2017, Box 2.3.1

Middle East and North Africa

January 2017, Box 2.4.1

South Asia
Sub-Saharan Africa

January 2017, Box 2.5.1
January 2017, Box 2.6.1

Forecast uncertainty
Quantifying uncertainties in global growth forecasts

June 2016, Special Focus 2

Cross-border spillovers
Who catches a cold when emerging markets sneeze?

January 2016, Chapter 3

Sources of the growth slowdown in BRICS

January 2016, Box 3.1

Understanding cross-border growth spillovers

January 2016, Box 3.2

Within-region spillovers

January 2016, Box 3.3

East Asia and PacificJanuary 2016, Box 2.1.1

Europe and Central Asia

January 2016, Box 2.2.1

Latin America and the Caribbean

January 2016, Box 2.3.1

Middle East and North Africa

January 2016, Box 2.4.1

South Asia

January 2016, Box 2.5.1

Sub-Saharan Africa

January 2016, Box 2.6.1

Having space and using it: Fiscal policy challenges and developing economies

January 2015, Chapter 3

Fiscal policy in low-income countries

January 2015, Box 3.1

What affects the size of fiscal multipliers?

January 2015, Box 3.2

Chile’s fiscal rule—an example of success

January 2015, Box 3.3

Narrow fiscal space and the risk of a debt crisis

January 2015, Box 3.4

Revenue mobilization in South Asia: Policy challenges and recommendations

January 2015, Box 2.3

Education demographics and global inequality

January 2018, Special Focus 2

Recent developments in emerging and developing country labor markets

June 2015, Box 1.3

Linkages between China and Sub-Saharan Africa

June 2015, Box 2.1

What does weak growth mean for poverty in the future?

January 2015, Box 1.1

What does a slowdown in China mean for Latin America and the Caribbean?

January 2015, Box 2.2

Fiscal space

Other topics

Commodity Markets
The role of major emerging markets in global commodity demandJune 2018, Special Focus 1

The role of the EM7 in commodity production

June 2018, SF1, Box SF1.1

Commodity consumption: Implications of government policies

June 2018, SF1, Box SF1.2

With the benefit of hindsight: The impact of the 2014–16 oil price collapse

January 2018, Special Focus 1

From commodity discovery to production: Vulnerabilities and policies in LICs

January 2016, Special Focus

After the commodities boom: What next for low-income countries?

June 2015, Special Focus 2

Low oil prices in perspective

June 2015, Box 1.2

Understanding the plunge in oil prices: Sources and implications

January 2015, Chapter 4

What do we know about the impact of oil prices on output and inflation? A brief survey

January 2015, Box 4.1

307

308

S E L E CTE D TO P I C S

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

Global Economic Prospects: Selected Topics, 2015-20
Globalization of Trade and Financial Flows
Poverty impact of food price shocks and policies

January 2019, Chapter 4

Arm’s-length trade: A source of post-crisis trade weakness

June 2017, Special Focus 2

The U.S. economy and the world

January 2017, Special Focus

Potential macroeconomic implications of the Trans-Pacific Partnership Agreement

January 2016, Chapter 4

Regulatory convergence in mega-regional trade agreements

January 2016, Box 4.1.1

China’s integration in global supply chains: Review and implications

January 2015, Box 2.1

Can remittances help promote consumption stability?

January 2015, Chapter 4

What lies behind the global trade slowdown?

January 2015, Chapter 4

Monetary and Exchange Rate Policies
The fourth wave: Rapid debt buildup

January 2020, Chapter 4

Price controls: Good intentions, bad outcomes

January 2020, Special Focus 1

Low for how much longer? Inflation in low-income countries

January 2020, Special Focus 2

Currency depreciation, inflation, and central bank independence

June 2019, Special Focus 1.2

The great disinflation

January 2019, Box 1.1

Corporate debt: Financial stability and investment implications

June 2018, Special Focus 2

Recent credit surge in historical context

June 2016, Special Focus 1

Peg and control? The links between exchange rate regimes and capital account policies

January 2016, Chapter 4

Negative interest rates in Europe: A glance at their causes and implications

June 2015, Box 1.1

Hoping for the best, preparing for the worst: Risks around U.S. rate liftoff and policy options

June 2015, Special Focus 1

Countercyclical monetary policy in emerging markets: Review and evidence

January 2015, Box 1.2

Fiscal Policies
The fourth wave: Rapid debt buildup

January 2020, Chapter 4

Debt: No free lunch

June 2019, Box 1.1

Debt in low-income countries: Evolution, implications, and remedies

January 2019, Chapter 4

Debt dynamics in emerging market and developing economies: Time to act?

June 2017, Special Focus 1

Having fiscal space and using it: Fiscal challenges in developing economies

January 2015, Chapter 3

Revenue mobilization in South Asia: Policy challenges and recommendations

January 2015, Box 2.3

Fiscal policy in low-income countries

January 2015, Box 3.1

What affects the size of fiscal multipliers?

January 2015, Box 3.2

Chile’s fiscal rule—an example of success

January 2015, Box 3.3

Narrow fiscal space and the risk of a debt crisis

January 2015, Box 3.4

G L O B A L E CO N O MI C P R OS P E C TS | J A N U A R Y 20 2 0

S E L E CTE D TO P I C S

Prospects Group:
Selected Other Publications on the Global Economy, 2015-20
Commodity Markets Outlook

Column1

Food price shocks: Channels and implications

April 2019, Special Focus

The implications of tariffs for commodity markets

October 2018, Box

The changing of the guard: Shifts in industrial commodity demand

October 2018, Special Focus

Oil exporters: Policies and challenges

April 2018, Special Focus

Investment weakness in commodity exporters

January 2017, Special Focus

OPEC in historical context: Commodity agreements and market fundamentals

October 2016, Special Focus

Energy and food prices: Moving in tandem?

July 2016, Special Focus

Resource development in an era of cheap commodities

April 2016, Special Focus

Weak growth in emerging market economies: What does it imply for commodity markets?

January 2016, Special Focus

Understanding El Niño: What does it mean for commodity markets?

October 2015, Special Focus

How important are China and India in global commodity consumption?

July 2015, Special Focus

Anatomy of the last four oil price crashes

April 2015, Special Focus

Putting the recent plunge in oil prices in perspective

January 2015, Special Focus

Inflation in Emerging and Developing Economies
Inflation: Concepts, evolution, and correlates

Chapter 1

Understanding global inflation synchronization

Chapter 2

Sources of inflation: Global and domestic drivers

Chapter 3

Inflation expectations: Review and evidence

Chapter 4

Inflation and exchange rate pass-throughChapter 5

Inflation in low-income countries

Chapter 6

Poverty impact of food price shocks and policies

Chapter 7

A Decade After the Global Recession: Lessons and Challenges for Emerging and Developing Economies
A Decade After the Global Recession: Lessons and Challenges

Chapter 1

What Happens During Global Recessions?

Chapter 2

Macroeconomic Developments

Chapter 3

Financial Market Developments

Chapter 4

Macroeconomic and Financial Sector Policies

Chapter 5

Prospects, Risks, and Vulnerabilities

Chapter 6

Policy Challenges

Chapter 7

The Role of the World Bank Group

Chapter 8

Global Waves of Debt: Causes and Consequences
Debt: Evolution, Causes, and Consequences

Chapter 1

Benefits and Costs of Debt: The Dose Makes the Poison

Chapter 2

Global Waves of Debt: What Goes up Must Come Down?

Chapter 3

The Fourth Wave: Ripple or Tsunami?

Chapter 4

Debt and Financial Crises: From Euphoria to Distress

Chapter 5

Policies: Turning Mistakes into Experience

Chapter 6

High-Frequency Monitoring
Global Monthly newsletter

Column1309

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G

lobal growth is projected to be slightly faster in 2020 thanthe post-crisis low registered last year. While growth could bestronger if reduced trade tensions lead to a sustained reduction in
uncertainty, the balance of risks to the outlook is to the downside.
Growth in emerging market and developing economies is also
expected to remain subdued, continuing a decade of disappointing
outcomes. A steep and widespread productivity growth slowdown
has been underway in these economies since the global financial
crisis, despite the largest, fastest, and most broad-based
accumulation of debt since the 1970s. In addition, many emerging
market and developing economies, including low-income countries,
face the challenge of phasing out price controls that impose
heavy fiscal cost and dampen investment. These circumstances
add urgency to the need to implement measures to rebuild
macroeconomic policy space and to undertake reforms to rekindle
productivity growth. These efforts need to be supplemented bypolicies to promote inclusive and sustainable long-term growth
and accelerate poverty alleviation.

Global Economic Prospects is a World Bank Group Flagship Reportthat examines global economic developments and prospects, witha special focus on emerging market and developing economies,on a semiannual basis (in January and June). The January edition
includes in-depth analyses of topical policy challenges facedby these economies, while the June edition contains shorter
analytical pieces.

SKU 211468